more changes to dim red slides
This commit is contained in:
mhjensen
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
|
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2,
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None,
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'___sec14'),
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||||
('Classical PCA Theorem', 2, None, '___sec15'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec16'),
|
||||
('Getting started with PCA', 2, None, '___sec17'),
|
||||
('Principal Component Analysis', 2, None, '___sec18'),
|
||||
('PCA and scikit-learn', 2, None, '___sec19'),
|
||||
('More on the PCA', 2, None, '___sec20'),
|
||||
('Incremental PCA', 2, None, '___sec21'),
|
||||
('Randomized PCA', 2, None, '___sec22'),
|
||||
('Kernel PCA', 2, None, '___sec23'),
|
||||
('LLE', 2, None, '___sec24'),
|
||||
('Other techniques', 2, None, '___sec25')]}
|
||||
('Towards the PCA theorem', 2, None, '___sec15'),
|
||||
('Classical PCA Theorem', 2, None, '___sec16'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec17'),
|
||||
('Getting started with PCA', 2, None, '___sec18'),
|
||||
('Principal Component Analysis', 2, None, '___sec19'),
|
||||
('PCA and scikit-learn', 2, None, '___sec20'),
|
||||
('More on the PCA', 2, None, '___sec21'),
|
||||
('Incremental PCA', 2, None, '___sec22'),
|
||||
('Randomized PCA', 2, None, '___sec23'),
|
||||
('Kernel PCA', 2, None, '___sec24'),
|
||||
('LLE', 2, None, '___sec25'),
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('Other techniques', 2, None, '___sec26')]}
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end of tocinfo -->
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<body>
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@@ -150,17 +151,18 @@ MathJax.Hub.Config({
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<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
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||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
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<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
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<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
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||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
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||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
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<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
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||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
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<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
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||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
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||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
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<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
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||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
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<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
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||||
|
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</ul>
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</li>
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@@ -219,7 +221,7 @@ MathJax.Hub.Config({
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<li><a href="._DimRed-bs008.html">9</a></li>
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<li><a href="._DimRed-bs009.html">10</a></li>
|
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<li><a href="">...</a></li>
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||||
<li><a href="._DimRed-bs026.html">27</a></li>
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<li><a href="._DimRed-bs027.html">28</a></li>
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<li><a href="._DimRed-bs001.html">»</a></li>
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</ul>
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<!-- ------------------- end of main content --------------- -->
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@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
('Classical PCA Theorem', 2, None, '___sec15'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec16'),
|
||||
('Getting started with PCA', 2, None, '___sec17'),
|
||||
('Principal Component Analysis', 2, None, '___sec18'),
|
||||
('PCA and scikit-learn', 2, None, '___sec19'),
|
||||
('More on the PCA', 2, None, '___sec20'),
|
||||
('Incremental PCA', 2, None, '___sec21'),
|
||||
('Randomized PCA', 2, None, '___sec22'),
|
||||
('Kernel PCA', 2, None, '___sec23'),
|
||||
('LLE', 2, None, '___sec24'),
|
||||
('Other techniques', 2, None, '___sec25')]}
|
||||
('Towards the PCA theorem', 2, None, '___sec15'),
|
||||
('Classical PCA Theorem', 2, None, '___sec16'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec17'),
|
||||
('Getting started with PCA', 2, None, '___sec18'),
|
||||
('Principal Component Analysis', 2, None, '___sec19'),
|
||||
('PCA and scikit-learn', 2, None, '___sec20'),
|
||||
('More on the PCA', 2, None, '___sec21'),
|
||||
('Incremental PCA', 2, None, '___sec22'),
|
||||
('Randomized PCA', 2, None, '___sec23'),
|
||||
('Kernel PCA', 2, None, '___sec24'),
|
||||
('LLE', 2, None, '___sec25'),
|
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('Other techniques', 2, None, '___sec26')]}
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||||
end of tocinfo -->
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<body>
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@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
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||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
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||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
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||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
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||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
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||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
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||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
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||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
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||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
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||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
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||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
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||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
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||||
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||||
</ul>
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||||
</li>
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||||
@@ -219,7 +221,7 @@ data.
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<li><a href="._DimRed-bs009.html">10</a></li>
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||||
<li><a href="._DimRed-bs010.html">11</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs002.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
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||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
('Classical PCA Theorem', 2, None, '___sec15'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec16'),
|
||||
('Getting started with PCA', 2, None, '___sec17'),
|
||||
('Principal Component Analysis', 2, None, '___sec18'),
|
||||
('PCA and scikit-learn', 2, None, '___sec19'),
|
||||
('More on the PCA', 2, None, '___sec20'),
|
||||
('Incremental PCA', 2, None, '___sec21'),
|
||||
('Randomized PCA', 2, None, '___sec22'),
|
||||
('Kernel PCA', 2, None, '___sec23'),
|
||||
('LLE', 2, None, '___sec24'),
|
||||
('Other techniques', 2, None, '___sec25')]}
|
||||
('Towards the PCA theorem', 2, None, '___sec15'),
|
||||
('Classical PCA Theorem', 2, None, '___sec16'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec17'),
|
||||
('Getting started with PCA', 2, None, '___sec18'),
|
||||
('Principal Component Analysis', 2, None, '___sec19'),
|
||||
('PCA and scikit-learn', 2, None, '___sec20'),
|
||||
('More on the PCA', 2, None, '___sec21'),
|
||||
('Incremental PCA', 2, None, '___sec22'),
|
||||
('Randomized PCA', 2, None, '___sec23'),
|
||||
('Kernel PCA', 2, None, '___sec24'),
|
||||
('LLE', 2, None, '___sec25'),
|
||||
('Other techniques', 2, None, '___sec26')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -218,7 +220,7 @@ ensures that all features are exactly between \( 0 \) and \( 1 \). The
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<li><a href="._DimRed-bs010.html">11</a></li>
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||||
<li><a href="._DimRed-bs011.html">12</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs003.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
('Classical PCA Theorem', 2, None, '___sec15'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec16'),
|
||||
('Getting started with PCA', 2, None, '___sec17'),
|
||||
('Principal Component Analysis', 2, None, '___sec18'),
|
||||
('PCA and scikit-learn', 2, None, '___sec19'),
|
||||
('More on the PCA', 2, None, '___sec20'),
|
||||
('Incremental PCA', 2, None, '___sec21'),
|
||||
('Randomized PCA', 2, None, '___sec22'),
|
||||
('Kernel PCA', 2, None, '___sec23'),
|
||||
('LLE', 2, None, '___sec24'),
|
||||
('Other techniques', 2, None, '___sec25')]}
|
||||
('Towards the PCA theorem', 2, None, '___sec15'),
|
||||
('Classical PCA Theorem', 2, None, '___sec16'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec17'),
|
||||
('Getting started with PCA', 2, None, '___sec18'),
|
||||
('Principal Component Analysis', 2, None, '___sec19'),
|
||||
('PCA and scikit-learn', 2, None, '___sec20'),
|
||||
('More on the PCA', 2, None, '___sec21'),
|
||||
('Incremental PCA', 2, None, '___sec22'),
|
||||
('Randomized PCA', 2, None, '___sec23'),
|
||||
('Kernel PCA', 2, None, '___sec24'),
|
||||
('LLE', 2, None, '___sec25'),
|
||||
('Other techniques', 2, None, '___sec26')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -221,7 +223,7 @@ techniques.
|
||||
<li><a href="._DimRed-bs011.html">12</a></li>
|
||||
<li><a href="._DimRed-bs012.html">13</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs004.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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|
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|
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<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -296,7 +298,7 @@ svm<span style="color: #666666">.</span>fit(X_train_scaled, y_train)
|
||||
<li><a href="._DimRed-bs012.html">13</a></li>
|
||||
<li><a href="._DimRed-bs013.html">14</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs005.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
|
||||
<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -246,7 +248,7 @@ svm<span style="color: #666666">.</span>fit(X_train_scaled, y_train)
|
||||
<li><a href="._DimRed-bs013.html">14</a></li>
|
||||
<li><a href="._DimRed-bs014.html">15</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs006.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -225,7 +227,7 @@ logreg<span style="color: #666666">.</span>fit(X_train_scaled, y_train)
|
||||
<li><a href="._DimRed-bs014.html">15</a></li>
|
||||
<li><a href="._DimRed-bs015.html">16</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs007.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
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|
||||
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|
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|
||||
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|
||||
('PCA and scikit-learn', 2, None, '___sec19'),
|
||||
('More on the PCA', 2, None, '___sec20'),
|
||||
('Incremental PCA', 2, None, '___sec21'),
|
||||
('Randomized PCA', 2, None, '___sec22'),
|
||||
('Kernel PCA', 2, None, '___sec23'),
|
||||
('LLE', 2, None, '___sec24'),
|
||||
('Other techniques', 2, None, '___sec25')]}
|
||||
('Towards the PCA theorem', 2, None, '___sec15'),
|
||||
('Classical PCA Theorem', 2, None, '___sec16'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec17'),
|
||||
('Getting started with PCA', 2, None, '___sec18'),
|
||||
('Principal Component Analysis', 2, None, '___sec19'),
|
||||
('PCA and scikit-learn', 2, None, '___sec20'),
|
||||
('More on the PCA', 2, None, '___sec21'),
|
||||
('Incremental PCA', 2, None, '___sec22'),
|
||||
('Randomized PCA', 2, None, '___sec23'),
|
||||
('Kernel PCA', 2, None, '___sec24'),
|
||||
('LLE', 2, None, '___sec25'),
|
||||
('Other techniques', 2, None, '___sec26')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -280,7 +282,7 @@ applications.
|
||||
<li><a href="._DimRed-bs015.html">16</a></li>
|
||||
<li><a href="._DimRed-bs016.html">17</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs008.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
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|
||||
('Prof of the PCA Theorem', 2, None, '___sec16'),
|
||||
('Getting started with PCA', 2, None, '___sec17'),
|
||||
('Principal Component Analysis', 2, None, '___sec18'),
|
||||
('PCA and scikit-learn', 2, None, '___sec19'),
|
||||
('More on the PCA', 2, None, '___sec20'),
|
||||
('Incremental PCA', 2, None, '___sec21'),
|
||||
('Randomized PCA', 2, None, '___sec22'),
|
||||
('Kernel PCA', 2, None, '___sec23'),
|
||||
('LLE', 2, None, '___sec24'),
|
||||
('Other techniques', 2, None, '___sec25')]}
|
||||
('Towards the PCA theorem', 2, None, '___sec15'),
|
||||
('Classical PCA Theorem', 2, None, '___sec16'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec17'),
|
||||
('Getting started with PCA', 2, None, '___sec18'),
|
||||
('Principal Component Analysis', 2, None, '___sec19'),
|
||||
('PCA and scikit-learn', 2, None, '___sec20'),
|
||||
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|
||||
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|
||||
('Randomized PCA', 2, None, '___sec23'),
|
||||
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|
||||
('LLE', 2, None, '___sec25'),
|
||||
('Other techniques', 2, None, '___sec26')]}
|
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end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -210,7 +212,7 @@ We have a data set defined by a design/feature matrix \( \boldsymbol{X} \) (see
|
||||
<li><a href="._DimRed-bs016.html">17</a></li>
|
||||
<li><a href="._DimRed-bs017.html">18</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs009.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
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|
||||
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|
||||
('Getting started with PCA', 2, None, '___sec17'),
|
||||
('Principal Component Analysis', 2, None, '___sec18'),
|
||||
('PCA and scikit-learn', 2, None, '___sec19'),
|
||||
('More on the PCA', 2, None, '___sec20'),
|
||||
('Incremental PCA', 2, None, '___sec21'),
|
||||
('Randomized PCA', 2, None, '___sec22'),
|
||||
('Kernel PCA', 2, None, '___sec23'),
|
||||
('LLE', 2, None, '___sec24'),
|
||||
('Other techniques', 2, None, '___sec25')]}
|
||||
('Towards the PCA theorem', 2, None, '___sec15'),
|
||||
('Classical PCA Theorem', 2, None, '___sec16'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec17'),
|
||||
('Getting started with PCA', 2, None, '___sec18'),
|
||||
('Principal Component Analysis', 2, None, '___sec19'),
|
||||
('PCA and scikit-learn', 2, None, '___sec20'),
|
||||
('More on the PCA', 2, None, '___sec21'),
|
||||
('Incremental PCA', 2, None, '___sec22'),
|
||||
('Randomized PCA', 2, None, '___sec23'),
|
||||
('Kernel PCA', 2, None, '___sec24'),
|
||||
('LLE', 2, None, '___sec25'),
|
||||
('Other techniques', 2, None, '___sec26')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -259,7 +261,7 @@ In the above example this is the function we constructed using <b>pandas</b>.
|
||||
<li><a href="._DimRed-bs017.html">18</a></li>
|
||||
<li><a href="._DimRed-bs018.html">19</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs010.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
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|
||||
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|
||||
('Getting started with PCA', 2, None, '___sec17'),
|
||||
('Principal Component Analysis', 2, None, '___sec18'),
|
||||
('PCA and scikit-learn', 2, None, '___sec19'),
|
||||
('More on the PCA', 2, None, '___sec20'),
|
||||
('Incremental PCA', 2, None, '___sec21'),
|
||||
('Randomized PCA', 2, None, '___sec22'),
|
||||
('Kernel PCA', 2, None, '___sec23'),
|
||||
('LLE', 2, None, '___sec24'),
|
||||
('Other techniques', 2, None, '___sec25')]}
|
||||
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|
||||
('Classical PCA Theorem', 2, None, '___sec16'),
|
||||
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|
||||
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|
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|
||||
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|
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|
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|
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|
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|
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|
||||
('Other techniques', 2, None, '___sec26')]}
|
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end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -254,7 +256,7 @@ $$
|
||||
<li><a href="._DimRed-bs018.html">19</a></li>
|
||||
<li><a href="._DimRed-bs019.html">20</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs011.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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||||
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|
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<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -242,7 +244,7 @@ C <span style="color: #666666">=</span> np<span style="color: #666666">.</span>c
|
||||
<li><a href="._DimRed-bs019.html">20</a></li>
|
||||
<li><a href="._DimRed-bs020.html">21</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs012.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
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||||
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||||
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||||
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|
||||
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|
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<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -244,7 +246,7 @@ The above procedure with <b>numpy</b> can be made more compact if we use <b>pand
|
||||
<li><a href="._DimRed-bs020.html">21</a></li>
|
||||
<li><a href="._DimRed-bs021.html">22</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs013.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
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||||
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|
||||
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|
||||
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||||
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|
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|
||||
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|
||||
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|
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||||
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|
||||
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||||
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||||
<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -226,7 +228,7 @@ We expand this model to the Franke function discussed above.
|
||||
<li><a href="._DimRed-bs021.html">22</a></li>
|
||||
<li><a href="._DimRed-bs022.html">23</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs014.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
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||||
('Classical PCA Theorem', 2, None, '___sec16'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec17'),
|
||||
('Getting started with PCA', 2, None, '___sec18'),
|
||||
('Principal Component Analysis', 2, None, '___sec19'),
|
||||
('PCA and scikit-learn', 2, None, '___sec20'),
|
||||
('More on the PCA', 2, None, '___sec21'),
|
||||
('Incremental PCA', 2, None, '___sec22'),
|
||||
('Randomized PCA', 2, None, '___sec23'),
|
||||
('Kernel PCA', 2, None, '___sec24'),
|
||||
('LLE', 2, None, '___sec25'),
|
||||
('Other techniques', 2, None, '___sec26')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -262,7 +264,7 @@ matrix.
|
||||
<li><a href="._DimRed-bs022.html">23</a></li>
|
||||
<li><a href="._DimRed-bs023.html">24</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs015.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
('Classical PCA Theorem', 2, None, '___sec15'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec16'),
|
||||
('Getting started with PCA', 2, None, '___sec17'),
|
||||
('Principal Component Analysis', 2, None, '___sec18'),
|
||||
('PCA and scikit-learn', 2, None, '___sec19'),
|
||||
('More on the PCA', 2, None, '___sec20'),
|
||||
('Incremental PCA', 2, None, '___sec21'),
|
||||
('Randomized PCA', 2, None, '___sec22'),
|
||||
('Kernel PCA', 2, None, '___sec23'),
|
||||
('LLE', 2, None, '___sec24'),
|
||||
('Other techniques', 2, None, '___sec25')]}
|
||||
('Towards the PCA theorem', 2, None, '___sec15'),
|
||||
('Classical PCA Theorem', 2, None, '___sec16'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec17'),
|
||||
('Getting started with PCA', 2, None, '___sec18'),
|
||||
('Principal Component Analysis', 2, None, '___sec19'),
|
||||
('PCA and scikit-learn', 2, None, '___sec20'),
|
||||
('More on the PCA', 2, None, '___sec21'),
|
||||
('Incremental PCA', 2, None, '___sec22'),
|
||||
('Randomized PCA', 2, None, '___sec23'),
|
||||
('Kernel PCA', 2, None, '___sec24'),
|
||||
('LLE', 2, None, '___sec25'),
|
||||
('Other techniques', 2, None, '___sec26')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -198,7 +200,7 @@ $$
|
||||
<p>
|
||||
If we then compute the expectation value
|
||||
$$
|
||||
\mathbb{E}[\boldsymbol{X}\boldsymbol{X}^T] = \frac{1}{n}\boldsymbol{X}=\begin{bmatrix}
|
||||
\mathbb{E}[\boldsymbol{X}\boldsymbol{X}^T] = \frac{1}{n}\boldsymbol{X}\boldsymbol{X}^T=\begin{bmatrix}
|
||||
x_{00}^2+x_{01}^2 & x_{00}x_{10}+x_{01}x_{11}\\
|
||||
x_{10}x_{00}+x_{01}x_{11} & x_{10}^2+x_{11}^2\\
|
||||
\end{bmatrix},
|
||||
@@ -208,9 +210,12 @@ which is just
|
||||
$$
|
||||
\boldsymbol{C}[\boldsymbol{x}_0,\boldsymbol{x}_1] = \boldsymbol{C}[\boldsymbol{x}]=\begin{bmatrix} \mathrm{var}[\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_1] \\
|
||||
\mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_0] & \mathrm{var}[\boldsymbol{x}_1] \\
|
||||
\end{bmatrix}.
|
||||
\end{bmatrix},
|
||||
$$
|
||||
|
||||
where we wrote $$\boldsymbol{C}[\boldsymbol{x}_0,\boldsymbol{x}_1] = \boldsymbol{C}[\boldsymbol{x}]$$ to indicate that this the covariance of the vectors \( \boldsymbol{x} \) of the design/feature matrix \( \boldsymbol{X} \).
|
||||
|
||||
<p>
|
||||
It is easy to generalize this to a matrix \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \).
|
||||
|
||||
<p>
|
||||
@@ -239,7 +244,7 @@ It is easy to generalize this to a matrix \( \boldsymbol{X}\in {\mathbb{R}}^{n\t
|
||||
<li><a href="._DimRed-bs023.html">24</a></li>
|
||||
<li><a href="._DimRed-bs024.html">25</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs016.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
('Classical PCA Theorem', 2, None, '___sec15'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec16'),
|
||||
('Getting started with PCA', 2, None, '___sec17'),
|
||||
('Principal Component Analysis', 2, None, '___sec18'),
|
||||
('PCA and scikit-learn', 2, None, '___sec19'),
|
||||
('More on the PCA', 2, None, '___sec20'),
|
||||
('Incremental PCA', 2, None, '___sec21'),
|
||||
('Randomized PCA', 2, None, '___sec22'),
|
||||
('Kernel PCA', 2, None, '___sec23'),
|
||||
('LLE', 2, None, '___sec24'),
|
||||
('Other techniques', 2, None, '___sec25')]}
|
||||
('Towards the PCA theorem', 2, None, '___sec15'),
|
||||
('Classical PCA Theorem', 2, None, '___sec16'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec17'),
|
||||
('Getting started with PCA', 2, None, '___sec18'),
|
||||
('Principal Component Analysis', 2, None, '___sec19'),
|
||||
('PCA and scikit-learn', 2, None, '___sec20'),
|
||||
('More on the PCA', 2, None, '___sec21'),
|
||||
('Incremental PCA', 2, None, '___sec22'),
|
||||
('Randomized PCA', 2, None, '___sec23'),
|
||||
('Kernel PCA', 2, None, '___sec24'),
|
||||
('LLE', 2, None, '___sec25'),
|
||||
('Other techniques', 2, None, '___sec26')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -176,7 +178,40 @@ MathJax.Hub.Config({
|
||||
<a name="part0016"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec15" class="anchor">Classical PCA Theorem </h2>
|
||||
<h2 id="___sec15" class="anchor">Towards the PCA theorem </h2>
|
||||
|
||||
<p>
|
||||
We have that the covariance matrix (the correlation matrix involves a simple rescaling) is given as
|
||||
$$
|
||||
\boldsymbol{C}[\boldsymbol{x}] = \frac{1}{n}\boldsymbol{X}\boldsymbol{X}^T= \mathbb{E}[\boldsymbol{X}\boldsymbol{X}^T].
|
||||
$$
|
||||
|
||||
Let us now assume that we can perform a series of orthogonal transformations where we employ some orthogonal matrices \( \boldsymbol{S} \).
|
||||
These matrices are defined as \( \boldsymbol{S}\in {\mathbb{R}}^{p\times p} \) and obey the orthogonality requirements \( \boldsymbol{S}\boldsymbol{S}^T=\boldsymbol{S}^T\boldsymbol{S}=\boldsymbol{I} \). The matrix can be written out in terms of the column vectors \( \boldsymbol{s}_i \) as \( \boldsymbol{S}=[\boldsymbol{s}_0,\boldsymbol{s}_1,\dots,\boldsymbol{s}_{p-1}] \) and \( \boldsymbol{s}_i \in {\mathbb{R}}^{p} \).
|
||||
|
||||
<p>
|
||||
Assume also that there is a transformation \( \boldsymbol{S}\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}^T=\boldsymbol{C}[\boldsymbol{y}] \) such that the new matrix \( \boldsymbol{C}[\boldsymbol{y}] \) is diagonal with elements \( [\lambda_0,\lambda_1,\lambda_2,\dots,\lambda_{p-1}] \).
|
||||
|
||||
<p>
|
||||
That is we have
|
||||
$$
|
||||
\boldsymbol{C}[\boldsymbol{y}] = \mathbb{E}[\boldsymbol{S}\boldsymbol{X}\boldsymbol{X}^T\boldsymbol{S}^T]=\boldsymbol{S}\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}^T,
|
||||
$$
|
||||
|
||||
since the matrix \( \boldsymbol{S} \) is not a data dependent matrix. Multiplying with \( \boldsymbol{S}^T \) from the left we have
|
||||
$$
|
||||
\boldsymbol{S}^T\boldsymbol{C}[\boldsymbol{y}] = \boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}^T,
|
||||
$$
|
||||
|
||||
and since \( \boldsymbol{C}[\boldsymbol{y}] \) is diagonal we have for a given eigenvalue \( i \) of the covariance matrix that
|
||||
|
||||
$$
|
||||
\boldsymbol{S}^T_i\lambda_i = \boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}^T_i.
|
||||
$$
|
||||
|
||||
<p>
|
||||
In the derivation of the PCA theorem we will assume that the eigenvalues are ordered in descending order, that is
|
||||
\( \lambda_0 > \lambda_1 > \dots > \lambda_{p-1} \).
|
||||
|
||||
<p>
|
||||
<p>
|
||||
@@ -204,7 +239,7 @@ MathJax.Hub.Config({
|
||||
<li><a href="._DimRed-bs024.html">25</a></li>
|
||||
<li><a href="._DimRed-bs025.html">26</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs017.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
('Classical PCA Theorem', 2, None, '___sec15'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec16'),
|
||||
('Getting started with PCA', 2, None, '___sec17'),
|
||||
('Principal Component Analysis', 2, None, '___sec18'),
|
||||
('PCA and scikit-learn', 2, None, '___sec19'),
|
||||
('More on the PCA', 2, None, '___sec20'),
|
||||
('Incremental PCA', 2, None, '___sec21'),
|
||||
('Randomized PCA', 2, None, '___sec22'),
|
||||
('Kernel PCA', 2, None, '___sec23'),
|
||||
('LLE', 2, None, '___sec24'),
|
||||
('Other techniques', 2, None, '___sec25')]}
|
||||
('Towards the PCA theorem', 2, None, '___sec15'),
|
||||
('Classical PCA Theorem', 2, None, '___sec16'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec17'),
|
||||
('Getting started with PCA', 2, None, '___sec18'),
|
||||
('Principal Component Analysis', 2, None, '___sec19'),
|
||||
('PCA and scikit-learn', 2, None, '___sec20'),
|
||||
('More on the PCA', 2, None, '___sec21'),
|
||||
('Incremental PCA', 2, None, '___sec22'),
|
||||
('Randomized PCA', 2, None, '___sec23'),
|
||||
('Kernel PCA', 2, None, '___sec24'),
|
||||
('LLE', 2, None, '___sec25'),
|
||||
('Other techniques', 2, None, '___sec26')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -176,7 +178,7 @@ MathJax.Hub.Config({
|
||||
<a name="part0017"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec16" class="anchor">Prof of the PCA Theorem </h2>
|
||||
<h2 id="___sec16" class="anchor">Classical PCA Theorem </h2>
|
||||
|
||||
<p>
|
||||
<p>
|
||||
@@ -203,6 +205,8 @@ MathJax.Hub.Config({
|
||||
<li><a href="._DimRed-bs024.html">25</a></li>
|
||||
<li><a href="._DimRed-bs025.html">26</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs018.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
('Classical PCA Theorem', 2, None, '___sec15'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec16'),
|
||||
('Getting started with PCA', 2, None, '___sec17'),
|
||||
('Principal Component Analysis', 2, None, '___sec18'),
|
||||
('PCA and scikit-learn', 2, None, '___sec19'),
|
||||
('More on the PCA', 2, None, '___sec20'),
|
||||
('Incremental PCA', 2, None, '___sec21'),
|
||||
('Randomized PCA', 2, None, '___sec22'),
|
||||
('Kernel PCA', 2, None, '___sec23'),
|
||||
('LLE', 2, None, '___sec24'),
|
||||
('Other techniques', 2, None, '___sec25')]}
|
||||
('Towards the PCA theorem', 2, None, '___sec15'),
|
||||
('Classical PCA Theorem', 2, None, '___sec16'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec17'),
|
||||
('Getting started with PCA', 2, None, '___sec18'),
|
||||
('Principal Component Analysis', 2, None, '___sec19'),
|
||||
('PCA and scikit-learn', 2, None, '___sec20'),
|
||||
('More on the PCA', 2, None, '___sec21'),
|
||||
('Incremental PCA', 2, None, '___sec22'),
|
||||
('Randomized PCA', 2, None, '___sec23'),
|
||||
('Kernel PCA', 2, None, '___sec24'),
|
||||
('LLE', 2, None, '___sec25'),
|
||||
('Other techniques', 2, None, '___sec26')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -176,18 +178,8 @@ MathJax.Hub.Config({
|
||||
<a name="part0018"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec17" class="anchor">Getting started with PCA </h2>
|
||||
<h2 id="___sec17" class="anchor">Prof of the PCA Theorem </h2>
|
||||
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #408080; font-style: italic"># Now add PCA</span>
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.decomposition</span> <span style="color: #008000; font-weight: bold">import</span> PCA
|
||||
pca <span style="color: #666666">=</span> PCA(n_components <span style="color: #666666">=</span> <span style="color: #666666">2</span>)
|
||||
pca<span style="color: #666666">.</span>fit(X_train_scaled)
|
||||
|
||||
X_pca <span style="color: #666666">=</span> pca<span style="color: #666666">.</span>transform(X_train_scaled)
|
||||
</pre></div>
|
||||
<p>
|
||||
<p>
|
||||
<!-- navigation buttons at the bottom of the page -->
|
||||
@@ -212,6 +204,7 @@ X_pca <span style="color: #666666">=</span> pca<span style="color: #666666">.</s
|
||||
<li><a href="._DimRed-bs024.html">25</a></li>
|
||||
<li><a href="._DimRed-bs025.html">26</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs019.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
('Classical PCA Theorem', 2, None, '___sec15'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec16'),
|
||||
('Getting started with PCA', 2, None, '___sec17'),
|
||||
('Principal Component Analysis', 2, None, '___sec18'),
|
||||
('PCA and scikit-learn', 2, None, '___sec19'),
|
||||
('More on the PCA', 2, None, '___sec20'),
|
||||
('Incremental PCA', 2, None, '___sec21'),
|
||||
('Randomized PCA', 2, None, '___sec22'),
|
||||
('Kernel PCA', 2, None, '___sec23'),
|
||||
('LLE', 2, None, '___sec24'),
|
||||
('Other techniques', 2, None, '___sec25')]}
|
||||
('Towards the PCA theorem', 2, None, '___sec15'),
|
||||
('Classical PCA Theorem', 2, None, '___sec16'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec17'),
|
||||
('Getting started with PCA', 2, None, '___sec18'),
|
||||
('Principal Component Analysis', 2, None, '___sec19'),
|
||||
('PCA and scikit-learn', 2, None, '___sec20'),
|
||||
('More on the PCA', 2, None, '___sec21'),
|
||||
('Incremental PCA', 2, None, '___sec22'),
|
||||
('Randomized PCA', 2, None, '___sec23'),
|
||||
('Kernel PCA', 2, None, '___sec24'),
|
||||
('LLE', 2, None, '___sec25'),
|
||||
('Other techniques', 2, None, '___sec26')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -176,38 +178,17 @@ MathJax.Hub.Config({
|
||||
<a name="part0019"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec18" class="anchor">Principal Component Analysis </h2>
|
||||
<div class="panel panel-default">
|
||||
<div class="panel-body">
|
||||
<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
|
||||
Principal Component Analysis (PCA) is by far the most popular dimensionality reduction algorithm.
|
||||
First it identifies the hyperplane that lies closest to the data, and then it projects the data onto it.
|
||||
<h2 id="___sec18" class="anchor">Getting started with PCA </h2>
|
||||
|
||||
<p>
|
||||
The following Python code uses NumPy’s <b>svd()</b> function to obtain all the principal components of the
|
||||
training set, then extracts the first two principal components
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>X_centered <span style="color: #666666">=</span> X <span style="color: #666666">-</span> X<span style="color: #666666">.</span>mean(axis<span style="color: #666666">=0</span>)
|
||||
U, s, V <span style="color: #666666">=</span> np<span style="color: #666666">.</span>linalg<span style="color: #666666">.</span>svd(X_centered)
|
||||
c1 <span style="color: #666666">=</span> V<span style="color: #666666">.</span>T[:, <span style="color: #666666">0</span>]
|
||||
c2 <span style="color: #666666">=</span> V<span style="color: #666666">.</span>T[:, <span style="color: #666666">1</span>]
|
||||
</pre></div>
|
||||
<p>
|
||||
PCA assumes that the dataset is centered around the origin. Scikit-Learn’s PCA classes take care of centering
|
||||
the data for you. However, if you implement PCA yourself (as in the preceding example), or if you use other libraries, don’t
|
||||
forget to center the data first.
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #408080; font-style: italic"># Now add PCA</span>
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.decomposition</span> <span style="color: #008000; font-weight: bold">import</span> PCA
|
||||
pca <span style="color: #666666">=</span> PCA(n_components <span style="color: #666666">=</span> <span style="color: #666666">2</span>)
|
||||
pca<span style="color: #666666">.</span>fit(X_train_scaled)
|
||||
|
||||
<p>
|
||||
Once you have identified all the principal components, you can reduce the dimensionality of the dataset
|
||||
down to \( d \) dimensions by projecting it onto the hyperplane defined by the first \( d \) principal components.
|
||||
Selecting this hyperplane ensures that the projection will preserve as much variance as possible.
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>W2 <span style="color: #666666">=</span> V<span style="color: #666666">.</span>T[:, :<span style="color: #666666">2</span>]
|
||||
X2D <span style="color: #666666">=</span> X_centered<span style="color: #666666">.</span>dot(W2)
|
||||
X_pca <span style="color: #666666">=</span> pca<span style="color: #666666">.</span>transform(X_train_scaled)
|
||||
</pre></div>
|
||||
<p>
|
||||
<p>
|
||||
@@ -232,6 +213,7 @@ X2D <span style="color: #666666">=</span> X_centered<span style="color: #666666"
|
||||
<li><a href="._DimRed-bs024.html">25</a></li>
|
||||
<li><a href="._DimRed-bs025.html">26</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs020.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
('Classical PCA Theorem', 2, None, '___sec15'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec16'),
|
||||
('Getting started with PCA', 2, None, '___sec17'),
|
||||
('Principal Component Analysis', 2, None, '___sec18'),
|
||||
('PCA and scikit-learn', 2, None, '___sec19'),
|
||||
('More on the PCA', 2, None, '___sec20'),
|
||||
('Incremental PCA', 2, None, '___sec21'),
|
||||
('Randomized PCA', 2, None, '___sec22'),
|
||||
('Kernel PCA', 2, None, '___sec23'),
|
||||
('LLE', 2, None, '___sec24'),
|
||||
('Other techniques', 2, None, '___sec25')]}
|
||||
('Towards the PCA theorem', 2, None, '___sec15'),
|
||||
('Classical PCA Theorem', 2, None, '___sec16'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec17'),
|
||||
('Getting started with PCA', 2, None, '___sec18'),
|
||||
('Principal Component Analysis', 2, None, '___sec19'),
|
||||
('PCA and scikit-learn', 2, None, '___sec20'),
|
||||
('More on the PCA', 2, None, '___sec21'),
|
||||
('Incremental PCA', 2, None, '___sec22'),
|
||||
('Randomized PCA', 2, None, '___sec23'),
|
||||
('Kernel PCA', 2, None, '___sec24'),
|
||||
('LLE', 2, None, '___sec25'),
|
||||
('Other techniques', 2, None, '___sec26')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -174,36 +176,41 @@ MathJax.Hub.Config({
|
||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||||
|
||||
<a name="part0020"></a>
|
||||
<!-- !split -->
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec19" class="anchor">PCA and scikit-learn </h2>
|
||||
<h2 id="___sec19" class="anchor">Principal Component Analysis </h2>
|
||||
<div class="panel panel-default">
|
||||
<div class="panel-body">
|
||||
<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
|
||||
Principal Component Analysis (PCA) is by far the most popular dimensionality reduction algorithm.
|
||||
First it identifies the hyperplane that lies closest to the data, and then it projects the data onto it.
|
||||
|
||||
<p>
|
||||
Scikit-Learn’s PCA class implements PCA using SVD decomposition just like we did before. The
|
||||
following code applies PCA to reduce the dimensionality of the dataset down to two dimensions (note
|
||||
that it automatically takes care of centering the data):
|
||||
The following Python code uses NumPy’s <b>svd()</b> function to obtain all the principal components of the
|
||||
training set, then extracts the first two principal components
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.decomposition</span> <span style="color: #008000; font-weight: bold">import</span> PCA
|
||||
pca <span style="color: #666666">=</span> PCA(n_components <span style="color: #666666">=</span> <span style="color: #666666">2</span>)
|
||||
X2D <span style="color: #666666">=</span> pca<span style="color: #666666">.</span>fit_transform(X)
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>X_centered <span style="color: #666666">=</span> X <span style="color: #666666">-</span> X<span style="color: #666666">.</span>mean(axis<span style="color: #666666">=0</span>)
|
||||
U, s, V <span style="color: #666666">=</span> np<span style="color: #666666">.</span>linalg<span style="color: #666666">.</span>svd(X_centered)
|
||||
c1 <span style="color: #666666">=</span> V<span style="color: #666666">.</span>T[:, <span style="color: #666666">0</span>]
|
||||
c2 <span style="color: #666666">=</span> V<span style="color: #666666">.</span>T[:, <span style="color: #666666">1</span>]
|
||||
</pre></div>
|
||||
<p>
|
||||
After fitting the PCA transformer to the dataset, you can access the principal components using the
|
||||
components variable (note that it contains the PCs as horizontal vectors, so, for example, the first
|
||||
principal component is equal to
|
||||
PCA assumes that the dataset is centered around the origin. Scikit-Learn’s PCA classes take care of centering
|
||||
the data for you. However, if you implement PCA yourself (as in the preceding example), or if you use other libraries, don’t
|
||||
forget to center the data first.
|
||||
|
||||
<p>
|
||||
Once you have identified all the principal components, you can reduce the dimensionality of the dataset
|
||||
down to \( d \) dimensions by projecting it onto the hyperplane defined by the first \( d \) principal components.
|
||||
Selecting this hyperplane ensures that the projection will preserve as much variance as possible.
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>pca<span style="color: #666666">.</span>components_<span style="color: #666666">.</span>T[:, <span style="color: #666666">0</span>])<span style="color: #666666">.</span>
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>W2 <span style="color: #666666">=</span> V<span style="color: #666666">.</span>T[:, :<span style="color: #666666">2</span>]
|
||||
X2D <span style="color: #666666">=</span> X_centered<span style="color: #666666">.</span>dot(W2)
|
||||
</pre></div>
|
||||
<p>
|
||||
Another very useful piece of information is the explained variance ratio of each principal component,
|
||||
available via the \( explained\_variance\_ratio \) variable. It indicates the proportion of the dataset’s
|
||||
variance that lies along the axis of each principal component.
|
||||
More material to come here.
|
||||
|
||||
<p>
|
||||
<p>
|
||||
<!-- navigation buttons at the bottom of the page -->
|
||||
@@ -226,6 +233,7 @@ More material to come here.
|
||||
<li><a href="._DimRed-bs024.html">25</a></li>
|
||||
<li><a href="._DimRed-bs025.html">26</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs021.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
('Classical PCA Theorem', 2, None, '___sec15'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec16'),
|
||||
('Getting started with PCA', 2, None, '___sec17'),
|
||||
('Principal Component Analysis', 2, None, '___sec18'),
|
||||
('PCA and scikit-learn', 2, None, '___sec19'),
|
||||
('More on the PCA', 2, None, '___sec20'),
|
||||
('Incremental PCA', 2, None, '___sec21'),
|
||||
('Randomized PCA', 2, None, '___sec22'),
|
||||
('Kernel PCA', 2, None, '___sec23'),
|
||||
('LLE', 2, None, '___sec24'),
|
||||
('Other techniques', 2, None, '___sec25')]}
|
||||
('Towards the PCA theorem', 2, None, '___sec15'),
|
||||
('Classical PCA Theorem', 2, None, '___sec16'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec17'),
|
||||
('Getting started with PCA', 2, None, '___sec18'),
|
||||
('Principal Component Analysis', 2, None, '___sec19'),
|
||||
('PCA and scikit-learn', 2, None, '___sec20'),
|
||||
('More on the PCA', 2, None, '___sec21'),
|
||||
('Incremental PCA', 2, None, '___sec22'),
|
||||
('Randomized PCA', 2, None, '___sec23'),
|
||||
('Kernel PCA', 2, None, '___sec24'),
|
||||
('LLE', 2, None, '___sec25'),
|
||||
('Other techniques', 2, None, '___sec26')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -174,35 +176,36 @@ MathJax.Hub.Config({
|
||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||||
|
||||
<a name="part0021"></a>
|
||||
<!-- !split -->
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec20" class="anchor">More on the PCA </h2>
|
||||
<h2 id="___sec20" class="anchor">PCA and scikit-learn </h2>
|
||||
|
||||
<p>
|
||||
Instead of arbitrarily choosing the number of dimensions to reduce down to, it is generally preferable to
|
||||
choose the number of dimensions that add up to a sufficiently large portion of the variance (e.g., 95%).
|
||||
Unless, of course, you are reducing dimensionality for data visualization — in that case you will
|
||||
generally want to reduce the dimensionality down to 2 or 3.
|
||||
The following code computes PCA without reducing dimensionality, then computes the minimum number
|
||||
of dimensions required to preserve 95% of the training set’s variance:
|
||||
Scikit-Learn’s PCA class implements PCA using SVD decomposition just like we did before. The
|
||||
following code applies PCA to reduce the dimensionality of the dataset down to two dimensions (note
|
||||
that it automatically takes care of centering the data):
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>pca <span style="color: #666666">=</span> PCA()
|
||||
pca<span style="color: #666666">.</span>fit(X)
|
||||
cumsum <span style="color: #666666">=</span> np<span style="color: #666666">.</span>cumsum(pca<span style="color: #666666">.</span>explained_variance_ratio_)
|
||||
d <span style="color: #666666">=</span> np<span style="color: #666666">.</span>argmax(cumsum <span style="color: #666666">>=</span> <span style="color: #666666">0.95</span>) <span style="color: #666666">+</span> <span style="color: #666666">1</span>
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.decomposition</span> <span style="color: #008000; font-weight: bold">import</span> PCA
|
||||
pca <span style="color: #666666">=</span> PCA(n_components <span style="color: #666666">=</span> <span style="color: #666666">2</span>)
|
||||
X2D <span style="color: #666666">=</span> pca<span style="color: #666666">.</span>fit_transform(X)
|
||||
</pre></div>
|
||||
<p>
|
||||
You could then set \( n\_components=d \) and run PCA again. However, there is a much better option: instead
|
||||
of specifying the number of principal components you want to preserve, you can set \( n\_components \) to be
|
||||
a float between 0.0 and 1.0, indicating the ratio of variance you wish to preserve:
|
||||
After fitting the PCA transformer to the dataset, you can access the principal components using the
|
||||
components variable (note that it contains the PCs as horizontal vectors, so, for example, the first
|
||||
principal component is equal to
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>pca <span style="color: #666666">=</span> PCA(n_components<span style="color: #666666">=0.95</span>)
|
||||
X_reduced <span style="color: #666666">=</span> pca<span style="color: #666666">.</span>fit_transform(X)
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>pca<span style="color: #666666">.</span>components_<span style="color: #666666">.</span>T[:, <span style="color: #666666">0</span>])<span style="color: #666666">.</span>
|
||||
</pre></div>
|
||||
<p>
|
||||
Another very useful piece of information is the explained variance ratio of each principal component,
|
||||
available via the \( explained\_variance\_ratio \) variable. It indicates the proportion of the dataset’s
|
||||
variance that lies along the axis of each principal component.
|
||||
More material to come here.
|
||||
|
||||
<p>
|
||||
<p>
|
||||
<!-- navigation buttons at the bottom of the page -->
|
||||
@@ -224,6 +227,7 @@ X_reduced <span style="color: #666666">=</span> pca<span style="color: #666666">
|
||||
<li><a href="._DimRed-bs024.html">25</a></li>
|
||||
<li><a href="._DimRed-bs025.html">26</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs022.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
('Classical PCA Theorem', 2, None, '___sec15'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec16'),
|
||||
('Getting started with PCA', 2, None, '___sec17'),
|
||||
('Principal Component Analysis', 2, None, '___sec18'),
|
||||
('PCA and scikit-learn', 2, None, '___sec19'),
|
||||
('More on the PCA', 2, None, '___sec20'),
|
||||
('Incremental PCA', 2, None, '___sec21'),
|
||||
('Randomized PCA', 2, None, '___sec22'),
|
||||
('Kernel PCA', 2, None, '___sec23'),
|
||||
('LLE', 2, None, '___sec24'),
|
||||
('Other techniques', 2, None, '___sec25')]}
|
||||
('Towards the PCA theorem', 2, None, '___sec15'),
|
||||
('Classical PCA Theorem', 2, None, '___sec16'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec17'),
|
||||
('Getting started with PCA', 2, None, '___sec18'),
|
||||
('Principal Component Analysis', 2, None, '___sec19'),
|
||||
('PCA and scikit-learn', 2, None, '___sec20'),
|
||||
('More on the PCA', 2, None, '___sec21'),
|
||||
('Incremental PCA', 2, None, '___sec22'),
|
||||
('Randomized PCA', 2, None, '___sec23'),
|
||||
('Kernel PCA', 2, None, '___sec24'),
|
||||
('LLE', 2, None, '___sec25'),
|
||||
('Other techniques', 2, None, '___sec26')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -176,15 +178,33 @@ MathJax.Hub.Config({
|
||||
<a name="part0022"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec21" class="anchor">Incremental PCA </h2>
|
||||
<h2 id="___sec21" class="anchor">More on the PCA </h2>
|
||||
|
||||
<p>
|
||||
One problem with the preceding implementation of PCA is that it requires the whole training set to fit in
|
||||
memory in order for the SVD algorithm to run. Fortunately, Incremental PCA (IPCA) algorithms have
|
||||
been developed: you can split the training set into mini-batches and feed an IPCA algorithm one minibatch
|
||||
at a time. This is useful for large training sets, and also to apply PCA online (i.e., on the fly, as new
|
||||
instances arrive).
|
||||
Instead of arbitrarily choosing the number of dimensions to reduce down to, it is generally preferable to
|
||||
choose the number of dimensions that add up to a sufficiently large portion of the variance (e.g., 95%).
|
||||
Unless, of course, you are reducing dimensionality for data visualization — in that case you will
|
||||
generally want to reduce the dimensionality down to 2 or 3.
|
||||
The following code computes PCA without reducing dimensionality, then computes the minimum number
|
||||
of dimensions required to preserve 95% of the training set’s variance:
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>pca <span style="color: #666666">=</span> PCA()
|
||||
pca<span style="color: #666666">.</span>fit(X)
|
||||
cumsum <span style="color: #666666">=</span> np<span style="color: #666666">.</span>cumsum(pca<span style="color: #666666">.</span>explained_variance_ratio_)
|
||||
d <span style="color: #666666">=</span> np<span style="color: #666666">.</span>argmax(cumsum <span style="color: #666666">>=</span> <span style="color: #666666">0.95</span>) <span style="color: #666666">+</span> <span style="color: #666666">1</span>
|
||||
</pre></div>
|
||||
<p>
|
||||
You could then set \( n\_components=d \) and run PCA again. However, there is a much better option: instead
|
||||
of specifying the number of principal components you want to preserve, you can set \( n\_components \) to be
|
||||
a float between 0.0 and 1.0, indicating the ratio of variance you wish to preserve:
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>pca <span style="color: #666666">=</span> PCA(n_components<span style="color: #666666">=0.95</span>)
|
||||
X_reduced <span style="color: #666666">=</span> pca<span style="color: #666666">.</span>fit_transform(X)
|
||||
</pre></div>
|
||||
<p>
|
||||
<p>
|
||||
<!-- navigation buttons at the bottom of the page -->
|
||||
@@ -205,6 +225,7 @@ instances arrive).
|
||||
<li><a href="._DimRed-bs024.html">25</a></li>
|
||||
<li><a href="._DimRed-bs025.html">26</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs023.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
('Classical PCA Theorem', 2, None, '___sec15'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec16'),
|
||||
('Getting started with PCA', 2, None, '___sec17'),
|
||||
('Principal Component Analysis', 2, None, '___sec18'),
|
||||
('PCA and scikit-learn', 2, None, '___sec19'),
|
||||
('More on the PCA', 2, None, '___sec20'),
|
||||
('Incremental PCA', 2, None, '___sec21'),
|
||||
('Randomized PCA', 2, None, '___sec22'),
|
||||
('Kernel PCA', 2, None, '___sec23'),
|
||||
('LLE', 2, None, '___sec24'),
|
||||
('Other techniques', 2, None, '___sec25')]}
|
||||
('Towards the PCA theorem', 2, None, '___sec15'),
|
||||
('Classical PCA Theorem', 2, None, '___sec16'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec17'),
|
||||
('Getting started with PCA', 2, None, '___sec18'),
|
||||
('Principal Component Analysis', 2, None, '___sec19'),
|
||||
('PCA and scikit-learn', 2, None, '___sec20'),
|
||||
('More on the PCA', 2, None, '___sec21'),
|
||||
('Incremental PCA', 2, None, '___sec22'),
|
||||
('Randomized PCA', 2, None, '___sec23'),
|
||||
('Kernel PCA', 2, None, '___sec24'),
|
||||
('LLE', 2, None, '___sec25'),
|
||||
('Other techniques', 2, None, '___sec26')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -176,18 +178,14 @@ MathJax.Hub.Config({
|
||||
<a name="part0023"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec22" class="anchor">Randomized PCA </h2>
|
||||
<h2 id="___sec22" class="anchor">Incremental PCA </h2>
|
||||
|
||||
<p>
|
||||
Scikit-Learn offers yet another option to perform PCA, called Randomized PCA. This is a stochastic
|
||||
algorithm that quickly finds an approximation of the first d principal components. Its computational
|
||||
complexity is \( O(m \times d^2)+O(d^3) \), instead of \( O(m \times n^2) + O(n^3) \), so it is dramatically faster than the
|
||||
previous algorithms when \( d \) is much smaller than \( n \).
|
||||
|
||||
<p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
One problem with the preceding implementation of PCA is that it requires the whole training set to fit in
|
||||
memory in order for the SVD algorithm to run. Fortunately, Incremental PCA (IPCA) algorithms have
|
||||
been developed: you can split the training set into mini-batches and feed an IPCA algorithm one minibatch
|
||||
at a time. This is useful for large training sets, and also to apply PCA online (i.e., on the fly, as new
|
||||
instances arrive).
|
||||
|
||||
<p>
|
||||
<p>
|
||||
@@ -208,6 +206,7 @@ previous algorithms when \( d \) is much smaller than \( n \).
|
||||
<li><a href="._DimRed-bs024.html">25</a></li>
|
||||
<li><a href="._DimRed-bs025.html">26</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs024.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
('Classical PCA Theorem', 2, None, '___sec15'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec16'),
|
||||
('Getting started with PCA', 2, None, '___sec17'),
|
||||
('Principal Component Analysis', 2, None, '___sec18'),
|
||||
('PCA and scikit-learn', 2, None, '___sec19'),
|
||||
('More on the PCA', 2, None, '___sec20'),
|
||||
('Incremental PCA', 2, None, '___sec21'),
|
||||
('Randomized PCA', 2, None, '___sec22'),
|
||||
('Kernel PCA', 2, None, '___sec23'),
|
||||
('LLE', 2, None, '___sec24'),
|
||||
('Other techniques', 2, None, '___sec25')]}
|
||||
('Towards the PCA theorem', 2, None, '___sec15'),
|
||||
('Classical PCA Theorem', 2, None, '___sec16'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec17'),
|
||||
('Getting started with PCA', 2, None, '___sec18'),
|
||||
('Principal Component Analysis', 2, None, '___sec19'),
|
||||
('PCA and scikit-learn', 2, None, '___sec20'),
|
||||
('More on the PCA', 2, None, '___sec21'),
|
||||
('Incremental PCA', 2, None, '___sec22'),
|
||||
('Randomized PCA', 2, None, '___sec23'),
|
||||
('Kernel PCA', 2, None, '___sec24'),
|
||||
('LLE', 2, None, '___sec25'),
|
||||
('Other techniques', 2, None, '___sec26')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -176,28 +178,14 @@ MathJax.Hub.Config({
|
||||
<a name="part0024"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec23" class="anchor">Kernel PCA </h2>
|
||||
<div class="panel panel-default">
|
||||
<div class="panel-body">
|
||||
<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
|
||||
<h2 id="___sec23" class="anchor">Randomized PCA </h2>
|
||||
|
||||
<p>
|
||||
The kernel trick is a mathematical technique that implicitly maps instances into a
|
||||
very high-dimensional space (called the feature space), enabling nonlinear classification and regression
|
||||
with Support Vector Machines. Recall that a linear decision boundary in the high-dimensional feature
|
||||
space corresponds to a complex nonlinear decision boundary in the original space.
|
||||
It turns out that the same trick can be applied to PCA, making it possible to perform complex nonlinear
|
||||
projections for dimensionality reduction. This is called Kernel PCA (kPCA). It is often good at
|
||||
preserving clusters of instances after projection, or sometimes even unrolling datasets that lie close to a
|
||||
twisted manifold.
|
||||
For example, the following code uses Scikit-Learn’s KernelPCA class to perform kPCA with an
|
||||
<p>
|
||||
Scikit-Learn offers yet another option to perform PCA, called Randomized PCA. This is a stochastic
|
||||
algorithm that quickly finds an approximation of the first d principal components. Its computational
|
||||
complexity is \( O(m \times d^2)+O(d^3) \), instead of \( O(m \times n^2) + O(n^3) \), so it is dramatically faster than the
|
||||
previous algorithms when \( d \) is much smaller than \( n \).
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.decomposition</span> <span style="color: #008000; font-weight: bold">import</span> KernelPCA
|
||||
rbf_pca <span style="color: #666666">=</span> KernelPCA(n_components <span style="color: #666666">=</span> <span style="color: #666666">2</span>, kernel<span style="color: #666666">=</span><span style="color: #BA2121">"rbf"</span>, gamma<span style="color: #666666">=0.04</span>)
|
||||
X_reduced <span style="color: #666666">=</span> rbf_pca<span style="color: #666666">.</span>fit_transform(X)
|
||||
</pre></div>
|
||||
<p>
|
||||
</div>
|
||||
</div>
|
||||
@@ -221,6 +209,7 @@ X_reduced <span style="color: #666666">=</span> rbf_pca<span style="color: #6666
|
||||
<li class="active"><a href="._DimRed-bs024.html">25</a></li>
|
||||
<li><a href="._DimRed-bs025.html">26</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs025.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('More on the PCA', 2, None, '___sec20'),
|
||||
('Incremental PCA', 2, None, '___sec21'),
|
||||
('Randomized PCA', 2, None, '___sec22'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('LLE', 2, None, '___sec25'),
|
||||
('Other techniques', 2, None, '___sec26')]}
|
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end of tocinfo -->
|
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|
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<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -176,14 +178,32 @@ MathJax.Hub.Config({
|
||||
<a name="part0025"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec24" class="anchor">LLE </h2>
|
||||
<h2 id="___sec24" class="anchor">Kernel PCA </h2>
|
||||
<div class="panel panel-default">
|
||||
<div class="panel-body">
|
||||
<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
|
||||
|
||||
<p>
|
||||
Locally Linear Embedding (LLE) is another very powerful nonlinear dimensionality reduction
|
||||
(NLDR) technique. It is a Manifold Learning technique that does not rely on projections like the previous
|
||||
algorithms. In a nutshell, LLE works by first measuring how each training instance linearly relates to its
|
||||
closest neighbors (c.n.), and then looking for a low-dimensional representation of the training set where
|
||||
these local relationships are best preserved (more details shortly).
|
||||
The kernel trick is a mathematical technique that implicitly maps instances into a
|
||||
very high-dimensional space (called the feature space), enabling nonlinear classification and regression
|
||||
with Support Vector Machines. Recall that a linear decision boundary in the high-dimensional feature
|
||||
space corresponds to a complex nonlinear decision boundary in the original space.
|
||||
It turns out that the same trick can be applied to PCA, making it possible to perform complex nonlinear
|
||||
projections for dimensionality reduction. This is called Kernel PCA (kPCA). It is often good at
|
||||
preserving clusters of instances after projection, or sometimes even unrolling datasets that lie close to a
|
||||
twisted manifold.
|
||||
For example, the following code uses Scikit-Learn’s KernelPCA class to perform kPCA with an
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.decomposition</span> <span style="color: #008000; font-weight: bold">import</span> KernelPCA
|
||||
rbf_pca <span style="color: #666666">=</span> KernelPCA(n_components <span style="color: #666666">=</span> <span style="color: #666666">2</span>, kernel<span style="color: #666666">=</span><span style="color: #BA2121">"rbf"</span>, gamma<span style="color: #666666">=0.04</span>)
|
||||
X_reduced <span style="color: #666666">=</span> rbf_pca<span style="color: #666666">.</span>fit_transform(X)
|
||||
</pre></div>
|
||||
<p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
<p>
|
||||
<p>
|
||||
@@ -202,6 +222,7 @@ these local relationships are best preserved (more details shortly).
|
||||
<li><a href="._DimRed-bs024.html">25</a></li>
|
||||
<li class="active"><a href="._DimRed-bs025.html">26</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs026.html">»</a></li>
|
||||
</ul>
|
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<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
|
||||
<ul class="dropdown-menu">
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs001.html#___sec0" style="font-size: 80%;">Reducing the number of degrees of freedom, overarching view</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs002.html#___sec1" style="font-size: 80%;">Preprocessing our data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs003.html#___sec2" style="font-size: 80%;">More preprocessing</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs004.html#___sec3" style="font-size: 80%;">Simple preprocessing examples, Franke function and regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs005.html#___sec4" style="font-size: 80%;">Simple preprocessing examples, breast cancer data and classification, Support Vector Machines</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs006.html#___sec5" style="font-size: 80%;">More on Cancer Data, now with Logistic Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs007.html#___sec6" style="font-size: 80%;">Why should we think of reducing the dimensionality</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs008.html#___sec7" style="font-size: 80%;">Basic ideas of the Principal Component Analysis (PCA)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs009.html#___sec8" style="font-size: 80%;">Introducing the Covariance and Correlation functions</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs010.html#___sec9" style="font-size: 80%;">Correlation Function and Design/Feature Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs011.html#___sec10" style="font-size: 80%;">Covariance Matrix Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs012.html#___sec11" style="font-size: 80%;">Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
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||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
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<!-- !split -->
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||||
<h2 id="___sec25" class="anchor">LLE </h2>
|
||||
|
||||
<p>
|
||||
Locally Linear Embedding (LLE) is another very powerful nonlinear dimensionality reduction
|
||||
(NLDR) technique. It is a Manifold Learning technique that does not rely on projections like the previous
|
||||
algorithms. In a nutshell, LLE works by first measuring how each training instance linearly relates to its
|
||||
closest neighbors (c.n.), and then looking for a low-dimensional representation of the training set where
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these local relationships are best preserved (more details shortly).
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
('Randomized PCA', 2, None, '___sec23'),
|
||||
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|
||||
('LLE', 2, None, '___sec25'),
|
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('Other techniques', 2, None, '___sec26')]}
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<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
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<!-- navigation toc: --> <li><a href="._DimRed-bs001.html#___sec0" style="font-size: 80%;">Reducing the number of degrees of freedom, overarching view</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs002.html#___sec1" style="font-size: 80%;">Preprocessing our data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs003.html#___sec2" style="font-size: 80%;">More preprocessing</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs004.html#___sec3" style="font-size: 80%;">Simple preprocessing examples, Franke function and regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs005.html#___sec4" style="font-size: 80%;">Simple preprocessing examples, breast cancer data and classification, Support Vector Machines</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs006.html#___sec5" style="font-size: 80%;">More on Cancer Data, now with Logistic Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs007.html#___sec6" style="font-size: 80%;">Why should we think of reducing the dimensionality</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs008.html#___sec7" style="font-size: 80%;">Basic ideas of the Principal Component Analysis (PCA)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs009.html#___sec8" style="font-size: 80%;">Introducing the Covariance and Correlation functions</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs010.html#___sec9" style="font-size: 80%;">Correlation Function and Design/Feature Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs011.html#___sec10" style="font-size: 80%;">Covariance Matrix Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs012.html#___sec11" style="font-size: 80%;">Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
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</li>
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<p> </p><p> </p><p> </p> <!-- add vertical space -->
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<a name="part0027"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec26" class="anchor">Other techniques </h2>
|
||||
|
||||
<p>
|
||||
There are many other dimensionality reduction techniques, several of which are available in Scikit-Learn.
|
||||
|
||||
<p>
|
||||
Here are some of the most popular:
|
||||
|
||||
<ul>
|
||||
<li> <b>Multidimensional Scaling (MDS)</b> reduces dimensionality while trying to preserve the distances between the instances.</li>
|
||||
<li> <b>Isomap</b> creates a graph by connecting each instance to its nearest neighbors, then reduces dimensionality while trying to preserve the geodesic distances between the instances.</li>
|
||||
<li> <b>t-Distributed Stochastic Neighbor Embedding</b> (t-SNE) reduces dimensionality while trying to keep similar instances close and dissimilar instances apart. It is mostly used for visualization, in particular to visualize clusters of instances in high-dimensional space (e.g., to visualize the MNIST images in 2D).</li>
|
||||
<li> Linear Discriminant Analysis (LDA) is actually a classification algorithm, but during training it learns the most discriminative axes between the classes, and these axes can then be used to define a hyperplane onto which to project the data. The benefit is that the projection will keep classes as far apart as possible, so LDA is a good technique to reduce dimensionality before running another classification algorithm such as a Support Vector Machine (SVM) classifier discussed in the SVM lectures.</li>
|
||||
</ul>
|
||||
|
||||
Here are other examples where we use the <b>DataFrame</b> functionality to handle arrays, now with more interesting features for us, namely numbers. We set up a matrix
|
||||
of dimensionality \( 10\times 5 \) and compute the mean value and standard deviation of each column. Similarly, we can perform mathematial operations like squaring the matrix elements and many other operations.
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">np</span>
|
||||
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">pandas</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">pd</span>
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">IPython.display</span> <span style="color: #008000; font-weight: bold">import</span> display
|
||||
np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>seed(<span style="color: #666666">100</span>)
|
||||
<span style="color: #408080; font-style: italic"># setting up a 10 x 5 matrix</span>
|
||||
rows <span style="color: #666666">=</span> <span style="color: #666666">10</span>
|
||||
cols <span style="color: #666666">=</span> <span style="color: #666666">5</span>
|
||||
a <span style="color: #666666">=</span> np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>randn(rows,cols)
|
||||
df <span style="color: #666666">=</span> pd<span style="color: #666666">.</span>DataFrame(a)
|
||||
display(df)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(df<span style="color: #666666">.</span>mean())
|
||||
<span style="color: #008000; font-weight: bold">print</span>(df<span style="color: #666666">.</span>std())
|
||||
display(df<span style="color: #666666">**2</span>)
|
||||
</pre></div>
|
||||
<p>
|
||||
Thereafter we can select specific columns only and plot final results
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>df<span style="color: #666666">.</span>columns <span style="color: #666666">=</span> [<span style="color: #BA2121">'First'</span>, <span style="color: #BA2121">'Second'</span>, <span style="color: #BA2121">'Third'</span>, <span style="color: #BA2121">'Fourth'</span>, <span style="color: #BA2121">'Fifth'</span>]
|
||||
df<span style="color: #666666">.</span>index <span style="color: #666666">=</span> np<span style="color: #666666">.</span>arange(<span style="color: #666666">10</span>)
|
||||
|
||||
display(df)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(df[<span style="color: #BA2121">'Second'</span>]<span style="color: #666666">.</span>mean() )
|
||||
<span style="color: #008000; font-weight: bold">print</span>(df<span style="color: #666666">.</span>info())
|
||||
<span style="color: #008000; font-weight: bold">print</span>(df<span style="color: #666666">.</span>describe())
|
||||
|
||||
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">pylab</span> <span style="color: #008000; font-weight: bold">import</span> plt, mpl
|
||||
plt<span style="color: #666666">.</span>style<span style="color: #666666">.</span>use(<span style="color: #BA2121">'seaborn'</span>)
|
||||
mpl<span style="color: #666666">.</span>rcParams[<span style="color: #BA2121">'font.family'</span>] <span style="color: #666666">=</span> <span style="color: #BA2121">'serif'</span>
|
||||
|
||||
df<span style="color: #666666">.</span>cumsum()<span style="color: #666666">.</span>plot(lw<span style="color: #666666">=2.0</span>, figsize<span style="color: #666666">=</span>(<span style="color: #666666">10</span>,<span style="color: #666666">6</span>))
|
||||
plt<span style="color: #666666">.</span>show()
|
||||
|
||||
|
||||
df<span style="color: #666666">.</span>plot<span style="color: #666666">.</span>bar(figsize<span style="color: #666666">=</span>(<span style="color: #666666">10</span>,<span style="color: #666666">6</span>), rot<span style="color: #666666">=15</span>)
|
||||
plt<span style="color: #666666">.</span>show()
|
||||
</pre></div>
|
||||
<p>
|
||||
We can produce a \( 4\times 4 \) matrix
|
||||
<p>
|
||||
|
||||
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
|
||||
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>b <span style="color: #666666">=</span> np<span style="color: #666666">.</span>arange(<span style="color: #666666">16</span>)<span style="color: #666666">.</span>reshape((<span style="color: #666666">4</span>,<span style="color: #666666">4</span>))
|
||||
<span style="color: #008000; font-weight: bold">print</span>(b)
|
||||
df1 <span style="color: #666666">=</span> pd<span style="color: #666666">.</span>DataFrame(b)
|
||||
<span style="color: #008000; font-weight: bold">print</span>(df1)
|
||||
</pre></div>
|
||||
<p>
|
||||
and many other operations.
|
||||
|
||||
<p>
|
||||
<!-- navigation buttons at the bottom of the page -->
|
||||
<ul class="pagination">
|
||||
<li><a href="._DimRed-bs026.html">«</a></li>
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|
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|
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||||
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||||
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|
||||
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|
||||
|
||||
|
||||
@@ -87,17 +87,18 @@ Automatically generated HTML file from DocOnce source
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
('Classical PCA Theorem', 2, None, '___sec15'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec16'),
|
||||
('Getting started with PCA', 2, None, '___sec17'),
|
||||
('Principal Component Analysis', 2, None, '___sec18'),
|
||||
('PCA and scikit-learn', 2, None, '___sec19'),
|
||||
('More on the PCA', 2, None, '___sec20'),
|
||||
('Incremental PCA', 2, None, '___sec21'),
|
||||
('Randomized PCA', 2, None, '___sec22'),
|
||||
('Kernel PCA', 2, None, '___sec23'),
|
||||
('LLE', 2, None, '___sec24'),
|
||||
('Other techniques', 2, None, '___sec25')]}
|
||||
('Towards the PCA theorem', 2, None, '___sec15'),
|
||||
('Classical PCA Theorem', 2, None, '___sec16'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec17'),
|
||||
('Getting started with PCA', 2, None, '___sec18'),
|
||||
('Principal Component Analysis', 2, None, '___sec19'),
|
||||
('PCA and scikit-learn', 2, None, '___sec20'),
|
||||
('More on the PCA', 2, None, '___sec21'),
|
||||
('Incremental PCA', 2, None, '___sec22'),
|
||||
('Randomized PCA', 2, None, '___sec23'),
|
||||
('Kernel PCA', 2, None, '___sec24'),
|
||||
('LLE', 2, None, '___sec25'),
|
||||
('Other techniques', 2, None, '___sec26')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -150,17 +151,18 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">Other techniques</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -219,7 +221,7 @@ MathJax.Hub.Config({
|
||||
<li><a href="._DimRed-bs008.html">9</a></li>
|
||||
<li><a href="._DimRed-bs009.html">10</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._DimRed-bs026.html">27</a></li>
|
||||
<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs001.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -868,7 +868,7 @@ $$
|
||||
If we then compute the expectation value
|
||||
<p> <br>
|
||||
$$
|
||||
\mathbb{E}[\boldsymbol{X}\boldsymbol{X}^T] = \frac{1}{n}\boldsymbol{X}=\begin{bmatrix}
|
||||
\mathbb{E}[\boldsymbol{X}\boldsymbol{X}^T] = \frac{1}{n}\boldsymbol{X}\boldsymbol{X}^T=\begin{bmatrix}
|
||||
x_{00}^2+x_{01}^2 & x_{00}x_{10}+x_{01}x_{11}\\
|
||||
x_{10}x_{00}+x_{01}x_{11} & x_{10}^2+x_{11}^2\\
|
||||
\end{bmatrix},
|
||||
@@ -880,26 +880,77 @@ which is just
|
||||
$$
|
||||
\boldsymbol{C}[\boldsymbol{x}_0,\boldsymbol{x}_1] = \boldsymbol{C}[\boldsymbol{x}]=\begin{bmatrix} \mathrm{var}[\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_1] \\
|
||||
\mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_0] & \mathrm{var}[\boldsymbol{x}_1] \\
|
||||
\end{bmatrix}.
|
||||
\end{bmatrix},
|
||||
$$
|
||||
<p> <br>
|
||||
|
||||
where we wrote <p> <br>
|
||||
$$\boldsymbol{C}[\boldsymbol{x}_0,\boldsymbol{x}_1] = \boldsymbol{C}[\boldsymbol{x}]$$
|
||||
<p> <br> to indicate that this the covariance of the vectors \( \boldsymbol{x} \) of the design/feature matrix \( \boldsymbol{X} \).
|
||||
|
||||
<p>
|
||||
It is easy to generalize this to a matrix \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \).
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec15">Classical PCA Theorem </h2>
|
||||
<h2 id="___sec15">Towards the PCA theorem </h2>
|
||||
|
||||
<p>
|
||||
We have that the covariance matrix (the correlation matrix involves a simple rescaling) is given as
|
||||
<p> <br>
|
||||
$$
|
||||
\boldsymbol{C}[\boldsymbol{x}] = \frac{1}{n}\boldsymbol{X}\boldsymbol{X}^T= \mathbb{E}[\boldsymbol{X}\boldsymbol{X}^T].
|
||||
$$
|
||||
<p> <br>
|
||||
|
||||
Let us now assume that we can perform a series of orthogonal transformations where we employ some orthogonal matrices \( \boldsymbol{S} \).
|
||||
These matrices are defined as \( \boldsymbol{S}\in {\mathbb{R}}^{p\times p} \) and obey the orthogonality requirements \( \boldsymbol{S}\boldsymbol{S}^T=\boldsymbol{S}^T\boldsymbol{S}=\boldsymbol{I} \). The matrix can be written out in terms of the column vectors \( \boldsymbol{s}_i \) as \( \boldsymbol{S}=[\boldsymbol{s}_0,\boldsymbol{s}_1,\dots,\boldsymbol{s}_{p-1}] \) and \( \boldsymbol{s}_i \in {\mathbb{R}}^{p} \).
|
||||
|
||||
<p>
|
||||
Assume also that there is a transformation \( \boldsymbol{S}\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}^T=\boldsymbol{C}[\boldsymbol{y}] \) such that the new matrix \( \boldsymbol{C}[\boldsymbol{y}] \) is diagonal with elements \( [\lambda_0,\lambda_1,\lambda_2,\dots,\lambda_{p-1}] \).
|
||||
|
||||
<p>
|
||||
That is we have
|
||||
<p> <br>
|
||||
$$
|
||||
\boldsymbol{C}[\boldsymbol{y}] = \mathbb{E}[\boldsymbol{S}\boldsymbol{X}\boldsymbol{X}^T\boldsymbol{S}^T]=\boldsymbol{S}\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}^T,
|
||||
$$
|
||||
<p> <br>
|
||||
|
||||
since the matrix \( \boldsymbol{S} \) is not a data dependent matrix. Multiplying with \( \boldsymbol{S}^T \) from the left we have
|
||||
<p> <br>
|
||||
$$
|
||||
\boldsymbol{S}^T\boldsymbol{C}[\boldsymbol{y}] = \boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}^T,
|
||||
$$
|
||||
<p> <br>
|
||||
|
||||
and since \( \boldsymbol{C}[\boldsymbol{y}] \) is diagonal we have for a given eigenvalue \( i \) of the covariance matrix that
|
||||
|
||||
<p> <br>
|
||||
$$
|
||||
\boldsymbol{S}^T_i\lambda_i = \boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}^T_i.
|
||||
$$
|
||||
<p> <br>
|
||||
|
||||
<p>
|
||||
In the derivation of the PCA theorem we will assume that the eigenvalues are ordered in descending order, that is
|
||||
\( \lambda_0 > \lambda_1 > \dots > \lambda_{p-1} \).
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec16">Prof of the PCA Theorem </h2>
|
||||
<h2 id="___sec16">Classical PCA Theorem </h2>
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec17">Getting started with PCA </h2>
|
||||
<h2 id="___sec17">Prof of the PCA Theorem </h2>
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec18">Getting started with PCA </h2>
|
||||
|
||||
<p>
|
||||
|
||||
@@ -915,7 +966,7 @@ X_pca = pca.transform(X_train_scaled)
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec18">Principal Component Analysis </h2>
|
||||
<h2 id="___sec19">Principal Component Analysis </h2>
|
||||
<div class="alert alert-block alert-block alert-text-normal">
|
||||
<b></b>
|
||||
<p>
|
||||
@@ -952,7 +1003,7 @@ X2D = X_centered.dot(W2)
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec19">PCA and scikit-learn </h2>
|
||||
<h2 id="___sec20">PCA and scikit-learn </h2>
|
||||
|
||||
<p>
|
||||
Scikit-Learn’s PCA class implements PCA using SVD decomposition just like we did before. The
|
||||
@@ -983,7 +1034,7 @@ More material to come here.
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec20">More on the PCA </h2>
|
||||
<h2 id="___sec21">More on the PCA </h2>
|
||||
|
||||
<p>
|
||||
Instead of arbitrarily choosing the number of dimensions to reduce down to, it is generally preferable to
|
||||
@@ -1014,7 +1065,7 @@ X_reduced = pca.fit_transform(X)
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec21">Incremental PCA </h2>
|
||||
<h2 id="___sec22">Incremental PCA </h2>
|
||||
|
||||
<p>
|
||||
One problem with the preceding implementation of PCA is that it requires the whole training set to fit in
|
||||
@@ -1026,7 +1077,7 @@ instances arrive).
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec22">Randomized PCA </h2>
|
||||
<h2 id="___sec23">Randomized PCA </h2>
|
||||
|
||||
<p>
|
||||
Scikit-Learn offers yet another option to perform PCA, called Randomized PCA. This is a stochastic
|
||||
@@ -1040,7 +1091,7 @@ previous algorithms when \( d \) is much smaller than \( n \).
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec23">Kernel PCA </h2>
|
||||
<h2 id="___sec24">Kernel PCA </h2>
|
||||
<div class="alert alert-block alert-block alert-text-normal">
|
||||
<b></b>
|
||||
<p>
|
||||
@@ -1066,7 +1117,7 @@ X_reduced = rbf_pca.fit_transform(X)
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec24">LLE </h2>
|
||||
<h2 id="___sec25">LLE </h2>
|
||||
|
||||
<p>
|
||||
Locally Linear Embedding (LLE) is another very powerful nonlinear dimensionality reduction
|
||||
@@ -1078,7 +1129,7 @@ these local relationships are best preserved (more details shortly).
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec25">Other techniques </h2>
|
||||
<h2 id="___sec26">Other techniques </h2>
|
||||
|
||||
<p>
|
||||
There are many other dimensionality reduction techniques, several of which are available in Scikit-Learn.
|
||||
|
||||
@@ -107,17 +107,18 @@ div { text-align: justify; text-justify: inter-word; }
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
('Classical PCA Theorem', 2, None, '___sec15'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec16'),
|
||||
('Getting started with PCA', 2, None, '___sec17'),
|
||||
('Principal Component Analysis', 2, None, '___sec18'),
|
||||
('PCA and scikit-learn', 2, None, '___sec19'),
|
||||
('More on the PCA', 2, None, '___sec20'),
|
||||
('Incremental PCA', 2, None, '___sec21'),
|
||||
('Randomized PCA', 2, None, '___sec22'),
|
||||
('Kernel PCA', 2, None, '___sec23'),
|
||||
('LLE', 2, None, '___sec24'),
|
||||
('Other techniques', 2, None, '___sec25')]}
|
||||
('Towards the PCA theorem', 2, None, '___sec15'),
|
||||
('Classical PCA Theorem', 2, None, '___sec16'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec17'),
|
||||
('Getting started with PCA', 2, None, '___sec18'),
|
||||
('Principal Component Analysis', 2, None, '___sec19'),
|
||||
('PCA and scikit-learn', 2, None, '___sec20'),
|
||||
('More on the PCA', 2, None, '___sec21'),
|
||||
('Incremental PCA', 2, None, '___sec22'),
|
||||
('Randomized PCA', 2, None, '___sec23'),
|
||||
('Kernel PCA', 2, None, '___sec24'),
|
||||
('LLE', 2, None, '___sec25'),
|
||||
('Other techniques', 2, None, '___sec26')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -845,7 +846,7 @@ $$
|
||||
<p>
|
||||
If we then compute the expectation value
|
||||
$$
|
||||
\mathbb{E}[\boldsymbol{X}\boldsymbol{X}^T] = \frac{1}{n}\boldsymbol{X}=\begin{bmatrix}
|
||||
\mathbb{E}[\boldsymbol{X}\boldsymbol{X}^T] = \frac{1}{n}\boldsymbol{X}\boldsymbol{X}^T=\begin{bmatrix}
|
||||
x_{00}^2+x_{01}^2 & x_{00}x_{10}+x_{01}x_{11}\\
|
||||
x_{10}x_{00}+x_{01}x_{11} & x_{10}^2+x_{11}^2\\
|
||||
\end{bmatrix},
|
||||
@@ -855,25 +856,66 @@ which is just
|
||||
$$
|
||||
\boldsymbol{C}[\boldsymbol{x}_0,\boldsymbol{x}_1] = \boldsymbol{C}[\boldsymbol{x}]=\begin{bmatrix} \mathrm{var}[\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_1] \\
|
||||
\mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_0] & \mathrm{var}[\boldsymbol{x}_1] \\
|
||||
\end{bmatrix}.
|
||||
\end{bmatrix},
|
||||
$$
|
||||
|
||||
where we wrote $$\boldsymbol{C}[\boldsymbol{x}_0,\boldsymbol{x}_1] = \boldsymbol{C}[\boldsymbol{x}]$$ to indicate that this the covariance of the vectors \( \boldsymbol{x} \) of the design/feature matrix \( \boldsymbol{X} \).
|
||||
|
||||
<p>
|
||||
It is easy to generalize this to a matrix \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \).
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec15">Classical PCA Theorem </h2>
|
||||
<h2 id="___sec15">Towards the PCA theorem </h2>
|
||||
|
||||
<p>
|
||||
We have that the covariance matrix (the correlation matrix involves a simple rescaling) is given as
|
||||
$$
|
||||
\boldsymbol{C}[\boldsymbol{x}] = \frac{1}{n}\boldsymbol{X}\boldsymbol{X}^T= \mathbb{E}[\boldsymbol{X}\boldsymbol{X}^T].
|
||||
$$
|
||||
|
||||
Let us now assume that we can perform a series of orthogonal transformations where we employ some orthogonal matrices \( \boldsymbol{S} \).
|
||||
These matrices are defined as \( \boldsymbol{S}\in {\mathbb{R}}^{p\times p} \) and obey the orthogonality requirements \( \boldsymbol{S}\boldsymbol{S}^T=\boldsymbol{S}^T\boldsymbol{S}=\boldsymbol{I} \). The matrix can be written out in terms of the column vectors \( \boldsymbol{s}_i \) as \( \boldsymbol{S}=[\boldsymbol{s}_0,\boldsymbol{s}_1,\dots,\boldsymbol{s}_{p-1}] \) and \( \boldsymbol{s}_i \in {\mathbb{R}}^{p} \).
|
||||
|
||||
<p>
|
||||
Assume also that there is a transformation \( \boldsymbol{S}\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}^T=\boldsymbol{C}[\boldsymbol{y}] \) such that the new matrix \( \boldsymbol{C}[\boldsymbol{y}] \) is diagonal with elements \( [\lambda_0,\lambda_1,\lambda_2,\dots,\lambda_{p-1}] \).
|
||||
|
||||
<p>
|
||||
That is we have
|
||||
$$
|
||||
\boldsymbol{C}[\boldsymbol{y}] = \mathbb{E}[\boldsymbol{S}\boldsymbol{X}\boldsymbol{X}^T\boldsymbol{S}^T]=\boldsymbol{S}\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}^T,
|
||||
$$
|
||||
|
||||
since the matrix \( \boldsymbol{S} \) is not a data dependent matrix. Multiplying with \( \boldsymbol{S}^T \) from the left we have
|
||||
$$
|
||||
\boldsymbol{S}^T\boldsymbol{C}[\boldsymbol{y}] = \boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}^T,
|
||||
$$
|
||||
|
||||
and since \( \boldsymbol{C}[\boldsymbol{y}] \) is diagonal we have for a given eigenvalue \( i \) of the covariance matrix that
|
||||
|
||||
$$
|
||||
\boldsymbol{S}^T_i\lambda_i = \boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}^T_i.
|
||||
$$
|
||||
|
||||
<p>
|
||||
In the derivation of the PCA theorem we will assume that the eigenvalues are ordered in descending order, that is
|
||||
\( \lambda_0 > \lambda_1 > \dots > \lambda_{p-1} \).
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec16">Prof of the PCA Theorem </h2>
|
||||
<h2 id="___sec16">Classical PCA Theorem </h2>
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec17">Getting started with PCA </h2>
|
||||
<h2 id="___sec17">Prof of the PCA Theorem </h2>
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec18">Getting started with PCA </h2>
|
||||
|
||||
<p>
|
||||
|
||||
@@ -888,7 +930,7 @@ X_pca = pca.transform(X_train_scaled)
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec18">Principal Component Analysis </h2>
|
||||
<h2 id="___sec19">Principal Component Analysis </h2>
|
||||
<div class="alert alert-block alert-block alert-text-normal">
|
||||
<b></b>
|
||||
<p>
|
||||
@@ -924,7 +966,7 @@ X2D = X_centered.dot(W2)
|
||||
<p>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec19">PCA and scikit-learn </h2>
|
||||
<h2 id="___sec20">PCA and scikit-learn </h2>
|
||||
|
||||
<p>
|
||||
Scikit-Learn’s PCA class implements PCA using SVD decomposition just like we did before. The
|
||||
@@ -955,7 +997,7 @@ More material to come here.
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec20">More on the PCA </h2>
|
||||
<h2 id="___sec21">More on the PCA </h2>
|
||||
|
||||
<p>
|
||||
Instead of arbitrarily choosing the number of dimensions to reduce down to, it is generally preferable to
|
||||
@@ -985,7 +1027,7 @@ X_reduced = pca.fit_transform(X)
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec21">Incremental PCA </h2>
|
||||
<h2 id="___sec22">Incremental PCA </h2>
|
||||
|
||||
<p>
|
||||
One problem with the preceding implementation of PCA is that it requires the whole training set to fit in
|
||||
@@ -997,7 +1039,7 @@ instances arrive).
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec22">Randomized PCA </h2>
|
||||
<h2 id="___sec23">Randomized PCA </h2>
|
||||
|
||||
<p>
|
||||
Scikit-Learn offers yet another option to perform PCA, called Randomized PCA. This is a stochastic
|
||||
@@ -1012,7 +1054,7 @@ previous algorithms when \( d \) is much smaller than \( n \).
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec23">Kernel PCA </h2>
|
||||
<h2 id="___sec24">Kernel PCA </h2>
|
||||
<div class="alert alert-block alert-block alert-text-normal">
|
||||
<b></b>
|
||||
<p>
|
||||
@@ -1041,7 +1083,7 @@ X_reduced = rbf_pca.fit_transform(X)
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec24">LLE </h2>
|
||||
<h2 id="___sec25">LLE </h2>
|
||||
|
||||
<p>
|
||||
Locally Linear Embedding (LLE) is another very powerful nonlinear dimensionality reduction
|
||||
@@ -1053,7 +1095,7 @@ these local relationships are best preserved (more details shortly).
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec25">Other techniques </h2>
|
||||
<h2 id="___sec26">Other techniques </h2>
|
||||
|
||||
<p>
|
||||
There are many other dimensionality reduction techniques, several of which are available in Scikit-Learn.
|
||||
|
||||
@@ -112,17 +112,18 @@ div { text-align: justify; text-justify: inter-word; }
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
('Classical PCA Theorem', 2, None, '___sec15'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec16'),
|
||||
('Getting started with PCA', 2, None, '___sec17'),
|
||||
('Principal Component Analysis', 2, None, '___sec18'),
|
||||
('PCA and scikit-learn', 2, None, '___sec19'),
|
||||
('More on the PCA', 2, None, '___sec20'),
|
||||
('Incremental PCA', 2, None, '___sec21'),
|
||||
('Randomized PCA', 2, None, '___sec22'),
|
||||
('Kernel PCA', 2, None, '___sec23'),
|
||||
('LLE', 2, None, '___sec24'),
|
||||
('Other techniques', 2, None, '___sec25')]}
|
||||
('Towards the PCA theorem', 2, None, '___sec15'),
|
||||
('Classical PCA Theorem', 2, None, '___sec16'),
|
||||
('Prof of the PCA Theorem', 2, None, '___sec17'),
|
||||
('Getting started with PCA', 2, None, '___sec18'),
|
||||
('Principal Component Analysis', 2, None, '___sec19'),
|
||||
('PCA and scikit-learn', 2, None, '___sec20'),
|
||||
('More on the PCA', 2, None, '___sec21'),
|
||||
('Incremental PCA', 2, None, '___sec22'),
|
||||
('Randomized PCA', 2, None, '___sec23'),
|
||||
('Kernel PCA', 2, None, '___sec24'),
|
||||
('LLE', 2, None, '___sec25'),
|
||||
('Other techniques', 2, None, '___sec26')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -850,7 +851,7 @@ $$
|
||||
<p>
|
||||
If we then compute the expectation value
|
||||
$$
|
||||
\mathbb{E}[\boldsymbol{X}\boldsymbol{X}^T] = \frac{1}{n}\boldsymbol{X}=\begin{bmatrix}
|
||||
\mathbb{E}[\boldsymbol{X}\boldsymbol{X}^T] = \frac{1}{n}\boldsymbol{X}\boldsymbol{X}^T=\begin{bmatrix}
|
||||
x_{00}^2+x_{01}^2 & x_{00}x_{10}+x_{01}x_{11}\\
|
||||
x_{10}x_{00}+x_{01}x_{11} & x_{10}^2+x_{11}^2\\
|
||||
\end{bmatrix},
|
||||
@@ -860,25 +861,66 @@ which is just
|
||||
$$
|
||||
\boldsymbol{C}[\boldsymbol{x}_0,\boldsymbol{x}_1] = \boldsymbol{C}[\boldsymbol{x}]=\begin{bmatrix} \mathrm{var}[\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_1] \\
|
||||
\mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_0] & \mathrm{var}[\boldsymbol{x}_1] \\
|
||||
\end{bmatrix}.
|
||||
\end{bmatrix},
|
||||
$$
|
||||
|
||||
where we wrote $$\boldsymbol{C}[\boldsymbol{x}_0,\boldsymbol{x}_1] = \boldsymbol{C}[\boldsymbol{x}]$$ to indicate that this the covariance of the vectors \( \boldsymbol{x} \) of the design/feature matrix \( \boldsymbol{X} \).
|
||||
|
||||
<p>
|
||||
It is easy to generalize this to a matrix \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \).
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec15">Classical PCA Theorem </h2>
|
||||
<h2 id="___sec15">Towards the PCA theorem </h2>
|
||||
|
||||
<p>
|
||||
We have that the covariance matrix (the correlation matrix involves a simple rescaling) is given as
|
||||
$$
|
||||
\boldsymbol{C}[\boldsymbol{x}] = \frac{1}{n}\boldsymbol{X}\boldsymbol{X}^T= \mathbb{E}[\boldsymbol{X}\boldsymbol{X}^T].
|
||||
$$
|
||||
|
||||
Let us now assume that we can perform a series of orthogonal transformations where we employ some orthogonal matrices \( \boldsymbol{S} \).
|
||||
These matrices are defined as \( \boldsymbol{S}\in {\mathbb{R}}^{p\times p} \) and obey the orthogonality requirements \( \boldsymbol{S}\boldsymbol{S}^T=\boldsymbol{S}^T\boldsymbol{S}=\boldsymbol{I} \). The matrix can be written out in terms of the column vectors \( \boldsymbol{s}_i \) as \( \boldsymbol{S}=[\boldsymbol{s}_0,\boldsymbol{s}_1,\dots,\boldsymbol{s}_{p-1}] \) and \( \boldsymbol{s}_i \in {\mathbb{R}}^{p} \).
|
||||
|
||||
<p>
|
||||
Assume also that there is a transformation \( \boldsymbol{S}\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}^T=\boldsymbol{C}[\boldsymbol{y}] \) such that the new matrix \( \boldsymbol{C}[\boldsymbol{y}] \) is diagonal with elements \( [\lambda_0,\lambda_1,\lambda_2,\dots,\lambda_{p-1}] \).
|
||||
|
||||
<p>
|
||||
That is we have
|
||||
$$
|
||||
\boldsymbol{C}[\boldsymbol{y}] = \mathbb{E}[\boldsymbol{S}\boldsymbol{X}\boldsymbol{X}^T\boldsymbol{S}^T]=\boldsymbol{S}\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}^T,
|
||||
$$
|
||||
|
||||
since the matrix \( \boldsymbol{S} \) is not a data dependent matrix. Multiplying with \( \boldsymbol{S}^T \) from the left we have
|
||||
$$
|
||||
\boldsymbol{S}^T\boldsymbol{C}[\boldsymbol{y}] = \boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}^T,
|
||||
$$
|
||||
|
||||
and since \( \boldsymbol{C}[\boldsymbol{y}] \) is diagonal we have for a given eigenvalue \( i \) of the covariance matrix that
|
||||
|
||||
$$
|
||||
\boldsymbol{S}^T_i\lambda_i = \boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}^T_i.
|
||||
$$
|
||||
|
||||
<p>
|
||||
In the derivation of the PCA theorem we will assume that the eigenvalues are ordered in descending order, that is
|
||||
\( \lambda_0 > \lambda_1 > \dots > \lambda_{p-1} \).
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec16">Prof of the PCA Theorem </h2>
|
||||
<h2 id="___sec16">Classical PCA Theorem </h2>
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec17">Getting started with PCA </h2>
|
||||
<h2 id="___sec17">Prof of the PCA Theorem </h2>
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec18">Getting started with PCA </h2>
|
||||
|
||||
<p>
|
||||
|
||||
@@ -893,7 +935,7 @@ X_pca <span style="color: #666666">=</span> pca<span style="color: #666666">.</s
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec18">Principal Component Analysis </h2>
|
||||
<h2 id="___sec19">Principal Component Analysis </h2>
|
||||
<div class="alert alert-block alert-block alert-text-normal">
|
||||
<b></b>
|
||||
<p>
|
||||
@@ -929,7 +971,7 @@ X2D <span style="color: #666666">=</span> X_centered<span style="color: #666666"
|
||||
<p>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec19">PCA and scikit-learn </h2>
|
||||
<h2 id="___sec20">PCA and scikit-learn </h2>
|
||||
|
||||
<p>
|
||||
Scikit-Learn’s PCA class implements PCA using SVD decomposition just like we did before. The
|
||||
@@ -960,7 +1002,7 @@ More material to come here.
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec20">More on the PCA </h2>
|
||||
<h2 id="___sec21">More on the PCA </h2>
|
||||
|
||||
<p>
|
||||
Instead of arbitrarily choosing the number of dimensions to reduce down to, it is generally preferable to
|
||||
@@ -990,7 +1032,7 @@ X_reduced <span style="color: #666666">=</span> pca<span style="color: #666666">
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec21">Incremental PCA </h2>
|
||||
<h2 id="___sec22">Incremental PCA </h2>
|
||||
|
||||
<p>
|
||||
One problem with the preceding implementation of PCA is that it requires the whole training set to fit in
|
||||
@@ -1002,7 +1044,7 @@ instances arrive).
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec22">Randomized PCA </h2>
|
||||
<h2 id="___sec23">Randomized PCA </h2>
|
||||
|
||||
<p>
|
||||
Scikit-Learn offers yet another option to perform PCA, called Randomized PCA. This is a stochastic
|
||||
@@ -1017,7 +1059,7 @@ previous algorithms when \( d \) is much smaller than \( n \).
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec23">Kernel PCA </h2>
|
||||
<h2 id="___sec24">Kernel PCA </h2>
|
||||
<div class="alert alert-block alert-block alert-text-normal">
|
||||
<b></b>
|
||||
<p>
|
||||
@@ -1046,7 +1088,7 @@ X_reduced <span style="color: #666666">=</span> rbf_pca<span style="color: #6666
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec24">LLE </h2>
|
||||
<h2 id="___sec25">LLE </h2>
|
||||
|
||||
<p>
|
||||
Locally Linear Embedding (LLE) is another very powerful nonlinear dimensionality reduction
|
||||
@@ -1058,7 +1100,7 @@ these local relationships are best preserved (more details shortly).
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec25">Other techniques </h2>
|
||||
<h2 id="___sec26">Other techniques </h2>
|
||||
|
||||
<p>
|
||||
There are many other dimensionality reduction techniques, several of which are available in Scikit-Learn.
|
||||
|
||||
@@ -892,7 +892,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"$$\n",
|
||||
"\\mathbb{E}[\\boldsymbol{X}\\boldsymbol{X}^T] = \\frac{1}{n}\\boldsymbol{X}=\\begin{bmatrix}\n",
|
||||
"\\mathbb{E}[\\boldsymbol{X}\\boldsymbol{X}^T] = \\frac{1}{n}\\boldsymbol{X}\\boldsymbol{X}^T=\\begin{bmatrix}\n",
|
||||
"x_{00}^2+x_{01}^2 & x_{00}x_{10}+x_{01}x_{11}\\\\\n",
|
||||
"x_{10}x_{00}+x_{01}x_{11} & x_{10}^2+x_{11}^2\\\\\n",
|
||||
"\\end{bmatrix},\n",
|
||||
@@ -913,7 +913,7 @@
|
||||
"$$\n",
|
||||
"\\boldsymbol{C}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] = \\boldsymbol{C}[\\boldsymbol{x}]=\\begin{bmatrix} \\mathrm{var}[\\boldsymbol{x}_0] & \\mathrm{cov}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] \\\\\n",
|
||||
" \\mathrm{cov}[\\boldsymbol{x}_1,\\boldsymbol{x}_0] & \\mathrm{var}[\\boldsymbol{x}_1] \\\\\n",
|
||||
" \\end{bmatrix}.\n",
|
||||
" \\end{bmatrix},\n",
|
||||
"$$"
|
||||
]
|
||||
},
|
||||
@@ -921,12 +921,89 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"where we wrote $$\\boldsymbol{C}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] = \\boldsymbol{C}[\\boldsymbol{x}]$$ to indicate that this the covariance of the vectors $\\boldsymbol{x}$ of the design/feature matrix $\\boldsymbol{X}$.\n",
|
||||
"\n",
|
||||
"It is easy to generalize this to a matrix $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"## Towards the PCA theorem\n",
|
||||
"\n",
|
||||
"We have that the covariance matrix (the correlation matrix involves a simple rescaling) is given as"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"$$\n",
|
||||
"\\boldsymbol{C}[\\boldsymbol{x}] = \\frac{1}{n}\\boldsymbol{X}\\boldsymbol{X}^T= \\mathbb{E}[\\boldsymbol{X}\\boldsymbol{X}^T].\n",
|
||||
"$$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Let us now assume that we can perform a series of orthogonal transformations where we employ some orthogonal matrices $\\boldsymbol{S}$.\n",
|
||||
"These matrices are defined as $\\boldsymbol{S}\\in {\\mathbb{R}}^{p\\times p}$ and obey the orthogonality requirements $\\boldsymbol{S}\\boldsymbol{S}^T=\\boldsymbol{S}^T\\boldsymbol{S}=\\boldsymbol{I}$. The matrix can be written out in terms of the column vectors $\\boldsymbol{s}_i$ as $\\boldsymbol{S}=[\\boldsymbol{s}_0,\\boldsymbol{s}_1,\\dots,\\boldsymbol{s}_{p-1}]$ and $\\boldsymbol{s}_i \\in {\\mathbb{R}}^{p}$.\n",
|
||||
"\n",
|
||||
"Assume also that there is a transformation $\\boldsymbol{S}\\boldsymbol{C}[\\boldsymbol{x}]\\boldsymbol{S}^T=\\boldsymbol{C}[\\boldsymbol{y}]$ such that the new matrix $\\boldsymbol{C}[\\boldsymbol{y}]$ is diagonal with elements $[\\lambda_0,\\lambda_1,\\lambda_2,\\dots,\\lambda_{p-1}]$. \n",
|
||||
"\n",
|
||||
"That is we have"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"$$\n",
|
||||
"\\boldsymbol{C}[\\boldsymbol{y}] = \\mathbb{E}[\\boldsymbol{S}\\boldsymbol{X}\\boldsymbol{X}^T\\boldsymbol{S}^T]=\\boldsymbol{S}\\boldsymbol{C}[\\boldsymbol{x}]\\boldsymbol{S}^T,\n",
|
||||
"$$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"since the matrix $\\boldsymbol{S}$ is not a data dependent matrix. Multiplying with $\\boldsymbol{S}^T$ from the left we have"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"$$\n",
|
||||
"\\boldsymbol{S}^T\\boldsymbol{C}[\\boldsymbol{y}] = \\boldsymbol{C}[\\boldsymbol{x}]\\boldsymbol{S}^T,\n",
|
||||
"$$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"and since $\\boldsymbol{C}[\\boldsymbol{y}]$ is diagonal we have for a given eigenvalue $i$ of the covariance matrix that"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"$$\n",
|
||||
"\\boldsymbol{S}^T_i\\lambda_i = \\boldsymbol{C}[\\boldsymbol{x}]\\boldsymbol{S}^T_i.\n",
|
||||
"$$"
|
||||
]
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|
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|
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"In the derivation of the PCA theorem we will assume that the eigenvalues are ordered in descending order, that is\n",
|
||||
"$\\lambda_0 > \\lambda_1 > \\dots > \\lambda_{p-1}$. \n",
|
||||
"\n",
|
||||
"## Classical PCA Theorem\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"## Prof of the PCA Theorem\n",
|
||||
"\n",
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"\n",
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
('Other techniques', 2, None, '___sec26')]}
|
||||
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|
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|
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|
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<!-- Bootstrap navigation bar -->
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<button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".navbar-responsive-collapse">
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<a class="navbar-brand" href="DimRed-bs.html">Data Analysis and Machine Learning: Preprocessing and Dimensionality Reduction</a>
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|
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<ul class="nav navbar-nav navbar-right">
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<li class="dropdown">
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<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
|
||||
<ul class="dropdown-menu">
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs001.html#___sec0" style="font-size: 80%;">Reducing the number of degrees of freedom, overarching view</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs002.html#___sec1" style="font-size: 80%;">Preprocessing our data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs003.html#___sec2" style="font-size: 80%;">More preprocessing</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs004.html#___sec3" style="font-size: 80%;">Simple preprocessing examples, Franke function and regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs005.html#___sec4" style="font-size: 80%;">Simple preprocessing examples, breast cancer data and classification, Support Vector Machines</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs006.html#___sec5" style="font-size: 80%;">More on Cancer Data, now with Logistic Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs007.html#___sec6" style="font-size: 80%;">Why should we think of reducing the dimensionality</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs008.html#___sec7" style="font-size: 80%;">Basic ideas of the Principal Component Analysis (PCA)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs009.html#___sec8" style="font-size: 80%;">Introducing the Covariance and Correlation functions</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs010.html#___sec9" style="font-size: 80%;">Correlation Function and Design/Feature Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs011.html#___sec10" style="font-size: 80%;">Covariance Matrix Examples</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs012.html#___sec11" style="font-size: 80%;">Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs013.html#___sec12" style="font-size: 80%;">Correlation Matrix with Pandas</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs014.html#___sec13" style="font-size: 80%;">Correlation Matrix with Pandas and the Franke function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs015.html#___sec14" style="font-size: 80%;">Rewriting the Covariance and/or Correlation Matrix</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs016.html#___sec15" style="font-size: 80%;">Towards the PCA theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs017.html#___sec16" style="font-size: 80%;">Classical PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs018.html#___sec17" style="font-size: 80%;">Prof of the PCA Theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs019.html#___sec18" style="font-size: 80%;">Getting started with PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs020.html#___sec19" style="font-size: 80%;">Principal Component Analysis</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs021.html#___sec20" style="font-size: 80%;">PCA and scikit-learn</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs022.html#___sec21" style="font-size: 80%;">More on the PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs023.html#___sec22" style="font-size: 80%;">Incremental PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs024.html#___sec23" style="font-size: 80%;">Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs025.html#___sec24" style="font-size: 80%;">Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs026.html#___sec25" style="font-size: 80%;">LLE</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._DimRed-bs027.html#___sec26" style="font-size: 80%;">Other techniques</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div> <!-- end of navigation bar -->
|
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|
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<div class="container">
|
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|
||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||||
|
||||
<a name="part0000"></a>
|
||||
<!-- ------------------- main content ---------------------- -->
|
||||
|
||||
|
||||
|
||||
<div class="jumbotron">
|
||||
<center><h1>Data Analysis and Machine Learning: Preprocessing and Dimensionality Reduction</h1></center> <!-- document title -->
|
||||
|
||||
<p>
|
||||
<!-- author(s): Morten Hjorth-Jensen -->
|
||||
|
||||
<center>
|
||||
<b>Morten Hjorth-Jensen</b> [1, 2]
|
||||
</center>
|
||||
|
||||
<p>
|
||||
<!-- institution(s) -->
|
||||
|
||||
<center>[1] <b>Department of Physics, University of Oslo</b></center>
|
||||
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
|
||||
<br>
|
||||
<p>
|
||||
<center><h4>Oct 22, 2019</h4></center> <!-- date -->
|
||||
<br>
|
||||
<p>
|
||||
|
||||
|
||||
<p><a href="._DimRed-bs001.html" class="btn btn-primary btn-lg">Read »</a></p>
|
||||
|
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|
||||
</div> <!-- end jumbotron -->
|
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|
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<p>
|
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<!-- navigation buttons at the bottom of the page -->
|
||||
<ul class="pagination">
|
||||
<li class="active"><a href="._DimRed-bs000.html">1</a></li>
|
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<li><a href="._DimRed-bs001.html">2</a></li>
|
||||
<li><a href="._DimRed-bs002.html">3</a></li>
|
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<li><a href="._DimRed-bs003.html">4</a></li>
|
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<li><a href="._DimRed-bs004.html">5</a></li>
|
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<li><a href="._DimRed-bs005.html">6</a></li>
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<li><a href="._DimRed-bs006.html">7</a></li>
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<li><a href="._DimRed-bs007.html">8</a></li>
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<li><a href="._DimRed-bs008.html">9</a></li>
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<li><a href="._DimRed-bs009.html">10</a></li>
|
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<li><a href="">...</a></li>
|
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<li><a href="._DimRed-bs027.html">28</a></li>
|
||||
<li><a href="._DimRed-bs001.html">»</a></li>
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</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
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|
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</div> <!-- end container -->
|
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<!-- include javascript, jQuery *first* -->
|
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<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js"></script>
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<script src="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/js/bootstrap.min.js"></script>
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<!-- Bootstrap footer
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||||
<footer>
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||||
<a href="http://..."><img width="250" align=right src="http://..."></a>
|
||||
</footer>
|
||||
-->
|
||||
|
||||
|
||||
<center style="font-size:80%">
|
||||
<!-- copyright --> © 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
|
||||
</center>
|
||||
|
||||
|
||||
</body>
|
||||
</html>
|
||||
|
||||
|
||||
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@@ -647,7 +647,7 @@ x_{10} & x_{11}\\
|
||||
If we then compute the expectation value
|
||||
!bt
|
||||
\[
|
||||
\mathbb{E}[\bm{X}\bm{X}^T] = \frac{1}{n}\bm{X}=\begin{bmatrix}
|
||||
\mathbb{E}[\bm{X}\bm{X}^T] = \frac{1}{n}\bm{X}\bm{X}^T=\begin{bmatrix}
|
||||
x_{00}^2+x_{01}^2 & x_{00}x_{10}+x_{01}x_{11}\\
|
||||
x_{10}x_{00}+x_{01}x_{11} & x_{10}^2+x_{11}^2\\
|
||||
\end{bmatrix},
|
||||
@@ -658,16 +658,56 @@ which is just
|
||||
\[
|
||||
\bm{C}[\bm{x}_0,\bm{x}_1] = \bm{C}[\bm{x}]=\begin{bmatrix} \mathrm{var}[\bm{x}_0] & \mathrm{cov}[\bm{x}_0,\bm{x}_1] \\
|
||||
\mathrm{cov}[\bm{x}_1,\bm{x}_0] & \mathrm{var}[\bm{x}_1] \\
|
||||
\end{bmatrix}.
|
||||
\end{bmatrix},
|
||||
\]
|
||||
!et
|
||||
where we wrote $$\bm{C}[\bm{x}_0,\bm{x}_1] = \bm{C}[\bm{x}]$$ to indicate that this the covariance of the vectors $\bm{x}$ of the design/feature matrix $\bm{X}$.
|
||||
|
||||
It is easy to generalize this to a matrix $\bm{X}\in {\mathbb{R}}^{n\times p}$.
|
||||
|
||||
|
||||
!split
|
||||
===== Towards the PCA theorem =====
|
||||
|
||||
We have that the covariance matrix (the correlation matrix involves a simple rescaling) is given as
|
||||
!bt
|
||||
\[
|
||||
\bm{C}[\bm{x}] = \frac{1}{n}\bm{X}\bm{X}^T= \mathbb{E}[\bm{X}\bm{X}^T].
|
||||
\]
|
||||
!et
|
||||
Let us now assume that we can perform a series of orthogonal transformations where we employ some orthogonal matrices $\bm{S}$.
|
||||
These matrices are defined as $\bm{S}\in {\mathbb{R}}^{p\times p}$ and obey the orthogonality requirements $\bm{S}\bm{S}^T=\bm{S}^T\bm{S}=\bm{I}$. The matrix can be written out in terms of the column vectors $\bm{s}_i$ as $\bm{S}=[\bm{s}_0,\bm{s}_1,\dots,\bm{s}_{p-1}]$ and $\bm{s}_i \in {\mathbb{R}}^{p}$.
|
||||
|
||||
Assume also that there is a transformation $\bm{S}\bm{C}[\bm{x}]\bm{S}^T=\bm{C}[\bm{y}]$ such that the new matrix $\bm{C}[\bm{y}]$ is diagonal with elements $[\lambda_0,\lambda_1,\lambda_2,\dots,\lambda_{p-1}]$.
|
||||
|
||||
That is we have
|
||||
!bt
|
||||
\[
|
||||
\bm{C}[\bm{y}] = \mathbb{E}[\bm{S}\bm{X}\bm{X}^T\bm{S}^T]=\bm{S}\bm{C}[\bm{x}]\bm{S}^T,
|
||||
\]
|
||||
!et
|
||||
since the matrix $\bm{S}$ is not a data dependent matrix. Multiplying with $\bm{S}^T$ from the left we have
|
||||
!bt
|
||||
\[
|
||||
\bm{S}^T\bm{C}[\bm{y}] = \bm{C}[\bm{x}]\bm{S}^T,
|
||||
\]
|
||||
!et
|
||||
and since $\bm{C}[\bm{y}]$ is diagonal we have for a given eigenvalue $i$ of the covariance matrix that
|
||||
|
||||
!bt
|
||||
\[
|
||||
\bm{S}^T_i\lambda_i = \bm{C}[\bm{x}]\bm{S}^T_i.
|
||||
\]
|
||||
!et
|
||||
|
||||
In the derivation of the PCA theorem we will assume that the eigenvalues are ordered in descending order, that is
|
||||
$\lambda_0 > \lambda_1 > \dots > \lambda_{p-1}$.
|
||||
|
||||
!split
|
||||
===== Classical PCA Theorem =====
|
||||
|
||||
|
||||
|
||||
!split
|
||||
===== Prof of the PCA Theorem =====
|
||||
|
||||
|
||||
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|
||||
This IPython notebook DimRed.ipynb does not require any additional
|
||||
programs.
|
||||
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@@ -0,0 +1,8 @@
|
||||
.DS_Store
|
||||
.svn
|
||||
log/*.log
|
||||
tmp/**
|
||||
node_modules/
|
||||
.sass-cache
|
||||
css/reveal.min.css
|
||||
js/reveal.min.js
|
||||
@@ -0,0 +1,5 @@
|
||||
language: node_js
|
||||
node_js:
|
||||
- 0.10
|
||||
before_script:
|
||||
- npm install -g grunt-cli
|
||||
@@ -0,0 +1,23 @@
|
||||
## Contributing
|
||||
|
||||
Please keep the [issue tracker](http://github.com/hakimel/reveal.js/issues) limited to **bug reports**, **feature requests** and **pull requests**.
|
||||
|
||||
|
||||
### Personal Support
|
||||
If you have personal support or setup questions the best place to ask those are [StackOverflow](http://stackoverflow.com/questions/tagged/reveal.js).
|
||||
|
||||
|
||||
### Bug Reports
|
||||
When reporting a bug make sure to include information about which browser and operating system you are on as well as the necessary steps to reproduce the issue. If possible please include a link to a sample presentation where the bug can be tested.
|
||||
|
||||
|
||||
### Pull Requests
|
||||
- Should follow the coding style of the file you work in, most importantly:
|
||||
- Tabs to indent
|
||||
- Single-quoted strings
|
||||
- Should be made towards the **dev branch**
|
||||
- Should be submitted from a feature/topic branch (not your master)
|
||||
|
||||
|
||||
### Plugins
|
||||
Please do not submit plugins as pull requests. They should be maintained in their own separate repository. More information here: https://github.com/hakimel/reveal.js/wiki/Plugin-Guidelines
|
||||
@@ -0,0 +1,140 @@
|
||||
/* global module:false */
|
||||
module.exports = function(grunt) {
|
||||
var port = grunt.option('port') || 8000;
|
||||
// Project configuration
|
||||
grunt.initConfig({
|
||||
pkg: grunt.file.readJSON('package.json'),
|
||||
meta: {
|
||||
banner:
|
||||
'/*!\n' +
|
||||
' * reveal.js <%= pkg.version %> (<%= grunt.template.today("yyyy-mm-dd, HH:MM") %>)\n' +
|
||||
' * http://lab.hakim.se/reveal-js\n' +
|
||||
' * MIT licensed\n' +
|
||||
' *\n' +
|
||||
' * Copyright (C) 2014 Hakim El Hattab, http://hakim.se\n' +
|
||||
' */'
|
||||
},
|
||||
|
||||
qunit: {
|
||||
files: [ 'test/*.html' ]
|
||||
},
|
||||
|
||||
uglify: {
|
||||
options: {
|
||||
banner: '<%= meta.banner %>\n'
|
||||
},
|
||||
build: {
|
||||
src: 'js/reveal.js',
|
||||
dest: 'js/reveal.min.js'
|
||||
}
|
||||
},
|
||||
|
||||
cssmin: {
|
||||
compress: {
|
||||
files: {
|
||||
'css/reveal.min.css': [ 'css/reveal.css' ]
|
||||
}
|
||||
}
|
||||
},
|
||||
|
||||
sass: {
|
||||
main: {
|
||||
files: {
|
||||
'css/theme/darkgray.css': 'css/theme/source/darkgray.scss',
|
||||
'css/theme/beigesmall.css': 'css/theme/source/beigesmall.scss',
|
||||
'css/theme/cbc.css': 'css/theme/source/cbc.scss',
|
||||
'css/theme/default.css': 'css/theme/source/default.scss',
|
||||
'css/theme/beige.css': 'css/theme/source/beige.scss',
|
||||
'css/theme/night.css': 'css/theme/source/night.scss',
|
||||
'css/theme/serif.css': 'css/theme/source/serif.scss',
|
||||
'css/theme/simple.css': 'css/theme/source/simple.scss',
|
||||
'css/theme/sky.css': 'css/theme/source/sky.scss',
|
||||
'css/theme/moon.css': 'css/theme/source/moon.scss',
|
||||
'css/theme/solarized.css': 'css/theme/source/solarized.scss',
|
||||
'css/theme/blood.css': 'css/theme/source/blood.scss'
|
||||
}
|
||||
}
|
||||
},
|
||||
|
||||
jshint: {
|
||||
options: {
|
||||
curly: false,
|
||||
eqeqeq: true,
|
||||
immed: true,
|
||||
latedef: true,
|
||||
newcap: true,
|
||||
noarg: true,
|
||||
sub: true,
|
||||
undef: true,
|
||||
eqnull: true,
|
||||
browser: true,
|
||||
expr: true,
|
||||
globals: {
|
||||
head: false,
|
||||
module: false,
|
||||
console: false,
|
||||
unescape: false
|
||||
}
|
||||
},
|
||||
files: [ 'Gruntfile.js', 'js/reveal.js' ]
|
||||
},
|
||||
|
||||
connect: {
|
||||
server: {
|
||||
options: {
|
||||
port: port,
|
||||
base: '.'
|
||||
}
|
||||
}
|
||||
},
|
||||
|
||||
zip: {
|
||||
'reveal-js-presentation.zip': [
|
||||
'index.html',
|
||||
'css/**',
|
||||
'js/**',
|
||||
'lib/**',
|
||||
'images/**',
|
||||
'plugin/**'
|
||||
]
|
||||
},
|
||||
|
||||
watch: {
|
||||
main: {
|
||||
files: [ 'Gruntfile.js', 'js/reveal.js', 'css/reveal.css' ],
|
||||
tasks: 'default'
|
||||
},
|
||||
theme: {
|
||||
files: [ 'css/theme/source/*.scss', 'css/theme/template/*.scss' ],
|
||||
tasks: 'themes'
|
||||
}
|
||||
}
|
||||
|
||||
});
|
||||
|
||||
// Dependencies
|
||||
grunt.loadNpmTasks( 'grunt-contrib-qunit' );
|
||||
grunt.loadNpmTasks( 'grunt-contrib-jshint' );
|
||||
grunt.loadNpmTasks( 'grunt-contrib-cssmin' );
|
||||
grunt.loadNpmTasks( 'grunt-contrib-uglify' );
|
||||
grunt.loadNpmTasks( 'grunt-contrib-watch' );
|
||||
grunt.loadNpmTasks( 'grunt-contrib-sass' );
|
||||
grunt.loadNpmTasks( 'grunt-contrib-connect' );
|
||||
grunt.loadNpmTasks( 'grunt-zip' );
|
||||
|
||||
// Default task
|
||||
grunt.registerTask( 'default', [ 'jshint', 'cssmin', 'uglify', 'qunit' ] );
|
||||
|
||||
// Theme task
|
||||
grunt.registerTask( 'themes', [ 'sass' ] );
|
||||
|
||||
// Package presentation to archive
|
||||
grunt.registerTask( 'package', [ 'default', 'zip' ] );
|
||||
|
||||
// Serve presentation locally
|
||||
grunt.registerTask( 'serve', [ 'connect', 'watch' ] );
|
||||
|
||||
// Run tests
|
||||
grunt.registerTask( 'test', [ 'jshint', 'qunit' ] );
|
||||
|
||||
};
|
||||
@@ -0,0 +1,19 @@
|
||||
Copyright (C) 2015 Hakim El Hattab, http://hakim.se
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in
|
||||
all copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
|
||||
THE SOFTWARE.
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,27 @@
|
||||
{
|
||||
"name": "reveal.js",
|
||||
"version": "3.1.0",
|
||||
"main": [
|
||||
"js/reveal.js",
|
||||
"css/reveal.css"
|
||||
],
|
||||
"homepage": "http://lab.hakim.se/reveal-js/",
|
||||
"license": "MIT",
|
||||
"description": "The HTML Presentation Framework",
|
||||
"authors": [
|
||||
"Hakim El Hattab <hakim.elhattab@gmail.com>"
|
||||
],
|
||||
"dependencies": {
|
||||
"headjs": "~0.9.6"
|
||||
},
|
||||
"repository": {
|
||||
"type": "git",
|
||||
"url": "git://github.com/hakimel/reveal.js.git"
|
||||
},
|
||||
"ignore": [
|
||||
"**/.*",
|
||||
"node_modules",
|
||||
"bower_components",
|
||||
"test"
|
||||
]
|
||||
}
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 9.8 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 2.9 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 2.5 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 2.1 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 2.1 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 18 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 11 KiB |
@@ -0,0 +1,202 @@
|
||||
/* Default Print Stylesheet Template
|
||||
by Rob Glazebrook of CSSnewbie.com
|
||||
Last Updated: June 4, 2008
|
||||
|
||||
Feel free (nay, compelled) to edit, append, and
|
||||
manipulate this file as you see fit. */
|
||||
|
||||
|
||||
@media print {
|
||||
|
||||
/* SECTION 1: Set default width, margin, float, and
|
||||
background. This prevents elements from extending
|
||||
beyond the edge of the printed page, and prevents
|
||||
unnecessary background images from printing */
|
||||
html {
|
||||
background: #fff;
|
||||
width: auto;
|
||||
height: auto;
|
||||
overflow: visible;
|
||||
}
|
||||
body {
|
||||
background: #fff;
|
||||
font-size: 20pt;
|
||||
width: auto;
|
||||
height: auto;
|
||||
border: 0;
|
||||
margin: 0 5%;
|
||||
padding: 0;
|
||||
overflow: visible;
|
||||
float: none !important;
|
||||
}
|
||||
|
||||
/* SECTION 2: Remove any elements not needed in print.
|
||||
This would include navigation, ads, sidebars, etc. */
|
||||
.nestedarrow,
|
||||
.controls,
|
||||
.fork-reveal,
|
||||
.share-reveal,
|
||||
.state-background,
|
||||
.reveal .progress,
|
||||
.reveal .backgrounds {
|
||||
display: none !important;
|
||||
}
|
||||
|
||||
/* SECTION 3: Set body font face, size, and color.
|
||||
Consider using a serif font for readability. */
|
||||
body, p, td, li, div {
|
||||
font-size: 20pt!important;
|
||||
font-family: Georgia, "Times New Roman", Times, serif !important;
|
||||
color: #000;
|
||||
}
|
||||
|
||||
/* SECTION 4: Set heading font face, sizes, and color.
|
||||
Differentiate your headings from your body text.
|
||||
Perhaps use a large sans-serif for distinction. */
|
||||
h1,h2,h3,h4,h5,h6 {
|
||||
color: #000!important;
|
||||
height: auto;
|
||||
line-height: normal;
|
||||
font-family: Georgia, "Times New Roman", Times, serif !important;
|
||||
text-shadow: 0 0 0 #000 !important;
|
||||
text-align: left;
|
||||
letter-spacing: normal;
|
||||
}
|
||||
/* Need to reduce the size of the fonts for printing */
|
||||
h1 { font-size: 28pt !important; }
|
||||
h2 { font-size: 24pt !important; }
|
||||
h3 { font-size: 22pt !important; }
|
||||
h4 { font-size: 22pt !important; font-variant: small-caps; }
|
||||
h5 { font-size: 21pt !important; }
|
||||
h6 { font-size: 20pt !important; font-style: italic; }
|
||||
|
||||
/* SECTION 5: Make hyperlinks more usable.
|
||||
Ensure links are underlined, and consider appending
|
||||
the URL to the end of the link for usability. */
|
||||
a:link,
|
||||
a:visited {
|
||||
color: #000 !important;
|
||||
font-weight: bold;
|
||||
text-decoration: underline;
|
||||
}
|
||||
/*
|
||||
.reveal a:link:after,
|
||||
.reveal a:visited:after {
|
||||
content: " (" attr(href) ") ";
|
||||
color: #222 !important;
|
||||
font-size: 90%;
|
||||
}
|
||||
*/
|
||||
|
||||
|
||||
/* SECTION 6: more reveal.js specific additions by @skypanther */
|
||||
ul, ol, div, p {
|
||||
visibility: visible;
|
||||
position: static;
|
||||
width: auto;
|
||||
height: auto;
|
||||
display: block;
|
||||
overflow: visible;
|
||||
margin: 0;
|
||||
text-align: left !important;
|
||||
}
|
||||
.reveal pre,
|
||||
.reveal table {
|
||||
margin-left: 0;
|
||||
margin-right: 0;
|
||||
}
|
||||
.reveal pre code {
|
||||
padding: 20px;
|
||||
border: 1px solid #ddd;
|
||||
}
|
||||
.reveal blockquote {
|
||||
margin: 20px 0;
|
||||
}
|
||||
.reveal .slides {
|
||||
position: static !important;
|
||||
width: auto !important;
|
||||
height: auto !important;
|
||||
|
||||
left: 0 !important;
|
||||
top: 0 !important;
|
||||
margin-left: 0 !important;
|
||||
margin-top: 0 !important;
|
||||
padding: 0 !important;
|
||||
zoom: 1 !important;
|
||||
|
||||
overflow: visible !important;
|
||||
display: block !important;
|
||||
|
||||
text-align: left !important;
|
||||
-webkit-perspective: none;
|
||||
-moz-perspective: none;
|
||||
-ms-perspective: none;
|
||||
perspective: none;
|
||||
|
||||
-webkit-perspective-origin: 50% 50%;
|
||||
-moz-perspective-origin: 50% 50%;
|
||||
-ms-perspective-origin: 50% 50%;
|
||||
perspective-origin: 50% 50%;
|
||||
}
|
||||
.reveal .slides section {
|
||||
visibility: visible !important;
|
||||
position: static !important;
|
||||
width: 100% !important;
|
||||
height: auto !important;
|
||||
display: block !important;
|
||||
overflow: visible !important;
|
||||
|
||||
left: 0 !important;
|
||||
top: 0 !important;
|
||||
margin-left: 0 !important;
|
||||
margin-top: 0 !important;
|
||||
padding: 60px 20px !important;
|
||||
z-index: auto !important;
|
||||
|
||||
opacity: 1 !important;
|
||||
|
||||
page-break-after: always !important;
|
||||
|
||||
-webkit-transform-style: flat !important;
|
||||
-moz-transform-style: flat !important;
|
||||
-ms-transform-style: flat !important;
|
||||
transform-style: flat !important;
|
||||
|
||||
-webkit-transform: none !important;
|
||||
-moz-transform: none !important;
|
||||
-ms-transform: none !important;
|
||||
transform: none !important;
|
||||
|
||||
-webkit-transition: none !important;
|
||||
-moz-transition: none !important;
|
||||
-ms-transition: none !important;
|
||||
transition: none !important;
|
||||
}
|
||||
.reveal .slides section.stack {
|
||||
padding: 0 !important;
|
||||
}
|
||||
.reveal section:last-of-type {
|
||||
page-break-after: avoid !important;
|
||||
}
|
||||
.reveal section .fragment {
|
||||
opacity: 1 !important;
|
||||
visibility: visible !important;
|
||||
|
||||
-webkit-transform: none !important;
|
||||
-moz-transform: none !important;
|
||||
-ms-transform: none !important;
|
||||
transform: none !important;
|
||||
}
|
||||
.reveal section img {
|
||||
display: block;
|
||||
margin: 15px 0px;
|
||||
background: rgba(255,255,255,1);
|
||||
border: 1px solid #666;
|
||||
box-shadow: none;
|
||||
}
|
||||
|
||||
.reveal section small {
|
||||
font-size: 0.8em;
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,157 @@
|
||||
/* Default Print Stylesheet Template
|
||||
by Rob Glazebrook of CSSnewbie.com
|
||||
Last Updated: June 4, 2008
|
||||
|
||||
Feel free (nay, compelled) to edit, append, and
|
||||
manipulate this file as you see fit. */
|
||||
|
||||
|
||||
/* SECTION 1: Set default width, margin, float, and
|
||||
background. This prevents elements from extending
|
||||
beyond the edge of the printed page, and prevents
|
||||
unnecessary background images from printing */
|
||||
|
||||
* {
|
||||
-webkit-print-color-adjust: exact;
|
||||
}
|
||||
|
||||
body {
|
||||
margin: 0 auto !important;
|
||||
border: 0;
|
||||
padding: 0;
|
||||
float: none !important;
|
||||
overflow: visible;
|
||||
}
|
||||
|
||||
html {
|
||||
width: 100%;
|
||||
height: 100%;
|
||||
overflow: visible;
|
||||
}
|
||||
|
||||
/* SECTION 2: Remove any elements not needed in print.
|
||||
This would include navigation, ads, sidebars, etc. */
|
||||
.nestedarrow,
|
||||
.reveal .controls,
|
||||
.reveal .progress,
|
||||
.reveal .slide-number,
|
||||
.reveal .playback,
|
||||
.reveal.overview,
|
||||
.fork-reveal,
|
||||
.share-reveal,
|
||||
.state-background {
|
||||
display: none !important;
|
||||
}
|
||||
|
||||
/* SECTION 3: Set body font face, size, and color.
|
||||
Consider using a serif font for readability. */
|
||||
body, p, td, li, div {
|
||||
|
||||
}
|
||||
|
||||
/* SECTION 4: Set heading font face, sizes, and color.
|
||||
Differentiate your headings from your body text.
|
||||
Perhaps use a large sans-serif for distinction. */
|
||||
h1,h2,h3,h4,h5,h6 {
|
||||
text-shadow: 0 0 0 #000 !important;
|
||||
}
|
||||
|
||||
.reveal pre code {
|
||||
overflow: hidden !important;
|
||||
font-family: Courier, 'Courier New', monospace !important;
|
||||
}
|
||||
|
||||
|
||||
/* SECTION 5: more reveal.js specific additions by @skypanther */
|
||||
ul, ol, div, p {
|
||||
visibility: visible;
|
||||
position: static;
|
||||
width: auto;
|
||||
height: auto;
|
||||
display: block;
|
||||
overflow: visible;
|
||||
margin: auto;
|
||||
}
|
||||
.reveal {
|
||||
width: auto !important;
|
||||
height: auto !important;
|
||||
overflow: hidden !important;
|
||||
}
|
||||
.reveal .slides {
|
||||
position: static;
|
||||
width: 100%;
|
||||
height: auto;
|
||||
|
||||
left: auto;
|
||||
top: auto;
|
||||
margin: 0 !important;
|
||||
padding: 0 !important;
|
||||
|
||||
overflow: visible;
|
||||
display: block;
|
||||
|
||||
-webkit-perspective: none;
|
||||
-moz-perspective: none;
|
||||
-ms-perspective: none;
|
||||
perspective: none;
|
||||
|
||||
-webkit-perspective-origin: 50% 50%; /* there isn't a none/auto value but 50-50 is the default */
|
||||
-moz-perspective-origin: 50% 50%;
|
||||
-ms-perspective-origin: 50% 50%;
|
||||
perspective-origin: 50% 50%;
|
||||
}
|
||||
.reveal .slides section {
|
||||
page-break-after: always !important;
|
||||
|
||||
visibility: visible !important;
|
||||
position: relative !important;
|
||||
display: block !important;
|
||||
position: relative !important;
|
||||
|
||||
margin: 0 !important;
|
||||
padding: 0 !important;
|
||||
box-sizing: border-box !important;
|
||||
min-height: 1px;
|
||||
|
||||
opacity: 1 !important;
|
||||
|
||||
-webkit-transform-style: flat !important;
|
||||
-moz-transform-style: flat !important;
|
||||
-ms-transform-style: flat !important;
|
||||
transform-style: flat !important;
|
||||
|
||||
-webkit-transform: none !important;
|
||||
-moz-transform: none !important;
|
||||
-ms-transform: none !important;
|
||||
transform: none !important;
|
||||
}
|
||||
.reveal section.stack {
|
||||
margin: 0 !important;
|
||||
padding: 0 !important;
|
||||
page-break-after: avoid !important;
|
||||
height: auto !important;
|
||||
min-height: auto !important;
|
||||
}
|
||||
.reveal img {
|
||||
box-shadow: none;
|
||||
}
|
||||
.reveal .roll {
|
||||
overflow: visible;
|
||||
line-height: 1em;
|
||||
}
|
||||
|
||||
/* Slide backgrounds are placed inside of their slide when exporting to PDF */
|
||||
.reveal section .slide-background {
|
||||
display: block !important;
|
||||
position: absolute;
|
||||
top: 0;
|
||||
left: 0;
|
||||
width: 100%;
|
||||
z-index: -1;
|
||||
}
|
||||
/* All elements should be above the slide-background */
|
||||
.reveal section>* {
|
||||
position: relative;
|
||||
z-index: 1;
|
||||
}
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,23 @@
|
||||
## Dependencies
|
||||
|
||||
Themes are written using Sass to keep things modular and reduce the need for repeated selectors across files. Make sure that you have the reveal.js development environment including the Grunt dependencies installed before proceding: https://github.com/hakimel/reveal.js#full-setup
|
||||
|
||||
## Creating a Theme
|
||||
|
||||
To create your own theme, start by duplicating any ```.scss``` file in [/css/theme/source](https://github.com/hakimel/reveal.js/blob/master/css/theme/source) and adding it to the compilation list in the [Gruntfile](https://github.com/hakimel/reveal.js/blob/master/Gruntfile.js).
|
||||
|
||||
Each theme file does four things in the following order:
|
||||
|
||||
1. **Include [/css/theme/template/mixins.scss](https://github.com/hakimel/reveal.js/blob/master/css/theme/template/mixins.scss)**
|
||||
Shared utility functions.
|
||||
|
||||
2. **Include [/css/theme/template/settings.scss](https://github.com/hakimel/reveal.js/blob/master/css/theme/template/settings.scss)**
|
||||
Declares a set of custom variables that the template file (step 4) expects. Can be overridden in step 3.
|
||||
|
||||
3. **Override**
|
||||
This is where you override the default theme. Either by specifying variables (see [settings.scss](https://github.com/hakimel/reveal.js/blob/master/css/theme/template/settings.scss) for reference) or by adding any selectors and styles you please.
|
||||
|
||||
4. **Include [/css/theme/template/theme.scss](https://github.com/hakimel/reveal.js/blob/master/css/theme/template/theme.scss)**
|
||||
The template theme file which will generate final CSS output based on the currently defined variables.
|
||||
|
||||
When you are done, run `grunt css-themes` to compile the Sass file to CSS and you are ready to use your new theme.
|
||||
@@ -0,0 +1,154 @@
|
||||
@import url(https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic);
|
||||
/**
|
||||
* Beige theme for reveal.js.
|
||||
*
|
||||
* Copyright (C) 2011-2012 Hakim El Hattab, http://hakim.se
|
||||
*/
|
||||
/* changed by hpl from 36px; */
|
||||
/* changed by hpl from 'League Gothic', Impact, sans-serif; */
|
||||
/* changed by hpl from uppercase; */
|
||||
@font-face {
|
||||
font-family: 'League Gothic';
|
||||
src: url("../../lib/font/league_gothic-webfont.eot");
|
||||
src: url("../../lib/font/league_gothic-webfont.eot?#iefix") format("embedded-opentype"), url("../../lib/font/league_gothic-webfont.woff") format("woff"), url("../../lib/font/league_gothic-webfont.ttf") format("truetype"), url("../../lib/font/league_gothic-webfont.svg#LeagueGothicRegular") format("svg");
|
||||
font-weight: normal;
|
||||
font-style: normal; }
|
||||
|
||||
/* changed (by hpl) from #333; */
|
||||
/*********************************************
|
||||
* GLOBAL STYLES
|
||||
*********************************************/
|
||||
body {
|
||||
background: #f7f2d3;
|
||||
background: -moz-radial-gradient(center, circle cover, white 0%, #f7f2d3 100%);
|
||||
background: -webkit-gradient(radial, center center, 0px, center center, 100%, color-stop(0%, white), color-stop(100%, #f7f2d3));
|
||||
background: -webkit-radial-gradient(center, circle cover, white 0%, #f7f2d3 100%);
|
||||
background: -o-radial-gradient(center, circle cover, white 0%, #f7f2d3 100%);
|
||||
background: -ms-radial-gradient(center, circle cover, white 0%, #f7f2d3 100%);
|
||||
background: radial-gradient(center, circle cover, white 0%, #f7f2d3 100%);
|
||||
background-color: #f7f3de; }
|
||||
|
||||
.reveal {
|
||||
font-family: "Lato", sans-serif;
|
||||
font-size: 30px;
|
||||
font-weight: normal;
|
||||
letter-spacing: -0.02em;
|
||||
color: #333333; }
|
||||
|
||||
::selection {
|
||||
color: white;
|
||||
background: rgba(79, 64, 28, 0.99);
|
||||
text-shadow: none; }
|
||||
|
||||
/*********************************************
|
||||
* HEADERS
|
||||
*********************************************/
|
||||
.reveal h1,
|
||||
.reveal h2,
|
||||
.reveal h3,
|
||||
.reveal h4,
|
||||
.reveal h5,
|
||||
.reveal h6 {
|
||||
margin: 0 0 20px 0;
|
||||
color: #222222;
|
||||
font-family: "Helvetica", Impact, sans-serif;
|
||||
line-height: 1.1em; /* changed (by hpl) from 0.9em */
|
||||
letter-spacing: 0.02em;
|
||||
text-transform: none;
|
||||
text-shadow: none; }
|
||||
|
||||
.reveal h1 {
|
||||
line-height: 1.2em;
|
||||
/* added by hpl */
|
||||
text-shadow: 0 1px 0 #cccccc, 0 2px 0 #c9c9c9, 0 3px 0 #bbbbbb, 0 4px 0 #b9b9b9, 0 5px 0 #aaaaaa, 0 6px 1px rgba(0, 0, 0, 0.1), 0 0 5px rgba(0, 0, 0, 0.1), 0 1px 3px rgba(0, 0, 0, 0.3), 0 3px 5px rgba(0, 0, 0, 0.2), 0 5px 10px rgba(0, 0, 0, 0.25), 0 20px 20px rgba(0, 0, 0, 0.15); }
|
||||
|
||||
/*********************************************
|
||||
* LINKS
|
||||
*********************************************/
|
||||
.reveal a:not(.image) {
|
||||
color: #8b743d;
|
||||
text-decoration: none;
|
||||
-webkit-transition: color .15s ease;
|
||||
-moz-transition: color .15s ease;
|
||||
-ms-transition: color .15s ease;
|
||||
-o-transition: color .15s ease;
|
||||
transition: color .15s ease; }
|
||||
|
||||
.reveal a:not(.image):hover {
|
||||
color: #c0a86e;
|
||||
text-shadow: none;
|
||||
border: none; }
|
||||
|
||||
.reveal .roll span:after {
|
||||
color: #fff;
|
||||
background: #564826; }
|
||||
|
||||
/*********************************************
|
||||
* IMAGES
|
||||
*********************************************/
|
||||
.reveal section img {
|
||||
margin: 15px 0px;
|
||||
background: rgba(255, 255, 255, 0.12);
|
||||
border: 4px solid #333333;
|
||||
box-shadow: 0 0 10px rgba(0, 0, 0, 0.15);
|
||||
-webkit-transition: all .2s linear;
|
||||
-moz-transition: all .2s linear;
|
||||
-ms-transition: all .2s linear;
|
||||
-o-transition: all .2s linear;
|
||||
transition: all .2s linear; }
|
||||
|
||||
.reveal a:hover img {
|
||||
background: rgba(255, 255, 255, 0.2);
|
||||
border-color: #8b743d;
|
||||
box-shadow: 0 0 20px rgba(0, 0, 0, 0.55); }
|
||||
|
||||
/*********************************************
|
||||
* NAVIGATION CONTROLS
|
||||
*********************************************/
|
||||
.reveal .controls div.navigate-left,
|
||||
.reveal .controls div.navigate-left.enabled {
|
||||
border-right-color: #8b743d; }
|
||||
|
||||
.reveal .controls div.navigate-right,
|
||||
.reveal .controls div.navigate-right.enabled {
|
||||
border-left-color: #8b743d; }
|
||||
|
||||
.reveal .controls div.navigate-up,
|
||||
.reveal .controls div.navigate-up.enabled {
|
||||
border-bottom-color: #8b743d; }
|
||||
|
||||
.reveal .controls div.navigate-down,
|
||||
.reveal .controls div.navigate-down.enabled {
|
||||
border-top-color: #8b743d; }
|
||||
|
||||
.reveal .controls div.navigate-left.enabled:hover {
|
||||
border-right-color: #c0a86e; }
|
||||
|
||||
.reveal .controls div.navigate-right.enabled:hover {
|
||||
border-left-color: #c0a86e; }
|
||||
|
||||
.reveal .controls div.navigate-up.enabled:hover {
|
||||
border-bottom-color: #c0a86e; }
|
||||
|
||||
.reveal .controls div.navigate-down.enabled:hover {
|
||||
border-top-color: #c0a86e; }
|
||||
|
||||
/*********************************************
|
||||
* PROGRESS BAR
|
||||
*********************************************/
|
||||
.reveal .progress {
|
||||
background: rgba(0, 0, 0, 0.2); }
|
||||
|
||||
.reveal .progress span {
|
||||
background: #8b743d;
|
||||
-webkit-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-moz-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-ms-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-o-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985); }
|
||||
|
||||
/*********************************************
|
||||
* SLIDE NUMBER
|
||||
*********************************************/
|
||||
.reveal .slide-number {
|
||||
color: #8b743d; }
|
||||
@@ -0,0 +1,155 @@
|
||||
@import url(https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic);
|
||||
/**
|
||||
* Beige theme for reveal.js.
|
||||
*
|
||||
* Copyright (C) 2011-2012 Hakim El Hattab, http://hakim.se
|
||||
*/
|
||||
/* changed by hpl from 36px; */
|
||||
/* changed by hpl from 'League Gothic', Impact, sans-serif; */
|
||||
/* changed by hpl from uppercase; */
|
||||
@font-face {
|
||||
font-family: 'League Gothic';
|
||||
src: url("../../lib/font/league_gothic-webfont.eot");
|
||||
src: url("../../lib/font/league_gothic-webfont.eot?#iefix") format("embedded-opentype"), url("../../lib/font/league_gothic-webfont.woff") format("woff"), url("../../lib/font/league_gothic-webfont.ttf") format("truetype"), url("../../lib/font/league_gothic-webfont.svg#LeagueGothicRegular") format("svg");
|
||||
font-weight: normal;
|
||||
font-style: normal; }
|
||||
|
||||
/* added/changed by hpl */
|
||||
/* changed (by hpl) from #333; */
|
||||
/*********************************************
|
||||
* GLOBAL STYLES
|
||||
*********************************************/
|
||||
body {
|
||||
background: #f7f2d3;
|
||||
background: -moz-radial-gradient(center, circle cover, white 0%, #f7f2d3 100%);
|
||||
background: -webkit-gradient(radial, center center, 0px, center center, 100%, color-stop(0%, white), color-stop(100%, #f7f2d3));
|
||||
background: -webkit-radial-gradient(center, circle cover, white 0%, #f7f2d3 100%);
|
||||
background: -o-radial-gradient(center, circle cover, white 0%, #f7f2d3 100%);
|
||||
background: -ms-radial-gradient(center, circle cover, white 0%, #f7f2d3 100%);
|
||||
background: radial-gradient(center, circle cover, white 0%, #f7f2d3 100%);
|
||||
background-color: #f7f3de; }
|
||||
|
||||
.reveal {
|
||||
font-family: "Lato", sans-serif;
|
||||
font-size: 25px;
|
||||
font-weight: normal;
|
||||
letter-spacing: -0.02em;
|
||||
color: #333333; }
|
||||
|
||||
::selection {
|
||||
color: white;
|
||||
background: rgba(79, 64, 28, 0.99);
|
||||
text-shadow: none; }
|
||||
|
||||
/*********************************************
|
||||
* HEADERS
|
||||
*********************************************/
|
||||
.reveal h1,
|
||||
.reveal h2,
|
||||
.reveal h3,
|
||||
.reveal h4,
|
||||
.reveal h5,
|
||||
.reveal h6 {
|
||||
margin: 0 0 20px 0;
|
||||
color: #222222;
|
||||
font-family: "Helvetica", Impact, sans-serif;
|
||||
line-height: 1.1em; /* changed (by hpl) from 0.9em */
|
||||
letter-spacing: 0.02em;
|
||||
text-transform: none;
|
||||
text-shadow: none; }
|
||||
|
||||
.reveal h1 {
|
||||
line-height: 1.2em;
|
||||
/* added by hpl */
|
||||
text-shadow: 0 1px 0 #cccccc, 0 2px 0 #c9c9c9, 0 3px 0 #bbbbbb, 0 4px 0 #b9b9b9, 0 5px 0 #aaaaaa, 0 6px 1px rgba(0, 0, 0, 0.1), 0 0 5px rgba(0, 0, 0, 0.1), 0 1px 3px rgba(0, 0, 0, 0.3), 0 3px 5px rgba(0, 0, 0, 0.2), 0 5px 10px rgba(0, 0, 0, 0.25), 0 20px 20px rgba(0, 0, 0, 0.15); }
|
||||
|
||||
/*********************************************
|
||||
* LINKS
|
||||
*********************************************/
|
||||
.reveal a:not(.image) {
|
||||
color: #8b743d;
|
||||
text-decoration: none;
|
||||
-webkit-transition: color .15s ease;
|
||||
-moz-transition: color .15s ease;
|
||||
-ms-transition: color .15s ease;
|
||||
-o-transition: color .15s ease;
|
||||
transition: color .15s ease; }
|
||||
|
||||
.reveal a:not(.image):hover {
|
||||
color: #c0a86e;
|
||||
text-shadow: none;
|
||||
border: none; }
|
||||
|
||||
.reveal .roll span:after {
|
||||
color: #fff;
|
||||
background: #564826; }
|
||||
|
||||
/*********************************************
|
||||
* IMAGES
|
||||
*********************************************/
|
||||
.reveal section img {
|
||||
margin: 15px 0px;
|
||||
background: rgba(255, 255, 255, 0.12);
|
||||
border: 4px solid #333333;
|
||||
box-shadow: 0 0 10px rgba(0, 0, 0, 0.15);
|
||||
-webkit-transition: all .2s linear;
|
||||
-moz-transition: all .2s linear;
|
||||
-ms-transition: all .2s linear;
|
||||
-o-transition: all .2s linear;
|
||||
transition: all .2s linear; }
|
||||
|
||||
.reveal a:hover img {
|
||||
background: rgba(255, 255, 255, 0.2);
|
||||
border-color: #8b743d;
|
||||
box-shadow: 0 0 20px rgba(0, 0, 0, 0.55); }
|
||||
|
||||
/*********************************************
|
||||
* NAVIGATION CONTROLS
|
||||
*********************************************/
|
||||
.reveal .controls div.navigate-left,
|
||||
.reveal .controls div.navigate-left.enabled {
|
||||
border-right-color: #8b743d; }
|
||||
|
||||
.reveal .controls div.navigate-right,
|
||||
.reveal .controls div.navigate-right.enabled {
|
||||
border-left-color: #8b743d; }
|
||||
|
||||
.reveal .controls div.navigate-up,
|
||||
.reveal .controls div.navigate-up.enabled {
|
||||
border-bottom-color: #8b743d; }
|
||||
|
||||
.reveal .controls div.navigate-down,
|
||||
.reveal .controls div.navigate-down.enabled {
|
||||
border-top-color: #8b743d; }
|
||||
|
||||
.reveal .controls div.navigate-left.enabled:hover {
|
||||
border-right-color: #c0a86e; }
|
||||
|
||||
.reveal .controls div.navigate-right.enabled:hover {
|
||||
border-left-color: #c0a86e; }
|
||||
|
||||
.reveal .controls div.navigate-up.enabled:hover {
|
||||
border-bottom-color: #c0a86e; }
|
||||
|
||||
.reveal .controls div.navigate-down.enabled:hover {
|
||||
border-top-color: #c0a86e; }
|
||||
|
||||
/*********************************************
|
||||
* PROGRESS BAR
|
||||
*********************************************/
|
||||
.reveal .progress {
|
||||
background: rgba(0, 0, 0, 0.2); }
|
||||
|
||||
.reveal .progress span {
|
||||
background: #8b743d;
|
||||
-webkit-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-moz-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-ms-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-o-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985); }
|
||||
|
||||
/*********************************************
|
||||
* SLIDE NUMBER
|
||||
*********************************************/
|
||||
.reveal .slide-number {
|
||||
color: #8b743d; }
|
||||
@@ -0,0 +1,273 @@
|
||||
@import url(../../lib/font/source-sans-pro/source-sans-pro.css);
|
||||
/**
|
||||
* Black theme for reveal.js. This is the opposite of the 'white' theme.
|
||||
*
|
||||
* Copyright (C) 2015 Hakim El Hattab, http://hakim.se
|
||||
*/
|
||||
section.has-light-background, section.has-light-background h1, section.has-light-background h2, section.has-light-background h3, section.has-light-background h4, section.has-light-background h5, section.has-light-background h6 {
|
||||
color: #222; }
|
||||
|
||||
/*********************************************
|
||||
* GLOBAL STYLES
|
||||
*********************************************/
|
||||
body {
|
||||
background: #222;
|
||||
background-color: #222; }
|
||||
|
||||
.reveal {
|
||||
font-family: 'Source Sans Pro', Helvetica, sans-serif;
|
||||
font-size: 30px; /* changed by hpl from 38px */
|
||||
font-weight: normal;
|
||||
color: #fff; }
|
||||
|
||||
::selection {
|
||||
color: #fff;
|
||||
background: #bee4fd;
|
||||
text-shadow: none; }
|
||||
|
||||
.reveal .slides > section, .reveal .slides > section > section {
|
||||
/* removed by hpl: line-height: 1.3; */
|
||||
font-weight: inherit; }
|
||||
|
||||
/*********************************************
|
||||
* HEADERS
|
||||
*********************************************/
|
||||
.reveal h1, .reveal h2, .reveal h3, .reveal h4, .reveal h5, .reveal h6 {
|
||||
margin: 0 0 20px 0;
|
||||
color: #fff;
|
||||
font-family: 'Source Sans Pro', Helvetica, sans-serif;
|
||||
font-weight: 600;
|
||||
line-height: 1.1em; /* changed by hpl from 1.2; */
|
||||
letter-spacing: normal;
|
||||
/* text-transform: uppercase; removed by hpl */
|
||||
text-shadow: none;
|
||||
word-wrap: break-word; }
|
||||
|
||||
.reveal h1 {
|
||||
line-height: 1.2em;
|
||||
}
|
||||
|
||||
/* Removed by hpl
|
||||
.reveal h1 {
|
||||
font-size: 2.5em; }
|
||||
|
||||
.reveal h2 {
|
||||
font-size: 1.6em; }
|
||||
|
||||
.reveal h3 {
|
||||
font-size: 1.3em; }
|
||||
|
||||
.reveal h4 {
|
||||
font-size: 1em; }
|
||||
*/
|
||||
|
||||
.reveal h1 {
|
||||
text-shadow: none; }
|
||||
|
||||
/*********************************************
|
||||
* OTHER
|
||||
*********************************************/
|
||||
.reveal p {
|
||||
margin: 20px 0;
|
||||
line-height: 1.3; }
|
||||
|
||||
/* Ensure certain elements are never larger than the slide itself */
|
||||
.reveal img, .reveal video, .reveal iframe {
|
||||
max-width: 95%;
|
||||
max-height: 95%; }
|
||||
|
||||
.reveal strong, .reveal b {
|
||||
font-weight: bold; }
|
||||
|
||||
.reveal em {
|
||||
font-style: italic; }
|
||||
|
||||
.reveal ol, .reveal dl, .reveal ul {
|
||||
display: inline-block;
|
||||
text-align: left;
|
||||
margin: 0 0 0 1em; }
|
||||
|
||||
.reveal ol {
|
||||
list-style-type: decimal; }
|
||||
|
||||
.reveal ul {
|
||||
list-style-type: disc; }
|
||||
|
||||
.reveal ul ul {
|
||||
list-style-type: square; }
|
||||
|
||||
.reveal ul ul ul {
|
||||
list-style-type: circle; }
|
||||
|
||||
.reveal ul ul, .reveal ul ol, .reveal ol ol, .reveal ol ul {
|
||||
display: block;
|
||||
margin-left: 40px; }
|
||||
|
||||
.reveal dt {
|
||||
font-weight: bold; }
|
||||
|
||||
.reveal dd {
|
||||
margin-left: 40px; }
|
||||
|
||||
.reveal q, .reveal blockquote {
|
||||
quotes: none; }
|
||||
|
||||
.reveal blockquote {
|
||||
display: block;
|
||||
position: relative;
|
||||
width: 70%;
|
||||
margin: 20px auto;
|
||||
padding: 5px;
|
||||
font-style: italic;
|
||||
background: rgba(255, 255, 255, 0.05);
|
||||
box-shadow: 0px 0px 2px rgba(0, 0, 0, 0.2); }
|
||||
|
||||
.reveal blockquote p:first-child, .reveal blockquote p:last-child {
|
||||
display: inline-block; }
|
||||
|
||||
.reveal q {
|
||||
font-style: italic; }
|
||||
|
||||
.reveal pre {
|
||||
display: block;
|
||||
position: relative;
|
||||
width: 90%;
|
||||
margin: 20px auto;
|
||||
text-align: left;
|
||||
font-size: 0.55em;
|
||||
font-family: monospace;
|
||||
line-height: 1.2em;
|
||||
word-wrap: break-word;
|
||||
box-shadow: 0px 0px 6px rgba(0, 0, 0, 0.3); }
|
||||
|
||||
.reveal code {
|
||||
font-family: monospace; }
|
||||
|
||||
.reveal pre code {
|
||||
display: block;
|
||||
padding: 5px;
|
||||
overflow: auto;
|
||||
max-height: 400px;
|
||||
word-wrap: normal;
|
||||
background: #3F3F3F;
|
||||
color: #DCDCDC; }
|
||||
|
||||
.reveal table {
|
||||
margin: auto;
|
||||
border-collapse: collapse;
|
||||
border-spacing: 0; }
|
||||
|
||||
.reveal table th {
|
||||
font-weight: bold; }
|
||||
|
||||
.reveal table th, .reveal table td {
|
||||
/*text-align: left; */ /* hpl modification */
|
||||
padding: 0.2em 0.5em 0.2em 0.5em;
|
||||
border-bottom: 1px solid; }
|
||||
|
||||
.reveal table th[align="center"], .reveal table td[align="center"] {
|
||||
text-align: center; }
|
||||
|
||||
.reveal table th[align="right"], .reveal table td[align="right"] {
|
||||
text-align: right; }
|
||||
|
||||
.reveal table tr:last-child td {
|
||||
border-bottom: none; }
|
||||
|
||||
.reveal sup {
|
||||
vertical-align: super; }
|
||||
|
||||
.reveal sub {
|
||||
vertical-align: sub; }
|
||||
|
||||
.reveal small {
|
||||
display: inline-block;
|
||||
font-size: 0.6em;
|
||||
line-height: 1.2em;
|
||||
vertical-align: top; }
|
||||
|
||||
.reveal small * {
|
||||
vertical-align: top; }
|
||||
|
||||
/*********************************************
|
||||
* LINKS
|
||||
*********************************************/
|
||||
.reveal a {
|
||||
color: #42affa;
|
||||
text-decoration: none;
|
||||
-webkit-transition: color 0.15s ease;
|
||||
-moz-transition: color 0.15s ease;
|
||||
transition: color 0.15s ease; }
|
||||
|
||||
.reveal a:hover {
|
||||
color: #8dcffc;
|
||||
text-shadow: none;
|
||||
border: none; }
|
||||
|
||||
.reveal .roll span:after {
|
||||
color: #fff;
|
||||
background: #068ee9; }
|
||||
|
||||
/*********************************************
|
||||
* IMAGES
|
||||
*********************************************/
|
||||
.reveal section img {
|
||||
margin: 15px 0px;
|
||||
background: rgba(255, 255, 255, 0.12);
|
||||
border: 4px solid #fff;
|
||||
box-shadow: 0 0 10px rgba(0, 0, 0, 0.15); }
|
||||
|
||||
.reveal a img {
|
||||
-webkit-transition: all 0.15s linear;
|
||||
-moz-transition: all 0.15s linear;
|
||||
transition: all 0.15s linear; }
|
||||
|
||||
.reveal a:hover img {
|
||||
background: rgba(255, 255, 255, 0.2);
|
||||
border-color: #42affa;
|
||||
box-shadow: 0 0 20px rgba(0, 0, 0, 0.55); }
|
||||
|
||||
/*********************************************
|
||||
* NAVIGATION CONTROLS
|
||||
*********************************************/
|
||||
.reveal .controls div.navigate-left, .reveal .controls div.navigate-left.enabled {
|
||||
border-right-color: #42affa; }
|
||||
|
||||
.reveal .controls div.navigate-right, .reveal .controls div.navigate-right.enabled {
|
||||
border-left-color: #42affa; }
|
||||
|
||||
.reveal .controls div.navigate-up, .reveal .controls div.navigate-up.enabled {
|
||||
border-bottom-color: #42affa; }
|
||||
|
||||
.reveal .controls div.navigate-down, .reveal .controls div.navigate-down.enabled {
|
||||
border-top-color: #42affa; }
|
||||
|
||||
.reveal .controls div.navigate-left.enabled:hover {
|
||||
border-right-color: #8dcffc; }
|
||||
|
||||
.reveal .controls div.navigate-right.enabled:hover {
|
||||
border-left-color: #8dcffc; }
|
||||
|
||||
.reveal .controls div.navigate-up.enabled:hover {
|
||||
border-bottom-color: #8dcffc; }
|
||||
|
||||
.reveal .controls div.navigate-down.enabled:hover {
|
||||
border-top-color: #8dcffc; }
|
||||
|
||||
/*********************************************
|
||||
* PROGRESS BAR
|
||||
*********************************************/
|
||||
.reveal .progress {
|
||||
background: rgba(0, 0, 0, 0.2); }
|
||||
|
||||
.reveal .progress span {
|
||||
background: #42affa;
|
||||
-webkit-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-moz-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985); }
|
||||
|
||||
/*********************************************
|
||||
* SLIDE NUMBER
|
||||
*********************************************/
|
||||
.reveal .slide-number {
|
||||
color: #42affa; }
|
||||
@@ -0,0 +1,180 @@
|
||||
@import url(https://fonts.googleapis.com/css?family=Ubuntu:300,700,300italic,700italic);
|
||||
/**
|
||||
* Blood theme for reveal.js
|
||||
* Author: Walther http://github.com/Walther
|
||||
*
|
||||
* Designed to be used with highlight.js theme
|
||||
* "monokai_sublime.css" available from
|
||||
* https://github.com/isagalaev/highlight.js/
|
||||
*
|
||||
* For other themes, change $codeBackground accordingly.
|
||||
*
|
||||
*/
|
||||
/* changed by hpl from 36px; */
|
||||
/* changed by hpl from 'League Gothic', Impact, sans-serif; */
|
||||
/* changed by hpl from uppercase; */
|
||||
/*********************************************
|
||||
* GLOBAL STYLES
|
||||
*********************************************/
|
||||
body {
|
||||
background: #222222;
|
||||
background: -moz-radial-gradient(center, circle cover, #626262 0%, #222222 100%);
|
||||
background: -webkit-gradient(radial, center center, 0px, center center, 100%, color-stop(0%, #626262), color-stop(100%, #222222));
|
||||
background: -webkit-radial-gradient(center, circle cover, #626262 0%, #222222 100%);
|
||||
background: -o-radial-gradient(center, circle cover, #626262 0%, #222222 100%);
|
||||
background: -ms-radial-gradient(center, circle cover, #626262 0%, #222222 100%);
|
||||
background: radial-gradient(center, circle cover, #626262 0%, #222222 100%);
|
||||
background-color: #2b2b2b; }
|
||||
|
||||
.reveal {
|
||||
font-family: Ubuntu, "sans-serif";
|
||||
font-size: 30px;
|
||||
font-weight: normal;
|
||||
letter-spacing: -0.02em;
|
||||
color: #eeeeee; }
|
||||
|
||||
::selection {
|
||||
color: white;
|
||||
background: #aa2233;
|
||||
text-shadow: none; }
|
||||
|
||||
/*********************************************
|
||||
* HEADERS
|
||||
*********************************************/
|
||||
.reveal h1,
|
||||
.reveal h2,
|
||||
.reveal h3,
|
||||
.reveal h4,
|
||||
.reveal h5,
|
||||
.reveal h6 {
|
||||
margin: 0 0 20px 0;
|
||||
color: #eeeeee;
|
||||
font-family: Ubuntu, "sans-serif";
|
||||
line-height: 1.1em; /* changed (by hpl) from 0.9em */
|
||||
letter-spacing: 0.02em;
|
||||
text-transform: none;
|
||||
text-shadow: 2px 2px 2px #222222; }
|
||||
|
||||
.reveal h1 {
|
||||
line-height: 1.2em;
|
||||
/* added by hpl */
|
||||
text-shadow: 0 1px 0 #cccccc, 0 2px 0 #c9c9c9, 0 3px 0 #bbbbbb, 0 4px 0 #b9b9b9, 0 5px 0 #aaaaaa, 0 6px 1px rgba(0, 0, 0, 0.1), 0 0 5px rgba(0, 0, 0, 0.1), 0 1px 3px rgba(0, 0, 0, 0.3), 0 3px 5px rgba(0, 0, 0, 0.2), 0 5px 10px rgba(0, 0, 0, 0.25), 0 20px 20px rgba(0, 0, 0, 0.15); }
|
||||
|
||||
/*********************************************
|
||||
* LINKS
|
||||
*********************************************/
|
||||
.reveal a:not(.image) {
|
||||
color: #aa2233;
|
||||
text-decoration: none;
|
||||
-webkit-transition: color .15s ease;
|
||||
-moz-transition: color .15s ease;
|
||||
-ms-transition: color .15s ease;
|
||||
-o-transition: color .15s ease;
|
||||
transition: color .15s ease; }
|
||||
|
||||
.reveal a:not(.image):hover {
|
||||
color: #dd5566;
|
||||
text-shadow: none;
|
||||
border: none; }
|
||||
|
||||
.reveal .roll span:after {
|
||||
color: #fff;
|
||||
background: #6a1520; }
|
||||
|
||||
/*********************************************
|
||||
* IMAGES
|
||||
*********************************************/
|
||||
.reveal section img {
|
||||
margin: 15px 0px;
|
||||
background: rgba(255, 255, 255, 0.12);
|
||||
border: 4px solid #eeeeee;
|
||||
box-shadow: 0 0 10px rgba(0, 0, 0, 0.15);
|
||||
-webkit-transition: all .2s linear;
|
||||
-moz-transition: all .2s linear;
|
||||
-ms-transition: all .2s linear;
|
||||
-o-transition: all .2s linear;
|
||||
transition: all .2s linear; }
|
||||
|
||||
.reveal a:hover img {
|
||||
background: rgba(255, 255, 255, 0.2);
|
||||
border-color: #aa2233;
|
||||
box-shadow: 0 0 20px rgba(0, 0, 0, 0.55); }
|
||||
|
||||
/*********************************************
|
||||
* NAVIGATION CONTROLS
|
||||
*********************************************/
|
||||
.reveal .controls div.navigate-left,
|
||||
.reveal .controls div.navigate-left.enabled {
|
||||
border-right-color: #aa2233; }
|
||||
|
||||
.reveal .controls div.navigate-right,
|
||||
.reveal .controls div.navigate-right.enabled {
|
||||
border-left-color: #aa2233; }
|
||||
|
||||
.reveal .controls div.navigate-up,
|
||||
.reveal .controls div.navigate-up.enabled {
|
||||
border-bottom-color: #aa2233; }
|
||||
|
||||
.reveal .controls div.navigate-down,
|
||||
.reveal .controls div.navigate-down.enabled {
|
||||
border-top-color: #aa2233; }
|
||||
|
||||
.reveal .controls div.navigate-left.enabled:hover {
|
||||
border-right-color: #dd5566; }
|
||||
|
||||
.reveal .controls div.navigate-right.enabled:hover {
|
||||
border-left-color: #dd5566; }
|
||||
|
||||
.reveal .controls div.navigate-up.enabled:hover {
|
||||
border-bottom-color: #dd5566; }
|
||||
|
||||
.reveal .controls div.navigate-down.enabled:hover {
|
||||
border-top-color: #dd5566; }
|
||||
|
||||
/*********************************************
|
||||
* PROGRESS BAR
|
||||
*********************************************/
|
||||
.reveal .progress {
|
||||
background: rgba(0, 0, 0, 0.2); }
|
||||
|
||||
.reveal .progress span {
|
||||
background: #aa2233;
|
||||
-webkit-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-moz-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-ms-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-o-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985); }
|
||||
|
||||
/*********************************************
|
||||
* SLIDE NUMBER
|
||||
*********************************************/
|
||||
.reveal .slide-number {
|
||||
color: #aa2233; }
|
||||
|
||||
.reveal p {
|
||||
font-weight: 300;
|
||||
text-shadow: 1px 1px #222222; }
|
||||
|
||||
.reveal h1,
|
||||
.reveal h2,
|
||||
.reveal h3,
|
||||
.reveal h4,
|
||||
.reveal h5,
|
||||
.reveal h6 {
|
||||
font-weight: 700; }
|
||||
|
||||
.reveal a:not(.image),
|
||||
.reveal a:not(.image):hover {
|
||||
text-shadow: 2px 2px 2px #000; }
|
||||
|
||||
.reveal small a:not(.image),
|
||||
.reveal small a:not(.image):hover {
|
||||
text-shadow: 1px 1px 1px #000; }
|
||||
|
||||
.reveal p code {
|
||||
background-color: #23241f;
|
||||
display: inline-block;
|
||||
border-radius: 7px; }
|
||||
|
||||
.reveal small code {
|
||||
vertical-align: baseline; }
|
||||
@@ -0,0 +1,144 @@
|
||||
@import url(https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic);
|
||||
/**
|
||||
* A simple theme for reveal.js presentations, similar
|
||||
* to the default theme. The accent color is darkblue.
|
||||
*
|
||||
* This theme is Copyright (C) 2012 Owen Versteeg, https://github.com/StereotypicalApps. It is MIT licensed.
|
||||
* reveal.js is Copyright (C) 2011-2012 Hakim El Hattab, http://hakim.se
|
||||
|
||||
* Center for Biomedical Computing theme made by Hans Petter Langtangen.
|
||||
*/
|
||||
/* changed by hpl from 36px; */
|
||||
/* changed by hpl from 'League Gothic', Impact, sans-serif; */
|
||||
/* changed by hpl from uppercase; */
|
||||
/*********************************************
|
||||
* GLOBAL STYLES
|
||||
*********************************************/
|
||||
body {
|
||||
background: white;
|
||||
background-color: white; }
|
||||
|
||||
.reveal {
|
||||
font-family: "Lato", sans-serif;
|
||||
font-size: 30px;
|
||||
font-weight: normal;
|
||||
letter-spacing: -0.02em;
|
||||
color: #404040; }
|
||||
|
||||
::selection {
|
||||
color: white;
|
||||
background: rgba(0, 0, 0, 0.99);
|
||||
text-shadow: none; }
|
||||
|
||||
/*********************************************
|
||||
* HEADERS
|
||||
*********************************************/
|
||||
.reveal h1,
|
||||
.reveal h2,
|
||||
.reveal h3,
|
||||
.reveal h4,
|
||||
.reveal h5,
|
||||
.reveal h6 {
|
||||
margin: 0 0 20px 0;
|
||||
color: #8a0808;
|
||||
font-family: "Helvetica", Impact, sans-serif;
|
||||
line-height: 1.1em; /* changed (by hpl) from 0.9em */
|
||||
letter-spacing: 0.02em;
|
||||
text-transform: none;
|
||||
text-shadow: none; }
|
||||
|
||||
.reveal h1 {
|
||||
line-height: 1.2em;
|
||||
/* added by hpl */
|
||||
text-shadow: 0px 0px 6px rgba(0, 0, 0, 0.2); }
|
||||
|
||||
/*********************************************
|
||||
* LINKS
|
||||
*********************************************/
|
||||
.reveal a:not(.image) {
|
||||
color: #8a0808;
|
||||
text-decoration: none;
|
||||
-webkit-transition: color .15s ease;
|
||||
-moz-transition: color .15s ease;
|
||||
-ms-transition: color .15s ease;
|
||||
-o-transition: color .15s ease;
|
||||
transition: color .15s ease; }
|
||||
|
||||
.reveal a:not(.image):hover {
|
||||
color: #ea0e0e;
|
||||
text-shadow: none;
|
||||
border: none; }
|
||||
|
||||
.reveal .roll span:after {
|
||||
color: #fff;
|
||||
background: #420404; }
|
||||
|
||||
/*********************************************
|
||||
* IMAGES
|
||||
*********************************************/
|
||||
.reveal section img {
|
||||
margin: 15px 0px;
|
||||
background: rgba(255, 255, 255, 0.12);
|
||||
border: 4px solid #404040;
|
||||
box-shadow: 0 0 10px rgba(0, 0, 0, 0.15);
|
||||
-webkit-transition: all .2s linear;
|
||||
-moz-transition: all .2s linear;
|
||||
-ms-transition: all .2s linear;
|
||||
-o-transition: all .2s linear;
|
||||
transition: all .2s linear; }
|
||||
|
||||
.reveal a:hover img {
|
||||
background: rgba(255, 255, 255, 0.2);
|
||||
border-color: #8a0808;
|
||||
box-shadow: 0 0 20px rgba(0, 0, 0, 0.55); }
|
||||
|
||||
/*********************************************
|
||||
* NAVIGATION CONTROLS
|
||||
*********************************************/
|
||||
.reveal .controls div.navigate-left,
|
||||
.reveal .controls div.navigate-left.enabled {
|
||||
border-right-color: #8a0808; }
|
||||
|
||||
.reveal .controls div.navigate-right,
|
||||
.reveal .controls div.navigate-right.enabled {
|
||||
border-left-color: #8a0808; }
|
||||
|
||||
.reveal .controls div.navigate-up,
|
||||
.reveal .controls div.navigate-up.enabled {
|
||||
border-bottom-color: #8a0808; }
|
||||
|
||||
.reveal .controls div.navigate-down,
|
||||
.reveal .controls div.navigate-down.enabled {
|
||||
border-top-color: #8a0808; }
|
||||
|
||||
.reveal .controls div.navigate-left.enabled:hover {
|
||||
border-right-color: #ea0e0e; }
|
||||
|
||||
.reveal .controls div.navigate-right.enabled:hover {
|
||||
border-left-color: #ea0e0e; }
|
||||
|
||||
.reveal .controls div.navigate-up.enabled:hover {
|
||||
border-bottom-color: #ea0e0e; }
|
||||
|
||||
.reveal .controls div.navigate-down.enabled:hover {
|
||||
border-top-color: #ea0e0e; }
|
||||
|
||||
/*********************************************
|
||||
* PROGRESS BAR
|
||||
*********************************************/
|
||||
.reveal .progress {
|
||||
background: rgba(0, 0, 0, 0.2); }
|
||||
|
||||
.reveal .progress span {
|
||||
background: #8a0808;
|
||||
-webkit-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-moz-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-ms-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-o-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985); }
|
||||
|
||||
/*********************************************
|
||||
* SLIDE NUMBER
|
||||
*********************************************/
|
||||
.reveal .slide-number {
|
||||
color: #8a0808; }
|
||||
@@ -0,0 +1,153 @@
|
||||
@import url(https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic);
|
||||
/**
|
||||
* Default theme for reveal.js.
|
||||
*
|
||||
* Copyright (C) 2011-2012 Hakim El Hattab, http://hakim.se
|
||||
*/
|
||||
/* changed by hpl from 36px; */
|
||||
/* changed by hpl from 'League Gothic', Impact, sans-serif; */
|
||||
/* changed by hpl from uppercase; */
|
||||
@font-face {
|
||||
font-family: 'League Gothic';
|
||||
src: url("../../lib/font/league_gothic-webfont.eot");
|
||||
src: url("../../lib/font/league_gothic-webfont.eot?#iefix") format("embedded-opentype"), url("../../lib/font/league_gothic-webfont.woff") format("woff"), url("../../lib/font/league_gothic-webfont.ttf") format("truetype"), url("../../lib/font/league_gothic-webfont.svg#LeagueGothicRegular") format("svg");
|
||||
font-weight: normal;
|
||||
font-style: normal; }
|
||||
|
||||
/*********************************************
|
||||
* GLOBAL STYLES
|
||||
*********************************************/
|
||||
body {
|
||||
background: #1c1e20;
|
||||
background: -moz-radial-gradient(center, circle cover, #555a5f 0%, #1c1e20 100%);
|
||||
background: -webkit-gradient(radial, center center, 0px, center center, 100%, color-stop(0%, #555a5f), color-stop(100%, #1c1e20));
|
||||
background: -webkit-radial-gradient(center, circle cover, #555a5f 0%, #1c1e20 100%);
|
||||
background: -o-radial-gradient(center, circle cover, #555a5f 0%, #1c1e20 100%);
|
||||
background: -ms-radial-gradient(center, circle cover, #555a5f 0%, #1c1e20 100%);
|
||||
background: radial-gradient(center, circle cover, #555a5f 0%, #1c1e20 100%);
|
||||
background-color: #2b2b2b; }
|
||||
|
||||
.reveal {
|
||||
font-family: "Lato", sans-serif;
|
||||
font-size: 30px;
|
||||
font-weight: normal;
|
||||
letter-spacing: -0.02em;
|
||||
color: #eeeeee; }
|
||||
|
||||
::selection {
|
||||
color: white;
|
||||
background: #ff5e99;
|
||||
text-shadow: none; }
|
||||
|
||||
/*********************************************
|
||||
* HEADERS
|
||||
*********************************************/
|
||||
.reveal h1,
|
||||
.reveal h2,
|
||||
.reveal h3,
|
||||
.reveal h4,
|
||||
.reveal h5,
|
||||
.reveal h6 {
|
||||
margin: 0 0 20px 0;
|
||||
color: #eeeeee;
|
||||
font-family: "Helvetica", Impact, sans-serif;
|
||||
line-height: 1.1em; /* changed (by hpl) from 0.9em */
|
||||
letter-spacing: 0.02em;
|
||||
text-transform: none;
|
||||
text-shadow: 0px 0px 6px rgba(0, 0, 0, 0.2); }
|
||||
|
||||
.reveal h1 {
|
||||
line-height: 1.2em;
|
||||
/* added by hpl */
|
||||
text-shadow: 0 1px 0 #cccccc, 0 2px 0 #c9c9c9, 0 3px 0 #bbbbbb, 0 4px 0 #b9b9b9, 0 5px 0 #aaaaaa, 0 6px 1px rgba(0, 0, 0, 0.1), 0 0 5px rgba(0, 0, 0, 0.1), 0 1px 3px rgba(0, 0, 0, 0.3), 0 3px 5px rgba(0, 0, 0, 0.2), 0 5px 10px rgba(0, 0, 0, 0.25), 0 20px 20px rgba(0, 0, 0, 0.15); }
|
||||
|
||||
/*********************************************
|
||||
* LINKS
|
||||
*********************************************/
|
||||
.reveal a:not(.image) {
|
||||
color: #13daec;
|
||||
text-decoration: none;
|
||||
-webkit-transition: color .15s ease;
|
||||
-moz-transition: color .15s ease;
|
||||
-ms-transition: color .15s ease;
|
||||
-o-transition: color .15s ease;
|
||||
transition: color .15s ease; }
|
||||
|
||||
.reveal a:not(.image):hover {
|
||||
color: #71e9f4;
|
||||
text-shadow: none;
|
||||
border: none; }
|
||||
|
||||
.reveal .roll span:after {
|
||||
color: #fff;
|
||||
background: #0d99a5; }
|
||||
|
||||
/*********************************************
|
||||
* IMAGES
|
||||
*********************************************/
|
||||
.reveal section img {
|
||||
margin: 15px 0px;
|
||||
background: rgba(255, 255, 255, 0.12);
|
||||
border: 4px solid #eeeeee;
|
||||
box-shadow: 0 0 10px rgba(0, 0, 0, 0.15);
|
||||
-webkit-transition: all .2s linear;
|
||||
-moz-transition: all .2s linear;
|
||||
-ms-transition: all .2s linear;
|
||||
-o-transition: all .2s linear;
|
||||
transition: all .2s linear; }
|
||||
|
||||
.reveal a:hover img {
|
||||
background: rgba(255, 255, 255, 0.2);
|
||||
border-color: #13daec;
|
||||
box-shadow: 0 0 20px rgba(0, 0, 0, 0.55); }
|
||||
|
||||
/*********************************************
|
||||
* NAVIGATION CONTROLS
|
||||
*********************************************/
|
||||
.reveal .controls div.navigate-left,
|
||||
.reveal .controls div.navigate-left.enabled {
|
||||
border-right-color: #13daec; }
|
||||
|
||||
.reveal .controls div.navigate-right,
|
||||
.reveal .controls div.navigate-right.enabled {
|
||||
border-left-color: #13daec; }
|
||||
|
||||
.reveal .controls div.navigate-up,
|
||||
.reveal .controls div.navigate-up.enabled {
|
||||
border-bottom-color: #13daec; }
|
||||
|
||||
.reveal .controls div.navigate-down,
|
||||
.reveal .controls div.navigate-down.enabled {
|
||||
border-top-color: #13daec; }
|
||||
|
||||
.reveal .controls div.navigate-left.enabled:hover {
|
||||
border-right-color: #71e9f4; }
|
||||
|
||||
.reveal .controls div.navigate-right.enabled:hover {
|
||||
border-left-color: #71e9f4; }
|
||||
|
||||
.reveal .controls div.navigate-up.enabled:hover {
|
||||
border-bottom-color: #71e9f4; }
|
||||
|
||||
.reveal .controls div.navigate-down.enabled:hover {
|
||||
border-top-color: #71e9f4; }
|
||||
|
||||
/*********************************************
|
||||
* PROGRESS BAR
|
||||
*********************************************/
|
||||
.reveal .progress {
|
||||
background: rgba(0, 0, 0, 0.2); }
|
||||
|
||||
.reveal .progress span {
|
||||
background: #13daec;
|
||||
-webkit-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-moz-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-ms-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-o-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985); }
|
||||
|
||||
/*********************************************
|
||||
* SLIDE NUMBER
|
||||
*********************************************/
|
||||
.reveal .slide-number {
|
||||
color: #13daec; }
|
||||
@@ -0,0 +1,153 @@
|
||||
@import url(https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic);
|
||||
/**
|
||||
* Default theme for reveal.js.
|
||||
*
|
||||
* Copyright (C) 2011-2012 Hakim El Hattab, http://hakim.se
|
||||
*/
|
||||
/* changed by hpl from 36px; */
|
||||
/* changed by hpl from 'League Gothic', Impact, sans-serif; */
|
||||
/* changed by hpl from uppercase; */
|
||||
@font-face {
|
||||
font-family: 'League Gothic';
|
||||
src: url("../../lib/font/league_gothic-webfont.eot");
|
||||
src: url("../../lib/font/league_gothic-webfont.eot?#iefix") format("embedded-opentype"), url("../../lib/font/league_gothic-webfont.woff") format("woff"), url("../../lib/font/league_gothic-webfont.ttf") format("truetype"), url("../../lib/font/league_gothic-webfont.svg#LeagueGothicRegular") format("svg");
|
||||
font-weight: normal;
|
||||
font-style: normal; }
|
||||
|
||||
/*********************************************
|
||||
* GLOBAL STYLES
|
||||
*********************************************/
|
||||
body {
|
||||
background: #1c1e20;
|
||||
background: -moz-radial-gradient(center, circle cover, #555a5f 0%, #1c1e20 100%);
|
||||
background: -webkit-gradient(radial, center center, 0px, center center, 100%, color-stop(0%, #555a5f), color-stop(100%, #1c1e20));
|
||||
background: -webkit-radial-gradient(center, circle cover, #555a5f 0%, #1c1e20 100%);
|
||||
background: -o-radial-gradient(center, circle cover, #555a5f 0%, #1c1e20 100%);
|
||||
background: -ms-radial-gradient(center, circle cover, #555a5f 0%, #1c1e20 100%);
|
||||
background: radial-gradient(center, circle cover, #555a5f 0%, #1c1e20 100%);
|
||||
background-color: #2b2b2b; }
|
||||
|
||||
.reveal {
|
||||
font-family: "Lato", sans-serif;
|
||||
font-size: 30px;
|
||||
font-weight: normal;
|
||||
letter-spacing: -0.02em;
|
||||
color: #eeeeee; }
|
||||
|
||||
::selection {
|
||||
color: white;
|
||||
background: #ff5e99;
|
||||
text-shadow: none; }
|
||||
|
||||
/*********************************************
|
||||
* HEADERS
|
||||
*********************************************/
|
||||
.reveal h1,
|
||||
.reveal h2,
|
||||
.reveal h3,
|
||||
.reveal h4,
|
||||
.reveal h5,
|
||||
.reveal h6 {
|
||||
margin: 0 0 20px 0;
|
||||
color: #eeeeee;
|
||||
font-family: "Helvetica", Impact, sans-serif;
|
||||
line-height: 1.1em; /* changed (by hpl) from 0.9em */
|
||||
letter-spacing: 0.02em;
|
||||
text-transform: none;
|
||||
text-shadow: 0px 0px 6px rgba(0, 0, 0, 0.2); }
|
||||
|
||||
.reveal h1 {
|
||||
line-height: 1.2em;
|
||||
/* added by hpl */
|
||||
text-shadow: 0 1px 0 #cccccc, 0 2px 0 #c9c9c9, 0 3px 0 #bbbbbb, 0 4px 0 #b9b9b9, 0 5px 0 #aaaaaa, 0 6px 1px rgba(0, 0, 0, 0.1), 0 0 5px rgba(0, 0, 0, 0.1), 0 1px 3px rgba(0, 0, 0, 0.3), 0 3px 5px rgba(0, 0, 0, 0.2), 0 5px 10px rgba(0, 0, 0, 0.25), 0 20px 20px rgba(0, 0, 0, 0.15); }
|
||||
|
||||
/*********************************************
|
||||
* LINKS
|
||||
*********************************************/
|
||||
.reveal a:not(.image) {
|
||||
color: #13daec;
|
||||
text-decoration: none;
|
||||
-webkit-transition: color .15s ease;
|
||||
-moz-transition: color .15s ease;
|
||||
-ms-transition: color .15s ease;
|
||||
-o-transition: color .15s ease;
|
||||
transition: color .15s ease; }
|
||||
|
||||
.reveal a:not(.image):hover {
|
||||
color: #71e9f4;
|
||||
text-shadow: none;
|
||||
border: none; }
|
||||
|
||||
.reveal .roll span:after {
|
||||
color: #fff;
|
||||
background: #0d99a5; }
|
||||
|
||||
/*********************************************
|
||||
* IMAGES
|
||||
*********************************************/
|
||||
.reveal section img {
|
||||
margin: 15px 0px;
|
||||
background: rgba(255, 255, 255, 0.12);
|
||||
border: 4px solid #eeeeee;
|
||||
box-shadow: 0 0 10px rgba(0, 0, 0, 0.15);
|
||||
-webkit-transition: all .2s linear;
|
||||
-moz-transition: all .2s linear;
|
||||
-ms-transition: all .2s linear;
|
||||
-o-transition: all .2s linear;
|
||||
transition: all .2s linear; }
|
||||
|
||||
.reveal a:hover img {
|
||||
background: rgba(255, 255, 255, 0.2);
|
||||
border-color: #13daec;
|
||||
box-shadow: 0 0 20px rgba(0, 0, 0, 0.55); }
|
||||
|
||||
/*********************************************
|
||||
* NAVIGATION CONTROLS
|
||||
*********************************************/
|
||||
.reveal .controls div.navigate-left,
|
||||
.reveal .controls div.navigate-left.enabled {
|
||||
border-right-color: #13daec; }
|
||||
|
||||
.reveal .controls div.navigate-right,
|
||||
.reveal .controls div.navigate-right.enabled {
|
||||
border-left-color: #13daec; }
|
||||
|
||||
.reveal .controls div.navigate-up,
|
||||
.reveal .controls div.navigate-up.enabled {
|
||||
border-bottom-color: #13daec; }
|
||||
|
||||
.reveal .controls div.navigate-down,
|
||||
.reveal .controls div.navigate-down.enabled {
|
||||
border-top-color: #13daec; }
|
||||
|
||||
.reveal .controls div.navigate-left.enabled:hover {
|
||||
border-right-color: #71e9f4; }
|
||||
|
||||
.reveal .controls div.navigate-right.enabled:hover {
|
||||
border-left-color: #71e9f4; }
|
||||
|
||||
.reveal .controls div.navigate-up.enabled:hover {
|
||||
border-bottom-color: #71e9f4; }
|
||||
|
||||
.reveal .controls div.navigate-down.enabled:hover {
|
||||
border-top-color: #71e9f4; }
|
||||
|
||||
/*********************************************
|
||||
* PROGRESS BAR
|
||||
*********************************************/
|
||||
.reveal .progress {
|
||||
background: rgba(0, 0, 0, 0.2); }
|
||||
|
||||
.reveal .progress span {
|
||||
background: #13daec;
|
||||
-webkit-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-moz-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-ms-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-o-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985); }
|
||||
|
||||
/*********************************************
|
||||
* SLIDE NUMBER
|
||||
*********************************************/
|
||||
.reveal .slide-number {
|
||||
color: #13daec; }
|
||||
@@ -0,0 +1,279 @@
|
||||
@import url(../../lib/font/league-gothic/league-gothic.css);
|
||||
@import url(https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic);
|
||||
/**
|
||||
* League theme for reveal.js.
|
||||
*
|
||||
* This was the default theme pre-3.0.0.
|
||||
*
|
||||
* Copyright (C) 2011-2012 Hakim El Hattab, http://hakim.se
|
||||
*/
|
||||
/*********************************************
|
||||
* GLOBAL STYLES
|
||||
*********************************************/
|
||||
body {
|
||||
background: #1c1e20;
|
||||
background: -moz-radial-gradient(center, circle cover, #555a5f 0%, #1c1e20 100%);
|
||||
background: -webkit-gradient(radial, center center, 0px, center center, 100%, color-stop(0%, #555a5f), color-stop(100%, #1c1e20));
|
||||
background: -webkit-radial-gradient(center, circle cover, #555a5f 0%, #1c1e20 100%);
|
||||
background: -o-radial-gradient(center, circle cover, #555a5f 0%, #1c1e20 100%);
|
||||
background: -ms-radial-gradient(center, circle cover, #555a5f 0%, #1c1e20 100%);
|
||||
background: radial-gradient(center, circle cover, #555a5f 0%, #1c1e20 100%);
|
||||
background-color: #2b2b2b; }
|
||||
|
||||
.reveal {
|
||||
font-family: 'Lato', sans-serif;
|
||||
font-size: 30px; /* changed by hpl from 36px */
|
||||
font-weight: normal;
|
||||
color: #eee; }
|
||||
|
||||
::selection {
|
||||
color: #fff;
|
||||
background: #FF5E99;
|
||||
text-shadow: none; }
|
||||
|
||||
.reveal .slides > section, .reveal .slides > section > section {
|
||||
/* removed by hpl: line-height: 1.3; */
|
||||
font-weight: inherit; }
|
||||
|
||||
/*********************************************
|
||||
* HEADERS
|
||||
*********************************************/
|
||||
.reveal h1, .reveal h2, .reveal h3, .reveal h4, .reveal h5, .reveal h6 {
|
||||
margin: 0 0 20px 0;
|
||||
color: #eee;
|
||||
font-family: 'Helvetica', Impact, sans-serif;
|
||||
font-weight: normal;
|
||||
line-height: 1.1em; /* changed by hpl from 1.2; */
|
||||
letter-spacing: normal;
|
||||
/* text-transform: uppercase; removed by hpl */
|
||||
text-shadow: 0px 0px 6px rgba(0, 0, 0, 0.2);
|
||||
word-wrap: break-word; }
|
||||
|
||||
.reveal h1 {
|
||||
line-height: 1.2em;
|
||||
}
|
||||
|
||||
/* removed by hpl:
|
||||
.reveal h1 {
|
||||
font-size: 3.77em; }
|
||||
|
||||
.reveal h2 {
|
||||
font-size: 2.11em; }
|
||||
|
||||
.reveal h3 {
|
||||
font-size: 1.55em; }
|
||||
|
||||
.reveal h4 {
|
||||
font-size: 1em; }
|
||||
*/
|
||||
|
||||
.reveal h1 {
|
||||
text-shadow: 0 1px 0 #ccc, 0 2px 0 #c9c9c9, 0 3px 0 #bbb, 0 4px 0 #b9b9b9, 0 5px 0 #aaa, 0 6px 1px rgba(0, 0, 0, 0.1), 0 0 5px rgba(0, 0, 0, 0.1), 0 1px 3px rgba(0, 0, 0, 0.3), 0 3px 5px rgba(0, 0, 0, 0.2), 0 5px 10px rgba(0, 0, 0, 0.25), 0 20px 20px rgba(0, 0, 0, 0.15); }
|
||||
|
||||
/*********************************************
|
||||
* OTHER
|
||||
*********************************************/
|
||||
.reveal p {
|
||||
margin: 20px 0;
|
||||
line-height: 1.3; }
|
||||
|
||||
/* Ensure certain elements are never larger than the slide itself */
|
||||
.reveal img, .reveal video, .reveal iframe {
|
||||
max-width: 95%;
|
||||
max-height: 95%; }
|
||||
|
||||
.reveal strong, .reveal b {
|
||||
font-weight: bold; }
|
||||
|
||||
.reveal em {
|
||||
font-style: italic; }
|
||||
|
||||
.reveal ol, .reveal dl, .reveal ul {
|
||||
display: inline-block;
|
||||
text-align: left;
|
||||
margin: 0 0 0 1em; }
|
||||
|
||||
.reveal ol {
|
||||
list-style-type: decimal; }
|
||||
|
||||
.reveal ul {
|
||||
list-style-type: disc; }
|
||||
|
||||
.reveal ul ul {
|
||||
list-style-type: square; }
|
||||
|
||||
.reveal ul ul ul {
|
||||
list-style-type: circle; }
|
||||
|
||||
.reveal ul ul, .reveal ul ol, .reveal ol ol, .reveal ol ul {
|
||||
display: block;
|
||||
margin-left: 40px; }
|
||||
|
||||
.reveal dt {
|
||||
font-weight: bold; }
|
||||
|
||||
.reveal dd {
|
||||
margin-left: 40px; }
|
||||
|
||||
.reveal q, .reveal blockquote {
|
||||
quotes: none; }
|
||||
|
||||
.reveal blockquote {
|
||||
display: block;
|
||||
position: relative;
|
||||
width: 70%;
|
||||
margin: 20px auto;
|
||||
padding: 5px;
|
||||
font-style: italic;
|
||||
background: rgba(255, 255, 255, 0.05);
|
||||
box-shadow: 0px 0px 2px rgba(0, 0, 0, 0.2); }
|
||||
|
||||
.reveal blockquote p:first-child, .reveal blockquote p:last-child {
|
||||
display: inline-block; }
|
||||
|
||||
.reveal q {
|
||||
font-style: italic; }
|
||||
|
||||
.reveal pre {
|
||||
display: block;
|
||||
position: relative;
|
||||
width: 90%;
|
||||
margin: 20px auto;
|
||||
text-align: left;
|
||||
font-size: 0.55em;
|
||||
font-family: monospace;
|
||||
line-height: 1.2em;
|
||||
word-wrap: break-word;
|
||||
box-shadow: 0px 0px 6px rgba(0, 0, 0, 0.3); }
|
||||
|
||||
.reveal code {
|
||||
font-family: monospace; }
|
||||
|
||||
.reveal pre code {
|
||||
display: block;
|
||||
padding: 5px;
|
||||
overflow: auto;
|
||||
max-height: 400px;
|
||||
word-wrap: normal;
|
||||
background: #3F3F3F;
|
||||
color: #DCDCDC; }
|
||||
|
||||
.reveal table {
|
||||
margin: auto;
|
||||
border-collapse: collapse;
|
||||
border-spacing: 0; }
|
||||
|
||||
.reveal table th {
|
||||
font-weight: bold; }
|
||||
|
||||
.reveal table th, .reveal table td {
|
||||
/* text-align: left; */ /* hpl modification */
|
||||
padding: 0.2em 0.5em 0.2em 0.5em;
|
||||
border-bottom: 1px solid; }
|
||||
|
||||
.reveal table th[align="center"], .reveal table td[align="center"] {
|
||||
text-align: center; }
|
||||
|
||||
.reveal table th[align="right"], .reveal table td[align="right"] {
|
||||
text-align: right; }
|
||||
|
||||
.reveal table tr:last-child td {
|
||||
border-bottom: none; }
|
||||
|
||||
.reveal sup {
|
||||
vertical-align: super; }
|
||||
|
||||
.reveal sub {
|
||||
vertical-align: sub; }
|
||||
|
||||
.reveal small {
|
||||
display: inline-block;
|
||||
font-size: 0.6em;
|
||||
line-height: 1.2em;
|
||||
vertical-align: top; }
|
||||
|
||||
.reveal small * {
|
||||
vertical-align: top; }
|
||||
|
||||
/*********************************************
|
||||
* LINKS
|
||||
*********************************************/
|
||||
.reveal a {
|
||||
color: #13DAEC;
|
||||
text-decoration: none;
|
||||
-webkit-transition: color 0.15s ease;
|
||||
-moz-transition: color 0.15s ease;
|
||||
transition: color 0.15s ease; }
|
||||
|
||||
.reveal a:hover {
|
||||
color: #71ebf4;
|
||||
text-shadow: none;
|
||||
border: none; }
|
||||
|
||||
.reveal .roll span:after {
|
||||
color: #fff;
|
||||
background: #0d9ba5; }
|
||||
|
||||
/*********************************************
|
||||
* IMAGES
|
||||
*********************************************/
|
||||
.reveal section img {
|
||||
margin: 15px 0px;
|
||||
background: rgba(255, 255, 255, 0.12);
|
||||
border: 4px solid #eee;
|
||||
box-shadow: 0 0 10px rgba(0, 0, 0, 0.15); }
|
||||
|
||||
.reveal a img {
|
||||
-webkit-transition: all 0.15s linear;
|
||||
-moz-transition: all 0.15s linear;
|
||||
transition: all 0.15s linear; }
|
||||
|
||||
.reveal a:hover img {
|
||||
background: rgba(255, 255, 255, 0.2);
|
||||
border-color: #13DAEC;
|
||||
box-shadow: 0 0 20px rgba(0, 0, 0, 0.55); }
|
||||
|
||||
/*********************************************
|
||||
* NAVIGATION CONTROLS
|
||||
*********************************************/
|
||||
.reveal .controls div.navigate-left, .reveal .controls div.navigate-left.enabled {
|
||||
border-right-color: #13DAEC; }
|
||||
|
||||
.reveal .controls div.navigate-right, .reveal .controls div.navigate-right.enabled {
|
||||
border-left-color: #13DAEC; }
|
||||
|
||||
.reveal .controls div.navigate-up, .reveal .controls div.navigate-up.enabled {
|
||||
border-bottom-color: #13DAEC; }
|
||||
|
||||
.reveal .controls div.navigate-down, .reveal .controls div.navigate-down.enabled {
|
||||
border-top-color: #13DAEC; }
|
||||
|
||||
.reveal .controls div.navigate-left.enabled:hover {
|
||||
border-right-color: #71ebf4; }
|
||||
|
||||
.reveal .controls div.navigate-right.enabled:hover {
|
||||
border-left-color: #71ebf4; }
|
||||
|
||||
.reveal .controls div.navigate-up.enabled:hover {
|
||||
border-bottom-color: #71ebf4; }
|
||||
|
||||
.reveal .controls div.navigate-down.enabled:hover {
|
||||
border-top-color: #71ebf4; }
|
||||
|
||||
/*********************************************
|
||||
* PROGRESS BAR
|
||||
*********************************************/
|
||||
.reveal .progress {
|
||||
background: rgba(0, 0, 0, 0.2); }
|
||||
|
||||
.reveal .progress span {
|
||||
background: #13DAEC;
|
||||
-webkit-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-moz-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985); }
|
||||
|
||||
/*********************************************
|
||||
* SLIDE NUMBER
|
||||
*********************************************/
|
||||
.reveal .slide-number {
|
||||
color: #13DAEC; }
|
||||
@@ -0,0 +1,153 @@
|
||||
@import url(https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic);
|
||||
/**
|
||||
* Solarized Dark theme for reveal.js.
|
||||
* Author: Achim Staebler
|
||||
*/
|
||||
/* changed by hpl from 36px; */
|
||||
/* changed by hpl from 'League Gothic', Impact, sans-serif; */
|
||||
/* changed by hpl from uppercase; */
|
||||
@font-face {
|
||||
font-family: 'League Gothic';
|
||||
src: url("../../lib/font/league_gothic-webfont.eot");
|
||||
src: url("../../lib/font/league_gothic-webfont.eot?#iefix") format("embedded-opentype"), url("../../lib/font/league_gothic-webfont.woff") format("woff"), url("../../lib/font/league_gothic-webfont.ttf") format("truetype"), url("../../lib/font/league_gothic-webfont.svg#LeagueGothicRegular") format("svg");
|
||||
font-weight: normal;
|
||||
font-style: normal; }
|
||||
|
||||
/**
|
||||
* Solarized colors by Ethan Schoonover
|
||||
*/
|
||||
html * {
|
||||
color-profile: sRGB;
|
||||
rendering-intent: auto; }
|
||||
|
||||
/*********************************************
|
||||
* GLOBAL STYLES
|
||||
*********************************************/
|
||||
body {
|
||||
background: #002b36;
|
||||
background-color: #002b36; }
|
||||
|
||||
.reveal {
|
||||
font-family: "Lato", sans-serif;
|
||||
font-size: 30px;
|
||||
font-weight: normal;
|
||||
letter-spacing: -0.02em;
|
||||
color: #93a1a1; }
|
||||
|
||||
::selection {
|
||||
color: white;
|
||||
background: #d33682;
|
||||
text-shadow: none; }
|
||||
|
||||
/*********************************************
|
||||
* HEADERS
|
||||
*********************************************/
|
||||
.reveal h1,
|
||||
.reveal h2,
|
||||
.reveal h3,
|
||||
.reveal h4,
|
||||
.reveal h5,
|
||||
.reveal h6 {
|
||||
margin: 0 0 20px 0;
|
||||
color: #eee8d5;
|
||||
font-family: "Helvetica", Impact, sans-serif;
|
||||
line-height: 1.1em; /* changed (by hpl) from 0.9em */
|
||||
letter-spacing: 0.02em;
|
||||
text-transform: none;
|
||||
text-shadow: none; }
|
||||
|
||||
.reveal h1 {
|
||||
line-height: 1.2em;
|
||||
/* added by hpl */
|
||||
text-shadow: 0px 0px 6px rgba(0, 0, 0, 0.2); }
|
||||
|
||||
/*********************************************
|
||||
* LINKS
|
||||
*********************************************/
|
||||
.reveal a:not(.image) {
|
||||
color: #268bd2;
|
||||
text-decoration: none;
|
||||
-webkit-transition: color .15s ease;
|
||||
-moz-transition: color .15s ease;
|
||||
-ms-transition: color .15s ease;
|
||||
-o-transition: color .15s ease;
|
||||
transition: color .15s ease; }
|
||||
|
||||
.reveal a:not(.image):hover {
|
||||
color: #78b9e6;
|
||||
text-shadow: none;
|
||||
border: none; }
|
||||
|
||||
.reveal .roll span:after {
|
||||
color: #fff;
|
||||
background: #1a6091; }
|
||||
|
||||
/*********************************************
|
||||
* IMAGES
|
||||
*********************************************/
|
||||
.reveal section img {
|
||||
margin: 15px 0px;
|
||||
background: rgba(255, 255, 255, 0.12);
|
||||
border: 4px solid #93a1a1;
|
||||
box-shadow: 0 0 10px rgba(0, 0, 0, 0.15);
|
||||
-webkit-transition: all .2s linear;
|
||||
-moz-transition: all .2s linear;
|
||||
-ms-transition: all .2s linear;
|
||||
-o-transition: all .2s linear;
|
||||
transition: all .2s linear; }
|
||||
|
||||
.reveal a:hover img {
|
||||
background: rgba(255, 255, 255, 0.2);
|
||||
border-color: #268bd2;
|
||||
box-shadow: 0 0 20px rgba(0, 0, 0, 0.55); }
|
||||
|
||||
/*********************************************
|
||||
* NAVIGATION CONTROLS
|
||||
*********************************************/
|
||||
.reveal .controls div.navigate-left,
|
||||
.reveal .controls div.navigate-left.enabled {
|
||||
border-right-color: #268bd2; }
|
||||
|
||||
.reveal .controls div.navigate-right,
|
||||
.reveal .controls div.navigate-right.enabled {
|
||||
border-left-color: #268bd2; }
|
||||
|
||||
.reveal .controls div.navigate-up,
|
||||
.reveal .controls div.navigate-up.enabled {
|
||||
border-bottom-color: #268bd2; }
|
||||
|
||||
.reveal .controls div.navigate-down,
|
||||
.reveal .controls div.navigate-down.enabled {
|
||||
border-top-color: #268bd2; }
|
||||
|
||||
.reveal .controls div.navigate-left.enabled:hover {
|
||||
border-right-color: #78b9e6; }
|
||||
|
||||
.reveal .controls div.navigate-right.enabled:hover {
|
||||
border-left-color: #78b9e6; }
|
||||
|
||||
.reveal .controls div.navigate-up.enabled:hover {
|
||||
border-bottom-color: #78b9e6; }
|
||||
|
||||
.reveal .controls div.navigate-down.enabled:hover {
|
||||
border-top-color: #78b9e6; }
|
||||
|
||||
/*********************************************
|
||||
* PROGRESS BAR
|
||||
*********************************************/
|
||||
.reveal .progress {
|
||||
background: rgba(0, 0, 0, 0.2); }
|
||||
|
||||
.reveal .progress span {
|
||||
background: #268bd2;
|
||||
-webkit-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-moz-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-ms-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-o-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985); }
|
||||
|
||||
/*********************************************
|
||||
* SLIDE NUMBER
|
||||
*********************************************/
|
||||
.reveal .slide-number {
|
||||
color: #268bd2; }
|
||||
@@ -0,0 +1,141 @@
|
||||
@import url(https://fonts.googleapis.com/css?family=Montserrat:700);
|
||||
@import url(https://fonts.googleapis.com/css?family=Open+Sans:400,700,400italic,700italic);
|
||||
/**
|
||||
* Black theme for reveal.js.
|
||||
*
|
||||
* Copyright (C) 2011-2012 Hakim El Hattab, http://hakim.se
|
||||
*/
|
||||
/* changed by hpl from 36px; */
|
||||
/* changed by hpl from 'League Gothic', Impact, sans-serif; */
|
||||
/* changed by hpl from uppercase; */
|
||||
/*********************************************
|
||||
* GLOBAL STYLES
|
||||
*********************************************/
|
||||
body {
|
||||
background: #111111;
|
||||
background-color: #111111; }
|
||||
|
||||
.reveal {
|
||||
font-family: "Open Sans", sans-serif;
|
||||
font-size: 30px;
|
||||
font-weight: normal;
|
||||
letter-spacing: -0.02em;
|
||||
color: #eeeeee; }
|
||||
|
||||
::selection {
|
||||
color: white;
|
||||
background: #e7ad52;
|
||||
text-shadow: none; }
|
||||
|
||||
/*********************************************
|
||||
* HEADERS
|
||||
*********************************************/
|
||||
.reveal h1,
|
||||
.reveal h2,
|
||||
.reveal h3,
|
||||
.reveal h4,
|
||||
.reveal h5,
|
||||
.reveal h6 {
|
||||
margin: 0 0 20px 0;
|
||||
color: #eeeeee;
|
||||
font-family: "Montserrat", Impact, sans-serif;
|
||||
line-height: 1.1em; /* changed (by hpl) from 0.9em */
|
||||
letter-spacing: -0.03em;
|
||||
text-transform: none;
|
||||
text-shadow: none; }
|
||||
|
||||
.reveal h1 {
|
||||
line-height: 1.2em;
|
||||
/* added by hpl */
|
||||
text-shadow: 0px 0px 6px rgba(0, 0, 0, 0.2); }
|
||||
|
||||
/*********************************************
|
||||
* LINKS
|
||||
*********************************************/
|
||||
.reveal a:not(.image) {
|
||||
color: #e7ad52;
|
||||
text-decoration: none;
|
||||
-webkit-transition: color .15s ease;
|
||||
-moz-transition: color .15s ease;
|
||||
-ms-transition: color .15s ease;
|
||||
-o-transition: color .15s ease;
|
||||
transition: color .15s ease; }
|
||||
|
||||
.reveal a:not(.image):hover {
|
||||
color: #f3d7ac;
|
||||
text-shadow: none;
|
||||
border: none; }
|
||||
|
||||
.reveal .roll span:after {
|
||||
color: #fff;
|
||||
background: #d08a1d; }
|
||||
|
||||
/*********************************************
|
||||
* IMAGES
|
||||
*********************************************/
|
||||
.reveal section img {
|
||||
margin: 15px 0px;
|
||||
background: rgba(255, 255, 255, 0.12);
|
||||
border: 4px solid #eeeeee;
|
||||
box-shadow: 0 0 10px rgba(0, 0, 0, 0.15);
|
||||
-webkit-transition: all .2s linear;
|
||||
-moz-transition: all .2s linear;
|
||||
-ms-transition: all .2s linear;
|
||||
-o-transition: all .2s linear;
|
||||
transition: all .2s linear; }
|
||||
|
||||
.reveal a:hover img {
|
||||
background: rgba(255, 255, 255, 0.2);
|
||||
border-color: #e7ad52;
|
||||
box-shadow: 0 0 20px rgba(0, 0, 0, 0.55); }
|
||||
|
||||
/*********************************************
|
||||
* NAVIGATION CONTROLS
|
||||
*********************************************/
|
||||
.reveal .controls div.navigate-left,
|
||||
.reveal .controls div.navigate-left.enabled {
|
||||
border-right-color: #e7ad52; }
|
||||
|
||||
.reveal .controls div.navigate-right,
|
||||
.reveal .controls div.navigate-right.enabled {
|
||||
border-left-color: #e7ad52; }
|
||||
|
||||
.reveal .controls div.navigate-up,
|
||||
.reveal .controls div.navigate-up.enabled {
|
||||
border-bottom-color: #e7ad52; }
|
||||
|
||||
.reveal .controls div.navigate-down,
|
||||
.reveal .controls div.navigate-down.enabled {
|
||||
border-top-color: #e7ad52; }
|
||||
|
||||
.reveal .controls div.navigate-left.enabled:hover {
|
||||
border-right-color: #f3d7ac; }
|
||||
|
||||
.reveal .controls div.navigate-right.enabled:hover {
|
||||
border-left-color: #f3d7ac; }
|
||||
|
||||
.reveal .controls div.navigate-up.enabled:hover {
|
||||
border-bottom-color: #f3d7ac; }
|
||||
|
||||
.reveal .controls div.navigate-down.enabled:hover {
|
||||
border-top-color: #f3d7ac; }
|
||||
|
||||
/*********************************************
|
||||
* PROGRESS BAR
|
||||
*********************************************/
|
||||
.reveal .progress {
|
||||
background: rgba(0, 0, 0, 0.2); }
|
||||
|
||||
.reveal .progress span {
|
||||
background: #e7ad52;
|
||||
-webkit-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-moz-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-ms-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-o-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985); }
|
||||
|
||||
/*********************************************
|
||||
* SLIDE NUMBER
|
||||
*********************************************/
|
||||
.reveal .slide-number {
|
||||
color: #e7ad52; }
|
||||
@@ -0,0 +1,143 @@
|
||||
/**
|
||||
* A simple theme for reveal.js presentations, similar
|
||||
* to the default theme. The accent color is brown.
|
||||
*
|
||||
* This theme is Copyright (C) 2012-2013 Owen Versteeg, http://owenversteeg.com - it is MIT licensed.
|
||||
*/
|
||||
/* changed by hpl from 36px; */
|
||||
/* changed by hpl from 'League Gothic', Impact, sans-serif; */
|
||||
/* changed by hpl from uppercase; */
|
||||
.reveal a:not(.image) {
|
||||
line-height: 1.3em; }
|
||||
|
||||
/*********************************************
|
||||
* GLOBAL STYLES
|
||||
*********************************************/
|
||||
body {
|
||||
background: #f0f1eb;
|
||||
background-color: #f0f1eb; }
|
||||
|
||||
.reveal {
|
||||
font-family: "Palatino Linotype", "Book Antiqua", Palatino, FreeSerif, serif;
|
||||
font-size: 30px;
|
||||
font-weight: normal;
|
||||
letter-spacing: -0.02em;
|
||||
color: black; }
|
||||
|
||||
::selection {
|
||||
color: white;
|
||||
background: #26351c;
|
||||
text-shadow: none; }
|
||||
|
||||
/*********************************************
|
||||
* HEADERS
|
||||
*********************************************/
|
||||
.reveal h1,
|
||||
.reveal h2,
|
||||
.reveal h3,
|
||||
.reveal h4,
|
||||
.reveal h5,
|
||||
.reveal h6 {
|
||||
margin: 0 0 20px 0;
|
||||
color: #383d3d;
|
||||
font-family: "Palatino Linotype", "Book Antiqua", Palatino, FreeSerif, serif;
|
||||
line-height: 1.1em; /* changed (by hpl) from 0.9em */
|
||||
letter-spacing: 0.02em;
|
||||
text-transform: none;
|
||||
text-shadow: none; }
|
||||
|
||||
.reveal h1 {
|
||||
line-height: 1.2em;
|
||||
/* added by hpl */
|
||||
text-shadow: 0px 0px 6px rgba(0, 0, 0, 0.2); }
|
||||
|
||||
/*********************************************
|
||||
* LINKS
|
||||
*********************************************/
|
||||
.reveal a:not(.image) {
|
||||
color: #51483d;
|
||||
text-decoration: none;
|
||||
-webkit-transition: color .15s ease;
|
||||
-moz-transition: color .15s ease;
|
||||
-ms-transition: color .15s ease;
|
||||
-o-transition: color .15s ease;
|
||||
transition: color .15s ease; }
|
||||
|
||||
.reveal a:not(.image):hover {
|
||||
color: #8b7c69;
|
||||
text-shadow: none;
|
||||
border: none; }
|
||||
|
||||
.reveal .roll span:after {
|
||||
color: #fff;
|
||||
background: #25211c; }
|
||||
|
||||
/*********************************************
|
||||
* IMAGES
|
||||
*********************************************/
|
||||
.reveal section img {
|
||||
margin: 15px 0px;
|
||||
background: rgba(255, 255, 255, 0.12);
|
||||
border: 4px solid black;
|
||||
box-shadow: 0 0 10px rgba(0, 0, 0, 0.15);
|
||||
-webkit-transition: all .2s linear;
|
||||
-moz-transition: all .2s linear;
|
||||
-ms-transition: all .2s linear;
|
||||
-o-transition: all .2s linear;
|
||||
transition: all .2s linear; }
|
||||
|
||||
.reveal a:hover img {
|
||||
background: rgba(255, 255, 255, 0.2);
|
||||
border-color: #51483d;
|
||||
box-shadow: 0 0 20px rgba(0, 0, 0, 0.55); }
|
||||
|
||||
/*********************************************
|
||||
* NAVIGATION CONTROLS
|
||||
*********************************************/
|
||||
.reveal .controls div.navigate-left,
|
||||
.reveal .controls div.navigate-left.enabled {
|
||||
border-right-color: #51483d; }
|
||||
|
||||
.reveal .controls div.navigate-right,
|
||||
.reveal .controls div.navigate-right.enabled {
|
||||
border-left-color: #51483d; }
|
||||
|
||||
.reveal .controls div.navigate-up,
|
||||
.reveal .controls div.navigate-up.enabled {
|
||||
border-bottom-color: #51483d; }
|
||||
|
||||
.reveal .controls div.navigate-down,
|
||||
.reveal .controls div.navigate-down.enabled {
|
||||
border-top-color: #51483d; }
|
||||
|
||||
.reveal .controls div.navigate-left.enabled:hover {
|
||||
border-right-color: #8b7c69; }
|
||||
|
||||
.reveal .controls div.navigate-right.enabled:hover {
|
||||
border-left-color: #8b7c69; }
|
||||
|
||||
.reveal .controls div.navigate-up.enabled:hover {
|
||||
border-bottom-color: #8b7c69; }
|
||||
|
||||
.reveal .controls div.navigate-down.enabled:hover {
|
||||
border-top-color: #8b7c69; }
|
||||
|
||||
/*********************************************
|
||||
* PROGRESS BAR
|
||||
*********************************************/
|
||||
.reveal .progress {
|
||||
background: rgba(0, 0, 0, 0.2); }
|
||||
|
||||
.reveal .progress span {
|
||||
background: #51483d;
|
||||
-webkit-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-moz-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-ms-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-o-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985); }
|
||||
|
||||
/*********************************************
|
||||
* SLIDE NUMBER
|
||||
*********************************************/
|
||||
.reveal .slide-number {
|
||||
color: #51483d; }
|
||||
@@ -0,0 +1,144 @@
|
||||
@import url(https://fonts.googleapis.com/css?family=News+Cycle:400,700);
|
||||
@import url(https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic);
|
||||
/**
|
||||
* A simple theme for reveal.js presentations, similar
|
||||
* to the default theme. The accent color is darkblue.
|
||||
*
|
||||
* This theme is Copyright (C) 2012 Owen Versteeg, https://github.com/StereotypicalApps. It is MIT licensed.
|
||||
* reveal.js is Copyright (C) 2011-2012 Hakim El Hattab, http://hakim.se
|
||||
*/
|
||||
/* changed by hpl from 36px; */
|
||||
/* changed by hpl from 'League Gothic', Impact, sans-serif; */
|
||||
/* changed by hpl from uppercase; */
|
||||
/* changed (by hpl) from 'News Cycle', Impact, sans-serif; */
|
||||
/*********************************************
|
||||
* GLOBAL STYLES
|
||||
*********************************************/
|
||||
body {
|
||||
background: white;
|
||||
background-color: white; }
|
||||
|
||||
.reveal {
|
||||
font-family: "Lato", sans-serif;
|
||||
font-size: 30px;
|
||||
font-weight: normal;
|
||||
letter-spacing: -0.02em;
|
||||
color: black; }
|
||||
|
||||
::selection {
|
||||
color: white;
|
||||
background: rgba(0, 0, 0, 0.99);
|
||||
text-shadow: none; }
|
||||
|
||||
/*********************************************
|
||||
* HEADERS
|
||||
*********************************************/
|
||||
.reveal h1,
|
||||
.reveal h2,
|
||||
.reveal h3,
|
||||
.reveal h4,
|
||||
.reveal h5,
|
||||
.reveal h6 {
|
||||
margin: 0 0 20px 0;
|
||||
color: black;
|
||||
font-family: "Helvetica", Impact, sans-serif;
|
||||
line-height: 1.1em; /* changed (by hpl) from 0.9em */
|
||||
letter-spacing: 0.02em;
|
||||
text-transform: none;
|
||||
text-shadow: none; }
|
||||
|
||||
.reveal h1 {
|
||||
line-height: 1.2em;
|
||||
/* added by hpl */
|
||||
text-shadow: 0px 0px 6px rgba(0, 0, 0, 0.2); }
|
||||
|
||||
/*********************************************
|
||||
* LINKS
|
||||
*********************************************/
|
||||
.reveal a:not(.image) {
|
||||
color: darkblue;
|
||||
text-decoration: none;
|
||||
-webkit-transition: color .15s ease;
|
||||
-moz-transition: color .15s ease;
|
||||
-ms-transition: color .15s ease;
|
||||
-o-transition: color .15s ease;
|
||||
transition: color .15s ease; }
|
||||
|
||||
.reveal a:not(.image):hover {
|
||||
color: #0000f1;
|
||||
text-shadow: none;
|
||||
border: none; }
|
||||
|
||||
.reveal .roll span:after {
|
||||
color: #fff;
|
||||
background: #00003f; }
|
||||
|
||||
/*********************************************
|
||||
* IMAGES
|
||||
*********************************************/
|
||||
.reveal section img {
|
||||
margin: 15px 0px;
|
||||
background: rgba(255, 255, 255, 0.12);
|
||||
border: 4px solid black;
|
||||
box-shadow: 0 0 10px rgba(0, 0, 0, 0.15);
|
||||
-webkit-transition: all .2s linear;
|
||||
-moz-transition: all .2s linear;
|
||||
-ms-transition: all .2s linear;
|
||||
-o-transition: all .2s linear;
|
||||
transition: all .2s linear; }
|
||||
|
||||
.reveal a:hover img {
|
||||
background: rgba(255, 255, 255, 0.2);
|
||||
border-color: darkblue;
|
||||
box-shadow: 0 0 20px rgba(0, 0, 0, 0.55); }
|
||||
|
||||
/*********************************************
|
||||
* NAVIGATION CONTROLS
|
||||
*********************************************/
|
||||
.reveal .controls div.navigate-left,
|
||||
.reveal .controls div.navigate-left.enabled {
|
||||
border-right-color: darkblue; }
|
||||
|
||||
.reveal .controls div.navigate-right,
|
||||
.reveal .controls div.navigate-right.enabled {
|
||||
border-left-color: darkblue; }
|
||||
|
||||
.reveal .controls div.navigate-up,
|
||||
.reveal .controls div.navigate-up.enabled {
|
||||
border-bottom-color: darkblue; }
|
||||
|
||||
.reveal .controls div.navigate-down,
|
||||
.reveal .controls div.navigate-down.enabled {
|
||||
border-top-color: darkblue; }
|
||||
|
||||
.reveal .controls div.navigate-left.enabled:hover {
|
||||
border-right-color: #0000f1; }
|
||||
|
||||
.reveal .controls div.navigate-right.enabled:hover {
|
||||
border-left-color: #0000f1; }
|
||||
|
||||
.reveal .controls div.navigate-up.enabled:hover {
|
||||
border-bottom-color: #0000f1; }
|
||||
|
||||
.reveal .controls div.navigate-down.enabled:hover {
|
||||
border-top-color: #0000f1; }
|
||||
|
||||
/*********************************************
|
||||
* PROGRESS BAR
|
||||
*********************************************/
|
||||
.reveal .progress {
|
||||
background: rgba(0, 0, 0, 0.2); }
|
||||
|
||||
.reveal .progress span {
|
||||
background: darkblue;
|
||||
-webkit-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-moz-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-ms-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-o-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985); }
|
||||
|
||||
/*********************************************
|
||||
* SLIDE NUMBER
|
||||
*********************************************/
|
||||
.reveal .slide-number {
|
||||
color: darkblue; }
|
||||
@@ -0,0 +1,144 @@
|
||||
@import url(https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic);
|
||||
/**
|
||||
* A simple theme for reveal.js presentations, similar
|
||||
* to the default theme. The accent color is darkblue.
|
||||
*
|
||||
* This theme is Copyright (C) 2012 Owen Versteeg, https://github.com/StereotypicalApps. It is MIT licensed.
|
||||
* reveal.js is Copyright (C) 2011-2012 Hakim El Hattab, http://hakim.se
|
||||
|
||||
* Simula theme made by Hans Petter Langtangen.
|
||||
*/
|
||||
/* changed by hpl from 36px; */
|
||||
/* changed by hpl from 'League Gothic', Impact, sans-serif; */
|
||||
/* changed by hpl from uppercase; */
|
||||
/*********************************************
|
||||
* GLOBAL STYLES
|
||||
*********************************************/
|
||||
body {
|
||||
background: white;
|
||||
background-color: white; }
|
||||
|
||||
.reveal {
|
||||
font-family: "Lato", sans-serif;
|
||||
font-size: 30px;
|
||||
font-weight: normal;
|
||||
letter-spacing: -0.02em;
|
||||
color: #404040; }
|
||||
|
||||
::selection {
|
||||
color: white;
|
||||
background: rgba(0, 0, 0, 0.99);
|
||||
text-shadow: none; }
|
||||
|
||||
/*********************************************
|
||||
* HEADERS
|
||||
*********************************************/
|
||||
.reveal h1,
|
||||
.reveal h2,
|
||||
.reveal h3,
|
||||
.reveal h4,
|
||||
.reveal h5,
|
||||
.reveal h6 {
|
||||
margin: 0 0 20px 0;
|
||||
color: #ff8800;
|
||||
font-family: "Helvetica", Impact, sans-serif;
|
||||
line-height: 1.1em; /* changed (by hpl) from 0.9em */
|
||||
letter-spacing: 0.02em;
|
||||
text-transform: none;
|
||||
text-shadow: none; }
|
||||
|
||||
.reveal h1 {
|
||||
line-height: 1.2em;
|
||||
/* added by hpl */
|
||||
text-shadow: 0px 0px 6px rgba(0, 0, 0, 0.2); }
|
||||
|
||||
/*********************************************
|
||||
* LINKS
|
||||
*********************************************/
|
||||
.reveal a:not(.image) {
|
||||
color: #ff8800;
|
||||
text-decoration: none;
|
||||
-webkit-transition: color .15s ease;
|
||||
-moz-transition: color .15s ease;
|
||||
-ms-transition: color .15s ease;
|
||||
-o-transition: color .15s ease;
|
||||
transition: color .15s ease; }
|
||||
|
||||
.reveal a:not(.image):hover {
|
||||
color: #ffb866;
|
||||
text-shadow: none;
|
||||
border: none; }
|
||||
|
||||
.reveal .roll span:after {
|
||||
color: #fff;
|
||||
background: #b35f00; }
|
||||
|
||||
/*********************************************
|
||||
* IMAGES
|
||||
*********************************************/
|
||||
.reveal section img {
|
||||
margin: 15px 0px;
|
||||
background: rgba(255, 255, 255, 0.12);
|
||||
border: 4px solid #404040;
|
||||
box-shadow: 0 0 10px rgba(0, 0, 0, 0.15);
|
||||
-webkit-transition: all .2s linear;
|
||||
-moz-transition: all .2s linear;
|
||||
-ms-transition: all .2s linear;
|
||||
-o-transition: all .2s linear;
|
||||
transition: all .2s linear; }
|
||||
|
||||
.reveal a:hover img {
|
||||
background: rgba(255, 255, 255, 0.2);
|
||||
border-color: #ff8800;
|
||||
box-shadow: 0 0 20px rgba(0, 0, 0, 0.55); }
|
||||
|
||||
/*********************************************
|
||||
* NAVIGATION CONTROLS
|
||||
*********************************************/
|
||||
.reveal .controls div.navigate-left,
|
||||
.reveal .controls div.navigate-left.enabled {
|
||||
border-right-color: #ff8800; }
|
||||
|
||||
.reveal .controls div.navigate-right,
|
||||
.reveal .controls div.navigate-right.enabled {
|
||||
border-left-color: #ff8800; }
|
||||
|
||||
.reveal .controls div.navigate-up,
|
||||
.reveal .controls div.navigate-up.enabled {
|
||||
border-bottom-color: #ff8800; }
|
||||
|
||||
.reveal .controls div.navigate-down,
|
||||
.reveal .controls div.navigate-down.enabled {
|
||||
border-top-color: #ff8800; }
|
||||
|
||||
.reveal .controls div.navigate-left.enabled:hover {
|
||||
border-right-color: #ffb866; }
|
||||
|
||||
.reveal .controls div.navigate-right.enabled:hover {
|
||||
border-left-color: #ffb866; }
|
||||
|
||||
.reveal .controls div.navigate-up.enabled:hover {
|
||||
border-bottom-color: #ffb866; }
|
||||
|
||||
.reveal .controls div.navigate-down.enabled:hover {
|
||||
border-top-color: #ffb866; }
|
||||
|
||||
/*********************************************
|
||||
* PROGRESS BAR
|
||||
*********************************************/
|
||||
.reveal .progress {
|
||||
background: rgba(0, 0, 0, 0.2); }
|
||||
|
||||
.reveal .progress span {
|
||||
background: #ff8800;
|
||||
-webkit-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-moz-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-ms-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-o-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985); }
|
||||
|
||||
/*********************************************
|
||||
* SLIDE NUMBER
|
||||
*********************************************/
|
||||
.reveal .slide-number {
|
||||
color: #ff8800; }
|
||||
@@ -0,0 +1,150 @@
|
||||
@import url(https://fonts.googleapis.com/css?family=Quicksand:400,700,400italic,700italic);
|
||||
@import url(https://fonts.googleapis.com/css?family=Open+Sans:400italic,700italic,400,700);
|
||||
/**
|
||||
* Sky theme for reveal.js.
|
||||
*
|
||||
* Copyright (C) 2011-2012 Hakim El Hattab, http://hakim.se
|
||||
*/
|
||||
/* changed by hpl from 36px; */
|
||||
/* changed by hpl from 'League Gothic', Impact, sans-serif; */
|
||||
/* changed by hpl from uppercase; */
|
||||
.reveal a:not(.image) {
|
||||
line-height: 1.3em; }
|
||||
|
||||
/*********************************************
|
||||
* GLOBAL STYLES
|
||||
*********************************************/
|
||||
body {
|
||||
background: #add9e4;
|
||||
background: -moz-radial-gradient(center, circle cover, #f7fbfc 0%, #add9e4 100%);
|
||||
background: -webkit-gradient(radial, center center, 0px, center center, 100%, color-stop(0%, #f7fbfc), color-stop(100%, #add9e4));
|
||||
background: -webkit-radial-gradient(center, circle cover, #f7fbfc 0%, #add9e4 100%);
|
||||
background: -o-radial-gradient(center, circle cover, #f7fbfc 0%, #add9e4 100%);
|
||||
background: -ms-radial-gradient(center, circle cover, #f7fbfc 0%, #add9e4 100%);
|
||||
background: radial-gradient(center, circle cover, #f7fbfc 0%, #add9e4 100%);
|
||||
background-color: #f7fbfc; }
|
||||
|
||||
.reveal {
|
||||
font-family: "Open Sans", sans-serif;
|
||||
font-size: 30px;
|
||||
font-weight: normal;
|
||||
letter-spacing: -0.02em;
|
||||
color: #333333; }
|
||||
|
||||
::selection {
|
||||
color: white;
|
||||
background: #134674;
|
||||
text-shadow: none; }
|
||||
|
||||
/*********************************************
|
||||
* HEADERS
|
||||
*********************************************/
|
||||
.reveal h1,
|
||||
.reveal h2,
|
||||
.reveal h3,
|
||||
.reveal h4,
|
||||
.reveal h5,
|
||||
.reveal h6 {
|
||||
margin: 0 0 20px 0;
|
||||
color: #333333;
|
||||
font-family: "Quicksand", sans-serif;
|
||||
line-height: 1.1em; /* changed (by hpl) from 0.9em */
|
||||
letter-spacing: -0.08em;
|
||||
text-transform: none;
|
||||
text-shadow: none; }
|
||||
|
||||
.reveal h1 {
|
||||
line-height: 1.2em;
|
||||
/* added by hpl */
|
||||
text-shadow: 0px 0px 6px rgba(0, 0, 0, 0.2); }
|
||||
|
||||
/*********************************************
|
||||
* LINKS
|
||||
*********************************************/
|
||||
.reveal a:not(.image) {
|
||||
color: #3b759e;
|
||||
text-decoration: none;
|
||||
-webkit-transition: color .15s ease;
|
||||
-moz-transition: color .15s ease;
|
||||
-ms-transition: color .15s ease;
|
||||
-o-transition: color .15s ease;
|
||||
transition: color .15s ease; }
|
||||
|
||||
.reveal a:not(.image):hover {
|
||||
color: #74a7cb;
|
||||
text-shadow: none;
|
||||
border: none; }
|
||||
|
||||
.reveal .roll span:after {
|
||||
color: #fff;
|
||||
background: #264c66; }
|
||||
|
||||
/*********************************************
|
||||
* IMAGES
|
||||
*********************************************/
|
||||
.reveal section img {
|
||||
margin: 15px 0px;
|
||||
background: rgba(255, 255, 255, 0.12);
|
||||
border: 4px solid #333333;
|
||||
box-shadow: 0 0 10px rgba(0, 0, 0, 0.15);
|
||||
-webkit-transition: all .2s linear;
|
||||
-moz-transition: all .2s linear;
|
||||
-ms-transition: all .2s linear;
|
||||
-o-transition: all .2s linear;
|
||||
transition: all .2s linear; }
|
||||
|
||||
.reveal a:hover img {
|
||||
background: rgba(255, 255, 255, 0.2);
|
||||
border-color: #3b759e;
|
||||
box-shadow: 0 0 20px rgba(0, 0, 0, 0.55); }
|
||||
|
||||
/*********************************************
|
||||
* NAVIGATION CONTROLS
|
||||
*********************************************/
|
||||
.reveal .controls div.navigate-left,
|
||||
.reveal .controls div.navigate-left.enabled {
|
||||
border-right-color: #3b759e; }
|
||||
|
||||
.reveal .controls div.navigate-right,
|
||||
.reveal .controls div.navigate-right.enabled {
|
||||
border-left-color: #3b759e; }
|
||||
|
||||
.reveal .controls div.navigate-up,
|
||||
.reveal .controls div.navigate-up.enabled {
|
||||
border-bottom-color: #3b759e; }
|
||||
|
||||
.reveal .controls div.navigate-down,
|
||||
.reveal .controls div.navigate-down.enabled {
|
||||
border-top-color: #3b759e; }
|
||||
|
||||
.reveal .controls div.navigate-left.enabled:hover {
|
||||
border-right-color: #74a7cb; }
|
||||
|
||||
.reveal .controls div.navigate-right.enabled:hover {
|
||||
border-left-color: #74a7cb; }
|
||||
|
||||
.reveal .controls div.navigate-up.enabled:hover {
|
||||
border-bottom-color: #74a7cb; }
|
||||
|
||||
.reveal .controls div.navigate-down.enabled:hover {
|
||||
border-top-color: #74a7cb; }
|
||||
|
||||
/*********************************************
|
||||
* PROGRESS BAR
|
||||
*********************************************/
|
||||
.reveal .progress {
|
||||
background: rgba(0, 0, 0, 0.2); }
|
||||
|
||||
.reveal .progress span {
|
||||
background: #3b759e;
|
||||
-webkit-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-moz-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-ms-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-o-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985); }
|
||||
|
||||
/*********************************************
|
||||
* SLIDE NUMBER
|
||||
*********************************************/
|
||||
.reveal .slide-number {
|
||||
color: #3b759e; }
|
||||
@@ -0,0 +1,153 @@
|
||||
@import url(https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic);
|
||||
/**
|
||||
* Solarized Light theme for reveal.js.
|
||||
* Author: Achim Staebler
|
||||
*/
|
||||
/* changed by hpl from 36px; */
|
||||
/* changed by hpl from 'League Gothic', Impact, sans-serif; */
|
||||
/* changed by hpl from uppercase; */
|
||||
@font-face {
|
||||
font-family: 'League Gothic';
|
||||
src: url("../../lib/font/league_gothic-webfont.eot");
|
||||
src: url("../../lib/font/league_gothic-webfont.eot?#iefix") format("embedded-opentype"), url("../../lib/font/league_gothic-webfont.woff") format("woff"), url("../../lib/font/league_gothic-webfont.ttf") format("truetype"), url("../../lib/font/league_gothic-webfont.svg#LeagueGothicRegular") format("svg");
|
||||
font-weight: normal;
|
||||
font-style: normal; }
|
||||
|
||||
/**
|
||||
* Solarized colors by Ethan Schoonover
|
||||
*/
|
||||
html * {
|
||||
color-profile: sRGB;
|
||||
rendering-intent: auto; }
|
||||
|
||||
/*********************************************
|
||||
* GLOBAL STYLES
|
||||
*********************************************/
|
||||
body {
|
||||
background: #fdf6e3;
|
||||
background-color: #fdf6e3; }
|
||||
|
||||
.reveal {
|
||||
font-family: "Lato", sans-serif;
|
||||
font-size: 30px;
|
||||
font-weight: normal;
|
||||
letter-spacing: -0.02em;
|
||||
color: #657b83; }
|
||||
|
||||
::selection {
|
||||
color: white;
|
||||
background: #d33682;
|
||||
text-shadow: none; }
|
||||
|
||||
/*********************************************
|
||||
* HEADERS
|
||||
*********************************************/
|
||||
.reveal h1,
|
||||
.reveal h2,
|
||||
.reveal h3,
|
||||
.reveal h4,
|
||||
.reveal h5,
|
||||
.reveal h6 {
|
||||
margin: 0 0 20px 0;
|
||||
color: #586e75;
|
||||
font-family: "Helvetica", Impact, sans-serif;
|
||||
line-height: 1.1em; /* changed (by hpl) from 0.9em */
|
||||
letter-spacing: 0.02em;
|
||||
text-transform: none;
|
||||
text-shadow: none; }
|
||||
|
||||
.reveal h1 {
|
||||
line-height: 1.2em;
|
||||
/* added by hpl */
|
||||
text-shadow: 0px 0px 6px rgba(0, 0, 0, 0.2); }
|
||||
|
||||
/*********************************************
|
||||
* LINKS
|
||||
*********************************************/
|
||||
.reveal a:not(.image) {
|
||||
color: #268bd2;
|
||||
text-decoration: none;
|
||||
-webkit-transition: color .15s ease;
|
||||
-moz-transition: color .15s ease;
|
||||
-ms-transition: color .15s ease;
|
||||
-o-transition: color .15s ease;
|
||||
transition: color .15s ease; }
|
||||
|
||||
.reveal a:not(.image):hover {
|
||||
color: #78b9e6;
|
||||
text-shadow: none;
|
||||
border: none; }
|
||||
|
||||
.reveal .roll span:after {
|
||||
color: #fff;
|
||||
background: #1a6091; }
|
||||
|
||||
/*********************************************
|
||||
* IMAGES
|
||||
*********************************************/
|
||||
.reveal section img {
|
||||
margin: 15px 0px;
|
||||
background: rgba(255, 255, 255, 0.12);
|
||||
border: 4px solid #657b83;
|
||||
box-shadow: 0 0 10px rgba(0, 0, 0, 0.15);
|
||||
-webkit-transition: all .2s linear;
|
||||
-moz-transition: all .2s linear;
|
||||
-ms-transition: all .2s linear;
|
||||
-o-transition: all .2s linear;
|
||||
transition: all .2s linear; }
|
||||
|
||||
.reveal a:hover img {
|
||||
background: rgba(255, 255, 255, 0.2);
|
||||
border-color: #268bd2;
|
||||
box-shadow: 0 0 20px rgba(0, 0, 0, 0.55); }
|
||||
|
||||
/*********************************************
|
||||
* NAVIGATION CONTROLS
|
||||
*********************************************/
|
||||
.reveal .controls div.navigate-left,
|
||||
.reveal .controls div.navigate-left.enabled {
|
||||
border-right-color: #268bd2; }
|
||||
|
||||
.reveal .controls div.navigate-right,
|
||||
.reveal .controls div.navigate-right.enabled {
|
||||
border-left-color: #268bd2; }
|
||||
|
||||
.reveal .controls div.navigate-up,
|
||||
.reveal .controls div.navigate-up.enabled {
|
||||
border-bottom-color: #268bd2; }
|
||||
|
||||
.reveal .controls div.navigate-down,
|
||||
.reveal .controls div.navigate-down.enabled {
|
||||
border-top-color: #268bd2; }
|
||||
|
||||
.reveal .controls div.navigate-left.enabled:hover {
|
||||
border-right-color: #78b9e6; }
|
||||
|
||||
.reveal .controls div.navigate-right.enabled:hover {
|
||||
border-left-color: #78b9e6; }
|
||||
|
||||
.reveal .controls div.navigate-up.enabled:hover {
|
||||
border-bottom-color: #78b9e6; }
|
||||
|
||||
.reveal .controls div.navigate-down.enabled:hover {
|
||||
border-top-color: #78b9e6; }
|
||||
|
||||
/*********************************************
|
||||
* PROGRESS BAR
|
||||
*********************************************/
|
||||
.reveal .progress {
|
||||
background: rgba(0, 0, 0, 0.2); }
|
||||
|
||||
.reveal .progress span {
|
||||
background: #268bd2;
|
||||
-webkit-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-moz-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-ms-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-o-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985); }
|
||||
|
||||
/*********************************************
|
||||
* SLIDE NUMBER
|
||||
*********************************************/
|
||||
.reveal .slide-number {
|
||||
color: #268bd2; }
|
||||
@@ -0,0 +1,50 @@
|
||||
/**
|
||||
* Beige theme for reveal.js.
|
||||
*
|
||||
* Copyright (C) 2011-2012 Hakim El Hattab, http://hakim.se
|
||||
*/
|
||||
|
||||
|
||||
// Default mixins and settings -----------------
|
||||
@import "../template/mixins";
|
||||
@import "../template/settings";
|
||||
// ---------------------------------------------
|
||||
|
||||
|
||||
|
||||
// Include theme-specific fonts
|
||||
@font-face {
|
||||
font-family: 'League Gothic';
|
||||
src: url('../../lib/font/league_gothic-webfont.eot');
|
||||
src: url('../../lib/font/league_gothic-webfont.eot?#iefix') format('embedded-opentype'),
|
||||
url('../../lib/font/league_gothic-webfont.woff') format('woff'),
|
||||
url('../../lib/font/league_gothic-webfont.ttf') format('truetype'),
|
||||
url('../../lib/font/league_gothic-webfont.svg#LeagueGothicRegular') format('svg');
|
||||
|
||||
font-weight: normal;
|
||||
font-style: normal;
|
||||
}
|
||||
|
||||
@import url(https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic);
|
||||
|
||||
|
||||
// Override theme settings (see ../template/settings.scss)
|
||||
$mainColor: #333;
|
||||
$headingColor: #222; /* changed (by hpl) from #333; */
|
||||
$headingTextShadow: none;
|
||||
$backgroundColor: #f7f3de;
|
||||
$linkColor: #8b743d;
|
||||
$linkColorHover: lighten( $linkColor, 20% );
|
||||
$selectionBackgroundColor: rgba(79, 64, 28, 0.99);
|
||||
$heading1TextShadow: 0 1px 0 #ccc, 0 2px 0 #c9c9c9, 0 3px 0 #bbb, 0 4px 0 #b9b9b9, 0 5px 0 #aaa, 0 6px 1px rgba(0,0,0,.1), 0 0 5px rgba(0,0,0,.1), 0 1px 3px rgba(0,0,0,.3), 0 3px 5px rgba(0,0,0,.2), 0 5px 10px rgba(0,0,0,.25), 0 20px 20px rgba(0,0,0,.15);
|
||||
|
||||
// Background generator
|
||||
@mixin bodyBackground() {
|
||||
@include radial-gradient( rgba(247,242,211,1), rgba(255,255,255,1) );
|
||||
}
|
||||
|
||||
|
||||
|
||||
// Theme template ------------------------------
|
||||
@import "../template/theme";
|
||||
// ---------------------------------------------
|
||||
@@ -0,0 +1,51 @@
|
||||
/**
|
||||
* Beige theme for reveal.js.
|
||||
*
|
||||
* Copyright (C) 2011-2012 Hakim El Hattab, http://hakim.se
|
||||
*/
|
||||
|
||||
|
||||
// Default mixins and settings -----------------
|
||||
@import "../template/mixins";
|
||||
@import "../template/settings";
|
||||
// ---------------------------------------------
|
||||
|
||||
|
||||
|
||||
// Include theme-specific fonts
|
||||
@font-face {
|
||||
font-family: 'League Gothic';
|
||||
src: url('../../lib/font/league_gothic-webfont.eot');
|
||||
src: url('../../lib/font/league_gothic-webfont.eot?#iefix') format('embedded-opentype'),
|
||||
url('../../lib/font/league_gothic-webfont.woff') format('woff'),
|
||||
url('../../lib/font/league_gothic-webfont.ttf') format('truetype'),
|
||||
url('../../lib/font/league_gothic-webfont.svg#LeagueGothicRegular') format('svg');
|
||||
|
||||
font-weight: normal;
|
||||
font-style: normal;
|
||||
}
|
||||
|
||||
@import url(https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic);
|
||||
|
||||
|
||||
// Override theme settings (see ../template/settings.scss)
|
||||
$mainColor: #333;
|
||||
$mainFontSize: 25px; /* added/changed by hpl */
|
||||
$headingColor: #222; /* changed (by hpl) from #333; */
|
||||
$headingTextShadow: none;
|
||||
$backgroundColor: #f7f3de;
|
||||
$linkColor: #8b743d;
|
||||
$linkColorHover: lighten( $linkColor, 20% );
|
||||
$selectionBackgroundColor: rgba(79, 64, 28, 0.99);
|
||||
$heading1TextShadow: 0 1px 0 #ccc, 0 2px 0 #c9c9c9, 0 3px 0 #bbb, 0 4px 0 #b9b9b9, 0 5px 0 #aaa, 0 6px 1px rgba(0,0,0,.1), 0 0 5px rgba(0,0,0,.1), 0 1px 3px rgba(0,0,0,.3), 0 3px 5px rgba(0,0,0,.2), 0 5px 10px rgba(0,0,0,.25), 0 20px 20px rgba(0,0,0,.15);
|
||||
|
||||
// Background generator
|
||||
@mixin bodyBackground() {
|
||||
@include radial-gradient( rgba(247,242,211,1), rgba(255,255,255,1) );
|
||||
}
|
||||
|
||||
|
||||
|
||||
// Theme template ------------------------------
|
||||
@import "../template/theme";
|
||||
// ---------------------------------------------
|
||||
@@ -0,0 +1,49 @@
|
||||
/**
|
||||
* Black theme for reveal.js. This is the opposite of the 'white' theme.
|
||||
*
|
||||
* Copyright (C) 2015 Hakim El Hattab, http://hakim.se
|
||||
*/
|
||||
|
||||
|
||||
// Default mixins and settings -----------------
|
||||
@import "../template/mixins";
|
||||
@import "../template/settings";
|
||||
// ---------------------------------------------
|
||||
|
||||
|
||||
// Include theme-specific fonts
|
||||
@import url(../../lib/font/source-sans-pro/source-sans-pro.css);
|
||||
|
||||
|
||||
// Override theme settings (see ../template/settings.scss)
|
||||
$backgroundColor: #222;
|
||||
|
||||
$mainColor: #fff;
|
||||
$headingColor: #fff;
|
||||
|
||||
$mainFontSize: 38px;
|
||||
$mainFont: 'Source Sans Pro', Helvetica, sans-serif;
|
||||
$headingFont: 'Source Sans Pro', Helvetica, sans-serif;
|
||||
$headingTextShadow: none;
|
||||
$headingLetterSpacing: normal;
|
||||
$headingTextTransform: uppercase;
|
||||
$headingFontWeight: 600;
|
||||
$linkColor: #42affa;
|
||||
$linkColorHover: lighten( $linkColor, 15% );
|
||||
$selectionBackgroundColor: lighten( $linkColor, 25% );
|
||||
|
||||
$heading1Size: 2.5em;
|
||||
$heading2Size: 1.6em;
|
||||
$heading3Size: 1.3em;
|
||||
$heading4Size: 1.0em;
|
||||
|
||||
section.has-light-background {
|
||||
&, h1, h2, h3, h4, h5, h6 {
|
||||
color: #222;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// Theme template ------------------------------
|
||||
@import "../template/theme";
|
||||
// ---------------------------------------------
|
||||
@@ -0,0 +1,91 @@
|
||||
/**
|
||||
* Blood theme for reveal.js
|
||||
* Author: Walther http://github.com/Walther
|
||||
*
|
||||
* Designed to be used with highlight.js theme
|
||||
* "monokai_sublime.css" available from
|
||||
* https://github.com/isagalaev/highlight.js/
|
||||
*
|
||||
* For other themes, change $codeBackground accordingly.
|
||||
*
|
||||
*/
|
||||
|
||||
// Default mixins and settings -----------------
|
||||
@import "../template/mixins";
|
||||
@import "../template/settings";
|
||||
// ---------------------------------------------
|
||||
|
||||
// Include theme-specific fonts
|
||||
|
||||
@import url(https://fonts.googleapis.com/css?family=Ubuntu:300,700,300italic,700italic);
|
||||
|
||||
// Colors used in the theme
|
||||
$blood: #a23;
|
||||
$coal: #222;
|
||||
$codeBackground: #23241f;
|
||||
|
||||
// Main text
|
||||
$mainFont: Ubuntu, 'sans-serif';
|
||||
$mainFontSize: 30px;
|
||||
$mainColor: #eee;
|
||||
|
||||
// Headings
|
||||
$headingFont: Ubuntu, 'sans-serif';
|
||||
$headingTextShadow: 2px 2px 2px $coal;
|
||||
|
||||
// h1 shadow, borrowed humbly from
|
||||
// (c) Default theme by Hakim El Hattab
|
||||
$heading1TextShadow: 0 1px 0 #ccc, 0 2px 0 #c9c9c9, 0 3px 0 #bbb, 0 4px 0 #b9b9b9, 0 5px 0 #aaa, 0 6px 1px rgba(0,0,0,.1), 0 0 5px rgba(0,0,0,.1), 0 1px 3px rgba(0,0,0,.3), 0 3px 5px rgba(0,0,0,.2), 0 5px 10px rgba(0,0,0,.25), 0 20px 20px rgba(0,0,0,.15);
|
||||
|
||||
// Links
|
||||
$linkColor: $blood;
|
||||
$linkColorHover: lighten( $linkColor, 20% );
|
||||
|
||||
// Text selection
|
||||
$selectionBackgroundColor: $blood;
|
||||
$selectionColor: #fff;
|
||||
|
||||
// Background generator
|
||||
@mixin bodyBackground() {
|
||||
@include radial-gradient( $coal, lighten( $coal, 25% ) );
|
||||
}
|
||||
|
||||
// Theme template ------------------------------
|
||||
@import "../template/theme";
|
||||
// ---------------------------------------------
|
||||
|
||||
// some overrides after theme template import
|
||||
|
||||
.reveal p {
|
||||
font-weight: 300;
|
||||
text-shadow: 1px 1px $coal;
|
||||
}
|
||||
|
||||
.reveal h1,
|
||||
.reveal h2,
|
||||
.reveal h3,
|
||||
.reveal h4,
|
||||
.reveal h5,
|
||||
.reveal h6 {
|
||||
font-weight: 700;
|
||||
}
|
||||
|
||||
.reveal a:not(.image),
|
||||
.reveal a:not(.image):hover {
|
||||
text-shadow: 2px 2px 2px #000;
|
||||
}
|
||||
|
||||
.reveal small a:not(.image),
|
||||
.reveal small a:not(.image):hover {
|
||||
text-shadow: 1px 1px 1px #000;
|
||||
}
|
||||
|
||||
.reveal p code {
|
||||
background-color: $codeBackground;
|
||||
display: inline-block;
|
||||
border-radius: 7px;
|
||||
}
|
||||
|
||||
.reveal small code {
|
||||
vertical-align: baseline;
|
||||
}
|
||||
@@ -0,0 +1,39 @@
|
||||
/**
|
||||
* A simple theme for reveal.js presentations, similar
|
||||
* to the default theme. The accent color is darkblue.
|
||||
*
|
||||
* This theme is Copyright (C) 2012 Owen Versteeg, https://github.com/StereotypicalApps. It is MIT licensed.
|
||||
* reveal.js is Copyright (C) 2011-2012 Hakim El Hattab, http://hakim.se
|
||||
|
||||
* Center for Biomedical Computing theme made by Hans Petter Langtangen.
|
||||
*/
|
||||
|
||||
|
||||
// Default mixins and settings -----------------
|
||||
@import "../template/mixins";
|
||||
@import "../template/settings";
|
||||
// ---------------------------------------------
|
||||
|
||||
|
||||
|
||||
// Include theme-specific fonts
|
||||
@import url(https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic);
|
||||
|
||||
|
||||
// Override theme settings (see ../template/settings.scss)
|
||||
$mainFont: 'Lato', sans-serif;
|
||||
$mainColor: #404040;
|
||||
$headingFont: 'Helvetica', Impact, sans-serif;
|
||||
$headingColor: #8A0808;
|
||||
$headingTextShadow: none;
|
||||
$headingTextTransform: none;
|
||||
$backgroundColor: #fff;
|
||||
$linkColor: #8A0808;
|
||||
$linkColorHover: lighten( $linkColor, 20% );
|
||||
$selectionBackgroundColor: rgba(0, 0, 0, 0.99);
|
||||
|
||||
|
||||
|
||||
// Theme template ------------------------------
|
||||
@import "../template/theme";
|
||||
// ---------------------------------------------
|
||||
@@ -0,0 +1,42 @@
|
||||
/**
|
||||
* Default theme for reveal.js.
|
||||
*
|
||||
* Copyright (C) 2011-2012 Hakim El Hattab, http://hakim.se
|
||||
*/
|
||||
|
||||
|
||||
// Default mixins and settings -----------------
|
||||
@import "../template/mixins";
|
||||
@import "../template/settings";
|
||||
// ---------------------------------------------
|
||||
|
||||
|
||||
|
||||
// Include theme-specific fonts
|
||||
@font-face {
|
||||
font-family: 'League Gothic';
|
||||
src: url('../../lib/font/league_gothic-webfont.eot');
|
||||
src: url('../../lib/font/league_gothic-webfont.eot?#iefix') format('embedded-opentype'),
|
||||
url('../../lib/font/league_gothic-webfont.woff') format('woff'),
|
||||
url('../../lib/font/league_gothic-webfont.ttf') format('truetype'),
|
||||
url('../../lib/font/league_gothic-webfont.svg#LeagueGothicRegular') format('svg');
|
||||
|
||||
font-weight: normal;
|
||||
font-style: normal;
|
||||
}
|
||||
|
||||
@import url(https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic);
|
||||
|
||||
// Override theme settings (see ../template/settings.scss)
|
||||
$heading1TextShadow: 0 1px 0 #ccc, 0 2px 0 #c9c9c9, 0 3px 0 #bbb, 0 4px 0 #b9b9b9, 0 5px 0 #aaa, 0 6px 1px rgba(0,0,0,.1), 0 0 5px rgba(0,0,0,.1), 0 1px 3px rgba(0,0,0,.3), 0 3px 5px rgba(0,0,0,.2), 0 5px 10px rgba(0,0,0,.25), 0 20px 20px rgba(0,0,0,.15);
|
||||
|
||||
// Background generator
|
||||
@mixin bodyBackground() {
|
||||
@include radial-gradient( rgba(28,30,32,1), rgba(85,90,95,1) );
|
||||
}
|
||||
|
||||
|
||||
|
||||
// Theme template ------------------------------
|
||||
@import "../template/theme";
|
||||
// ---------------------------------------------
|
||||
@@ -0,0 +1,42 @@
|
||||
/**
|
||||
* Default theme for reveal.js.
|
||||
*
|
||||
* Copyright (C) 2011-2012 Hakim El Hattab, http://hakim.se
|
||||
*/
|
||||
|
||||
|
||||
// Default mixins and settings -----------------
|
||||
@import "../template/mixins";
|
||||
@import "../template/settings";
|
||||
// ---------------------------------------------
|
||||
|
||||
|
||||
|
||||
// Include theme-specific fonts
|
||||
@font-face {
|
||||
font-family: 'League Gothic';
|
||||
src: url('../../lib/font/league_gothic-webfont.eot');
|
||||
src: url('../../lib/font/league_gothic-webfont.eot?#iefix') format('embedded-opentype'),
|
||||
url('../../lib/font/league_gothic-webfont.woff') format('woff'),
|
||||
url('../../lib/font/league_gothic-webfont.ttf') format('truetype'),
|
||||
url('../../lib/font/league_gothic-webfont.svg#LeagueGothicRegular') format('svg');
|
||||
|
||||
font-weight: normal;
|
||||
font-style: normal;
|
||||
}
|
||||
|
||||
@import url(https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic);
|
||||
|
||||
// Override theme settings (see ../template/settings.scss)
|
||||
$heading1TextShadow: 0 1px 0 #ccc, 0 2px 0 #c9c9c9, 0 3px 0 #bbb, 0 4px 0 #b9b9b9, 0 5px 0 #aaa, 0 6px 1px rgba(0,0,0,.1), 0 0 5px rgba(0,0,0,.1), 0 1px 3px rgba(0,0,0,.3), 0 3px 5px rgba(0,0,0,.2), 0 5px 10px rgba(0,0,0,.25), 0 20px 20px rgba(0,0,0,.15);
|
||||
|
||||
// Background generator
|
||||
@mixin bodyBackground() {
|
||||
@include radial-gradient( rgba(28,30,32,1), rgba(85,90,95,1) );
|
||||
}
|
||||
|
||||
|
||||
|
||||
// Theme template ------------------------------
|
||||
@import "../template/theme";
|
||||
// ---------------------------------------------
|
||||
@@ -0,0 +1,34 @@
|
||||
/**
|
||||
* League theme for reveal.js.
|
||||
*
|
||||
* This was the default theme pre-3.0.0.
|
||||
*
|
||||
* Copyright (C) 2011-2012 Hakim El Hattab, http://hakim.se
|
||||
*/
|
||||
|
||||
|
||||
// Default mixins and settings -----------------
|
||||
@import "../template/mixins";
|
||||
@import "../template/settings";
|
||||
// ---------------------------------------------
|
||||
|
||||
|
||||
|
||||
// Include theme-specific fonts
|
||||
@import url(../../lib/font/league-gothic/league-gothic.css);
|
||||
@import url(https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic);
|
||||
|
||||
// Override theme settings (see ../template/settings.scss)
|
||||
$headingTextShadow: 0px 0px 6px rgba(0,0,0,0.2);
|
||||
$heading1TextShadow: 0 1px 0 #ccc, 0 2px 0 #c9c9c9, 0 3px 0 #bbb, 0 4px 0 #b9b9b9, 0 5px 0 #aaa, 0 6px 1px rgba(0,0,0,.1), 0 0 5px rgba(0,0,0,.1), 0 1px 3px rgba(0,0,0,.3), 0 3px 5px rgba(0,0,0,.2), 0 5px 10px rgba(0,0,0,.25), 0 20px 20px rgba(0,0,0,.15);
|
||||
|
||||
// Background generator
|
||||
@mixin bodyBackground() {
|
||||
@include radial-gradient( rgba(28,30,32,1), rgba(85,90,95,1) );
|
||||
}
|
||||
|
||||
|
||||
|
||||
// Theme template ------------------------------
|
||||
@import "../template/theme";
|
||||
// ---------------------------------------------
|
||||
@@ -0,0 +1,68 @@
|
||||
/**
|
||||
* Solarized Dark theme for reveal.js.
|
||||
* Author: Achim Staebler
|
||||
*/
|
||||
|
||||
|
||||
// Default mixins and settings -----------------
|
||||
@import "../template/mixins";
|
||||
@import "../template/settings";
|
||||
// ---------------------------------------------
|
||||
|
||||
|
||||
|
||||
// Include theme-specific fonts
|
||||
@font-face {
|
||||
font-family: 'League Gothic';
|
||||
src: url('../../lib/font/league_gothic-webfont.eot');
|
||||
src: url('../../lib/font/league_gothic-webfont.eot?#iefix') format('embedded-opentype'),
|
||||
url('../../lib/font/league_gothic-webfont.woff') format('woff'),
|
||||
url('../../lib/font/league_gothic-webfont.ttf') format('truetype'),
|
||||
url('../../lib/font/league_gothic-webfont.svg#LeagueGothicRegular') format('svg');
|
||||
|
||||
font-weight: normal;
|
||||
font-style: normal;
|
||||
}
|
||||
|
||||
@import url(https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic);
|
||||
|
||||
/**
|
||||
* Solarized colors by Ethan Schoonover
|
||||
*/
|
||||
html * {
|
||||
color-profile: sRGB;
|
||||
rendering-intent: auto;
|
||||
}
|
||||
|
||||
// Solarized colors
|
||||
$base03: #002b36;
|
||||
$base02: #073642;
|
||||
$base01: #586e75;
|
||||
$base00: #657b83;
|
||||
$base0: #839496;
|
||||
$base1: #93a1a1;
|
||||
$base2: #eee8d5;
|
||||
$base3: #fdf6e3;
|
||||
$yellow: #b58900;
|
||||
$orange: #cb4b16;
|
||||
$red: #dc322f;
|
||||
$magenta: #d33682;
|
||||
$violet: #6c71c4;
|
||||
$blue: #268bd2;
|
||||
$cyan: #2aa198;
|
||||
$green: #859900;
|
||||
|
||||
// Override theme settings (see ../template/settings.scss)
|
||||
$mainColor: $base1;
|
||||
$headingColor: $base2;
|
||||
$headingTextShadow: none;
|
||||
$backgroundColor: $base03;
|
||||
$linkColor: $blue;
|
||||
$linkColorHover: lighten( $linkColor, 20% );
|
||||
$selectionBackgroundColor: $magenta;
|
||||
|
||||
|
||||
|
||||
// Theme template ------------------------------
|
||||
@import "../template/theme";
|
||||
// ---------------------------------------------
|
||||
@@ -0,0 +1,35 @@
|
||||
/**
|
||||
* Black theme for reveal.js.
|
||||
*
|
||||
* Copyright (C) 2011-2012 Hakim El Hattab, http://hakim.se
|
||||
*/
|
||||
|
||||
|
||||
// Default mixins and settings -----------------
|
||||
@import "../template/mixins";
|
||||
@import "../template/settings";
|
||||
// ---------------------------------------------
|
||||
|
||||
|
||||
// Include theme-specific fonts
|
||||
@import url(https://fonts.googleapis.com/css?family=Montserrat:700);
|
||||
@import url(https://fonts.googleapis.com/css?family=Open+Sans:400,700,400italic,700italic);
|
||||
|
||||
|
||||
// Override theme settings (see ../template/settings.scss)
|
||||
$backgroundColor: #111;
|
||||
|
||||
$mainFont: 'Open Sans', sans-serif;
|
||||
$linkColor: #e7ad52;
|
||||
$linkColorHover: lighten( $linkColor, 20% );
|
||||
$headingFont: 'Montserrat', Impact, sans-serif;
|
||||
$headingTextShadow: none;
|
||||
$headingLetterSpacing: -0.03em;
|
||||
$headingTextTransform: none;
|
||||
$selectionBackgroundColor: #e7ad52;
|
||||
$mainFontSize: 30px;
|
||||
|
||||
|
||||
// Theme template ------------------------------
|
||||
@import "../template/theme";
|
||||
// ---------------------------------------------
|
||||
@@ -0,0 +1,35 @@
|
||||
/**
|
||||
* A simple theme for reveal.js presentations, similar
|
||||
* to the default theme. The accent color is brown.
|
||||
*
|
||||
* This theme is Copyright (C) 2012-2013 Owen Versteeg, http://owenversteeg.com - it is MIT licensed.
|
||||
*/
|
||||
|
||||
|
||||
// Default mixins and settings -----------------
|
||||
@import "../template/mixins";
|
||||
@import "../template/settings";
|
||||
// ---------------------------------------------
|
||||
|
||||
|
||||
|
||||
// Override theme settings (see ../template/settings.scss)
|
||||
$mainFont: 'Palatino Linotype', 'Book Antiqua', Palatino, FreeSerif, serif;
|
||||
$mainColor: #000;
|
||||
$headingFont: 'Palatino Linotype', 'Book Antiqua', Palatino, FreeSerif, serif;
|
||||
$headingColor: #383D3D;
|
||||
$headingTextShadow: none;
|
||||
$headingTextTransform: none;
|
||||
$backgroundColor: #F0F1EB;
|
||||
$linkColor: #51483D;
|
||||
$linkColorHover: lighten( $linkColor, 20% );
|
||||
$selectionBackgroundColor: #26351C;
|
||||
|
||||
.reveal a:not(.image) {
|
||||
line-height: 1.3em;
|
||||
}
|
||||
|
||||
|
||||
// Theme template ------------------------------
|
||||
@import "../template/theme";
|
||||
// ---------------------------------------------
|
||||
@@ -0,0 +1,38 @@
|
||||
/**
|
||||
* A simple theme for reveal.js presentations, similar
|
||||
* to the default theme. The accent color is darkblue.
|
||||
*
|
||||
* This theme is Copyright (C) 2012 Owen Versteeg, https://github.com/StereotypicalApps. It is MIT licensed.
|
||||
* reveal.js is Copyright (C) 2011-2012 Hakim El Hattab, http://hakim.se
|
||||
*/
|
||||
|
||||
|
||||
// Default mixins and settings -----------------
|
||||
@import "../template/mixins";
|
||||
@import "../template/settings";
|
||||
// ---------------------------------------------
|
||||
|
||||
|
||||
|
||||
// Include theme-specific fonts
|
||||
@import url(https://fonts.googleapis.com/css?family=News+Cycle:400,700);
|
||||
@import url(https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic);
|
||||
|
||||
|
||||
// Override theme settings (see ../template/settings.scss)
|
||||
$mainFont: 'Lato', sans-serif;
|
||||
$mainColor: #000;
|
||||
$headingFont: 'Helvetica', Impact, sans-serif; /* changed (by hpl) from 'News Cycle', Impact, sans-serif; */
|
||||
$headingColor: #000;
|
||||
$headingTextShadow: none;
|
||||
$headingTextTransform: none;
|
||||
$backgroundColor: #fff;
|
||||
$linkColor: #00008B;
|
||||
$linkColorHover: lighten( $linkColor, 20% );
|
||||
$selectionBackgroundColor: rgba(0, 0, 0, 0.99);
|
||||
|
||||
|
||||
|
||||
// Theme template ------------------------------
|
||||
@import "../template/theme";
|
||||
// ---------------------------------------------
|
||||
@@ -0,0 +1,39 @@
|
||||
/**
|
||||
* A simple theme for reveal.js presentations, similar
|
||||
* to the default theme. The accent color is darkblue.
|
||||
*
|
||||
* This theme is Copyright (C) 2012 Owen Versteeg, https://github.com/StereotypicalApps. It is MIT licensed.
|
||||
* reveal.js is Copyright (C) 2011-2012 Hakim El Hattab, http://hakim.se
|
||||
|
||||
* Simula theme made by Hans Petter Langtangen.
|
||||
*/
|
||||
|
||||
|
||||
// Default mixins and settings -----------------
|
||||
@import "../template/mixins";
|
||||
@import "../template/settings";
|
||||
// ---------------------------------------------
|
||||
|
||||
|
||||
|
||||
// Include theme-specific fonts
|
||||
@import url(https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic);
|
||||
|
||||
|
||||
// Override theme settings (see ../template/settings.scss)
|
||||
$mainFont: 'Lato', sans-serif;
|
||||
$mainColor: #404040;
|
||||
$headingFont: 'Helvetica', Impact, sans-serif;
|
||||
$headingColor: #ff8800;
|
||||
$headingTextShadow: none;
|
||||
$headingTextTransform: none;
|
||||
$backgroundColor: #fff;
|
||||
$linkColor: #ff8800;
|
||||
$linkColorHover: lighten( $linkColor, 20% );
|
||||
$selectionBackgroundColor: rgba(0, 0, 0, 0.99);
|
||||
|
||||
|
||||
|
||||
// Theme template ------------------------------
|
||||
@import "../template/theme";
|
||||
// ---------------------------------------------
|
||||
@@ -0,0 +1,46 @@
|
||||
/**
|
||||
* Sky theme for reveal.js.
|
||||
*
|
||||
* Copyright (C) 2011-2012 Hakim El Hattab, http://hakim.se
|
||||
*/
|
||||
|
||||
|
||||
// Default mixins and settings -----------------
|
||||
@import "../template/mixins";
|
||||
@import "../template/settings";
|
||||
// ---------------------------------------------
|
||||
|
||||
|
||||
|
||||
// Include theme-specific fonts
|
||||
@import url(https://fonts.googleapis.com/css?family=Quicksand:400,700,400italic,700italic);
|
||||
@import url(https://fonts.googleapis.com/css?family=Open+Sans:400italic,700italic,400,700);
|
||||
|
||||
|
||||
// Override theme settings (see ../template/settings.scss)
|
||||
$mainFont: 'Open Sans', sans-serif;
|
||||
$mainColor: #333;
|
||||
$headingFont: 'Quicksand', sans-serif;
|
||||
$headingColor: #333;
|
||||
$headingLetterSpacing: -0.08em;
|
||||
$headingTextShadow: none;
|
||||
$backgroundColor: #f7fbfc;
|
||||
$linkColor: #3b759e;
|
||||
$linkColorHover: lighten( $linkColor, 20% );
|
||||
$selectionBackgroundColor: #134674;
|
||||
|
||||
// Fix links so they are not cut off
|
||||
.reveal a:not(.image) {
|
||||
line-height: 1.3em;
|
||||
}
|
||||
|
||||
// Background generator
|
||||
@mixin bodyBackground() {
|
||||
@include radial-gradient( #add9e4, #f7fbfc );
|
||||
}
|
||||
|
||||
|
||||
|
||||
// Theme template ------------------------------
|
||||
@import "../template/theme";
|
||||
// ---------------------------------------------
|
||||
@@ -0,0 +1,74 @@
|
||||
/**
|
||||
* Solarized Light theme for reveal.js.
|
||||
* Author: Achim Staebler
|
||||
*/
|
||||
|
||||
|
||||
// Default mixins and settings -----------------
|
||||
@import "../template/mixins";
|
||||
@import "../template/settings";
|
||||
// ---------------------------------------------
|
||||
|
||||
|
||||
|
||||
// Include theme-specific fonts
|
||||
@font-face {
|
||||
font-family: 'League Gothic';
|
||||
src: url('../../lib/font/league_gothic-webfont.eot');
|
||||
src: url('../../lib/font/league_gothic-webfont.eot?#iefix') format('embedded-opentype'),
|
||||
url('../../lib/font/league_gothic-webfont.woff') format('woff'),
|
||||
url('../../lib/font/league_gothic-webfont.ttf') format('truetype'),
|
||||
url('../../lib/font/league_gothic-webfont.svg#LeagueGothicRegular') format('svg');
|
||||
|
||||
font-weight: normal;
|
||||
font-style: normal;
|
||||
}
|
||||
|
||||
@import url(https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic);
|
||||
|
||||
|
||||
/**
|
||||
* Solarized colors by Ethan Schoonover
|
||||
*/
|
||||
html * {
|
||||
color-profile: sRGB;
|
||||
rendering-intent: auto;
|
||||
}
|
||||
|
||||
// Solarized colors
|
||||
$base03: #002b36;
|
||||
$base02: #073642;
|
||||
$base01: #586e75;
|
||||
$base00: #657b83;
|
||||
$base0: #839496;
|
||||
$base1: #93a1a1;
|
||||
$base2: #eee8d5;
|
||||
$base3: #fdf6e3;
|
||||
$yellow: #b58900;
|
||||
$orange: #cb4b16;
|
||||
$red: #dc322f;
|
||||
$magenta: #d33682;
|
||||
$violet: #6c71c4;
|
||||
$blue: #268bd2;
|
||||
$cyan: #2aa198;
|
||||
$green: #859900;
|
||||
|
||||
// Override theme settings (see ../template/settings.scss)
|
||||
$mainColor: $base00;
|
||||
$headingColor: $base01;
|
||||
$headingTextShadow: none;
|
||||
$backgroundColor: $base3;
|
||||
$linkColor: $blue;
|
||||
$linkColorHover: lighten( $linkColor, 20% );
|
||||
$selectionBackgroundColor: $magenta;
|
||||
|
||||
// Background generator
|
||||
// @mixin bodyBackground() {
|
||||
// @include radial-gradient( rgba($base3,1), rgba(lighten($base3, 20%),1) );
|
||||
// }
|
||||
|
||||
|
||||
|
||||
// Theme template ------------------------------
|
||||
@import "../template/theme";
|
||||
// ---------------------------------------------
|
||||
@@ -0,0 +1,49 @@
|
||||
/**
|
||||
* White theme for reveal.js. This is the opposite of the 'black' theme.
|
||||
*
|
||||
* Copyright (C) 2015 Hakim El Hattab, http://hakim.se
|
||||
*/
|
||||
|
||||
|
||||
// Default mixins and settings -----------------
|
||||
@import "../template/mixins";
|
||||
@import "../template/settings";
|
||||
// ---------------------------------------------
|
||||
|
||||
|
||||
// Include theme-specific fonts
|
||||
@import url(../../lib/font/source-sans-pro/source-sans-pro.css);
|
||||
|
||||
|
||||
// Override theme settings (see ../template/settings.scss)
|
||||
$backgroundColor: #fff;
|
||||
|
||||
$mainColor: #222;
|
||||
$headingColor: #222;
|
||||
|
||||
$mainFontSize: 38px;
|
||||
$mainFont: 'Source Sans Pro', Helvetica, sans-serif;
|
||||
$headingFont: 'Source Sans Pro', Helvetica, sans-serif;
|
||||
$headingTextShadow: none;
|
||||
$headingLetterSpacing: normal;
|
||||
$headingTextTransform: uppercase;
|
||||
$headingFontWeight: 600;
|
||||
$linkColor: #2a76dd;
|
||||
$linkColorHover: lighten( $linkColor, 15% );
|
||||
$selectionBackgroundColor: lighten( $linkColor, 25% );
|
||||
|
||||
$heading1Size: 2.5em;
|
||||
$heading2Size: 1.6em;
|
||||
$heading3Size: 1.3em;
|
||||
$heading4Size: 1.0em;
|
||||
|
||||
section.has-dark-background {
|
||||
&, h1, h2, h3, h4, h5, h6 {
|
||||
color: #fff;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// Theme template ------------------------------
|
||||
@import "../template/theme";
|
||||
// ---------------------------------------------
|
||||
@@ -0,0 +1,273 @@
|
||||
@import url(../../lib/font/source-sans-pro/source-sans-pro.css);
|
||||
/**
|
||||
* White theme for reveal.js. This is the opposite of the 'black' theme.
|
||||
*
|
||||
* Copyright (C) 2015 Hakim El Hattab, http://hakim.se
|
||||
*/
|
||||
section.has-dark-background, section.has-dark-background h1, section.has-dark-background h2, section.has-dark-background h3, section.has-dark-background h4, section.has-dark-background h5, section.has-dark-background h6 {
|
||||
color: #fff; }
|
||||
|
||||
/*********************************************
|
||||
* GLOBAL STYLES
|
||||
*********************************************/
|
||||
body {
|
||||
background: #fff;
|
||||
background-color: #fff; }
|
||||
|
||||
.reveal {
|
||||
font-family: 'Source Sans Pro', Helvetica, sans-serif;
|
||||
font-size: 30px; /* changed by hpl from 38px */
|
||||
font-weight: normal;
|
||||
color: #222; }
|
||||
|
||||
::selection {
|
||||
color: #fff;
|
||||
background: #98bdef;
|
||||
text-shadow: none; }
|
||||
|
||||
.reveal .slides > section, .reveal .slides > section > section {
|
||||
line-height: 1.1em; /* changed by hpl from 1.2; */
|
||||
font-weight: inherit; }
|
||||
|
||||
/*********************************************
|
||||
* HEADERS
|
||||
*********************************************/
|
||||
.reveal h1, .reveal h2, .reveal h3, .reveal h4, .reveal h5, .reveal h6 {
|
||||
margin: 0 0 20px 0;
|
||||
color: #222;
|
||||
font-family: 'Source Sans Pro', Helvetica, sans-serif;
|
||||
font-weight: 600;
|
||||
line-height: 1.1em; /* changed by hpl from 1.2; */
|
||||
letter-spacing: normal;
|
||||
/* text-transform: uppercase; removed by hpl */
|
||||
text-shadow: none;
|
||||
word-wrap: break-word; }
|
||||
|
||||
.reveal h1 {
|
||||
line-height: 1.2em;
|
||||
}
|
||||
|
||||
/* removed by hpl
|
||||
.reveal h1 {
|
||||
font-size: 2.5em; }
|
||||
|
||||
.reveal h2 {
|
||||
font-size: 1.6em; }
|
||||
|
||||
.reveal h3 {
|
||||
font-size: 1.3em; }
|
||||
|
||||
.reveal h4 {
|
||||
font-size: 1em; }
|
||||
*/
|
||||
|
||||
.reveal h1 {
|
||||
text-shadow: none; }
|
||||
|
||||
/*********************************************
|
||||
* OTHER
|
||||
*********************************************/
|
||||
.reveal p {
|
||||
margin: 20px 0;
|
||||
line-height: 1.3; }
|
||||
|
||||
/* Ensure certain elements are never larger than the slide itself */
|
||||
.reveal img, .reveal video, .reveal iframe {
|
||||
max-width: 95%;
|
||||
max-height: 95%; }
|
||||
|
||||
.reveal strong, .reveal b {
|
||||
font-weight: bold; }
|
||||
|
||||
.reveal em {
|
||||
font-style: italic; }
|
||||
|
||||
.reveal ol, .reveal dl, .reveal ul {
|
||||
display: inline-block;
|
||||
text-align: left;
|
||||
margin: 0 0 0 1em; }
|
||||
|
||||
.reveal ol {
|
||||
list-style-type: decimal; }
|
||||
|
||||
.reveal ul {
|
||||
list-style-type: disc; }
|
||||
|
||||
.reveal ul ul {
|
||||
list-style-type: square; }
|
||||
|
||||
.reveal ul ul ul {
|
||||
list-style-type: circle; }
|
||||
|
||||
.reveal ul ul, .reveal ul ol, .reveal ol ol, .reveal ol ul {
|
||||
display: block;
|
||||
margin-left: 40px; }
|
||||
|
||||
.reveal dt {
|
||||
font-weight: bold; }
|
||||
|
||||
.reveal dd {
|
||||
margin-left: 40px; }
|
||||
|
||||
.reveal q, .reveal blockquote {
|
||||
quotes: none; }
|
||||
|
||||
.reveal blockquote {
|
||||
display: block;
|
||||
position: relative;
|
||||
width: 70%;
|
||||
margin: 20px auto;
|
||||
padding: 5px;
|
||||
font-style: italic;
|
||||
background: rgba(255, 255, 255, 0.05);
|
||||
box-shadow: 0px 0px 2px rgba(0, 0, 0, 0.2); }
|
||||
|
||||
.reveal blockquote p:first-child, .reveal blockquote p:last-child {
|
||||
display: inline-block; }
|
||||
|
||||
.reveal q {
|
||||
font-style: italic; }
|
||||
|
||||
.reveal pre {
|
||||
display: block;
|
||||
position: relative;
|
||||
width: 90%;
|
||||
margin: 20px auto;
|
||||
text-align: left;
|
||||
font-size: 0.55em;
|
||||
font-family: monospace;
|
||||
line-height: 1.2em;
|
||||
word-wrap: break-word;
|
||||
box-shadow: 0px 0px 6px rgba(0, 0, 0, 0.3); }
|
||||
|
||||
.reveal code {
|
||||
font-family: monospace; }
|
||||
|
||||
.reveal pre code {
|
||||
display: block;
|
||||
padding: 5px;
|
||||
overflow: auto;
|
||||
max-height: 400px;
|
||||
word-wrap: normal;
|
||||
background: #3F3F3F;
|
||||
color: #DCDCDC; }
|
||||
|
||||
.reveal table {
|
||||
margin: auto;
|
||||
border-collapse: collapse;
|
||||
border-spacing: 0; }
|
||||
|
||||
.reveal table th {
|
||||
font-weight: bold; }
|
||||
|
||||
.reveal table th, .reveal table td {
|
||||
/* text-align: left; */ /* hpl modification */
|
||||
padding: 0.2em 0.5em 0.2em 0.5em;
|
||||
border-bottom: 1px solid; }
|
||||
|
||||
.reveal table th[align="center"], .reveal table td[align="center"] {
|
||||
text-align: center; }
|
||||
|
||||
.reveal table th[align="right"], .reveal table td[align="right"] {
|
||||
text-align: right; }
|
||||
|
||||
.reveal table tr:last-child td {
|
||||
border-bottom: none; }
|
||||
|
||||
.reveal sup {
|
||||
vertical-align: super; }
|
||||
|
||||
.reveal sub {
|
||||
vertical-align: sub; }
|
||||
|
||||
.reveal small {
|
||||
display: inline-block;
|
||||
font-size: 0.6em;
|
||||
line-height: 1.2em;
|
||||
vertical-align: top; }
|
||||
|
||||
.reveal small * {
|
||||
vertical-align: top; }
|
||||
|
||||
/*********************************************
|
||||
* LINKS
|
||||
*********************************************/
|
||||
.reveal a {
|
||||
color: #2a76dd;
|
||||
text-decoration: none;
|
||||
-webkit-transition: color 0.15s ease;
|
||||
-moz-transition: color 0.15s ease;
|
||||
transition: color 0.15s ease; }
|
||||
|
||||
.reveal a:hover {
|
||||
color: #6ca2e8;
|
||||
text-shadow: none;
|
||||
border: none; }
|
||||
|
||||
.reveal .roll span:after {
|
||||
color: #fff;
|
||||
background: #1a54a1; }
|
||||
|
||||
/*********************************************
|
||||
* IMAGES
|
||||
*********************************************/
|
||||
.reveal section img {
|
||||
margin: 15px 0px;
|
||||
background: rgba(255, 255, 255, 0.12);
|
||||
border: 4px solid #222;
|
||||
box-shadow: 0 0 10px rgba(0, 0, 0, 0.15); }
|
||||
|
||||
.reveal a img {
|
||||
-webkit-transition: all 0.15s linear;
|
||||
-moz-transition: all 0.15s linear;
|
||||
transition: all 0.15s linear; }
|
||||
|
||||
.reveal a:hover img {
|
||||
background: rgba(255, 255, 255, 0.2);
|
||||
border-color: #2a76dd;
|
||||
box-shadow: 0 0 20px rgba(0, 0, 0, 0.55); }
|
||||
|
||||
/*********************************************
|
||||
* NAVIGATION CONTROLS
|
||||
*********************************************/
|
||||
.reveal .controls div.navigate-left, .reveal .controls div.navigate-left.enabled {
|
||||
border-right-color: #2a76dd; }
|
||||
|
||||
.reveal .controls div.navigate-right, .reveal .controls div.navigate-right.enabled {
|
||||
border-left-color: #2a76dd; }
|
||||
|
||||
.reveal .controls div.navigate-up, .reveal .controls div.navigate-up.enabled {
|
||||
border-bottom-color: #2a76dd; }
|
||||
|
||||
.reveal .controls div.navigate-down, .reveal .controls div.navigate-down.enabled {
|
||||
border-top-color: #2a76dd; }
|
||||
|
||||
.reveal .controls div.navigate-left.enabled:hover {
|
||||
border-right-color: #6ca2e8; }
|
||||
|
||||
.reveal .controls div.navigate-right.enabled:hover {
|
||||
border-left-color: #6ca2e8; }
|
||||
|
||||
.reveal .controls div.navigate-up.enabled:hover {
|
||||
border-bottom-color: #6ca2e8; }
|
||||
|
||||
.reveal .controls div.navigate-down.enabled:hover {
|
||||
border-top-color: #6ca2e8; }
|
||||
|
||||
/*********************************************
|
||||
* PROGRESS BAR
|
||||
*********************************************/
|
||||
.reveal .progress {
|
||||
background: rgba(0, 0, 0, 0.2); }
|
||||
|
||||
.reveal .progress span {
|
||||
background: #2a76dd;
|
||||
-webkit-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
-moz-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
|
||||
transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985); }
|
||||
|
||||
/*********************************************
|
||||
* SLIDE NUMBER
|
||||
*********************************************/
|
||||
.reveal .slide-number {
|
||||
color: #2a76dd; }
|
||||
@@ -0,0 +1,411 @@
|
||||
<!doctype html>
|
||||
<html lang="en">
|
||||
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
|
||||
<title>reveal.js - The HTML Presentation Framework</title>
|
||||
|
||||
<meta name="description" content="A framework for easily creating beautiful presentations using HTML">
|
||||
<meta name="author" content="Hakim El Hattab">
|
||||
|
||||
<meta name="apple-mobile-web-app-capable" content="yes" />
|
||||
<meta name="apple-mobile-web-app-status-bar-style" content="black-translucent" />
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no, minimal-ui">
|
||||
|
||||
<link rel="stylesheet" href="css/reveal.css">
|
||||
<link rel="stylesheet" href="css/theme/black.css" id="theme">
|
||||
|
||||
<!-- Code syntax highlighting -->
|
||||
<link rel="stylesheet" href="lib/css/zenburn.css">
|
||||
|
||||
<!-- Printing and PDF exports -->
|
||||
<script>
|
||||
var link = document.createElement( 'link' );
|
||||
link.rel = 'stylesheet';
|
||||
link.type = 'text/css';
|
||||
link.href = window.location.search.match( /print-pdf/gi ) ? 'css/print/pdf.css' : 'css/print/paper.css';
|
||||
document.getElementsByTagName( 'head' )[0].appendChild( link );
|
||||
</script>
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="lib/js/html5shiv.js"></script>
|
||||
<![endif]-->
|
||||
</head>
|
||||
|
||||
<body>
|
||||
|
||||
<div class="reveal">
|
||||
|
||||
<!-- Any section element inside of this container is displayed as a slide -->
|
||||
<div class="slides">
|
||||
<section>
|
||||
<h1>Reveal.js</h1>
|
||||
<h3>The HTML Presentation Framework</h3>
|
||||
<p>
|
||||
<small>Created by <a href="http://hakim.se">Hakim El Hattab</a> / <a href="http://twitter.com/hakimel">@hakimel</a></small>
|
||||
</p>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h2>Hello There</h2>
|
||||
<p>
|
||||
reveal.js enables you to create beautiful interactive slide decks using HTML. This presentation will show you examples of what it can do.
|
||||
</p>
|
||||
</section>
|
||||
|
||||
<!-- Example of nested vertical slides -->
|
||||
<section>
|
||||
<section>
|
||||
<h2>Vertical Slides</h2>
|
||||
<p>Slides can be nested inside of each other.</p>
|
||||
<p>Use the <em>Space</em> key to navigate through all slides.</p>
|
||||
<br>
|
||||
<a href="#" class="navigate-down">
|
||||
<img width="178" height="238" data-src="https://s3.amazonaws.com/hakim-static/reveal-js/arrow.png" alt="Down arrow">
|
||||
</a>
|
||||
</section>
|
||||
<section>
|
||||
<h2>Basement Level 1</h2>
|
||||
<p>Nested slides are useful for adding additional detail underneath a high level horizontal slide.</p>
|
||||
</section>
|
||||
<section>
|
||||
<h2>Basement Level 2</h2>
|
||||
<p>That's it, time to go back up.</p>
|
||||
<br>
|
||||
<a href="#/2">
|
||||
<img width="178" height="238" data-src="https://s3.amazonaws.com/hakim-static/reveal-js/arrow.png" alt="Up arrow" style="transform: rotate(180deg); -webkit-transform: rotate(180deg);">
|
||||
</a>
|
||||
</section>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h2>Slides</h2>
|
||||
<p>
|
||||
Not a coder? Not a problem. There's a fully-featured visual editor for authoring these, try it out at <a href="http://slides.com" target="_blank">http://slides.com</a>.
|
||||
</p>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h2>Point of View</h2>
|
||||
<p>
|
||||
Press <strong>ESC</strong> to enter the slide overview.
|
||||
</p>
|
||||
<p>
|
||||
Hold down alt and click on any element to zoom in on it using <a href="http://lab.hakim.se/zoom-js">zoom.js</a>. Alt + click anywhere to zoom back out.
|
||||
</p>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h2>Touch Optimized</h2>
|
||||
<p>
|
||||
Presentations look great on touch devices, like mobile phones and tablets. Simply swipe through your slides.
|
||||
</p>
|
||||
</section>
|
||||
|
||||
<section data-markdown>
|
||||
<script type="text/template">
|
||||
## Markdown support
|
||||
|
||||
Write content using inline or external Markdown.
|
||||
Instructions and more info available in the [readme](https://github.com/hakimel/reveal.js#markdown).
|
||||
|
||||
```
|
||||
<section data-markdown>
|
||||
## Markdown support
|
||||
|
||||
Write content using inline or external Markdown.
|
||||
Instructions and more info available in the [readme](https://github.com/hakimel/reveal.js#markdown).
|
||||
</section>
|
||||
```
|
||||
</script>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<section id="fragments">
|
||||
<h2>Fragments</h2>
|
||||
<p>Hit the next arrow...</p>
|
||||
<p class="fragment">... to step through ...</p>
|
||||
<p><span class="fragment">... a</span> <span class="fragment">fragmented</span> <span class="fragment">slide.</span></p>
|
||||
|
||||
<aside class="notes">
|
||||
This slide has fragments which are also stepped through in the notes window.
|
||||
</aside>
|
||||
</section>
|
||||
<section>
|
||||
<h2>Fragment Styles</h2>
|
||||
<p>There's different types of fragments, like:</p>
|
||||
<p class="fragment grow">grow</p>
|
||||
<p class="fragment shrink">shrink</p>
|
||||
<p class="fragment fade-out">fade-out</p>
|
||||
<p class="fragment current-visible">current-visible</p>
|
||||
<p class="fragment highlight-red">highlight-red</p>
|
||||
<p class="fragment highlight-blue">highlight-blue</p>
|
||||
</section>
|
||||
</section>
|
||||
|
||||
<section id="transitions">
|
||||
<h2>Transition Styles</h2>
|
||||
<p>
|
||||
You can select from different transitions, like: <br>
|
||||
<a href="?transition=none#/transitions">None</a> -
|
||||
<a href="?transition=fade#/transitions">Fade</a> -
|
||||
<a href="?transition=slide#/transitions">Slide</a> -
|
||||
<a href="?transition=convex#/transitions">Convex</a> -
|
||||
<a href="?transition=concave#/transitions">Concave</a> -
|
||||
<a href="?transition=zoom#/transitions">Zoom</a>
|
||||
</p>
|
||||
</section>
|
||||
|
||||
<section id="themes">
|
||||
<h2>Themes</h2>
|
||||
<p>
|
||||
reveal.js comes with a few themes built in: <br>
|
||||
<!-- Hacks to swap themes after the page has loaded. Not flexible and only intended for the reveal.js demo deck. -->
|
||||
<a href="#" onclick="document.getElementById('theme').setAttribute('href','css/theme/black.css'); return false;">Black (default)</a> -
|
||||
<a href="#" onclick="document.getElementById('theme').setAttribute('href','css/theme/white.css'); return false;">White</a> -
|
||||
<a href="#" onclick="document.getElementById('theme').setAttribute('href','css/theme/league.css'); return false;">League</a> -
|
||||
<a href="#" onclick="document.getElementById('theme').setAttribute('href','css/theme/sky.css'); return false;">Sky</a> -
|
||||
<a href="#" onclick="document.getElementById('theme').setAttribute('href','css/theme/beige.css'); return false;">Beige</a> -
|
||||
<a href="#" onclick="document.getElementById('theme').setAttribute('href','css/theme/simple.css'); return false;">Simple</a> <br>
|
||||
<a href="#" onclick="document.getElementById('theme').setAttribute('href','css/theme/serif.css'); return false;">Serif</a> -
|
||||
<a href="#" onclick="document.getElementById('theme').setAttribute('href','css/theme/blood.css'); return false;">Blood</a> -
|
||||
<a href="#" onclick="document.getElementById('theme').setAttribute('href','css/theme/night.css'); return false;">Night</a> -
|
||||
<a href="#" onclick="document.getElementById('theme').setAttribute('href','css/theme/moon.css'); return false;">Moon</a> -
|
||||
<a href="#" onclick="document.getElementById('theme').setAttribute('href','css/theme/solarized.css'); return false;">Solarized</a>
|
||||
</p>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<section data-background="#dddddd">
|
||||
<h2>Slide Backgrounds</h2>
|
||||
<p>
|
||||
Set <code>data-background="#dddddd"</code> on a slide to change the background color. All CSS color formats are supported.
|
||||
</p>
|
||||
<a href="#" class="navigate-down">
|
||||
<img width="178" height="238" data-src="https://s3.amazonaws.com/hakim-static/reveal-js/arrow.png" alt="Down arrow">
|
||||
</a>
|
||||
</section>
|
||||
<section data-background="https://s3.amazonaws.com/hakim-static/reveal-js/image-placeholder.png">
|
||||
<h2>Image Backgrounds</h2>
|
||||
<pre><code><section data-background="image.png"></code></pre>
|
||||
</section>
|
||||
<section data-background="https://s3.amazonaws.com/hakim-static/reveal-js/image-placeholder.png" data-background-repeat="repeat" data-background-size="100px">
|
||||
<h2>Tiled Backgrounds</h2>
|
||||
<pre><code style="word-wrap: break-word;"><section data-background="image.png" data-background-repeat="repeat" data-background-size="100px"></code></pre>
|
||||
</section>
|
||||
<section data-background-video="https://s3.amazonaws.com/static.slid.es/site/homepage/v1/homepage-video-editor.mp4,https://s3.amazonaws.com/static.slid.es/site/homepage/v1/homepage-video-editor.webm" data-background-color="#000000">
|
||||
<div style="background-color: rgba(0, 0, 0, 0.9); color: #fff; padding: 20px;">
|
||||
<h2>Video Backgrounds</h2>
|
||||
<pre><code style="word-wrap: break-word;"><section data-background-video="video.mp4,video.webm"></code></pre>
|
||||
</div>
|
||||
</section>
|
||||
<section data-background="http://i.giphy.com/90F8aUepslB84.gif">
|
||||
<h2>... and GIFs!</h2>
|
||||
</section>
|
||||
</section>
|
||||
|
||||
<section data-transition="slide" data-background="#4d7e65" data-background-transition="zoom">
|
||||
<h2>Background Transitions</h2>
|
||||
<p>
|
||||
Different background transitions are available via the backgroundTransition option. This one's called "zoom".
|
||||
</p>
|
||||
<pre><code>Reveal.configure({ backgroundTransition: 'zoom' })</code></pre>
|
||||
</section>
|
||||
|
||||
<section data-transition="slide" data-background="#b5533c" data-background-transition="zoom">
|
||||
<h2>Background Transitions</h2>
|
||||
<p>
|
||||
You can override background transitions per-slide.
|
||||
</p>
|
||||
<pre><code style="word-wrap: break-word;"><section data-background-transition="zoom"></code></pre>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h2>Pretty Code</h2>
|
||||
<pre><code data-trim contenteditable>
|
||||
function linkify( selector ) {
|
||||
if( supports3DTransforms ) {
|
||||
|
||||
var nodes = document.querySelectorAll( selector );
|
||||
|
||||
for( var i = 0, len = nodes.length; i < len; i++ ) {
|
||||
var node = nodes[i];
|
||||
|
||||
if( !node.className ) {
|
||||
node.className += ' roll';
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
</code></pre>
|
||||
<p>Code syntax highlighting courtesy of <a href="http://softwaremaniacs.org/soft/highlight/en/description/">highlight.js</a>.</p>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h2>Marvelous List</h2>
|
||||
<ul>
|
||||
<li>No order here</li>
|
||||
<li>Or here</li>
|
||||
<li>Or here</li>
|
||||
<li>Or here</li>
|
||||
</ul>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h2>Fantastic Ordered List</h2>
|
||||
<ol>
|
||||
<li>One is smaller than...</li>
|
||||
<li>Two is smaller than...</li>
|
||||
<li>Three!</li>
|
||||
</ol>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h2>Tabular Tables</h2>
|
||||
<table>
|
||||
<thead>
|
||||
<tr>
|
||||
<th>Item</th>
|
||||
<th>Value</th>
|
||||
<th>Quantity</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr>
|
||||
<td>Apples</td>
|
||||
<td>$1</td>
|
||||
<td>7</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Lemonade</td>
|
||||
<td>$2</td>
|
||||
<td>18</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Bread</td>
|
||||
<td>$3</td>
|
||||
<td>2</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h2>Clever Quotes</h2>
|
||||
<p>
|
||||
These guys come in two forms, inline: <q cite="http://searchservervirtualization.techtarget.com/definition/Our-Favorite-Technology-Quotations">
|
||||
“The nice thing about standards is that there are so many to choose from”</q> and block:
|
||||
</p>
|
||||
<blockquote cite="http://searchservervirtualization.techtarget.com/definition/Our-Favorite-Technology-Quotations">
|
||||
“For years there has been a theory that millions of monkeys typing at random on millions of typewriters would
|
||||
reproduce the entire works of Shakespeare. The Internet has proven this theory to be untrue.”
|
||||
</blockquote>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h2>Intergalactic Interconnections</h2>
|
||||
<p>
|
||||
You can link between slides internally,
|
||||
<a href="#/2/3">like this</a>.
|
||||
</p>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h2>Speaker View</h2>
|
||||
<p>There's a <a href="https://github.com/hakimel/reveal.js#speaker-notes">speaker view</a>. It includes a timer, preview of the upcoming slide as well as your speaker notes.</p>
|
||||
<p>Press the <em>S</em> key to try it out.</p>
|
||||
|
||||
<aside class="notes">
|
||||
Oh hey, these are some notes. They'll be hidden in your presentation, but you can see them if you open the speaker notes window (hit 's' on your keyboard).
|
||||
</aside>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h2>Export to PDF</h2>
|
||||
<p>Presentations can be <a href="https://github.com/hakimel/reveal.js#pdf-export">exported to PDF</a>, here's an example:</p>
|
||||
<iframe src="//www.slideshare.net/slideshow/embed_code/42840540" width="445" height="355" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" style="border:3px solid #666; margin-bottom:5px; max-width: 100%;" allowfullscreen> </iframe>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h2>Global State</h2>
|
||||
<p>
|
||||
Set <code>data-state="something"</code> on a slide and <code>"something"</code>
|
||||
will be added as a class to the document element when the slide is open. This lets you
|
||||
apply broader style changes, like switching the page background.
|
||||
</p>
|
||||
</section>
|
||||
|
||||
<section data-state="customevent">
|
||||
<h2>State Events</h2>
|
||||
<p>
|
||||
Additionally custom events can be triggered on a per slide basis by binding to the <code>data-state</code> name.
|
||||
</p>
|
||||
<pre><code class="javascript" data-trim contenteditable style="font-size: 18px;">
|
||||
Reveal.addEventListener( 'customevent', function() {
|
||||
console.log( '"customevent" has fired' );
|
||||
} );
|
||||
</code></pre>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h2>Take a Moment</h2>
|
||||
<p>
|
||||
Press B or . on your keyboard to pause the presentation. This is helpful when you're on stage and want to take distracting slides off the screen.
|
||||
</p>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h2>Much more</h2>
|
||||
<ul>
|
||||
<li>Right-to-left support</li>
|
||||
<li><a href="https://github.com/hakimel/reveal.js#api">Extensive JavaScript API</a></li>
|
||||
<li><a href="https://github.com/hakimel/reveal.js#auto-sliding">Auto-progression</a></li>
|
||||
<li><a href="https://github.com/hakimel/reveal.js#parallax-background">Parallax backgrounds</a></li>
|
||||
<li><a href="https://github.com/hakimel/reveal.js#keyboard-bindings">Custom keyboard bindings</a></li>
|
||||
</ul>
|
||||
</section>
|
||||
|
||||
<section style="text-align: left;">
|
||||
<h1>THE END</h1>
|
||||
<p>
|
||||
- <a href="http://slides.com">Try the online editor</a> <br>
|
||||
- <a href="https://github.com/hakimel/reveal.js">Source code & documentation</a>
|
||||
</p>
|
||||
</section>
|
||||
|
||||
</div>
|
||||
|
||||
</div>
|
||||
|
||||
<script src="lib/js/head.min.js"></script>
|
||||
<script src="js/reveal.js"></script>
|
||||
|
||||
<script>
|
||||
|
||||
// Full list of configuration options available at:
|
||||
// https://github.com/hakimel/reveal.js#configuration
|
||||
Reveal.initialize({
|
||||
controls: true,
|
||||
progress: true,
|
||||
history: true,
|
||||
center: true,
|
||||
|
||||
transition: 'slide', // none/fade/slide/convex/concave/zoom
|
||||
|
||||
// Optional reveal.js plugins
|
||||
dependencies: [
|
||||
{ src: 'lib/js/classList.js', condition: function() { return !document.body.classList; } },
|
||||
{ src: 'plugin/markdown/marked.js', condition: function() { return !!document.querySelector( '[data-markdown]' ); } },
|
||||
{ src: 'plugin/markdown/markdown.js', condition: function() { return !!document.querySelector( '[data-markdown]' ); } },
|
||||
{ src: 'plugin/highlight/highlight.js', async: true, condition: function() { return !!document.querySelector( 'pre code' ); }, callback: function() { hljs.initHighlightingOnLoad(); } },
|
||||
{ src: 'plugin/zoom-js/zoom.js', async: true },
|
||||
{ src: 'plugin/notes/notes.js', async: true }
|
||||
]
|
||||
});
|
||||
|
||||
</script>
|
||||
|
||||
</body>
|
||||
</html>
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user