updating files

2020-09-19 22:18:32 +02:00
parent e3609dc85f
commit 666100d74a
20 changed files with 142 additions and 142 deletions
@@ -222,7 +222,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Sep 18, 2020</h4></center> <!-- date -->
+<center><h4>Sep 19, 2020</h4></center> <!-- date -->
 <br>
 <p>

@@ -232,21 +232,21 @@ polydegree <span style="color: #666666">=</span> np<span style="color: #666666">
 x_train, x_test, y_train, y_test <span style="color: #666666">=</span> train_test_split(x, y, test_size<span style="color: #666666">=0.2</span>)

 <span style="color: #008000; font-weight: bold">for</span> degree <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(maxdegree):
-    model <span style="color: #666666">=</span> make_pipeline(PolynomialFeatures(degree<span style="color: #666666">=</span>degree), LinearRegression(fit_intercept<span style="color: #666666">=</span><span style="color: #008000">False</span>))
+    model <span style="color: #666666">=</span> make_pipeline(PolynomialFeatures(degree<span style="color: #666666">=</span>degree), LinearRegression(fit_intercept<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">False</span>))
    y_pred <span style="color: #666666">=</span> np<span style="color: #666666">.</span>empty((y_test<span style="color: #666666">.</span>shape[<span style="color: #666666">0</span>], n_boostraps))
    <span style="color: #008000; font-weight: bold">for</span> i <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(n_boostraps):
        x_, y_ <span style="color: #666666">=</span> resample(x_train, y_train)
        y_pred[:, i] <span style="color: #666666">=</span> model<span style="color: #666666">.</span>fit(x_, y_)<span style="color: #666666">.</span>predict(x_test)<span style="color: #666666">.</span>ravel()

    polydegree[degree] <span style="color: #666666">=</span> degree
-    error[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>mean((y_test <span style="color: #666666">-</span> y_pred)<span style="color: #666666">**2</span>, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000">True</span>) )
-    bias[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( (y_test <span style="color: #666666">-</span> np<span style="color: #666666">.</span>mean(y_pred, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000">True</span>))<span style="color: #666666">**2</span> )
-    variance[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>var(y_pred, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000">True</span>) )
-    <span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&#39;Polynomial degree:&#39;</span>, degree)
-    <span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&#39;Error:&#39;</span>, error[degree])
-    <span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&#39;Bias^2:&#39;</span>, bias[degree])
-    <span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&#39;Var:&#39;</span>, variance[degree])
-    <span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&#39;{} &gt;= {} + {} = {}&#39;</span><span style="color: #666666">.</span>format(error[degree], bias[degree], variance[degree], bias[degree]<span style="color: #666666">+</span>variance[degree]))
+    error[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>mean((y_test <span style="color: #666666">-</span> y_pred)<span style="color: #666666">**2</span>, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>) )
+    bias[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( (y_test <span style="color: #666666">-</span> np<span style="color: #666666">.</span>mean(y_pred, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>))<span style="color: #666666">**2</span> )
+    variance[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>var(y_pred, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>) )
+    <span style="color: #008000">print</span>(<span style="color: #BA2121">&#39;Polynomial degree:&#39;</span>, degree)
+    <span style="color: #008000">print</span>(<span style="color: #BA2121">&#39;Error:&#39;</span>, error[degree])
+    <span style="color: #008000">print</span>(<span style="color: #BA2121">&#39;Bias^2:&#39;</span>, bias[degree])
+    <span style="color: #008000">print</span>(<span style="color: #BA2121">&#39;Var:&#39;</span>, variance[degree])
+    <span style="color: #008000">print</span>(<span style="color: #BA2121">&#39;</span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121"> &gt;= </span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121"> + </span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121"> = </span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121">&#39;</span><span style="color: #666666">.</span>format(error[degree], bias[degree], variance[degree], bias[degree]<span style="color: #666666">+</span>variance[degree]))

 plt<span style="color: #666666">.</span>plot(polydegree, error, label<span style="color: #666666">=</span><span style="color: #BA2121">&#39;Error&#39;</span>)
 plt<span style="color: #666666">.</span>plot(polydegree, bias, label<span style="color: #666666">=</span><span style="color: #BA2121">&#39;bias&#39;</span>)
@@ -229,7 +229,7 @@ MathJax.Hub.Config({
 <span style="color: #BA2121; font-style: italic">training data.</span>
 <span style="color: #BA2121; font-style: italic">&quot;&quot;&quot;</span>

-<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #19177C">__doc__</span>)
+<span style="color: #008000">print</span>(<span style="color: #19177C">__doc__</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">matplotlib.pyplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
@@ -256,7 +256,7 @@ plt<span style="color: #666666">.</span>figure(figsize<span style="color: #66666
    plt<span style="color: #666666">.</span>setp(ax, xticks<span style="color: #666666">=</span>(), yticks<span style="color: #666666">=</span>())

    polynomial_features <span style="color: #666666">=</span> PolynomialFeatures(degree<span style="color: #666666">=</span>degrees[i],
-                                             include_bias<span style="color: #666666">=</span><span style="color: #008000">False</span>)
+                                             include_bias<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">False</span>)
    linear_regression <span style="color: #666666">=</span> LinearRegression()
    pipeline <span style="color: #666666">=</span> Pipeline([(<span style="color: #BA2121">&quot;polynomial_features&quot;</span>, polynomial_features),
                         (<span style="color: #BA2121">&quot;linear_regression&quot;</span>, linear_regression)])
@@ -275,7 +275,7 @@ plt<span style="color: #666666">.</span>figure(figsize<span style="color: #66666
    plt<span style="color: #666666">.</span>xlim((<span style="color: #666666">0</span>, <span style="color: #666666">1</span>))
    plt<span style="color: #666666">.</span>ylim((<span style="color: #666666">-2</span>, <span style="color: #666666">2</span>))
    plt<span style="color: #666666">.</span>legend(loc<span style="color: #666666">=</span><span style="color: #BA2121">&quot;best&quot;</span>)
-    plt<span style="color: #666666">.</span>title(<span style="color: #BA2121">&quot;Degree {}</span><span style="color: #BB6622; font-weight: bold">\n</span><span style="color: #BA2121">MSE = {:.2e}(+/- {:.2e})&quot;</span><span style="color: #666666">.</span>format(
+    plt<span style="color: #666666">.</span>title(<span style="color: #BA2121">&quot;Degree </span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BB6622; font-weight: bold">\n</span><span style="color: #BA2121">MSE = </span><span style="color: #BB6688; font-weight: bold">{:.2e}</span><span style="color: #BA2121">(+/- </span><span style="color: #BB6688; font-weight: bold">{:.2e}</span><span style="color: #BA2121">)&quot;</span><span style="color: #666666">.</span>format(
        degrees[i], <span style="color: #666666">-</span>scores<span style="color: #666666">.</span>mean(), scores<span style="color: #666666">.</span>std()))
 plt<span style="color: #666666">.</span>show()
 </pre></div>
@@ -236,7 +236,7 @@ with their respective energies.
 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">scipy.linalg</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">scl</span>
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> train_test_split
 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">tqdm</span>
-sns<span style="color: #666666">.</span>set(color_codes<span style="color: #666666">=</span><span style="color: #008000">True</span>)
+sns<span style="color: #666666">.</span>set(color_codes<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>)
 cmap_args<span style="color: #666666">=</span><span style="color: #008000">dict</span>(vmin<span style="color: #666666">=-1.</span>, vmax<span style="color: #666666">=1.</span>, cmap<span style="color: #666666">=</span><span style="color: #BA2121">&#39;seismic&#39;</span>)

 L <span style="color: #666666">=</span> <span style="color: #666666">40</span>
@@ -245,7 +245,7 @@ X_test_own <span style="color: #666666">=</span> np<span style="color: #666666">

 <!-- 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">def</span> <span style="color: #0000FF">ols_inv</span>(x: np<span style="color: #666666">.</span>ndarray, y: np<span style="color: #666666">.</span>ndarray) <span style="color: #666666">-&gt;</span> np<span style="color: #666666">.</span>ndarray:
-    <span style="color: #008000; font-weight: bold">return</span> scl<span style="color: #666666">.</span>inv(x<span style="color: #666666">.</span>T @ x) @ (x<span style="color: #666666">.</span>T @ y)
+    <span style="color: #008000; font-weight: bold">return</span> scl<span style="color: #666666">.</span>inv(x<span style="color: #666666">.</span>T <span style="color: #666666">@</span> x) <span style="color: #666666">@</span> (x<span style="color: #666666">.</span>T <span style="color: #666666">@</span> y)
 beta <span style="color: #666666">=</span> ols_inv(X_train_own, y_train)
 </pre></div>
 <p>
@@ -247,7 +247,7 @@ linear system as an equation would reduce this down to
 <!-- 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">def</span> <span style="color: #0000FF">ols_svd</span>(x: np<span style="color: #666666">.</span>ndarray, y: np<span style="color: #666666">.</span>ndarray) <span style="color: #666666">-&gt;</span> np<span style="color: #666666">.</span>ndarray:
    u, s, v <span style="color: #666666">=</span> scl<span style="color: #666666">.</span>svd(x)
-    <span style="color: #008000; font-weight: bold">return</span> v<span style="color: #666666">.</span>T @ scl<span style="color: #666666">.</span>pinv(scl<span style="color: #666666">.</span>diagsvd(s, u<span style="color: #666666">.</span>shape[<span style="color: #666666">0</span>], v<span style="color: #666666">.</span>shape[<span style="color: #666666">0</span>])) @ u<span style="color: #666666">.</span>T @ y
+    <span style="color: #008000; font-weight: bold">return</span> v<span style="color: #666666">.</span>T <span style="color: #666666">@</span> scl<span style="color: #666666">.</span>pinv(scl<span style="color: #666666">.</span>diagsvd(s, u<span style="color: #666666">.</span>shape[<span style="color: #666666">0</span>], v<span style="color: #666666">.</span>shape[<span style="color: #666666">0</span>])) <span style="color: #666666">@</span> u<span style="color: #666666">.</span>T <span style="color: #666666">@</span> y
 </pre></div>
 <p>

@@ -236,7 +236,7 @@ We will look at a system of \( L = 40 \) spins with a coupling constant of \( J
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> train_test_split
 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">sklearn.linear_model</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">skl</span>
 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">tqdm</span>
-sns<span style="color: #666666">.</span>set(color_codes<span style="color: #666666">=</span><span style="color: #008000">True</span>)
+sns<span style="color: #666666">.</span>set(color_codes<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>)
 cmap_args<span style="color: #666666">=</span><span style="color: #008000">dict</span>(vmin<span style="color: #666666">=-1.</span>, vmax<span style="color: #666666">=1.</span>, cmap<span style="color: #666666">=</span><span style="color: #BA2121">&#39;seismic&#39;</span>)

 L <span style="color: #666666">=</span> <span style="color: #666666">40</span>
@@ -229,7 +229,7 @@ colors <span style="color: #666666">=</span> {
        lambdas,
        train_errors[key],
        colors[key],
-        label<span style="color: #666666">=</span><span style="color: #BA2121">&quot;Train {0}&quot;</span><span style="color: #666666">.</span>format(key),
+        label<span style="color: #666666">=</span><span style="color: #BA2121">&quot;Train </span><span style="color: #BB6688; font-weight: bold">{0}</span><span style="color: #BA2121">&quot;</span><span style="color: #666666">.</span>format(key),
        linewidth<span style="color: #666666">=4.0</span>
    )

@@ -238,7 +238,7 @@ colors <span style="color: #666666">=</span> {
        lambdas,
        test_errors[key],
        colors[key] <span style="color: #666666">+</span> <span style="color: #BA2121">&quot;--&quot;</span>,
-        label<span style="color: #666666">=</span><span style="color: #BA2121">&quot;Test {0}&quot;</span><span style="color: #666666">.</span>format(key),
+        label<span style="color: #666666">=</span><span style="color: #BA2121">&quot;Test </span><span style="color: #BB6688; font-weight: bold">{0}</span><span style="color: #BA2121">&quot;</span><span style="color: #666666">.</span>format(key),
        linewidth<span style="color: #666666">=4.0</span>
    )
 plt<span style="color: #666666">.</span>legend(loc<span style="color: #666666">=</span><span style="color: #BA2121">&quot;best&quot;</span>, fontsize<span style="color: #666666">=18</span>)
@@ -206,7 +206,7 @@ MathJax.Hub.Config({
 <h2 id="___sec19" class="anchor">Friday September 18: Intro to Logistic Regression </h2>

 <p>
-<a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureSeptember18.mp4?vrtx=view-as-webpage" target="_self">Video of Lecture</a> and <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesSeptember18.pdf" target="_self">link to handwritten notes</a>.
+<a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h20/forelesningsvideoer/LectureSeptember18.mp4?vrtx=view-as-webpage" target="_self">Video of Lecture</a> and <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesSeptember18.pdf" target="_self">link to handwritten notes</a>.

 <p>
 <p>
@@ -246,7 +246,7 @@ DATA_ID <span style="color: #666666">=</span> <span style="color: #BA2121">&quot
    <span style="color: #008000; font-weight: bold">return</span> os<span style="color: #666666">.</span>path<span style="color: #666666">.</span>join(DATA_ID, dat_id)

 <span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">save_fig</span>(fig_id):
-    plt<span style="color: #666666">.</span>savefig(image_path(fig_id) <span style="color: #666666">+</span> <span style="color: #BA2121">&quot;.png&quot;</span>, format<span style="color: #666666">=</span><span style="color: #BA2121">&#39;png&#39;</span>)
+    plt<span style="color: #666666">.</span>savefig(image_path(fig_id) <span style="color: #666666">+</span> <span style="color: #BA2121">&quot;.png&quot;</span>, <span style="color: #008000">format</span><span style="color: #666666">=</span><span style="color: #BA2121">&#39;png&#39;</span>)

 infile <span style="color: #666666">=</span> <span style="color: #008000">open</span>(data_path(<span style="color: #BA2121">&quot;chddata.csv&quot;</span>),<span style="color: #BA2121">&#39;r&#39;</span>)

@@ -229,7 +229,7 @@ ax <span style="color: #666666">=</span> fig<span style="color: #666666">.</span
 ax<span style="color: #666666">.</span>plot(z, sigma)
 ax<span style="color: #666666">.</span>set_ylim([<span style="color: #666666">-0.1</span>, <span style="color: #666666">1.1</span>])
 ax<span style="color: #666666">.</span>set_xlim([<span style="color: #666666">-5</span>,<span style="color: #666666">5</span>])
-ax<span style="color: #666666">.</span>grid(<span style="color: #008000">True</span>)
+ax<span style="color: #666666">.</span>grid(<span style="color: #008000; font-weight: bold">True</span>)
 ax<span style="color: #666666">.</span>set_xlabel(<span style="color: #BA2121">&#39;z&#39;</span>)
 ax<span style="color: #666666">.</span>set_title(<span style="color: #BA2121">&#39;sigmoid function&#39;</span>)

@@ -245,7 +245,7 @@ ax <span style="color: #666666">=</span> fig<span style="color: #666666">.</span
 ax<span style="color: #666666">.</span>plot(z, step)
 ax<span style="color: #666666">.</span>set_ylim([<span style="color: #666666">-0.5</span>, <span style="color: #666666">1.5</span>])
 ax<span style="color: #666666">.</span>set_xlim([<span style="color: #666666">-5</span>,<span style="color: #666666">5</span>])
-ax<span style="color: #666666">.</span>grid(<span style="color: #008000">True</span>)
+ax<span style="color: #666666">.</span>grid(<span style="color: #008000; font-weight: bold">True</span>)
 ax<span style="color: #666666">.</span>set_xlabel(<span style="color: #BA2121">&#39;z&#39;</span>)
 ax<span style="color: #666666">.</span>set_title(<span style="color: #BA2121">&#39;step function&#39;</span>)

@@ -260,7 +260,7 @@ ax <span style="color: #666666">=</span> fig<span style="color: #666666">.</span
 ax<span style="color: #666666">.</span>plot(z, t)
 ax<span style="color: #666666">.</span>set_ylim([<span style="color: #666666">-1.0</span>, <span style="color: #666666">1.0</span>])
 ax<span style="color: #666666">.</span>set_xlim([<span style="color: #666666">-2*</span>mt<span style="color: #666666">.</span>pi,<span style="color: #666666">2*</span>mt<span style="color: #666666">.</span>pi])
-ax<span style="color: #666666">.</span>grid(<span style="color: #008000">True</span>)
+ax<span style="color: #666666">.</span>grid(<span style="color: #008000; font-weight: bold">True</span>)
 ax<span style="color: #666666">.</span>set_xlabel(<span style="color: #BA2121">&#39;z&#39;</span>)
 ax<span style="color: #666666">.</span>set_title(<span style="color: #BA2121">&#39;tanh function&#39;</span>)

@@ -223,12 +223,12 @@ classification.
 cancer <span style="color: #666666">=</span> load_breast_cancer()

 X_train, X_test, y_train, y_test <span style="color: #666666">=</span> train_test_split(cancer<span style="color: #666666">.</span>data,cancer<span style="color: #666666">.</span>target,random_state<span style="color: #666666">=0</span>)
-<span style="color: #008000; font-weight: bold">print</span>(X_train<span style="color: #666666">.</span>shape)
-<span style="color: #008000; font-weight: bold">print</span>(X_test<span style="color: #666666">.</span>shape)
+<span style="color: #008000">print</span>(X_train<span style="color: #666666">.</span>shape)
+<span style="color: #008000">print</span>(X_test<span style="color: #666666">.</span>shape)
 <span style="color: #408080; font-style: italic"># Logistic Regression</span>
 logreg <span style="color: #666666">=</span> LogisticRegression(solver<span style="color: #666666">=</span><span style="color: #BA2121">&#39;lbfgs&#39;</span>)
 logreg<span style="color: #666666">.</span>fit(X_train, y_train)
-<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&quot;Test set accuracy with Logistic Regression: {:.2f}&quot;</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test,y_test)))
+<span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;Test set accuracy with Logistic Regression: </span><span style="color: #BB6688; font-weight: bold">{:.2f}</span><span style="color: #BA2121">&quot;</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test,y_test)))
 <span style="color: #408080; font-style: italic">#now scale the data</span>
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.preprocessing</span> <span style="color: #008000; font-weight: bold">import</span> StandardScaler
 scaler <span style="color: #666666">=</span> StandardScaler()
@@ -237,7 +237,7 @@ X_train_scaled <span style="color: #666666">=</span> scaler<span style="color: #
 X_test_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_test)
 <span style="color: #408080; font-style: italic"># Logistic Regression</span>
 logreg<span style="color: #666666">.</span>fit(X_train_scaled, y_train)
-<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&quot;Test set accuracy Logistic Regression with scaled data: {:.2f}&quot;</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test_scaled,y_test)))
+<span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;Test set accuracy Logistic Regression with scaled data: </span><span style="color: #BB6688; font-weight: bold">{:.2f}</span><span style="color: #BA2121">&quot;</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test_scaled,y_test)))
 </pre></div>
 <p>
 <p>
@@ -243,7 +243,7 @@ correlation_matrix <span style="color: #666666">=</span> cancerpd<span style="co
 <span style="color: #408080; font-style: italic"># use the heatmap function from seaborn to plot the correlation matrix</span>
 <span style="color: #408080; font-style: italic"># annot = True to print the values inside the square</span>
 plt<span style="color: #666666">.</span>figure(figsize<span style="color: #666666">=</span>(<span style="color: #666666">15</span>,<span style="color: #666666">8</span>))
-sns<span style="color: #666666">.</span>heatmap(data<span style="color: #666666">=</span>correlation_matrix, annot<span style="color: #666666">=</span><span style="color: #008000">True</span>)
+sns<span style="color: #666666">.</span>heatmap(data<span style="color: #666666">=</span>correlation_matrix, annot<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>)
 plt<span style="color: #666666">.</span>show()
 </pre></div>
 <p>
@@ -217,12 +217,12 @@ MathJax.Hub.Config({
 cancer <span style="color: #666666">=</span> load_breast_cancer()

 X_train, X_test, y_train, y_test <span style="color: #666666">=</span> train_test_split(cancer<span style="color: #666666">.</span>data,cancer<span style="color: #666666">.</span>target,random_state<span style="color: #666666">=0</span>)
-<span style="color: #008000; font-weight: bold">print</span>(X_train<span style="color: #666666">.</span>shape)
-<span style="color: #008000; font-weight: bold">print</span>(X_test<span style="color: #666666">.</span>shape)
+<span style="color: #008000">print</span>(X_train<span style="color: #666666">.</span>shape)
+<span style="color: #008000">print</span>(X_test<span style="color: #666666">.</span>shape)
 <span style="color: #408080; font-style: italic"># Logistic Regression</span>
 logreg <span style="color: #666666">=</span> LogisticRegression(solver<span style="color: #666666">=</span><span style="color: #BA2121">&#39;lbfgs&#39;</span>)
 logreg<span style="color: #666666">.</span>fit(X_train, y_train)
-<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&quot;Test set accuracy with Logistic Regression: {:.2f}&quot;</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test,y_test)))
+<span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;Test set accuracy with Logistic Regression: </span><span style="color: #BB6688; font-weight: bold">{:.2f}</span><span style="color: #BA2121">&quot;</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test,y_test)))
 <span style="color: #408080; font-style: italic">#now scale the data</span>
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.preprocessing</span> <span style="color: #008000; font-weight: bold">import</span> StandardScaler
 scaler <span style="color: #666666">=</span> StandardScaler()
@@ -231,20 +231,20 @@ X_train_scaled <span style="color: #666666">=</span> scaler<span style="color: #
 X_test_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_test)
 <span style="color: #408080; font-style: italic"># Logistic Regression</span>
 logreg<span style="color: #666666">.</span>fit(X_train_scaled, y_train)
-<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&quot;Test set accuracy Logistic Regression with scaled data: {:.2f}&quot;</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test_scaled,y_test)))
+<span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;Test set accuracy Logistic Regression with scaled data: </span><span style="color: #BB6688; font-weight: bold">{:.2f}</span><span style="color: #BA2121">&quot;</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test_scaled,y_test)))


 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.preprocessing</span> <span style="color: #008000; font-weight: bold">import</span> LabelEncoder
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> cross_validate
 <span style="color: #408080; font-style: italic">#Cross validation</span>
 accuracy <span style="color: #666666">=</span> cross_validate(logreg,X_test_scaled,y_test,cv<span style="color: #666666">=10</span>)[<span style="color: #BA2121">&#39;test_score&#39;</span>]
-<span style="color: #008000; font-weight: bold">print</span>(accuracy)
-<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&quot;Test set accuracy with Logistic Regression  and scaled data: {:.2f}&quot;</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test_scaled,y_test)))
+<span style="color: #008000">print</span>(accuracy)
+<span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;Test set accuracy with Logistic Regression  and scaled data: </span><span style="color: #BB6688; font-weight: bold">{:.2f}</span><span style="color: #BA2121">&quot;</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test_scaled,y_test)))


 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">scikitplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">skplt</span>
 y_pred <span style="color: #666666">=</span> logreg<span style="color: #666666">.</span>predict(X_test_scaled)
-skplt<span style="color: #666666">.</span>metrics<span style="color: #666666">.</span>plot_confusion_matrix(y_test, y_pred, normalize<span style="color: #666666">=</span><span style="color: #008000">True</span>)
+skplt<span style="color: #666666">.</span>metrics<span style="color: #666666">.</span>plot_confusion_matrix(y_test, y_pred, normalize<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>)
 plt<span style="color: #666666">.</span>show()
 y_probas <span style="color: #666666">=</span> logreg<span style="color: #666666">.</span>predict_proba(X_test_scaled)
 skplt<span style="color: #666666">.</span>metrics<span style="color: #666666">.</span>plot_roc(y_test, y_probas)
@@ -222,7 +222,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Sep 18, 2020</h4></center> <!-- date -->
+<center><h4>Sep 19, 2020</h4></center> <!-- date -->
 <br>
 <p>

@@ -148,7 +148,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>&nbsp;<br>
-<center><h4>Sep 18, 2020</h4></center> <!-- date -->
+<center><h4>Sep 19, 2020</h4></center> <!-- date -->
 <br>
 <p>

@@ -456,21 +456,21 @@ polydegree = np.zeros(maxdegree)
 x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=<span style="color: #B452CD">0.2</span>)

 <span style="color: #8B008B; font-weight: bold">for</span> degree <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(maxdegree):
-    model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=<span style="color: #658b00">False</span>))
+    model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=<span style="color: #8B008B; font-weight: bold">False</span>))
    y_pred = np.empty((y_test.shape[<span style="color: #B452CD">0</span>], n_boostraps))
    <span style="color: #8B008B; font-weight: bold">for</span> i <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(n_boostraps):
        x_, y_ = resample(x_train, y_train)
        y_pred[:, i] = model.fit(x_, y_).predict(x_test).ravel()

    polydegree[degree] = degree
-    error[degree] = np.mean( np.mean((y_test - y_pred)**<span style="color: #B452CD">2</span>, axis=<span style="color: #B452CD">1</span>, keepdims=<span style="color: #658b00">True</span>) )
-    bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=<span style="color: #B452CD">1</span>, keepdims=<span style="color: #658b00">True</span>))**<span style="color: #B452CD">2</span> )
-    variance[degree] = np.mean( np.var(y_pred, axis=<span style="color: #B452CD">1</span>, keepdims=<span style="color: #658b00">True</span>) )
-    <span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&#39;Polynomial degree:&#39;</span>, degree)
-    <span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&#39;Error:&#39;</span>, error[degree])
-    <span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&#39;Bias^2:&#39;</span>, bias[degree])
-    <span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&#39;Var:&#39;</span>, variance[degree])
-    <span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&#39;{} &gt;= {} + {} = {}&#39;</span>.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
+    error[degree] = np.mean( np.mean((y_test - y_pred)**<span style="color: #B452CD">2</span>, axis=<span style="color: #B452CD">1</span>, keepdims=<span style="color: #8B008B; font-weight: bold">True</span>) )
+    bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=<span style="color: #B452CD">1</span>, keepdims=<span style="color: #8B008B; font-weight: bold">True</span>))**<span style="color: #B452CD">2</span> )
+    variance[degree] = np.mean( np.var(y_pred, axis=<span style="color: #B452CD">1</span>, keepdims=<span style="color: #8B008B; font-weight: bold">True</span>) )
+    <span style="color: #658b00">print</span>(<span style="color: #CD5555">&#39;Polynomial degree:&#39;</span>, degree)
+    <span style="color: #658b00">print</span>(<span style="color: #CD5555">&#39;Error:&#39;</span>, error[degree])
+    <span style="color: #658b00">print</span>(<span style="color: #CD5555">&#39;Bias^2:&#39;</span>, bias[degree])
+    <span style="color: #658b00">print</span>(<span style="color: #CD5555">&#39;Var:&#39;</span>, variance[degree])
+    <span style="color: #658b00">print</span>(<span style="color: #CD5555">&#39;{} &gt;= {} + {} = {}&#39;</span>.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))

 plt.plot(polydegree, error, label=<span style="color: #CD5555">&#39;Error&#39;</span>)
 plt.plot(polydegree, bias, label=<span style="color: #CD5555">&#39;bias&#39;</span>)
@@ -508,7 +508,7 @@ plt.show()
 <span style="color: #CD5555">training data.</span>
 <span style="color: #CD5555">&quot;&quot;&quot;</span>

-<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #00688B">__doc__</span>)
+<span style="color: #658b00">print</span>(<span style="color: #00688B">__doc__</span>)

 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
@@ -535,7 +535,7 @@ plt.figure(figsize=(<span style="color: #B452CD">14</span>, <span style="color:
    plt.setp(ax, xticks=(), yticks=())

    polynomial_features = PolynomialFeatures(degree=degrees[i],
-                                             include_bias=<span style="color: #658b00">False</span>)
+                                             include_bias=<span style="color: #8B008B; font-weight: bold">False</span>)
    linear_regression = LinearRegression()
    pipeline = Pipeline([(<span style="color: #CD5555">&quot;polynomial_features&quot;</span>, polynomial_features),
                         (<span style="color: #CD5555">&quot;linear_regression&quot;</span>, linear_regression)])
@@ -641,7 +641,7 @@ with their respective energies.
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">scipy.linalg</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">scl</span>
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">tqdm</span>
-sns.set(color_codes=<span style="color: #658b00">True</span>)
+sns.set(color_codes=<span style="color: #8B008B; font-weight: bold">True</span>)
 cmap_args=<span style="color: #658b00">dict</span>(vmin=-<span style="color: #B452CD">1.</span>, vmax=<span style="color: #B452CD">1.</span>, cmap=<span style="color: #CD5555">&#39;seismic&#39;</span>)

 L = <span style="color: #B452CD">40</span>
@@ -768,7 +768,7 @@ X_test_own = np.concatenate(

 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
 <div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">def</span> <span style="color: #008b45">ols_inv</span>(x: np.ndarray, y: np.ndarray) -&gt; np.ndarray:
-    <span style="color: #8B008B; font-weight: bold">return</span> scl.inv(x.T <span style="color: #a61717; background-color: #e3d2d2">@</span> x) <span style="color: #a61717; background-color: #e3d2d2">@</span> (x.T <span style="color: #a61717; background-color: #e3d2d2">@</span> y)
+    <span style="color: #8B008B; font-weight: bold">return</span> scl.inv(x.T @ x) @ (x.T @ y)
 beta = ols_inv(X_train_own, y_train)
 </pre></div>
 </section>
@@ -825,7 +825,7 @@ linear system as an equation would reduce this down to
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
 <div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">def</span> <span style="color: #008b45">ols_svd</span>(x: np.ndarray, y: np.ndarray) -&gt; np.ndarray:
    u, s, v = scl.svd(x)
-    <span style="color: #8B008B; font-weight: bold">return</span> v.T <span style="color: #a61717; background-color: #e3d2d2">@</span> scl.pinv(scl.diagsvd(s, u.shape[<span style="color: #B452CD">0</span>], v.shape[<span style="color: #B452CD">0</span>])) <span style="color: #a61717; background-color: #e3d2d2">@</span> u.T <span style="color: #a61717; background-color: #e3d2d2">@</span> y
+    <span style="color: #8B008B; font-weight: bold">return</span> v.T @ scl.pinv(scl.diagsvd(s, u.shape[<span style="color: #B452CD">0</span>], v.shape[<span style="color: #B452CD">0</span>])) @ u.T @ y
 </pre></div>
 <p>

@@ -903,7 +903,7 @@ We will look at a system of \( L = 40 \) spins with a coupling constant of \( J
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">sklearn.linear_model</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">skl</span>
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">tqdm</span>
-sns.set(color_codes=<span style="color: #658b00">True</span>)
+sns.set(color_codes=<span style="color: #8B008B; font-weight: bold">True</span>)
 cmap_args=<span style="color: #658b00">dict</span>(vmin=-<span style="color: #B452CD">1.</span>, vmax=<span style="color: #B452CD">1.</span>, cmap=<span style="color: #CD5555">&#39;seismic&#39;</span>)

 L = <span style="color: #B452CD">40</span>
@@ -1197,7 +1197,7 @@ other models for all values of \( \lambda \).
 <h2 id="___sec19">Friday September 18: Intro to Logistic Regression </h2>

 <p>
-<a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureSeptember18.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture</a> and <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesSeptember18.pdf" target="_blank">link to handwritten notes</a>.
+<a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h20/forelesningsvideoer/LectureSeptember18.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture</a> and <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesSeptember18.pdf" target="_blank">link to handwritten notes</a>.
 </section>


@@ -1384,7 +1384,7 @@ DATA_ID = <span style="color: #CD5555">&quot;DataFiles/&quot;</span>
    <span style="color: #8B008B; font-weight: bold">return</span> os.path.join(DATA_ID, dat_id)

 <span style="color: #8B008B; font-weight: bold">def</span> <span style="color: #008b45">save_fig</span>(fig_id):
-    plt.savefig(image_path(fig_id) + <span style="color: #CD5555">&quot;.png&quot;</span>, format=<span style="color: #CD5555">&#39;png&#39;</span>)
+    plt.savefig(image_path(fig_id) + <span style="color: #CD5555">&quot;.png&quot;</span>, <span style="color: #658b00">format</span>=<span style="color: #CD5555">&#39;png&#39;</span>)

 infile = <span style="color: #658b00">open</span>(data_path(<span style="color: #CD5555">&quot;chddata.csv&quot;</span>),<span style="color: #CD5555">&#39;r&#39;</span>)

@@ -1500,7 +1500,7 @@ ax = fig.add_subplot(<span style="color: #B452CD">111</span>)
 ax.plot(z, sigma)
 ax.set_ylim([-<span style="color: #B452CD">0.1</span>, <span style="color: #B452CD">1.1</span>])
 ax.set_xlim([-<span style="color: #B452CD">5</span>,<span style="color: #B452CD">5</span>])
-ax.grid(<span style="color: #658b00">True</span>)
+ax.grid(<span style="color: #8B008B; font-weight: bold">True</span>)
 ax.set_xlabel(<span style="color: #CD5555">&#39;z&#39;</span>)
 ax.set_title(<span style="color: #CD5555">&#39;sigmoid function&#39;</span>)

@@ -1516,7 +1516,7 @@ ax = fig.add_subplot(<span style="color: #B452CD">111</span>)
 ax.plot(z, step)
 ax.set_ylim([-<span style="color: #B452CD">0.5</span>, <span style="color: #B452CD">1.5</span>])
 ax.set_xlim([-<span style="color: #B452CD">5</span>,<span style="color: #B452CD">5</span>])
-ax.grid(<span style="color: #658b00">True</span>)
+ax.grid(<span style="color: #8B008B; font-weight: bold">True</span>)
 ax.set_xlabel(<span style="color: #CD5555">&#39;z&#39;</span>)
 ax.set_title(<span style="color: #CD5555">&#39;step function&#39;</span>)

@@ -1531,7 +1531,7 @@ ax = fig.add_subplot(<span style="color: #B452CD">111</span>)
 ax.plot(z, t)
 ax.set_ylim([-<span style="color: #B452CD">1.0</span>, <span style="color: #B452CD">1.0</span>])
 ax.set_xlim([-<span style="color: #B452CD">2</span>*mt.pi,<span style="color: #B452CD">2</span>*mt.pi])
-ax.grid(<span style="color: #658b00">True</span>)
+ax.grid(<span style="color: #8B008B; font-weight: bold">True</span>)
 ax.set_xlabel(<span style="color: #CD5555">&#39;z&#39;</span>)
 ax.set_title(<span style="color: #CD5555">&#39;tanh function&#39;</span>)

@@ -1793,12 +1793,12 @@ classification.
 cancer = load_breast_cancer()

 X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=<span style="color: #B452CD">0</span>)
-<span style="color: #8B008B; font-weight: bold">print</span>(X_train.shape)
-<span style="color: #8B008B; font-weight: bold">print</span>(X_test.shape)
+<span style="color: #658b00">print</span>(X_train.shape)
+<span style="color: #658b00">print</span>(X_test.shape)
 <span style="color: #228B22"># Logistic Regression</span>
 logreg = LogisticRegression(solver=<span style="color: #CD5555">&#39;lbfgs&#39;</span>)
 logreg.fit(X_train, y_train)
-<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&quot;Test set accuracy with Logistic Regression: {:.2f}&quot;</span>.format(logreg.score(X_test,y_test)))
+<span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;Test set accuracy with Logistic Regression: {:.2f}&quot;</span>.format(logreg.score(X_test,y_test)))
 <span style="color: #228B22">#now scale the data</span>
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.preprocessing</span> <span style="color: #8B008B; font-weight: bold">import</span> StandardScaler
 scaler = StandardScaler()
@@ -1807,7 +1807,7 @@ X_train_scaled = scaler.transform(X_train)
 X_test_scaled = scaler.transform(X_test)
 <span style="color: #228B22"># Logistic Regression</span>
 logreg.fit(X_train_scaled, y_train)
-<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&quot;Test set accuracy Logistic Regression with scaled data: {:.2f}&quot;</span>.format(logreg.score(X_test_scaled,y_test)))
+<span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;Test set accuracy Logistic Regression with scaled data: {:.2f}&quot;</span>.format(logreg.score(X_test_scaled,y_test)))
 </pre></div>
 </section>

@@ -1853,7 +1853,7 @@ correlation_matrix = cancerpd.corr().round(<span style="color: #B452CD">1</span>
 <span style="color: #228B22"># use the heatmap function from seaborn to plot the correlation matrix</span>
 <span style="color: #228B22"># annot = True to print the values inside the square</span>
 plt.figure(figsize=(<span style="color: #B452CD">15</span>,<span style="color: #B452CD">8</span>))
-sns.heatmap(data=correlation_matrix, annot=<span style="color: #658b00">True</span>)
+sns.heatmap(data=correlation_matrix, annot=<span style="color: #8B008B; font-weight: bold">True</span>)
 plt.show()
 </pre></div>
 </section>
@@ -1913,12 +1913,12 @@ applications. This will be discussed later this semester (<a href="https://compp
 cancer = load_breast_cancer()

 X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=<span style="color: #B452CD">0</span>)
-<span style="color: #8B008B; font-weight: bold">print</span>(X_train.shape)
-<span style="color: #8B008B; font-weight: bold">print</span>(X_test.shape)
+<span style="color: #658b00">print</span>(X_train.shape)
+<span style="color: #658b00">print</span>(X_test.shape)
 <span style="color: #228B22"># Logistic Regression</span>
 logreg = LogisticRegression(solver=<span style="color: #CD5555">&#39;lbfgs&#39;</span>)
 logreg.fit(X_train, y_train)
-<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&quot;Test set accuracy with Logistic Regression: {:.2f}&quot;</span>.format(logreg.score(X_test,y_test)))
+<span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;Test set accuracy with Logistic Regression: {:.2f}&quot;</span>.format(logreg.score(X_test,y_test)))
 <span style="color: #228B22">#now scale the data</span>
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.preprocessing</span> <span style="color: #8B008B; font-weight: bold">import</span> StandardScaler
 scaler = StandardScaler()
@@ -1927,20 +1927,20 @@ X_train_scaled = scaler.transform(X_train)
 X_test_scaled = scaler.transform(X_test)
 <span style="color: #228B22"># Logistic Regression</span>
 logreg.fit(X_train_scaled, y_train)
-<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&quot;Test set accuracy Logistic Regression with scaled data: {:.2f}&quot;</span>.format(logreg.score(X_test_scaled,y_test)))
+<span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;Test set accuracy Logistic Regression with scaled data: {:.2f}&quot;</span>.format(logreg.score(X_test_scaled,y_test)))


 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.preprocessing</span> <span style="color: #8B008B; font-weight: bold">import</span> LabelEncoder
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> cross_validate
 <span style="color: #228B22">#Cross validation</span>
 accuracy = cross_validate(logreg,X_test_scaled,y_test,cv=<span style="color: #B452CD">10</span>)[<span style="color: #CD5555">&#39;test_score&#39;</span>]
-<span style="color: #8B008B; font-weight: bold">print</span>(accuracy)
-<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&quot;Test set accuracy with Logistic Regression  and scaled data: {:.2f}&quot;</span>.format(logreg.score(X_test_scaled,y_test)))
+<span style="color: #658b00">print</span>(accuracy)
+<span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;Test set accuracy with Logistic Regression  and scaled data: {:.2f}&quot;</span>.format(logreg.score(X_test_scaled,y_test)))


 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">scikitplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">skplt</span>
 y_pred = logreg.predict(X_test_scaled)
-skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=<span style="color: #658b00">True</span>)
+skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=<span style="color: #8B008B; font-weight: bold">True</span>)
 plt.show()
 y_probas = logreg.predict_proba(X_test_scaled)
 skplt.metrics.plot_roc(y_test, y_probas)
@@ -144,7 +144,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Sep 18, 2020</h4></center> <!-- date -->
+<center><h4>Sep 19, 2020</h4></center> <!-- date -->
 <br>
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -428,21 +428,21 @@ polydegree = np.zeros(maxdegree)
 x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=<span style="color: #B452CD">0.2</span>)

 <span style="color: #8B008B; font-weight: bold">for</span> degree <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(maxdegree):
-    model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=<span style="color: #658b00">False</span>))
+    model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=<span style="color: #8B008B; font-weight: bold">False</span>))
    y_pred = np.empty((y_test.shape[<span style="color: #B452CD">0</span>], n_boostraps))
    <span style="color: #8B008B; font-weight: bold">for</span> i <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(n_boostraps):
        x_, y_ = resample(x_train, y_train)
        y_pred[:, i] = model.fit(x_, y_).predict(x_test).ravel()

    polydegree[degree] = degree
-    error[degree] = np.mean( np.mean((y_test - y_pred)**<span style="color: #B452CD">2</span>, axis=<span style="color: #B452CD">1</span>, keepdims=<span style="color: #658b00">True</span>) )
-    bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=<span style="color: #B452CD">1</span>, keepdims=<span style="color: #658b00">True</span>))**<span style="color: #B452CD">2</span> )
-    variance[degree] = np.mean( np.var(y_pred, axis=<span style="color: #B452CD">1</span>, keepdims=<span style="color: #658b00">True</span>) )
-    <span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&#39;Polynomial degree:&#39;</span>, degree)
-    <span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&#39;Error:&#39;</span>, error[degree])
-    <span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&#39;Bias^2:&#39;</span>, bias[degree])
-    <span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&#39;Var:&#39;</span>, variance[degree])
-    <span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&#39;{} &gt;= {} + {} = {}&#39;</span>.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
+    error[degree] = np.mean( np.mean((y_test - y_pred)**<span style="color: #B452CD">2</span>, axis=<span style="color: #B452CD">1</span>, keepdims=<span style="color: #8B008B; font-weight: bold">True</span>) )
+    bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=<span style="color: #B452CD">1</span>, keepdims=<span style="color: #8B008B; font-weight: bold">True</span>))**<span style="color: #B452CD">2</span> )
+    variance[degree] = np.mean( np.var(y_pred, axis=<span style="color: #B452CD">1</span>, keepdims=<span style="color: #8B008B; font-weight: bold">True</span>) )
+    <span style="color: #658b00">print</span>(<span style="color: #CD5555">&#39;Polynomial degree:&#39;</span>, degree)
+    <span style="color: #658b00">print</span>(<span style="color: #CD5555">&#39;Error:&#39;</span>, error[degree])
+    <span style="color: #658b00">print</span>(<span style="color: #CD5555">&#39;Bias^2:&#39;</span>, bias[degree])
+    <span style="color: #658b00">print</span>(<span style="color: #CD5555">&#39;Var:&#39;</span>, variance[degree])
+    <span style="color: #658b00">print</span>(<span style="color: #CD5555">&#39;{} &gt;= {} + {} = {}&#39;</span>.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))

 plt.plot(polydegree, error, label=<span style="color: #CD5555">&#39;Error&#39;</span>)
 plt.plot(polydegree, bias, label=<span style="color: #CD5555">&#39;bias&#39;</span>)
@@ -479,7 +479,7 @@ plt.show()
 <span style="color: #CD5555">training data.</span>
 <span style="color: #CD5555">&quot;&quot;&quot;</span>

-<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #00688B">__doc__</span>)
+<span style="color: #658b00">print</span>(<span style="color: #00688B">__doc__</span>)

 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">np</span>
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
@@ -506,7 +506,7 @@ plt.figure(figsize=(<span style="color: #B452CD">14</span>, <span style="color:
    plt.setp(ax, xticks=(), yticks=())

    polynomial_features = PolynomialFeatures(degree=degrees[i],
-                                             include_bias=<span style="color: #658b00">False</span>)
+                                             include_bias=<span style="color: #8B008B; font-weight: bold">False</span>)
    linear_regression = LinearRegression()
    pipeline = Pipeline([(<span style="color: #CD5555">&quot;polynomial_features&quot;</span>, polynomial_features),
                         (<span style="color: #CD5555">&quot;linear_regression&quot;</span>, linear_regression)])
@@ -608,7 +608,7 @@ with their respective energies.
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">scipy.linalg</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">scl</span>
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">tqdm</span>
-sns.set(color_codes=<span style="color: #658b00">True</span>)
+sns.set(color_codes=<span style="color: #8B008B; font-weight: bold">True</span>)
 cmap_args=<span style="color: #658b00">dict</span>(vmin=-<span style="color: #B452CD">1.</span>, vmax=<span style="color: #B452CD">1.</span>, cmap=<span style="color: #CD5555">&#39;seismic&#39;</span>)

 L = <span style="color: #B452CD">40</span>
@@ -724,7 +724,7 @@ X_test_own = np.concatenate(

 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
 <div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">def</span> <span style="color: #008b45">ols_inv</span>(x: np.ndarray, y: np.ndarray) -&gt; np.ndarray:
-    <span style="color: #8B008B; font-weight: bold">return</span> scl.inv(x.T <span style="color: #a61717; background-color: #e3d2d2">@</span> x) <span style="color: #a61717; background-color: #e3d2d2">@</span> (x.T <span style="color: #a61717; background-color: #e3d2d2">@</span> y)
+    <span style="color: #8B008B; font-weight: bold">return</span> scl.inv(x.T @ x) @ (x.T @ y)
 beta = ols_inv(X_train_own, y_train)
 </pre></div>
 <p>
@@ -774,7 +774,7 @@ linear system as an equation would reduce this down to
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
 <div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">def</span> <span style="color: #008b45">ols_svd</span>(x: np.ndarray, y: np.ndarray) -&gt; np.ndarray:
    u, s, v = scl.svd(x)
-    <span style="color: #8B008B; font-weight: bold">return</span> v.T <span style="color: #a61717; background-color: #e3d2d2">@</span> scl.pinv(scl.diagsvd(s, u.shape[<span style="color: #B452CD">0</span>], v.shape[<span style="color: #B452CD">0</span>])) <span style="color: #a61717; background-color: #e3d2d2">@</span> u.T <span style="color: #a61717; background-color: #e3d2d2">@</span> y
+    <span style="color: #8B008B; font-weight: bold">return</span> v.T @ scl.pinv(scl.diagsvd(s, u.shape[<span style="color: #B452CD">0</span>], v.shape[<span style="color: #B452CD">0</span>])) @ u.T @ y
 </pre></div>
 <p>

@@ -850,7 +850,7 @@ We will look at a system of \( L = 40 \) spins with a coupling constant of \( J
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">sklearn.linear_model</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">skl</span>
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">tqdm</span>
-sns.set(color_codes=<span style="color: #658b00">True</span>)
+sns.set(color_codes=<span style="color: #8B008B; font-weight: bold">True</span>)
 cmap_args=<span style="color: #658b00">dict</span>(vmin=-<span style="color: #B452CD">1.</span>, vmax=<span style="color: #B452CD">1.</span>, cmap=<span style="color: #CD5555">&#39;seismic&#39;</span>)

 L = <span style="color: #B452CD">40</span>
@@ -1133,7 +1133,7 @@ other models for all values of \( \lambda \).
 <h2 id="___sec19">Friday September 18: Intro to Logistic Regression </h2>

 <p>
-<a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureSeptember18.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture</a> and <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesSeptember18.pdf" target="_blank">link to handwritten notes</a>.
+<a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h20/forelesningsvideoer/LectureSeptember18.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture</a> and <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesSeptember18.pdf" target="_blank">link to handwritten notes</a>.

 <p>
 <!-- !split  -->
@@ -1316,7 +1316,7 @@ DATA_ID = <span style="color: #CD5555">&quot;DataFiles/&quot;</span>
    <span style="color: #8B008B; font-weight: bold">return</span> os.path.join(DATA_ID, dat_id)

 <span style="color: #8B008B; font-weight: bold">def</span> <span style="color: #008b45">save_fig</span>(fig_id):
-    plt.savefig(image_path(fig_id) + <span style="color: #CD5555">&quot;.png&quot;</span>, format=<span style="color: #CD5555">&#39;png&#39;</span>)
+    plt.savefig(image_path(fig_id) + <span style="color: #CD5555">&quot;.png&quot;</span>, <span style="color: #658b00">format</span>=<span style="color: #CD5555">&#39;png&#39;</span>)

 infile = <span style="color: #658b00">open</span>(data_path(<span style="color: #CD5555">&quot;chddata.csv&quot;</span>),<span style="color: #CD5555">&#39;r&#39;</span>)

@@ -1427,7 +1427,7 @@ ax = fig.add_subplot(<span style="color: #B452CD">111</span>)
 ax.plot(z, sigma)
 ax.set_ylim([-<span style="color: #B452CD">0.1</span>, <span style="color: #B452CD">1.1</span>])
 ax.set_xlim([-<span style="color: #B452CD">5</span>,<span style="color: #B452CD">5</span>])
-ax.grid(<span style="color: #658b00">True</span>)
+ax.grid(<span style="color: #8B008B; font-weight: bold">True</span>)
 ax.set_xlabel(<span style="color: #CD5555">&#39;z&#39;</span>)
 ax.set_title(<span style="color: #CD5555">&#39;sigmoid function&#39;</span>)

@@ -1443,7 +1443,7 @@ ax = fig.add_subplot(<span style="color: #B452CD">111</span>)
 ax.plot(z, step)
 ax.set_ylim([-<span style="color: #B452CD">0.5</span>, <span style="color: #B452CD">1.5</span>])
 ax.set_xlim([-<span style="color: #B452CD">5</span>,<span style="color: #B452CD">5</span>])
-ax.grid(<span style="color: #658b00">True</span>)
+ax.grid(<span style="color: #8B008B; font-weight: bold">True</span>)
 ax.set_xlabel(<span style="color: #CD5555">&#39;z&#39;</span>)
 ax.set_title(<span style="color: #CD5555">&#39;step function&#39;</span>)

@@ -1458,7 +1458,7 @@ ax = fig.add_subplot(<span style="color: #B452CD">111</span>)
 ax.plot(z, t)
 ax.set_ylim([-<span style="color: #B452CD">1.0</span>, <span style="color: #B452CD">1.0</span>])
 ax.set_xlim([-<span style="color: #B452CD">2</span>*mt.pi,<span style="color: #B452CD">2</span>*mt.pi])
-ax.grid(<span style="color: #658b00">True</span>)
+ax.grid(<span style="color: #8B008B; font-weight: bold">True</span>)
 ax.set_xlabel(<span style="color: #CD5555">&#39;z&#39;</span>)
 ax.set_title(<span style="color: #CD5555">&#39;tanh function&#39;</span>)

@@ -1685,12 +1685,12 @@ classification.
 cancer = load_breast_cancer()

 X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=<span style="color: #B452CD">0</span>)
-<span style="color: #8B008B; font-weight: bold">print</span>(X_train.shape)
-<span style="color: #8B008B; font-weight: bold">print</span>(X_test.shape)
+<span style="color: #658b00">print</span>(X_train.shape)
+<span style="color: #658b00">print</span>(X_test.shape)
 <span style="color: #228B22"># Logistic Regression</span>
 logreg = LogisticRegression(solver=<span style="color: #CD5555">&#39;lbfgs&#39;</span>)
 logreg.fit(X_train, y_train)
-<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&quot;Test set accuracy with Logistic Regression: {:.2f}&quot;</span>.format(logreg.score(X_test,y_test)))
+<span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;Test set accuracy with Logistic Regression: {:.2f}&quot;</span>.format(logreg.score(X_test,y_test)))
 <span style="color: #228B22">#now scale the data</span>
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.preprocessing</span> <span style="color: #8B008B; font-weight: bold">import</span> StandardScaler
 scaler = StandardScaler()
@@ -1699,7 +1699,7 @@ X_train_scaled = scaler.transform(X_train)
 X_test_scaled = scaler.transform(X_test)
 <span style="color: #228B22"># Logistic Regression</span>
 logreg.fit(X_train_scaled, y_train)
-<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&quot;Test set accuracy Logistic Regression with scaled data: {:.2f}&quot;</span>.format(logreg.score(X_test_scaled,y_test)))
+<span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;Test set accuracy Logistic Regression with scaled data: {:.2f}&quot;</span>.format(logreg.score(X_test_scaled,y_test)))
 </pre></div>
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -1744,7 +1744,7 @@ correlation_matrix = cancerpd.corr().round(<span style="color: #B452CD">1</span>
 <span style="color: #228B22"># use the heatmap function from seaborn to plot the correlation matrix</span>
 <span style="color: #228B22"># annot = True to print the values inside the square</span>
 plt.figure(figsize=(<span style="color: #B452CD">15</span>,<span style="color: #B452CD">8</span>))
-sns.heatmap(data=correlation_matrix, annot=<span style="color: #658b00">True</span>)
+sns.heatmap(data=correlation_matrix, annot=<span style="color: #8B008B; font-weight: bold">True</span>)
 plt.show()
 </pre></div>
 <p>
@@ -1803,12 +1803,12 @@ applications. This will be discussed later this semester (<a href="https://compp
 cancer = load_breast_cancer()

 X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=<span style="color: #B452CD">0</span>)
-<span style="color: #8B008B; font-weight: bold">print</span>(X_train.shape)
-<span style="color: #8B008B; font-weight: bold">print</span>(X_test.shape)
+<span style="color: #658b00">print</span>(X_train.shape)
+<span style="color: #658b00">print</span>(X_test.shape)
 <span style="color: #228B22"># Logistic Regression</span>
 logreg = LogisticRegression(solver=<span style="color: #CD5555">&#39;lbfgs&#39;</span>)
 logreg.fit(X_train, y_train)
-<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&quot;Test set accuracy with Logistic Regression: {:.2f}&quot;</span>.format(logreg.score(X_test,y_test)))
+<span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;Test set accuracy with Logistic Regression: {:.2f}&quot;</span>.format(logreg.score(X_test,y_test)))
 <span style="color: #228B22">#now scale the data</span>
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.preprocessing</span> <span style="color: #8B008B; font-weight: bold">import</span> StandardScaler
 scaler = StandardScaler()
@@ -1817,20 +1817,20 @@ X_train_scaled = scaler.transform(X_train)
 X_test_scaled = scaler.transform(X_test)
 <span style="color: #228B22"># Logistic Regression</span>
 logreg.fit(X_train_scaled, y_train)
-<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&quot;Test set accuracy Logistic Regression with scaled data: {:.2f}&quot;</span>.format(logreg.score(X_test_scaled,y_test)))
+<span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;Test set accuracy Logistic Regression with scaled data: {:.2f}&quot;</span>.format(logreg.score(X_test_scaled,y_test)))


 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.preprocessing</span> <span style="color: #8B008B; font-weight: bold">import</span> LabelEncoder
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> cross_validate
 <span style="color: #228B22">#Cross validation</span>
 accuracy = cross_validate(logreg,X_test_scaled,y_test,cv=<span style="color: #B452CD">10</span>)[<span style="color: #CD5555">&#39;test_score&#39;</span>]
-<span style="color: #8B008B; font-weight: bold">print</span>(accuracy)
-<span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&quot;Test set accuracy with Logistic Regression  and scaled data: {:.2f}&quot;</span>.format(logreg.score(X_test_scaled,y_test)))
+<span style="color: #658b00">print</span>(accuracy)
+<span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;Test set accuracy with Logistic Regression  and scaled data: {:.2f}&quot;</span>.format(logreg.score(X_test_scaled,y_test)))


 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">scikitplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">skplt</span>
 y_pred = logreg.predict(X_test_scaled)
-skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=<span style="color: #658b00">True</span>)
+skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=<span style="color: #8B008B; font-weight: bold">True</span>)
 plt.show()
 y_probas = logreg.predict_proba(X_test_scaled)
 skplt.metrics.plot_roc(y_test, y_probas)
@@ -149,7 +149,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Sep 18, 2020</h4></center> <!-- date -->
+<center><h4>Sep 19, 2020</h4></center> <!-- date -->
 <br>
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -433,21 +433,21 @@ polydegree <span style="color: #666666">=</span> np<span style="color: #666666">
 x_train, x_test, y_train, y_test <span style="color: #666666">=</span> train_test_split(x, y, test_size<span style="color: #666666">=0.2</span>)

 <span style="color: #008000; font-weight: bold">for</span> degree <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(maxdegree):
-    model <span style="color: #666666">=</span> make_pipeline(PolynomialFeatures(degree<span style="color: #666666">=</span>degree), LinearRegression(fit_intercept<span style="color: #666666">=</span><span style="color: #008000">False</span>))
+    model <span style="color: #666666">=</span> make_pipeline(PolynomialFeatures(degree<span style="color: #666666">=</span>degree), LinearRegression(fit_intercept<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">False</span>))
    y_pred <span style="color: #666666">=</span> np<span style="color: #666666">.</span>empty((y_test<span style="color: #666666">.</span>shape[<span style="color: #666666">0</span>], n_boostraps))
    <span style="color: #008000; font-weight: bold">for</span> i <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(n_boostraps):
        x_, y_ <span style="color: #666666">=</span> resample(x_train, y_train)
        y_pred[:, i] <span style="color: #666666">=</span> model<span style="color: #666666">.</span>fit(x_, y_)<span style="color: #666666">.</span>predict(x_test)<span style="color: #666666">.</span>ravel()

    polydegree[degree] <span style="color: #666666">=</span> degree
-    error[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>mean((y_test <span style="color: #666666">-</span> y_pred)<span style="color: #666666">**2</span>, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000">True</span>) )
-    bias[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( (y_test <span style="color: #666666">-</span> np<span style="color: #666666">.</span>mean(y_pred, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000">True</span>))<span style="color: #666666">**2</span> )
-    variance[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>var(y_pred, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000">True</span>) )
-    <span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&#39;Polynomial degree:&#39;</span>, degree)
-    <span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&#39;Error:&#39;</span>, error[degree])
-    <span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&#39;Bias^2:&#39;</span>, bias[degree])
-    <span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&#39;Var:&#39;</span>, variance[degree])
-    <span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&#39;{} &gt;= {} + {} = {}&#39;</span><span style="color: #666666">.</span>format(error[degree], bias[degree], variance[degree], bias[degree]<span style="color: #666666">+</span>variance[degree]))
+    error[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>mean((y_test <span style="color: #666666">-</span> y_pred)<span style="color: #666666">**2</span>, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>) )
+    bias[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( (y_test <span style="color: #666666">-</span> np<span style="color: #666666">.</span>mean(y_pred, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>))<span style="color: #666666">**2</span> )
+    variance[degree] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>var(y_pred, axis<span style="color: #666666">=1</span>, keepdims<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>) )
+    <span style="color: #008000">print</span>(<span style="color: #BA2121">&#39;Polynomial degree:&#39;</span>, degree)
+    <span style="color: #008000">print</span>(<span style="color: #BA2121">&#39;Error:&#39;</span>, error[degree])
+    <span style="color: #008000">print</span>(<span style="color: #BA2121">&#39;Bias^2:&#39;</span>, bias[degree])
+    <span style="color: #008000">print</span>(<span style="color: #BA2121">&#39;Var:&#39;</span>, variance[degree])
+    <span style="color: #008000">print</span>(<span style="color: #BA2121">&#39;</span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121"> &gt;= </span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121"> + </span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121"> = </span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121">&#39;</span><span style="color: #666666">.</span>format(error[degree], bias[degree], variance[degree], bias[degree]<span style="color: #666666">+</span>variance[degree]))

 plt<span style="color: #666666">.</span>plot(polydegree, error, label<span style="color: #666666">=</span><span style="color: #BA2121">&#39;Error&#39;</span>)
 plt<span style="color: #666666">.</span>plot(polydegree, bias, label<span style="color: #666666">=</span><span style="color: #BA2121">&#39;bias&#39;</span>)
@@ -484,7 +484,7 @@ plt<span style="color: #666666">.</span>show()
 <span style="color: #BA2121; font-style: italic">training data.</span>
 <span style="color: #BA2121; font-style: italic">&quot;&quot;&quot;</span>

-<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #19177C">__doc__</span>)
+<span style="color: #008000">print</span>(<span style="color: #19177C">__doc__</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">matplotlib.pyplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
@@ -511,7 +511,7 @@ plt<span style="color: #666666">.</span>figure(figsize<span style="color: #66666
    plt<span style="color: #666666">.</span>setp(ax, xticks<span style="color: #666666">=</span>(), yticks<span style="color: #666666">=</span>())

    polynomial_features <span style="color: #666666">=</span> PolynomialFeatures(degree<span style="color: #666666">=</span>degrees[i],
-                                             include_bias<span style="color: #666666">=</span><span style="color: #008000">False</span>)
+                                             include_bias<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">False</span>)
    linear_regression <span style="color: #666666">=</span> LinearRegression()
    pipeline <span style="color: #666666">=</span> Pipeline([(<span style="color: #BA2121">&quot;polynomial_features&quot;</span>, polynomial_features),
                         (<span style="color: #BA2121">&quot;linear_regression&quot;</span>, linear_regression)])
@@ -530,7 +530,7 @@ plt<span style="color: #666666">.</span>figure(figsize<span style="color: #66666
    plt<span style="color: #666666">.</span>xlim((<span style="color: #666666">0</span>, <span style="color: #666666">1</span>))
    plt<span style="color: #666666">.</span>ylim((<span style="color: #666666">-2</span>, <span style="color: #666666">2</span>))
    plt<span style="color: #666666">.</span>legend(loc<span style="color: #666666">=</span><span style="color: #BA2121">&quot;best&quot;</span>)
-    plt<span style="color: #666666">.</span>title(<span style="color: #BA2121">&quot;Degree {}</span><span style="color: #BB6622; font-weight: bold">\n</span><span style="color: #BA2121">MSE = {:.2e}(+/- {:.2e})&quot;</span><span style="color: #666666">.</span>format(
+    plt<span style="color: #666666">.</span>title(<span style="color: #BA2121">&quot;Degree </span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BB6622; font-weight: bold">\n</span><span style="color: #BA2121">MSE = </span><span style="color: #BB6688; font-weight: bold">{:.2e}</span><span style="color: #BA2121">(+/- </span><span style="color: #BB6688; font-weight: bold">{:.2e}</span><span style="color: #BA2121">)&quot;</span><span style="color: #666666">.</span>format(
        degrees[i], <span style="color: #666666">-</span>scores<span style="color: #666666">.</span>mean(), scores<span style="color: #666666">.</span>std()))
 plt<span style="color: #666666">.</span>show()
 </pre></div>
@@ -613,7 +613,7 @@ with their respective energies.
 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">scipy.linalg</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">scl</span>
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> train_test_split
 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">tqdm</span>
-sns<span style="color: #666666">.</span>set(color_codes<span style="color: #666666">=</span><span style="color: #008000">True</span>)
+sns<span style="color: #666666">.</span>set(color_codes<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>)
 cmap_args<span style="color: #666666">=</span><span style="color: #008000">dict</span>(vmin<span style="color: #666666">=-1.</span>, vmax<span style="color: #666666">=1.</span>, cmap<span style="color: #666666">=</span><span style="color: #BA2121">&#39;seismic&#39;</span>)

 L <span style="color: #666666">=</span> <span style="color: #666666">40</span>
@@ -729,7 +729,7 @@ X_test_own <span style="color: #666666">=</span> np<span style="color: #666666">

 <!-- 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">def</span> <span style="color: #0000FF">ols_inv</span>(x: np<span style="color: #666666">.</span>ndarray, y: np<span style="color: #666666">.</span>ndarray) <span style="color: #666666">-&gt;</span> np<span style="color: #666666">.</span>ndarray:
-    <span style="color: #008000; font-weight: bold">return</span> scl<span style="color: #666666">.</span>inv(x<span style="color: #666666">.</span>T @ x) @ (x<span style="color: #666666">.</span>T @ y)
+    <span style="color: #008000; font-weight: bold">return</span> scl<span style="color: #666666">.</span>inv(x<span style="color: #666666">.</span>T <span style="color: #666666">@</span> x) <span style="color: #666666">@</span> (x<span style="color: #666666">.</span>T <span style="color: #666666">@</span> y)
 beta <span style="color: #666666">=</span> ols_inv(X_train_own, y_train)
 </pre></div>
 <p>
@@ -779,7 +779,7 @@ linear system as an equation would reduce this down to
 <!-- 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">def</span> <span style="color: #0000FF">ols_svd</span>(x: np<span style="color: #666666">.</span>ndarray, y: np<span style="color: #666666">.</span>ndarray) <span style="color: #666666">-&gt;</span> np<span style="color: #666666">.</span>ndarray:
    u, s, v <span style="color: #666666">=</span> scl<span style="color: #666666">.</span>svd(x)
-    <span style="color: #008000; font-weight: bold">return</span> v<span style="color: #666666">.</span>T @ scl<span style="color: #666666">.</span>pinv(scl<span style="color: #666666">.</span>diagsvd(s, u<span style="color: #666666">.</span>shape[<span style="color: #666666">0</span>], v<span style="color: #666666">.</span>shape[<span style="color: #666666">0</span>])) @ u<span style="color: #666666">.</span>T @ y
+    <span style="color: #008000; font-weight: bold">return</span> v<span style="color: #666666">.</span>T <span style="color: #666666">@</span> scl<span style="color: #666666">.</span>pinv(scl<span style="color: #666666">.</span>diagsvd(s, u<span style="color: #666666">.</span>shape[<span style="color: #666666">0</span>], v<span style="color: #666666">.</span>shape[<span style="color: #666666">0</span>])) <span style="color: #666666">@</span> u<span style="color: #666666">.</span>T <span style="color: #666666">@</span> y
 </pre></div>
 <p>

@@ -855,7 +855,7 @@ We will look at a system of \( L = 40 \) spins with a coupling constant of \( J
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> train_test_split
 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">sklearn.linear_model</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">skl</span>
 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">tqdm</span>
-sns<span style="color: #666666">.</span>set(color_codes<span style="color: #666666">=</span><span style="color: #008000">True</span>)
+sns<span style="color: #666666">.</span>set(color_codes<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>)
 cmap_args<span style="color: #666666">=</span><span style="color: #008000">dict</span>(vmin<span style="color: #666666">=-1.</span>, vmax<span style="color: #666666">=1.</span>, cmap<span style="color: #666666">=</span><span style="color: #BA2121">&#39;seismic&#39;</span>)

 L <span style="color: #666666">=</span> <span style="color: #666666">40</span>
@@ -1109,7 +1109,7 @@ colors <span style="color: #666666">=</span> {
        lambdas,
        train_errors[key],
        colors[key],
-        label<span style="color: #666666">=</span><span style="color: #BA2121">&quot;Train {0}&quot;</span><span style="color: #666666">.</span>format(key),
+        label<span style="color: #666666">=</span><span style="color: #BA2121">&quot;Train </span><span style="color: #BB6688; font-weight: bold">{0}</span><span style="color: #BA2121">&quot;</span><span style="color: #666666">.</span>format(key),
        linewidth<span style="color: #666666">=4.0</span>
    )

@@ -1118,7 +1118,7 @@ colors <span style="color: #666666">=</span> {
        lambdas,
        test_errors[key],
        colors[key] <span style="color: #666666">+</span> <span style="color: #BA2121">&quot;--&quot;</span>,
-        label<span style="color: #666666">=</span><span style="color: #BA2121">&quot;Test {0}&quot;</span><span style="color: #666666">.</span>format(key),
+        label<span style="color: #666666">=</span><span style="color: #BA2121">&quot;Test </span><span style="color: #BB6688; font-weight: bold">{0}</span><span style="color: #BA2121">&quot;</span><span style="color: #666666">.</span>format(key),
        linewidth<span style="color: #666666">=4.0</span>
    )
 plt<span style="color: #666666">.</span>legend(loc<span style="color: #666666">=</span><span style="color: #BA2121">&quot;best&quot;</span>, fontsize<span style="color: #666666">=18</span>)
@@ -1138,7 +1138,7 @@ other models for all values of \( \lambda \).
 <h2 id="___sec19">Friday September 18: Intro to Logistic Regression </h2>

 <p>
-<a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureSeptember18.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture</a> and <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesSeptember18.pdf" target="_blank">link to handwritten notes</a>.
+<a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h20/forelesningsvideoer/LectureSeptember18.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture</a> and <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesSeptember18.pdf" target="_blank">link to handwritten notes</a>.

 <p>
 <!-- !split  -->
@@ -1321,7 +1321,7 @@ DATA_ID <span style="color: #666666">=</span> <span style="color: #BA2121">&quot
    <span style="color: #008000; font-weight: bold">return</span> os<span style="color: #666666">.</span>path<span style="color: #666666">.</span>join(DATA_ID, dat_id)

 <span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">save_fig</span>(fig_id):
-    plt<span style="color: #666666">.</span>savefig(image_path(fig_id) <span style="color: #666666">+</span> <span style="color: #BA2121">&quot;.png&quot;</span>, format<span style="color: #666666">=</span><span style="color: #BA2121">&#39;png&#39;</span>)
+    plt<span style="color: #666666">.</span>savefig(image_path(fig_id) <span style="color: #666666">+</span> <span style="color: #BA2121">&quot;.png&quot;</span>, <span style="color: #008000">format</span><span style="color: #666666">=</span><span style="color: #BA2121">&#39;png&#39;</span>)

 infile <span style="color: #666666">=</span> <span style="color: #008000">open</span>(data_path(<span style="color: #BA2121">&quot;chddata.csv&quot;</span>),<span style="color: #BA2121">&#39;r&#39;</span>)

@@ -1432,7 +1432,7 @@ ax <span style="color: #666666">=</span> fig<span style="color: #666666">.</span
 ax<span style="color: #666666">.</span>plot(z, sigma)
 ax<span style="color: #666666">.</span>set_ylim([<span style="color: #666666">-0.1</span>, <span style="color: #666666">1.1</span>])
 ax<span style="color: #666666">.</span>set_xlim([<span style="color: #666666">-5</span>,<span style="color: #666666">5</span>])
-ax<span style="color: #666666">.</span>grid(<span style="color: #008000">True</span>)
+ax<span style="color: #666666">.</span>grid(<span style="color: #008000; font-weight: bold">True</span>)
 ax<span style="color: #666666">.</span>set_xlabel(<span style="color: #BA2121">&#39;z&#39;</span>)
 ax<span style="color: #666666">.</span>set_title(<span style="color: #BA2121">&#39;sigmoid function&#39;</span>)

@@ -1448,7 +1448,7 @@ ax <span style="color: #666666">=</span> fig<span style="color: #666666">.</span
 ax<span style="color: #666666">.</span>plot(z, step)
 ax<span style="color: #666666">.</span>set_ylim([<span style="color: #666666">-0.5</span>, <span style="color: #666666">1.5</span>])
 ax<span style="color: #666666">.</span>set_xlim([<span style="color: #666666">-5</span>,<span style="color: #666666">5</span>])
-ax<span style="color: #666666">.</span>grid(<span style="color: #008000">True</span>)
+ax<span style="color: #666666">.</span>grid(<span style="color: #008000; font-weight: bold">True</span>)
 ax<span style="color: #666666">.</span>set_xlabel(<span style="color: #BA2121">&#39;z&#39;</span>)
 ax<span style="color: #666666">.</span>set_title(<span style="color: #BA2121">&#39;step function&#39;</span>)

@@ -1463,7 +1463,7 @@ ax <span style="color: #666666">=</span> fig<span style="color: #666666">.</span
 ax<span style="color: #666666">.</span>plot(z, t)
 ax<span style="color: #666666">.</span>set_ylim([<span style="color: #666666">-1.0</span>, <span style="color: #666666">1.0</span>])
 ax<span style="color: #666666">.</span>set_xlim([<span style="color: #666666">-2*</span>mt<span style="color: #666666">.</span>pi,<span style="color: #666666">2*</span>mt<span style="color: #666666">.</span>pi])
-ax<span style="color: #666666">.</span>grid(<span style="color: #008000">True</span>)
+ax<span style="color: #666666">.</span>grid(<span style="color: #008000; font-weight: bold">True</span>)
 ax<span style="color: #666666">.</span>set_xlabel(<span style="color: #BA2121">&#39;z&#39;</span>)
 ax<span style="color: #666666">.</span>set_title(<span style="color: #BA2121">&#39;tanh function&#39;</span>)

@@ -1690,12 +1690,12 @@ classification.
 cancer <span style="color: #666666">=</span> load_breast_cancer()

 X_train, X_test, y_train, y_test <span style="color: #666666">=</span> train_test_split(cancer<span style="color: #666666">.</span>data,cancer<span style="color: #666666">.</span>target,random_state<span style="color: #666666">=0</span>)
-<span style="color: #008000; font-weight: bold">print</span>(X_train<span style="color: #666666">.</span>shape)
-<span style="color: #008000; font-weight: bold">print</span>(X_test<span style="color: #666666">.</span>shape)
+<span style="color: #008000">print</span>(X_train<span style="color: #666666">.</span>shape)
+<span style="color: #008000">print</span>(X_test<span style="color: #666666">.</span>shape)
 <span style="color: #408080; font-style: italic"># Logistic Regression</span>
 logreg <span style="color: #666666">=</span> LogisticRegression(solver<span style="color: #666666">=</span><span style="color: #BA2121">&#39;lbfgs&#39;</span>)
 logreg<span style="color: #666666">.</span>fit(X_train, y_train)
-<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&quot;Test set accuracy with Logistic Regression: {:.2f}&quot;</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test,y_test)))
+<span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;Test set accuracy with Logistic Regression: </span><span style="color: #BB6688; font-weight: bold">{:.2f}</span><span style="color: #BA2121">&quot;</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test,y_test)))
 <span style="color: #408080; font-style: italic">#now scale the data</span>
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.preprocessing</span> <span style="color: #008000; font-weight: bold">import</span> StandardScaler
 scaler <span style="color: #666666">=</span> StandardScaler()
@@ -1704,7 +1704,7 @@ X_train_scaled <span style="color: #666666">=</span> scaler<span style="color: #
 X_test_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_test)
 <span style="color: #408080; font-style: italic"># Logistic Regression</span>
 logreg<span style="color: #666666">.</span>fit(X_train_scaled, y_train)
-<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&quot;Test set accuracy Logistic Regression with scaled data: {:.2f}&quot;</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test_scaled,y_test)))
+<span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;Test set accuracy Logistic Regression with scaled data: </span><span style="color: #BB6688; font-weight: bold">{:.2f}</span><span style="color: #BA2121">&quot;</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test_scaled,y_test)))
 </pre></div>
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -1749,7 +1749,7 @@ correlation_matrix <span style="color: #666666">=</span> cancerpd<span style="co
 <span style="color: #408080; font-style: italic"># use the heatmap function from seaborn to plot the correlation matrix</span>
 <span style="color: #408080; font-style: italic"># annot = True to print the values inside the square</span>
 plt<span style="color: #666666">.</span>figure(figsize<span style="color: #666666">=</span>(<span style="color: #666666">15</span>,<span style="color: #666666">8</span>))
-sns<span style="color: #666666">.</span>heatmap(data<span style="color: #666666">=</span>correlation_matrix, annot<span style="color: #666666">=</span><span style="color: #008000">True</span>)
+sns<span style="color: #666666">.</span>heatmap(data<span style="color: #666666">=</span>correlation_matrix, annot<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>)
 plt<span style="color: #666666">.</span>show()
 </pre></div>
 <p>
@@ -1808,12 +1808,12 @@ applications. This will be discussed later this semester (<a href="https://compp
 cancer <span style="color: #666666">=</span> load_breast_cancer()

 X_train, X_test, y_train, y_test <span style="color: #666666">=</span> train_test_split(cancer<span style="color: #666666">.</span>data,cancer<span style="color: #666666">.</span>target,random_state<span style="color: #666666">=0</span>)
-<span style="color: #008000; font-weight: bold">print</span>(X_train<span style="color: #666666">.</span>shape)
-<span style="color: #008000; font-weight: bold">print</span>(X_test<span style="color: #666666">.</span>shape)
+<span style="color: #008000">print</span>(X_train<span style="color: #666666">.</span>shape)
+<span style="color: #008000">print</span>(X_test<span style="color: #666666">.</span>shape)
 <span style="color: #408080; font-style: italic"># Logistic Regression</span>
 logreg <span style="color: #666666">=</span> LogisticRegression(solver<span style="color: #666666">=</span><span style="color: #BA2121">&#39;lbfgs&#39;</span>)
 logreg<span style="color: #666666">.</span>fit(X_train, y_train)
-<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&quot;Test set accuracy with Logistic Regression: {:.2f}&quot;</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test,y_test)))
+<span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;Test set accuracy with Logistic Regression: </span><span style="color: #BB6688; font-weight: bold">{:.2f}</span><span style="color: #BA2121">&quot;</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test,y_test)))
 <span style="color: #408080; font-style: italic">#now scale the data</span>
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.preprocessing</span> <span style="color: #008000; font-weight: bold">import</span> StandardScaler
 scaler <span style="color: #666666">=</span> StandardScaler()
@@ -1822,20 +1822,20 @@ X_train_scaled <span style="color: #666666">=</span> scaler<span style="color: #
 X_test_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_test)
 <span style="color: #408080; font-style: italic"># Logistic Regression</span>
 logreg<span style="color: #666666">.</span>fit(X_train_scaled, y_train)
-<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&quot;Test set accuracy Logistic Regression with scaled data: {:.2f}&quot;</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test_scaled,y_test)))
+<span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;Test set accuracy Logistic Regression with scaled data: </span><span style="color: #BB6688; font-weight: bold">{:.2f}</span><span style="color: #BA2121">&quot;</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test_scaled,y_test)))


 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.preprocessing</span> <span style="color: #008000; font-weight: bold">import</span> LabelEncoder
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> cross_validate
 <span style="color: #408080; font-style: italic">#Cross validation</span>
 accuracy <span style="color: #666666">=</span> cross_validate(logreg,X_test_scaled,y_test,cv<span style="color: #666666">=10</span>)[<span style="color: #BA2121">&#39;test_score&#39;</span>]
-<span style="color: #008000; font-weight: bold">print</span>(accuracy)
-<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&quot;Test set accuracy with Logistic Regression  and scaled data: {:.2f}&quot;</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test_scaled,y_test)))
+<span style="color: #008000">print</span>(accuracy)
+<span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;Test set accuracy with Logistic Regression  and scaled data: </span><span style="color: #BB6688; font-weight: bold">{:.2f}</span><span style="color: #BA2121">&quot;</span><span style="color: #666666">.</span>format(logreg<span style="color: #666666">.</span>score(X_test_scaled,y_test)))


 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">scikitplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">skplt</span>
 y_pred <span style="color: #666666">=</span> logreg<span style="color: #666666">.</span>predict(X_test_scaled)
-skplt<span style="color: #666666">.</span>metrics<span style="color: #666666">.</span>plot_confusion_matrix(y_test, y_pred, normalize<span style="color: #666666">=</span><span style="color: #008000">True</span>)
+skplt<span style="color: #666666">.</span>metrics<span style="color: #666666">.</span>plot_confusion_matrix(y_test, y_pred, normalize<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>)
 plt<span style="color: #666666">.</span>show()
 y_probas <span style="color: #666666">=</span> logreg<span style="color: #666666">.</span>predict_proba(X_test_scaled)
 skplt<span style="color: #666666">.</span>metrics<span style="color: #666666">.</span>plot_roc(y_test, y_probas)
@@ -10,7 +10,7 @@
    "<!-- Author: -->  \n",
    "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
    "\n",
-    "Date: **Sep 18, 2020**\n",
+    "Date: **Sep 19, 2020**\n",
    "\n",
    "Copyright 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
    "\n",
@@ -1436,7 +1436,7 @@
    "\n",
    "## Friday September 18: Intro to Logistic Regression\n",
    "\n",
-    "[Video of Lecture](https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureSeptember18.mp4?vrtx=view-as-webpage) and [link to handwritten notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesSeptember18.pdf).\n",
+    "[Video of Lecture](https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h20/forelesningsvideoer/LectureSeptember18.mp4?vrtx=view-as-webpage) and [link to handwritten notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesSeptember18.pdf).\n",
    "\n",
    "\n",
    "\n",
@@ -2369,5 +2369,5 @@
 ],
 "metadata": {},
 "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }