diff --git a/doc/pub/LogReg/html/._LogReg-bs000.html b/doc/pub/LogReg/html/._LogReg-bs000.html
index bbb583e11..d4f17bb42 100644
--- a/doc/pub/LogReg/html/._LogReg-bs000.html
+++ b/doc/pub/LogReg/html/._LogReg-bs000.html
@@ -49,7 +49,8 @@ Automatically generated HTML file from DocOnce source
None,
'___sec4'),
('Maximum likelihood', 2, None, '___sec5'),
- ('Minimizing the cross entropy', 2, None, '___sec6')]}
+ ('Minimizing the cross entropy', 2, None, '___sec6'),
+ ('A _scikit-learn_ example', 2, None, '___sec7')]}
end of tocinfo -->
@@ -94,6 +95,7 @@ MathJax.Hub.Config({
The cross-entropy as a cost function for logistic regression Maximum likelihood Minimizing the cross entropy A scikit-learn example [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
-
Sep 17, 2018 Sep 20, 2018
@@ -143,6 +145,7 @@ MathJax.Hub.Config({
7 8 9 10 » The cross-entropy as a cost function for logistic regression Maximum likelihood Minimizing the cross entropy A scikit-learn example 7 8 9 10 » The cross-entropy as a cost function for logistic regression Maximum likelihood Minimizing the cross entropy A scikit-learn example 7 8 9 10 » The cross-entropy as a cost function for logistic regression Maximum likelihood Minimizing the cross entropy A scikit-learn example 7 8 9 10 » The cross-entropy as a cost function for logistic regression Maximum likelihood Minimizing the cross entropy A scikit-learn example 7 8 9 10 » The cross-entropy as a cost function for logistic regression Maximum likelihood Minimizing the cross entropy A scikit-learn example 7 8 9 10 » The cross-entropy as a cost function for logistic regression Maximum likelihood Minimizing the cross entropy A scikit-learn example 7 8 9 10 » The cross-entropy as a cost function for logistic regression Maximum likelihood Minimizing the cross entropy A scikit-learn example 7 8 9 10 » The cross-entropy as a cost function for logistic regression Maximum likelihood Minimizing the cross entropy A scikit-learn example
diff --git a/doc/pub/LogReg/html/._LogReg-bs009.html b/doc/pub/LogReg/html/._LogReg-bs009.html
new file mode 100644
index 000000000..f81543409
--- /dev/null
+++ b/doc/pub/LogReg/html/._LogReg-bs009.html
@@ -0,0 +1,177 @@
+
+
+
+Data Analysis and Machine Learning: Logistic Regression
+
+
+
+
+
+
+
+
+
+
A scikit-learn example
+
+
+
+
+
import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+iris = datasets. load_iris()
+list (iris. keys())
+['data' , 'target_names' , 'feature_names' , 'target' , 'DESCR' ]
+X = iris["data" ][:, 3 :] # petal width
+y = (iris["target" ] == 2 ). astype(np. int) # 1 if Iris-Virginica, else 0
+
+from sklearn.linear_model import LogisticRegression
+log_reg = LogisticRegression()
+log_reg. fit(X, y)
+
+X_new = np. linspace(0 , 3 , 1000 ). reshape(-1 , 1 )
+y_proba = log_reg. predict_proba(X_new)
+plt. plot(X_new, y_proba[:, 1 ], "g-" , label= "Iris-Virginica" )
+plt. plot(X_new, y_proba[:, 0 ], "b--" , label= "Not Iris-Virginica" )
+plt. show()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/doc/pub/LogReg/html/LogReg-bs.html b/doc/pub/LogReg/html/LogReg-bs.html
index bbb583e11..d4f17bb42 100644
--- a/doc/pub/LogReg/html/LogReg-bs.html
+++ b/doc/pub/LogReg/html/LogReg-bs.html
@@ -49,7 +49,8 @@ Automatically generated HTML file from DocOnce source
None,
'___sec4'),
('Maximum likelihood', 2, None, '___sec5'),
- ('Minimizing the cross entropy', 2, None, '___sec6')]}
+ ('Minimizing the cross entropy', 2, None, '___sec6'),
+ ('A _scikit-learn_ example', 2, None, '___sec7')]}
end of tocinfo -->
@@ -94,6 +95,7 @@ MathJax.Hub.Config({
The cross-entropy as a cost function for logistic regression Maximum likelihood Minimizing the cross entropy A scikit-learn example [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
-
Sep 17, 2018 Sep 20, 2018
@@ -143,6 +145,7 @@ MathJax.Hub.Config({
7 8 9 10 » [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
Sep 17, 2018 Sep 20, 2018
@@ -356,6 +356,34 @@ Here we need gradient descent methods!
+
+A scikit-learn example
+
+
+
+
+
import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+iris = datasets.load_iris()
+list (iris.keys())
+['data' , 'target_names' , 'feature_names' , 'target' , 'DESCR' ]
+X = iris["data" ][:, 3 :] # petal width
+y = (iris["target" ] == 2 ).astype(np.int) # 1 if Iris-Virginica, else 0
+
+from sklearn.linear_model import LogisticRegression
+log_reg = LogisticRegression()
+log_reg.fit(X, y)
+
+X_new = np.linspace(0 , 3 , 1000 ).reshape(-1 , 1 )
+y_proba = log_reg.predict_proba(X_new)
+plt.plot(X_new, y_proba[:, 1 ], "g-" , label="Iris-Virginica" )
+plt.plot(X_new, y_proba[:, 0 ], "b--" , label="Not Iris-Virginica" )
+plt.show()
+
+
+
diff --git a/doc/pub/LogReg/html/LogReg-solarized.html b/doc/pub/LogReg/html/LogReg-solarized.html
index ff268fe82..e482c3596 100644
--- a/doc/pub/LogReg/html/LogReg-solarized.html
+++ b/doc/pub/LogReg/html/LogReg-solarized.html
@@ -43,7 +43,8 @@ div { text-align: justify; text-justify: inter-word; }
None,
'___sec4'),
('Maximum likelihood', 2, None, '___sec5'),
- ('Minimizing the cross entropy', 2, None, '___sec6')]}
+ ('Minimizing the cross entropy', 2, None, '___sec6'),
+ ('A _scikit-learn_ example', 2, None, '___sec7')]}
end of tocinfo -->
@@ -85,7 +86,7 @@ MathJax.Hub.Config({
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
-
Sep 17, 2018 Sep 20, 2018
@@ -266,6 +267,35 @@ f(z)[1-f(z)] \). This equation defines a transcendental equation for
be written in a closed form.
Here we need gradient descent methods!
+
+
A scikit-learn example
+
+
+
+
+
import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+iris = datasets.load_iris()
+list (iris.keys())
+['data' , 'target_names' , 'feature_names' , 'target' , 'DESCR' ]
+X = iris["data" ][:, 3 :] # petal width
+y = (iris["target" ] == 2 ).astype(np.int) # 1 if Iris-Virginica, else 0
+
+from sklearn.linear_model import LogisticRegression
+log_reg = LogisticRegression()
+log_reg.fit(X, y)
+
+X_new = np.linspace(0 , 3 , 1000 ).reshape(-1 , 1 )
+y_proba = log_reg.predict_proba(X_new)
+plt.plot(X_new, y_proba[:, 1 ], "g-" , label="Iris-Virginica" )
+plt.plot(X_new, y_proba[:, 0 ], "b--" , label="Not Iris-Virginica" )
+plt.show()
+
+
diff --git a/doc/pub/LogReg/html/LogReg.html b/doc/pub/LogReg/html/LogReg.html
index e63170fc4..889e00fb5 100644
--- a/doc/pub/LogReg/html/LogReg.html
+++ b/doc/pub/LogReg/html/LogReg.html
@@ -48,7 +48,8 @@ div { text-align: justify; text-justify: inter-word; }
None,
'___sec4'),
('Maximum likelihood', 2, None, '___sec5'),
- ('Minimizing the cross entropy', 2, None, '___sec6')]}
+ ('Minimizing the cross entropy', 2, None, '___sec6'),
+ ('A _scikit-learn_ example', 2, None, '___sec7')]}
end of tocinfo -->
@@ -90,7 +91,7 @@ MathJax.Hub.Config({
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
-
Sep 17, 2018 Sep 20, 2018
@@ -271,6 +272,35 @@ f(z)[1-f(z)] \). This equation defines a transcendental equation for
be written in a closed form.
Here we need gradient descent methods!
+
+
A scikit-learn example
+
+
+
+
+
import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+iris = datasets. load_iris()
+list (iris. keys())
+['data' , 'target_names' , 'feature_names' , 'target' , 'DESCR' ]
+X = iris["data" ][:, 3 :] # petal width
+y = (iris["target" ] == 2 ). astype(np. int) # 1 if Iris-Virginica, else 0
+
+from sklearn.linear_model import LogisticRegression
+log_reg = LogisticRegression()
+log_reg. fit(X, y)
+
+X_new = np. linspace(0 , 3 , 1000 ). reshape(-1 , 1 )
+y_proba = log_reg. predict_proba(X_new)
+plt. plot(X_new, y_proba[:, 1 ], "g-" , label= "Iris-Virginica" )
+plt. plot(X_new, y_proba[:, 0 ], "b--" , label= "Not Iris-Virginica" )
+plt. show()
+
+
diff --git a/doc/pub/LogReg/ipynb/LogReg.ipynb b/doc/pub/LogReg/ipynb/LogReg.ipynb
index 4c9b78863..90c236b5c 100644
--- a/doc/pub/LogReg/ipynb/LogReg.ipynb
+++ b/doc/pub/LogReg/ipynb/LogReg.ipynb
@@ -10,7 +10,7 @@
" \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 17, 2018**\n",
+ "Date: **Sep 20, 2018**\n",
"\n",
"Copyright 1999-2018, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
@@ -285,7 +285,40 @@
"f(z)[1-f(z)]$. This equation defines a transcendental equation for\n",
"$\\mathbf{w}$, the solution of which, unlike linear regression, cannot\n",
"be written in a closed form. \n",
- "Here we need gradient descent methods!"
+ "Here we need gradient descent methods!\n",
+ "\n",
+ "\n",
+ "## A **scikit-learn** example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "from sklearn import datasets\n",
+ "iris = datasets.load_iris()\n",
+ "list(iris.keys())\n",
+ "['data', 'target_names', 'feature_names', 'target', 'DESCR']\n",
+ "X = iris[\"data\"][:, 3:] # petal width\n",
+ "y = (iris[\"target\"] == 2).astype(np.int) # 1 if Iris-Virginica, else 0\n",
+ "\n",
+ "from sklearn.linear_model import LogisticRegression\n",
+ "log_reg = LogisticRegression()\n",
+ "log_reg.fit(X, y)\n",
+ "\n",
+ "X_new = np.linspace(0, 3, 1000).reshape(-1, 1)\n",
+ "y_proba = log_reg.predict_proba(X_new)\n",
+ "plt.plot(X_new, y_proba[:, 1], \"g-\", label=\"Iris-Virginica\")\n",
+ "plt.plot(X_new, y_proba[:, 0], \"b--\", label=\"Not Iris-Virginica\")\n",
+ "plt.show()"
]
}
],
diff --git a/doc/pub/LogReg/ipynb/ipynb-LogReg-src.tar.gz b/doc/pub/LogReg/ipynb/ipynb-LogReg-src.tar.gz
index 95a287a49..901d9ce8b 100644
Binary files a/doc/pub/LogReg/ipynb/ipynb-LogReg-src.tar.gz and b/doc/pub/LogReg/ipynb/ipynb-LogReg-src.tar.gz differ
diff --git a/doc/pub/LogReg/pdf/LogReg-beamer-handouts2x3.pdf b/doc/pub/LogReg/pdf/LogReg-beamer-handouts2x3.pdf
index 03f53c2e5..4e19fd9be 100644
Binary files a/doc/pub/LogReg/pdf/LogReg-beamer-handouts2x3.pdf and b/doc/pub/LogReg/pdf/LogReg-beamer-handouts2x3.pdf differ
diff --git a/doc/pub/LogReg/pdf/LogReg-beamer.pdf b/doc/pub/LogReg/pdf/LogReg-beamer.pdf
index 59633c0de..2bff2cb51 100644
Binary files a/doc/pub/LogReg/pdf/LogReg-beamer.pdf and b/doc/pub/LogReg/pdf/LogReg-beamer.pdf differ
diff --git a/doc/pub/LogReg/pdf/LogReg-minted.pdf b/doc/pub/LogReg/pdf/LogReg-minted.pdf
index 3e659592b..e321dd04d 100644
Binary files a/doc/pub/LogReg/pdf/LogReg-minted.pdf and b/doc/pub/LogReg/pdf/LogReg-minted.pdf differ
diff --git a/doc/src/LogisticRegression/LogReg.do.txt b/doc/src/LogisticRegression/LogReg.do.txt
index 3c60670e4..0f5ea1bec 100644
--- a/doc/src/LogisticRegression/LogReg.do.txt
+++ b/doc/src/LogisticRegression/LogReg.do.txt
@@ -151,3 +151,29 @@ f(z)[1-f(z)]$. This equation defines a transcendental equation for
$\mathbf{w}$, the solution of which, unlike linear regression, cannot
be written in a closed form.
Here we need gradient descent methods!
+
+
+!split
+===== A _scikit-learn_ example =====
+
+!bc pycod
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+iris = datasets.load_iris()
+list(iris.keys())
+['data', 'target_names', 'feature_names', 'target', 'DESCR']
+X = iris["data"][:, 3:] # petal width
+y = (iris["target"] == 2).astype(np.int) # 1 if Iris-Virginica, else 0
+
+from sklearn.linear_model import LogisticRegression
+log_reg = LogisticRegression()
+log_reg.fit(X, y)
+
+X_new = np.linspace(0, 3, 1000).reshape(-1, 1)
+y_proba = log_reg.predict_proba(X_new)
+plt.plot(X_new, y_proba[:, 1], "g-", label="Iris-Virginica")
+plt.plot(X_new, y_proba[:, 0], "b--", label="Not Iris-Virginica")
+plt.show()
+
+!ec