update on log reg with examples

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