From 958cfbe131e95e099e33bf24aa0adda504ee743e Mon Sep 17 00:00:00 2001
From: Andreas Mueller <amueller@ais.uni-bonn.de>
Date: Fri, 27 Apr 2012 22:53:09 +0200
Subject: [PATCH] COSMIT cleanup + pep8 in examples

---
 examples/exercises/plot_cv_diabetes.py   | 10 +++---
 examples/exercises/plot_iris_exercise.py | 45 +++++++++++-------------
 examples/linear_model/plot_logistic.py   | 16 +++++----
 3 files changed, 33 insertions(+), 38 deletions(-)

diff --git a/examples/exercises/plot_cv_diabetes.py b/examples/exercises/plot_cv_diabetes.py
index 6a0182afaa..65efd6fd99 100644
--- a/examples/exercises/plot_cv_diabetes.py
+++ b/examples/exercises/plot_cv_diabetes.py
@@ -34,17 +34,15 @@ pl.figure(1, figsize=(2.5, 2))
 pl.clf()
 pl.axes([.1, .25, .8, .7])
 pl.semilogx(alphas, scores)
-pl.semilogx(alphas, np.array(scores) + np.array(scores_std)/20, 'b--')
-pl.semilogx(alphas, np.array(scores) - np.array(scores_std)/20, 'b--')
+pl.semilogx(alphas, np.array(scores) + np.array(scores_std) / 20, 'b--')
+pl.semilogx(alphas, np.array(scores) - np.array(scores_std) / 20, 'b--')
 pl.yticks(())
 pl.ylabel('CV score')
 pl.xlabel('alpha')
 pl.axhline(np.max(scores), linestyle='--', color='.5')
-pl.text(2e-4, np.max(scores)+1e-4, '.489')
+pl.text(2e-4, np.max(scores) + 1e-4, '.489')
 
-################################################################################
+##############################################################################
 # Bonus: how much can you trust the selection of alpha?
-from scikits.learn import cross_val
 k_fold = cross_validation.KFold(len(X), 3)
 print [lasso.fit(X[train], y[train]).alpha for train, _ in k_fold]
-
diff --git a/examples/exercises/plot_iris_exercise.py b/examples/exercises/plot_iris_exercise.py
index d0984b066f..72bb6df0db 100644
--- a/examples/exercises/plot_iris_exercise.py
+++ b/examples/exercises/plot_iris_exercise.py
@@ -3,25 +3,22 @@
 SVM Exercise
 ================================
 
-This exercise is used in the
-:ref:`using_kernels_tut` part of the 
-:ref:`supervised_learning_tut` section of the
-:ref:`stat_learn_tut_index`.
+This exercise is used in the :ref:`using_kernels_tut` part of the
+:ref:`supervised_learning_tut` section of the :ref:`stat_learn_tut_index`.
 """
 print __doc__
 
 
-
 import numpy as np
 import pylab as pl
-from scikits.learn import datasets, svm
+from sklearn import datasets, svm
 
 iris = datasets.load_iris()
 X = iris.data
 y = iris.target
 
-X = X[y!=0, :2]
-y = y[y!=0, :2]
+X = X[y != 0, :2]
+y = y[y != 0, :2]
 
 n_sample = len(X)
 
@@ -30,12 +27,12 @@ order = np.random.permutation(n_sample)
 X = X[order]
 y = y[order].astype(np.float)
 
-X_train = X[:.9*n_sample]
-y_train = y[:.9*n_sample]
-X_test = X[.9*n_sample:]
-y_test = y[.9*n_sample:]
+X_train = X[:.9 * n_sample]
+y_train = y[:.9 * n_sample]
+X_test = X[.9 * n_sample:]
+y_test = y[.9 * n_sample:]
 
-h = .02 # step size in the mesh
+h = .02  # step size in the mesh
 
 # fit the model
 for fig_num, kernel in enumerate(('linear', 'rbf', 'poly')):
@@ -45,19 +42,19 @@ for fig_num, kernel in enumerate(('linear', 'rbf', 'poly')):
 
     pl.figure(fig_num)
     pl.clf()
-    pl.scatter(X[:,0], X[:,1], c=y, zorder=10)
+    pl.scatter(X[:, 0], X[:, 1], c=y, zorder=10)
 
     # Circle out the test data
-    pl.scatter(X_test[:,0], X_test[:, 1],
+    pl.scatter(X_test[:, 0], X_test[:, 1],
             s=80, facecolors='none', zorder=10)
 
     pl.axis('tight')
-    x_min = X[:,0].min()
-    x_max = X[:,0].max()
-    y_min = X[:,1].min()
-    y_max = X[:,1].max()
-    y_min = X[:,1].min()
-    y_max = X[:,1].max()
+    x_min = X[:, 0].min()
+    x_max = X[:, 0].max()
+    y_min = X[:, 1].min()
+    y_max = X[:, 1].max()
+    y_min = X[:, 1].min()
+    y_max = X[:, 1].max()
 
     XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]
     Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])
@@ -65,10 +62,8 @@ for fig_num, kernel in enumerate(('linear', 'rbf', 'poly')):
     # Put the result into a color plot
     Z = Z.reshape(XX.shape)
     pl.pcolormesh(XX, YY, Z > 0)
-    pl.contour(XX, YY, Z, colors=['k', 'k', 'k'], 
-              linestyles=['--', '-', '--'], 
+    pl.contour(XX, YY, Z, colors=['k', 'k', 'k'],
+              linestyles=['--', '-', '--'],
               levels=[-.5, 0, .5])
 
     pl.title(kernel)
-
-
diff --git a/examples/linear_model/plot_logistic.py b/examples/linear_model/plot_logistic.py
index ff1adbd098..6e5089357a 100644
--- a/examples/linear_model/plot_logistic.py
+++ b/examples/linear_model/plot_logistic.py
@@ -7,7 +7,7 @@
 Logit function
 =========================================================
 Show in the plot is how the logistic regression would, in this
-synthetic dataset, classify values as either 0 or 1, 
+synthetic dataset, classify values as either 0 or 1,
 i.e. class one or two, using the logit-curve.
 
 """
@@ -20,7 +20,7 @@ print __doc__
 import numpy as np
 import pylab as pl
 
-from scikits.learn import linear_model
+from sklearn import linear_model
 
 # this is our test set, it's just a straight line with some
 # gaussian noise
@@ -29,8 +29,8 @@ n_samples = 100
 np.random.seed(0)
 X = np.random.normal(size=n_samples)
 y = (X > 0).astype(np.float)
-X[X>0] *= 4
-X += .3*np.random.normal(size=n_samples)
+X[X > 0] *= 4
+X += .3 * np.random.normal(size=n_samples)
 
 X = X[:, np.newaxis]
 # run the classifier
@@ -42,14 +42,16 @@ pl.figure(1, figsize=(4, 3))
 pl.clf()
 pl.scatter(X.ravel(), y, color='black', zorder=20)
 X_test = np.linspace(-5, 10, 300)
+
+
 def model(x):
-    return 1/(1+np.exp(-x))
-loss = model(X_test*clf.coef_ + clf.intercept_).ravel()
+    return 1 / (1 + np.exp(-x))
+loss = model(X_test * clf.coef_ + clf.intercept_).ravel()
 pl.plot(X_test, loss, color='blue', linewidth=3)
 
 ols = linear_model.LinearRegression()
 ols.fit(X, y)
-pl.plot(X_test, ols.coef_*X_test + ols.intercept_, linewidth=1)
+pl.plot(X_test, ols.coef_ * X_test + ols.intercept_, linewidth=1)
 pl.axhline(.5, color='.5')
 
 pl.ylabel('y')
-- 
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