DOC: Moving examples in a separate directory, final commit (hopefully)

git-svn-id: https://scikit-learn.svn.sourceforge.net/svnroot/scikit-learn/trunk@703 22fbfee3-77ab-4535-9bad-27d1bd3bc7d8

examples/svm/README.txt

+
+SVM: support vector machine
+------------------------------
+
+Examples concerning the `scikits.learn.svm` package.
+

examples/svm/plot_svm.py

+"""
+==========================
+Linear SVM classifier
+==========================
+
+Simple usage of Support Vector Machines to classify a sample. It will
+plot the decision surface and the support vectors.
+
+"""
+import numpy as np
+import pylab as pl
+from scikits.learn import svm, datasets
+
+# import some data to play with
+iris = datasets.load_iris()
+X = iris.data[:, :2] # we only take the first two features. We could
+                     # avoid this ugly slicing by using a two-dim dataset
+Y = iris.target
+
+h=.02 # step size in the mesh
+
+# we create an instance of SVM and fit out data. We do not scale our
+# data since we want to plot the support vectors
+clf = svm.SVC(kernel='linear')
+clf.fit(X, Y)
+
+# Plot the decision boundary. For that, we will asign a color to each
+# point in the mesh [x_min, m_max]x[y_min, y_max].
+x_min, x_max = X[:,0].min()-1, X[:,0].max()+1
+y_min, y_max = X[:,1].min()-1, X[:,1].max()+1
+xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
+Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
+
+# Put the result into a color plot
+Z = Z.reshape(xx.shape)
+pl.set_cmap(pl.cm.Paired)
+pl.pcolormesh(xx, yy, Z)
+
+# Plot also the training points
+pl.scatter(X[:,0], X[:,1], c=Y)
+# and the support vectors
+pl.scatter(clf.support_[:,0], clf.support_[:, 1], marker='+')
+pl.title('3-Class classification using Support Vector Machine. \n' + \
+         'Support Vectors are hightlighted with a +')
+pl.axis('tight')
+pl.show()

examples/svm/plot_svm_hyperplane.py

+"""
+===========================================
+SVM: Maximum separating margin hyperplane
+===========================================
+
+"""
+
+import numpy as np
+import pylab as pl
+from scikits.learn import svm
+
+# we create 40 separable points
+np.random.seed(0)
+X = np.r_[np.random.randn(20, 2) - [2,2], np.random.randn(20, 2) + [2, 2]]
+Y = [0]*20 + [1]*20
+
+# fit the model
+clf = svm.SVC(kernel='linear')
+clf.fit(X, Y)
+
+# get the separating hyperplane
+w = np.dot(clf.dual_coef_[0], clf.support_)
+a = -w[0]/w[1]
+xx = np.linspace(-5, 5)
+yy = a*xx + (clf.rho_[0])/w[1]
+
+# plot the parallels to the separating hyperplane that pass through the
+# support vectors
+b = clf.support_[0]
+yy_down = a*xx + (b[1] - a*b[0])
+b = clf.support_[-1]
+yy_up = a*xx + (b[1] - a*b[0])
+
+# plot the line, the points, and the nearest vectors to the plane
+pl.set_cmap(pl.cm.Paired)
+pl.plot(xx, yy, 'k-')
+pl.plot(xx, yy_down, 'k--')
+pl.plot(xx, yy_up, 'k--')
+pl.scatter(X[:,0], X[:,1], c=Y)
+pl.scatter(clf.support_[:,0], clf.support_[:,1], marker='+')
+
+pl.axis('tight')
+pl.show()
+

examples/svm/plot_svm_nonlinear.py

+"""
+=================
+Non-linear SVM
+=================
+
+"""
+
+import numpy as np
+import pylab as pl
+from scikits.learn import svm
+
+xx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500))
+np.random.seed(0)
+X = np.random.randn(300, 2)
+Y = np.logical_xor(X[:,0]>0, X[:,1]>0)
+
+# fit the model
+clf = svm.SVC(impl='nu_svc', kernel='rbf', C=100)
+clf.fit(X, Y)
+
+# plot the line, the points, and the nearest vectors to the plane
+Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
+Z = Z.reshape(xx.shape)
+
+pl.set_cmap(pl.cm.Paired)
+pl.pcolormesh(xx, yy, Z)
+pl.scatter(X[:,0], X[:,1], c=Y)
+
+pl.axis('tight')
+pl.show()
+

examples/svm/plot_svm_oneclass.py

+"""
+==================
+One-class SVM
+==================
+"""
+
+import numpy as np
+import pylab as pl
+from scikits.learn import svm
+
+xx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500))
+X = np.random.randn(100, 2)
+Y = [0]*100
+
+# fit the model
+clf = svm.OneClassSVM(nu=0.5)
+clf.fit(X, Y)
+
+# plot the line, the points, and the nearest vectors to the plane
+Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
+Z = Z.reshape(xx.shape)
+
+pl.set_cmap(pl.cm.Paired)
+pl.pcolormesh(xx, yy, Z)
+pl.scatter(X[:,0], X[:,1], c=Y)
+pl.scatter(clf.support_[:,0], clf.support_[:,1], c='black')
+pl.axis('tight')
+pl.show()