I want to plot the data according to the classes given using Support Vector Machine. I am stuck in this code.
Error is shown at -> Z = Z.reshape(XX.shape)
Please note the code is imported from sklearn-svm . When I try to change the datasets given to the application the above error is shown.
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm,datasets
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
Y = np.array(Y)
# figure number
fignum = 1
# fit the model
for kernel in ('linear', 'poly', 'rbf'):
clf = svm.SVC(kernel=kernel, gamma=2)
clf.fit(X, Y)
# plot the line, the points, and the nearest vectors to the plane
plt.figure(fignum, figsize=(4, 3))
plt.clf()
plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=80,
facecolors='none', zorder=10)
plt.scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap=plt.cm.Paired)
plt.axis('tight')
x_min = 0
x_max = 10
y_min = 0
y_max = 10
XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]
Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])
# Put the result into a color plot
Z = Z.reshape(XX.shape)
#print Z
plt.figure(fignum, figsize=(4, 3))
plt.pcolormesh(XX, YY, Z > 0, cmap=plt.cm.Paired)
plt.contour(XX, YY, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'],levels=[-.5, 0, .5])
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.xticks(())
plt.yticks(())
fignum = fignum + 1
plt.show()
XX
and YY
both have the shape (200, 200)
, but your Z
has shape (40000, 3)
. So XX
and YY
both contain 40000 values, while Z has 120000 values and can therefore not simply be reshaped to the shape of XX
or YY
as you would lose values. The call to decision_function()
does not return a matrix of shape (N_samples, 1)
as you seem to expect. Instead, according to scikit documentation , it returns (N_samples, n_class * (n_class-1) / 2)
. You can replace the call with predict()
to get the (probably) desired result, ie
Z = clf.predict(np.c_[XX.ravel(), YY.ravel()])
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