Added SVM for Non Linear Data

Classification/NonLiniearSVM.py

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+# importing libraries
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.datasets import make_circles
+from mpl_toolkits.mplot3d import Axes3D
+
+# generating data
+X, Y = make_circles(n_samples = 500, noise = 0.02)
+
+# visualizing data
+plt.scatter(X[:, 0], X[:, 1], c = Y, marker = '.')
+plt.show()
+
+
+# adding a new dimension to X
+X1 = X[:, 0].reshape((-1, 1))
+X2 = X[:, 1].reshape((-1, 1))
+X3 = (X1**2 + X2**2)
+X = np.hstack((X, X3))
+
+# visualizing data in higher dimension
+fig = plt.figure()
+axes = fig.add_subplot(111, projection = '3d')
+axes.scatter(X1, X2, X1**2 + X2**2, c = Y, depthshade = True)
+plt.show()
+
+# create support vector classifier using a linear kernel
+from sklearn import svm
+
+svc = svm.SVC(kernel = 'linear')
+svc.fit(X, Y)
+w = svc.coef_
+b = svc.intercept_
+
+# plotting the separating hyperplane
+x1 = X[:, 0].reshape((-1, 1))
+x2 = X[:, 1].reshape((-1, 1))
+x1, x2 = np.meshgrid(x1, x2)
+x3 = -(w[0][0]*x1 + w[0][1]*x2 + b) / w[0][2]
+
+fig = plt.figure()
+axes2 = fig.add_subplot(111, projection = '3d')
+axes2.scatter(X1, X2, X1**2 + X2**2, c = Y, depthshade = True)
+axes1 = fig.gca(projection = '3d')
+axes1.plot_surface(x1, x2, x3, alpha = 0.01)
+plt.show()