From ba452284496012605402a30b56d26de7b7f0bb0c Mon Sep 17 00:00:00 2001
From: Ayush Tiwari <36932180+GameLogist@users.noreply.github.com>
Date: Fri, 29 Oct 2021 02:36:40 +0530
Subject: [PATCH] Added SVM for Non Linear Data

---
 Classification/NonLiniearSVM.py | 46 +++++++++++++++++++++++++++++++++
 1 file changed, 46 insertions(+)
 create mode 100644 Classification/NonLiniearSVM.py

diff --git a/Classification/NonLiniearSVM.py b/Classification/NonLiniearSVM.py
new file mode 100644
index 0000000..88d9cbf
--- /dev/null
+++ b/Classification/NonLiniearSVM.py
@@ -0,0 +1,46 @@
+# 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()