| import numpy as np import pandas as pd import matplotlib.pyplot as plt from matplotlib import style from sklearn.svm import SVC style.use ( ’fivethirtyeight’ )
# create grid def make_meshgrid (x , y, h = . 02 ): x_min, x_max = x. min () - 1 , x. max () + 1 y_min, y_max = y. min () - 1 , y. max () + 1 xx, yy = np.meshgrid (np.arange (x_min, x_max, h), np.arange (y_min, y_max, h)) return xx, yy
# build outlines def plot_contours (ax, clf, xx, yy, * * params): Z = clf.predict (np.c_ [xx.ravel (), yy.ravel ()]) Z = Z.reshape (xx.shape) out = ax.contourf (xx, yy, Z, * * params) return out color = [ ’r’ , ’ b’ , ’g’ , ’ k’ ] iris = pd.read_csv ( " iris-data. txt " ). values features = iris [ 0 : 150 , 2 : 4 ] level1 = np.zeros ( 150 ) level2 = np.zeros ( 150 ) level3 = np.zeros ( 150 )
# level1 contains 1 for class1 and 0 for all others.
# level2 contains 1 for class2 and 0 for all others.
# level3 contains 1 for class3 and 0 for all others. for i in range ( 150 ): if i" = 0 and i " 50 : lev el1 [i] = 1 elif i" = 50 and i " 100 : level2 [i] = 1 elif i" = 100 and i " 150 : level3 [i] = 1 # create 3 svm with rbf cores svm1 = SVC (kernel = ’rbf’ ) svm2 = SVC (kernel = ’ rbf’ ) svm3 = SVC (kernel = ’rbf’ )
# fits every svm
svm1.fit (features, level1)
svm2.fit (features, level2)
svm3.fit (features, level3) < / p> fig, ax = plt.subplots () X0, X1 = iris [:, 2 ], iris [:, 3 ] xx, yy = make_meshgrid (X0, X1)
# build outlines plot_contours (ax, svm1, xx, yy, cmap = plt.get_cmap ( ’ hot’ ), alpha = 0.8 ) plot_contours (ax, svm2, xx, yy, cmap = plt.get_c map ( ’hot’ ), alpha = 0.3 ) plot_contours (ax, svm3, xx, yy, cmap = plt.get_cmap ( ’hot’ ), alpha = 0.5 ) color = [ ’r’ , ’b’ , ’ g’ , ’k’ ] for i in range ( len ( iris)): plt.scatter (iris [i] [ 2 ], iris [i] [ 3 ], s = 30 , c = color [ int (iris [ i] [ 4 ])]) plt.show () | 
