[1], [1], [1], [1], [1], [1]] X=np.array(X).transpose() print X.shape y=np.array(y).flatten(1) y[y==0]=-1 print y.shape def Gauss_kernel(x,z,sigma=1): return np.exp(-np.sum((x-z)**2)/(2*sigma**2)) svms=SVMC(X,y,kernel=Gauss_kernel) svms.train() print len(svms.supportVec),"SupportVectors:" for i in range(len(svms.supportVec)): t=svms.supportVec[i] print svms.X[:,t] svms.error(X,y) for i in range(svms.M): if svms.y[i]==-1: plt.plot(svms.X[0,i],svms.X[1,i],'or') elif svms.y[i]==1: plt.plot(svms.X[0,i],svms.X[1,i],'ob') for i in svms.supportVec: plt.plot(svms.X[0,i],svms.X[1,i],'oy') plt.show()
y = [[0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [1], [1], [1], [1],
[1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1],
[1], [1], [1], [1], [1], [1], [1]]
X = np.array(X).transpose()
print X.shape
y = np.array(y).flatten(1)
y[y == 0] = -1
print y.shape
def Gauss_kernel(x, z, sigma=1):
return np.exp(-np.sum((x - z)**2) / (2 * sigma**2))
svms = SVMC(X, y, C=12, kernel=Gauss_kernel)
svms.train()
print len(svms.supportVec), "SupportVectors:"
for i in range(len(svms.supportVec)):
t = svms.supportVec[i]
print svms.X[:, t]
svms.error(X, y)
for i in range(svms.M):
if svms.y[i] == -1:
plt.plot(svms.X[0, i], svms.X[1, i], 'or')
elif svms.y[i] == 1:
plt.plot(svms.X[0, i], svms.X[1, i], 'ob')
for i in svms.supportVec:
plt.plot(svms.X[0, i], svms.X[1, i], 'oy')
plt.show()
[1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1]] X=np.array(X).transpose() print X.shape y=np.array(y).flatten(1) y[y==0]=-1 print y.shape svms=SVMC(X,y) svms.train() print len(svms.supportVec) for i in range(len(svms.supportVec)): t=svms.supportVec[i] print svms.X[:,t] svms.prints_test_linear()
import numpy as np
import scipy as sp
from dml.SVM import SVMC
X = [[6.6, 9.5], [7.6, 11.5], [9.95, 12.85], [13.25, 13.6], [12.5, 11.75],
[10, 10.6], [7.85, 10.45], [9.8, 11.3], [11.8, 13.35], [7.25, 13.5],
[5.4, 10.55], [8.45, 10.1], [11.25, 13], [13, 16.55], [6.35, 6.1],
[7.85, 4.35], [11.05, 4.1], [13.3, 6.3], [12.8, 8.5], [11.05, 7.9],
[8.65, 5.9], [8.3, 4.65], [13.05, 6.35], [10.8, 6.95], [9.4, 5.9],
[8.7, 6.2], [7.75, 5.85], [9.3, 5.1], [11.65, 5.65], [12.35, 6.25],
[9.2, 6.8], [9.85, 6.25]]
y = [[0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [1],
[1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1],
[1], [1]]
X = np.array(X).transpose()
print X.shape
y = np.array(y).flatten(1)
y[y == 0] = -1
print y.shape
svms = SVMC(X, y)
svms.train()
print len(svms.supportVec)
for i in range(len(svms.supportVec)):
t = svms.supportVec[i]
print svms.X[:, t]
svms.prints_test_linear()