Created
December 16, 2018 13:58
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線形SVMの実装
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import numpy as np | |
from scipy.optimize import minimize | |
from matplotlib import pyplot as plt | |
ONE_DATA = 30 | |
x = np.vstack((np.random.normal(loc=-2, scale=1.0, size=(ONE_DATA, 2)),\ | |
np.random.normal(loc=2, scale=1.0, size=(ONE_DATA, 2)))) | |
y = np.hstack((np.repeat(-1, ONE_DATA), np.repeat(1, ONE_DATA))) | |
def get_obj(x, y): | |
def obj(alpha): | |
second = np.array([alpha[i]*alpha[j]*y[i]*y[j]*np.dot(x[i], x[j]) \ | |
for i in range(len(alpha)) for j in range(len(alpha))]) | |
return - np.sum(alpha) + .5 * np.sum(second) | |
return obj | |
def get_g(i): | |
def g(alpha): | |
return alpha[i] | |
return g | |
def get_h(y): | |
def h(alpha): | |
return np.sum([alpha[i]*y[i] for i in range(len(alpha))]) | |
return h | |
cons = [ | |
{'type': 'eq', 'fun': get_h(y)} | |
] | |
for i in range(len(x)): | |
cons.append({'type': 'ineq', 'fun': get_g(i)}) | |
res = minimize(get_obj(x, y), np.zeros(len(x)), constraints=cons, method="SLSQP") | |
alpha_hat = res.x | |
tol = 1e-10 | |
support_index = np.where(alpha_hat > tol)[0] | |
non_suppoert_index = np.where(alpha_hat <= tol)[0] | |
alpha_hat[non_suppoert_index] = 0 | |
w = np.sum([alpha_hat[i]*y[i]*x[i] for i in range(len(x))], axis=0) | |
x_m = x[support_index][np.where(y[support_index]==-1)] | |
x_p = x[support_index][np.where(y[support_index]==1)] | |
b = - ( np.dot(w, x_p[0]) + np.dot(w, x_m[0]) ) / 2 | |
def classifier(w1, w2, b, x): | |
return -w1/w2*x - b/w2 | |
for xi, yi in zip(x, y): | |
if (xi[0] in x_m and xi[1] in x_m) or \ | |
(xi[0] in x_p and xi[1] in x_p): | |
plt.plot(xi[0], xi[1], 'gv') | |
continue | |
if yi == 1: | |
plt.plot(xi[0], xi[1], 'ro') | |
else: | |
plt.plot(xi[0], xi[1], 'bx') | |
x = np.linspace(-5, 5, 100) | |
y = classifier(w[0], w[1], b, x) | |
plt.plot(x, y) | |
plt.xlim([-5, 5]) | |
plt.ylim([-5, 5]) | |
plt.show() |
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