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from numpy import loadtxt, where, zeros, e, array, log, ones, append, linspace | |
from pylab import scatter, show, legend, xlabel, ylabel, contour, title | |
from scipy.optimize import fmin_bfgs | |
def sigmoid(X): | |
'''Compute the sigmoid function ''' | |
#d = zeros(shape=(X.shape)) | |
den = 1.0 + e ** (-1.0 * X) | |
d = 1.0 / den | |
return d | |
def cost_function_reg(theta, X, y, l): | |
'''Compute the cost and partial derivatives as grads | |
''' | |
h = sigmoid(X.dot(theta)) | |
thetaR = theta[1:, 0] | |
J = (1.0 / m) * ((-y.T.dot(log(h))) - ((1 - y.T).dot(log(1.0 - h)))) \ | |
+ (l / (2.0 * m)) * (thetaR.T.dot(thetaR)) | |
delta = h - y | |
sumdelta = delta.T.dot(X[:, 1]) | |
grad1 = (1.0 / m) * sumdelta | |
XR = X[:, 1:X.shape[1]] | |
sumdelta = delta.T.dot(XR) | |
grad = (1.0 / m) * (sumdelta + l * thetaR) | |
out = zeros(shape=(grad.shape[0], grad.shape[1] + 1)) | |
out[:, 0] = grad1 | |
out[:, 1:] = grad | |
return J.flatten(), out.T.flatten() | |
m, n = X.shape | |
y.shape = (m, 1) | |
it = map_feature(X[:, 0], X[:, 1]) | |
#Initialize theta parameters | |
initial_theta = zeros(shape=(it.shape[1], 1)) | |
#Set regularization parameter lambda to 1 | |
l = 1 | |
# Compute and display initial cost and gradient for regularized logistic | |
# regression | |
cost, grad = cost_function_reg(initial_theta, it, y, l) | |
def decorated_cost(theta): | |
return cost_function_reg(theta, it, y, l) | |
print fmin_bfgs(decorated_cost, initial_theta, maxfun=400) |
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