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decision_boundary.py
#Plot Boundary
u = linspace(-1, 1.5, 50)
v = linspace(-1, 1.5, 50)
z = zeros(shape=(len(u), len(v)))
for i in range(len(u)):
for j in range(len(v)):
z[i, j] = (map_feature(array(u[i]), array(v[j])).dot(array(theta)))
z = z.T
contour(u, v, z)
title('lambda = %f' % l)
xlabel('Microchip Test 1')
ylabel('Microchip Test 2')
legend(['y = 1', 'y = 0', 'Decision boundary'])
show()
def predict(theta, X):
'''Predict whether the label
is 0 or 1 using learned logistic
regression parameters '''
m, n = X.shape
p = zeros(shape=(m, 1))
h = sigmoid(X.dot(theta.T))
for it in range(0, h.shape[0]):
if h[it] > 0.5:
p[it, 0] = 1
else:
p[it, 0] = 0
return p
#% Compute accuracy on our training set
p = predict(array(theta), it)
print 'Train Accuracy: %f' % ((y[where(p == y)].size / float(y.size)) * 100.0)

how do you tell which contour line is the real decision boundary? since there are so many of them...

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