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Logistic prediction
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(
for it in range(0, h.shape[0]):
if h[it] > 0.5:
p[it, 0] = 1
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)

Simplified version of this one:

def predict(theta, X, y):
    p_1 = sigmoid(, theta))
    return p_1 > 0.5

You can run it just as shown above.

Beautiful :)

Where's the full set of code please?..

inwins27 commented Jun 7, 2017

p = predict(array(theta), it)
I am getting a name error: theta not defined

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