Logistic prediction

prediction.py

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)

Simplified version of this one:

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

You can run it just as shown above.

@waylonflinn nice!

Beautiful :)

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

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