def run_feedforward_nn_for_samples(theta_matrix_1, theta_matrix_2, samples, true_output):
assert(mathutil.is_np_2d_array(samples))
num_samples, num_features = samples.shape
predicted_y = np.empty((num_samples,))
for row_idx, sample in enumerate(samples):
predicted_y[row_idx] = run_feedforward_nn_for_sample(theta_matrix_1, theta_matrix_2, sample)
num_correctly_classified = np.count_nonzero(true_output[:,0] == predicted_y)
print(num_correctly_classified/num_samples)
def reshape_to_np_1darray(x): assert(mathutil.is_np_2d_array(x)) return x.reshape((-1))
