from __future__ import print_function

import numpy

train_data = (((5, 2, 3), 15), ((6, 5, 9), 25),

((11, 12, 13), 41), ((1, 1, 1), 8), ((11, 12, 13), 41))

test_data = (((515, 22, 13), 555), ((61, 35, 49), 150))

parameter_vector = [2, 4, 1, 5]

m = len(train_data)

LEARNING_RATE = 0.009

def _error(example_no, data_set='train'):

return calculate_hypothesis_value(example_no, data_set) - output(example_no, data_set)

def _hypothesis_value(data_input_tuple):

return hyp_val

def output(example_no, data_set):

return test_data[example_no][1]

def calculate_hypothesis_value(example_no, data_set):

return _hypothesis_value(test_data[example_no][0])

def summation_of_cost_derivative(index, end=m):

return summation_value

def get_cost_derivative(index):

return cost_derivative_value

def run_gradient_descent():

print(("Number of iterations:", j))

def test_gradient_descent():

for i in range(len(test_data)):

print(("Actual output value:", output(i, 'test')))

print(("Hypothesis output:", calculate_hypothesis_value(i, 'test')))

if __name__ == '__main__':

run_gradient_descent()

print("\nTesting gradient descent for a linear hypothesis function.\n")

test_gradient_descent()