__author__ = "Bharat Kul Ratan"

import random
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd


def plot_points():
    x_coordinates = pd.Series(random.sample(range(1, 20), 10), name='x_axis')
    y_coordinates = list(map(lambda x: 2*x+3, x_coordinates))
    # Add a random value, so that y values does not lie on the equation exactly
    y_coordinates = pd.Series([y + random.randint(-5, 5) for y in y_coordinates], name='y_axis')

    x_and_y = [{'x': item[0], 'y': item[1]} for item in zip(x_coordinates, y_coordinates)]
    sns.regplot(x=x_coordinates, y=y_coordinates, fit_reg=False)
    plt.show()
    return x_and_y


def mse(slope, intercept, points):
    error = 0
    for item in points:
        predicted = item['x'] * slope + intercept
        output = item['y']
        error += ((predicted - output) ** 2)
    error /= len(points)
    return error


def update_weights(slope, intercept, learning_rate, points):
    m_update = 0
    c_update = 0
    for item in points:
        m_update += -2 * item['x'] * (item['y'] - (slope * item['x'] + intercept))
        c_update += -2 * (item['y'] - (slope * item['x'] + intercept))

    slope -= (m_update/float(len(points))) * learning_rate
    intercept -= (c_update/float(len(points))) * learning_rate
    return slope, intercept


def run():
    x_and_y = plot_points()
    check_values = 1
    m = 1
    c = 0
    for i in range(100000):
        mse_error = mse(m, c, x_and_y)
        if i == check_values:
            print('slope=', round(m, 3), 'intercept=', round(c, 3), 'mse=', round(mse_error, 3))
            check_values *= 10
        m, c = update_weights(m, c, 0.001, x_and_y)


if __name__ == '__main__':
    run()