ml8.py

import numpy as np
from statistics import mean
import matplotlib.pyplot as plt
from matplotlib import style
import random
style.use('ggplot')

xs = np.array([1,2,3,4,5,6], dtype=np.float64)
ys = np.array([5,4,6,5,6,7], dtype=np.float64)


def create_dataset(hm, variance, step=2, correlation=False):
    val = 1
    ys = []
    for i in range(hm):
        y = val + random.randrange(-variance, variance)
        ys.append(y)
        if correlation and correlation == 'pos':
            val += step
        elif correlation and correlation == 'neg':
            val -= step
    xs = [i for i in range(hm)]
    return np.array(xs, dtype=np.float64), np.array(ys, dtype=np.float64)


def best_fit_slope_and_intercept(xs,ys):
    m = (((mean(xs) * mean(ys)) - mean(xs*ys)) /
          ((mean(xs)**2) - mean(xs**2)))
    b = mean(ys) - m*mean(xs)
    return m, b


# variance
def squared_error(ys_orig, ys_line):
    return sum((ys_line - ys_orig) ** 2)


def coefficient_of_determination(ys_orig, ys_line):
    y_mean_line = [mean(ys_orig) for _ in ys_orig]
    squared_error_regr = squared_error(ys_orig, ys_line)
    squared_error_mean = squared_error(ys_orig, y_mean_line)
    return 1 - (squared_error_regr / squared_error_mean)


xs, ys = create_dataset(40, 10, 2, 'pos')
m, b = best_fit_slope_and_intercept(xs,ys)

regression_line = [(m*x)+b for x in xs]

predict_x = 8
predict_y = (m*predict_x)+b

r_squared = coefficient_of_determination(ys, regression_line)
print(r_squared)

plt.scatter(xs,ys,color='#003F72')
plt.scatter(predict_x,predict_y, color='g')
plt.plot(xs, regression_line)
plt.show()