from statistics import mean
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
import random
style.use('fivethirtyeight')
#xs = np.array([1,2,3,4,5,6],dtype=np.float64)
#ys = np.array([5,4,6,4,6,7],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(len(ys))]
    
            
    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)*mean(xs))-mean(xs*xs)))
    b = mean(ys)-m * mean(xs)

    return m,b
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 y in ys_orig]
    squared_error_regr = squared_error(ys_orig, ys_line)
    squared_error_y_mean = squared_error(ys_orig,y_mean_line)
    return 1 - (squared_error_regr/squared_error_y_mean)

xs, ys = create_dataset(40, 10, 2, correlation = '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)
#print(predict_y)
plt.scatter(xs,ys)
plt.scatter(predict_x,predict_y, s=100,color='g')
plt.plot(xs,regression_line)
plt.show()




    

8e5c49b5aa2a4699cf7d468aadf9f37291d3cbc6
<mask token>


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


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 y in ys_orig]
    squared_error_regr = squared_error(ys_orig, ys_line)
    squared_error_y_mean = squared_error(ys_orig, y_mean_line)
    return 1 - squared_error_regr / squared_error_y_mean


<mask token>

<mask token>


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(len(ys))]
    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) * mean(xs) - mean
        (xs * xs))
    b = mean(ys) - m * mean(xs)
    return m, b


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 y in ys_orig]
    squared_error_regr = squared_error(ys_orig, ys_line)
    squared_error_y_mean = squared_error(ys_orig, y_mean_line)
    return 1 - squared_error_regr / squared_error_y_mean


<mask token>

<mask token>
style.use('fivethirtyeight')


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(len(ys))]
    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) * mean(xs) - mean
        (xs * xs))
    b = mean(ys) - m * mean(xs)
    return m, b


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 y in ys_orig]
    squared_error_regr = squared_error(ys_orig, ys_line)
    squared_error_y_mean = squared_error(ys_orig, y_mean_line)
    return 1 - squared_error_regr / squared_error_y_mean


xs, ys = create_dataset(40, 10, 2, correlation='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)
plt.scatter(predict_x, predict_y, s=100, color='g')
plt.plot(xs, regression_line)
plt.show()

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


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(len(ys))]
    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) * mean(xs) - mean
        (xs * xs))
    b = mean(ys) - m * mean(xs)
    return m, b


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 y in ys_orig]
    squared_error_regr = squared_error(ys_orig, ys_line)
    squared_error_y_mean = squared_error(ys_orig, y_mean_line)
    return 1 - squared_error_regr / squared_error_y_mean


xs, ys = create_dataset(40, 10, 2, correlation='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)
plt.scatter(predict_x, predict_y, s=100, color='g')
plt.plot(xs, regression_line)
plt.show()

from statistics import mean
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
import random
style.use('fivethirtyeight')
#xs = np.array([1,2,3,4,5,6],dtype=np.float64)
#ys = np.array([5,4,6,4,6,7],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(len(ys))]
    
            
    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)*mean(xs))-mean(xs*xs)))
    b = mean(ys)-m * mean(xs)

    return m,b
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 y in ys_orig]
    squared_error_regr = squared_error(ys_orig, ys_line)
    squared_error_y_mean = squared_error(ys_orig,y_mean_line)
    return 1 - (squared_error_regr/squared_error_y_mean)

xs, ys = create_dataset(40, 10, 2, correlation = '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)
#print(predict_y)
plt.scatter(xs,ys)
plt.scatter(predict_x,predict_y, s=100,color='g')
plt.plot(xs,regression_line)
plt.show()




    
