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([5,4,6,5,6,7], 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(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)
print(m,b)

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([5,4,6,5,6,7], 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(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)
print(m,b)

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()

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