#
#
# Regression and Classification programming exercises
#
#


#
#	In this exercise we will be taking a small data set and computing a linear function
#	that fits it, by hand.
#

#	the data set

import numpy as np

sleep = [5,6,7,8,10]
scores = [65,51,75,75,86]


def compute_regression(sleep,scores):

    #	First, compute the average amount of each list

    avg_sleep = np.mean(sleep)
    avg_scores = np.mean(scores)

    #	Then normalize the lists by subtracting the mean value from each entry

    normalized_sleep = sleep - avg_sleep
    normalized_scores = scores - avg_scores

    #	Compute the slope of the line by taking the sum over each student
    #	of the product of their normalized sleep times their normalized test score.
    #	Then divide this by the sum of squares of the normalized sleep times.

    slope = sum(normalized_sleep*normalized_scores) /  sum(normalized_sleep * normalized_sleep)


    #	Finally, We have a linear function of the form
    #	y - avg_y = slope * ( x - avg_x )
    #	Rewrite this function in the form
    #	y = m * x + b
    #	Then return the values m, b

    m = slope
    b = avg_scores - (slope * avg_sleep)

    return m,b


if __name__=="__main__":
    m,b = compute_regression(sleep,scores)
    print "Your linear model is y={}*x+{}".format(m,b)

#
#
# Regression and Classification programming exercises
#
#


#
#	In this exercise we will be taking a small data set and computing a linear function
#	that fits it, by hand.
#

#	the data set

import numpy as np

sleep = [5,6,7,8,10]
scores = [65,51,75,75,86]


def compute_regression(sleep,scores):

    #	First, compute the average amount of each list

    avg_sleep = np.mean(sleep)
    avg_scores = np.mean(scores)

    #	Then normalize the lists by subtracting the mean value from each entry

    normalized_sleep = sleep - avg_sleep
    normalized_scores = scores - avg_scores

    #	Compute the slope of the line by taking the sum over each student
    #	of the product of their normalized sleep times their normalized test score.
    #	Then divide this by the sum of squares of the normalized sleep times.

    slope = sum(normalized_sleep*normalized_scores) /  sum(normalized_sleep * normalized_sleep)


    #	Finally, We have a linear function of the form
    #	y - avg_y = slope * ( x - avg_x )
    #	Rewrite this function in the form
    #	y = m * x + b
    #	Then return the values m, b

    m = slope
    b = avg_scores - (slope * avg_sleep)

    return m,b


if __name__=="__main__":
    m,b = compute_regression(sleep,scores)
    print "Your linear model is y={}*x+{}".format(m,b)

# # # Regression and Classification programming exercises # # # #	In this exercise we will be taking a small data set and computing a linear function #	that fits it, by hand. # #	the data set #	First, compute the average amount of each list #	Then normalize the lists by subtracting the mean value from each entry #	Compute the slope of the line by taking the sum over each student #	of the product of their normalized sleep times their normalized test score. #	Then divide this by the sum of squares of the normalized sleep times. #	Finally, We have a linear function of the form #	y - avg_y = slope * ( x - avg_x ) #	Rewrite this function in the form #	y = m * x + b #	Then return the values m, b