Linear Regression for single variable. For multivariable check out my gradientDescent.py

linearRegression.py

import numpy as np
import dataMunge, pdb
from sklearn import linear_model

input = [
	[95, 85],
	[85, 95],
	[80, 70],
	[70, 65],
	[60, 70]
]

#http://aimotion.blogspot.com/2011/10/machine-learning-with-python-linear.html


class LinearRegression:

	equation, first_coefficient, second_coefficient, coefficient_of_determination = None,  None, None, None
	clf = linear_model.LinearRegression()

	def __init__(self, **kwargs):
		pass 

	def solve(self, array=input, print_results=True):

		#Lets fit the data
		#self.multivariateRegression(input,output, filtered_input, print_results)

		Array = np.array( array )
		if ( len(Array[0]) == 2 ):
			self.solveCoefficients( Array[:,0], Array[:,1] )



		X, Y = [ [i] for i in Array[:,0] ], Array[:,1] 

				
		self.clf.fit ( X, Y  )
		self.coe = self.clf.score( X, Y)
		self.data_labels = ['x'] + ['x' + str(i) for i in range(len(self.clf.coef_))]

		if print_results:
			message ="\nEquation via sklearn: %.3f + "  % self.clf.intercept_;
			message += ' + '.join( [str(x[0])+x[1] for x in zip(np.around(self.clf.coef_, decimals=3), self.data_labels)] )
			message +=".  Coefficient of Determination is %.3f." % self.coe
			print message
	

	#Solve for b0 and b1
	def solveCoefficients(self, input, output):

		array_len = len(input)

		input_average, output_average = np.mean(input), np.mean(output)

		input_deviation, output_deviation = np.subtract( input, input_average),\
						 np.subtract( output, output_average)

		sum_of_squares_input = np.square( np.subtract(input, input_average) )

		self.first_coefficient = sum( np.multiply( input_deviation, output_deviation))/ \
				sum(sum_of_squares_input)

		self.second_coefficient = np.subtract( output_average, \
			np.multiply( self.first_coefficient, input_average ) )

		
		coe = (1.0/array_len) * sum(np.multiply(input_deviation, output_deviation) )/ (np.std(input)* np.std(output) )

		self.coefficient_of_determination = coe*coe

		print "Equation: %.3fx + %.3f. Coefficient of Determination is %.3f"  \
		% ( self.second_coefficient, self.first_coefficient,  self.coefficient_of_determination)

		return self.first_coefficient, self.second_coefficient