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Update LinearRegression.py

added example for single and multi variable cases

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LinearRegression.py

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`‎LinearRegression.py‎`

Lines changed: 12 additions & 10 deletions

Original file line number	Diff line number	Diff line change
`@@ -117,27 +117,29 @@ def lin_reg(X,theta):`
`117`	`117`
`118`	`118`
`119`	`119`
`120`		`-def prep_data(X,y,test_size):`
`121`		`- X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.4)`
`122`		`- return X_train,X_test,y_train,y_test`
`123`		`-`
`124`		`-`
`125`		`-`
`126`	`120`	`"""`
`127`	`121`	`Loading and training on toy dataset (boston land prices)`
`128`	`122`	`"""`
`129`	`123`
`130`	`124`	`boston = datasets.load_boston()`
`131`	`125`
`132`		`-`
	`126`	`+"""`
	`127`	`+linear regression with multiple variables`
	`128`	`+"""`
`133`	`129`	`X = boston.data`
`134`	`130`	`y = boston.target`
`135`	`131`
	`132`	`+"""`
	`133`	`+#linear regression with single variable`
	`134`	`+X = np.asmatrix(boston.data[:,0]).T #taking only the 1st column`
	`135`	`+y = boston.target`
	`136`	`+"""`
`136`	`137`
`137`		`-`
`138`	`138`
`139`	`139`	`X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.4)`
`140`	`140`
	`141`	`+`
	`142`	`+`
`141`	`143`	`normal_eqn_theta = np.asmatrix(normal_eqn_theta(X_train,y_train)).T`
`142`	`144`
`143`	`145`
`@@ -151,5 +153,5 @@ def prep_data(X,y,test_size):`
`151`	`153`
`152`	`154`
`153`	`155`
`154`		`-print "MSE for normal_eqn Theta is: ", mean_squared_error(y_test, pred1)`
`155`		`-print "MSE for gradient_desc Theta is: ", mean_squared_error(y_test, pred2)`
	`156`	`+print "MSE for prediction using normal_eqn Theta is: ", mean_squared_error(y_test, pred1)`
	`157`	`+print "MSE for prediction using gradient_desc Theta is: ", mean_squared_error(y_test, pred2)`

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