def housing():
train, train_target, test, test_target = load_boston_house()
normalize_columns = [0, 1, 2, 6, 7, 9, 10, 11, 12]
normalize(train, normalize_columns)
normalize(test, normalize_columns)
train = append_new_column(train, 1.0, 0)
test = append_new_column(test, 1.0, 0)
lr = LinearRegression()
lr.fit(train, train_target)
print '=============Train Data Result============'
predict = lr.predict(train)
print "mse: ", mse(predict, train_target), " rmse: ", rmse(predict, train_target), " mae: ", mae(predict,
train_target)
print '=============Test Data Result============'
predict = lr.predict(test)
print "mse: ", mse(predict, test_target), " rmse: ", rmse(predict, test_target), " mae: ", mae(predict, test_target)
def regression_tree():
print "=========Start Train=============="
train, train_target, test, test_target = load_boston_house()
print len(train), len(test)
classifier = tree.RegressionTree()
classifier = classifier.fit(train, train_target, 2, -1)
print "=========Finish Train=============="
print "=========Tree=============="
print_tree(classifier.root)
print '=============Train Data Result============'
predict = classifier.predict(train)
print "mse: ", mse(predict, train_target), " rmse: ", rmse(predict, train_target), " mae: ", mae(predict,
train_target)
print '=============Test Data Result============'
predict = classifier.predict(test)
print "mse: ", mse(predict, test_target), " rmse: ", rmse(predict, test_target), " mae: ", mae(predict, test_target)
def housing():
train, train_target, test, test_target = load_boston_house()
scaler = normalize(train)
scaler.scale_test(test)
train = append_new_column(train, 1.0, 0)
test = append_new_column(test, 1.0, 0)
lr = StochasticGradientDescendingRegression()
# lr = GradientDescendingRegression()
lr.fit(train, train_target, 0.0001, 500)
print '---------------Stochastic Gradient----------'
print '=============Train Data Result============'
predict = lr.predict(train)
print "mse: ", mse(predict, train_target), " rmse: ", rmse(predict, train_target), " mae: ", mae(predict,
train_target)
print '=============Test Data Result============'
predict = lr.predict(test)
print "mse: ", mse(predict, test_target), " rmse: ", rmse(predict, test_target), " mae: ", mae(predict, test_target)
print '------------- Normal Equation--------------'
lr = LinearRegression()
lr.fit(train, train_target)
print '=============Train Data Result============'
predict = lr.predict(train)
print "mse: ", mse(predict, train_target), " rmse: ", rmse(predict, train_target), " mae: ", mae(predict,
train_target)
print '=============Test Data Result============'
predict = lr.predict(test)
print "mse: ", mse(predict, test_target), " rmse: ", rmse(predict, test_target), " mae: ", mae(predict, test_target)
print '---------------Regression Tree-----------'
lr = RegressionTree()
lr.fit(train, train_target, 2, 0)
print '=============Train Data Result============'
predict = lr.predict(train)
print "mse: ", mse(predict, train_target), " rmse: ", rmse(predict, train_target), " mae: ", mae(predict,
train_target)
print '=============Test Data Result============'
predict = lr.predict(test)
print "mse: ", mse(predict, test_target), " rmse: ", rmse(predict, test_target), " mae: ", mae(predict, test_target)
def housing():
train, train_target, test, test_target = load_boston_house()
normalize_columns = [0, 1, 2, 6, 7, 9, 10, 11, 12]
normalize(train, normalize_columns)
normalize(test, normalize_columns)
train = append_new_column(train, 1.0, 0)
test = append_new_column(test, 1.0, 0)
lr = LinearRegression()
lr.fit(train, train_target)
print '=============Train Data Result============'
predict = lr.predict(train)
print "mse: ", mse(predict, train_target), " rmse: ", rmse(
predict, train_target), " mae: ", mae(predict, train_target)
print '=============Test Data Result============'
predict = lr.predict(test)
print "mse: ", mse(predict, test_target), " rmse: ", rmse(
predict, test_target), " mae: ", mae(predict, test_target)
def regression_tree():
print "=========Start Train=============="
train, train_target, test, test_target = load_boston_house()
print len(train), len(test)
classifier = tree.RegressionTree()
classifier = classifier.fit(train, train_target, 2, -1)
print "=========Finish Train=============="
print "=========Tree=============="
print_tree(classifier.root)
print '=============Train Data Result============'
predict = classifier.predict(train)
print "mse: ", mse(predict, train_target), " rmse: ", rmse(
predict, train_target), " mae: ", mae(predict, train_target)
print '=============Test Data Result============'
predict = classifier.predict(test)
print "mse: ", mse(predict, test_target), " rmse: ", rmse(
predict, test_target), " mae: ", mae(predict, test_target)