コード例 #1
0
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)
コード例 #2
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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)
コード例 #3
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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)
コード例 #4
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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)
コード例 #5
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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)