コード例 #1
0
def main():
    data = DataHelper.Data()
    x_train, y_train, x_test, y_test, _ = data.loadData("hw2_data_1.txt", 2, 70)

    epoch = 3
    weights = np.ones(len(x_train)) / len(x_train)
    adaboost = Adaboost(weights, epoch, DecisionRule())
    adaboost.train(x_train, y_train)
    prediction = adaboost.predict(x_test)
    print("Error rate for %d iterations is %.2f%%" % (epoch, adaboost.evaluate(prediction, y_test)))

    epoch = 5
    weights = np.ones(len(x_train)) / len(x_train)
    adaboost = Adaboost(weights, epoch, DecisionRule())
    adaboost.train(x_train, y_train)
    prediction = adaboost.predict(x_test)
    print("Error rate for %d iterations is %.2f%%" % (epoch, adaboost.evaluate(prediction, y_test)))

    epoch = 10
    weights = np.ones(len(x_train)) / len(x_train)
    adaboost = Adaboost(weights, epoch, DecisionRule())
    adaboost.train(x_train, y_train)
    prediction = adaboost.predict(x_test)
    print("Error rate for %d iterations is %.2f%%" % (epoch, adaboost.evaluate(prediction, y_test)))

    epoch = 20
    weights = np.ones(len(x_train)) / len(x_train)
    adaboost = Adaboost(weights, epoch, DecisionRule())
    adaboost.train(x_train, y_train)
    prediction = adaboost.predict(x_test)
    print("Error rate for %d iterations is %.2f%%" % (epoch, adaboost.evaluate(prediction, y_test)))
コード例 #2
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def main():
    data = DataHelper.Data()
    x_train, y_train, x_test, y_test, _ = data.loadData(
        "hw2_data_2.txt", 20, 700)

    # radial kernel
    svm_radial = SVM(x_train,
                     y_train,
                     x_test,
                     y_test,
                     kernel="RADIAL",
                     gamma_range=np.logspace(-3, 2, 6))
    svm_radial.run()

    # sigmoid kernel
    svm_sigmoid = SVM(x_train,
                      y_train,
                      x_test,
                      y_test,
                      kernel="SIGMOID",
                      gamma_range=np.logspace(-3, 2, 6))
    svm_sigmoid.run()

    # polynomial kernel
    svm_poly = SVM(x_train,
                   y_train,
                   x_test,
                   y_test,
                   kernel="POLYNOMIAL",
                   degree_range=range(1, 11))
    svm_poly.run()

    plt.show()
コード例 #3
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def main():
    data = DataHelper.Data()
    x_train, y_train, x_test, y_test, attr_list = data.loadData(
        "hw2_data_2.txt", 20, 700)
    # print(x_train.shape, y_train.shape, x_test.shape, y_test.shape, attr_list.shape)
    gb = GradientBoosting(x_train, y_train, x_test, y_test, attr_list)
    gb.fit()
コード例 #4
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def main():
    data = DataHelper.Data()
    x_train, y_train, x_test, y_test, _ = data.loadData(
        "hw2_data_2.txt", 20, 700)
    mars = MARS(x_train, y_train, x_test, y_test)
    mars.fit()
    print("The testing error rate for MARS classifier is: %.4f" %
          mars.evaluate())
コード例 #5
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def main():
    data = DataHelper.Data()
    x_train, y_train, x_test, y_test, _ = data.loadData(
        "hw2_data_1.txt", 2, 70)  # load data
    weights = np.ones(x_train.shape[1] + 1)
    epochs = 50
    model = perceptron(weights, learningRate=1, epoch=epochs)
    model.train(x_train, y_train)
    print("The error rate for perceptron after %i epochs is %.2f %%" %
          (epochs, model.evaluate(x_test, y_test)))