def main_ncc():
    mnist = MNIST.MNISTData('MNIST_Light/*/*.png')

    train_features, test_features, train_labels, test_labels = mnist.get_data()

    ncc = NearestCentriodClassifier()
    ncc.fit(train_features, train_labels, 10)

    #ncc.visaulizeLabel(3, (20,20))

    y_pred = ncc.predict(test_features)

    print("Classification report SKLearn GNB:\n%s\n" %
          (metrics.classification_report(test_labels, y_pred)))
    print("Confusion matrix SKLearn GNB:\n%s" %
          metrics.confusion_matrix(test_labels, y_pred))
示例#2
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def main():
    mnist = MNIST.MNISTData('MNIST_Light/*/*.png')

    train_features, test_features, train_labels, test_labels = mnist.get_data()

    mnist.visualize_random()

    gnb = GaussianNB()
    gnb.fit(train_features, train_labels)
    y_pred = gnb.predict(test_features)

    print("Classification report SKLearn GNB:\n%s\n" %
          (metrics.classification_report(test_labels, y_pred)))
    print("Confusion matrix SKLearn GNB:\n%s" %
          metrics.confusion_matrix(test_labels, y_pred))

    mnist.visualize_wrong_class(y_pred, 8)
示例#3
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def main():
    mnist = MNIST.MNISTData(
        "/Users/duy/Documents/code/lund/EDAN95_applied_ai_lund/lab_5_nb/MNIST_Light/*/*.png"
    )

    train_features, test_features, train_labels, test_labels = mnist.get_data()

    mnist.visualize_random()

    gnb = GaussianNB()
    gnb.fit(train_features, train_labels)
    y_pred = gnb.predict(test_features)

    print("Classification report SKLearn GNB:\n%s\n" %
          (metrics.classification_report(test_labels, y_pred)))
    print("Confusion matrix SKLearn GNB:\n%s" %
          metrics.confusion_matrix(test_labels, y_pred))

    mnist.visualize_wrong_class(y_pred, 8)
示例#4
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def MNIST_light(normalized=True):
    mnist = MNIST.MNISTData('MNIST_Light/*/*.png')
    train_features, test_features, train_labels, test_labels = mnist.get_data(
        normalized)
    return train_features, test_features, train_labels, test_labels