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))
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)
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)
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
