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