def main():
'''
Main function
:return:
NAN
'''
# Load Data
x_train, y_train, x_test, y_test, label_dict = load_mnist(
which_type='fashion', threshold=0.5)
# Get the Model
nbc = NaiveBayesClassifier()
# Train
nbc.fit(x_train, y_train)
# Test
predictions = nbc.predict(x_test)
# Evaluate accuracy
accuracy = np.sum(np.uint8(predictions == y_test)) / len(y_test)
print("Accuracy: ", accuracy)
# Show Confusion Matrix
plot_confusion_matrix(targets=y_test,
predictions=predictions,
classes=[label_dict[l] for l in label_dict])
# Plot predictions
plt.figure()
while True:
idx = np.random.randint(0, x_test.shape[0])
x = x_test[idx]
p = predictions[idx]
y = y_test[idx]
plt.imshow(x, cmap='gray')
plt.title('Target: {}, Prediction: {}'.format(label_dict[int(y)],
label_dict[int(p)]))
plt.waitforbuttonpress()
def main():
""" Main function """
# load data
x_train, y_train, x_test, y_test, label_dict = load_mnist(
which_type='digits', threshold=0.5)
# get the model
nbc = NaiveBayesClassifier()
# train
nbc.fit(x_train, y_train)
# test
predictions = nbc.predict(x_test)
# evaluate performances
accuracy = np.sum(np.uint8(predictions == y_test)) / len(y_test)
print('Accuracy: {}'.format(accuracy))
# show confusion matrix
plot_confusion_matrix(targets=y_test,
predictions=predictions,
classes=[label_dict[l] for l in label_dict])
# plot predictions
plt.figure()
while True:
idx = np.random.randint(0, x_test.shape[0])
x = x_test[idx]
p = predictions[idx]
y = y_test[idx]
plt.imshow(x, cmap='gray')
plt.title('Target: {}, Prediction: {}'.format(label_dict[int(y)],
label_dict[int(p)]))
plt.waitforbuttonpress()
print() print(classification_report(y_test, predictions)) ''' ### K-NEAREST NEIGHBORS ### ''' from sklearn.datasets import load_iris from knn import KNN X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33) model = KNN() model.fit(X_train, y_train) predictions = model.predict(X_test) print(classification_report(y_test, predictions)) ''' ### NAIVE BAYES CLASSIFIER ### from sklearn.datasets import load_wine from naive_bayes import NaiveBayesClassifier X, y = load_wine(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33) model = NaiveBayesClassifier() model.fit(X_train, y_train) predictions = model.predict(X_test) print(classification_report(y_test, predictions))
import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from naive_bayes import NaiveBayesClassifier
def compute_accuracy(y_true, y_pred):
return np.sum(y_true == y_pred) / len(y_true)
if __name__ == '__main__':
X, y = make_classification(n_samples=1000, n_features=10, n_classes=2)
X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.2, shuffle=True)
clf = NaiveBayesClassifier()
clf.fit(X_train, Y_train)
predictions = clf.predict(X_test)
accuracy = compute_accuracy(Y_test, predictions)
print("The accuracy of the model is: {}".format(accuracy))