from __future__ import division, print_function
from sklearn import datasets
import numpy as np
from mlfromscratch.utils import train_test_split, normalize, accuracy_score, Plot
from mlfromscratch.supervised_learning import NaiveBayes
def main():
data = datasets.load_digits()
X = normalize(data.data)
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
clf = NaiveBayes()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
# Reduce dimension to two using PCA and plot the results
Plot().plot_in_2d(X_test, y_pred, title="Naive Bayes", accuracy=accuracy, legend_labels=data.target_names)
if __name__ == "__main__":
main()
