# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import sys
import load_datasets
import NeuralNet  # importer la classe du Réseau de Neurones
import DecisionTree  # importer la classe de l'Arbre de Décision
# importer d'autres fichiers et classes si vous en avez développés
# importer d'autres bibliothèques au besoin, sauf celles qui font du machine learning

train1, train_labels1, test1, test_labels1 = ld.load_iris_dataset(train_ratio = 0.7)
train2, train_labels2, test2, test_labels2 = ld.load_monks_dataset(1)
train3, train_labels3, test3, test_labels3 = ld.load_monks_dataset(2)
train4, train_labels4, test4, test_labels4 = ld.load_monks_dataset(3)
train5, train_labels5, test5, test_labels5 = ld.load_congressional_dataset(train_ratio = 0.7)


dt1 = DecisionTree.DecisionTree(attribute_type="continuous")
dt1.train(train1, train_labels1)
dt1.predict(test1[0],test_labels1[0])
dt1.test(test1, test_labels1)

dt2 = DecisionTree.DecisionTree(attribute_type="discrete")
dt2.train(train2, train_labels2)
dt2.tree
dt2.predict(test2[0],test_labels2[0])
dt2.test(test2, test_labels2)

dt3 = DecisionTree.DecisionTree(attribute_type="discrete")
dt3.train(train3, train_labels3)
dt3.tree
dt3.predict(test3[0],test_labels3[0])
dt3.test(test3, test_labels3)

dt4 = DecisionTree.DecisionTree(attribute_type="discrete")
dt4.train(train4, train_labels4)
dt4.tree
dt4.predict(test4[0],test_labels4[0])
dt4.test(test4, test_labels4)

dt5 = DecisionTree.DecisionTree(attribute_type="discrete")
dt5.train(train5, train_labels5)
dt5.predict(test5[0],test_labels5[0])
dt5.test(test5, test_labels5)

nn1 = NeuralNet.NeuralNet(np.array([4,4,4,1]))
nn1.feed_forward(train1[0])
nn1.train(train1, train_labels1, 0.1)