#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue May 28 15:38:30 2019

@author: student
"""

from sklearn import datasets
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
from sklearn.model_selection import train_test_split


iris = datasets.load_iris()

X = iris.data

y_dozmiany = iris.target
y_dozmiany = np.where(y_dozmiany==0, 4, y_dozmiany)
y_dozmiany = np.where(y_dozmiany==1, 0, y_dozmiany)
y_dozmiany = np.where(y_dozmiany==2, 0, y_dozmiany)
Y = np.where(y_dozmiany==4, 1, y_dozmiany)


X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=42)


model = Sequential()
model.add(Dense(12,input_shape=(4,), activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=150, batch_size=10)





predictions = model.predict(X_test)
# round predictions
rounded = [round(x[0]) for x in predictions]
print(rounded[0:10])
print(y_test[0:10])