/*******************************************************************************
 * Copyright (c) 2010 Haifeng Li
 * Modifications copyright (C) 2017 Sam Erickson
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *******************************************************************************/
package smile.regression;

import org.junit.After;
import org.junit.AfterClass;
import org.junit.Before;
import org.junit.BeforeClass;
import org.junit.Test;
import smile.data.AttributeDataset;
import smile.data.parser.ArffParser;
import smile.validation.CrossValidation;
import smile.math.Math;

/**
 * 
 * @author Sam Erickson
 */
public class NeuralNetworkTest {
    public NeuralNetworkTest(){
    }
    @BeforeClass
    public static void setUpClass() throws Exception {
    }

    @AfterClass
    public static void tearDownClass() throws Exception {
    }
    
    @Before
    public void setUp() {
    }
    
    @After
    public void tearDown() {
    }
    public void test(NeuralNetwork.ActivationFunction activation, String dataset, String url, int response) {
        System.out.println(dataset + "\t" + activation);
        ArffParser parser = new ArffParser();
        parser.setResponseIndex(response);
        try {
            AttributeDataset data = parser.parse(smile.data.parser.IOUtils.getTestDataFile(url));
            double[] datay = data.toArray(new double[data.size()]);
            double[][] datax = data.toArray(new double[data.size()][]);
            
            int n = datax.length;
            int p = datax[0].length;
            double[] mux = Math.colMeans(datax);
            double[] sdx = Math.colSds(datax);
            double muy = Math.mean(datay);
            double sdy = Math.sd(datay);
            for (int i = 0; i < n; i++) {
                datay[i]=(datay[i]-muy)/sdy;
                for (int j = 0; j < p; j++) {
                    datax[i][j] = (datax[i][j] - mux[j]) / sdx[j];
                }
            }
            
            int k = 10;

            CrossValidation cv = new CrossValidation(n, k);
            double rss = 0.0;
            double ad = 0.0;
            for (int i = 0; i < k; i++) {
                double[][] trainx = smile.math.Math.slice(datax, cv.train[i]);
                double[] trainy = smile.math.Math.slice(datay, cv.train[i]);
                double[][] testx = smile.math.Math.slice(datax, cv.test[i]);
                double[] testy = smile.math.Math.slice(datay, cv.test[i]);

                NeuralNetwork neuralNetwork = new NeuralNetwork(activation,new int[]{datax[0].length,10,10,1});
                neuralNetwork.learn(trainx,trainy);

                for (int j = 0; j < testx.length; j++) {
                    double r = testy[j] - neuralNetwork.predict(testx[j]);
                    rss += r * r;
                }
            }

            System.out.format("10-CV MSE = %.4f%n", rss/n);
         } catch (Exception ex) {
             System.err.println(ex);
         }
    }
    /**
     * Test of learn method, of class NeuralNetwork.
     */
    @Test
    public void testLogisticSigmoid() {
        test(NeuralNetwork.ActivationFunction.LOGISTIC_SIGMOID, "CPU", "weka/cpu.arff", 6);
        //test(NeuralNetwork.ActivationFunction.LOGISTIC_SIGMOID, "2dplanes", "weka/regression/2dplanes.arff", 6);
        test(NeuralNetwork.ActivationFunction.LOGISTIC_SIGMOID, "abalone", "weka/regression/abalone.arff", 8);
        //test(NeuralNetwork.ActivationFunction.LOGISTIC_SIGMOID, "ailerons", "weka/regression/ailerons.arff", 40);
        //test(NeuralNetwork.ActivationFunction.LOGISTIC_SIGMOID, "bank32nh", "weka/regression/bank32nh.arff", 32);
        test(NeuralNetwork.ActivationFunction.LOGISTIC_SIGMOID, "cal_housing", "weka/regression/cal_housing.arff", 8);
        //test(NeuralNetworkRegressor.ActivationFunction.LOGISTIC_SIGMOID, "puma8nh", "weka/regression/puma8nh.arff", 8);
        test(NeuralNetwork.ActivationFunction.LOGISTIC_SIGMOID, "kin8nm", "weka/regression/kin8nm.arff", 8);
    }
    
    /**
     * Test of learn method, of class NeuralNetwork.
     */
    @Test
    public void testTanh() {
        test(NeuralNetwork.ActivationFunction.TANH, "CPU", "weka/cpu.arff", 6);
        //test(NeuralNetworkRegressor.ActivationFunction.TANH, "2dplanes", "weka/regression/2dplanes.arff", 6);
        test(NeuralNetwork.ActivationFunction.TANH, "abalone", "weka/regression/abalone.arff", 8);
        //test(NeuralNetworkRegressor.ActivationFunction.TANH, "ailerons", "weka/regression/ailerons.arff", 40);
        //test(NeuralNetworkRegressor.ActivationFunction.TANH, "bank32nh", "weka/regression/bank32nh.arff", 32);
        test(NeuralNetwork.ActivationFunction.TANH, "cal_housing", "weka/regression/cal_housing.arff", 8);
        //test(NeuralNetworkRegressor.ActivationFunction.TANH, "puma8nh", "weka/regression/puma8nh.arff", 8);
        test(NeuralNetwork.ActivationFunction.TANH, "kin8nm", "weka/regression/kin8nm.arff", 8);
    }
}