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package org.deeplearning4j.eval;

import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.transforms.Abs;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.NDArrayIndex;

import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;

/**
 * Evaluation method for the evaluation of regression algorithms.

 * Provides the following metrics, for each column:

 * - MSE: mean squared error

 * - MAE: mean absolute error

 * - RMSE: root mean squared error

 * - RSE: relative squared error

 * - correlation coefficient

 * See for example: http://www.saedsayad.com/model_evaluation_r.htm
 * For classification, see {@link Evaluation}
 *
 * @author Alex Black
 */
public class RegressionEvaluation {

    public static final int DEFAULT_PRECISION = 5;

    private List columnNames;
    private int precision;
    private int exampleCount = 0;
    private INDArray labelsSumPerColumn;    //sum(actual) per column -> used to calculate mean
    private INDArray sumSquaredErrorsPerColumn;     //(predicted - actual)^2
    private INDArray sumAbsErrorsPerColumn;         //abs(predicted-actial)
    private INDArray currentMean;
    private INDArray currentPredictionMean;
    private INDArray m2Actual;

    private INDArray sumOfProducts;
    private INDArray sumSquaredLabels;
    private INDArray sumSquaredPredicted;

    /** Create a regression evaluation object with the specified number of columns, and default precision
     * for the stats() method.
     * @param nColumns Number of columns
     */
    public RegressionEvaluation(int nColumns) {
        this(createDefaultColumnNames(nColumns), DEFAULT_PRECISION);
    }

    /** Create a regression evaluation object with the specified number of columns, and specified precision
     * for the stats() method.
     * @param nColumns Number of columns
     */
    public RegressionEvaluation(int nColumns, int precision) {
        this(createDefaultColumnNames(nColumns), precision);
    }

    /** Create a regression evaluation object with default precision for the stats() method
     * @param columnNames Names of the columns
     */
    public RegressionEvaluation(String... columnNames) {
        this(Arrays.asList(columnNames), DEFAULT_PRECISION);
    }

    /** Create a regression evaluation object with default precision for the stats() method
     * @param columnNames Names of the columns
     */
    public RegressionEvaluation(List columnNames) {
        this(columnNames, DEFAULT_PRECISION);
    }

    /** Create a regression evaluation object with specified precision for the stats() method
     * @param columnNames Names of the columns
     */
    public RegressionEvaluation(List columnNames, int precision) {
        this.columnNames = columnNames;
        this.precision = precision;

        int n = columnNames.size();
        labelsSumPerColumn = Nd4j.zeros(n);
        sumSquaredErrorsPerColumn = Nd4j.zeros(n);
        sumAbsErrorsPerColumn = Nd4j.zeros(n);
        currentMean = Nd4j.zeros(n);
        m2Actual = Nd4j.zeros(n);

        currentPredictionMean = Nd4j.zeros(n);
        sumOfProducts = Nd4j.zeros(n);
        sumSquaredLabels = Nd4j.zeros(n);
        sumSquaredPredicted = Nd4j.zeros(n);
    }

    private static List createDefaultColumnNames(int nColumns) {
        List list = new ArrayList<>(nColumns);
        for (int i = 0; i < nColumns; i++) list.add("col_" + i);
        return list;
    }


    public void eval(INDArray labels, INDArray predictions) {
        //References for the calculations is this section:
        //https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Online_algorithm
        //https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient#For_a_sample
        //Doing online calculation of means, sum of squares, etc.

        labelsSumPerColumn.addi(labels.sum(0));

        INDArray error = predictions.sub(labels);
        INDArray absErrorSum = Nd4j.getExecutioner().execAndReturn(new Abs(error.dup())).sum(0);
        INDArray squaredErrorSum = error.mul(error).sum(0);

        sumAbsErrorsPerColumn.addi(absErrorSum);
        sumSquaredErrorsPerColumn.addi(squaredErrorSum);

        sumOfProducts.addi(labels.mul(predictions).sum(0));

        sumSquaredLabels.addi(labels.mul(labels).sum(0));
        sumSquaredPredicted.addi(predictions.mul(predictions).sum(0));


        int nRows = labels.size(0);
        for( int i=0; i