/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2014 by RapidMiner and the contributors
 *
 *  Complete list of developers available at our web site:
 *
 *       http://rapidminer.com
 *
 *  This program is free software: you can redistribute it and/or modify
 *  it under the terms of the GNU Affero General Public License as published by
 *  the Free Software Foundation, either version 3 of the License, or
 *  (at your option) any later version.
 *
 *  This program is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU Affero General Public License for more details.
 *
 *  You should have received a copy of the GNU Affero General Public License
 *  along with this program.  If not, see http://www.gnu.org/licenses/.
 */
package com.rapidminer.operator.visualization.dependencies;

import Jama.Matrix;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.Operator;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.ProcessSetupError.Severity;
import com.rapidminer.operator.ports.InputPort;
import com.rapidminer.operator.ports.OutputPort;
import com.rapidminer.operator.ports.metadata.AttributeMetaData;
import com.rapidminer.operator.ports.metadata.ExampleSetMetaData;
import com.rapidminer.operator.ports.metadata.ExampleSetPrecondition;
import com.rapidminer.operator.ports.metadata.SimpleMetaDataError;
import com.rapidminer.parameter.UndefinedParameterError;
import com.rapidminer.tools.math.matrix.CovarianceMatrix;

/** This operator calculates the covariances between all attributes
 *  of the input example set and returns a covariance matrix object
 *  which can be visualized.
 * 
 *  @author Ingo Mierswa
 */
public class CovarianceMatrixOperator extends Operator {

    private InputPort exampleSetInput = getInputPorts().createPort("example set");

    private OutputPort exampleSetOutput = getOutputPorts().createPort("example set");
    private OutputPort covarianceOutput = getOutputPorts().createPort("covariance");

    public CovarianceMatrixOperator(OperatorDescription description) {
        super(description);

        exampleSetInput.addPrecondition(new ExampleSetPrecondition(exampleSetInput) {
            @Override
            public void makeAdditionalChecks(ExampleSetMetaData emd) throws UndefinedParameterError {
                for (AttributeMetaData amd: emd.getAllAttributes()) {
                    if (!amd.isSpecial() && !amd.isNumerical()) {
                        exampleSetInput.addError(new SimpleMetaDataError(Severity.WARNING, exampleSetInput, "not_defined_on_nominal", "Covariance"));
                        break;
                    }
                }
                super.makeAdditionalChecks(emd);
            }
        });

        getTransformer().addPassThroughRule(exampleSetInput, exampleSetOutput);
        getTransformer().addGenerationRule(covarianceOutput, NumericalMatrix.class);
    }

    @Override
    public void doWork() throws OperatorException {
        ExampleSet exampleSet = exampleSetInput.getData(ExampleSet.class);
        String[] columnNames = new String[exampleSet.getAttributes().size()];
        boolean[] isNominal = new boolean[columnNames.length];
        int counter = 0;
        for (Attribute attribute : exampleSet.getAttributes()) {
            columnNames[counter] = attribute.getName();
            if (attribute.isNominal())
                isNominal[counter] = true;
            counter++;
        }
        Matrix covarianceMatrix = CovarianceMatrix.getCovarianceMatrix(exampleSet);

        // setting all nominal colums on NaN
        double[][] matrix = covarianceMatrix.getArray();
        for (int i = 0; i < covarianceMatrix.getColumnDimension(); i++) {
            for (int j = 0; j < covarianceMatrix.getRowDimension(); j++) {
                if (isNominal[i] || isNominal[j])
                    matrix[i][j] = Double.NaN;
            }
        }

        exampleSetOutput.deliver(exampleSet);
        covarianceOutput.deliver(new NumericalMatrix("Covariance", columnNames, covarianceMatrix, true));
    }
}