/*
 * Encog(tm) Core v3.4 - Java Version
 * http://www.heatonresearch.com/encog/
 * https://github.com/encog/encog-java-core
 
 * Copyright 2008-2016 Heaton Research, Inc.
 *
 * 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.
 *   
 * For more information on Heaton Research copyrights, licenses 
 * and trademarks visit:
 * http://www.heatonresearch.com/copyright
 */
package org.encog.app.analyst.util;

import org.encog.EncogError;
import org.encog.app.analyst.AnalystFileFormat;
import org.encog.app.analyst.EncogAnalyst;
import org.encog.app.analyst.analyze.PerformAnalysis;
import org.encog.app.analyst.csv.TimeSeriesUtil;
import org.encog.app.analyst.csv.normalize.AnalystNormalizeCSV;
import org.encog.app.analyst.script.normalize.AnalystField;
import org.encog.app.analyst.script.prop.ScriptProperties;
import org.encog.ml.data.MLData;
import org.encog.ml.data.MLDataPair;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataPair;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.util.arrayutil.NormalizationAction;
import org.encog.util.csv.CSVFormat;
import org.encog.util.csv.ReadCSV;
import org.encog.util.logging.EncogLogging;

import java.io.File;

/**
 * Provides an interface to the analyst usually used by other programs.
 */
public class AnalystUtility {
	/**
	 * The analyst we are using.
	 */
	final private EncogAnalyst analyst;

	/**
	 * Construct the analyst utility.
	 * @param theAnalyst The analyst that we are using.
	 */
	public AnalystUtility(EncogAnalyst theAnalyst) {
		this.analyst = theAnalyst;
	}

	/**
	 * Encode fields, using the analyst.
	 * @param includeInput Should we include the input fields.
	 * @param includeOutput Should we include the output fields.
	 * @param rawData The raw data to encode from.
	 * @param encodedData The data to encode to.
	 */
	public void encode(
			boolean includeInput,
			boolean includeOutput,
			double[] rawData,
			MLData encodedData) {
		int rawIndex = 0;
		int outputIndex = 0;

		for (final AnalystField stat : analyst.getScript().getNormalize()
				.getNormalizedFields()) {

			if (stat.isIgnored()) {
				continue;
			}

			if (stat.isOutput() && !includeOutput ) {
				continue;
			}

			if( stat.isInput() && !includeInput ) {
				continue;
			}

			if (stat.getAction() == NormalizationAction.Normalize) {
				encodedData.setData(outputIndex++, stat.normalize(rawData[rawIndex++]));
			} else if (stat.getAction() == NormalizationAction.PassThrough) {
				encodedData.setData(outputIndex++, rawData[rawIndex++]);
			} else {
				final double[] d = stat.encode(rawData[rawIndex++]);
				for (final double element : d) {
					encodedData.setData(outputIndex++, element);
				}
			}
		}
	}

	/**
	 * Load a CSV file into an MLDataSet.
	 * @param file The file to load.
	 * @return The loaded data set.
	 */
	public MLDataSet loadCSV(File file) {
		if (this.analyst == null) {
			throw new EncogError(
					"Can't normalize yet, file has not been analyzed.");
		}

		MLDataSet result = new BasicMLDataSet();

		int inputCount = this.analyst.determineInputCount();
		int outputCount = this.analyst.determineOutputCount();
		int totalCount = inputCount+outputCount;

		boolean headers = this.analyst.getScript().getProperties()
				.getPropertyBoolean(ScriptProperties.SETUP_CONFIG_INPUT_HEADERS);

		final CSVFormat format = this.analyst.getScript().determineFormat();

		CSVHeaders analystHeaders = new CSVHeaders(file, headers,
				format);

		ReadCSV csv = new ReadCSV(file.toString(), headers, format);

		for (final AnalystField field : analyst.getScript().getNormalize()
				.getNormalizedFields()) {
			field.init();
		}

		TimeSeriesUtil series = new TimeSeriesUtil(analyst,true,
				analystHeaders.getHeaders());


		try {
			// write file contents
			while (csv.next()) {

				double[] output = AnalystNormalizeCSV.extractFields(
						this.analyst, analystHeaders, csv, totalCount,
						false);

				if (series.getTotalDepth() > 1) {
					output = series.process(output);
				}

				MLDataPair pair = BasicMLDataPair.createPair(inputCount,outputCount);
				for(int i=0;i