/*
 * 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.ml.bayesian;

import java.io.InputStream;
import java.io.OutputStream;
import java.util.Map;

import org.encog.ml.bayesian.query.BayesianQuery;
import org.encog.ml.bayesian.query.enumerate.EnumerationQuery;
import org.encog.ml.bayesian.query.sample.SamplingQuery;
import org.encog.ml.bayesian.table.TableLine;
import org.encog.persist.EncogFileSection;
import org.encog.persist.EncogPersistor;
import org.encog.persist.EncogReadHelper;
import org.encog.persist.EncogWriteHelper;

/**
 * Persist a Bayesian network.
 */
public class PersistBayes implements EncogPersistor {

	/**
	 * @return The file version.
	 */
	@Override
	public final int getFileVersion() {
		return 1;
	}

	/**
	 * {@inheritDoc}
	 */
	@Override
	public final Object read(final InputStream is) {
		final BayesianNetwork result = new BayesianNetwork();
		final EncogReadHelper in = new EncogReadHelper(is);
		EncogFileSection section;
		String queryType = "";
		String queryStr = "";
		String contentsStr = "";

		while ((section = in.readNextSection()) != null) {
			if (section.getSectionName().equals("BAYES-NETWORK")
					&& section.getSubSectionName().equals("BAYES-PARAM")) {
				final Map params = section.parseParams();
				queryType = params.get("queryType");
				queryStr = params.get("query");
				contentsStr = params.get("contents");
			}
			if (section.getSectionName().equals("BAYES-NETWORK")
					&& section.getSubSectionName().equals("BAYES-TABLE")) {

				result.setContents(contentsStr);
				
				// first, define relationships (1st pass)
				for (String line : section.getLines()) {
					result.defineRelationship(line);
				}

				result.finalizeStructure();

				// now define the probabilities (2nd pass)
				for (String line : section.getLines()) {
					result.defineProbability(line);
				}
			}
			if (section.getSectionName().equals("BAYES-NETWORK")
					&& section.getSubSectionName().equals("BAYES-PROPERTIES")) {
				final Map params = section.parseParams();
				result.getProperties().putAll(params);
			}
		}

		// define query, if it exists
		if (queryType.length() > 0) {
			BayesianQuery query = null;
			if (queryType.equals("EnumerationQuery")) {
				query = new EnumerationQuery(result);
			} else {
				query = new SamplingQuery(result);
			}

			if (query != null && queryStr.length()>0) {
				result.setQuery(query);
				result.defineClassificationStructure(queryStr);
			}
		}

		return result;
	}

	/**
	 * {@inheritDoc}
	 */
	@Override
	public final void save(final OutputStream os, final Object obj) {
		final EncogWriteHelper out = new EncogWriteHelper(os);
		final BayesianNetwork b = (BayesianNetwork) obj;
		out.addSection("BAYES-NETWORK");
		out.addSubSection("BAYES-PARAM");
		String queryType = "";
		String queryStr = b.getClassificationStructure();

		if( b.getQuery()!=null ) {
			queryType = b.getQuery().getClass().getSimpleName();
		}
		
		out.writeProperty("queryType", queryType);
		out.writeProperty("query", queryStr);
		out.writeProperty("contents", b.getContents());
		out.addSubSection("BAYES-PROPERTIES");
		out.addProperties(b.getProperties());

		out.addSubSection("BAYES-TABLE");
		for (BayesianEvent event : b.getEvents()) {
			for (TableLine line : event.getTable().getLines()) {
				if (line == null)
					continue;
				StringBuilder str = new StringBuilder();
				str.append("P(");

				str.append(BayesianEvent.formatEventName(event, line.getResult()));

				if (event.getParents().size() > 0) {
					str.append("|");
				}

				int index = 0;
				boolean first = true;
				for (BayesianEvent parentEvent : event.getParents()) {
					if (!first) {
						str.append(",");
					}
					first = false;
					int arg = line.getArguments()[index++];
					if (parentEvent.isBoolean()) {
						if (arg==0) {
							str.append("+");
						} else {
							str.append("-");
						}
					}
					str.append(parentEvent.getLabel());
					if (!parentEvent.isBoolean()) {
						str.append("=");
						if( arg>=parentEvent.getChoices().size()) {
							throw new BayesianError("Argument value " + arg + " is out of range for event " + parentEvent.toString());
						}
						str.append(parentEvent.getChoice(arg));
					}
				}
				str.append(")=");
				str.append(line.getProbability());
				str.append("\n");
				out.write(str.toString());
			}
		}

		out.flush();
	}

	/**
	 * {@inheritDoc}
	 */
	@Override
	public String getPersistClassString() {
		return "BayesianNetwork";
	}
}