/*
 * 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.script.normalize;

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

import org.encog.app.analyst.AnalystError;
import org.encog.app.analyst.missing.DiscardMissing;
import org.encog.app.analyst.missing.HandleMissingValues;
import org.encog.app.analyst.missing.MeanAndModeMissing;
import org.encog.app.analyst.missing.NegateMissing;
import org.encog.app.analyst.script.AnalystClassItem;
import org.encog.app.analyst.script.AnalystScript;
import org.encog.app.analyst.script.DataField;
import org.encog.app.analyst.script.prop.ScriptProperties;
import org.encog.util.arrayutil.ClassItem;
import org.encog.util.arrayutil.NormalizationAction;

/**
 * This class holds information about the fields that the Encog Analyst will
 * normalize.
 * 
 */
public class AnalystNormalize {

	/**
	 * The normalized fields.  These fields define the order and format 
	 * that data will be presented to the ML method.
	 */
	private final List normalizedFields 
		= new ArrayList();
	
	/**
	 * The parent script.
	 */
	private AnalystScript script;

	/**
	 * Construct the object.
	 * @param theScript The script.
	 */
	public AnalystNormalize(AnalystScript theScript) {
		this.script = theScript;
	}
	
	/**
	 * @return Calculate the input columns.
	 */
	public int calculateInputColumns() {
		int result = 0;
		for (final AnalystField field : this.normalizedFields) {
			if (field.isInput()) {
				result += field.getColumnsNeeded();
			}
		}
		return result;
	}

	/**
	 * Calculate the output columns.
	 * @return The output columns.
	 */
	public int calculateOutputColumns() {
		int result = 0;
		for (final AnalystField field : this.normalizedFields) {
			if (field.isOutput()) {
				result += field.getColumnsNeeded();
			}
		}
		return result;
	}

	/**
	 * @return Count the active fields.
	 */
	public int countActiveFields() {
		int result = 0;
		for (final AnalystField field : this.normalizedFields) {
			if (field.getAction() != NormalizationAction.Ignore) {
				result++;
			}
		}
		return result;
	}

	/**
	 * @return the normalizedFields
	 */
	public List getNormalizedFields() {
		return this.normalizedFields;
	}

	/**
	 * Init the normalized fields.
	 * @param script The script.
	 */
	public void init(final AnalystScript script) {

		if (this.normalizedFields == null) {
			return;
		}

		for (final AnalystField norm : this.normalizedFields) {
			final DataField f = script.findDataField(norm.getName());

			if (f == null) {
				throw new AnalystError("Normalize specifies unknown field: "
						+ norm.getName());
			}

			if (norm.getAction() == NormalizationAction.Normalize) {
				norm.setActualHigh(f.getMax());
				norm.setActualLow(f.getMin());
			}

			if ((norm.getAction() == NormalizationAction.Equilateral)
					|| (norm.getAction() == NormalizationAction.OneOf)
					|| (norm.getAction() == NormalizationAction.SingleField)) {

				int index = 0;
				for (final AnalystClassItem item : f.getClassMembers()) {
					norm.getClasses().add(
							new ClassItem(item.getName(), index++));
				}
			}
		}
	}

	/** {@inheritDoc} */
	@Override
	public String toString() {
		final StringBuilder result = new StringBuilder("[");
		result.append(getClass().getSimpleName());
		result.append(": ");
		if (this.normalizedFields != null) {
			result.append(this.normalizedFields.toString());
		}
		result.append("]");
		return result.toString();
	}

	/**
	 * @return the missingValues
	 */
	public HandleMissingValues getMissingValues() {
		final String type = this.script.getProperties().getPropertyString(
				ScriptProperties.ML_CONFIG_TYPE);

		if( type.equals("DiscardMissing") ) {
			return new DiscardMissing();
		} else if( type.equals("MeanAndModeMissing") ) {
			return new MeanAndModeMissing();
		} else if( type.equals("NegateMissing") ) {
			return new NegateMissing();
		} else {
			return new DiscardMissing();
		}
	}

	/**
	 * @param missingValues the missingValues to set
	 */
	public void setMissingValues(HandleMissingValues missingValues) {
		this.script.getProperties().setProperty(
				ScriptProperties.NORMALIZE_MISSING_VALUES, missingValues.getClass().getSimpleName());		
	}	
}