/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 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.commands;
import java.io.File;
import java.util.Random;
import org.encog.app.analyst.AnalystError;
import org.encog.app.analyst.EncogAnalyst;
import org.encog.app.analyst.script.DataField;
import org.encog.app.analyst.script.ml.ScriptOpcode;
import org.encog.app.analyst.script.normalize.AnalystField;
import org.encog.app.analyst.script.prop.ScriptProperties;
import org.encog.ml.MLMethod;
import org.encog.ml.bayesian.BayesianNetwork;
import org.encog.ml.data.buffer.EncogEGBFile;
import org.encog.ml.factory.MLMethodFactory;
import org.encog.ml.prg.VariableMapping;
import org.encog.ml.prg.expvalue.ValueType;
import org.encog.ml.prg.extension.EncogOpcodeRegistry;
import org.encog.ml.prg.extension.ProgramExtensionTemplate;
import org.encog.ml.prg.generator.RampedHalfAndHalf;
import org.encog.ml.prg.train.PrgPopulation;
import org.encog.persist.EncogDirectoryPersistence;
import org.encog.util.logging.EncogLogging;
/**
* The Encog Analyst create command. This command is used to create a Machine
* Learning method.
*
*/
public class CmdCreate extends Cmd {
/**
* The name of this command.
*/
public static final String COMMAND_NAME = "CREATE";
/**
* Construct the create command.
*
* @param theAnalyst
* The analyst to use.
*/
public CmdCreate(final EncogAnalyst theAnalyst) {
super(theAnalyst);
}
/**
* {@inheritDoc}
*/
@Override
public boolean executeCommand(final String args) {
// get filenames
final String trainingID = getProp().getPropertyString(
ScriptProperties.ML_CONFIG_TRAINING_FILE);
final String resourceID = getProp().getPropertyString(
ScriptProperties.ML_CONFIG_MACHINE_LEARNING_FILE);
final File trainingFile = getScript().resolveFilename(trainingID);
final File resourceFile = getScript().resolveFilename(resourceID);
final String type = getProp().getPropertyString(
ScriptProperties.ML_CONFIG_TYPE);
final String arch = getProp().getPropertyString(
ScriptProperties.ML_CONFIG_ARCHITECTURE);
EncogLogging.log(EncogLogging.LEVEL_DEBUG, "Beginning create");
EncogLogging.log(EncogLogging.LEVEL_DEBUG, "training file:"
+ trainingID);
EncogLogging.log(EncogLogging.LEVEL_DEBUG, "resource file:"
+ resourceID);
EncogLogging.log(EncogLogging.LEVEL_DEBUG, "type:" + type);
EncogLogging.log(EncogLogging.LEVEL_DEBUG, "arch:" + arch);
final EncogEGBFile egb = new EncogEGBFile(trainingFile);
egb.open();
final int input = egb.getInputCount();
final int ideal = egb.getIdealCount();
egb.close();
final MLMethodFactory factory = new MLMethodFactory();
final MLMethod obj = factory.create(type, arch, input, ideal);
if (obj instanceof BayesianNetwork) {
final String query = getProp().getPropertyString(
ScriptProperties.ML_CONFIG_QUERY);
((BayesianNetwork) obj).defineClassificationStructure(query);
} else if (obj instanceof PrgPopulation) {
handlePrgPopulation((PrgPopulation) obj);
}
EncogDirectoryPersistence.saveObject(resourceFile, obj);
return false;
}
private void handlePrgPopulation(PrgPopulation pop) {
// create the variables
int classType = 0;
pop.getContext().clearDefinedVariables();
for(AnalystField field : getScript().getNormalize().getNormalizedFields() ) {
DataField df = getScript().findDataField(field.getName());
String varName = field.getName();
VariableMapping mapping;
switch( field.getAction() ) {
case Ignore:
mapping = null;
break;
case PassThrough:
if( df.isInteger() ) {
mapping = new VariableMapping(varName, ValueType.intType);
} else if( df.isReal() ) {
mapping = new VariableMapping(varName, ValueType.floatingType);
} else {
mapping = new VariableMapping(varName, ValueType.stringType);
}
break;
case Equilateral:
case OneOf:
case Normalize:
mapping = new VariableMapping(varName, ValueType.floatingType);
break;
case SingleField:
if( df.isClass() ) {
mapping = new VariableMapping(varName, ValueType.enumType, classType++, df.getClassMembers().size() );
} else if( df.isInteger() ) {
mapping = new VariableMapping(varName, ValueType.intType );
} else {
mapping = new VariableMapping(varName, ValueType.floatingType);
}
break;
default:
throw new AnalystError("Unknown normalization action: " + field.getAction().toString());
}
if( field.isOutput() ) {
pop.getContext().setResult(mapping);
} else {
pop.getContext().defineVariable(mapping);
}
}
// populate the opcodes
if (this.getScript().getOpcodes().size() > 0) {
for (ScriptOpcode op : this.getScript().getOpcodes()) {
ProgramExtensionTemplate temp = EncogOpcodeRegistry.INSTANCE
.findOpcode(op.getName(), op.getArgCount());
pop.getContext().getFunctions().addExtension(temp);
}
}
// generate initial population
RampedHalfAndHalf generate = new RampedHalfAndHalf(pop.getContext(), 1, 6);
generate.generate(new Random(), pop);
}
/**
* {@inheritDoc}
*/
@Override
public String getName() {
return CmdCreate.COMMAND_NAME;
}
}