/*
* Encog(tm) Core Unit Tests v3.0 - Java Version
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
* Copyright 2008-2011 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.factory;
import org.encog.EncogError;
import org.encog.engine.network.activation.ActivationLinear;
import org.encog.engine.network.activation.ActivationTANH;
import org.encog.ml.svm.SVM;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.rbf.RBFNetwork;
import junit.framework.Assert;
import junit.framework.TestCase;
public class TestMLMethodFactory extends TestCase {
public static final String TYPE_FEEDFORWARD = "feedforward";
public static final String TYPE_RBFNETWORK = "rbfnetwork";
public static final String TYPE_SVM = "svm";
public static final String TYPE_SOM = "som";
public void testFactoryFeedforward() {
String architecture = "?:B->TANH->3->LINEAR->?:B";
MLMethodFactory factory = new MLMethodFactory();
BasicNetwork network = (BasicNetwork)factory.create(MLMethodFactory.TYPE_FEEDFORWARD, architecture, 1, 4);
Assert.assertTrue(network.isLayerBiased(0));
Assert.assertFalse(network.isLayerBiased(1));
Assert.assertTrue(network.isLayerBiased(2));
Assert.assertEquals(3, network.getLayerCount());
Assert.assertTrue(network.getActivation(0) instanceof ActivationLinear );
Assert.assertTrue(network.getActivation(1) instanceof ActivationTANH );
Assert.assertTrue(network.getActivation(2) instanceof ActivationLinear );
Assert.assertEquals(18,network.encodedArrayLength());
Assert.assertEquals(1,network.getLayerNeuronCount(0));
Assert.assertEquals(3,network.getLayerNeuronCount(1));
Assert.assertEquals(4,network.getLayerNeuronCount(2));
}
private void expectError(String t, String a) {
MLMethodFactory factory = new MLMethodFactory();
try {
factory.create(t, a, 2, 1);
Assert.assertTrue(false);
} catch(EncogError e) {
// good
}
}
public void testFactoryFeedforwardError() {
String ARC1 = "?->3->ERROR->?";
String ARC2 = "?->?->?";
String ARC3 = "?->3->0->?";
expectError(MLMethodFactory.TYPE_FEEDFORWARD, ARC1);
expectError(MLMethodFactory.TYPE_FEEDFORWARD, ARC2);
expectError(MLMethodFactory.TYPE_FEEDFORWARD, ARC3);
}
public void testFactoryRBF() {
String architecture = "?->GAUSSIAN(c=4)->?";
MLMethodFactory factory = new MLMethodFactory();
RBFNetwork network = (RBFNetwork)factory.create(MLMethodFactory.TYPE_RBFNETWORK, architecture, 1, 4);
Assert.assertEquals(1,network.getInputCount());
Assert.assertEquals(4,network.getOutputCount());
Assert.assertEquals(4,network.getRBF().length);
}
public void testFactorySVM() {
String architecture = "?->C(KERNEL=RBF,TYPE=NEW)->?";
MLMethodFactory factory = new MLMethodFactory();
SVM network = (SVM)factory.create(MLMethodFactory.TYPE_SVM, architecture, 4, 1);
Assert.assertEquals(4,network.getInputCount());
Assert.assertEquals(1,network.getOutputCount());
}
public void testFactorySOM() {
}
}