Author: tommaso
Date: Mon Nov 19 09:05:33 2012
New Revision: 1411106
URL: http://svn.apache.org/viewvc?rev=1411106&view=rev
Log:
refactored NNFactoryTest, added test for BasicPerceptron
Added:
labs/yay/trunk/core/src/test/java/org/apache/yay/BasicPerceptronTest.java
(with props)
Modified:
labs/yay/trunk/core/src/main/java/org/apache/yay/BasicPerceptron.java
labs/yay/trunk/core/src/test/java/org/apache/yay/NeuralNetworkFactoryTest.java
Modified: labs/yay/trunk/core/src/main/java/org/apache/yay/BasicPerceptron.java
URL:
http://svn.apache.org/viewvc/labs/yay/trunk/core/src/main/java/org/apache/yay/BasicPerceptron.java?rev=1411106&r1=1411105&r2=1411106&view=diff
==============================================================================
--- labs/yay/trunk/core/src/main/java/org/apache/yay/BasicPerceptron.java
(original)
+++ labs/yay/trunk/core/src/main/java/org/apache/yay/BasicPerceptron.java Mon
Nov 19 09:05:33 2012
@@ -38,7 +38,7 @@ public class BasicPerceptron implements
*
* @param inputWeights the array of starting weights for the perceptron
*/
- public BasicPerceptron(double[] inputWeights) {
+ public BasicPerceptron(double... inputWeights) {
this.perceptronNeuron = new BinaryThresholdNeuron(0, inputWeights);
this.currentWeights = inputWeights;
}
Added: labs/yay/trunk/core/src/test/java/org/apache/yay/BasicPerceptronTest.java
URL:
http://svn.apache.org/viewvc/labs/yay/trunk/core/src/test/java/org/apache/yay/BasicPerceptronTest.java?rev=1411106&view=auto
==============================================================================
--- labs/yay/trunk/core/src/test/java/org/apache/yay/BasicPerceptronTest.java
(added)
+++ labs/yay/trunk/core/src/test/java/org/apache/yay/BasicPerceptronTest.java
Mon Nov 19 09:05:33 2012
@@ -0,0 +1,74 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you 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.
+ */
+package org.apache.yay;
+
+import java.util.Vector;
+
+import org.junit.Test;
+
+import static org.junit.Assert.assertEquals;
+
+/**
+ * Testcase for {@link BasicPerceptron}
+ */
+public class BasicPerceptronTest {
+
+ @Test
+ public void testLearnPhase() throws Exception {
+ BasicPerceptron basicPerceptron = new BasicPerceptron(1, 2, 3, 4);
+ basicPerceptron.learn(createTrainingExample(1d, 4d, 5d, 6d),
+ createTrainingExample(1d, 5d, 6d, 0.5d),
+ createTrainingExample(0.1d, 9d, 4d, 1.9d),
+ createTrainingExample(0.11d, 4d, 2.6d, 9.5d));
+ }
+
+ @Test
+ public void testPredictionPhase() throws Exception {
+ BasicPerceptron basicPerceptron = new BasicPerceptron(1, 2, 3, 4);
+ basicPerceptron.learn(createTrainingExample(1d, 4d, 5d, 6d),
+ createTrainingExample(1d, 5d, 6d, 0.5d),
+ createTrainingExample(0.1d, 9d, 4d, 1.9d),
+ createTrainingExample(0.11d, 4d, 2.6d, 9.5d));
+ Double output = basicPerceptron.predict(createTrainingExample(0d, 1d,
6d, 0.4d));
+ assertEquals(Double.valueOf(1d), output);
+ }
+
+ private TrainingExample<Double, Double> createTrainingExample(final Double
output, final Double... params) {
+ return new TrainingExample<Double, Double>() {
+ @Override
+ public Vector<Feature<Double>> getFeatureVector() {
+ Vector<Feature<Double>> features = new
Vector<Feature<Double>>();
+ Feature<Double> byasFeature = new Feature<Double>();
+ byasFeature.setValue(1d);
+ features.add(byasFeature);
+ for (Double d : params) {
+ Feature<Double> feature = new Feature<Double>();
+ feature.setValue(d);
+ features.add(feature);
+ }
+ return features;
+ }
+
+ @Override
+ public Double getOutput() {
+ return output;
+ }
+ };
+ }
+}
Propchange:
labs/yay/trunk/core/src/test/java/org/apache/yay/BasicPerceptronTest.java
------------------------------------------------------------------------------
svn:eol-style = native
Modified:
labs/yay/trunk/core/src/test/java/org/apache/yay/NeuralNetworkFactoryTest.java
URL:
http://svn.apache.org/viewvc/labs/yay/trunk/core/src/test/java/org/apache/yay/NeuralNetworkFactoryTest.java?rev=1411106&r1=1411105&r2=1411106&view=diff
==============================================================================
---
labs/yay/trunk/core/src/test/java/org/apache/yay/NeuralNetworkFactoryTest.java
(original)
+++
labs/yay/trunk/core/src/test/java/org/apache/yay/NeuralNetworkFactoryTest.java
Mon Nov 19 09:05:33 2012
@@ -35,7 +35,7 @@ public class NeuralNetworkFactoryTest {
double[][] weights = {{-30d, 20d, 20d}};
WeightsMatrix singleAndLayerWeights = new WeightsMatrix(weights);
WeightsMatrix[] andWeightsMatrixSet = new
WeightsMatrix[]{singleAndLayerWeights};
- NeuralNetwork<Double,Double> andNN = NeuralNetworkFactory.create(new
LinkedList<TrainingExample<Double, Double>>(), andWeightsMatrixSet, new
VoidLearningStrategy<Double, Double>(), new FeedForwardStrategy(new
SigmoidFunction()));
+ NeuralNetwork<Double,Double> andNN = createFFNN(andWeightsMatrixSet);
assertEquals(0l, Math.round(andNN.predict(createSample(1d, 0d))));
assertEquals(0l, Math.round(andNN.predict(createSample(0d, 1d))));
assertEquals(0l, Math.round(andNN.predict(createSample(0d, 0d))));
@@ -47,7 +47,7 @@ public class NeuralNetworkFactoryTest {
double[][] weights = {{-10d, 20d, 20d}};
WeightsMatrix singleOrLayerWeights = new WeightsMatrix(weights);
WeightsMatrix[] orWeightsMatrixSet = new
WeightsMatrix[]{singleOrLayerWeights};
- NeuralNetwork<Double,Double> orNN = NeuralNetworkFactory.create(new
LinkedList<TrainingExample<Double, Double>>(), orWeightsMatrixSet, new
VoidLearningStrategy<Double, Double>(), new FeedForwardStrategy(new
SigmoidFunction()));
+ NeuralNetwork<Double,Double> orNN = createFFNN(orWeightsMatrixSet);
assertEquals(1l, Math.round(orNN.predict(createSample(1d, 0d))));
assertEquals(1l, Math.round(orNN.predict(createSample(0d, 1d))));
assertEquals(0l, Math.round(orNN.predict(createSample(0d, 0d))));
@@ -59,7 +59,7 @@ public class NeuralNetworkFactoryTest {
double[][] weights = {{10d, -20d}};
WeightsMatrix singleNotLayerWeights = new WeightsMatrix(weights);
WeightsMatrix[] notWeightsMatrixSet = new
WeightsMatrix[]{singleNotLayerWeights};
- NeuralNetwork<Double,Double> orNN = NeuralNetworkFactory.create(new
LinkedList<TrainingExample<Double, Double>>(), notWeightsMatrixSet, new
VoidLearningStrategy<Double, Double>(), new FeedForwardStrategy(new
SigmoidFunction()));
+ NeuralNetwork<Double,Double> orNN = createFFNN(notWeightsMatrixSet);
assertEquals(1l, Math.round(orNN.predict(createSample(0d))));
assertEquals(0l, Math.round(orNN.predict(createSample(1d))));
}
@@ -69,13 +69,20 @@ public class NeuralNetworkFactoryTest {
WeightsMatrix firstNorLayerWeights = new WeightsMatrix(new double[][]{{0,
0, 0},{-30d, 20d, 20d}, {10d, -20d, -20d}});
WeightsMatrix secondNorLayerWeights = new WeightsMatrix(new
double[][]{{-10d, 20d, 20d}});
WeightsMatrix[] norWeightsMatrixSet = new
WeightsMatrix[]{firstNorLayerWeights,secondNorLayerWeights};
- NeuralNetwork<Double,Double> norNN = NeuralNetworkFactory.create(new
LinkedList<TrainingExample<Double, Double>>(), norWeightsMatrixSet, new
VoidLearningStrategy<Double, Double>(), new FeedForwardStrategy(new
SigmoidFunction()));
+ NeuralNetwork<Double,Double> norNN = createFFNN(norWeightsMatrixSet);
assertEquals(0l, Math.round(norNN.predict(createSample(1d, 0d))));
assertEquals(0l, Math.round(norNN.predict(createSample(0d, 1d))));
assertEquals(1l, Math.round(norNN.predict(createSample(0d, 0d))));
assertEquals(1l, Math.round(norNN.predict(createSample(1d, 1d))));
}
+ private NeuralNetwork<Double, Double> createFFNN(WeightsMatrix[]
andWeightsMatrixSet)
+ throws CreationException {
+ return NeuralNetworkFactory.create(new LinkedList<TrainingExample<Double,
Double>>(),
+ andWeightsMatrixSet, new VoidLearningStrategy<Double, Double>(),
+ new FeedForwardStrategy(new SigmoidFunction()));
+ }
+
private Example<Double> createSample(final Double... params) {
return new Example<Double>() {
@Override
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