Author: tommaso
Date: Tue May 29 07:39:01 2012
New Revision: 1343582
URL: http://svn.apache.org/viewvc?rev=1343582&view=rev
Log:
added test for an AND NN creation based on FF strategy
Added:
labs/yay/trunk/core/src/test/java/org/apache/yay/NeuralNetworkFactoryTest.java
Added:
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=1343582&view=auto
==============================================================================
---
labs/yay/trunk/core/src/test/java/org/apache/yay/NeuralNetworkFactoryTest.java
(added)
+++
labs/yay/trunk/core/src/test/java/org/apache/yay/NeuralNetworkFactoryTest.java
Tue May 29 07:39:01 2012
@@ -0,0 +1,64 @@
+/*
+ * 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 org.junit.Test;
+
+import java.util.HashSet;
+import java.util.LinkedList;
+import java.util.Set;
+import java.util.Vector;
+
+import static org.junit.Assert.assertEquals;
+
+/**
+ * Testcase for {@link NeuralNetworkFactory}
+ */
+public class NeuralNetworkFactoryTest {
+
+ @Test
+ public void andNNCreationTest() throws Exception {
+ Set<WeightsMatrix> andWeightsMatrixSet = new HashSet<WeightsMatrix>();
+ double[][] weights = {{-30d, 20d, 20d}};
+ WeightsMatrix singleAndLayerWeights = new WeightsMatrix(weights);
+ andWeightsMatrixSet.add(singleAndLayerWeights);
+ NeuralNetwork<Double,Double> andNN = NeuralNetworkFactory.create(new
LinkedList<TrainingExample<Double, Double>>(), andWeightsMatrixSet, new
VoidLearningStrategy(), new FeedForwardStrategy(new SigmoidFunction()));
+ assertEquals(0l, Math.round(andNN.predict(createSample(1d, 0d))));
+ assertEquals(1l, Math.round(andNN.predict(createSample(1d, 1d))));
+ }
+
+ private Example<Double> createSample(final Double x, final Double y) {
+ return new Example<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);
+ Feature<Double> trueFeature = new Feature<Double>();
+ trueFeature.setValue(x);
+ features.add(trueFeature);
+ Feature<Double> falseFeature = new Feature<Double>();
+ falseFeature.setValue(y);
+ features.add(falseFeature);
+ return features;
+ }
+ };
+ }
+}
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