multiplevalidationsample.java

一个纯java写的神经网络源代码

    private double[][] constructLearningData(int learningPatternNumber){
        // Define the number of columns
        int columns = 5;
        // Define the Learning Data array
        double[][] learningData = new double[learningPatternNumber][columns];
        // Define the randomized Learning Data
        // Select the not Randomized patterns
        
        // The Simple Parity Input Data
        double[][] simpleParityData = {
            {0.0,0.0,0.0,0.0,1.0},
            {0.0,0.0,0.0,1.0,0.0},
            {0.0,0.0,1.0,0.0,0.0},
            {0.0,0.0,1.0,1.0,1.0},
            {0.0,1.0,0.0,0.0,0.0},
            {0.0,1.0,0.0,1.0,1.0},
            {0.0,1.0,1.0,0.0,1.0},
            {0.0,1.0,1.0,1.0,0.0},
            {1.0,0.0,0.0,0.0,0.0},
            {1.0,0.0,0.0,1.0,1.0},
            {1.0,0.0,1.0,0.0,1.0},
            {1.0,0.0,1.0,1.0,0.0},
            {1.0,1.0,0.0,0.0,1.0},
            {1.0,1.0,0.0,1.0,0.0},
            {1.0,1.0,1.0,0.0,0.0},
            {1.0,1.0,1.0,1.0,1.0}};
            
        for (int i = 0;i<learningPatternNumber;i++){
            // Construct the each pattern of the Learning Data
            learningData[i][0] = simpleParityData[i][0];
            learningData[i][1] = simpleParityData[i][1];
            learningData[i][2] = simpleParityData[i][2];
            learningData[i][3] = simpleParityData[i][3];
            learningData[i][4] = simpleParityData[i][4];
        }
        return learningData;
    }
    
    /** Constructs the network Test Data based on the GUI options - Class Method
     * @param testPatternNumber The int Number of Test Patterns
     * @return The Test Patterns Vector
     */
    private double[][] constructTestData(int testPatternNumber){
        // Define the number of columns
        int columns = 5;
        
        // Define the Learning Data array
        double[][] testData = new double[testPatternNumber][columns];
        // Define the randomized Learning Data
        // Select the not Randomized patterns
        // The Simple Parity Input Data
        double[][] simpleParityData = {{0.0,0.0,0.0,0.0,1.0},{0.0,0.0,0.0,1.0,0.0},{0.0,0.0,1.0,0.0,0.0},{0.0,0.0,1.0,1.0,1.0},{0.0,1.0,0.0,0.0,0.0},{0.0,1.0,0.0,1.0,1.0},{0.0,1.0,1.0,0.0,1.0},{0.0,1.0,1.0,1.0,0.0},{1.0,0.0,0.0,0.0,0.0},{1.0,0.0,0.0,1.0,1.0},{1.0,0.0,1.0,0.0,1.0},{1.0,0.0,1.0,1.0,0.0},{1.0,1.0,0.0,0.0,1.0},{1.0,1.0,0.0,1.0,0.0},{1.0,1.0,1.0,0.0,0.0},{1.0,1.0,1.0,1.0,1.0}};
        for (int i = 0;i<testPatternNumber;i++){
            // Construct the each pattern of the Test Data
            testData[i][0] = simpleParityData[i][0];
            testData[i][1] = simpleParityData[i][1];
            testData[i][2] = simpleParityData[i][2];
            testData[i][3] = simpleParityData[i][3];
            testData[i][4] = simpleParityData[i][4];
        }
        return testData;
    }
    
    private NeuralNet initializeModularParity(int learningPatternNumber,int testPatternNumber){
        // Initialize the neural networks
        NeuralNet network = new NeuralNet();
        NestedNeuralLayer firstNetwork = new NestedNeuralLayer();
        NestedNeuralLayer secondNetwork = new NestedNeuralLayer();
        // Set the First & Second network properties
        firstNetwork.setNeuralNet(xorNet);
        secondNetwork.setNeuralNet(xorNet);
        firstNetwork.setLayerName("First Network");
        secondNetwork.setLayerName("Second Network");
        //firstNetwork.setLearning(true);
        //secondNetwork.setLearning(true);
        // Define & Initialize the Learning & Test inputs
        double[][] learningData=constructLearningData(learningPatternNumber);
        double[][] testData=constructTestData(testPatternNumber);
        // Define & Initialize the network layers and Define the layer names
        LinearLayer inputFirst=new LinearLayer();
        LinearLayer inputSecond=new LinearLayer();
        SigmoidLayer hidden=new SigmoidLayer();
        SigmoidLayer output=new SigmoidLayer();
        inputFirst.setLayerName("First Input Third Network Layer");
        inputSecond.setLayerName("Second Input Third Network Layer");
        hidden.setLayerName("Hidden Third Network Layer");
        output.setLayerName("Output Third Network Layer");
        // Define the number of neurons for each layer
        inputFirst.setRows(1);
        inputSecond.setRows(1);
        hidden.setRows(2);
        output.setRows(1);
        // Define the first network output -> input connection for the third network
        DirectSynapse firstSynapseOI=new DirectSynapse();
        firstSynapseOI.setName("First OI Synapse");
        // Define the second network output -> input connection for the third network
        DirectSynapse secondSynapseOI=new DirectSynapse();
        secondSynapseOI.setName("First OI Synapse");
        // Define the first input -> hidden connection for the third network
        FullSynapse firstSynapseIH=new FullSynapse();
        firstSynapseIH.setName("First IH Synapse");
        // Define the Second input -> hidden connection for the third network
        FullSynapse secondSynapseIH=new FullSynapse();
        secondSynapseIH.setName("Second IH Synapse");
        // Define the hidden -> output connection for the third network
        FullSynapse synapseHO=new FullSynapse();
        synapseHO.setName("HO Synapse");
        // Connect the First Network with the First Input Third Network Layer
        NeuralNetFactory.connect(firstNetwork,firstSynapseOI,inputFirst);
        // Connect the Second Network with the Second Input Third Network Layer
        NeuralNetFactory.connect(secondNetwork,secondSynapseOI,inputSecond);
        // Connect the First Input Third Network Layer with the Hidden Third Network Layer
        NeuralNetFactory.connect(inputFirst,firstSynapseIH,hidden);
        // Connect the Second Input Third Network Layer with the Hidden Third Network Layer
        NeuralNetFactory.connect(inputSecond,secondSynapseIH,hidden);
        // Connect the Hidden Third Network Layer with the Output Third Network Layer
        NeuralNetFactory.connect(hidden,synapseHO,output);
        // Define & Initialize the First Learning Input Synapse
        MemoryInputSynapse firstLearningInputSynapse=NeuralNetFactory.createInput("First Learning Input Synapse",learningData,1,1,2);
        // Define the First Test Input Synapse
        MemoryInputSynapse firstTestInputSynapse=NeuralNetFactory.createInput("First Test Input Synapse",testData,1,1,2);
        // Initialize the First Input Switch Synapse
        LearningSwitch firstInputSwitch=NeuralNetFactory.createSwitch("First Input Switch Synapse",firstLearningInputSynapse,firstTestInputSynapse);
        // Connect the First Input Switch Synapse to the First Network
        firstNetwork.addInputSynapse(firstInputSwitch);
        // Define & Initialize the Second Learning Input Synapse
        MemoryInputSynapse secondLearningInputSynapse=NeuralNetFactory.createInput("Second Learning Input Synapse",learningData,1,3,4);
        // Define the Second Test Input Synapse
        MemoryInputSynapse secondTestInputSynapse=NeuralNetFactory.createInput("Second Test Input Synapse",testData,1,3,4);
        // Initialize the Second Input Switch Synapse
        LearningSwitch secondInputSwitch=NeuralNetFactory.createSwitch("Second Input Switch Synapse",secondLearningInputSynapse,secondTestInputSynapse);
        secondInputSwitch.setStepCounter(false);
        // Connect the Second Input Switch Synapse to the Second Network
        secondNetwork.addInputSynapse(secondInputSwitch);
        // Define the Trainer Input Switch
        MemoryInputSynapse learningDesiredSynapse=NeuralNetFactory.createInput("Learning Desired Synapse",learningData,1,5,5);
        // Define the Test Input Synapse
        MemoryInputSynapse testDesiredSynapse=NeuralNetFactory.createInput("Test Desired Synapse",testData,1,5,5);
        // Initialize the Input Switch Synapse
        LearningSwitch learningSwitch=NeuralNetFactory.createSwitch("Learning Switch Synapse",learningDesiredSynapse,testDesiredSynapse);
        // Define the Trainer and link it to the Monitor
        TeachingSynapse trainer=new TeachingSynapse();
        trainer.setName("Modular Parity Trainer Synapse");
        // Connect the Teacher to the Output Layer
        output.addOutputSynapse(trainer);
        // Connect the Learning Switch Synapse to the Trainer
        trainer.setDesired(learningSwitch);
        // Define the Output Synapse Memory (Data)
        MemoryOutputSynapse outputMemoryData=new MemoryOutputSynapse();
        outputMemoryData.setName("Output Data");
        // Connect the Output Memory Synapse (Data) to the Output Third Network Layer
        output.addOutputSynapse(outputMemoryData);
        // Incorpore the network components to the NeuralNet object
        network.addLayer(firstNetwork);
        network.addLayer(secondNetwork);
        network.addLayer(inputFirst);
        network.addLayer(inputSecond);
        network.addLayer(hidden);
        network.addLayer(output);
        network.setTeacher(trainer);
        network.getMonitor().setLearningRate(0.5);
        network.getMonitor().setMomentum(0.5);
        network.getMonitor().setTotCicles(totCycles);
        return network;
    }
    /** Initializes the EETNN Neural Network I - Class Method
     * @param learningPatternNumber Number of Learning Patterns
     * @param testPatternNumber Number of Test Patterns
     */
    private NeuralNet initializeNetworkI(int learningPatternNumber,int testPatternNumber){
        // Initialize the neural network
        NeuralNet network=new NeuralNet();
        // Define & Initialize the network layers and Define the layer names
        LinearLayer input=new LinearLayer();
        SigmoidLayer hidden=new SigmoidLayer();
        SigmoidLayer output=new SigmoidLayer();
        input.setLayerName("Input Layer");
        hidden.setLayerName("Hidden Layer");
        output.setLayerName("Output Layer");
        // Define the number of neurons for each layer
        input.setRows(2);
        hidden.setRows(2);
        output.setRows(1);
        // Define the input -> hidden connection
        FullSynapse synapseIH=new FullSynapse();
        synapseIH.setName("IH Synapse");
        // Define the hidden -> output connection
        FullSynapse synapseHO=new FullSynapse();
        synapseHO.setName("HO Synapse");
        // Connect the Input Layer with the Hidden Layer
        NeuralNetFactory.connect(input,synapseIH,hidden);
        // Connect the Hidden Layer with the Output Layer
        NeuralNetFactory.connect(hidden,synapseHO,output);
        // Define & Initialize the Learning Input Synapse
        XLSInputSynapse learningInputSynapse = new XLSInputSynapse();
        learningInputSynapse.setName("Learning Input Synapse");
        learningInputSynapse.setInputFile(new File("/tmp/wine.xls"));
        learningInputSynapse.setAdvancedColumnSelector("6,7");
        learningInputSynapse.setSheetName("wine.data");
        learningInputSynapse.setFirstRow(2);
        learningInputSynapse.setLastRow(100);
        // Define the Test Input Synapse
        XLSInputSynapse testInputSynapse = new XLSInputSynapse();
        testInputSynapse.setName("Test Input Synapse");
        testInputSynapse.setInputFile(new File("/tmp/wine.xls"));
        testInputSynapse.setAdvancedColumnSelector("6,7");
        testInputSynapse.setSheetName("wine.data");
        testInputSynapse.setFirstRow(2);
        testInputSynapse.setLastRow(100);
        // Initialize the Input Switch Synapse
        LearningSwitch inputSwitch=NeuralNetFactory.createSwitch("Input Switch Synapse",learningInputSynapse,testInputSynapse);
        // Connect the Input Switch Synapse to the Input Layer
        input.addInputSynapse(inputSwitch);
        // Define the Trainer's Learning Input Synapse
        XLSInputSynapse learningDesiredSynapse = new XLSInputSynapse();
        learningDesiredSynapse.setName("Learning Desired Synapse");
        learningDesiredSynapse.setInputFile(new File("/tmp/wine.xls"));
        learningDesiredSynapse.setAdvancedColumnSelector("8");
        learningDesiredSynapse.setSheetName("wine.data");
        learningDesiredSynapse.setFirstRow(2);
        learningDesiredSynapse.setLastRow(100);
        // Define the Trainer's Test Input Synapse
        XLSInputSynapse testDesiredSynapse = new XLSInputSynapse();
        testDesiredSynapse.setName("Test Desired Synapse");
        testDesiredSynapse.setInputFile(new File("/tmp/wine.xls"));
        testDesiredSynapse.setAdvancedColumnSelector("8");
        testDesiredSynapse.setSheetName("wine.data");
        testDesiredSynapse.setFirstRow(2);
        testDesiredSynapse.setLastRow(100);
        // Initialize the Input Switch Synapse
        LearningSwitch learningSwitch=NeuralNetFactory.createSwitch("Learning Switch Synapse",learningDesiredSynapse,testDesiredSynapse);
        // Define the Trainer and link it to the Monitor
        TeachingSynapse trainer=new TeachingSynapse();
        trainer.setName("EETNN Trainer Synapse");
        // Connect the Teacher to the Output Layer
        output.addOutputSynapse(trainer);
        // Connect the Learning Switch Synapse to the Trainer
        trainer.setDesired(learningSwitch);
        // Define the Output Synapse Memory (Data)
    /*XLSOutputSynapse outputMemoryData=new XLSOutputSynapse();
    outputMemoryData.setName("Output Data");
    outputMemoryData.setFileName("/tmp/OutputData.xls");
    outputMemoryData.setSheetName("wine.data");
    // Connect the Output Memory Synapse (Data) to the Output
    output.addOutputSynapse(outputMemoryData); */
        // Incorpore the network components to the NeuralNet object
        network.addLayer(input);
        network.addLayer(hidden);
        network.addLayer(output);
        network.setTeacher(trainer);
        return network;
    }
    
}