Java Code Examples for org.neuroph.nnet.MultiLayerPerceptron#calculate()

@Test
public void testXorMSE() {
    MultiLayerPerceptron myMlPerceptron = new MultiLayerPerceptron(TransferFunctionType.SIGMOID, 2, 3, 1);
    myMlPerceptron.randomizeWeights(new WeightsRandomizer(new Random(123)));

    myMlPerceptron.setLearningRule(instance);
    myMlPerceptron.learn(xorDataSet);

    MeanSquaredError mse = new MeanSquaredError();
    for (DataSetRow testSetRow : xorDataSet.getRows()) {
        myMlPerceptron.setInput(testSetRow.getInput());
        myMlPerceptron.calculate();
        double[] networkOutput = myMlPerceptron.getOutput();
        mse.addPatternError(networkOutput, testSetRow.getDesiredOutput());
    }
    assertTrue(mse.getTotalError() < maxError);
}
 

@Test
public void testXorMSE() {
    MultiLayerPerceptron myMlPerceptron = new MultiLayerPerceptron(TransferFunctionType.SIGMOID, 2, 3, 1);
    myMlPerceptron.randomizeWeights(new WeightsRandomizer(new Random(123)));

    myMlPerceptron.setLearningRule(instance);
    myMlPerceptron.learn(xorDataSet);

    MeanSquaredError mse = new MeanSquaredError();
    for (DataSetRow testSetRow : xorDataSet.getRows()) {
        myMlPerceptron.setInput(testSetRow.getInput());
        myMlPerceptron.calculate();
        double[] networkOutput = myMlPerceptron.getOutput();
        mse.addPatternError(networkOutput, testSetRow.getDesiredOutput());
    }
    assertTrue(mse.getTotalError() < maxError);
}
 

/**
 * Runs the specified network using the double array as input data. This data should
 * be normalized or things will get real weird, real quick.
 * 
 * @param network
 *          The MLP network to be run
 * @param input
 *          The normalized input data to use to run the network
 * @return double[] - The network's "answers" from running the network. For the networks
 *         CTV'd with input data created by DataCreator, this means two doubles, whose range is
 *         between 0.0 and 1.0:
 *         <ol>
 *         <li>ret[0] - The probability the Home team wins (Away team loses)</li>
 *         <li>ret[1] - The probability the Home team loses (Away team wins)</li>
 *         </ol>
 */
protected double[] runNetwork(MultiLayerPerceptron network, double[] input) {
  double[] ret;
  network.setInput(input);
  network.calculate();
  // Return value is the network's output
  ret = network.getOutput();
  if (log.isTraceEnabled()) {
    StringBuilder sb = new StringBuilder();
    sb.append("Comparison: Input to Output:\n");
    sb.append("Input : ");
    sb.append(Arrays.toString(input));
    sb.append('\n');
    sb.append("Output: ");
    sb.append(Arrays.toString(ret));
    log.trace(sb.toString());
  }
  if (log.isTraceEnabled()) {
    log.trace("Network Input : " + Arrays.toString(input));
    log.trace("Network Output: " + Arrays.toString(ret));
  }
  return ret;
}
 

private void testNeuralNetwork(MultiLayerPerceptron neuralNet, DataSet testSet) {
    // not implemented
    for (DataSetRow testSetRow : testSet.getRows()) {
        neuralNet.setInput(testSetRow.getInput());
        neuralNet.calculate();
    }
}
 

/**
 * Runs this sample
 */    
public static void main(String[] args) throws FileNotFoundException, IOException, ClassNotFoundException, SQLException {

    // create neural network
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(2, 3, 1);

    // Load the database driver
    Class.forName("sun.jdbc.odbc.JdbcOdbcDriver");
    // Get a connection to the database
    String dbName = "neuroph";
    String dbUser = "root";
    String dbPass = "";
    // create a connection to database
    Connection connection = DriverManager.getConnection("jdbc:mysql://localhost:3306/" + dbName, dbUser, dbPass);

    // ise this sql to get input from database table
    String inputSql = "SELECT * FROM input_data";        
    // create dinput adapter using specidfied database connection and sql query
    JDBCInputAdapter in = new JDBCInputAdapter(connection, inputSql);
    String outputTable = "output_data"; // write output to this table
    // create output adapter using specified connection and output table
    JDBCOutputAdapter out = new JDBCOutputAdapter(connection, outputTable);

    
    double[] input;
    // read input using input adapter
    while ((input = in.readInput()) != null) {
        neuralNet.setInput(input);
        neuralNet.calculate();
        double[] output = neuralNet.getOutput();
        // and write output using output aadapter
        out.writeOutput(output);
    }

    in.close();
    out.close();
    connection.close();
}
 

/**
 * Runs this sample
 */    
public static void main(String[] args) throws FileNotFoundException, IOException {
    
    // create neural network
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(2, 3, 1); 
    
    // use file provided in org.neuroph.sample.data package
    String inputFileName = FileIOSample.class.getResource("data/xor_data.txt").getFile();
    // create file input adapter using specifed file
    FileInputAdapter fileIn = new FileInputAdapter(inputFileName);
    // create file output  adapter using specified file name
    FileOutputAdapter fileOut = new FileOutputAdapter("some_output_file.txt");
          
    
    double[] input; // input buffer used for reading network input from file
    // read network input using input adapter
    while( (input = fileIn.readInput()) != null) {
        // feed neywork with input    
        neuralNet.setInput(input);
        // calculate network ...
        neuralNet.calculate();  
        // .. and get network output
        double[] output = neuralNet.getOutput();
        // write network output using output adapter
        fileOut.writeOutput(output);
    }
    
    // close input and output files
    fileIn.close();
    fileOut.close();     
    
    // Also note that shorter way for this is using org.neuroph.util.io.IOHelper class
}