Examples with Perceptron org.knime.base.data.neural.Perceptron used on opensource projects

Example 11 with Perceptron

use of org.knime.base.data.neural.Perceptron in project knime-core by knime.

the class PMMLNeuralNetworkTranslator method addOutputLayer.

/**
 * Writes the PMML output layer of the MLP.
 *
 * @param nnModel
 *            the neural network model.
 * @param mlp
 *            the underlying {@link MultiLayerPerceptron}.
 * @param spec
 *            the port object spec
 */
protected void addOutputLayer(final NeuralNetwork nnModel, final MultiLayerPerceptron mlp, final PMMLPortObjectSpec spec) {
    int lastlayer = mlp.getNrLayers() - 1;
    String targetCol = spec.getTargetFields().iterator().next();
    Layer outputlayer = mlp.getLayer(lastlayer);
    Perceptron[] outputperceptrons = outputlayer.getPerceptrons();
    HashMap<DataCell, Integer> outputmap = mlp.getClassMapping();
    NeuralOutputs neuralOuts = nnModel.addNewNeuralOutputs();
    neuralOuts.setNumberOfOutputs(BigInteger.valueOf(outputperceptrons.length));
    for (int i = 0; i < outputperceptrons.length; i++) {
        NeuralOutput neuralOutput = neuralOuts.addNewNeuralOutput();
        neuralOutput.setOutputNeuron(lastlayer + "," + i);
        // search corresponding output value
        String colname = "";
        for (Entry<DataCell, Integer> e : outputmap.entrySet()) {
            if (e.getValue().equals(i)) {
                colname = ((StringValue) e.getKey()).getStringValue();
            }
        }
        DerivedField df = neuralOutput.addNewDerivedField();
        df.setOptype(OPTYPE.CATEGORICAL);
        df.setDataType(DATATYPE.STRING);
        if (mlp.getMode() == MultiLayerPerceptron.CLASSIFICATION_MODE) {
            df.setOptype(OPTYPE.CATEGORICAL);
            df.setDataType(DATATYPE.STRING);
        } else if (mlp.getMode() == MultiLayerPerceptron.REGRESSION_MODE) {
            df.setOptype(OPTYPE.CONTINUOUS);
            df.setDataType(DATATYPE.DOUBLE);
        }
        if (mlp.getMode() == MultiLayerPerceptron.CLASSIFICATION_MODE) {
            NormDiscrete normDiscrete = df.addNewNormDiscrete();
            normDiscrete.setField(targetCol);
            normDiscrete.setValue(colname);
        } else if (mlp.getMode() == MultiLayerPerceptron.REGRESSION_MODE) {
            FieldRef fieldRef = df.addNewFieldRef();
            fieldRef.setField(targetCol);
        }
    }
}

Example 12 with Perceptron

use of org.knime.base.data.neural.Perceptron in project knime-core by knime.

the class PMMLNeuralNetworkTranslator method initInputLayer.

/**
 * @param nnModel the PMML neural network model
 */
private void initInputLayer(final NeuralNetwork nnModel) {
    NeuralInputs neuralInputs = nnModel.getNeuralInputs();
    m_idPosMap = new HashMap<String, Integer>();
    m_curPerceptrons = new Vector<Perceptron>();
    m_inputmap = new HashMap<String, Integer>();
    m_counter = 0;
    m_curLayer = 0;
    for (NeuralInput ni : neuralInputs.getNeuralInputArray()) {
        m_curPercpetronID = ni.getId();
        String fieldName = m_nameMapper.getColumnName(ni.getDerivedField().getFieldRef().getField());
        Perceptron p = new InputPerceptron();
        p.setClassValue(fieldName);
        m_inputmap.put(fieldName, m_counter);
        m_curPerceptrons.add(p);
        m_idPosMap.put(m_curPercpetronID, m_counter);
        m_counter++;
    }
    Perceptron[] curPerceptrons = new Perceptron[m_curPerceptrons.size()];
    curPerceptrons = m_curPerceptrons.toArray(curPerceptrons);
    m_predLayer = new InputLayer(curPerceptrons);
    m_allLayers.add(m_curLayer, new InputLayer(curPerceptrons));
    m_predPerceptrons = curPerceptrons;
    m_predidPosMap = new HashMap<String, Integer>(m_idPosMap);
}

Example 13 with Perceptron

use of org.knime.base.data.neural.Perceptron in project knime-core by knime.

the class PMMLNeuralNetworkTranslator method addLayer.

/**
 * Writes a layer of the MLP.
 *
 * @param nnModel
 *            the NeuralNetwork model.
 * @param mlp
 *            the underlying {@link MultiLayerPerceptron}.
 * @param layer
 *            the number of the current layer.
 */
protected void addLayer(final NeuralNetwork nnModel, final MultiLayerPerceptron mlp, final int layer) {
    Layer curLayer = mlp.getLayer(layer);
    Perceptron[] perceptrons = curLayer.getPerceptrons();
    AttributesImpl atts = new AttributesImpl();
    atts.addAttribute(null, null, "numberOfNeurons", CDATA, "" + perceptrons.length);
    NeuralLayer neuralLayer = nnModel.addNewNeuralLayer();
    for (int i = 0; i < perceptrons.length; i++) {
        Neuron neuron = neuralLayer.addNewNeuron();
        neuron.setId(layer + "," + i);
        neuron.setBias(-1 * perceptrons[i].getThreshold());
        double[] weights = perceptrons[i].getWeights();
        int predLayerLength = weights.length;
        for (int j = 0; j < predLayerLength; j++) {
            Con con = neuron.addNewCon();
            con.setFrom((layer - 1) + "," + j);
            con.setWeight(weights[j]);
        }
    }
}