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

Example 1 with Perceptron

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

the class PMMLNeuralNetworkTranslator method addInputLayer.

/**
 * Writes the PMML input layer of the MLP.
 *
 * @param nnModel
 *            the Neural Network model.
 * @param mlp
 *            the underlying {@link MultiLayerPerceptron}.
 */
protected void addInputLayer(final NeuralNetwork nnModel, final MultiLayerPerceptron mlp) {
    Layer inputlayer = mlp.getLayer(0);
    Perceptron[] inputperceptrons = inputlayer.getPerceptrons();
    HashMap<String, Integer> inputmap = mlp.getInputMapping();
    NeuralInputs neuralInputs = nnModel.addNewNeuralInputs();
    neuralInputs.setNumberOfInputs(BigInteger.valueOf(inputperceptrons.length));
    for (int i = 0; i < inputperceptrons.length; i++) {
        NeuralInput neuralInput = neuralInputs.addNewNeuralInput();
        neuralInput.setId(0 + "," + i);
        // search corresponding input column
        String colname = "";
        for (Entry<String, Integer> e : inputmap.entrySet()) {
            if (e.getValue().equals(i)) {
                colname = e.getKey();
            }
        }
        DerivedField df = neuralInput.addNewDerivedField();
        df.setOptype(OPTYPE.CONTINUOUS);
        df.setDataType(DATATYPE.DOUBLE);
        FieldRef fieldRef = df.addNewFieldRef();
        fieldRef.setField(m_nameMapper.getDerivedFieldName(colname));
    }
}

Example 2 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 3 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]);
        }
    }
}

Example 4 with Perceptron

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

the class PMMLNeuralNetworkTranslator method initiateFinalLayer.

/**
 * @param nnModel
 *            the PMML neural network model
 */
private void initiateFinalLayer(final NeuralNetwork nnModel) {
    NeuralLayer hiddenLayer = nnModel.getNeuralLayerArray(nnModel.getNeuralLayerArray().length - 1);
    m_counter = 0;
    m_idPosMap = new HashMap<String, Integer>();
    m_curLayer++;
    m_curPerceptrons = new Vector<Perceptron>();
    for (Neuron neuron : hiddenLayer.getNeuronArray()) {
        m_weights = new double[m_predPerceptrons.length];
        m_curPercpetronID = neuron.getId();
        m_curThreshold = -1 * neuron.getBias();
        for (Con con : neuron.getConArray()) {
            String fromID = con.getFrom();
            double weight = con.getWeight();
            int pos = m_predidPosMap.get(fromID);
            m_weights[pos] = weight;
        }
        Perceptron p = new SigmoidPerceptron(m_weights, m_predPerceptrons);
        p.setThreshold(m_curThreshold);
        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_allLayers.add(m_curLayer, new HiddenLayer(m_predLayer, curPerceptrons));
    m_predLayer = m_allLayers.get(m_curLayer);
    m_predPerceptrons = curPerceptrons;
    m_predidPosMap = new HashMap<String, Integer>(m_idPosMap);
}

Example 5 with Perceptron

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

the class PMMLNeuralNetworkTranslator method addInputLayer.

/**
 * Writes the PMML input layer of the MLP.
 *
 * @param nnModel
 *            the Neural Network model.
 * @param mlp
 *            the underlying {@link MultiLayerPerceptron}.
 */
protected void addInputLayer(final NeuralNetwork nnModel, final MultiLayerPerceptron mlp) {
    Layer inputlayer = mlp.getLayer(0);
    Perceptron[] inputperceptrons = inputlayer.getPerceptrons();
    HashMap<String, Integer> inputmap = mlp.getInputMapping();
    NeuralInputs neuralInputs = nnModel.addNewNeuralInputs();
    neuralInputs.setNumberOfInputs(BigInteger.valueOf(inputperceptrons.length));
    for (int i = 0; i < inputperceptrons.length; i++) {
        NeuralInput neuralInput = neuralInputs.addNewNeuralInput();
        neuralInput.setId(0 + "," + i);
        // search corresponding input column
        String colname = "";
        for (Entry<String, Integer> e : inputmap.entrySet()) {
            if (e.getValue().equals(i)) {
                colname = e.getKey();
            }
        }
        DerivedField df = neuralInput.addNewDerivedField();
        df.setOptype(OPTYPE.CONTINUOUS);
        df.setDataType(DATATYPE.DOUBLE);
        FieldRef fieldRef = df.addNewFieldRef();
        fieldRef.setField(m_nameMapper.getDerivedFieldName(colname));
    }
}