Java 类weka.core.pmml.NormContinuous 实例源码

项目：repo.kmeanspp.silhouette_score 文件：NeuralNetwork.java

protected NeuralOutputs(Element outputs, MiningSchema miningSchema) throws Exception {
  m_classAttribute = miningSchema.getMiningSchemaAsInstances().classAttribute();

  int vals = (m_classAttribute.isNumeric())
    ? 1
    : m_classAttribute.numValues();

  m_outputNeurons = new String[vals];
  m_categoricalIndexes = new int[vals];

  NodeList outputL = outputs.getElementsByTagName("NeuralOutput");
  if (outputL.getLength() != m_outputNeurons.length) {
    throw new Exception("[NeuralOutputs] the number of neural outputs does not match "
        + "the number expected!");
  }

  for (int i = 0; i < outputL.getLength(); i++) {
    Node outputN = outputL.item(i);
    if (outputN.getNodeType() == Node.ELEMENT_NODE) {
      Element outputE = (Element)outputN;
      // get the ID for this output neuron
      m_outputNeurons[i] = outputE.getAttribute("outputNeuron");

      if (m_classAttribute.isNumeric()) {
        // get the single norm continuous
        NodeList contL = outputE.getElementsByTagName("NormContinuous");
        if (contL.getLength() != 1) {
          throw new Exception("[NeuralOutputs] Should be exactly one norm continuous element "
              + "for numeric class!");
        }
        Node normContNode = contL.item(0);
        String attName = ((Element)normContNode).getAttribute("field");
        Attribute dummyTargetDef = new Attribute(attName);
        ArrayList<Attribute> dummyFieldDefs = new ArrayList<Attribute>();
        dummyFieldDefs.add(dummyTargetDef);

        m_regressionMapping = new NormContinuous((Element)normContNode, 
            FieldMetaInfo.Optype.CONTINUOUS, dummyFieldDefs);
        break;
      } else {
        // we just need to grab the categorical value (out of the NormDiscrete element)
        // that this output neuron is associated with
        NodeList discL = outputE.getElementsByTagName("NormDiscrete");
        if (discL.getLength() != 1) {
          throw new Exception("[NeuralOutputs] Should be only one norm discrete element "
              + "per derived field/neural output for a nominal class!");
        }
        Node normDiscNode = discL.item(0);
        String attValue = ((Element)normDiscNode).getAttribute("value");
        int index = m_classAttribute.indexOfValue(attValue);
        if (index < 0) {
          throw new Exception("[NeuralOutputs] Can't find specified target value "
              + attValue + " in class attribute " + m_classAttribute.name());
        }
        m_categoricalIndexes[i] = index;
      }
    }
  }
}

项目：autoweka 文件：NeuralNetwork.java

protected NeuralOutputs(Element outputs, MiningSchema miningSchema) throws Exception {
  m_classAttribute = miningSchema.getMiningSchemaAsInstances().classAttribute();

  int vals = (m_classAttribute.isNumeric())
    ? 1
    : m_classAttribute.numValues();

  m_outputNeurons = new String[vals];
  m_categoricalIndexes = new int[vals];

  NodeList outputL = outputs.getElementsByTagName("NeuralOutput");
  if (outputL.getLength() != m_outputNeurons.length) {
    throw new Exception("[NeuralOutputs] the number of neural outputs does not match "
        + "the number expected!");
  }

  for (int i = 0; i < outputL.getLength(); i++) {
    Node outputN = outputL.item(i);
    if (outputN.getNodeType() == Node.ELEMENT_NODE) {
      Element outputE = (Element)outputN;
      // get the ID for this output neuron
      m_outputNeurons[i] = outputE.getAttribute("outputNeuron");

      if (m_classAttribute.isNumeric()) {
        // get the single norm continuous
        NodeList contL = outputE.getElementsByTagName("NormContinuous");
        if (contL.getLength() != 1) {
          throw new Exception("[NeuralOutputs] Should be exactly one norm continuous element "
              + "for numeric class!");
        }
        Node normContNode = contL.item(0);
        String attName = ((Element)normContNode).getAttribute("field");
        Attribute dummyTargetDef = new Attribute(attName);
        ArrayList<Attribute> dummyFieldDefs = new ArrayList<Attribute>();
        dummyFieldDefs.add(dummyTargetDef);

        m_regressionMapping = new NormContinuous((Element)normContNode, 
            FieldMetaInfo.Optype.CONTINUOUS, dummyFieldDefs);
        break;
      } else {
        // we just need to grab the categorical value (out of the NormDiscrete element)
        // that this output neuron is associated with
        NodeList discL = outputE.getElementsByTagName("NormDiscrete");
        if (discL.getLength() != 1) {
          throw new Exception("[NeuralOutputs] Should be only one norm discrete element "
              + "per derived field/neural output for a nominal class!");
        }
        Node normDiscNode = discL.item(0);
        String attValue = ((Element)normDiscNode).getAttribute("value");
        int index = m_classAttribute.indexOfValue(attValue);
        if (index < 0) {
          throw new Exception("[NeuralOutputs] Can't find specified target value "
              + attValue + " in class attribute " + m_classAttribute.name());
        }
        m_categoricalIndexes[i] = index;
      }
    }
  }
}

项目：umple 文件：NeuralNetwork.java

protected NeuralOutputs(Element outputs, MiningSchema miningSchema) throws Exception {
  m_classAttribute = miningSchema.getMiningSchemaAsInstances().classAttribute();

  int vals = (m_classAttribute.isNumeric())
    ? 1
    : m_classAttribute.numValues();

  m_outputNeurons = new String[vals];
  m_categoricalIndexes = new int[vals];

  NodeList outputL = outputs.getElementsByTagName("NeuralOutput");
  if (outputL.getLength() != m_outputNeurons.length) {
    throw new Exception("[NeuralOutputs] the number of neural outputs does not match "
        + "the number expected!");
  }

  for (int i = 0; i < outputL.getLength(); i++) {
    Node outputN = outputL.item(i);
    if (outputN.getNodeType() == Node.ELEMENT_NODE) {
      Element outputE = (Element)outputN;
      // get the ID for this output neuron
      m_outputNeurons[i] = outputE.getAttribute("outputNeuron");

      if (m_classAttribute.isNumeric()) {
        // get the single norm continuous
        NodeList contL = outputE.getElementsByTagName("NormContinuous");
        if (contL.getLength() != 1) {
          throw new Exception("[NeuralOutputs] Should be exactly one norm continuous element "
              + "for numeric class!");
        }
        Node normContNode = contL.item(0);
        String attName = ((Element)normContNode).getAttribute("field");
        Attribute dummyTargetDef = new Attribute(attName);
        ArrayList<Attribute> dummyFieldDefs = new ArrayList<Attribute>();
        dummyFieldDefs.add(dummyTargetDef);

        m_regressionMapping = new NormContinuous((Element)normContNode, 
            FieldMetaInfo.Optype.CONTINUOUS, dummyFieldDefs);
        break;
      } else {
        // we just need to grab the categorical value (out of the NormDiscrete element)
        // that this output neuron is associated with
        NodeList discL = outputE.getElementsByTagName("NormDiscrete");
        if (discL.getLength() != 1) {
          throw new Exception("[NeuralOutputs] Should be only one norm discrete element "
              + "per derived field/neural output for a nominal class!");
        }
        Node normDiscNode = discL.item(0);
        String attValue = ((Element)normDiscNode).getAttribute("value");
        int index = m_classAttribute.indexOfValue(attValue);
        if (index < 0) {
          throw new Exception("[NeuralOutputs] Can't find specified target value "
              + attValue + " in class attribute " + m_classAttribute.name());
        }
        m_categoricalIndexes[i] = index;
      }
    }
  }
}