Java Utils.minIndex方法代碼示例

import weka.core.Utils; //導入方法依賴的package包/類
/**
  * Returns class probabilities. When minimum expected cost approach is chosen,
  * returns probability one for class with the minimum expected misclassification
  * cost. Otherwise it returns the probability distribution returned by
  * the base classifier.
  *
  * @param instance the instance to be classified
  * @return the computed distribution for the given instance
  * @throws Exception if instance could not be classified
  * successfully */
 public double[] distributionForInstance(Instance instance) throws Exception {

   if (!m_MinimizeExpectedCost) {
     return m_Classifier.distributionForInstance(instance);
   }
   double [] pred = m_Classifier.distributionForInstance(instance);
   double [] costs = m_CostMatrix.expectedCosts(pred, instance);
   /*
   for (int i = 0; i < pred.length; i++) {
     System.out.print(pred[i] + " ");
   }
   System.out.println();
   for (int i = 0; i < costs.length; i++) {
     System.out.print(costs[i] + " ");
   }
   System.out.println("\n");
   */

   // This is probably not ideal
   int classIndex = Utils.minIndex(costs);
   for (int i = 0; i  < pred.length; i++) {
     if (i == classIndex) {
pred[i] = 1.0;
     } else {
pred[i] = 0.0;
     }
   }
   return pred; 
 }
 

import weka.core.Utils; //導入方法依賴的package包/類
/**
 * Compute the output. Either a probability distribution or a single
 * value (regression).
 * 
 * @param incoming the values from the last hidden layer
 * @param preds the array to fill with predicted values
 * @throws Exception if there is a problem computing the output
 */
protected void getOuput(HashMap<String, Double> incoming, double[] preds) throws Exception {
  
  if (preds.length != m_outputNeurons.length) {
    throw new Exception("[NeuralOutputs] Incorrect number of predictions requested: "
        + preds.length + "requested, " + m_outputNeurons.length + " expected");
  }
  for (int i = 0; i < m_outputNeurons.length; i++) {
    Double neuronOut = incoming.get(m_outputNeurons[i]);
    if (neuronOut == null) {
      throw new Exception("[NeuralOutputs] Unable to find output neuron "
          + m_outputNeurons[i] + " in the incoming HashMap!!");
    }
    if (m_classAttribute.isNumeric()) {
      // will be only one output neuron anyway
      preds[0] = neuronOut.doubleValue();
      
      preds[0] = m_regressionMapping.getResultInverse(preds);
    } else {

      // clip at zero
      // preds[m_categoricalIndexes[i]] = (neuronOut < 0) ? 0.0 : neuronOut;
      preds[m_categoricalIndexes[i]] = neuronOut;
    }
  }
  
  if (m_classAttribute.isNominal()) {
    // check for negative values and adjust
    double min = preds[Utils.minIndex(preds)];
    if (min < 0) {
      for (int i = 0; i < preds.length; i++) {
        preds[i] -= min;
      }
    }
    // do a simplemax normalization
    Utils.normalize(preds);
  }
}
 

import weka.core.Utils; //導入方法依賴的package包/類
double eval(double[] args) {
  return args[Utils.minIndex(args)];
}