Examples of ArrayDoubleList

org.apache.commons.collections.primitives.ArrayDoubleList
An {@link DoubleList} backed by an array of doubles.This implementation supports all optional methods. @since Commons Primitives 1.0 @version $Revision: 1.3 $ $Date: 2003/10/16 20:49:36 $ @author Rodney Waldhoff
org.joda.primitives.list.impl.ArrayDoubleList

Examples of org.apache.commons.collections.primitives.ArrayDoubleList

      }
   }


   private int findClaster(Sample sample, int clastersCount)
   {
      DoubleList clasters = new ArrayDoubleList();


      int inputsCount = sample.getInput().size();
      for (int i = 0; i < clastersCount; ++i)
      {
         clasters.add(sample.getInput().get(sample.getInput().size() - clastersCount + i));
      }


      return JNMFMathUtils.indexOfMaxElement(clasters);
   }

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Examples of org.apache.commons.collections.primitives.ArrayDoubleList

   public DoubleList transform(DoubleList vector, VariableType type)
   {
      Validate.isTrue(inClassValue != outClassValue, "inClassValue == outClassValue");
      Validate.isTrue(vector.size() > 0, "Vector calcCountOfItems is 0");


      DoubleList result = new ArrayDoubleList();


      int closestValueIndex = -1;
      double minDisnance = Double.MAX_VALUE;
      for (int i = 0; i < vector.size(); ++i)
      {
         double distance = Math.abs(vector.get(i) - inClassValue);
         if (distance < minDisnance)
         {
            closestValueIndex = i;
            minDisnance = distance;
         }
      }


      double res = closestValueIndex + 1;


      if (classToValueTable != null)
      {
         res = classToValueTable.get(res);
      }


      result.add(res);


      return result;
   }

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Examples of org.apache.commons.collections.primitives.ArrayDoubleList


   public DoubleList getResult()
   {
      Layer last = builder.getNetwork().getLayers().getLast();


      DoubleList result = new ArrayDoubleList();
      for (Neuron neuron : last.getNeurons())
      {
         result.add(neuron.getActivation());
      }


      return result;
   }

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Examples of org.apache.commons.collections.primitives.ArrayDoubleList

    * @return the results of execution of neural network
    */
   public DoubleList getResult()
   {
      Layer lastLayer = layers.getLast();
      DoubleList result = new ArrayDoubleList();


      for (Neuron n : lastLayer.getNeurons())
      {
         result.add(n.getActivation());
      }


      return result;
   }

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Examples of org.apache.commons.collections.primitives.ArrayDoubleList

      int[] result = new int[getOutputNeurons().size()];


      int pCount = outputNeuronsInfo.values().iterator().next().size();
      for (int i = 0; i < pCount; ++i)
      {
         DoubleList activations = new ArrayDoubleList();
         DoubleList targetValues = new ArrayDoubleList();


         for (Neuron outputNeuron : getOutputNeurons())
         {
            NeuronStepInfo step = outputNeuronsInfo.get(outputNeuron).get(i);
            activations.add(step.activation);
            targetValues.add(step.targetValue);
         }


         int indexOfMax = JNMFMathUtils.indexOfMaxElement(activations);
         if (indexOfMax == JNMFMathUtils.indexOfMaxElement(targetValues))
         {

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Examples of org.apache.commons.collections.primitives.ArrayDoubleList

      DoubleList bestWeights = null;
      double minNextE = Double.MAX_VALUE;


      for (int i = 0; i < 100; ++i)
      {
         DoubleList weights = new ArrayDoubleList(outputsCount);
         for (int j = 0; j < outputsCount; ++j)
         {
            double w = JNMFMathUtils.reflectToInterval(rand.nextDouble(), 0, 1, -1, 1);


            weights.add(w);
         }


         double nextE = calcNextE(candidate, weights);


         if (nextE < minNextE)

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Examples of org.apache.commons.collections.primitives.ArrayDoubleList


      for (Neuron candidateNeuron : candidates)
      {
         List<NeuronStepInfo> candidateInfo = candidateNeuronsInfo.get(candidateNeuron);


         DoubleList sigmas = new ArrayDoubleList();


         for (int p = 0; p < pCount; ++p)
         {
            double sigmaP = 0;
            for (int j = 0; j < outputNeuronsCount; ++j)
            {
               List<NeuronStepInfo> outputInfo = outputNeuronsInfo.get(getOutputNeurons().get(j));


               double avE = calcAvE(outputInfo);


               NeuronStepInfo stepPInfo = outputInfo.get(p);
               IFunction deriviative = candidateNeuron.getActivationFunction().getDerivative();
               sigmaP += Math.signum(calcCj(j, candidateNeuron)) * (stepPInfo.e - avE) *
                       deriviative.calc(candidateInfo.get(p).net);
            }


            sigmas.add(sigmaP);
         }


         sigmaPValues.put(candidateNeuron, sigmas);
      }
   }

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Examples of org.apache.commons.collections.primitives.ArrayDoubleList

      signalAfterTranslationEvent();
   }


   public DoubleList getOutput()
   {
      DoubleList output = new ArrayDoubleList();
      for (Neuron neuron : getNeurons())
      {
         output.add(neuron.getActivation());
      }


      return output;
   }

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Examples of org.apache.commons.collections.primitives.ArrayDoubleList

         {
            sum[i] = sum[i] + partialResult.get(i);
         }
      }


      DoubleList result = new ArrayDoubleList();
      for (int i = 0; i < sum.length; ++i)
      {
         result.add(sum[i] / forecastPerformers.size());
      }


      return result;
   }

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Examples of org.apache.commons.collections.primitives.ArrayDoubleList

   protected void calcAllErrors()
   {
      signalNewNetworkErrorCalculdated(this.sumError / (double) learningData.size());
      if (learningData.isSplitedOnClasses())
      {
         DoubleList notGuessedPercentByClasses = new ArrayDoubleList();
         for (int i = 0; i < learningData.getOutputsCount(); ++i)
         {
            double persent =
                    (double) notGuessedClasses[i] / (double) learningData.calcCountOfItems(i);


            if (Double.isNaN(persent))
            {
               persent = 0;
            }


            notGuessedPercentByClasses.add(persent);
         }


         signalNewNotGuessedLearningDataPercentCalculated(
                 (double) totalNotGuessedClasses / (double) learningData.size(),
                 notGuessedPercentByClasses);

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