Examples of org.apache.mahout.math.DenseVector.assign()

Class org.apache.mahout.math.DenseVector

Examples of org.apache.mahout.math.DenseVector.assign()

org.apache.mahout.math.DenseVector.assign()

    DenseVector initialProbabilities = new DenseVector(nrOfHiddenStates);


    // assign pseudo count to avoid zero probabilities
    transitionMatrix.assign(pseudoCount);
    emissionMatrix.assign(pseudoCount);
    initialProbabilities.assign(pseudoCount);


    // now loop over the sequences to count the number of transitions
    Iterator<int[]> hiddenSequenceIt = hiddenSequences.iterator();
    Iterator<int[]> observedSequenceIt = observedSequences.iterator();
    while (hiddenSequenceIt.hasNext() && observedSequenceIt.hasNext()) {

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    }
  }


  private static Vector randomVector(final Random gen, int n) {
    Vector x = new DenseVector(n);
    x.assign(new UnaryFunction() {
      @Override
      public double apply(double v) {
        return gen.nextGaussian();
      }
    });

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    sealed = false;
  }


  private void regularizeAll() {
    Vector all = new DenseVector(beta.numCols());
    all.assign(1);
    regularize(all);
  }


  @Override
  public void close() {

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    // target variable of 0, the last 30 a target of 1.  The remaining columns are are random noise.
    input = readCsv("sgd.csv");


    // regenerate the target variable
    Vector target = new DenseVector(60);
    target.assign(0);
    target.viewPart(30, 30).assign(1);
    return target;
  }


  private static void train(Matrix input, Vector target, OnlineLearner lr) {

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  @Override
  public Vector classify(Vector instance) {
    Vector r = new DenseVector(numCategories() - 1);
    BinaryFunction scale = Functions.plusMult(1.0 / models.size());
    for (OnlineLogisticRegression model : models) {
      r.assign(model.classify(instance), scale);
    }
    return r;
  }


  @Override

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  @Override
  public Vector classifyNoLink(Vector instance) {
    Vector r = new DenseVector(numCategories() - 1);
    BinaryFunction scale = Functions.plusMult(1.0 / models.size());
    for (OnlineLogisticRegression model : models) {
      r.assign(model.classifyNoLink(instance), scale);
    }
    return r;
  }


  @Override

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    assertEquals(1 / 6.0, m.get(2, 3), 1.0e-9);


    Vector x = new DenseVector(new double[]{5, -120, 630, -1120, 630});


    Vector b = new DenseVector(5);
    b.assign(1);


    assertEquals(0, m.times(x).minus(b).norm(2), 1.0e-9);


    LSMR r = new LSMR();
    Vector x1 = r.solve(m, b);

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    transitionMatrix.assign(pseudoCount);
    emissionMatrix.assign(pseudoCount);
    // given no prior knowledge, we have to assume that all initial hidden
    // states are equally likely
    DenseVector initialProbabilities = new DenseVector(nrOfHiddenStates);
    initialProbabilities.assign(1.0 / nrOfHiddenStates);


    // now loop over the sequences to count the number of transitions
    countTransitions(transitionMatrix, emissionMatrix, observedSequence,
        hiddenSequence);

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    DenseVector initialProbabilities = new DenseVector(nrOfHiddenStates);


    // assign pseudo count to avoid zero probabilities
    transitionMatrix.assign(pseudoCount);
    emissionMatrix.assign(pseudoCount);
    initialProbabilities.assign(pseudoCount);


    // now loop over the sequences to count the number of transitions
    Iterator<int[]> hiddenSequenceIt = hiddenSequences.iterator();
    Iterator<int[]> observedSequenceIt = observedSequences.iterator();
    while (hiddenSequenceIt.hasNext() && observedSequenceIt.hasNext()) {

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  public DenseVector inputToHidden(Vector input) {
    DenseVector activations = new DenseVector(numHidden);
    for (int i = 0; i < numHidden; i++) {
      activations.setQuick(i, hiddenWeights[i].dot(input));
    }
    activations.assign(hiddenBias, Functions.PLUS);
    activations.assign(Functions.min(40.0)).assign(Functions.max(-40));
    activations.assign(Functions.SIGMOID);
    return activations;
  }

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