Package weka.core.matrix

Examples of weka.core.matrix.Matrix.transpose()

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    Matrix thisPoint = new Matrix(1, 2);
    thisPoint.set(0, 0, x);
    thisPoint.set(0, 1, m_ConstDelta);
    return Math.exp(-thisPoint.times(m_CovarianceInverse).
        times(thisPoint.transpose()).get(0, 0)
        / 2) / (Math.sqrt(TWO_PI) * m_Determinant);
  }
 
  /**
   * Constructor
 
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    }
    Matrix trainMatrix = new Matrix(trainValues);
    // svd requires rows >= columns, so transpose data if necessary
    if (m_numAttributes < m_numInstances) {
      m_transpose = true;
      trainMatrix = trainMatrix.transpose();
    }
    SingularValueDecomposition trainSVD = trainMatrix.svd();
    m_u = trainSVD.getU(); // left singular vectors
    m_s = trainSVD.getS(); // singular values
    m_v = trainSVD.getV(); // right singular vectors
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    Matrix thisPoint = new Matrix(1, 2);
    thisPoint.set(0, 0, x);
    thisPoint.set(0, 1, m_ConstDelta);
    return Math.exp(-thisPoint.times(m_CovarianceInverse).
        times(thisPoint.transpose()).get(0, 0)
        / 2) / (Math.sqrt(TWO_PI) * m_Determinant);
  }
 
  /**
   * Constructor
 
View Full Code Here
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    }
    Matrix trainMatrix = new Matrix(trainValues);
    // svd requires rows >= columns, so transpose data if necessary
    if (m_numAttributes < m_numInstances) {
      m_transpose = true;
      trainMatrix = trainMatrix.transpose();
    }
    SingularValueDecomposition trainSVD = trainMatrix.svd();
    m_u = trainSVD.getU(); // left singular vectors
    m_s = trainSVD.getS(); // singular values
    m_v = trainSVD.getV(); // right singular vectors
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    Matrix k = new Matrix(m_NumTrain, 1);
    for (int i = 0; i < m_NumTrain; i++) {
      k.set(i, 0, m_kernel.eval(-1, i, inst));
    }

    double result = k.transpose().times(m_t).get(0, 0) + m_avg_target;
    result = (result - m_Blin) / m_Alin;

    return result;

  }
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    Matrix k = new Matrix(m_NumTrain, 1);
    for (int i = 0; i < m_NumTrain; i++) {
      k.set(i, 0, m_kernel.eval(-1, i, inst));
    }

    double estimate = k.transpose().times(m_t).get(0, 0) + m_avg_target;

    double sigma = computeStdDev(inst, k);

    confidenceLevel = 1.0 - ((1.0 - confidenceLevel) / 2.0);
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    Matrix k = new Matrix(m_NumTrain, 1);
    for (int i = 0; i < m_NumTrain; i++) {
      k.set(i, 0, m_kernel.eval(-1, i, inst));
    }
   
    double estimate = k.transpose().times(m_t).get(0, 0) + m_avg_target;

    double sigma = computeStdDev(inst, k);
   
    // transform to GP space
    value = value * m_Alin + m_Blin;
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    Matrix thisPoint = new Matrix(1, 2);
    thisPoint.set(0, 0, x);
    thisPoint.set(0, 1, m_ConstDelta);
    return Math.exp(-thisPoint.times(m_CovarianceInverse).
        times(thisPoint.transpose()).get(0, 0)
        / 2) / (Math.sqrt(TWO_PI) * m_Determinant);
  }
 
  /**
   * Constructor
 
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