本文整理了Java中Jama.Matrix.getColumnPackedCopy()方法的一些代码示例，展示了Matrix.getColumnPackedCopy()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Matrix.getColumnPackedCopy()方法的具体详情如下：
包路径：Jama.Matrix
类名称：Matrix
方法名：getColumnPackedCopy

Matrix.getColumnPackedCopy介绍

暂无

代码示例

代码示例来源：origin: marytts/marytts

residuals = r.getColumnPackedCopy();
predictedValues = p.getColumnPackedCopy();
correlation = MathUtils.correlation(predictedValues, y.getColumnPackedCopy());

代码示例来源：origin: marytts/marytts

residuals = r.getColumnPackedCopy();
predictedValues = p.getColumnPackedCopy();
correlation = MathUtils.correlation(predictedValues, y.getColumnPackedCopy());

代码示例来源：origin: marytts/marytts

residuals = r.getColumnPackedCopy();
predictedValues = p.getColumnPackedCopy();
for (int i = 0; i < predictedValues.length; i++)
  if (predictedValues[i] < 0.0)
correlation = MathUtils.correlation(predictedValues, indVar.getColumnPackedCopy());

代码示例来源：origin: marytts/marytts

residuals = r.getColumnPackedCopy();
predictedValues = p.getColumnPackedCopy();
for (int i = 0; i < predictedValues.length; i++)
  if (predictedValues[i] < 0.0)
correlation = MathUtils.correlation(predictedValues, indVar.getColumnPackedCopy());

代码示例来源：origin: h2oai/h2o-3

xrow = tmp.getColumnPackedCopy();

代码示例来源：origin: openimaj/openimaj

/**
 * Compute the low rank estimate of the given vector
 * 
 * @param in
 *            the vector
 * @return the low-rank projection of the vector
 */
public double[] project(double[] in) {
  return W.times(new Matrix(new double[][] { in }).transpose()).getColumnPackedCopy();
}

代码示例来源：origin: openimaj/openimaj

private double[] project(double[] vector) {
  final Matrix vec = new Matrix(1, vector.length);
  final double[][] vecarr = vec.getArray();
  for (int i = 0; i < vector.length; i++)
    vecarr[0][i] = vector[i] - mean[i];
  return vec.times(basis).getColumnPackedCopy();
}

代码示例来源：origin: senbox-org/s1tbx

private static double[] computePolynomialCoefficients(
    double slantRangeToFirstPixel, double slantRangeToMidPixel, double slantRangeToLastPixel, int imageWidth) {
  final int firstPixel = 0;
  final int midPixel = imageWidth / 2;
  final int lastPixel = imageWidth - 1;
  final double[] idxArray = {firstPixel, midPixel, lastPixel};
  final double[] rangeArray = {slantRangeToFirstPixel, slantRangeToMidPixel, slantRangeToLastPixel};
  final Matrix A = Maths.createVandermondeMatrix(idxArray, 2);
  final Matrix b = new Matrix(rangeArray, 3);
  final Matrix x = A.solve(b);
  return x.getColumnPackedCopy();
}

代码示例来源：origin: senbox-org/s1tbx

private static double[] computePolynomialCoefficients(
    double slantRangeToFirstPixel, double slantRangeToMidPixel, double slantRangeToLastPixel, int imageWidth) {
  final int firstPixel = 0;
  final int midPixel = imageWidth / 2;
  final int lastPixel = imageWidth - 1;
  final double[] idxArray = {firstPixel, midPixel, lastPixel};
  final double[] rangeArray = {slantRangeToFirstPixel, slantRangeToMidPixel, slantRangeToLastPixel};
  final Matrix A = Maths.createVandermondeMatrix(idxArray, 2);
  final Matrix b = new Matrix(rangeArray, 3);
  final Matrix x = A.solve(b);
  return x.getColumnPackedCopy();
}

代码示例来源：origin: senbox-org/s1tbx

private static double[] computePolynomialCoefficients(
    double slantRangeToFirstPixel, double slantRangeToMidPixel, double slantRangeToLastPixel, int imageWidth) {
  final int firstPixel = 0;
  final int midPixel = imageWidth / 2;
  final int lastPixel = imageWidth - 1;
  final double[] idxArray = {firstPixel, midPixel, lastPixel};
  final double[] rangeArray = {slantRangeToFirstPixel, slantRangeToMidPixel, slantRangeToLastPixel};
  final Matrix A = Maths.createVandermondeMatrix(idxArray, 2);
  final Matrix b = new Matrix(rangeArray, 3);
  final Matrix x = A.solve(b);
  return x.getColumnPackedCopy();
}

代码示例来源：origin: org.openimaj/image-feature-extraction

private double[] project(double[] vector) {
  final Matrix vec = new Matrix(1, vector.length);
  final double[][] vecarr = vec.getArray();
  for (int i = 0; i < vector.length; i++)
    vecarr[0][i] = vector[i] - mean[i];
  return vec.times(basis).getColumnPackedCopy();
}

代码示例来源：origin: openimaj/openimaj

@Override
public double[] whiten(double[] vector) {
  final double[] normVec = ns.normalise(vector);
  final Matrix vec = new Matrix(new double[][] { normVec });
  return vec.times(transform).getColumnPackedCopy();
}

代码示例来源：origin: senbox-org/s1tbx

private static double[] computePolynomialCoefficients(
    double slantRangeToFirstPixel, double slantRangeToMidPixel, double slantRangeToLastPixel, int imageWidth) {
  final int firstPixel = 0;
  final int midPixel = imageWidth / 2;
  final int lastPixel = imageWidth - 1;
  final double[] idxArray = {firstPixel, midPixel, lastPixel};
  final double[] rangeArray = {slantRangeToFirstPixel, slantRangeToMidPixel, slantRangeToLastPixel};
  final Matrix A = Maths.createVandermondeMatrix(idxArray, 2);
  final Matrix b = new Matrix(rangeArray, 3);
  final Matrix x = A.solve(b);
  return x.getColumnPackedCopy();
}

代码示例来源：origin: openimaj/openimaj

/**
 * Project a vector by the basis. The vector is normalised by subtracting
 * the mean and then multiplied by the basis.
 * 
 * @param vector
 *            the vector to project
 * @return projected vector
 */
public double[] project(double[] vector) {
  final Matrix vec = new Matrix(1, vector.length);
  final double[][] vecarr = vec.getArray();
  for (int i = 0; i < vector.length; i++)
    vecarr[0][i] = vector[i] - mean[i];
  return vec.times(basis).getColumnPackedCopy();
}

代码示例来源：origin: openimaj/openimaj

/**
   * Project a vector by the basis. The vector
   * is normalised by subtracting the mean and
   * then multiplied by the basis.
   * @param vector the vector to project
   * @return projected vector
   */
  public double[] project(double [] vector) {
    Matrix vec = new Matrix(1, vector.length);
    final double[][] vecarr = vec.getArray();
    
    for (int i=0; i<vector.length; i++)
      vecarr[0][i] = vector[i] - mean[i];
    
    return vec.times(eigenvectors).getColumnPackedCopy();
  }
}

代码示例来源：origin: sc.fiji/TrackMate_

/**
 * Runs the prediction step of the Kalman filter and returns the state
 * predicted by the evolution process.
 * 
 * @return a new <code>double[]</code> of 6 elements containing the
 *         predicted state: <code>x, y, z, vx, vy, vz</code> with velocity
 *         in <code>length/frame</code> units.
 * 
 */
public double[] predict()
{
  Xp = A.times( X );
  P = A.times( P.times( A.transpose() ) ).plus( Q );
  return Xp.getColumnPackedCopy();
}

代码示例来源：origin: openimaj/openimaj

/**
 * Generate a new "observation" as a linear combination of
 * the eigenvectors (ev): mean + ev * scaling.
 * <p>
 * If the scaling vector is shorter than the number of
 * components, it will be zero-padded. If it is longer,
 * it will be truncated.
 * 
 * @param scalings the weighting for each eigenvector
 * @return generated observation
 */
public double [] generate(double[] scalings) {
  Matrix scale = new Matrix(this.eigenvalues.length, 1);
  
  for (int i=0; i<Math.min(eigenvalues.length, scalings.length); i++)
    scale.set(i, 0, scalings[i]);
  
  Matrix meanMatrix = new Matrix(new double[][]{mean}).transpose();
  
  return meanMatrix.plus(eigenvectors.times(scale)).getColumnPackedCopy();
}

代码示例来源：origin: openimaj/openimaj

/**
 * Generate a new "observation" as a linear combination of the principal
 * components (PC): mean + PC * scaling.
 * 
 * If the scaling vector is shorter than the number of components, it will
 * be zero-padded. If it is longer, it will be truncated.
 * 
 * @param scalings
 *            the weighting for each PC
 * @return generated observation
 */
public double[] generate(double[] scalings) {
  final Matrix scale = new Matrix(this.eigenvalues.length, 1);
  for (int i = 0; i < Math.min(eigenvalues.length, scalings.length); i++)
    scale.set(i, 0, scalings[i]);
  final Matrix meanMatrix = new Matrix(new double[][] { mean }).transpose();
  return meanMatrix.plus(basis.times(scale)).getColumnPackedCopy();
}

代码示例来源：origin: fiji/TrackMate

/**
 * Runs the prediction step of the Kalman filter and returns the state
 * predicted by the evolution process.
 * 
 * @return a new <code>double[]</code> of 6 elements containing the
 *         predicted state: <code>x, y, z, vx, vy, vz</code> with velocity
 *         in <code>length/frame</code> units.
 * 
 */
public double[] predict()
{
  Xp = A.times( X );
  P = A.times( P.times( A.transpose() ) ).plus( Q );
  return Xp.getColumnPackedCopy();
}

代码示例来源：origin: percyliang/fig

public double [] sample(Random random)
{
 Matrix L = getChol().getL();
 // start with a vector of iid std normals
 Matrix stdNormal = new Matrix(dim(), 1);
 for (int i = 0; i < dim(); i++)
 {
  stdNormal.set(i, 0, SampleUtils.sampleGaussian(random));
 }
 Matrix result = L.times(stdNormal);
 result.plusEquals(mean);
 return result.getColumnPackedCopy();
}
public double[] sampleObject(Random random) { return sample(random); }