本文整理了Java中Jama.Matrix.transpose()方法的一些代码示例，展示了Matrix.transpose()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.transpose()方法的具体详情如下：
包路径：Jama.Matrix
类名称：Matrix
方法名：transpose

Matrix.transpose介绍

暂无

代码示例

代码示例来源：origin: marytts/marytts

public double[][] getDataProjected(Matrix data, boolean debug) {
  // Project the original data set
  Matrix dataProjected;
  dataProjected = PC.transpose().times(data);
  if (debug) {
    System.out.println("Data projected:");
    dataProjected.print(dataProjected.getRowDimension(), 3);
  }
  return dataProjected.getArray();
}

代码示例来源：origin: marytts/marytts

public double[][] getDataProjected(Matrix data, boolean debug) {
  // Project the original data set
  Matrix dataProjected;
  dataProjected = PC.transpose().times(data);
  if (debug) {
    System.out.println("Data projected:");
    dataProjected.print(dataProjected.getRowDimension(), 3);
  }
  return dataProjected.getArray();
}

代码示例来源：origin: h2oai/h2o-3

Matrix x_r = new Matrix(xxchol.getL()).transpose();
x_r = x_r.times(Math.sqrt(nobs));
Matrix rrmul = x_r.times(yt_r.transpose());
SingularValueDecomposition rrsvd = new SingularValueDecomposition(rrmul);   // RS' = U \Sigma V'
double[] sval = rrsvd.getSingularValues();  // get singular values as double array
 Matrix eigvec = yt_qr.getQ().times(rrsvd.getV());

代码示例来源：origin: marytts/marytts

Matrix Y = data.transpose();
Y = Y.times(1.0 / Math.sqrt(N - 1));
Matrix projectedData = PC.transpose().times(data);
Matrix covProjectedData = projectedData.times(projectedData.transpose());

代码示例来源：origin: marytts/marytts

Matrix Y = data.transpose();
Y = Y.times(1.0 / Math.sqrt(N - 1));
Matrix projectedData = PC.transpose().times(data);
Matrix covProjectedData = projectedData.times(projectedData.transpose());

代码示例来源：origin: marytts/marytts

covariance = data.times(data.transpose());
covariance = covariance.times(1.0 / (N - 1));
if (debug) {
  System.out.println("Covariance");
Matrix projectedData = PC.transpose().times(data);
Matrix covProjectedData = projectedData.times(projectedData.transpose());

代码示例来源：origin: marytts/marytts

covariance = data.times(data.transpose());
covariance = covariance.times(1.0 / (N - 1));
if (debug) {
  System.out.println("Covariance");
Matrix projectedData = PC.transpose().times(data);
Matrix covProjectedData = projectedData.times(projectedData.transpose());

代码示例来源：origin: openimaj/openimaj

static Matrix computeEssentialMatrix(CameraIntrinsics ci, Matrix F) {
  return ci.calibrationMatrix.transpose().times(F).times(ci.calibrationMatrix);
}

代码示例来源：origin: org.openimaj/sandbox

static Matrix computeEssentialMatrix(CameraIntrinsics ci, Matrix F) {
  return ci.calibrationMatrix.transpose().times(F).times(ci.calibrationMatrix);
}

代码示例来源：origin: percyliang/fig

public MultGaussianSuffStats(double[] x) {
 sum = new Matrix(x, x.length);
 outerproducts = sum.times(sum.transpose());
 n = 1;
}
public MultGaussianSuffStats(MultGaussianSuffStats stats) {

代码示例来源：origin: bcdev/beam

/**
 * @param pt point
 * @return the square of the  Mahalanobis distance of the point from center of gravity
 */
public double distancesqu(double[] pt) {
  double[] df = new double[pt.length];
  for (int k = 0; k < pt.length; k++){
    df[k] = pt[k] - this.cog[k];
  }
  Matrix dfm = new Matrix(df, pt.length);
  return Math.abs(dfm.transpose().times(this.covinv.times(dfm)).getArray()[0][0]);
}

代码示例来源：origin: us.ihmc/ihmc-robotics-toolkit

public static Matrix getCovarianceMatrix(Matrix m)
{
 // TODO: Suggestion for javadoc comments:
 // This method assumes sample sets in rows
 int N = m.getColumnDimension();
 Matrix ret = subtractAverageColumnFromEachRow(m);
 ret = ret.times(ret.transpose());
 ret = ret.times(1.0 / (N));
 return ret;
}

代码示例来源：origin: openimaj/openimaj

@Override
public double[] predict(double[] data) {
  final double[][] corrected = new double[][] { new double[data.length + 1] };
  corrected[0][0] = 1;
  System.arraycopy(data, 0, corrected[0], 1, data.length);
  final Matrix x = new Matrix(corrected);
  return x.times(this.weights).transpose().getArray()[0];
}

代码示例来源：origin: us.ihmc/IHMCRoboticsToolkit

public static Matrix getCovarianceMatrix(Matrix m)
{
 // TODO: Suggestion for javadoc comments:
 // This method assumes sample sets in rows
 int N = m.getColumnDimension();
 Matrix ret = subtractAverageColumnFromEachRow(m);
 ret = ret.times(ret.transpose());
 ret = ret.times(1.0 / (N));
 return ret;
}

代码示例来源：origin: openimaj/openimaj

/**
 * De-normalise a fundamental estimate. Use when {@link #estimate(List)} was
 * called with pre-normalised data.
 *
 * @param norms
 *            the normalisation transforms
 */
public void denormaliseFundamental(Pair<Matrix> norms) {
  this.fundamental = norms.secondObject().transpose().times(fundamental).times(norms.firstObject());
}

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

/**
 * Compute the covariance matrix of the given samples (assumed each sample is a
 * row).
 *
 * @param m
 *            the samples matrix
 * @return the covariance matrix
 */
public static Matrix covariance(Matrix m) {
  final int N = m.getRowDimension();
  return times(m.transpose().times(m), 1.0 / (N > 1 ? N - 1 : N));
}

代码示例来源：origin: openimaj/openimaj

@Override
public double estimateLogProbability(double[] sample) {
  final Matrix xm = new Matrix(1, N);
  for (int i = 0; i < N; i++)
    xm.set(0, i, sample[i] - mean.get(0, i));
  final Matrix xmt = xm.transpose();
  final double v = xm.times(inv_covar.times(xmt)).get(0, 0);
  return Math.log(pdf_const_factor) + (-0.5 * v);
}

代码示例来源：origin: openimaj/openimaj

private IndependentPair<Matrix, Matrix> decorrelate(Matrix meanCentredX) {
  final Matrix C = MatrixUtils.covariance(meanCentredX.transpose());
  final Matrix CC = MatrixUtils.invSqrtSym(C);
  return IndependentPair.pair(CC.times(meanCentredX), CC);
}

代码示例来源：origin: org.openimaj/sandbox

private IndependentPair<Matrix, Matrix> decorrelate(Matrix meanCentredX) {
  final Matrix C = MatrixUtils.covariance(meanCentredX.transpose());
  final Matrix CC = MatrixUtils.invSqrtSym(C);
  return IndependentPair.pair(CC.times(meanCentredX), CC);
}