Jama.Matrix.getRowDimension()方法的使用及代码示例

x33g5p2x  于2022-01-24 转载在 其他  
字(9.2k)|赞(0)|评价(0)|浏览(159)

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

Matrix.getRowDimension介绍

暂无

代码示例

代码示例来源：origin: marytts/marytts

public void printPricipalComponents(Vector<String> factors) {
  System.out.println("PCs:");
  for (int j = 0; j < PC.getColumnDimension(); j++) {
    System.out.println("PC(" + j + ")");
    for (int i = 0; i < PC.getRowDimension(); i++)
      System.out.format(" %s %.5f\n", factors.elementAt(i), PC.get(i, j));
  }
}

代码示例来源：origin: marytts/marytts

public void multipleLinearRegression(String fileName, boolean interceptTerm) {
  try {
    BufferedReader reader = new BufferedReader(new FileReader(fileName));
    Matrix data = Matrix.read(reader);
    reader.close();
    int rows = data.getRowDimension() - 1;
    int cols = data.getColumnDimension() - 1;
    Matrix indVar = data.getMatrix(0, rows, 0, 0); // dataVowels(:,0) -> col 0 is the independent variable
    data = data.getMatrix(0, rows, 1, cols); // dataVowels(:,1:cols) -> dependent variables
    multipleLinearRegression(indVar, data, interceptTerm);
  } catch (Exception e) {
    throw new RuntimeException("Problem reading file " + fileName, e);
  }
}

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

int M = data.getRowDimension();
int N = data.getColumnDimension();
      data.set(i, j, ((data.get(i, j) - mn) / sd));
  } else {
      data.set(i, j, (data.get(i, j) - mn));
  data.print(data.getRowDimension(), 3);
V = new double[svd.getS().getRowDimension()];
for (int i = 0; i < svd.getS().getRowDimension(); i++) {
  V[i] = svd.getS().get(i, i);
  if (debug)
    System.out.println(V[i]);
if (debug) {
  System.out.println("PC:");
  PC.print(PC.getRowDimension(), 3);
varianceProportion = new double[covProjectedData.getColumnDimension()];
double sumPropVar = 0.0; // sum of the proportion of variance
for (int j = 0; j < covProjectedData.getColumnDimension(); j++) {
  varianceProportion[j] = covProjectedData.get(j, j);
  sumPropVar += varianceProportion[j];

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

private void copyArray(Matrix vectorIn, double[] out) {
  for (int i = 0; i < vectorIn.getRowDimension(); i++) {
    out[i] = vectorIn.get(i, 0);
  }
}

代码示例来源：origin: org.openimaj/FaceTracker

void initShape(Rectangle r, Matrix shape) {
  assert ((shape.getRowDimension() == _rshape.getRowDimension()) && (shape
      .getColumnDimension() == _rshape.getColumnDimension()));
  int n = _rshape.getRowDimension() / 2;
  double a = r.width * Math.cos(_simil[1]) * _simil[0] + 1;
  double b = r.width * Math.sin(_simil[1]) * _simil[0];
  double tx = r.x + (int) (r.width / 2) + r.width * _simil[2];
  double ty = r.y + (int) (r.height / 2) + r.height * _simil[3];
  double[][] s = _rshape.getArray();
  double[][] d = shape.getArray();
  for (int i = 0; i < n; i++) {
    d[i][0] = a * s[i][0] - b * s[i + n][0] + tx;
    d[i + n][0] = b * s[i][0] + a * s[i + n][0] + ty;
  }
}

代码示例来源：origin: marytts/marytts

public void multipleLinearRegression(Matrix datay, Matrix dataX, boolean interceptTerm) {
  b0Term = interceptTerm;
  if (interceptTerm) { // first column of X is filled with 1s if b_0 != 0
    int row = dataX.getRowDimension();
    int col = dataX.getColumnDimension();
    Matrix B = new Matrix(row, col + 1);
    Matrix ones = new Matrix(row, 1);
    for (int i = 0; i < row; i++)
      ones.set(i, 0, 1.0);
    B.setMatrix(0, row - 1, 0, 0, ones);
    B.setMatrix(0, row - 1, 1, col, dataX);
    multipleLinearRegression(datay, B);
  } else {
    multipleLinearRegression(datay, dataX);
  }
}

代码示例来源：origin: marytts/marytts

private int[] checkMeanColumns(String dataFile, int Y[], String[] features) {
  try {
    BufferedReader reader = new BufferedReader(new FileReader(dataFile));
    Matrix data = Matrix.read(reader);
    reader.close();
    data = data.transpose(); // then I have easy access to the columns
    int rows = data.getRowDimension() - 1;
    int cols = data.getColumnDimension() - 1;
    data = data.getMatrix(0, rows, 1, cols); // dataVowels(:,1:cols) -> dependent variables
    int M = data.getRowDimension();
    double mn;
    for (int i = 0; i < M; i++) {
      mn = MathUtils.mean(data.getArray()[i]);
      if (mn == 0.0) {
        System.out.println("Removing feature: " + features[i] + " from list of features because it has mean=0.0");
        Y = MathUtils.removeIndex(Y, i);
      }
    }
  } catch (Exception e) {
    throw new RuntimeException("Problem reading file " + dataFile, e);
  }
  System.out.println();
  return Y;
}

代码示例来源：origin: marytts/marytts

y = datay;
b = X.solve(y);
coeffs = new double[b.getRowDimension()];
for (int j = 0; j < b.getRowDimension(); j++) {
  coeffs[j] = b.get(j, 0);

代码示例来源：origin: marytts/marytts

Matrix data = Matrix.read(reader);
reader.close();
int rows = data.getRowDimension() - 1;
int cols = data.getColumnDimension() - 1;
  if (b.getRowDimension() == numCoeff) {
      int row = data.getRowDimension();
      int col = data.getColumnDimension();

代码示例来源：origin: h2oai/h2o-3

model._output._v = svdJ.getU().getMatrix(0, atqJ.getRowDimension() - 1, 0, _parms._nv - 1).getArray();

代码示例来源：origin: sc.fiji/TrakEM2_

public void fit( final double x[][], final double y[][], final double lambda )
{
  final double[][] expandedX = kernelExpandMatrixNormalize( x );
  final Matrix phiX = new Matrix( expandedX, expandedX.length, length );
  final Matrix phiXTransp = phiX.transpose();
  final Matrix phiXProduct = phiXTransp.times( phiX );
  final int l = phiXProduct.getRowDimension();
  final double lambda2 = 2 * lambda;
  for (int i = 0; i < l; ++i )
    phiXProduct.set( i, i, phiXProduct.get( i, i ) + lambda2 );
  final Matrix phiXPseudoInverse = phiXProduct.inverse();
  final Matrix phiXProduct2 = phiXPseudoInverse.times( phiXTransp );
  final Matrix betaMatrix = phiXProduct2.times( new Matrix( y, y.length, 2 ) );
  setBeta( betaMatrix.getArray() );
}

代码示例来源：origin: marytts/marytts

public void printPricipalComponents(Vector<String> factors) {
  System.out.println("PCs:");
  for (int j = 0; j < PC.getColumnDimension(); j++) {
    System.out.println("PC(" + j + ")");
    for (int i = 0; i < PC.getRowDimension(); i++)
      System.out.format(" %s %.5f\n", factors.elementAt(i), PC.get(i, j));
  }
}

代码示例来源：origin: marytts/marytts

int M = data.getRowDimension();
int N = data.getColumnDimension();
      data.set(i, j, ((data.get(i, j) - mn) / sd));
  } else {
      data.set(i, j, (data.get(i, j) - mn));
  data.print(data.getRowDimension(), 3);
V = new double[svd.getS().getRowDimension()];
for (int i = 0; i < svd.getS().getRowDimension(); i++) {
  V[i] = svd.getS().get(i, i);
  if (debug)
    System.out.println(V[i]);
if (debug) {
  System.out.println("PC:");
  PC.print(PC.getRowDimension(), 3);
varianceProportion = new double[covProjectedData.getColumnDimension()];
double sumPropVar = 0.0; // sum of the proportion of variance
for (int j = 0; j < covProjectedData.getColumnDimension(); j++) {
  varianceProportion[j] = covProjectedData.get(j, j);
  sumPropVar += varianceProportion[j];

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

private void copyArray(Matrix vectorIn, double[] out) {
  for (int i = 0; i < vectorIn.getRowDimension(); i++) {
    out[i] = vectorIn.get(i, 0);
  }
}

代码示例来源：origin: openimaj/openimaj

static void writeMat(BufferedWriter s, Matrix M) throws IOException {
  final int r = M.getRowDimension();
  final int c = M.getColumnDimension();
  s.write(r + " " + c + " 0"); // type always 0 for java version as its
                  // ignored
  final double[][] Mv = M.getArray();
  for (int rr = 0; rr < r; rr++)
    for (int cc = 0; cc < c; cc++)
      s.write(Mv[rr][cc] + " ");
}

代码示例来源：origin: marytts/marytts

public void multipleLinearRegression(Matrix datay, Matrix dataX, boolean interceptTerm) {
  b0Term = interceptTerm;
  if (interceptTerm) { // first column of X is filled with 1s if b_0 != 0
    int row = dataX.getRowDimension();
    int col = dataX.getColumnDimension();
    Matrix B = new Matrix(row, col + 1);
    Matrix ones = new Matrix(row, 1);
    for (int i = 0; i < row; i++)
      ones.set(i, 0, 1.0);
    B.setMatrix(0, row - 1, 0, 0, ones);
    B.setMatrix(0, row - 1, 1, col, dataX);
    multipleLinearRegression(datay, B);
  } else {
    multipleLinearRegression(datay, dataX);
  }
}

代码示例来源：origin: marytts/marytts

public void multipleLinearRegression(String fileName, boolean interceptTerm) {
  try {
    BufferedReader reader = new BufferedReader(new FileReader(fileName));
    Matrix data = Matrix.read(reader);
    reader.close();
    int rows = data.getRowDimension() - 1;
    int cols = data.getColumnDimension() - 1;
    Matrix indVar = data.getMatrix(0, rows, 0, 0); // dataVowels(:,0) -> col 0 is the independent variable
    data = data.getMatrix(0, rows, 1, cols); // dataVowels(:,1:cols) -> dependent variables
    multipleLinearRegression(indVar, data, interceptTerm);
  } catch (Exception e) {
    throw new RuntimeException("Problem reading file " + fileName, e);
  }
}

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

private int[] checkMeanColumns(String dataFile, int Y[], String[] features) {
  try {
    BufferedReader reader = new BufferedReader(new FileReader(dataFile));
    Matrix data = Matrix.read(reader);
    reader.close();
    data = data.transpose(); // then I have easy access to the columns
    int rows = data.getRowDimension() - 1;
    int cols = data.getColumnDimension() - 1;
    data = data.getMatrix(0, rows, 1, cols); // dataVowels(:,1:cols) -> dependent variables
    int M = data.getRowDimension();
    double mn;
    for (int i = 0; i < M; i++) {
      mn = MathUtils.mean(data.getArray()[i]);
      if (mn == 0.0) {
        System.out.println("Removing feature: " + features[i] + " from list of features because it has mean=0.0");
        Y = MathUtils.removeIndex(Y, i);
      }
    }
  } catch (Exception e) {
    throw new RuntimeException("Problem reading file " + dataFile, e);
  }
  System.out.println();
  return Y;
}

内容来源于网络，如有侵权，请联系作者删除！

Matrix

相关文章

    查看更多

    热门标签

    更多
    JavaquerypythonNode开发语言requestUtil数据库Table后端算法LoggerMessageElementParser

    最新文章

    更多
    • 蜀ICP备2022004421号-2 NULL123 https://www.null123.com © All rights reserved
    • 本站内容来源互联网,如果侵犯您的权益请联系我们删除, 联系方式：448109455@qq.com