Jama.Matrix.getMatrix()方法的使用及代码示例

x33g5p2x  于2022-01-24 转载在 其他  
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本文整理了Java中Jama.Matrix.getMatrix()方法的一些代码示例，展示了Matrix.getMatrix()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.getMatrix()方法的具体详情如下：
包路径：Jama.Matrix
类名称：Matrix
方法名：getMatrix

Matrix.getMatrix介绍

暂无

代码示例

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 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

throw new RuntimeException("Problem reading file, rowIni < rowend" + rowIni + " < " + rowEnd);
  Matrix indVar = data.getMatrix(rowIni, rowEnd, indVariable, indVariable); // dataVowels(:,0) -> last col is the
  data = data.getMatrix(rowIni, rowEnd, c); // the dependent variables correspond to the column indices in c
  multipleLinearRegression(indVar, data, interceptTerm);
} catch (Exception e) {

代码示例来源：origin: marytts/marytts

throw new RuntimeException("Problem reading file, rowIni < rowend" + rowIni + " < " + rowEnd);
  Matrix indVar = data.getMatrix(rowIni, rowEnd, indVariable, indVariable); // dataVowels(:,0) -> last col is the
  data = data.getMatrix(rowIni, rowEnd, c); // the dependent variables correspond to the column indices in c
  multipleLinearRegression(indVar, data, interceptTerm);
} catch (Exception e) {

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

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

/***
 * PCA
 * 
 * @param fileName
 *            data one column per dimension or linguistic factor
 * @param eigen
 *            if true use eigenvalues, if false use svd (recomended)
 * @param scale
 *            if true use z-normalisation (recomended), if false substract off the mean for ecah dimension
 */
public void principalComponentAnalysis(String fileName, boolean eigen, boolean scale) {
  try {
    BufferedReader reader = new BufferedReader(new FileReader(fileName));
    Matrix data = Matrix.read(reader);
    int rows = data.getRowDimension() - 1;
    int cols = data.getColumnDimension() - 1;
    data = data.getMatrix(0, rows, 1, cols); // dataVowels(:,1:cols) -> dependent variables
    if (eigen)
      eigenPCA(data.transpose(), scale, false);
    else
      svdPCA(data.transpose(), scale, false);
  } catch (Exception e) {
    throw new RuntimeException("Problem reading file " + fileName, e);
  }
}

代码示例来源：origin: marytts/marytts

/***
 * PCA
 * 
 * @param fileName
 *            data one column per dimension or linguistic factor
 * @param eigen
 *            if true use eigenvalues, if false use svd (recomended)
 * @param scale
 *            if true use z-normalisation (recomended), if false substract off the mean for ecah dimension
 */
public void principalComponentAnalysis(String fileName, boolean eigen, boolean scale) {
  try {
    BufferedReader reader = new BufferedReader(new FileReader(fileName));
    Matrix data = Matrix.read(reader);
    int rows = data.getRowDimension() - 1;
    int cols = data.getColumnDimension() - 1;
    data = data.getMatrix(0, rows, 1, cols); // dataVowels(:,1:cols) -> dependent variables
    if (eigen)
      eigenPCA(data.transpose(), scale, false);
    else
      svdPCA(data.transpose(), scale, false);
  } catch (Exception e) {
    throw new RuntimeException("Problem reading file " + fileName, e);
  }
}

代码示例来源：origin: marytts/marytts

int cols = dataVowels.getColumnDimension() - 1;
Matrix indVar = dataVowels.getMatrix(0, rows, 0, 0); // dataVowels(:,0) -> col 0 is the independent variable
dataVowels = dataVowels.getMatrix(0, rows, 1, cols); // dataVowels(:,1:cols) -> dependent variables

代码示例来源：origin: marytts/marytts

int cols = dataVowels.getColumnDimension() - 1;
Matrix indVar = dataVowels.getMatrix(0, rows, 0, 0); // dataVowels(:,0) -> col 0 is the independent variable
dataVowels = dataVowels.getMatrix(0, rows, 1, cols); // dataVowels(:,1:cols) -> dependent variables

代码示例来源：origin: marytts/marytts

throw new RuntimeException("Problem reading file, rowIni < rowend" + rowIni + " < " + rowEnd);
Matrix indVar = data.getMatrix(rowIni, rowEnd, indVariable, indVariable); // dataVowels(:,0) -> last col is the
data = data.getMatrix(rowIni, rowEnd, c); // the dependent variables correspond to the column indices in c

代码示例来源：origin: marytts/marytts

throw new RuntimeException("Problem reading file, rowIni < rowend" + rowIni + " < " + rowEnd);
Matrix indVar = data.getMatrix(rowIni, rowEnd, indVariable, indVariable); // dataVowels(:,0) -> last col is the
data = data.getMatrix(rowIni, rowEnd, c); // the dependent variables correspond to the column indices in c

代码示例来源：origin: h2oai/h2o-3

public Frame makeUVec(SVDModel model, String u_name, Frame u, Frame qfrm, Matrix atqJ, SingularValueDecomposition svdJ ) {
 model._output._u_key = Key.make(u_name);
 double[][] svdJ_u = svdJ.getV().getMatrix(0, atqJ.getColumnDimension() - 1, 0,
     _parms._nv - 1).getArray();
 DataInfo qinfo = new DataInfo(qfrm, null, true, DataInfo.TransformType.NONE,
     false, false, false);
 DKV.put(qinfo._key, qinfo);
 BMulTask btsk = new BMulTask(_job._key, qinfo, ArrayUtils.transpose(svdJ_u));
 btsk.doAll(_parms._nv, Vec.T_NUM, qinfo._adaptedFrame);
 qinfo.remove();
 return btsk.outputFrame(model._output._u_key, null, null);
//  DKV.remove(qinfo._key);
}
@Override

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: openimaj/openimaj

/**
 * Select a subset of the principal components. Calling this method throws
 * away any extra basis vectors and eigenvalues.
 * 
 * @param n
 */
public void selectSubset(int n) {
  if (n >= eigenvalues.length)
    return;
  basis = basis.getMatrix(0, basis.getRowDimension() - 1, 0, n - 1);
  eigenvalues = Arrays.copyOf(eigenvalues, n);
}

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

public static Matrix getRowNumber(int i, Matrix m)
{
 Matrix ret = m.getMatrix(i, i, 0, m.getColumnDimension() - 1);
 return ret;
}

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

public static Matrix getRowNumber(int i, Matrix m)
{
 Matrix ret = m.getMatrix(i, i, 0, m.getColumnDimension() - 1);
 return ret;
}

代码示例来源：origin: stackoverflow.com

Matrix matrix;
matrix = new Matrix();
matrix.set(3, 5, "You");
matrix.set(0, 0, "Hi");

for (ArrayList<String> list : matrix.getMatrix()) {
  for (String value : list) {
    System.out.print(value + "\t");
  }
  System.out.println();
}

代码示例来源：origin: cmu-phil/tetrad

private static Matrix sumColumns(Matrix a){
  Matrix sum = new Matrix(1,a.getColumnDimension());
  for(int i=0; i<a.getRowDimension(); i++)
    sum.plusEquals(a.getMatrix(i,i,0,a.getColumnDimension()-1));
  return sum;
}

代码示例来源：origin: cmu-phil/tetrad

/**
 * Sums across the rows of the <code>Matrix</code> and return the result as a single column <code>MAtrix</code>
 * @param A  input <code>Matrix</code>
 * @return result
 */
public static Matrix sumRows(Matrix A){
  Matrix sum = new Matrix(A.getRowDimension(),1);
  for(int i=0; i<A.getColumnDimension(); i++)
    sum.plusEquals(A.getMatrix(0,A.getRowDimension()-1,i,i));
  return sum;
}

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