Jama.Matrix.read()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.read()方法的一些代码示例，展示了Matrix.read()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.read()方法的具体详情如下：
包路径：Jama.Matrix
类名称：Matrix
方法名：read

Matrix.read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

try {
  BufferedReader reader = new BufferedReader(new FileReader(fileName));
  Matrix data = Matrix.read(reader);
  reader.close();
  int rows = data.getRowDimension() - 1;

代码示例来源：origin: marytts/marytts

try {
  BufferedReader reader = new BufferedReader(new FileReader(fileName));
  Matrix data = Matrix.read(reader);
  reader.close();
  int rows = data.getRowDimension() - 1;

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

public void predictValues(String fileName, int indVariable, int[] c, boolean interceptTerm, int rowIni, int rowEnd) {
  try {
    BufferedReader reader = new BufferedReader(new FileReader(fileName));
    Matrix data = Matrix.read(reader);
    reader.close();
    int rows = data.getRowDimension() - 1;

代码示例来源：origin: marytts/marytts

public void predictValues(String fileName, int indVariable, int[] c, boolean interceptTerm, int rowIni, int rowEnd) {
  try {
    BufferedReader reader = new BufferedReader(new FileReader(fileName));
    Matrix data = Matrix.read(reader);
    reader.close();
    int rows = data.getRowDimension() - 1;

代码示例来源：origin: marytts/marytts

Matrix dataVowels = Matrix.read(reader);
int rows = dataVowels.getRowDimension() - 1;
int cols = dataVowels.getColumnDimension() - 1;

代码示例来源：origin: marytts/marytts

Matrix dataVowels = Matrix.read(reader);
int rows = dataVowels.getRowDimension() - 1;
int cols = dataVowels.getColumnDimension() - 1;

代码示例来源：origin: bcdev/beam

int weighted = Integer.valueOf(stok.nextToken());
int dimension = Integer.valueOf(stok.nextToken());
double[][] df = Matrix.read(in).getArray();
this.npoints = df.length;
this.np = this.npoints;

代码示例来源：origin: bcdev/beam

@Override
  protected void setUp() throws Exception {
    endmembers = Matrix.read(getResourceReader("endmember-spectra.csv"));
    spectra = Matrix.read(getResourceReader("pixel-spectra.csv"));
  }
}

代码示例来源：origin: de.dfki.mary/marytts-runtime

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: bcdev/beam

public void testUnconstrainedUnmixing() throws IOException {
  SpectralUnmixing mlm = new UnconstrainedLSU(endmembers.getArray());
  Matrix abundUnconstrBeam = new Matrix(mlm.unmix(spectra.getArray()));
  Matrix abundUnconstrEnvi = Matrix.read(getResourceReader("abundances-unconstr-envi.csv"));
  Matrix abundUnconstrExpected = Matrix.read(getResourceReader("abundances-unconstr-expected.csv"));
  assertEquals("Difference of abundances (BEAM minus ENVI, unconstrained)",
      0.0,
      maxAbs(abundUnconstrBeam.minus(abundUnconstrEnvi)),
      1e-4);
  assertEquals("Difference of abundances (BEAM minus EXPECTED, unconstrained)",
      0.0,
      maxAbs(abundUnconstrBeam.minus(abundUnconstrExpected)),
      1e-7);
}

代码示例来源：origin: bcdev/beam

public void testConstrainedUnmixing() throws IOException {
  SpectralUnmixing mlmC = new ConstrainedLSU(endmembers.getArray());
  Matrix abundConstrBeam = new Matrix(mlmC.unmix(spectra.getArray()));
  
  Matrix abundConstrEnvi = Matrix.read(getResourceReader("abundances-constr-envi.csv"));
  Matrix abundConstrExpected = Matrix.read(getResourceReader("abundances-constr-expected.csv"));
  assertEquals("Difference of abundances (BEAM minus ENVI, constrained)",
      0.0,
      maxAbs(abundConstrBeam.minus(abundConstrEnvi)),
      1e-2);
  assertEquals("Difference of abundances (BEAM minus EXPECTED, constrained)",
      0.0,
      maxAbs(abundConstrBeam.minus(abundConstrExpected)),
      1e-7);
  assertEquals("Sum of abundances must be 1 (constrained)",
      0.0,
      maxAbsDeltaRowSumFromOne(abundConstrBeam),
      1e-15);
}

代码示例来源：origin: gov.nist.math/jama

A.print(FILE,fmt,10);
FILE.close();
R = Matrix.read(new BufferedReader(new FileReader("JamaTestMatrix.out")));
if (A.minus(R).norm1() < .001 ) {
  try_success("print()/read()...","");
  A.print(FILE,fmt,10);
  FILE.close();
  R = Matrix.read(new BufferedReader(new FileReader("JamaTestMatrix.out")));
  if (A.minus(R).norm1() < .001 ) {
   try_success("print()/read()...","");

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