Java Matrix类代码示例

import weka.core.matrix.Matrix; //导入依赖的package包/类
/**
 * Loads the correlation/covariance matrix from a file.
 * 
 * @throws IOException if a problem occurs
 */
protected void loadMatrix() throws IOException {
  File f = new File(m_pathToMatrix);

  if (!f.exists()) {
    throw new IOException("The matrix file '" + m_pathToMatrix
      + "' does not seem to exist on the file system!");
  }

  BufferedReader br = null;
  try {
    br = new BufferedReader(new FileReader(m_pathToMatrix));
    try {
      m_matrix = new Matrix(br);
    } catch (Exception e) {
      throw new IOException(e);
    }
    br.close();
    br = null;
  } finally {
    if (br != null) {
      br.close();
    }
  }
}
 

import weka.core.matrix.Matrix; //导入依赖的package包/类
/**
 * Classifies a given instance.
 * 
 * @param inst the instance to be classified
 * @return the classification
 * @throws Exception if instance could not be classified successfully
 */
@Override
public double classifyInstance(Instance inst) throws Exception {

  // Filter instance
  inst = filterInstance(inst);

  // Build K vector
  Matrix k = new Matrix(m_NumTrain, 1);
  for (int i = 0; i < m_NumTrain; i++) {
    k.set(i, 0, m_kernel.eval(-1, i, inst));
  }

  double result = k.transpose().times(m_t).get(0, 0) + m_avg_target;
  result = (result - m_Blin) / m_Alin;

  return result;

}
 

import weka.core.matrix.Matrix; //导入依赖的package包/类
/**
 * Computes standard deviation for given instance, without transforming target
 * back into original space.
 */
protected double computeStdDev(Instance inst, Matrix k) throws Exception {

  double kappa = m_kernel.eval(-1, -1, inst) + m_deltaSquared;

  double s = 0;
  int n = m_L.length;
  for (int i = 0; i < n; i++) {
    double t = 0;
    for (int j = 0; j < n; j++) {
      t -= k.get(j, 0) * (i > j ? m_L[i][j] : m_L[j][i]);
    }
    s += t * k.get(i, 0);
  }

  double sigma = m_delta;
  if (kappa > s) {
    sigma = Math.sqrt(kappa - s);
  }

  return sigma;
}
 

import weka.core.matrix.Matrix; //导入依赖的package包/类
/**
 * Returns natural logarithm of density estimate for given value based on
 * given instance.
 * 
 * @param instance the instance to make the prediction for.
 * @param value the value to make the prediction for.
 * @return the natural logarithm of the density estimate
 * @exception Exception if the density cannot be computed
 */
@Override
public double logDensity(Instance inst, double value) throws Exception {

  inst = filterInstance(inst);

  // Build K vector (and Kappa)
  Matrix k = new Matrix(m_NumTrain, 1);
  for (int i = 0; i < m_NumTrain; i++) {
    k.set(i, 0, m_kernel.eval(-1, i, inst));
  }

  double estimate = k.transpose().times(m_t).get(0, 0) + m_avg_target;

  double sigma = computeStdDev(inst, k);

  // transform to GP space
  value = value * m_Alin + m_Blin;
  // center around estimate
  value = value - estimate;
  double z = -Math.log(sigma * Math.sqrt(2 * Math.PI)) - value * value
    / (2.0 * sigma * sigma);

  return z + Math.log(m_Alin);
}
 

import weka.core.matrix.Matrix; //导入依赖的package包/类
/**
 * Classifies a given instance.
 * 
 * @param inst
 *            the instance to be classified
 * @return the classification
 * @throws Exception
 *             if instance could not be classified successfully
 */
public double classifyInstance(Instance inst) throws Exception {

  // Filter instance
  inst = filterInstance(inst);

  // Build K vector
  Matrix k = new Matrix(m_NumTrain, 1);
  for (int i = 0; i < m_NumTrain; i++) {
    k.set(i, 0, m_kernel.eval(-1, i, inst));
  }

  double result = k.transpose().times(m_t).get(0, 0) + m_avg_target;
  result = (result - m_Blin) / m_Alin;

  return result;

}
 

import weka.core.matrix.Matrix; //导入依赖的package包/类
/**
 * Computes standard deviation for given instance, without
 * transforming target back into original space.
 */
protected double computeStdDev(Instance inst, Matrix k) throws Exception {

  double kappa = m_kernel.eval(-1, -1, inst) + m_delta * m_delta;

  double s = 0;
  int n = m_L.length;
  for (int i = 0; i < n; i++) {
    double t = 0;
    for (int j = 0; j < n; j++) {
      t -= k.get(j,0) * (i>j? m_L[i][j] : m_L[j][i]);
    }			
    s += t * k.get(i,0);
  }

  double sigma = m_delta;
  if (kappa > s) {
    sigma = Math.sqrt(kappa - s);
  }

  return sigma;
}
 

import weka.core.matrix.Matrix; //导入依赖的package包/类
/**
 * Returns natural logarithm of density estimate for given value based on given instance.
 *   
 * @param instance the instance to make the prediction for.
 * @param value the value to make the prediction for.
 * @return the natural logarithm of the density estimate
 * @exception Exception if the density cannot be computed
 */
public double logDensity(Instance inst, double value) throws Exception {
  
  inst = filterInstance(inst);

  // Build K vector (and Kappa)
  Matrix k = new Matrix(m_NumTrain, 1);
  for (int i = 0; i < m_NumTrain; i++) {
    k.set(i, 0, m_kernel.eval(-1, i, inst));
  }
  
  double estimate = k.transpose().times(m_t).get(0, 0) + m_avg_target;

  double sigma = computeStdDev(inst, k);
  
  // transform to GP space
  value = value * m_Alin + m_Blin;
  // center around estimate
  value = value - estimate;
  double z = -Math.log(sigma * Math.sqrt(2 * Math.PI)) 
    - value * value /(2.0*sigma*sigma); 
  
  return z + Math.log(m_Alin);
}
 

import weka.core.matrix.Matrix; //导入依赖的package包/类
/** Creates a default PrecomputedKernelMatrixKernell */
public Kernel getKernel() {
  PrecomputedKernelMatrixKernel pc = new PrecomputedKernelMatrixKernel();

  // load kernel matrix
  try {
    pc.setKernelMatrix(
       new Matrix(
          new InputStreamReader(ClassLoader.getSystemResourceAsStream(
                "weka/classifiers/data/test.matrix"))));
  } catch (Exception e) {
    e.printStackTrace();
  }

  return pc;
}
 

import weka.core.matrix.Matrix; //导入依赖的package包/类
/**
 * Compute the product among the matrix and the vector
 *
 * @param current
 *            The matrix.
 * @param vectorX
 *            The vector.
 */
public void productVector(Matrix current, double[] vectorX) {

	for (int m = 0; m < vectorX.length; m++) {
		for (int nn = 0; nn < vectorX.length; nn++) {
			current.set(m, nn, vectorX[m] * vectorX[nn]);
		}
	}
}
 

import weka.core.matrix.Matrix; //导入依赖的package包/类
public static Image getHeatMapForMatrix(Matrix matrix,
  List<String> rowAttNames) {

  double[][] m = matrix.getArray();

  // generate the heat map
  // need to reverse the order of the rows
  double[][] mm = new double[m.length][];
  for (int i = 0; i < m.length; i++) {
    mm[m.length - 1 - i] = m[i];
  }
  String[] xLabels = new String[rowAttNames.size()];
  String[] yLabels = new String[rowAttNames.size()];
  for (int i = 0; i < rowAttNames.size(); i++) {
    xLabels[i] = rowAttNames.get(i);
    yLabels[rowAttNames.size() - 1 - i] = rowAttNames.get(i);
  }
  HeatChart map = new HeatChart(mm, true);
  map.setTitle("Correlation matrix heat map");
  map.setCellSize(new java.awt.Dimension(30, 30));
  map.setHighValueColour(java.awt.Color.RED);
  map.setLowValueColour(java.awt.Color.BLUE);
  map.setXValues(xLabels);
  map.setYValues(yLabels);

  return map.getChartImage();
}
 

import weka.core.matrix.Matrix; //导入依赖的package包/类
/**
 * Generates a heat map from a matrix of correlations
 * 
 * @param matrix a Matrix (expected to hold correlation values between -1 and
 *          1)
 * @param rowAttNames a list of labels for the columns/rows
 * @return an Image holding the heat map
 */
public static Image getHeatMapForMatrix(Matrix matrix,
  List<String> rowAttNames) {

  double[][] m = matrix.getArray();

  // generate the heat map
  // need to reverse the order of the rows
  double[][] mm = new double[m.length][];
  for (int i = 0; i < m.length; i++) {
    mm[m.length - 1 - i] = m[i];
  }
  String[] xLabels = new String[rowAttNames.size()];
  String[] yLabels = new String[rowAttNames.size()];
  for (int i = 0; i < rowAttNames.size(); i++) {
    xLabels[i] = rowAttNames.get(i);
    yLabels[rowAttNames.size() - 1 - i] = rowAttNames.get(i);
  }
  HeatChart map = new HeatChart(mm, true);
  map.setTitle("Correlation matrix heat map");
  map.setCellSize(new java.awt.Dimension(30, 30));
  map.setHighValueColour(java.awt.Color.RED);
  map.setLowValueColour(java.awt.Color.BLUE);
  map.setXValues(xLabels);
  map.setYValues(yLabels);

  return map.getChartImage();
}
 

import weka.core.matrix.Matrix; //导入依赖的package包/类
/**
 * Returns the standard errors of slope and intercept for a simple linear
 * regression model: y = a + bx. The first element is the standard error of
 * slope, the second element is standard error of intercept.
 * 
 * @param data (the data set)
 * @param chosen (chosen x-attribute)
 * @param slope (slope determined by simple linear regression model)
 * @param intercept (intercept determined by simple linear regression model)
 * @param df (number of instances - 2)
 * 
 * @return array of standard errors of slope and intercept
 * @throws Exception if there is a missing class value in data
 */
public static double[] calculateStdErrorOfCoef(Instances data,
  Attribute chosen, double slope, double intercept, int df) throws Exception {
  // calculate sum of squared residuals, mean squared error
  double ssr = calculateSSR(data, chosen, slope, intercept);
  double mse = ssr / df;

  /*
   * put data into 2-D array with 2 columns first column is value of chosen
   * attribute second column is constant (1's)
   */
  double[][] array = new double[data.numInstances()][2];
  for (int i = 0; i < data.numInstances(); i++) {
    array[i][0] = data.instance(i).value(chosen);
    array[i][1] = 1.0;
  }

  /*
   * linear algebra calculation: covariance matrix = mse * (XtX)^-1 diagonal
   * of covariance matrix is square of standard error of coefficients
   */
  Matrix X = new Matrix(array);
  Matrix Xt = X.transpose();
  Matrix XtX = Xt.times(X);
  Matrix inverse = XtX.inverse();
  Matrix cov = inverse.times(mse);

  double[] result = new double[2];
  for (int i = 0; i < 2; i++) {
    result[i] = Math.sqrt(cov.get(i, i));
  }

  return result;
}
 

import weka.core.matrix.Matrix; //导入依赖的package包/类
/**
 * Gives standard deviation of the prediction at the given instance.
 * 
 * @param inst the instance to get the standard deviation for
 * @return the standard deviation
 * @throws Exception if computation fails
 */
public double getStandardDeviation(Instance inst) throws Exception {

  inst = filterInstance(inst);

  // Build K vector (and Kappa)
  Matrix k = new Matrix(m_NumTrain, 1);
  for (int i = 0; i < m_NumTrain; i++) {
    k.set(i, 0, m_kernel.eval(-1, i, inst));
  }

  return computeStdDev(inst, k) / m_Alin;
}
 

import weka.core.matrix.Matrix; //导入依赖的package包/类
/** Calculate covariance and value means */
private void calculateCovariance() {

  double sumValues = 0, sumConds = 0;
  for (int i = 0; i < m_Values.size(); i++) {
    sumValues += m_Values.elementAt(i).doubleValue()
      * m_Weights.elementAt(i).doubleValue();
    sumConds += m_CondValues.elementAt(i).doubleValue()
      * m_Weights.elementAt(i).doubleValue();
  }
  m_ValueMean = sumValues / m_SumOfWeights;
  m_CondMean = sumConds / m_SumOfWeights;
  double c00 = 0, c01 = 0, c10 = 0, c11 = 0;
  for (int i = 0; i < m_Values.size(); i++) {
    double x = m_Values.elementAt(i).doubleValue();
    double y = m_CondValues.elementAt(i).doubleValue();
    double weight = m_Weights.elementAt(i).doubleValue();
    c00 += (x - m_ValueMean) * (x - m_ValueMean) * weight;
    c01 += (x - m_ValueMean) * (y - m_CondMean) * weight;
    c11 += (y - m_CondMean) * (y - m_CondMean) * weight;
  }
  c00 /= (m_SumOfWeights - 1.0);
  c01 /= (m_SumOfWeights - 1.0);
  c10 = c01;
  c11 /= (m_SumOfWeights - 1.0);
  m_Covariance = new Matrix(2, 2);
  m_Covariance.set(0, 0, c00);
  m_Covariance.set(0, 1, c01);
  m_Covariance.set(1, 0, c10);
  m_Covariance.set(1, 1, c11);
}
 

import weka.core.matrix.Matrix; //导入依赖的package包/类
/**
 * Returns value for normal kernel
 *
 * @param x the argument to the kernel function
 * @param variance the variance
 * @return the value for a normal kernel
 */
private double normalKernel(double x) {
  
  Matrix thisPoint = new Matrix(1, 2);
  thisPoint.set(0, 0, x);
  thisPoint.set(0, 1, m_ConstDelta);
  return Math.exp(-thisPoint.times(m_CovarianceInverse).
      times(thisPoint.transpose()).get(0, 0) 
      / 2) / (Math.sqrt(TWO_PI) * m_Determinant);
}