本文整理汇总了Java中org.apache.commons.math3.linear.EigenDecomposition.getV方法的典型用法代码示例。如果您正苦于以下问题：Java EigenDecomposition.getV方法的具体用法？Java EigenDecomposition.getV怎么用？Java EigenDecomposition.getV使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类org.apache.commons.math3.linear.EigenDecomposition的用法示例。

在下文中一共展示了EigenDecomposition.getV方法的7个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Java代码示例。

示例1: root

import org.apache.commons.math3.linear.EigenDecomposition; //导入方法依赖的package包/类
/**
 * @param p
 * @return the p-th root matrix of this matrix
 */
public Matrix root (double p) {
	if (!isSquare())
		throw new IllegalArgumentException("No sqaure matrix.");
	if (p==1) return this;
	if (p<1) throw new IllegalArgumentException(String.format("Illegal fractional root: %f", p));
	EigenDecomposition ev = new EigenDecomposition(this);
	RealMatrix V = ev.getV();
	RealMatrix D = ev.getD();
	for (int i=0; i<D.getRowDimension(); i++) {
		if (D.getEntry(i,i)<0)
			throw new IllegalStateException("Root of the matrix is not defined.");
		D.setEntry(i,i,Math.pow(D.getEntry(i,i),1./p));
	}
	RealMatrix B = V.multiply(D).multiply(new LUDecomposition(V).getSolver().getInverse());
	return new Matrix(B.getData());
} 

示例2: conditionCovarianceMatrix

import org.apache.commons.math3.linear.EigenDecomposition; //导入方法依赖的package包/类
/**
 * Conditions the supplied covariance matrix by enforcing
 * positive eigenvalues along its main diagonal. 
 * @param S original covariance matrix
 * @return modified covariance matrix
 */
private RealMatrix conditionCovarianceMatrix(RealMatrix S) {
	EigenDecomposition ed = new EigenDecomposition(S);  // S  ->  V . D . V^T
	RealMatrix V  = ed.getV();
	RealMatrix D  = ed.getD();	// diagonal matrix of eigenvalues
	RealMatrix VT = ed.getVT();
	for (int i = 0; i < D.getRowDimension(); i++) {
		D.setEntry(i, i, Math.max(D.getEntry(i, i), 10E-6));	// setting eigenvalues to zero is not enough!
	}
	return V.multiply(D).multiply(VT);
} 

示例3: matrixLog

import org.apache.commons.math3.linear.EigenDecomposition; //导入方法依赖的package包/类
static double[][] matrixLog (double[][] matrix) {
	if (matrix.length==1)
		return new double[][]{{Math.log(matrix[0][0])}};
	if (matrix.length!=matrix[0].length)
		throw new IllegalArgumentException("No sqaure matrix.");
	RealMatrix A = new Array2DRowRealMatrix(matrix);
	EigenDecomposition ev = new EigenDecomposition(A);
	RealMatrix V = ev.getV();
	RealMatrix Vinv = (new LUDecomposition(V).getSolver().getInverse());
	RealMatrix logA = Vinv.multiply(A).multiply(V);
	for (int i=0; i<matrix.length; i++)
		logA.setEntry(i, i,Math.log(logA.getEntry(i, i)));
	return V.multiply(logA).multiply(Vinv).getData();
} 

示例4: eigen_bak

import org.apache.commons.math3.linear.EigenDecomposition; //导入方法依赖的package包/类
/**
 * Calculates the eigen decomposition of a real matrix. The eigen
 * decomposition of matrix A is a set of two matrices: V and D such that A =
 * V × D × VT. A, V and D are all m × m matrices.
 *
 * @param a Given matrix.
 * @return Result W/V arrays.
 */
public static Array[] eigen_bak(Array a) {
    int m = a.getShape()[0];
    Array Wa;
    Array Va = Array.factory(DataType.DOUBLE, new int[]{m, m});
    double[][] aa = (double[][]) ArrayUtil.copyToNDJavaArray(a);
    RealMatrix matrix = new Array2DRowRealMatrix(aa, false);
    EigenDecomposition decomposition = new EigenDecomposition(matrix);
    if (decomposition.hasComplexEigenvalues()) {
        Wa = Array.factory(DataType.OBJECT, new int[]{m});
        double[] rev = decomposition.getRealEigenvalues();
        double[] iev = decomposition.getImagEigenvalues();
        for (int i = 0; i < m; i++) {
            Wa.setObject(i, new Complex(rev[i], iev[i]));
            RealVector v = decomposition.getEigenvector(i);
            for (int j = 0; j < v.getDimension(); j++) {
                Va.setDouble(j * m + i, v.getEntry(j));
            }
        }
    } else {
        RealMatrix V = decomposition.getV();
        RealMatrix D = decomposition.getD();
        Wa = Array.factory(DataType.DOUBLE, new int[]{m});
        for (int i = 0; i < m; i++) {
            for (int j = 0; j < m; j++) {
                Va.setDouble(i * m + (m - j - 1), V.getEntry(i, j));
                if (i == j) {
                    Wa.setDouble(m - i - 1, D.getEntry(i, j));
                }
            }
        }
    }

    return new Array[]{Wa, Va};
} 

示例5: componize

import org.apache.commons.math3.linear.EigenDecomposition; //导入方法依赖的package包/类
public void componize(double [][] covariate){
	
	// replace the initial data set the same values minus the means from each variable-column
	
for (int j=0; j < covariate[0].length; j++ ) {
	
	DescriptiveStatistics stats = new DescriptiveStatistics();	
			
  	 for  (int k=0 ; k < covariate.length ; k++){
  	 stats.addValue(covariate[k][j]);}
	
	double mean=stats.getMean();

	for  (int k=0 ; k < covariate.length ; k++){
	   covariate[k][j]= covariate[k][j]-mean;}

// here ends the iteration that replaces all values with the value minus the mean of the respective column		
}	

// We get the Covariance matrix for the "adjusted by mean" data set
RealMatrix covariance_matrix=  new PearsonsCorrelation(covariate).getCorrelationMatrix();		

// we get the Eigen values and the Eigen Vectors Via Eigen Value Decomposition.

EigenDecomposition Eig = new EigenDecomposition(covariance_matrix, 1);	
// get the Eigen Values	
double Eigenvaluess [] =Eig.getRealEigenvalues();	
// Get the Eigen vectors	
RealMatrix Eigenvec = Eig.getV();	
double [][] EigenVecors=Eigenvec.getData();    
//Sort everything to get the Vectors with the highest significance	
SortTableDouble sort = new SortTableDouble();   
 sort.sorting (Eigenvaluess,EigenVecors );    
 EigenVecor =sort.value_matrix();	
 Eigenvalues=sort.scanned_values();
 Perchentages= new double[Eigenvalues.length];
	 for  (int k=0 ; k < Eigenvalues.length ; k++){
		Perchentages[k]= Eigenvalues[k]/Eigenvalues.length	;} 
} 

示例6: reduceTraining

import org.apache.commons.math3.linear.EigenDecomposition; //导入方法依赖的package包/类
public double[][] reduceTraining(double[][] data, String[] classLabels) {
   assert(data.length > 0);
   assert(data[0].length == super.inputSize);

   double[][] reducedData = changingValueReducer.reduceTraining(data, classLabels);

   RealMatrix dataMatrix = new Array2DRowRealMatrix(reducedData);

   // NOTE(eriq): The math says that we should first center our data.
   //  But, centering the data (subtracting the mean) gives up much worse results...
   // RealMatrix centeredData = dataMatrix.subtract(getMeanMatrix(dataMatrix));
   RealMatrix centeredData = dataMatrix;

   Covariance covariance = new Covariance(reducedData);
   EigenDecomposition eigenDecomp = new EigenDecomposition(covariance.getCovarianceMatrix());

   // Get the eigen vectors.
   // Transpose them (each eigen vector is now in a row).
   // Take the top |reducedFeatureVectorLength| vectors.
   // TODO(eriq): Verify |reducedFeatureVectorLength| > |num reduced features|.
   // NOTE(eriq): Are the eigen vectors along the vertical or horizontal.
   // transformationMatrix = eigenDecomp.getV().transpose();
   transformationMatrix = eigenDecomp.getV();

   // Get only the top |super.outputSize| eigen vectors.
   transformationMatrix = transformationMatrix.getSubMatrix(0, super.outputSize - 1, 0, reducedData[0].length - 1);

   RealMatrix finalData = transformationMatrix.multiply(centeredData.transpose()).transpose();

   return finalData.getData();
} 

示例7: computeEigen

import org.apache.commons.math3.linear.EigenDecomposition; //导入方法依赖的package包/类
/**
 * Function to perform Eigen decomposition on a given matrix.
 * Input must be a symmetric matrix.
 * 
 * @param in matrix object
 * @return array of matrix blocks
 * @throws DMLRuntimeException if DMLRuntimeException occurs
 */
private static MatrixBlock[] computeEigen(MatrixObject in)
	throws DMLRuntimeException 
{
	if ( in.getNumRows() != in.getNumColumns() ) {
		throw new DMLRuntimeException("Eigen Decomposition can only be done on a square matrix. Input matrix is rectangular (rows=" + in.getNumRows() + ", cols="+ in.getNumColumns() +")");
	}
	
	Array2DRowRealMatrix matrixInput = DataConverter.convertToArray2DRowRealMatrix(in);
	
	EigenDecomposition eigendecompose = new EigenDecomposition(matrixInput);
	RealMatrix eVectorsMatrix = eigendecompose.getV();
	double[][] eVectors = eVectorsMatrix.getData();
	double[] eValues = eigendecompose.getRealEigenvalues();
	
	//Sort the eigen values (and vectors) in increasing order (to be compatible w/ LAPACK.DSYEVR())
	int n = eValues.length;
	for (int i = 0; i < n; i++) {
	    int k = i;
	    double p = eValues[i];
	    for (int j = i + 1; j < n; j++) {
	        if (eValues[j] < p) {
	            k = j;
	            p = eValues[j];
	        }
	    }
	    if (k != i) {
	        eValues[k] = eValues[i];
	        eValues[i] = p;
	        for (int j = 0; j < n; j++) {
	            p = eVectors[j][i];
	            eVectors[j][i] = eVectors[j][k];
	            eVectors[j][k] = p;
	        }
	    }
	}

	MatrixBlock mbValues = DataConverter.convertToMatrixBlock(eValues, true);
	MatrixBlock mbVectors = DataConverter.convertToMatrixBlock(eVectors);

	return new MatrixBlock[] { mbValues, mbVectors };
} 

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