本文整理汇总了Java中cern.colt.matrix.linalg.EigenvalueDecomposition类的典型用法代码示例。如果您正苦于以下问题：Java EigenvalueDecomposition类的具体用法？Java EigenvalueDecomposition怎么用？Java EigenvalueDecomposition使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
EigenvalueDecomposition类属于cern.colt.matrix.linalg包，在下文中一共展示了EigenvalueDecomposition类的12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: getRoots
import cern.colt.matrix.linalg.EigenvalueDecomposition; //导入依赖的package包/类
/**
* {@inheritDoc}
*/
@Override
public Double[] getRoots(final RealPolynomialFunction1D function) {
Validate.notNull(function, "function");
final double[] coeffs = function.getCoefficients();
final int l = coeffs.length - 1;
final DoubleMatrix2D hessian = DoubleFactory2D.dense.make(l, l);
for (int i = 0; i < l; i++) {
hessian.setQuick(0, i, -coeffs[l - i - 1] / coeffs[l]);
for (int j = 1; j < l; j++) {
hessian.setQuick(j, i, 0);
if (i != l - 1) {
hessian.setQuick(i + 1, i, 1);
}
}
}
final double[] d = new EigenvalueDecomposition(hessian).getRealEigenvalues().toArray();
final Double[] result = new Double[d.length];
for (int i = 0; i < d.length; i++) {
result[i] = d[i];
}
return result;
}
开发者ID:DevStreet，项目名称:FinanceAnalytics，代码行数:26，代码来源:EigenvaluePolynomialRootFinder.java
示例2: getGt
import cern.colt.matrix.linalg.EigenvalueDecomposition; //导入依赖的package包/类
private static DoubleMatrix2D getGt(final DenseDoubleMatrix2D p, final DenseDoubleMatrix2D q, double lambda) {
final int K = p.columns();
DenseDoubleMatrix2D A1 = new DenseDoubleMatrix2D(K, K);
q.zMult(q, A1, 1.0, 0.0, true, false);
for (int k = 0; k < K; k++) {
A1.setQuick(k, k, lambda + A1.getQuick(k, k));
}
EigenvalueDecomposition eig = new EigenvalueDecomposition(A1);
DoubleMatrix1D d = eig.getRealEigenvalues();
DoubleMatrix2D gt = eig.getV();
for (int k = 0; k < K; k++) {
double a = sqrt(d.get(k));
gt.viewColumn(k).assign(x -> a * x);
}
return gt;
}
开发者ID:RankSys，项目名称:RankSys，代码行数:20，代码来源:PZTFactorizer.java
示例3: SolveSpectralClustering
import cern.colt.matrix.linalg.EigenvalueDecomposition; //导入依赖的package包/类
/**
* Finds the clusters using algorithm version 1.
*
* @param similarityMatrix
* the similarity matrix
* @param numCluster
* the number of clusters to construct
*/
public static void SolveSpectralClustering(DenseDoubleMatrix2D similarityMatrix, int numCluster,
SimilarityMatrixTransformer transformer) {
for (int r = 0; r < similarityMatrix.rows(); r++) {
for (int c = 0; c < similarityMatrix.columns(); c++) {
System.out.print(similarityMatrix.getQuick(r, c) + " ");
}
System.out.println();
}
DenseDoubleMatrix2D weightedMatrix = transformer.transform(similarityMatrix);
DenseDoubleMatrix2D laplacianMatrix = getLaplacian(weightedMatrix);
EigenvalueDecomposition eigenDecomposition = new EigenvalueDecomposition(laplacianMatrix);
DoubleMatrix2D eigenVectors = eigenDecomposition.getV();
DoubleMatrix1D eigenValues = eigenDecomposition.getRealEigenvalues();
System.out.println("EigenValues:");
EigenValueSortHelper[] eigenvaluesToSort = new EigenValueSortHelper[eigenValues.size()];
for (int iEigen = 0; iEigen < eigenValues.size(); iEigen++) {
eigenvaluesToSort[iEigen] = new EigenValueSortHelper(eigenValues.getQuick(iEigen), iEigen);
System.out.print(eigenValues.getQuick(iEigen) + " ");
}
System.out.println();
Arrays.sort(eigenvaluesToSort);
for (int iEigen = 0; iEigen < eigenValues.size(); iEigen++) {
System.out.print(eigenvaluesToSort[iEigen].getIndex() + ":"
+ eigenValues.getQuick(eigenvaluesToSort[iEigen].getIndex()) + " ");
}
System.out.println();
System.out.println("EigenVectors:");
for (int iEigen = 0; iEigen < eigenVectors.columns(); iEigen++) {
System.out.print("[");
for (int r = 0; r < eigenVectors.rows(); r++) {
System.out.print(eigenVectors.getQuick(r, eigenvaluesToSort[iEigen].getIndex()) + " ");
}
System.out.println("]");
}
}
开发者ID:dexyko，项目名称:spectralclustering，代码行数:46，代码来源:SpectralClusteringSolver.java
示例4: getEigenvectors
import cern.colt.matrix.linalg.EigenvalueDecomposition; //导入依赖的package包/类
public double[][] getEigenvectors(){
EigenvalueDecomposition eigenDecomp = new EigenvalueDecomposition(matrix);
DoubleMatrix2D eigenVectorMatrix = eigenDecomp.getV();
return eigenVectorMatrix.toArray();
}
开发者ID:rafaelmss，项目名称:GraphClustering，代码行数:7，代码来源:MatrixColt.java
示例5: calEigen
import cern.colt.matrix.linalg.EigenvalueDecomposition; //导入依赖的package包/类
public int calEigen() {
EigenvalueDecomposition eigen_cal = new EigenvalueDecomposition(
x_matrix);
eigen_matrix = eigen_cal.getD();
@SuppressWarnings("unused")
int[] result = getEigen();
sort();
return 0;
}
开发者ID:kewei-ma，项目名称:GeDBIT，代码行数:10，代码来源:Eigen.java
示例6: calculateEigenvalues
import cern.colt.matrix.linalg.EigenvalueDecomposition; //导入依赖的package包/类
private void calculateEigenvalues(DoubleMatrix2D matrix) {
EigenvalueDecomposition evd = new EigenvalueDecomposition(matrix);
DoubleMatrix2D D = evd.getD();
DoubleMatrix2D V = evd.getV();
System.out.println("here");
}
开发者ID:max6cn，项目名称:jmt，代码行数:7，代码来源:RandomNetworkGenerator.java
示例7: getSpectralRadius
import cern.colt.matrix.linalg.EigenvalueDecomposition; //导入依赖的package包/类
public static double getSpectralRadius(DoubleMatrix2D A){
EigenvalueDecomposition eigen = new EigenvalueDecomposition(A);
DoubleMatrix1D eigenValues = eigen.getRealEigenvalues();
return eigenValues.assign(Functions.abs).viewSorted().get(eigenValues.size()-1);
}
开发者ID:wil3，项目名称:lacus，代码行数:7，代码来源:MLMatrixUtils.java
示例8: runPCA
import cern.colt.matrix.linalg.EigenvalueDecomposition; //导入依赖的package包/类
static public EigenvalueDecomposition runPCA(DoubleMatrix2D matrix) {
return runPCA(matrix, true);
}
开发者ID:kewei-ma，项目名称:GeDBIT，代码行数:4，代码来源:PCAMR.java
示例9: EMPCA
import cern.colt.matrix.linalg.EigenvalueDecomposition; //导入依赖的package包/类
/**
* Compute Principal Component Analysis with EM method.
*
* @param matrix
* the matrix to do PCA, needs not to be centerized already. This
* method will centerize it. Each row is a data point.
* @param pcNum
* @return a {@link DoubleMatrix2D} of size [pcNum x (dim +1)], the first
* column is the variance of each PC in descending order. The remain
* of each row is a PC
*/
static DoubleMatrix2D EMPCA(DoubleMatrix2D matrix, final int pcNum) {
// System.out.println(matrix);
if (matrix instanceof LargeDenseDoubleMatrix2D)
return EMPCALarge((LargeDenseDoubleMatrix2D) matrix, pcNum);
final int iterNum = 20;
// final int size = matrix.rows();
final int dim = matrix.columns();
// center the matrix
// System.out.println(matrix);
centerize(matrix);
DoubleMatrix2D data = matrix.viewDice();
// initialization
Random r = new Random();
double[][] CData = new double[dim][pcNum];
for (int i = 0; i < dim; i++)
for (int j = 0; j < pcNum; j++)
CData[i][j] = r.nextDouble() - 0.5;
DoubleMatrix2D C = new DenseDoubleMatrix2D(CData);
DoubleMatrix2D x = null;
Algebra alg = new Algebra();
for (int i = 0; i < iterNum; i++) {
x = alg.mult(
alg.mult(alg.inverse(alg.mult(C.viewDice(), C)),
C.viewDice()), data);
C = alg.mult(alg.mult(data, x.viewDice()),
alg.inverse(alg.mult(x, x.viewDice())));
}
C = orthonormalization(C);
DoubleMatrix2D cov = Statistic.covariance(alg.mult(C.viewDice(), data)
.viewDice());
EigenvalueDecomposition evd = new EigenvalueDecomposition(cov);
DenseDoubleMatrix2D result = new DenseDoubleMatrix2D(dim + 1, pcNum);
result.viewPart(1, 0, dim, pcNum).assign(
alg.mult(C, evd.getV().viewColumnFlip()));
result.viewRow(0).assign(evd.getRealEigenvalues().viewFlip());
return result.viewDice();
}
开发者ID:kewei-ma，项目名称:GeDBIT，代码行数:59，代码来源:PCAMR.java
示例10: EMPCALarge
import cern.colt.matrix.linalg.EigenvalueDecomposition; //导入依赖的package包/类
static DoubleMatrix2D EMPCALarge(LargeDenseDoubleMatrix2D matrix,
final int pcNum) {
final int iterNum = 20;
final int dim = matrix.columns();
// center the matrix
centerize(matrix);
// initialization
Random r = new Random();
double[][] CData = new double[dim][pcNum];
for (int i = 0; i < dim; i++)
for (int j = 0; j < pcNum; j++)
CData[i][j] = r.nextDouble() - 0.5;
DoubleMatrix2D C = new DenseDoubleMatrix2D(CData);
DoubleMatrix2D x = null;
Algebra alg = new Algebra();
for (int i = 0; i < iterNum; i++) {
x = LargeDenseDoubleMatrix2D.mult(LargeDenseDoubleMatrix2D.mult(alg
.inverse(LargeDenseDoubleMatrix2D.mult(C, true, C, false)),
false, C, true), false, matrix, true);
C = LargeDenseDoubleMatrix2D.mult(LargeDenseDoubleMatrix2D.mult(
matrix, true, x, true), false, alg
.inverse(LargeDenseDoubleMatrix2D.mult(x, false, x, true)),
false);
}
C = orthonormalization(C);
DoubleMatrix2D cov = LargeDenseDoubleMatrix2D
.covariance(LargeDenseDoubleMatrix2D.mult(C.viewDice(), false,
matrix, true), true);
EigenvalueDecomposition evd = new EigenvalueDecomposition(cov);
DenseDoubleMatrix2D result = new DenseDoubleMatrix2D(dim + 1, pcNum);
result.viewPart(1, 0, dim, pcNum).assign(
alg.mult(C, evd.getV().viewColumnFlip()));
result.viewRow(0).assign(evd.getRealEigenvalues().viewFlip());
return result.viewDice();
}
开发者ID:kewei-ma，项目名称:GeDBIT，代码行数:44，代码来源:PCAMR.java
示例11: EMPCA
import cern.colt.matrix.linalg.EigenvalueDecomposition; //导入依赖的package包/类
/**
* Compute Principal Component Analysis with EM method.
*
* @param matrix
* the matrix to do PCA, needs not to be centerized already. This
* method will centerize it. Each row is a data point.
* @param pcNum
* @return a {@link DoubleMatrix2D} of size [pcNum x (dim +1)], the first
* column is the variance of each PC in descending order. The remain
* of each row is a PC
*/
static DoubleMatrix2D EMPCA(DoubleMatrix2D matrix, final int pcNum) {
// System.out.println(matrix);
if (matrix instanceof LargeDenseDoubleMatrix2D)
return EMPCALarge((LargeDenseDoubleMatrix2D) matrix, pcNum);
final int iterNum = 20;
// final int size = matrix.rows();
final int dim = matrix.columns();
// center the matrix
// System.out.println(matrix);
centerize(matrix);
DoubleMatrix2D data = matrix.viewDice();
// initialization
Random r = new Random();
double[][] CData = new double[dim][pcNum];
for (int i = 0; i < dim; i++)
for (int j = 0; j < pcNum; j++)
CData[i][j] = r.nextDouble() - 0.5;
DoubleMatrix2D C = new DenseDoubleMatrix2D(CData);
DoubleMatrix2D x = null;
Algebra alg = new Algebra();
for (int i = 0; i < iterNum; i++) {
x = alg.mult(
alg.mult(alg.inverse(alg.mult(C.viewDice(), C)),
C.viewDice()), data);
C = alg.mult(alg.mult(data, x.viewDice()),
alg.inverse(alg.mult(x, x.viewDice())));
}
C = orthonormalization(C);
DoubleMatrix2D cov = Statistic.covariance(alg.mult(C.viewDice(), data)
.viewDice());
EigenvalueDecomposition evd = new EigenvalueDecomposition(cov);
DenseDoubleMatrix2D result = new DenseDoubleMatrix2D(dim + 1, pcNum);
result.viewPart(1, 0, dim, pcNum).assign(
alg.mult(C, evd.getV().viewColumnFlip()));
result.viewRow(0).assign(evd.getRealEigenvalues().viewFlip());
return result.viewDice();
}
开发者ID:kewei-ma，项目名称:GeDBIT，代码行数:59，代码来源:PCA.java
示例12: EMPCALarge
import cern.colt.matrix.linalg.EigenvalueDecomposition; //导入依赖的package包/类
static DoubleMatrix2D EMPCALarge(LargeDenseDoubleMatrix2D matrix,
final int pcNum) {
final int iterNum = 20;
final int dim = matrix.columns();
// center the matrix
centerize(matrix);
// initialization
Random r = new Random();
double[][] CData = new double[dim][pcNum];
for (int i = 0; i < dim; i++)
for (int j = 0; j < pcNum; j++)
CData[i][j] = r.nextDouble() - 0.5;
DoubleMatrix2D C = new DenseDoubleMatrix2D(CData);
DoubleMatrix2D x = null;
Algebra alg = new Algebra();
for (int i = 0; i < iterNum; i++) {
x = LargeDenseDoubleMatrix2D.mult(LargeDenseDoubleMatrix2D.mult(alg
.inverse(LargeDenseDoubleMatrix2D.mult(C, true, C, false)),
false, C, true), false, matrix, true);
C = LargeDenseDoubleMatrix2D.mult(LargeDenseDoubleMatrix2D.mult(
matrix, true, x, true), false, alg
.inverse(LargeDenseDoubleMatrix2D.mult(x, false, x, true)),
false);
}
C = orthonormalization(C);
DoubleMatrix2D cov = LargeDenseDoubleMatrix2D
.covariance(LargeDenseDoubleMatrix2D.mult(C.viewDice(), false,
matrix, true), true);
EigenvalueDecomposition evd = new EigenvalueDecomposition(cov);
DenseDoubleMatrix2D result = new DenseDoubleMatrix2D(dim + 1, pcNum);
result.viewPart(1, 0, dim, pcNum).assign(
alg.mult(C, evd.getV().viewColumnFlip()));
result.viewRow(0).assign(evd.getRealEigenvalues().viewFlip());
return result.viewDice();
}
开发者ID:kewei-ma，项目名称:GeDBIT，代码行数:43，代码来源:PCA.java
注：本文中的cern.colt.matrix.linalg.EigenvalueDecomposition类示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。 |
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