本文整理匯總了Java中org.apache.commons.math3.linear.Array2DRowRealMatrix類的典型用法代碼示例。如果您正苦於以下問題：Java Array2DRowRealMatrix類的具體用法？Java Array2DRowRealMatrix怎麽用？Java Array2DRowRealMatrix使用的例子？那麽, 這裏精選的類代碼示例或許可以為您提供幫助。
Array2DRowRealMatrix類屬於org.apache.commons.math3.linear包，在下文中一共展示了Array2DRowRealMatrix類的15個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Java代碼示例。
示例1: apacheCommonsExample
import org.apache.commons.math3.linear.Array2DRowRealMatrix; //導入依賴的package包/類
public void apacheCommonsExample() {
double[][] A = {
{0.1950, 0.0311},
{0.3588, 0.2203},
{0.1716, 0.5931},
{0.2105, 0.3242}};
double[][] B = {
{0.0502, 0.9823, 0.9472},
{0.5732, 0.2694, 0.916}};
RealMatrix aRealMatrix = new Array2DRowRealMatrix(A);
RealMatrix bRealMatrix = new Array2DRowRealMatrix(B);
RealMatrix cRealMatrix = aRealMatrix.multiply(bRealMatrix);
System.out.println();
for (int i = 0; i < cRealMatrix.getRowDimension(); i++) {
System.out.println(cRealMatrix.getRowVector(i));
}
}
開發者ID:PacktPublishing，項目名稱:Machine-Learning-End-to-Endguide-for-Java-developers，代碼行數:21，代碼來源:MathExamples.java
示例2: RotationMeasurementModel
import org.apache.commons.math3.linear.Array2DRowRealMatrix; //導入依賴的package包/類
public RotationMeasurementModel()
{
super();
// H = measurementMatrix
measurementMatrix = new Array2DRowRealMatrix(new double[][]
{
{ 1, 0, 0, 0 },
{ 0, 1, 0, 0 },
{ 0, 0, 1, 0 },
{ 0, 0, 0, 1 } });
// R = measurementNoise
measurementNoise = new Array2DRowRealMatrix(new double[][]
{
{ noiseCoefficient, 0, 0, 0 },
{ 0, noiseCoefficient, 0, 0 },
{ 0, 0, noiseCoefficient, 0 },
{ 0, 0, 0, noiseCoefficient } });
}
示例3: nextWishart
import org.apache.commons.math3.linear.Array2DRowRealMatrix; //導入依賴的package包/類
public RealMatrix nextWishart(double df, Cholesky invscale) {
int d = invscale.getL().getColumnDimension();
Array2DRowRealMatrix A = new Array2DRowRealMatrix(d,d);
ArrayRealVector v = new ArrayRealVector(d);
for (int i=0; i<d; i++) {
v.setEntry(i, sqrt(nextChiSquared(df-i)));
for (int j=0; j<i; j++) {
v.setEntry(j, 0.0);
}
for (int j=i+1; j<d; j++) {
v.setEntry(j, nextGaussian());
}
A.setColumnVector(i, invscale.solveLT(v));
}
return A.multiply(A.transpose());
}
示例4: main
import org.apache.commons.math3.linear.Array2DRowRealMatrix; //導入依賴的package包/類
public static void main(String[] args) {
// test wishart
double[][] s = {{2.0,1.0,0.0},{1.0,2.0,1.0},{0.0,1.0,2.0}};
RealMatrix S = new Array2DRowRealMatrix(s);
Cholesky C = new Cholesky(S);
double df = 2.4;
RealMatrix sum = new Array2DRowRealMatrix(3,3);
for (int i=0; i<100000; i++) {
RealMatrix sample = generator.nextWishart(df, C);
sum = sum.add(sample);
}
sum = sum.scalarMultiply(1.0/100000.0);
System.out.println(sum.getRowVector(0));
System.out.println(sum.getRowVector(1));
System.out.println(sum.getRowVector(2));
}
示例5: getSolver
import org.apache.commons.math3.linear.Array2DRowRealMatrix; //導入依賴的package包/類
/**
* @param data dense matrix represented in row-major form
* @return solver for the system Ax = b
*/
static Solver getSolver(double[][] data) {
if (data == null) {
return null;
}
RealMatrix M = new Array2DRowRealMatrix(data, false);
double infNorm = M.getNorm();
double singularityThreshold = infNorm * SINGULARITY_THRESHOLD_RATIO;
RRQRDecomposition decomposition = new RRQRDecomposition(M, singularityThreshold);
DecompositionSolver solver = decomposition.getSolver();
if (solver.isNonSingular()) {
return new Solver(solver);
}
// Otherwise try to report apparent rank
int apparentRank = decomposition.getRank(0.01); // Better value?
log.warn("{} x {} matrix is near-singular (threshold {}). Add more data or decrease the " +
"number of features, to <= about {}",
M.getRowDimension(),
M.getColumnDimension(),
singularityThreshold,
apparentRank);
throw new SingularMatrixSolverException(apparentRank, "Apparent rank: " + apparentRank);
}
示例6: createX
import org.apache.commons.math3.linear.Array2DRowRealMatrix; //導入依賴的package包/類
/**
* Creates the X design matrix for this regression model
* @return the X design matrix
*/
RealMatrix createX() {
final int n = frame.rows().count();
final int offset = hasIntercept() ? 1 : 0;
final int p = hasIntercept() ? regressors.size() + 1 : regressors.size();
final int[] colIndexes = regressors.stream().mapToInt(k -> frame.cols().ordinalOf(k)).toArray();
final RealMatrix x = new Array2DRowRealMatrix(n, p);
for (int i = 0; i < n; ++i) {
x.setEntry(i, 0, 1d);
for (int j = offset; j < p; ++j) {
final double value = frame.data().getDouble(i, colIndexes[j - offset]);
x.setEntry(i, j, value);
}
}
return x;
}
示例7: testCreateLargeMatrix
import org.apache.commons.math3.linear.Array2DRowRealMatrix; //導入依賴的package包/類
@Test
public void testCreateLargeMatrix(){
// Creates a large PoN of junk values and simply tests that these can be written and read.
// Make a big, fake set of read counts.
final int numRows = 2500000;
final int numCols = 10;
final double mean = 3e-7;
final double sigma = 1e-9;
final RealMatrix bigCounts = createMatrixOfGaussianValues(numRows, numCols, mean, sigma);
final File tempOutputHD5 = Utils.createTempFile("big-ol-", ".hd5");
final HDF5File hdf5File = new HDF5File(tempOutputHD5, HDF5File.OpenMode.CREATE);
final String hdf5Path = "/test/m";
hdf5File.makeDoubleMatrix(hdf5Path, bigCounts.getData());
hdf5File.close();
final HDF5File hdf5FileForReading = new HDF5File(tempOutputHD5, HDF5File.OpenMode.READ_ONLY);
final double[][] result = hdf5FileForReading.readDoubleMatrix(hdf5Path);
final RealMatrix resultAsRealMatrix = new Array2DRowRealMatrix(result);
Assert.assertTrue(resultAsRealMatrix.getRowDimension() == numRows);
Assert.assertTrue(resultAsRealMatrix.getColumnDimension() == numCols);
final RealMatrix readMatrix = new Array2DRowRealMatrix(result);
assertEqualsMatrix(readMatrix, bigCounts, false);
}
示例8: getCorrelationCoefficients
import org.apache.commons.math3.linear.Array2DRowRealMatrix; //導入依賴的package包/類
/**
* Gets the correlation coefficients.
*
* @param data the data
* @return the correlation coefficients
*/
protected List<Double> getCorrelationCoefficients(final double[][] data) {
int n = data.length;
int m = data[0].length;
List<Double> correlationCoefficients = new LinkedList<Double>();
for (int i = 0; i < n; i++) {
double[][] x = new double[n - 1][m];
int k = 0;
for (int j = 0; j < n; j++) {
if (j != i) {
x[k++] = data[j];
}
}
// Transpose the matrix so that it fits the linear model
double[][] xT = new Array2DRowRealMatrix(x).transpose().getData();
// RSquare is the "coefficient of determination"
correlationCoefficients.add(MathUtil.createLinearRegression(xT,
data[i]).calculateRSquared());
}
return correlationCoefficients;
}
開發者ID:gmartinezramirez，項目名稱:Fog-Computing-Mobile-Architecture，代碼行數:29，代碼來源:PowerVmSelectionPolicyMaximumCorrelation.java
示例9: testToeplitz
import org.apache.commons.math3.linear.Array2DRowRealMatrix; //導入依賴的package包/類
@Test
public void testToeplitz() {
RealMatrix[] c = new RealMatrix[] {new Array2DRowRealMatrix(new double[] {1}),
new Array2DRowRealMatrix(new double[] {2}),
new Array2DRowRealMatrix(new double[] {3})};
RealMatrix[][] A = MatrixUtils.toeplitz(c, 3);
// 1 2 3
// 2 1 2
// 3 2 1
Assert.assertArrayEquals(new RealMatrix[] {new Array2DRowRealMatrix(new double[] {1}),
new Array2DRowRealMatrix(new double[] {2}),
new Array2DRowRealMatrix(new double[] {3})}, A[0]);
Assert.assertArrayEquals(new RealMatrix[] {new Array2DRowRealMatrix(new double[] {2}),
new Array2DRowRealMatrix(new double[] {1}),
new Array2DRowRealMatrix(new double[] {2})}, A[1]);
Assert.assertArrayEquals(new RealMatrix[] {new Array2DRowRealMatrix(new double[] {3}),
new Array2DRowRealMatrix(new double[] {2}),
new Array2DRowRealMatrix(new double[] {1})}, A[2]);
}
示例10: testFlatten2D
import org.apache.commons.math3.linear.Array2DRowRealMatrix; //導入依賴的package包/類
@Test
public void testFlatten2D() {
RealMatrix[] m1 = new RealMatrix[] {
new Array2DRowRealMatrix(new double[][] {new double[] {1, 2, 3},
new double[] {4, 5, 6}}),
new Array2DRowRealMatrix(new double[][] {new double[] {7, 8, 9},
new double[] {10, 11, 12}}),
new Array2DRowRealMatrix(new double[][] {new double[] {13, 14, 15},
new double[] {16, 17, 18}})};
double[] actual = MatrixUtils.flatten(m1);
double[] expected = new double[18];
for (int i = 0; i < expected.length; i++) {
expected[i] = i + 1;
}
Assert.assertArrayEquals(expected, actual, 0.d);
}
示例11: testUnflatten2D
import org.apache.commons.math3.linear.Array2DRowRealMatrix; //導入依賴的package包/類
@Test
public void testUnflatten2D() {
double[] data = new double[24];
for (int i = 0; i < data.length; i++) {
data[i] = i + 1;
}
RealMatrix[] actual = MatrixUtils.unflatten(data, 2, 3, 4);
RealMatrix[] expected = new RealMatrix[] {
new Array2DRowRealMatrix(new double[][] {new double[] {1, 2, 3},
new double[] {4, 5, 6}}),
new Array2DRowRealMatrix(new double[][] {new double[] {7, 8, 9},
new double[] {10, 11, 12}}),
new Array2DRowRealMatrix(new double[][] {new double[] {13, 14, 15},
new double[] {16, 17, 18}}),
new Array2DRowRealMatrix(new double[][] {new double[] {19, 20, 21},
new double[] {22, 23, 24}})};
Assert.assertArrayEquals(expected, actual);
}
示例12: testPower1
import org.apache.commons.math3.linear.Array2DRowRealMatrix; //導入依賴的package包/類
@Test
public void testPower1() {
RealMatrix A = new Array2DRowRealMatrix(new double[][] {new double[] {1, 2, 3},
new double[] {4, 5, 6}});
double[] x = new double[3];
x[0] = Math.random();
x[1] = Math.random();
x[2] = Math.random();
double[] u = new double[2];
double[] v = new double[3];
double s = MatrixUtils.power1(A, x, 2, u, v);
SingularValueDecomposition svdA = new SingularValueDecomposition(A);
Assert.assertArrayEquals(svdA.getU().getColumn(0), u, 0.001d);
Assert.assertArrayEquals(svdA.getV().getColumn(0), v, 0.001d);
Assert.assertEquals(svdA.getSingularValues()[0], s, 0.001d);
}
示例13: testLanczosTridiagonalization
import org.apache.commons.math3.linear.Array2DRowRealMatrix; //導入依賴的package包/類
@Test
public void testLanczosTridiagonalization() {
// Symmetric matrix
RealMatrix C = new Array2DRowRealMatrix(new double[][] {new double[] {1, 2, 3, 4},
new double[] {2, 1, 4, 3}, new double[] {3, 4, 1, 2}, new double[] {4, 3, 2, 1}});
// naive initial vector
double[] a = new double[] {1, 1, 1, 1};
RealMatrix actual = new Array2DRowRealMatrix(new double[4][4]);
MatrixUtils.lanczosTridiagonalization(C, a, actual);
RealMatrix expected = new Array2DRowRealMatrix(new double[][] {new double[] {40, 60, 0, 0},
new double[] {60, 10, 120, 0}, new double[] {0, 120, 10, 120},
new double[] {0, 0, 120, 10}});
Assert.assertEquals(expected, actual);
}
示例14: testTridiagonalEigen
import org.apache.commons.math3.linear.Array2DRowRealMatrix; //導入依賴的package包/類
@Test
public void testTridiagonalEigen() {
// Tridiagonal Matrix
RealMatrix T = new Array2DRowRealMatrix(new double[][] {new double[] {40, 60, 0, 0},
new double[] {60, 10, 120, 0}, new double[] {0, 120, 10, 120},
new double[] {0, 0, 120, 10}});
double[] eigvals = new double[4];
RealMatrix eigvecs = new Array2DRowRealMatrix(new double[4][4]);
MatrixUtils.tridiagonalEigen(T, 2, eigvals, eigvecs);
RealMatrix actual = eigvecs.multiply(eigvecs.transpose());
RealMatrix expected = new Array2DRowRealMatrix(new double[4][4]);
for (int i = 0; i < 4; i++) {
expected.setEntry(i, i, 1);
}
for (int i = 0; i < 4; i++) {
for (int j = 0; j < 4; j++) {
Assert.assertEquals(expected.getEntry(i, j), actual.getEntry(i, j), 0.001d);
}
}
}
示例15: value
import org.apache.commons.math3.linear.Array2DRowRealMatrix; //導入依賴的package包/類
public Pair<RealVector, RealMatrix> value(final RealVector aprioriApproximation) {
int maskCount = allMaskValues.size();
int groupCount = table.getGroupsNames().size();
RealMatrix aprioriApproximationMatrix = new Array2DRowRealMatrix(aprioriApproximation.getDimension(), 1);
aprioriApproximationMatrix.setColumn(0, aprioriApproximation.toArray());
RealVector approximation = maskProbabilitiesForSources.multiply(aprioriApproximationMatrix).getColumnVector(0);
approximation = normalizeVector(approximation);
RealVector differences = observedFrequencies.subtract(approximation);
RealMatrix jacobian = new Array2DRowRealMatrix(maskCount, groupCount);
for (int i = 0; i < groupCount; i++) {
RealVector partialDifferences = observedFrequencies.subtract(maskProbabilitiesForSources.getColumnVector(i).mapMultiply(aprioriApproximation.getEntry(i)));
jacobian.setColumn(i, partialDifferences.toArray());//.ebeDivide(differences).toArray());
//if (i == 0) System.out.println(partialDifferences);
}
return new Pair<>(differences, jacobian);
}