List of usage examples for org.apache.commons.math3.linear Array2DRowRealMatrix Array2DRowRealMatrix
public Array2DRowRealMatrix(final double[][] d, final boolean copyArray) throws DimensionMismatchException, NoDataException, NullArgumentException
From source file:msi.gama.util.matrix.GamaFloatMatrix.java
RealMatrix getRealMatrix() {
final RealMatrix realMatrix = new Array2DRowRealMatrix(this.numRows, this.numCols);
for (int i = 0; i < this.numRows; i++) {
for (int j = 0; j < this.numCols; j++) {
realMatrix.setEntry(i, j, this.get(null, j, i));
}// w w w . jav a 2 s . c om
}
return realMatrix;
}
From source file:com.clust4j.data.DataSet.java
public void sortAscInPlace(int colIdx) { if (colIdx < 0 || colIdx >= data.getColumnDimension()) throw new IllegalArgumentException("col out of bounds"); double[][] dataRef = data.getDataRef(); data = new Array2DRowRealMatrix(MatUtils.sortAscByCol(dataRef, colIdx), false); }
From source file:edu.stanford.cfuller.colocalization3d.correction.Correction.java
/** * Applies an existing correction to a single x-y position in the Image plane. * * @param x The x-position at which to apply the correction. * @param y The y-position at which to apply the correction. * @return A RealVector containing 3 elements-- the magnitude of the correction in the x, y, and z dimensions, in that order. *//*from w w w . jav a 2 s . c o m*/ public RealVector correctPosition(double x, double y) throws UnableToCorrectException { RealVector corrections = new ArrayRealVector(3, 0.0); RealVector distsToCentroids = this.getPositionsForCorrection().getColumnVector(0).mapSubtract(x) .mapToSelf(new Power(2)); distsToCentroids = distsToCentroids .add(this.getPositionsForCorrection().getColumnVector(1).mapSubtract(y).mapToSelf(new Power(2))); distsToCentroids.mapToSelf(new Sqrt()); RealVector distRatio = distsToCentroids.ebeDivide(this.getDistanceCutoffs()); RealVector distRatioBin = new ArrayRealVector(distRatio.getDimension(), 0.0); for (int i = 0; i < distRatio.getDimension(); i++) { if (distRatio.getEntry(i) <= 1) distRatioBin.setEntry(i, 1.0); } RealVector weights = distRatio.map(new Power(2.0)).mapMultiplyToSelf(-3).mapAddToSelf(1) .add(distRatio.map(new Power(3.0)).mapMultiplyToSelf(2)); weights = weights.ebeMultiply(distRatioBin); double sumWeights = 0; int countWeights = 0; for (int i = 0; i < weights.getDimension(); i++) { if (weights.getEntry(i) > 0) { sumWeights += weights.getEntry(i); countWeights++; } } if (countWeights == 0) { // this means there were no points in the correction dataset near the position being corrected. throw (new UnableToCorrectException( "Incomplete coverage in correction dataset at (x,y) = (" + x + ", " + y + ").")); } RealMatrix cX = new Array2DRowRealMatrix(countWeights, this.getCorrectionX().getColumnDimension()); RealMatrix cY = new Array2DRowRealMatrix(countWeights, this.getCorrectionX().getColumnDimension()); RealMatrix cZ = new Array2DRowRealMatrix(countWeights, this.getCorrectionX().getColumnDimension()); RealVector xVec = new ArrayRealVector(countWeights, 0.0); RealVector yVec = new ArrayRealVector(countWeights, 0.0); RealVector keptWeights = new ArrayRealVector(countWeights, 0.0); int keptCounter = 0; for (int i = 0; i < weights.getDimension(); i++) { if (weights.getEntry(i) > 0) { cX.setRowVector(keptCounter, this.getCorrectionX().getRowVector(i)); cY.setRowVector(keptCounter, this.getCorrectionY().getRowVector(i)); cZ.setRowVector(keptCounter, this.getCorrectionZ().getRowVector(i)); xVec.setEntry(keptCounter, x - this.getPositionsForCorrection().getEntry(i, 0)); yVec.setEntry(keptCounter, y - this.getPositionsForCorrection().getEntry(i, 1)); keptWeights.setEntry(keptCounter, weights.getEntry(i)); keptCounter++; } } double xCorr = 0; double yCorr = 0; double zCorr = 0; RealMatrix allCorrectionParameters = new Array2DRowRealMatrix(countWeights, numberOfCorrectionParameters); RealVector ones = new ArrayRealVector(countWeights, 1.0); allCorrectionParameters.setColumnVector(0, ones); allCorrectionParameters.setColumnVector(1, xVec); allCorrectionParameters.setColumnVector(2, yVec); allCorrectionParameters.setColumnVector(3, xVec.map(new Power(2))); allCorrectionParameters.setColumnVector(4, yVec.map(new Power(2))); allCorrectionParameters.setColumnVector(5, xVec.ebeMultiply(yVec)); for (int i = 0; i < countWeights; i++) { xCorr += allCorrectionParameters.getRowVector(i).dotProduct(cX.getRowVector(i)) * keptWeights.getEntry(i); yCorr += allCorrectionParameters.getRowVector(i).dotProduct(cY.getRowVector(i)) * keptWeights.getEntry(i); zCorr += allCorrectionParameters.getRowVector(i).dotProduct(cZ.getRowVector(i)) * keptWeights.getEntry(i); } xCorr /= sumWeights; yCorr /= sumWeights; zCorr /= sumWeights; corrections.setEntry(0, xCorr); corrections.setEntry(1, yCorr); corrections.setEntry(2, zCorr); return corrections; }
From source file:com.clust4j.data.DataSet.java
public void sortDescInPlace(int colIdx) { if (colIdx < 0 || colIdx >= data.getColumnDimension()) throw new IllegalArgumentException("col out of bounds"); double[][] dataRef = data.getDataRef(); data = new Array2DRowRealMatrix(MatUtils.sortDescByCol(dataRef, colIdx), false); }
From source file:com.clust4j.algo.NearestNeighborsTests.java
/** * Assert that when all of the matrix entries are exactly the same, * the algorithm will still converge, yet produce one label: 0 *///from www. jav a 2 s .c o m @Override @Test public void testAllSame() { final double[][] x = MatUtils.rep(-1, 6, 6); final Array2DRowRealMatrix X = new Array2DRowRealMatrix(x, false); Neighborhood neighb = new NearestNeighbors(X, new NearestNeighborsParameters(3).setVerbose(true)).fit() .getNeighbors(); assertTrue(new MatUtils.MatSeries(neighb.getDistances(), Inequality.EQUAL_TO, 0).all()); System.out.println(); /* * Test def constructor */ neighb = new NearestNeighbors(X, new NearestNeighborsParameters().setVerbose(true)).fit().getNeighbors(); assertTrue(new MatUtils.MatSeries(neighb.getDistances(), Inequality.EQUAL_TO, 0).all()); System.out.println(); /* * Test BallTree */ neighb = new NearestNeighbors(X, new NearestNeighborsParameters().setVerbose(true).setAlgorithm(NeighborsAlgorithm.BALL_TREE)).fit() .getNeighbors(); assertTrue(new MatUtils.MatSeries(neighb.getDistances(), Inequality.EQUAL_TO, 0).all()); System.out.println(); }
From source file:com.clust4j.algo.KMeansTests.java
/** * Assert that when all of the matrix entries are exactly the same, * the algorithm will still converge, yet produce one label: 0 *///from ww w. j av a 2 s . c om @Override @Test public void testAllSame() { final double[][] x = MatUtils.rep(-1, 3, 3); final Array2DRowRealMatrix X = new Array2DRowRealMatrix(x, false); int[] labels = new KMeans(X, new KMeansParameters(3).setVerbose(true)).fit().getLabels(); assertTrue(new VecUtils.IntSeries(labels, Inequality.EQUAL_TO, 0).all()); System.out.println(); }
From source file:com.clust4j.algo.MeanShiftTests.java
@Test public void testAutoEstimationWithScale() { Array2DRowRealMatrix iris = (Array2DRowRealMatrix) new StandardScaler().fit(data_).transform(data_); final double[][] X = iris.getData(); // MS estimates bw at 1.6041295821313855 final double bandwidth = 1.6041295821313855; assertTrue(Precision.equals(/* w w w . j ava2 s.c o m*/ MeanShift.autoEstimateBW(iris, 0.3, Distance.EUCLIDEAN, GlobalState.DEFAULT_RANDOM_STATE, false), bandwidth, 1e-9)); assertTrue(Precision.equals( MeanShift.autoEstimateBW(iris, 0.3, Distance.EUCLIDEAN, GlobalState.DEFAULT_RANDOM_STATE, true), bandwidth, 1e-9)); // Asserting fit works without breaking things... RadiusNeighbors r = new RadiusNeighbors(iris, new RadiusNeighborsParameters(bandwidth)).fit(); TreeSet<MeanShiftSeed> centers = new TreeSet<>(); for (double[] seed : X) centers.add(MeanShift.singleSeed(seed, r, X, 300)); assertTrue(centers.size() == 4); double[][] expected_dists = new double[][] { new double[] { 0.50161528154395962, -0.31685274298813487, 0.65388162422893481, 0.65270450741975761 }, new double[] { 0.52001211065400177, -0.29561728795619946, 0.67106269515983397, 0.67390853215763813 }, new double[] { 0.54861244890482475, -0.25718786696105495, 0.68964559485632182, 0.69326664641211422 }, new double[] { -1.0595457115461515, 0.74408909010240054, -1.2995708885010491, -1.2545442961404225 } }; int[] expected_centers = new int[] { 82, 80, 77, 45 }; int idx = 0; for (MeanShiftSeed seed : centers) { assertTrue(VecUtils.equalsWithTolerance(seed.dists, expected_dists[idx], 1e-1)); assertTrue(seed.count == expected_centers[idx]); idx++; } ArrayList<EntryPair<double[], Integer>> center_intensity = new ArrayList<>(); for (MeanShiftSeed seed : centers) { if (null != seed) { center_intensity.add(seed.getPair()); } } final ArrayList<EntryPair<double[], Integer>> sorted_by_intensity = center_intensity; // test getting the unique vals idx = 0; final int m_prime = sorted_by_intensity.size(); final Array2DRowRealMatrix sorted_centers = new Array2DRowRealMatrix(m_prime, iris.getColumnDimension()); for (Map.Entry<double[], Integer> e : sorted_by_intensity) sorted_centers.setRow(idx++, e.getKey()); // Create a boolean mask, init true final boolean[] unique = new boolean[m_prime]; for (int i = 0; i < unique.length; i++) unique[i] = true; // Fit the new neighbors model RadiusNeighbors nbrs = new RadiusNeighbors(sorted_centers, new RadiusNeighborsParameters(bandwidth).setVerbose(false)).fit(); // Iterate over sorted centers and query radii int[] indcs; double[] center; for (int i = 0; i < m_prime; i++) { if (unique[i]) { center = sorted_centers.getRow(i); indcs = nbrs.getNeighbors(new double[][] { center }, bandwidth, false).getIndices()[0]; for (int id : indcs) { unique[id] = false; } unique[i] = true; // Keep this as true } } // Now assign the centroids... int redundant_ct = 0; final ArrayList<double[]> centroids = new ArrayList<>(); for (int i = 0; i < unique.length; i++) { if (unique[i]) { centroids.add(sorted_centers.getRow(i)); } } redundant_ct = unique.length - centroids.size(); assertTrue(redundant_ct == 2); assertTrue(centroids.size() == 2); assertTrue(VecUtils.equalsWithTolerance(centroids.get(0), new double[] { 0.4999404345258691, -0.3157948009929614, 0.6516983739795399, 0.6505251874544873 }, 1e-6)); assertTrue(VecUtils.equalsExactly(centroids.get(1), new double[] { -1.0560079864392702, 0.7416046454700266, -1.295231741534238, -1.2503554887998656 })); // also put the centroids into a matrix. We have to // wait to perform this op, because we have to know // the size of centroids first... Array2DRowRealMatrix clust_centers = new Array2DRowRealMatrix(centroids.size(), iris.getColumnDimension()); for (int i = 0; i < clust_centers.getRowDimension(); i++) clust_centers.setRow(i, centroids.get(i)); // The final nearest neighbors model -- if this works, we are in the clear... new NearestNeighbors(clust_centers, new NearestNeighborsParameters(1)).fit(); }
From source file:ch.epfl.leb.sass.models.psfs.internal.GibsonLanniPSF.java
/** * Computes a digital representation of the PSF. * //www . ja v a 2 s .co m * @param z The stage displacement. * @return An image stack of the PSF. **/ private void computeDigitalPSF(double z) { // Has a PSF has already been computed for this emitter's z-plane? Long zDiscrete = getNearestZPlane(this.eZ); if (interpolators.containsKey(zDiscrete)) { // PSF already computed for this z-plane, so don't do anything. return; } // Otherwise, compute the PSF and store the result in the hash map double x0 = (this.sizeX - 1) / 2.0D; double y0 = (this.sizeY - 1) / 2.0D; double xp = x0; double yp = y0; //ImageStack stack = new ImageStack(this.sizeX, this.sizeY); int maxRadius = (int) Math .round(Math.sqrt((this.sizeX - x0) * (this.sizeX - x0) + (this.sizeY - y0) * (this.sizeY - y0))) + 1; double[] r = new double[maxRadius * this.oversampling]; double[] h = new double[r.length]; double a = 0.0D; double b = Math.min(1.0D, this.ns / this.NA); double k0 = 2 * Math.PI / this.wavelength; double factor1 = this.MINWAVELENGTH / this.wavelength; double factor = factor1 * this.NA / 1.4; double deltaRho = (b - a) / (this.numSamples - 1); // basis construction double rho = 0.0D; double am = 0.0; double[][] Basis = new double[this.numSamples][this.numBasis]; BesselJ bj0 = new BesselJ(0); BesselJ bj1 = new BesselJ(1); for (int m = 0; m < this.numBasis; m++) { am = (3 * m + 1) * factor; // am = (3 * m + 1); for (int rhoi = 0; rhoi < this.numSamples; rhoi++) { rho = rhoi * deltaRho; Basis[rhoi][m] = bj0.value(am * rho); } } // compute the function to be approximated double ti = 0.0D; double OPD = 0; double W = 0; double[][] Coef = new double[1][this.numBasis * 2]; double[][] Ffun = new double[this.numSamples][2]; // Oil thickness. ti = (this.ti0 + z); double sqNA = this.NA * this.NA; double rhoNA2; for (int rhoi = 0; rhoi < this.numSamples; rhoi++) { rho = rhoi * deltaRho; rhoNA2 = rho * rho * sqNA; // OPD in the sample OPD = this.eZ * Math.sqrt(this.ns * this.ns - rhoNA2); // OPD in the immersion medium OPD += ti * Math.sqrt(this.ni * this.ni - rhoNA2) - this.ti0 * Math.sqrt(this.ni0 * this.ni0 - rhoNA2); // OPD in the coverslip OPD += this.tg * Math.sqrt(this.ng * this.ng - rhoNA2) - this.tg0 * Math.sqrt(this.ng0 * this.ng0 - rhoNA2); W = k0 * OPD; Ffun[rhoi][0] = Math.cos(W); Ffun[rhoi][1] = Math.sin(W); } // solve the linear system // begin....... (Using Common Math) RealMatrix coefficients = new Array2DRowRealMatrix(Basis, false); RealMatrix rhsFun = new Array2DRowRealMatrix(Ffun, false); DecompositionSolver solver = null; if (this.solverName.equals("svd")) { // slower but more accurate solver = new SingularValueDecomposition(coefficients).getSolver(); } else { // faster, less accurate solver = new QRDecomposition(coefficients).getSolver(); } RealMatrix solution = solver.solve(rhsFun); Coef = solution.getData(); // end....... double[][] RM = new double[this.numBasis][r.length]; double beta = 0.0D; double rm = 0.0D; for (int n = 0; n < r.length; n++) { r[n] = (n * 1.0 / this.oversampling); beta = k0 * this.NA * r[n] * this.resPSF; for (int m = 0; m < this.numBasis; m++) { am = (3 * m + 1) * factor; rm = am * bj1.value(am * b) * bj0.value(beta * b) * b; rm = rm - beta * b * bj0.value(am * b) * bj1.value(beta * b); RM[m][n] = rm / (am * am - beta * beta); } } // obtain one component double maxValue = 0.0D; for (int n = 0; n < r.length; n++) { double realh = 0.0D; double imgh = 0.0D; for (int m = 0; m < this.numBasis; m++) { realh = realh + RM[m][n] * Coef[m][0]; imgh = imgh + RM[m][n] * Coef[m][1]; } h[n] = realh * realh + imgh * imgh; } // Interpolate the PSF onto a 2D grid of physical coordinates double[] pixel = new double[this.sizeX * this.sizeY]; for (int x = 0; x < this.sizeX; x++) { for (int y = 0; y < this.sizeY; y++) { // Distance in pixels from the center of the array double rPixel = Math.sqrt((x - xp) * (x - xp) + (y - yp) * (y - yp)); // Find the index of the PSF h array that matches this distance int index = (int) Math.floor(rPixel * this.oversampling); // Perform a linear interpolation from h onto the current point. // (1 / this.oversampling) is the distance between samples in // the h array in units of pixels in the final output array. double value = h[index] + (h[(index + 1)] - h[index]) * (rPixel - r[index]) * this.oversampling; pixel[(x + this.sizeX * y)] = value; } } // Compute the constant that normalizes the PSF to its area double normConst = 0; for (int x = 0; x < this.sizeX; x++) { for (int y = 0; y < this.sizeY; y++) { normConst += pixel[x + this.sizeX * y] * this.resPSF * this.resPSF; } } // Compute the (discrete) cumulative distribution function. // First, compute the sums in the y-direction. double[] CDF = new double[this.sizeX * this.sizeY]; double sum = 0; for (int x = 0; x < this.sizeX; x++) { sum = 0; for (int y = 0; y < this.sizeY; y++) { // Normalize to the integrated area pixel[x + this.sizeX * y] /= normConst; // Increment the sum in the y-direction and the CDF sum += pixel[x + this.sizeX * y] * this.resPSF; CDF[x + this.sizeX * y] = sum; } } // Next, compute the sums in the x-direction. for (int y = 0; y < this.sizeY; y++) { sum = 0; for (int x = 0; x < this.sizeX; x++) { // Increment the sum in the x-direction sum += CDF[x + y * this.sizeX] * this.resPSF; CDF[x + y * this.sizeX] = sum; } } // Construct the x-coordinates double[] mgridX = new double[this.sizeX]; for (int x = 0; x < this.sizeX; x++) { mgridX[x] = (x - 0.5 * (this.sizeX - 1)) * this.resPSF; } // Construct the y-coordinates double[] mgridY = new double[this.sizeY]; for (int y = 0; y < this.sizeY; y++) { mgridY[y] = (y - 0.5 * (this.sizeY - 1)) * this.resPSF; } //stack.addSlice(new FloatProcessor(this.sizeX, this.sizeY, pixel)); //stack.addSlice(new FloatProcessor(this.sizeX, this.sizeY, CDF)); // Reshape CDF for interpolation double[][] rCDF = new double[this.sizeY][this.sizeX]; for (int x = 0; x < this.sizeX; x++) { for (int y = 0; y < this.sizeY; y++) { rCDF[y][x] = CDF[x + y * this.sizeX]; } } // Compute the interpolating spline for this PSF. this.interpCDF = new PiecewiseBicubicSplineInterpolatingFunction(mgridX, mgridY, rCDF); interpolators.put(zDiscrete, new PiecewiseBicubicSplineInterpolatingFunction(mgridX, mgridY, rCDF)); }
From source file:com.clust4j.algo.HDBSCANTests.java
@Test public void compareToPckg() { Array2DRowRealMatrix X = new Array2DRowRealMatrix(MatUtils.reshape(new double[] { 0.58459246, 0.2411591, 0.54266953, 0.80298748, 0.7108317, 0.77419375, 0.19460038, 0.51769224, 0.80581355, 0.06109043, 0.57755264, 0.48690635, 0.4698578, 0.68256655, 0.35583625, 0.33956817, 0.46084149, 0.2266772, 0.78013553, 0.84169725, 0.45929076, 0.5763663, 0.85034392, 0.42344478, 0.08823549 }, 5, 5), false); HDBSCAN h = new HDBSCAN(X).fit(); assertTrue(VecUtils.equalsExactly(h.getLabels(), VecUtils.repInt(-1, 5))); h = new HDBSCAN(X, new HDBSCANParameters().setAlgo(HDBSCAN_Algorithm.PRIMS_KDTREE)).fit(); assertTrue(VecUtils.equalsExactly(h.getLabels(), VecUtils.repInt(-1, 5))); h = new HDBSCAN(X, new HDBSCANParameters().setAlgo(HDBSCAN_Algorithm.PRIMS_BALLTREE)).fit(); assertTrue(VecUtils.equalsExactly(h.getLabels(), VecUtils.repInt(-1, 5))); // Test on IRIS h = new HDBSCAN(iris, new HDBSCANParameters().setAlgo(HDBSCAN_Algorithm.GENERIC)).fit(); int[] expectedLabels = new NoiseyLabelEncoder(expected_iris_labs).fit().getEncodedLabels(); assertTrue(VecUtils.equalsExactly(expectedLabels, h.getLabels())); h = new HDBSCAN(X, new HDBSCANParameters().setAlgo(HDBSCAN_Algorithm.PRIMS_KDTREE)).fit(); System.out.println(Arrays.toString(h.getLabels())); }
From source file:com.ibm.bi.dml.runtime.util.DataConverter.java
/** * Helper method that converts SystemML matrix variable (<code>varname</code>) into a Array2DRowRealMatrix format, * which is useful in invoking Apache CommonsMath. * /* w w w. j a va 2s. c o m*/ * @param ec * @param varname * @return * @throws DMLRuntimeException */ public static Array2DRowRealMatrix convertToArray2DRowRealMatrix(MatrixObject mo) throws DMLRuntimeException { Matrix.ValueType vt = (mo.getValueType() == ValueType.DOUBLE ? Matrix.ValueType.Double : Matrix.ValueType.Integer); Matrix mathInput = new Matrix(mo.getFileName(), mo.getNumRows(), mo.getNumColumns(), vt); mathInput.setMatrixObject(mo); double[][] data = mathInput.getMatrixAsDoubleArray(); Array2DRowRealMatrix matrixInput = new Array2DRowRealMatrix(data, false); return matrixInput; }