List of usage examples for com.google.common.primitives Floats toArray
public static float[] toArray(Collection<? extends Number> collection)
From source file:demo.project.model.StackedRankColumnSpec.java
@Override public void load(ARankColumnModel model) { super.load(model); StackedRankColumnModel m = (StackedRankColumnModel) model; m.setAlignment(singleAlignment);// ww w .ja v a2 s.c o m m.setAlignment(alignment); m.setCompressed(compressed); m.setWeights(Floats.toArray(weights)); }
From source file:net.larry1123.elec.util.config.fieldhanders.floats.FloatArrayListFieldHandler.java
/** * {@inheritDoc}//from w ww . j a v a 2 s.c o m */ @Override public void setToFile(ArrayList<Float> value) { if (CollectionUtils.isNotEmpty(value)) { getPropertiesFile().setFloatArray(getPropertyKey(), Floats.toArray(value), getSpacer()); } }
From source file:org.terasology.persistence.typeHandling.coreTypes.FloatTypeHandler.java
@Override public PersistedData serializeCollection(Collection<Float> value, SerializationContext context) { return context.create(Floats.toArray(value)); }
From source file:juicebox.tools.utils.juicer.hiccups.GPUController.java
public GPUOutputContainer process(MatrixZoomData zd, double[] normalizationVector, double[] expectedVector, int[] rowBounds, int[] columnBounds, int matrixSize, float[] thresholdBL, float[] thresholdDonut, float[] thresholdH, float[] thresholdV, NormalizationType normalizationType) throws NegativeArraySizeException, IOException { RealMatrix localizedRegionData = HiCFileTools.extractLocalBoundedRegion(zd, rowBounds[0], rowBounds[1], columnBounds[0], columnBounds[1], matrixSize, matrixSize, normalizationType); float[] observedVals = Floats .toArray(Doubles.asList(MatrixTools.flattenedRowMajorOrderMatrix(localizedRegionData))); // slice KR vector to localized region float[] distanceExpectedKRVector = Floats.toArray(Doubles.asList(expectedVector)); float[] kr1CPU = Floats .toArray(Doubles.asList(Arrays.copyOfRange(normalizationVector, rowBounds[0], rowBounds[1]))); float[] kr2CPU = Floats .toArray(Doubles.asList(Arrays.copyOfRange(normalizationVector, columnBounds[0], columnBounds[1]))); if (kr1CPU.length < matrixSize) kr1CPU = ArrayTools.padEndOfArray(kr1CPU, matrixSize, Float.NaN); if (kr2CPU.length < matrixSize) kr2CPU = ArrayTools.padEndOfArray(kr2CPU, matrixSize, Float.NaN); float[] boundRowIndex = new float[1]; boundRowIndex[0] = rowBounds[0];// ww w . j a va 2s .c om float[] boundColumnIndex = new float[1]; boundColumnIndex[0] = columnBounds[0]; //long gpu_time1 = System.currentTimeMillis(); // transfer host (CPU) memory to device (GPU) memory CUdeviceptr observedKRGPU = GPUHelper.allocateInput(observedVals); CUdeviceptr expectedDistanceVectorGPU = GPUHelper.allocateInput(distanceExpectedKRVector); CUdeviceptr kr1GPU = GPUHelper.allocateInput(kr1CPU); CUdeviceptr kr2GPU = GPUHelper.allocateInput(kr2CPU); CUdeviceptr thresholdBLGPU = GPUHelper.allocateInput(thresholdBL); CUdeviceptr thresholdDonutGPU = GPUHelper.allocateInput(thresholdDonut); CUdeviceptr thresholdHGPU = GPUHelper.allocateInput(thresholdH); CUdeviceptr thresholdVGPU = GPUHelper.allocateInput(thresholdV); CUdeviceptr boundRowIndexGPU = GPUHelper.allocateInput(boundRowIndex); CUdeviceptr boundColumnIndexGPU = GPUHelper.allocateInput(boundColumnIndex); // create empty gpu arrays for the results int flattenedSize = matrixSize * matrixSize; CUdeviceptr expectedBLGPU = GPUHelper.allocateOutput(flattenedSize, Sizeof.FLOAT); CUdeviceptr expectedDonutGPU = GPUHelper.allocateOutput(flattenedSize, Sizeof.FLOAT); CUdeviceptr expectedHGPU = GPUHelper.allocateOutput(flattenedSize, Sizeof.FLOAT); CUdeviceptr expectedVGPU = GPUHelper.allocateOutput(flattenedSize, Sizeof.FLOAT); CUdeviceptr binBLGPU = GPUHelper.allocateOutput(flattenedSize, Sizeof.FLOAT); CUdeviceptr binDonutGPU = GPUHelper.allocateOutput(flattenedSize, Sizeof.FLOAT); CUdeviceptr binHGPU = GPUHelper.allocateOutput(flattenedSize, Sizeof.FLOAT); CUdeviceptr binVGPU = GPUHelper.allocateOutput(flattenedSize, Sizeof.FLOAT); CUdeviceptr observedGPU = GPUHelper.allocateOutput(flattenedSize, Sizeof.FLOAT); CUdeviceptr peakGPU = GPUHelper.allocateOutput(flattenedSize, Sizeof.FLOAT); // call the kernel on the card kernelLauncher.call( // inputs observedKRGPU, // output expectedBLGPU, expectedDonutGPU, expectedHGPU, expectedVGPU, observedGPU, binBLGPU, binDonutGPU, binHGPU, binVGPU, peakGPU, // thresholds thresholdBLGPU, thresholdDonutGPU, thresholdHGPU, thresholdVGPU, // distance expected expectedDistanceVectorGPU, // kr kr1GPU, kr2GPU, // bounds boundRowIndexGPU, boundColumnIndexGPU); // initialize memory to store GPU results float[] expectedBLResult = new float[flattenedSize]; float[] expectedDonutResult = new float[flattenedSize]; float[] expectedHResult = new float[flattenedSize]; float[] expectedVResult = new float[flattenedSize]; float[] binBLResult = new float[flattenedSize]; float[] binDonutResult = new float[flattenedSize]; float[] binHResult = new float[flattenedSize]; float[] binVResult = new float[flattenedSize]; float[] observedResult = new float[flattenedSize]; float[] peakResult = new float[flattenedSize]; // transfer device (GPU) memory to host (CPU) memory cuMemcpyDtoH(Pointer.to(expectedBLResult), expectedBLGPU, flattenedSize * Sizeof.FLOAT); cuMemcpyDtoH(Pointer.to(expectedDonutResult), expectedDonutGPU, flattenedSize * Sizeof.FLOAT); cuMemcpyDtoH(Pointer.to(expectedHResult), expectedHGPU, flattenedSize * Sizeof.FLOAT); cuMemcpyDtoH(Pointer.to(expectedVResult), expectedVGPU, flattenedSize * Sizeof.FLOAT); cuMemcpyDtoH(Pointer.to(binBLResult), binBLGPU, flattenedSize * Sizeof.FLOAT); cuMemcpyDtoH(Pointer.to(binDonutResult), binDonutGPU, flattenedSize * Sizeof.FLOAT); cuMemcpyDtoH(Pointer.to(binHResult), binHGPU, flattenedSize * Sizeof.FLOAT); cuMemcpyDtoH(Pointer.to(binVResult), binVGPU, flattenedSize * Sizeof.FLOAT); cuMemcpyDtoH(Pointer.to(observedResult), observedGPU, flattenedSize * Sizeof.FLOAT); cuMemcpyDtoH(Pointer.to(peakResult), peakGPU, flattenedSize * Sizeof.FLOAT); //long gpu_time2 = System.currentTimeMillis(); //System.out.println("GPU Time: " + (gpu_time2-gpu_time1)); int finalWidthX = rowBounds[5] - rowBounds[4]; int finalWidthY = columnBounds[5] - columnBounds[4]; // x2, y2 not inclusive here int x1 = rowBounds[2]; int y1 = columnBounds[2]; int x2 = x1 + finalWidthX; int y2 = y1 + finalWidthY; //System.out.println("flat = "+flattenedSize+" n = "+matrixSize+" x1 = "+x1+" x2 = "+x2+" y1 = "+y1+" y2 ="+y2); float[][] observedDenseCPU = GPUHelper.GPUArraytoCPUMatrix(observedResult, matrixSize, x1, x2, y1, y2); float[][] peakDenseCPU = GPUHelper.GPUArraytoCPUMatrix(peakResult, matrixSize, x1, x2, y1, y2); float[][] binBLDenseCPU = GPUHelper.GPUArraytoCPUMatrix(binBLResult, matrixSize, x1, x2, y1, y2); float[][] binDonutDenseCPU = GPUHelper.GPUArraytoCPUMatrix(binDonutResult, matrixSize, x1, x2, y1, y2); float[][] binHDenseCPU = GPUHelper.GPUArraytoCPUMatrix(binHResult, matrixSize, x1, x2, y1, y2); float[][] binVDenseCPU = GPUHelper.GPUArraytoCPUMatrix(binVResult, matrixSize, x1, x2, y1, y2); float[][] expectedBLDenseCPU = GPUHelper.GPUArraytoCPUMatrix(expectedBLResult, matrixSize, x1, x2, y1, y2); float[][] expectedDonutDenseCPU = GPUHelper.GPUArraytoCPUMatrix(expectedDonutResult, matrixSize, x1, x2, y1, y2); float[][] expectedHDenseCPU = GPUHelper.GPUArraytoCPUMatrix(expectedHResult, matrixSize, x1, x2, y1, y2); float[][] expectedVDenseCPU = GPUHelper.GPUArraytoCPUMatrix(expectedVResult, matrixSize, x1, x2, y1, y2); GPUHelper.freeUpMemory(new CUdeviceptr[] { observedKRGPU, expectedDistanceVectorGPU, kr1GPU, kr2GPU, thresholdBLGPU, thresholdDonutGPU, thresholdHGPU, thresholdVGPU, boundRowIndexGPU, boundColumnIndexGPU, expectedBLGPU, expectedDonutGPU, expectedHGPU, expectedVGPU, binBLGPU, binDonutGPU, binHGPU, binVGPU, observedGPU, peakGPU }); return new GPUOutputContainer(observedDenseCPU, peakDenseCPU, binBLDenseCPU, binDonutDenseCPU, binHDenseCPU, binVDenseCPU, expectedBLDenseCPU, expectedDonutDenseCPU, expectedHDenseCPU, expectedVDenseCPU); }
From source file:xfel.mods.arp.base.utils.reflection.PrimitiveTypeHelper.java
@Override public Object convertToTypeArray(Collection<?> collection) { return Floats.toArray((Collection<? extends Number>) collection); }
From source file:org.caleydo.core.io.parser.ascii.LinearDataParser.java
@Override protected void parseFile(BufferedReader reader) throws IOException { // prepare for id setting of column IDs IDMappingManager columnIDMappingManager; IDType internalColumnIDType;// ww w . j av a2 s .c o m IDType externalColumnIDType = IDType.getIDType(dataSetDescription.getColumnIDSpecification().getIdType()); IDTypeParsingRules parsingRules = null; if (dataSetDescription.getColumnIDSpecification().getIdTypeParsingRules() != null) parsingRules = dataSetDescription.getColumnIDSpecification().getIdTypeParsingRules(); else if (externalColumnIDType.getIdTypeParsingRules() != null) parsingRules = externalColumnIDType.getIdTypeParsingRules(); if (!dataDomain.getDataSetDescription().isTransposeMatrix()) { columnIDMappingManager = dataDomain.getDimensionIDMappingManager(); internalColumnIDType = dataDomain.getDimensionIDType(); } else { columnIDMappingManager = dataDomain.getRecordIDMappingManager(); internalColumnIDType = dataDomain.getRecordIDType(); } MappingType columnMappingType = columnIDMappingManager.createMap(internalColumnIDType, externalColumnIDType, false, true); // ------------- ID parsing stuff ------------------------------ IDSpecification rowIDSpecification = dataSetDescription.getRowIDSpecification(); IDCategory rowIDCategory = IDCategory.getIDCategory(rowIDSpecification.getIdCategory()); IDType externalRowIDType = IDType.getIDType(rowIDSpecification.getIdType()); IDType internalRowIDType; if (dataDomain.isColumnDimension()) internalRowIDType = dataDomain.getRecordIDType(); else internalRowIDType = dataDomain.getDimensionIDType(); IDMappingManager rowIDMappingManager = IDMappingManagerRegistry.get().getIDMappingManager(rowIDCategory); MappingType rowMappingType = rowIDMappingManager.createMap(internalRowIDType, externalRowIDType, false, true); int columnOfRowIDs = dataSetDescription.getColumnOfRowIds(); int columnOfColumnIDs = dataSetDescription.getRowOfColumnIDs(); // fixme this is a hack needed to use the same datasetdescription int columnOfData = dataSetDescription.getParsingRules().iterator().next().getFromColumn(); for (int headerCount = 0; headerCount < dataSetDescription.getNumberOfHeaderLines(); headerCount++) { reader.readLine(); } String line; while ((line = reader.readLine()) != null) { String[] splitLine = line.split(dataSetDescription.getDelimiter()); String columnID = splitLine[columnOfColumnIDs]; String rowID = splitLine[columnOfRowIDs]; Integer columnNumber = columnIDMappingManager.getID(externalColumnIDType, internalColumnIDType, columnID); if (columnNumber == null) { columnNumber = targetRawContainer.size(); columnIDMappingManager.addMapping(columnMappingType, columnNumber, columnID); int nrRows = 0; if (targetRawContainer.size() > 0) { nrRows = targetRawContainer.get(0).size(); } ArrayList<Float> newList = new ArrayList<Float>(nrRows); for (int i = 0; i < nrRows; i++) { newList.add(Float.NaN); } targetRawContainer.add(newList); } Integer rowNumber = rowIDMappingManager.getID(externalRowIDType, internalRowIDType, rowID); ArrayList<Float> column = targetRawContainer.get(columnNumber); if (rowNumber == null) { rowNumber = column.size(); rowIDMappingManager.addMapping(rowMappingType, rowNumber, rowID); for (ArrayList<Float> tColumn : targetRawContainer) { tColumn.add(Float.NaN); } } try { float data = Float.parseFloat(splitLine[columnOfData]); column.set(rowNumber, data); } catch (NumberFormatException nfe) { // nothing to do, is already NAN } } int depth = targetRawContainer.get(0).size(); for (ArrayList<Float> tColumn : targetRawContainer) { if (depth != tColumn.size()) throw new IllegalStateException( "Columns don't have the same length" + depth + " / " + tColumn.size()); float[] fColumn = Floats.toArray(tColumn); FloatContainer container = new FloatContainer(fColumn); NumericalColumn<FloatContainer, Float> column = new NumericalColumn<>( dataSetDescription.getDataDescription()); column.setRawData(container); dataDomain.getTable().addColumn(column); } }
From source file:org.apache.flink.examples.java.ml.GPULinearRegressionWithPointers.java
private static DataP getPointersToData(String fileLocation) { ArrayList<Float> xs = new ArrayList<>(); ArrayList<Float> ys = new ArrayList<>(); try {/*from ww w . j ava 2 s . c o m*/ BufferedReader br = new BufferedReader(new FileReader(fileLocation)); String line = br.readLine(); while (line != null) { String[] tokens = line.split(" "); xs.add(Float.valueOf(tokens[0])); ys.add(Float.valueOf(tokens[1])); line = br.readLine(); } br.close(); } catch (Exception e) { e.printStackTrace(); } cuInit(0); CUdevice device = new CUdevice(); cuDeviceGet(device, 0); CUcontext context = new CUcontext(); cuCtxCreate(context, 0, device); // get input x data from elements CUdeviceptr pxs = new CUdeviceptr(); CUdeviceptr pys = new CUdeviceptr(); CUdeviceptr pxsr = new CUdeviceptr(); CUdeviceptr pysr = new CUdeviceptr(); CUdeviceptr n = new CUdeviceptr(); cuMemAlloc(pxs, Sizeof.FLOAT * xs.size()); cuMemAlloc(pys, Sizeof.FLOAT * ys.size()); cuMemAlloc(pxsr, Sizeof.FLOAT * xs.size()); cuMemAlloc(pysr, Sizeof.FLOAT * ys.size()); cuMemAlloc(n, Sizeof.INT); float[] vxs = Floats.toArray(xs); float[] vys = Floats.toArray(ys); cuMemcpyHtoD(pxs, Pointer.to(vxs), Sizeof.FLOAT * xs.size()); cuMemcpyHtoD(pys, Pointer.to(vys), Sizeof.FLOAT * ys.size()); cuMemcpyHtoD(n, Pointer.to(new int[] { xs.size() }), Sizeof.INT); return new DataP(pxs, pys, pxsr, pysr, n, vxs.length); }
From source file:com.vertigo.familyplot.library.HorizontalBarGraph.java
private float[] getFloats() { return Floats.toArray(Collections2.transform(datapoints, new Function<Entry, Float>() { @Override//w w w . ja va2 s. c om public Float apply(Entry input) { return input.getValue(); } })); }
From source file:edu.illinois.cs.cogcomp.sl.util.FeatureVectorBuffer.java
public IFeatureVector toFeatureVector(boolean sorted) { if (!sorted)//from www . j a v a 2 s .com return new SparseFeatureVector(Ints.toArray(idxList), Floats.toArray(valList), false); if (idxList.size() == 0) return new SparseFeatureVector(new int[0], new float[0]); // sort items Integer[] idxs = new Integer[idxList.size()]; for (int i = 0; i < idxs.length; i++) idxs[i] = i; Arrays.sort(idxs, new Comparator<Integer>() { public int compare(Integer o1, Integer o2) { return Integer.compare(idxList.get(o1), idxList.get(o2)); } }); int preIdx = -1; if (sorted && idxList.get(idxs[0]) < 0) { logger.error("Feature vector index should start at 1. Please shift your feature vector " + "index by 1 using shift(int offset) function . See readme for details."); throw new IllegalArgumentException("index must be >= 1"); } int numNonZeroFeature = 0; for (int i = 0; i < idxs.length; i++) { if (preIdx == idxList.get(idxs[i])) { continue; } numNonZeroFeature++; preIdx = idxList.get(idxs[i]); } int[] indices = new int[numNonZeroFeature]; float[] values = new float[numNonZeroFeature]; numNonZeroFeature = 0; preIdx = -1; for (int i = 0; i < idxList.size(); i++) { if (preIdx == idxList.get(idxs[i])) { values[numNonZeroFeature - 1] += valList.get(idxs[i]); continue; } indices[numNonZeroFeature] = idxList.get(idxs[i]); values[numNonZeroFeature] = valList.get(idxs[i]); preIdx = idxList.get(idxs[i]); numNonZeroFeature++; } return new SparseFeatureVector(indices, values, sorted); }
From source file:com.galois.qrstream.qrpipe.Receive.java
/** * Update IProgress with QR finder points that were * found during the QR decoding. It orders the (x,y) * coordinates as follows: [x1,y2,x2,y2...xi,yi...]. *///from ww w . j a v a2 s .c om private void displayQRFinderPoints(Iterable<Result> decodedQRCodes) { List<Float> list = Lists.newArrayList(); for (Result qr : decodedQRCodes) { ResultPoint[] points = qr.getResultPoints(); if (points != null) { for (ResultPoint point : points) { if (point != null) { list.add(point.getX()); list.add(point.getY()); } } } } progress.drawFinderPoints(Floats.toArray(list)); }