Examples with IntDoubleVector com.tencent.angel.ml.math2.vector.IntDoubleVector used on opensource projects

Example 86 with IntDoubleVector

use of com.tencent.angel.ml.math2.vector.IntDoubleVector in project angel by Tencent.

the class BinaryMatrixExecutor method apply.

private static Matrix apply(BlasDoubleMatrix mat, IntDoubleVector v, int idx, boolean onCol, Binary op) {
    double[] data = mat.getData();
    int m = mat.getNumRows(), n = mat.getNumCols();
    int size = v.size();
    byte[] flag = null;
    if (!v.isDense()) {
        flag = new byte[v.getDim()];
    }
    if (onCol && op.isInplace()) {
        if (v.isDense()) {
            double[] values = v.getStorage().getValues();
            for (int i = 0; i < m; i++) {
                data[i * n + idx] = op.apply(data[i * n + idx], values[i]);
            }
        } else if (v.isSparse()) {
            ObjectIterator<Int2DoubleMap.Entry> iter = v.getStorage().entryIterator();
            while (iter.hasNext()) {
                Int2DoubleMap.Entry entry = iter.next();
                int i = entry.getIntKey();
                flag[i] = 1;
                data[i * n + idx] = op.apply(data[i * n + idx], entry.getDoubleValue());
            }
        } else {
            // sorted
            int[] idxs = v.getStorage().getIndices();
            double[] values = v.getStorage().getValues();
            for (int k = 0; k < size; k++) {
                int i = idxs[k];
                flag[i] = 1;
                data[i * n + idx] = op.apply(data[i * n + idx], values[k]);
            }
        }
        if (!v.isDense()) {
            switch(op.getOpType()) {
                case INTERSECTION:
                    for (int i = 0; i < m; i++) {
                        if (flag[i] == 0) {
                            data[i * n + idx] = 0;
                        }
                    }
                case UNION:
                    break;
                case ALL:
                    for (int i = 0; i < m; i++) {
                        if (flag[i] == 0) {
                            data[i * n + idx] = op.apply(data[i * n + idx], 0);
                        }
                    }
            }
        }
        return mat;
    } else if (onCol && !op.isInplace()) {
        double[] newData;
        if (op.getOpType() == INTERSECTION) {
            newData = new double[m * n];
        } else {
            newData = ArrayCopy.copy(data);
        }
        if (v.isDense()) {
            double[] values = v.getStorage().getValues();
            for (int i = 0; i < m; i++) {
                newData[i * n + idx] = op.apply(data[i * n + idx], values[i]);
            }
        } else if (v.isSparse()) {
            ObjectIterator<Int2DoubleMap.Entry> iter = v.getStorage().entryIterator();
            while (iter.hasNext()) {
                Int2DoubleMap.Entry entry = iter.next();
                int i = entry.getIntKey();
                flag[i] = 1;
                newData[i * n + idx] = op.apply(data[i * n + idx], entry.getDoubleValue());
            }
        } else {
            // sorted
            int[] idxs = v.getStorage().getIndices();
            double[] values = v.getStorage().getValues();
            for (int k = 0; k < size; k++) {
                int i = idxs[k];
                flag[i] = 1;
                newData[i * n + idx] = op.apply(data[i * n + idx], values[k]);
            }
        }
        if (!v.isDense()) {
            switch(op.getOpType()) {
                case INTERSECTION:
                    break;
                case UNION:
                    break;
                case ALL:
                    for (int i = 0; i < m; i++) {
                        if (flag[i] == 0) {
                            newData[i * n + idx] = op.apply(data[i * n + idx], 0);
                        }
                    }
            }
        }
        return new BlasDoubleMatrix(mat.getMatrixId(), mat.getClock(), m, n, newData);
    } else if (!onCol && op.isInplace()) {
        if (v.isDense()) {
            double[] values = v.getStorage().getValues();
            for (int j = 0; j < n; j++) {
                data[idx * n + j] = op.apply(data[idx * n + j], values[j]);
            }
        } else if (v.isSparse()) {
            ObjectIterator<Int2DoubleMap.Entry> iter = v.getStorage().entryIterator();
            while (iter.hasNext()) {
                Int2DoubleMap.Entry entry = iter.next();
                int j = entry.getIntKey();
                flag[j] = 1;
                data[idx * n + j] = op.apply(data[idx * n + j], entry.getDoubleValue());
            }
        } else {
            // sorted
            int[] idxs = v.getStorage().getIndices();
            double[] values = v.getStorage().getValues();
            for (int k = 0; k < size; k++) {
                int j = idxs[k];
                flag[j] = 1;
                data[idx * n + j] = op.apply(data[idx * n + j], values[k]);
            }
        }
        if (!v.isDense()) {
            switch(op.getOpType()) {
                case INTERSECTION:
                    for (int j = 0; j < n; j++) {
                        if (flag[j] == 0) {
                            data[idx * n + j] = 0;
                        }
                    }
                case UNION:
                    break;
                case ALL:
                    for (int j = 0; j < n; j++) {
                        if (flag[j] == 0) {
                            data[idx * n + j] = op.apply(data[idx * n + j], 0);
                        }
                    }
            }
        }
        return mat;
    } else {
        double[] newData;
        if (op.getOpType() == INTERSECTION) {
            newData = new double[m * n];
        } else {
            newData = ArrayCopy.copy(data);
        }
        if (v.isDense()) {
            double[] values = v.getStorage().getValues();
            for (int j = 0; j < n; j++) {
                newData[idx * n + j] = op.apply(data[idx * n + j], values[j]);
            }
        } else if (v.isSparse()) {
            ObjectIterator<Int2DoubleMap.Entry> iter = v.getStorage().entryIterator();
            while (iter.hasNext()) {
                Int2DoubleMap.Entry entry = iter.next();
                int j = entry.getIntKey();
                flag[j] = 1;
                newData[idx * n + j] = op.apply(data[idx * n + j], entry.getDoubleValue());
            }
        } else {
            // sorted
            int[] idxs = v.getStorage().getIndices();
            double[] values = v.getStorage().getValues();
            for (int k = 0; k < size; k++) {
                int j = idxs[k];
                flag[j] = 1;
                newData[idx * n + j] = op.apply(data[idx * n + j], values[k]);
            }
        }
        if (!v.isDense()) {
            switch(op.getOpType()) {
                case INTERSECTION:
                    break;
                case UNION:
                    break;
                case ALL:
                    for (int j = 0; j < n; j++) {
                        if (flag[j] == 0) {
                            newData[idx * n + j] = op.apply(data[idx * n + j], 0);
                        }
                    }
            }
        }
        return new BlasDoubleMatrix(mat.getMatrixId(), mat.getClock(), m, n, newData);
    }
}

Example 87 with IntDoubleVector

use of com.tencent.angel.ml.math2.vector.IntDoubleVector in project angel by Tencent.

the class UpdatePSFTest method testSparseDoubleUDF.

public void testSparseDoubleUDF() throws Exception {
    Worker worker = LocalClusterContext.get().getWorker(workerAttempt0Id).getWorker();
    MatrixClient client1 = worker.getPSAgent().getMatrixClient(SPARSE_DOUBLE_MAT, 0);
    int matrixW1Id = client1.getMatrixId();
    // genIndexs(feaNum, nnz);
    int[] index = new int[feaNum];
    for (int i = 0; i < index.length; i++) {
        index[i] = i;
    }
    IntDoubleVector deltaVec = new IntDoubleVector(feaNum, new IntDoubleSparseVectorStorage(feaNum, nnz));
    for (int i = 0; i < index.length; i++) {
        deltaVec.set(index[i], index[i]);
    }
    // for (int i = 0; i < feaNum; i++) {
    // deltaVec.set(i, i);
    // }
    deltaVec.setRowId(0);
    Vector[] updates = new Vector[1];
    updates[0] = deltaVec;
    client1.asyncUpdate(new IncrementRows(new IncrementRowsParam(matrixW1Id, updates))).get();
    IntDoubleVector row = (IntDoubleVector) client1.getRow(0);
    for (int id : index) {
        // System.out.println("id=" + id + ", value=" + row.get(id));
        Assert.assertEquals(row.get(id), deltaVec.get(id), 0);
    }
    Assert.assertTrue(index.length == row.size());
}

Example 88 with IntDoubleVector

use of com.tencent.angel.ml.math2.vector.IntDoubleVector in project angel by Tencent.

the class IndexGetRowTask method run.

@Override
public void run(TaskContext taskContext) throws AngelException {
    try {
        MatrixClient matrixClient = taskContext.getMatrix(IndexGetRowTest.DENSE_DOUBLE_MAT);
        MatrixMeta matrixMeta = PSAgentContext.get().getMatrixMetaManager().getMatrixMeta(IndexGetRowTest.DENSE_DOUBLE_MAT);
        int[] indices = genIndexs((int) matrixMeta.getColNum(), IndexGetRowTest.nnz);
        IntDoubleVector delta = VFactory.sparseDoubleVector((int) matrixMeta.getColNum(), IndexGetRowTest.nnz);
        for (int i = 0; i < IndexGetRowTest.nnz; i++) {
            delta.set(indices[i], indices[i]);
        }
        IntDoubleVector delta1 = VFactory.denseDoubleVector((int) matrixMeta.getColNum());
        for (int i = 0; i < IndexGetRowTest.colNum; i++) {
            delta1.set(i, i);
        }
        LOG.info("delta use " + delta.getType() + " type storage");
        int testNum = 500;
        long startTs = System.currentTimeMillis();
        LOG.info("for sparse delta type");
        conf.setBoolean("use.new.split", false);
        for (int i = 0; i < testNum; i++) {
            matrixClient.increment(0, delta, true);
            if (i > 0 && i % 10 == 0) {
                LOG.info("increment old use time = " + (System.currentTimeMillis() - startTs) / i);
            }
        }
        conf.setBoolean("use.new.split", true);
        startTs = System.currentTimeMillis();
        for (int i = 0; i < testNum; i++) {
            matrixClient.increment(0, delta, true);
            // IntDoubleVector vector = (IntDoubleVector) ((GetRowResult) matrixClient.get(func)).getRow();
            if (i > 0 && i % 10 == 0) {
                LOG.info("increment new use time = " + (System.currentTimeMillis() - startTs) / i);
            }
        }
        LOG.info("for dense delta type");
        conf.setBoolean("use.new.split", false);
        for (int i = 0; i < testNum; i++) {
            matrixClient.increment(0, delta1, true);
            if (i > 0 && i % 10 == 0) {
                LOG.info("increment old use time = " + (System.currentTimeMillis() - startTs) / i);
            }
        }
        conf.setBoolean("use.new.split", true);
        startTs = System.currentTimeMillis();
        for (int i = 0; i < testNum; i++) {
            matrixClient.increment(0, delta1, true);
            // IntDoubleVector vector = (IntDoubleVector) ((GetRowResult) matrixClient.get(func)).getRow();
            if (i > 0 && i % 10 == 0) {
                LOG.info("increment new use time = " + (System.currentTimeMillis() - startTs) / i);
            }
        }
    } catch (InvalidParameterException e) {
        LOG.error("get matrix failed ", e);
        throw new AngelException(e);
    }
}

Example 89 with IntDoubleVector

use of com.tencent.angel.ml.math2.vector.IntDoubleVector in project angel by Tencent.

the class RBCompLongDoubleMatrix method diag.

@Override
public Vector diag() {
    double[] resArr = new double[rows.length];
    for (int i = 0; i < rows.length; i++) {
        if (null == rows[i]) {
            resArr[i] = 0;
        } else {
            resArr[i] = rows[i].get(i);
        }
    }
    IntDoubleDenseVectorStorage storage = new IntDoubleDenseVectorStorage(resArr);
    return new IntDoubleVector(getMatrixId(), 0, getClock(), resArr.length, storage);
}

Example 90 with IntDoubleVector

use of com.tencent.angel.ml.math2.vector.IntDoubleVector in project angel by Tencent.

the class RBIntDoubleMatrix method diag.

@Override
public Vector diag() {
    double[] resArr = new double[rows.length];
    for (int i = 0; i < rows.length; i++) {
        if (null == rows[i]) {
            resArr[i] = 0;
        } else {
            resArr[i] = rows[i].get(i);
        }
    }
    IntDoubleDenseVectorStorage storage = new IntDoubleDenseVectorStorage(resArr);
    return new IntDoubleVector(getMatrixId(), 0, getClock(), resArr.length, storage);
}