Examples with BlasDoubleMatrix com.tencent.angel.ml.math2.matrix.BlasDoubleMatrix used on opensource projects

Example 16 with BlasDoubleMatrix

use of com.tencent.angel.ml.math2.matrix.BlasDoubleMatrix in project angel by Tencent.

the class DotMatrixExecutor method apply.

private static Vector apply(BlasDoubleMatrix mat, boolean trans, IntDoubleVector v) {
    int m = mat.getNumRows(), n = mat.getNumCols();
    double[] resArr;
    if (trans) {
        assert m == v.getDim();
        resArr = new double[n];
    } else {
        assert n == v.getDim();
        resArr = new double[m];
    }
    int r = mat.getNumRows(), c = mat.getNumCols();
    double[] data = mat.getData();
    if (v.isDense()) {
        double[] tempArray = v.getStorage().getValues();
        if (trans) {
            blas.dgemv("N", c, r, 1.0, data, c, tempArray, 1, 0.0, resArr, 1);
        } else {
            blas.dgemv("T", c, r, 1.0, data, c, tempArray, 1, 0.0, resArr, 1);
        }
    } else if (v.isSparse()) {
        if (trans) {
            for (int j = 0; j < c; j++) {
                ObjectIterator<Int2DoubleMap.Entry> iter = v.getStorage().entryIterator();
                while (iter.hasNext()) {
                    Int2DoubleMap.Entry entry = iter.next();
                    int i = entry.getIntKey();
                    resArr[j] += data[i * c + j] * entry.getDoubleValue();
                }
            }
        } else {
            for (int i = 0; i < r; i++) {
                ObjectIterator<Int2DoubleMap.Entry> iter = v.getStorage().entryIterator();
                while (iter.hasNext()) {
                    Int2DoubleMap.Entry entry = iter.next();
                    int j = entry.getIntKey();
                    resArr[i] += data[i * c + j] * entry.getDoubleValue();
                }
            }
        }
    } else {
        // sorted
        if (trans) {
            for (int j = 0; j < r; j++) {
                int[] idxs = v.getStorage().getIndices();
                double[] vals = v.getStorage().getValues();
                for (int k = 0; k < idxs.length; k++) {
                    resArr[j] += data[idxs[k] * c + j] * vals[k];
                }
            }
        } else {
            for (int i = 0; i < r; i++) {
                int[] idxs = v.getStorage().getIndices();
                double[] vals = v.getStorage().getValues();
                for (int k = 0; k < idxs.length; k++) {
                    resArr[i] += data[i * c + idxs[k]] * vals[k];
                }
            }
        }
    }
    IntDoubleDenseVectorStorage storage = new IntDoubleDenseVectorStorage(resArr);
    return new IntDoubleVector(v.getMatrixId(), v.getClock(), 0, resArr.length, storage);
}

Example 17 with BlasDoubleMatrix

use of com.tencent.angel.ml.math2.matrix.BlasDoubleMatrix in project angel by Tencent.

the class DotMatrixExecutor method apply.

private static Matrix apply(BlasDoubleMatrix mat1, boolean trans1, RBLongDoubleMatrix mat2, boolean trans2) {
    if (trans1 && !trans2) {
        int outputRows = mat1.getNumCols();
        LongDoubleVector[] rows = new LongDoubleVector[outputRows];
        for (int i = 0; i < outputRows; i++) {
            Vector col = mat1.getCol(i);
            rows[i] = (LongDoubleVector) mat2.transDot(col);
        }
        return MFactory.rbLongDoubleMatrix(rows);
    } else if (!trans1 && !trans2) {
        int outputRows = mat1.getNumRows();
        LongDoubleVector[] rows = new LongDoubleVector[outputRows];
        for (int i = 0; i < outputRows; i++) {
            Vector row = mat1.getRow(i);
            rows[i] = (LongDoubleVector) mat2.transDot(row);
        }
        return MFactory.rbLongDoubleMatrix(rows);
    } else {
        throw new AngelException("the operation is not supported!");
    }
}

Example 18 with BlasDoubleMatrix

use of com.tencent.angel.ml.math2.matrix.BlasDoubleMatrix in project angel by Tencent.

the class DotMatrixExecutor method apply.

private static Vector apply(BlasDoubleMatrix mat, boolean trans, IntLongVector v) {
    int m = mat.getNumRows(), n = mat.getNumCols();
    double[] resArr;
    if (trans) {
        assert m == v.getDim();
        resArr = new double[n];
    } else {
        assert n == v.getDim();
        resArr = new double[m];
    }
    int r = mat.getNumRows(), c = mat.getNumCols();
    double[] data = mat.getData();
    if (v.isDense()) {
        double[] tempArray = ArrayCopy.copy(v.getStorage().getValues(), new double[v.getDim()]);
        if (trans) {
            blas.dgemv("N", c, r, 1.0, data, c, tempArray, 1, 0.0, resArr, 1);
        } else {
            blas.dgemv("T", c, r, 1.0, data, c, tempArray, 1, 0.0, resArr, 1);
        }
    } else if (v.isSparse()) {
        if (trans) {
            for (int j = 0; j < c; j++) {
                ObjectIterator<Int2LongMap.Entry> iter = v.getStorage().entryIterator();
                while (iter.hasNext()) {
                    Int2LongMap.Entry entry = iter.next();
                    int i = entry.getIntKey();
                    resArr[j] += data[i * c + j] * entry.getLongValue();
                }
            }
        } else {
            for (int i = 0; i < r; i++) {
                ObjectIterator<Int2LongMap.Entry> iter = v.getStorage().entryIterator();
                while (iter.hasNext()) {
                    Int2LongMap.Entry entry = iter.next();
                    int j = entry.getIntKey();
                    resArr[i] += data[i * c + j] * entry.getLongValue();
                }
            }
        }
    } else {
        // sorted
        if (trans) {
            for (int j = 0; j < r; j++) {
                int[] idxs = v.getStorage().getIndices();
                long[] vals = v.getStorage().getValues();
                for (int k = 0; k < idxs.length; k++) {
                    resArr[j] += data[idxs[k] * c + j] * vals[k];
                }
            }
        } else {
            for (int i = 0; i < r; i++) {
                int[] idxs = v.getStorage().getIndices();
                long[] vals = v.getStorage().getValues();
                for (int k = 0; k < idxs.length; k++) {
                    resArr[i] += data[i * c + idxs[k]] * vals[k];
                }
            }
        }
    }
    IntDoubleDenseVectorStorage storage = new IntDoubleDenseVectorStorage(resArr);
    return new IntDoubleVector(v.getMatrixId(), v.getClock(), 0, resArr.length, storage);
}

Example 19 with BlasDoubleMatrix

use of com.tencent.angel.ml.math2.matrix.BlasDoubleMatrix in project angel by Tencent.

the class DotMatrixExecutor method apply.

private static Matrix apply(BlasDoubleMatrix mat1, boolean trans1, RBIntDoubleMatrix mat2, boolean trans2) {
    if (trans1 && !trans2) {
        int outputRows = mat1.getNumCols();
        IntDoubleVector[] rows = new IntDoubleVector[outputRows];
        for (int i = 0; i < outputRows; i++) {
            Vector col = mat1.getCol(i);
            rows[i] = (IntDoubleVector) mat2.transDot(col);
        }
        return MFactory.rbIntDoubleMatrix(rows);
    } else if (!trans1 && !trans2) {
        int outputRows = mat1.getNumRows();
        IntDoubleVector[] rows = new IntDoubleVector[outputRows];
        for (int i = 0; i < outputRows; i++) {
            Vector row = mat1.getRow(i);
            rows[i] = (IntDoubleVector) mat2.transDot(row);
        }
        return MFactory.rbIntDoubleMatrix(rows);
    } else {
        throw new AngelException("the operation is not supported!");
    }
}

Example 20 with BlasDoubleMatrix

use of com.tencent.angel.ml.math2.matrix.BlasDoubleMatrix in project angel by Tencent.

the class BinaryMatrixExecutor method apply.

private static Matrix apply(BlasDoubleMatrix mat, IntIntVector 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()) {
            int[] 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<Int2IntMap.Entry> iter = v.getStorage().entryIterator();
            while (iter.hasNext()) {
                Int2IntMap.Entry entry = iter.next();
                int i = entry.getIntKey();
                flag[i] = 1;
                data[i * n + idx] = op.apply(data[i * n + idx], entry.getIntValue());
            }
        } else {
            // sorted
            int[] idxs = v.getStorage().getIndices();
            int[] 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()) {
            int[] 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<Int2IntMap.Entry> iter = v.getStorage().entryIterator();
            while (iter.hasNext()) {
                Int2IntMap.Entry entry = iter.next();
                int i = entry.getIntKey();
                flag[i] = 1;
                newData[i * n + idx] = op.apply(data[i * n + idx], entry.getIntValue());
            }
        } else {
            // sorted
            int[] idxs = v.getStorage().getIndices();
            int[] 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()) {
            int[] 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<Int2IntMap.Entry> iter = v.getStorage().entryIterator();
            while (iter.hasNext()) {
                Int2IntMap.Entry entry = iter.next();
                int j = entry.getIntKey();
                flag[j] = 1;
                data[idx * n + j] = op.apply(data[idx * n + j], entry.getIntValue());
            }
        } else {
            // sorted
            int[] idxs = v.getStorage().getIndices();
            int[] 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()) {
            int[] 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<Int2IntMap.Entry> iter = v.getStorage().entryIterator();
            while (iter.hasNext()) {
                Int2IntMap.Entry entry = iter.next();
                int j = entry.getIntKey();
                flag[j] = 1;
                newData[idx * n + j] = op.apply(data[idx * n + j], entry.getIntValue());
            }
        } else {
            // sorted
            int[] idxs = v.getStorage().getIndices();
            int[] 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);
    }
}