Examples with Scalar org.apache.sysml.udf.Scalar used on opensource projects

Example 16 with Scalar

use of org.apache.sysml.udf.Scalar in project incubator-systemml by apache.

the class TimeWrapper method execute.

@Override
public void execute() {
    long present = System.currentTimeMillis();
    _ret = new Scalar(ScalarValueType.Double, String.valueOf(present));
}

Example 17 with Scalar

use of org.apache.sysml.udf.Scalar in project systemml by apache.

the class DynamicWriteMatrixCP method execute.

@Override
public void execute() {
    boolean success = false;
    try {
        Matrix mat = (Matrix) this.getFunctionInput(0);
        String fname = ((Scalar) this.getFunctionInput(1)).getValue();
        String format = ((Scalar) this.getFunctionInput(2)).getValue();
        MatrixObject mo = mat.getMatrixObject();
        MatrixCharacteristics mc = mo.getMatrixCharacteristics();
        OutputInfo oi = OutputInfo.stringToOutputInfo(format);
        MatrixBlock mb = mo.acquireRead();
        DataConverter.writeMatrixToHDFS(mb, fname, oi, mc);
        mo.release();
        success = true;
    } catch (Exception e) {
        throw new RuntimeException("Error executing dynamic write of matrix", e);
    }
    _success = new Scalar(ScalarValueType.Boolean, String.valueOf(success));
}

Example 18 with Scalar

use of org.apache.sysml.udf.Scalar in project systemml by apache.

the class MultiInputCbind method execute.

@Override
public void execute() {
    int numInputs = Integer.parseInt(((Scalar) getFunctionInput(0)).getValue());
    spagetize = Boolean.parseBoolean(((Scalar) getFunctionInput(1)).getValue());
    // Compute output dimensions
    numRetCols = 0;
    if (spagetize) {
        // Assumption the inputs are of same shape
        MatrixBlock in = ((Matrix) getFunctionInput(2)).getMatrixObject().acquireRead();
        numRetRows = in.getNumRows() * in.getNumColumns();
        numRetCols = numInputs;
        ((Matrix) getFunctionInput(2)).getMatrixObject().release();
    } else {
        for (int inputID = 2; inputID < numInputs + 2; inputID++) {
            MatrixBlock in = ((Matrix) getFunctionInput(inputID)).getMatrixObject().acquireRead();
            numRetRows = in.getNumRows();
            numRetCols += in.getNumColumns();
            ((Matrix) getFunctionInput(inputID)).getMatrixObject().release();
        }
    }
    allocateOutput();
    // Performs cbind (cbind (cbind ( X1, X2 ), X3 ), X4)
    double[] retData = retMB.getDenseBlockValues();
    int startColumn = 0;
    for (int inputID = 2; inputID < numInputs + 2; inputID++) {
        MatrixBlock in = ((Matrix) getFunctionInput(inputID)).getMatrixObject().acquireRead();
        if (spagetize && in.getNumRows() * in.getNumColumns() != numRetRows) {
            throw new RuntimeException("Expected the inputs to be of same size when spagetization is turned on.");
        }
        int inputNumCols = in.getNumColumns();
        if (in.isInSparseFormat()) {
            Iterator<IJV> iter = in.getSparseBlockIterator();
            while (iter.hasNext()) {
                IJV ijv = iter.next();
                if (spagetize) {
                    // Perform matrix(X1, rows=length(X1), cols=1) operation before cbind
                    // Output Column ID = inputID-2 for all elements of inputs
                    int outputRowIndex = ijv.getI() * inputNumCols + ijv.getJ();
                    int outputColIndex = inputID - 2;
                    retData[(int) (outputRowIndex * retMB.getNumColumns() + outputColIndex)] = ijv.getV();
                } else {
                    // Traditional cbind
                    // Row ID remains the same as that of input
                    int outputRowIndex = ijv.getI();
                    int outputColIndex = ijv.getJ() + startColumn;
                    retData[(int) (outputRowIndex * retMB.getNumColumns() + outputColIndex)] = ijv.getV();
                }
            }
        } else {
            double[] denseBlock = in.getDenseBlockValues();
            if (denseBlock != null) {
                if (spagetize) {
                    // Perform matrix(X1, rows=length(X1), cols=1) operation before cbind
                    // Output Column ID = inputID-2 for all elements of inputs
                    int j = inputID - 2;
                    for (int i = 0; i < numRetRows; i++) {
                        retData[(int) (i * numRetCols + j)] = denseBlock[i];
                    }
                } else {
                    // Row ID remains the same as that of input
                    for (int i = 0; i < retMB.getNumRows(); i++) {
                        for (int j = 0; j < inputNumCols; j++) {
                            int outputColIndex = j + startColumn;
                            retData[(int) (i * numRetCols + outputColIndex)] = denseBlock[i * inputNumCols + j];
                        }
                    }
                }
            }
        }
        ((Matrix) getFunctionInput(inputID)).getMatrixObject().release();
        startColumn += inputNumCols;
    }
    retMB.recomputeNonZeros();
    try {
        retMB.examSparsity();
        ret.setMatrixDoubleArray(retMB, OutputInfo.BinaryBlockOutputInfo, InputInfo.BinaryBlockInputInfo);
    } catch (DMLRuntimeException e) {
        throw new RuntimeException("Error while executing MultiInputCbind", e);
    } catch (IOException e) {
        throw new RuntimeException("Error while executing MultiInputCbind", e);
    }
}

Example 19 with Scalar

use of org.apache.sysml.udf.Scalar in project systemml by apache.

the class CumSumProd method execute.

@Override
public void execute() {
    X = ((Matrix) getFunctionInput(0)).getMatrixObject().acquireRead();
    C = ((Matrix) getFunctionInput(1)).getMatrixObject().acquireRead();
    if (X.getNumRows() != C.getNumRows())
        throw new RuntimeException("Number of rows of X and C should match");
    if (X.getNumColumns() != C.getNumColumns() && C.getNumColumns() != 1)
        throw new RuntimeException("Incorrect Number of columns of X and C (Expected C to be of same dimension or a vector)");
    start = Double.parseDouble(((Scalar) getFunctionInput(2)).getValue());
    isReverse = Boolean.parseBoolean(((Scalar) getFunctionInput(3)).getValue());
    numRetRows = X.getNumRows();
    numRetCols = X.getNumColumns();
    allocateOutput();
    // Copy X to Y
    denseBlock = retMB.getDenseBlockValues();
    if (X.isInSparseFormat()) {
        Iterator<IJV> iter = X.getSparseBlockIterator();
        while (iter.hasNext()) {
            IJV ijv = iter.next();
            denseBlock[ijv.getI() * numRetCols + ijv.getJ()] = ijv.getV();
        }
    } else {
        if (X.getDenseBlock() != null)
            System.arraycopy(X.getDenseBlockValues(), 0, denseBlock, 0, denseBlock.length);
    }
    if (!isReverse) {
        // Y [1, ] = X [1, ] + C [1, ] * start;
        // Y [i+1, ] = X [i+1, ] + C [i+1, ] * Y [i, ]
        addCNConstant(0, start);
        for (int i = 1; i < numRetRows; i++) {
            addC(i, true);
        }
    } else {
        // Y [m, ] = X [m, ] + C [m, ] * start;
        // Y [i-1, ] = X [i-1, ] + C [i-1, ] * Y [i, ]
        addCNConstant(numRetRows - 1, start);
        for (int i = numRetRows - 2; i >= 0; i--) {
            addC(i, false);
        }
    }
    ((Matrix) getFunctionInput(1)).getMatrixObject().release();
    ((Matrix) getFunctionInput(0)).getMatrixObject().release();
    retMB.recomputeNonZeros();
    try {
        retMB.examSparsity();
        ret.setMatrixDoubleArray(retMB, OutputInfo.BinaryBlockOutputInfo, InputInfo.BinaryBlockInputInfo);
    } catch (DMLRuntimeException e) {
        throw new RuntimeException("Error while executing CumSumProd", e);
    } catch (IOException e) {
        throw new RuntimeException("Error while executing CumSumProd", e);
    }
}