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

Example 6 with Scalar

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

the class DynamicReadMatrixRcCP method execute.

@Override
public void execute() {
    try {
        String fname = ((Scalar) this.getFunctionInput(0)).getValue();
        Integer m = Integer.parseInt(((Scalar) this.getFunctionInput(1)).getValue());
        Integer n = Integer.parseInt(((Scalar) this.getFunctionInput(2)).getValue());
        String format = ((Scalar) this.getFunctionInput(3)).getValue();
        InputInfo ii = InputInfo.stringToInputInfo(format);
        OutputInfo oi = OutputInfo.BinaryBlockOutputInfo;
        String fnameTmp = createOutputFilePathAndName("TMP");
        _ret = new Matrix(fnameTmp, m, n, ValueType.Double);
        MatrixBlock mbTmp = DataConverter.readMatrixFromHDFS(fname, ii, m, n, ConfigurationManager.getBlocksize(), ConfigurationManager.getBlocksize());
        _ret.setMatrixDoubleArray(mbTmp, oi, ii);
        _rc = new Scalar(ScalarValueType.Integer, "0");
    // NOTE: The packagesupport wrapper creates a new MatrixObjectNew with the given
    // matrix block. This leads to a dirty state of the new object. Hence, the resulting
    // intermediate plan variable will be exported in front of MR jobs and during this export
    // the format will be changed to binary block (the contract of external functions),
    // no matter in which format the original matrix was.
    } catch (Exception e) {
        _rc = new Scalar(ScalarValueType.Integer, "1");
    // throw new PackageRuntimeException("Error executing dynamic read of matrix",e);
    }
}

Example 7 with Scalar

use of org.apache.sysml.udf.Scalar in project incubator-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 8 with Scalar

use of org.apache.sysml.udf.Scalar in project incubator-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 9 with Scalar

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

the class BinningWrapper method execute.

@Override
public void execute() {
    try {
        // get input parameters (input matrix assumed to be sorted)
        Matrix inM = (Matrix) getFunctionInput(0);
        double[][] col = inM.getMatrixAsDoubleArray();
        int binsize = Integer.parseInt(((Scalar) getFunctionInput(1)).getValue());
        int numbins = Integer.parseInt(((Scalar) getFunctionInput(2)).getValue());
        int nrowX = (int) inM.getNumRows();
        // execute binning (extend bins for duplicates)
        double[] col_bins = new double[numbins + 1];
        int pos_col = 0;
        int bin_id = 0;
        col_bins[0] = col[0][0];
        while (pos_col < nrowX - 1 && bin_id < numbins) {
            // for all bins
            pos_col = (pos_col + binsize >= nrowX) ? nrowX - 1 : pos_col + binsize;
            double end_val = col[pos_col][0];
            col_bins[bin_id + 1] = end_val;
            // pull all duplicates in current bin
            boolean cont = true;
            while (cont && pos_col < nrowX - 1) {
                if (end_val == col[pos_col + 1][0])
                    pos_col++;
                else
                    cont = false;
            }
            bin_id++;
        }
        // prepare results
        int num_bins_defined = bin_id;
        for (int i = 0; i < num_bins_defined; i++) col_bins[i] = (col_bins[i] + col_bins[i + 1]) / 2;
        // create and copy output matrix
        String dir = createOutputFilePathAndName(OUTPUT_FILE);
        _bins = new Matrix(dir, col_bins.length, 1, ValueType.Double);
        _bins.setMatrixDoubleArray(col_bins);
        _defBins = new Scalar(ScalarValueType.Integer, String.valueOf(num_bins_defined));
    } catch (Exception e) {
        throw new RuntimeException("Error executing external order function", e);
    }
}

Example 10 with Scalar

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

the class DynamicReadMatrixCP method execute.

@Override
public void execute() {
    try {
        String fname = ((Scalar) this.getFunctionInput(0)).getValue();
        Integer m = Integer.parseInt(((Scalar) this.getFunctionInput(1)).getValue());
        Integer n = Integer.parseInt(((Scalar) this.getFunctionInput(2)).getValue());
        String format = ((Scalar) this.getFunctionInput(3)).getValue();
        InputInfo ii = InputInfo.stringToInputInfo(format);
        OutputInfo oi = OutputInfo.BinaryBlockOutputInfo;
        MatrixBlock mbTmp = DataConverter.readMatrixFromHDFS(fname, ii, m, n, ConfigurationManager.getBlocksize(), ConfigurationManager.getBlocksize());
        String fnameTmp = createOutputFilePathAndName("TMP");
        _ret = new Matrix(fnameTmp, m, n, ValueType.Double);
        _ret.setMatrixDoubleArray(mbTmp, oi, ii);
    // NOTE: The packagesupport wrapper creates a new MatrixObjectNew with the given
    // matrix block. This leads to a dirty state of the new object. Hence, the resulting
    // intermediate plan variable will be exported in front of MR jobs and during this export
    // the format will be changed to binary block (the contract of external functions),
    // no matter in which format the original matrix was.
    } catch (Exception e) {
        throw new RuntimeException("Error executing dynamic read of matrix", e);
    }
}