Examples with Ctable org.apache.sysml.lops.Ctable used on opensource projects

Example 6 with Ctable

use of org.apache.sysml.lops.Ctable in project incubator-systemml by apache.

the class CtableCPInstruction method processInstruction.

@Override
public void processInstruction(ExecutionContext ec) {
    MatrixBlock matBlock1 = ec.getMatrixInput(input1.getName(), getExtendedOpcode());
    MatrixBlock matBlock2 = null, wtBlock = null;
    double cst1, cst2;
    CTableMap resultMap = new CTableMap(EntryType.INT);
    MatrixBlock resultBlock = null;
    Ctable.OperationTypes ctableOp = findCtableOperation();
    ctableOp = _isExpand ? Ctable.OperationTypes.CTABLE_EXPAND_SCALAR_WEIGHT : ctableOp;
    long outputDim1 = (_dim1Literal ? (long) Double.parseDouble(_outDim1) : (ec.getScalarInput(_outDim1, ValueType.DOUBLE, false)).getLongValue());
    long outputDim2 = (_dim2Literal ? (long) Double.parseDouble(_outDim2) : (ec.getScalarInput(_outDim2, ValueType.DOUBLE, false)).getLongValue());
    boolean outputDimsKnown = (outputDim1 != -1 && outputDim2 != -1);
    if (outputDimsKnown) {
        int inputRows = matBlock1.getNumRows();
        int inputCols = matBlock1.getNumColumns();
        boolean sparse = MatrixBlock.evalSparseFormatInMemory(outputDim1, outputDim2, inputRows * inputCols);
        // blocks because it would implicitly turn the O(N) algorithm into O(N log N).
        if (!sparse)
            resultBlock = new MatrixBlock((int) outputDim1, (int) outputDim2, false);
    }
    if (_isExpand) {
        resultBlock = new MatrixBlock(matBlock1.getNumRows(), Integer.MAX_VALUE, true);
    }
    switch(ctableOp) {
        case // (VECTOR)
        CTABLE_TRANSFORM:
            // F=ctable(A,B,W)
            matBlock2 = ec.getMatrixInput(input2.getName(), getExtendedOpcode());
            wtBlock = ec.getMatrixInput(input3.getName(), getExtendedOpcode());
            matBlock1.ctableOperations((SimpleOperator) _optr, matBlock2, wtBlock, resultMap, resultBlock);
            break;
        case // (VECTOR/MATRIX)
        CTABLE_TRANSFORM_SCALAR_WEIGHT:
            // F = ctable(A,B) or F = ctable(A,B,1)
            matBlock2 = ec.getMatrixInput(input2.getName(), getExtendedOpcode());
            cst1 = ec.getScalarInput(input3.getName(), input3.getValueType(), input3.isLiteral()).getDoubleValue();
            matBlock1.ctableOperations((SimpleOperator) _optr, matBlock2, cst1, _ignoreZeros, resultMap, resultBlock);
            break;
        case // (VECTOR)
        CTABLE_EXPAND_SCALAR_WEIGHT:
            // F = ctable(seq,A) or F = ctable(seq,B,1)
            matBlock2 = ec.getMatrixInput(input2.getName(), getExtendedOpcode());
            cst1 = ec.getScalarInput(input3.getName(), input3.getValueType(), input3.isLiteral()).getDoubleValue();
            // only resultBlock.rlen known, resultBlock.clen set in operation
            matBlock1.ctableOperations((SimpleOperator) _optr, matBlock2, cst1, resultBlock);
            break;
        case // (VECTOR)
        CTABLE_TRANSFORM_HISTOGRAM:
            // F=ctable(A,1) or F = ctable(A,1,1)
            cst1 = ec.getScalarInput(input2.getName(), input2.getValueType(), input2.isLiteral()).getDoubleValue();
            cst2 = ec.getScalarInput(input3.getName(), input3.getValueType(), input3.isLiteral()).getDoubleValue();
            matBlock1.ctableOperations((SimpleOperator) _optr, cst1, cst2, resultMap, resultBlock);
            break;
        case // (VECTOR)
        CTABLE_TRANSFORM_WEIGHTED_HISTOGRAM:
            // F=ctable(A,1,W)
            wtBlock = ec.getMatrixInput(input3.getName(), getExtendedOpcode());
            cst1 = ec.getScalarInput(input2.getName(), input2.getValueType(), input2.isLiteral()).getDoubleValue();
            matBlock1.ctableOperations((SimpleOperator) _optr, cst1, wtBlock, resultMap, resultBlock);
            break;
        default:
            throw new DMLRuntimeException("Encountered an invalid ctable operation (" + ctableOp + ") while executing instruction: " + this.toString());
    }
    if (input1.getDataType() == DataType.MATRIX)
        ec.releaseMatrixInput(input1.getName(), getExtendedOpcode());
    if (input2.getDataType() == DataType.MATRIX)
        ec.releaseMatrixInput(input2.getName(), getExtendedOpcode());
    if (input3.getDataType() == DataType.MATRIX)
        ec.releaseMatrixInput(input3.getName(), getExtendedOpcode());
    if (resultBlock == null) {
        // decided for hash-aggregation just to prevent inefficiency in case of sparse outputs.
        if (outputDimsKnown)
            resultBlock = DataConverter.convertToMatrixBlock(resultMap, (int) outputDim1, (int) outputDim2);
        else
            resultBlock = DataConverter.convertToMatrixBlock(resultMap);
    } else
        resultBlock.examSparsity();
    // such as ctable expand (guarded by released input memory)
    if (checkGuardedRepresentationChange(matBlock1, matBlock2, resultBlock)) {
        resultBlock.examSparsity();
    }
    ec.setMatrixOutput(output.getName(), resultBlock, getExtendedOpcode());
}