Examples with KahanObject org.apache.sysml.runtime.instructions.cp.KahanObject used on opensource projects

Example 86 with KahanObject

use of org.apache.sysml.runtime.instructions.cp.KahanObject in project incubator-systemml by apache.

the class Mean method execute.

@Override
public Data execute(Data in1, double in2, double count) {
    KahanObject kahanObj = (KahanObject) in1;
    double delta = (in2 - kahanObj._sum) / count;
    _plus.execute(in1, delta);
    return kahanObj;
}

Example 87 with KahanObject

use of org.apache.sysml.runtime.instructions.cp.KahanObject in project incubator-systemml by apache.

the class MatrixBlock method incrementalAggregate.

@Override
public void incrementalAggregate(AggregateOperator aggOp, MatrixValue newWithCorrection) {
    // assert(aggOp.correctionExists);
    MatrixBlock newWithCor = checkType(newWithCorrection);
    KahanObject buffer = new KahanObject(0, 0);
    if (aggOp.correctionLocation == CorrectionLocationType.LASTROW) {
        if (aggOp.increOp.fn instanceof KahanPlus) {
            LibMatrixAgg.aggregateBinaryMatrix(newWithCor, this, aggOp);
        } else {
            for (int r = 0; r < rlen - 1; r++) for (int c = 0; c < clen; c++) {
                buffer._sum = this.quickGetValue(r, c);
                buffer._correction = this.quickGetValue(r + 1, c);
                buffer = (KahanObject) aggOp.increOp.fn.execute(buffer, newWithCor.quickGetValue(r, c), newWithCor.quickGetValue(r + 1, c));
                quickSetValue(r, c, buffer._sum);
                quickSetValue(r + 1, c, buffer._correction);
            }
        }
    } else if (aggOp.correctionLocation == CorrectionLocationType.LASTCOLUMN) {
        if (aggOp.increOp.fn instanceof Builtin && (((Builtin) (aggOp.increOp.fn)).bFunc == Builtin.BuiltinCode.MAXINDEX || ((Builtin) (aggOp.increOp.fn)).bFunc == Builtin.BuiltinCode.MININDEX)) {
            // modified, the other needs to be changed to match.
            for (int r = 0; r < rlen; r++) {
                double currMaxValue = quickGetValue(r, 1);
                long newMaxIndex = (long) newWithCor.quickGetValue(r, 0);
                double newMaxValue = newWithCor.quickGetValue(r, 1);
                double update = aggOp.increOp.fn.execute(newMaxValue, currMaxValue);
                if (2.0 == update) {
                    // Return value of 2 ==> both values the same, break ties
                    // in favor of higher index.
                    long curMaxIndex = (long) quickGetValue(r, 0);
                    quickSetValue(r, 0, Math.max(curMaxIndex, newMaxIndex));
                } else if (1.0 == update) {
                    // Return value of 1 ==> new value is better; use its index
                    quickSetValue(r, 0, newMaxIndex);
                    quickSetValue(r, 1, newMaxValue);
                } else {
                // Other return value ==> current answer is best
                }
            }
        // *** END HACK ***
        } else {
            if (aggOp.increOp.fn instanceof KahanPlus) {
                LibMatrixAgg.aggregateBinaryMatrix(newWithCor, this, aggOp);
            } else {
                for (int r = 0; r < rlen; r++) for (int c = 0; c < clen - 1; c++) {
                    buffer._sum = this.quickGetValue(r, c);
                    buffer._correction = this.quickGetValue(r, c + 1);
                    buffer = (KahanObject) aggOp.increOp.fn.execute(buffer, newWithCor.quickGetValue(r, c), newWithCor.quickGetValue(r, c + 1));
                    quickSetValue(r, c, buffer._sum);
                    quickSetValue(r, c + 1, buffer._correction);
                }
            }
        }
    } else if (aggOp.correctionLocation == CorrectionLocationType.LASTTWOROWS) {
        double n, n2, mu2;
        for (int r = 0; r < rlen - 2; r++) for (int c = 0; c < clen; c++) {
            buffer._sum = this.quickGetValue(r, c);
            n = this.quickGetValue(r + 1, c);
            buffer._correction = this.quickGetValue(r + 2, c);
            mu2 = newWithCor.quickGetValue(r, c);
            n2 = newWithCor.quickGetValue(r + 1, c);
            n = n + n2;
            double toadd = (mu2 - buffer._sum) * n2 / n;
            buffer = (KahanObject) aggOp.increOp.fn.execute(buffer, toadd);
            quickSetValue(r, c, buffer._sum);
            quickSetValue(r + 1, c, n);
            quickSetValue(r + 2, c, buffer._correction);
        }
    } else if (aggOp.correctionLocation == CorrectionLocationType.LASTTWOCOLUMNS) {
        double n, n2, mu2;
        for (int r = 0; r < rlen; r++) for (int c = 0; c < clen - 2; c++) {
            buffer._sum = this.quickGetValue(r, c);
            n = this.quickGetValue(r, c + 1);
            buffer._correction = this.quickGetValue(r, c + 2);
            mu2 = newWithCor.quickGetValue(r, c);
            n2 = newWithCor.quickGetValue(r, c + 1);
            n = n + n2;
            double toadd = (mu2 - buffer._sum) * n2 / n;
            buffer = (KahanObject) aggOp.increOp.fn.execute(buffer, toadd);
            quickSetValue(r, c, buffer._sum);
            quickSetValue(r, c + 1, n);
            quickSetValue(r, c + 2, buffer._correction);
        }
    } else if (aggOp.correctionLocation == CorrectionLocationType.LASTFOURROWS && aggOp.increOp.fn instanceof CM && ((CM) aggOp.increOp.fn).getAggOpType() == AggregateOperationTypes.VARIANCE) {
        // create buffers to store results
        CM_COV_Object cbuff_curr = new CM_COV_Object();
        CM_COV_Object cbuff_part = new CM_COV_Object();
        // perform incremental aggregation
        for (int r = 0; r < rlen - 4; r++) for (int c = 0; c < clen; c++) {
            // extract current values: { var | mean, count, m2 correction, mean correction }
            // note: m2 = var * (n - 1)
            // count
            cbuff_curr.w = quickGetValue(r + 2, c);
            // m2
            cbuff_curr.m2._sum = quickGetValue(r, c) * (cbuff_curr.w - 1);
            // mean
            cbuff_curr.mean._sum = quickGetValue(r + 1, c);
            cbuff_curr.m2._correction = quickGetValue(r + 3, c);
            cbuff_curr.mean._correction = quickGetValue(r + 4, c);
            // extract partial values: { var | mean, count, m2 correction, mean correction }
            // note: m2 = var * (n - 1)
            // count
            cbuff_part.w = newWithCor.quickGetValue(r + 2, c);
            // m2
            cbuff_part.m2._sum = newWithCor.quickGetValue(r, c) * (cbuff_part.w - 1);
            // mean
            cbuff_part.mean._sum = newWithCor.quickGetValue(r + 1, c);
            cbuff_part.m2._correction = newWithCor.quickGetValue(r + 3, c);
            cbuff_part.mean._correction = newWithCor.quickGetValue(r + 4, c);
            // calculate incremental aggregated variance
            cbuff_curr = (CM_COV_Object) aggOp.increOp.fn.execute(cbuff_curr, cbuff_part);
            // store updated values: { var | mean, count, m2 correction, mean correction }
            double var = cbuff_curr.getRequiredResult(AggregateOperationTypes.VARIANCE);
            quickSetValue(r, c, var);
            // mean
            quickSetValue(r + 1, c, cbuff_curr.mean._sum);
            // count
            quickSetValue(r + 2, c, cbuff_curr.w);
            quickSetValue(r + 3, c, cbuff_curr.m2._correction);
            quickSetValue(r + 4, c, cbuff_curr.mean._correction);
        }
    } else if (aggOp.correctionLocation == CorrectionLocationType.LASTFOURCOLUMNS && aggOp.increOp.fn instanceof CM && ((CM) aggOp.increOp.fn).getAggOpType() == AggregateOperationTypes.VARIANCE) {
        // create buffers to store results
        CM_COV_Object cbuff_curr = new CM_COV_Object();
        CM_COV_Object cbuff_part = new CM_COV_Object();
        // perform incremental aggregation
        for (int r = 0; r < rlen; r++) for (int c = 0; c < clen - 4; c++) {
            // extract current values: { var | mean, count, m2 correction, mean correction }
            // note: m2 = var * (n - 1)
            // count
            cbuff_curr.w = quickGetValue(r, c + 2);
            // m2
            cbuff_curr.m2._sum = quickGetValue(r, c) * (cbuff_curr.w - 1);
            // mean
            cbuff_curr.mean._sum = quickGetValue(r, c + 1);
            cbuff_curr.m2._correction = quickGetValue(r, c + 3);
            cbuff_curr.mean._correction = quickGetValue(r, c + 4);
            // extract partial values: { var | mean, count, m2 correction, mean correction }
            // note: m2 = var * (n - 1)
            // count
            cbuff_part.w = newWithCor.quickGetValue(r, c + 2);
            // m2
            cbuff_part.m2._sum = newWithCor.quickGetValue(r, c) * (cbuff_part.w - 1);
            // mean
            cbuff_part.mean._sum = newWithCor.quickGetValue(r, c + 1);
            cbuff_part.m2._correction = newWithCor.quickGetValue(r, c + 3);
            cbuff_part.mean._correction = newWithCor.quickGetValue(r, c + 4);
            // calculate incremental aggregated variance
            cbuff_curr = (CM_COV_Object) aggOp.increOp.fn.execute(cbuff_curr, cbuff_part);
            // store updated values: { var | mean, count, m2 correction, mean correction }
            double var = cbuff_curr.getRequiredResult(AggregateOperationTypes.VARIANCE);
            quickSetValue(r, c, var);
            // mean
            quickSetValue(r, c + 1, cbuff_curr.mean._sum);
            // count
            quickSetValue(r, c + 2, cbuff_curr.w);
            quickSetValue(r, c + 3, cbuff_curr.m2._correction);
            quickSetValue(r, c + 4, cbuff_curr.mean._correction);
        }
    } else
        throw new DMLRuntimeException("unrecognized correctionLocation: " + aggOp.correctionLocation);
}

Example 88 with KahanObject

use of org.apache.sysml.runtime.instructions.cp.KahanObject in project incubator-systemml by apache.

the class MatrixBlock method sparseAggregateUnaryHelp.

private void sparseAggregateUnaryHelp(AggregateUnaryOperator op, MatrixBlock result, int blockingFactorRow, int blockingFactorCol, MatrixIndexes indexesIn) {
    // initialize result
    if (op.aggOp.initialValue != 0)
        result.reset(result.rlen, result.clen, op.aggOp.initialValue);
    CellIndex tempCellIndex = new CellIndex(-1, -1);
    KahanObject buffer = new KahanObject(0, 0);
    if (sparse && sparseBlock != null) {
        SparseBlock a = sparseBlock;
        for (int r = 0; r < Math.min(rlen, a.numRows()); r++) {
            if (a.isEmpty(r))
                continue;
            int apos = a.pos(r);
            int alen = a.size(r);
            int[] aix = a.indexes(r);
            double[] aval = a.values(r);
            for (int i = apos; i < apos + alen; i++) {
                tempCellIndex.set(r, aix[i]);
                op.indexFn.execute(tempCellIndex, tempCellIndex);
                incrementalAggregateUnaryHelp(op.aggOp, result, tempCellIndex.row, tempCellIndex.column, aval[i], buffer);
            }
        }
    } else if (!sparse && denseBlock != null) {
        DenseBlock a = getDenseBlock();
        for (int i = 0; i < rlen; i++) for (int j = 0; j < clen; j++) {
            tempCellIndex.set(i, j);
            op.indexFn.execute(tempCellIndex, tempCellIndex);
            incrementalAggregateUnaryHelp(op.aggOp, result, tempCellIndex.row, tempCellIndex.column, a.get(i, j), buffer);
        }
    }
}

Example 89 with KahanObject

use of org.apache.sysml.runtime.instructions.cp.KahanObject in project incubator-systemml by apache.

the class MatrixPackedCell method incrementalAggregate.

// with corrections
@Override
public void incrementalAggregate(AggregateOperator aggOp, MatrixValue newWithCorrection) {
    MatrixPackedCell newWithCor = checkType(newWithCorrection);
    if (aggOp.correctionLocation == CorrectionLocationType.NONE || aggOp.correctionLocation == CorrectionLocationType.LASTROW || aggOp.correctionLocation == CorrectionLocationType.LASTCOLUMN) {
        checkAndAllocateSpace(1);
        KahanObject buffer = new KahanObject(value, extras[0]);
        buffer = (KahanObject) aggOp.increOp.fn.execute(buffer, newWithCor.value, newWithCor.getExtraByPostition(0));
        value = buffer._sum;
        extras[0] = buffer._correction;
    } else if (aggOp.correctionLocation == CorrectionLocationType.LASTROW || aggOp.correctionLocation == CorrectionLocationType.LASTTWOCOLUMNS) {
        checkAndAllocateSpace(2);
        KahanObject buffer = new KahanObject(value, extras[0]);
        buffer._sum = value;
        double n = extras[0];
        buffer._correction = extras[1];
        double mu2 = newWithCor.value;
        double n2 = newWithCor.getExtraByPostition(0);
        n = n + n2;
        double toadd = (mu2 - buffer._sum) * n2 / n;
        buffer = (KahanObject) aggOp.increOp.fn.execute(buffer, toadd);
        value = buffer._sum;
        extras[0] = n;
        extras[1] = buffer._correction;
    } else
        throw new DMLRuntimeException("unrecognized correctionLocation: " + aggOp.correctionLocation);
}

Example 90 with KahanObject

use of org.apache.sysml.runtime.instructions.cp.KahanObject in project incubator-systemml by apache.

the class EncoderMVImpute method parseMethodsAndReplacments.

private void parseMethodsAndReplacments(JSONObject parsedSpec) throws JSONException {
    JSONArray mvspec = (JSONArray) parsedSpec.get(TfUtils.TXMETHOD_IMPUTE);
    _mvMethodList = new MVMethod[mvspec.size()];
    _replacementList = new String[mvspec.size()];
    _meanList = new KahanObject[mvspec.size()];
    _countList = new long[mvspec.size()];
    for (int i = 0; i < mvspec.size(); i++) {
        JSONObject mvobj = (JSONObject) mvspec.get(i);
        _mvMethodList[i] = MVMethod.valueOf(mvobj.get("method").toString().toUpperCase());
        if (_mvMethodList[i] == MVMethod.CONSTANT) {
            _replacementList[i] = mvobj.getString("value").toString();
        }
        _meanList[i] = new KahanObject(0, 0);
    }
}