io.questdb.griffin.engine.functions.groupby.AbstractCovarGroupByFunction Maven / Gradle / Ivy

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 *  Copyright (c) 2014-2019 Appsicle
 *  Copyright (c) 2019-2023 QuestDB
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 *  you may not use this file except in compliance with the License.
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 *  http://www.apache.org/licenses/LICENSE-2.0
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 *  distributed under the License is distributed on an "AS IS" BASIS,
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package io.questdb.griffin.engine.functions.groupby;

import io.questdb.cairo.ArrayColumnTypes;
import io.questdb.cairo.ColumnType;
import io.questdb.cairo.map.MapValue;
import io.questdb.cairo.sql.Function;
import io.questdb.cairo.sql.Record;
import io.questdb.griffin.engine.functions.BinaryFunction;
import io.questdb.griffin.engine.functions.DoubleFunction;
import io.questdb.griffin.engine.functions.GroupByFunction;
import io.questdb.std.Numbers;
import org.jetbrains.annotations.NotNull;

/**
 * The abstract class, in addition, provides a method to aggregate bivariate/regression statistics.
 * We use an algorithm similar to B.P. Welford's which works by first aggregating sum of squares of
 * independent and dependent variables Sxy = sum[(X - meanX) * (Y - meanY)].
 * Computation of covariance is then simple, e.g. covariance = Sxy / (n - 1)
 *
 * @see Welford's algorithm
 */
public abstract class AbstractCovarGroupByFunction extends DoubleFunction implements GroupByFunction, BinaryFunction {
    protected final Function xFunction;
    protected final Function yFunction;
    protected int valueIndex;

    protected AbstractCovarGroupByFunction(@NotNull Function arg0, @NotNull Function arg1) {
        this.xFunction = arg0;
        this.yFunction = arg1;
    }

    @Override
    public void computeFirst(MapValue mapValue, Record record) {
        final double x = xFunction.getDouble(record);
        final double y = yFunction.getDouble(record);
        mapValue.putDouble(valueIndex, 0);
        mapValue.putDouble(valueIndex + 1, 0);
        mapValue.putDouble(valueIndex + 2, 0);
        mapValue.putLong(valueIndex + 3, 0);

        if (Numbers.isFinite(x) && Numbers.isFinite(y)) {
            aggregate(mapValue, x, y);
        }
    }

    @Override
    public void computeNext(MapValue mapValue, Record record) {
        final double x = xFunction.getDouble(record);
        final double y = yFunction.getDouble(record);
        if (Numbers.isFinite(x) && Numbers.isFinite(y)) {
            aggregate(mapValue, x, y);
        }
    }

    @Override
    public Function getLeft() {
        return xFunction;
    }

    @Override
    public Function getRight() {
        return yFunction;
    }

    @Override
    public int getValueIndex() {
        return valueIndex;
    }

    @Override
    public boolean isConstant() {
        return false;
    }

    @Override
    public boolean isParallelismSupported() {
        return false;
    }

    @Override
    public void pushValueTypes(ArrayColumnTypes columnTypes) {
        this.valueIndex = columnTypes.getColumnCount();
        columnTypes.add(ColumnType.DOUBLE);
        columnTypes.add(ColumnType.DOUBLE);
        columnTypes.add(ColumnType.DOUBLE);
        columnTypes.add(ColumnType.LONG);
    }

    @Override
    public void setDouble(MapValue mapValue, double value) {
        mapValue.putDouble(valueIndex + 2, value);
        mapValue.putLong(valueIndex + 3, 1L);
    }

    @Override
    public void setNull(MapValue mapValue) {
        mapValue.putDouble(valueIndex, Double.NaN);
        mapValue.putDouble(valueIndex + 1, Double.NaN);
        mapValue.putDouble(valueIndex + 2, Double.NaN);
        mapValue.putLong(valueIndex + 3, 0);
    }

    @Override
    public void setValueIndex(int valueIndex) {
        this.valueIndex = valueIndex;
    }

    protected void aggregate(MapValue mapValue, double x, double y) {
        double meanX = mapValue.getDouble(valueIndex);
        double meanY = mapValue.getDouble(valueIndex + 1);
        double sumXY = mapValue.getDouble(valueIndex + 2);
        long count = mapValue.getLong(valueIndex + 3) + 1;

        double oldMeanX = meanX;
        meanX += (x - meanX) / count;
        meanY += (y - meanY) / count;
        sumXY += (x - oldMeanX) * (y - meanY);

        mapValue.putDouble(valueIndex, meanX);
        mapValue.putDouble(valueIndex + 1, meanY);
        mapValue.putDouble(valueIndex + 2, sumXY);
        mapValue.addLong(valueIndex + 3, 1L);
    }
}