100 lines
3.6 KiB
Java
100 lines
3.6 KiB
Java
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package com.google.common.math;
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import com.google.common.base.Preconditions;
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import com.google.common.primitives.Doubles;
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/* loaded from: classes2.dex */
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public final class PairedStatsAccumulator {
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private final StatsAccumulator xStats = new StatsAccumulator();
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private final StatsAccumulator yStats = new StatsAccumulator();
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private double sumOfProductsOfDeltas = 0.0d;
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private double ensurePositive(double d) {
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if (d > 0.0d) {
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return d;
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}
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return Double.MIN_VALUE;
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}
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public final void add(double d, double d2) {
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this.xStats.add(d);
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if (!Doubles.isFinite(d) || !Doubles.isFinite(d2)) {
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this.sumOfProductsOfDeltas = Double.NaN;
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} else if (this.xStats.count() > 1) {
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this.sumOfProductsOfDeltas += (d - this.xStats.mean()) * (d2 - this.yStats.mean());
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}
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this.yStats.add(d2);
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}
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public final void addAll(PairedStats pairedStats) {
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if (pairedStats.count() == 0) {
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return;
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}
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this.xStats.addAll(pairedStats.xStats());
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if (this.yStats.count() == 0) {
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this.sumOfProductsOfDeltas = pairedStats.sumOfProductsOfDeltas();
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} else {
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this.sumOfProductsOfDeltas += pairedStats.sumOfProductsOfDeltas() + ((pairedStats.xStats().mean() - this.xStats.mean()) * (pairedStats.yStats().mean() - this.yStats.mean()) * pairedStats.count());
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}
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this.yStats.addAll(pairedStats.yStats());
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}
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public final PairedStats snapshot() {
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return new PairedStats(this.xStats.snapshot(), this.yStats.snapshot(), this.sumOfProductsOfDeltas);
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}
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public final long count() {
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return this.xStats.count();
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}
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public final Stats xStats() {
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return this.xStats.snapshot();
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}
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public final Stats yStats() {
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return this.yStats.snapshot();
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}
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public final double populationCovariance() {
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Preconditions.checkState(count() != 0);
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return this.sumOfProductsOfDeltas / count();
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}
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public final double sampleCovariance() {
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Preconditions.checkState(count() > 1);
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return this.sumOfProductsOfDeltas / (count() - 1);
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}
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public final double pearsonsCorrelationCoefficient() {
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Preconditions.checkState(count() > 1);
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if (Double.isNaN(this.sumOfProductsOfDeltas)) {
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return Double.NaN;
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}
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double sumOfSquaresOfDeltas = this.xStats.sumOfSquaresOfDeltas();
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double sumOfSquaresOfDeltas2 = this.yStats.sumOfSquaresOfDeltas();
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Preconditions.checkState(sumOfSquaresOfDeltas > 0.0d);
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Preconditions.checkState(sumOfSquaresOfDeltas2 > 0.0d);
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return ensureInUnitRange(this.sumOfProductsOfDeltas / Math.sqrt(ensurePositive(sumOfSquaresOfDeltas * sumOfSquaresOfDeltas2)));
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}
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public final LinearTransformation leastSquaresFit() {
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Preconditions.checkState(count() > 1);
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if (Double.isNaN(this.sumOfProductsOfDeltas)) {
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return LinearTransformation.forNaN();
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}
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double sumOfSquaresOfDeltas = this.xStats.sumOfSquaresOfDeltas();
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if (sumOfSquaresOfDeltas <= 0.0d) {
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Preconditions.checkState(this.yStats.sumOfSquaresOfDeltas() > 0.0d);
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return LinearTransformation.vertical(this.xStats.mean());
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}
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if (this.yStats.sumOfSquaresOfDeltas() > 0.0d) {
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return LinearTransformation.mapping(this.xStats.mean(), this.yStats.mean()).withSlope(this.sumOfProductsOfDeltas / sumOfSquaresOfDeltas);
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}
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return LinearTransformation.horizontal(this.yStats.mean());
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}
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private static double ensureInUnitRange(double d) {
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return Doubles.constrainToRange(d, -1.0d, 1.0d);
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}
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}
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