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Java example source code file (PairedStats.java)

This example Java source code file (PairedStats.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

annotation, beta, bytebuffer, bytes, expected, gwtincompatible, lineartransformation, nan, nio, object, override, pairedstats, serializable, stats, string

The PairedStats.java Java example source code

/*
 * Copyright (C) 2012 The Guava Authors
 *
 * Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
 * in compliance with the License. You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software distributed under the License
 * is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
 * or implied. See the License for the specific language governing permissions and limitations under
 * the License.
 */

package com.google.common.math;

import static com.google.common.base.Preconditions.checkArgument;
import static com.google.common.base.Preconditions.checkNotNull;
import static com.google.common.base.Preconditions.checkState;
import static java.lang.Double.NaN;
import static java.lang.Double.doubleToLongBits;
import static java.lang.Double.isNaN;

import com.google.common.annotations.Beta;
import com.google.common.annotations.GwtIncompatible;
import com.google.common.base.MoreObjects;
import com.google.common.base.Objects;

import java.io.Serializable;
import java.nio.ByteBuffer;
import java.nio.ByteOrder;

import javax.annotation.Nullable;

/**
 * An immutable value object capturing some basic statistics about a collection of paired double
 * values (e.g. points on a plane). Build instances with {@link PairedStatsAccumulator#snapshot}.
 *
 * @author Pete Gillin
 * @since 20.0
 */
@Beta
@GwtIncompatible
public final class PairedStats implements Serializable {

  private final Stats xStats;
  private final Stats yStats;
  private final double sumOfProductsOfDeltas;

  /**
   * Internal constructor. Users should use {@link PairedStatsAccumulator#snapshot}.
   *
   * <p>To ensure that the created instance obeys its contract, the parameters should satisfy the
   * following constraints. This is the callers responsibility and is not enforced here.
   * <ul>
   * <li>Both {@code xStats} and {@code yStats} must have the same {@count}.
   * <li>If that {@code count} is 1, {@code sumOfProductsOfDeltas} must be exactly 0.0.
   * <li>If that {@code count} is more than 1, {@code sumOfProductsOfDeltas} must be finite.
   * </ul>
   */
  PairedStats(Stats xStats, Stats yStats, double sumOfProductsOfDeltas) {
    this.xStats = xStats;
    this.yStats = yStats;
    this.sumOfProductsOfDeltas = sumOfProductsOfDeltas;
  }

  /**
   * Returns the number of pairs in the dataset.
   */
  public long count() {
    return xStats.count();
  }

  /**
   * Returns the statistics on the {@code x} values alone.
   */
  public Stats xStats() {
    return xStats;
  }

  /**
   * Returns the statistics on the {@code y} values alone.
   */
  public Stats yStats() {
    return yStats;
  }

  /**
   * Returns the population covariance of the values. The count must be non-zero.
   *
   * <p>This is guaranteed to return zero if the dataset contains a single pair of finite values. It
   * is not guaranteed to return zero when the dataset consists of the same pair of values multiple
   * times, due to numerical errors.
   *
   * <h3>Non-finite values
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY},
   * {@link Double#NEGATIVE_INFINITY}, or {@link Double#NaN}) then the result is {@link Double#NaN}.
   *
   * @throws IllegalStateException if the dataset is empty
   */
  public double populationCovariance() {
    checkState(count() != 0);
    return sumOfProductsOfDeltas / count();
  }

  /**
   * Returns the sample covariance of the values. The count must be greater than one.
   *
   * <p>This is not guaranteed to return zero when the dataset consists of the same pair of values
   * multiple times, due to numerical errors.
   *
   * <h3>Non-finite values
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY},
   * {@link Double#NEGATIVE_INFINITY}, or {@link Double#NaN}) then the result is {@link Double#NaN}.
   *
   * @throws IllegalStateException if the dataset is empty or contains a single pair of values
   */
  public double sampleCovariance() {
    checkState(count() > 1);
    return sumOfProductsOfDeltas / (count() - 1);
  }

  /**
   * Returns the <a href="http://mathworld.wolfram.com/CorrelationCoefficient.html">Pearson's or
   * product-moment correlation coefficient</a> of the values. The count must greater than one, and
   * the {@code x} and {@code y} values must both have non-zero population variance (i.e.
   * {@code xStats().populationVariance() > 0.0 && yStats().populationVariance() > 0.0}). The result
   * is not guaranteed to be exactly +/-1 even when the data are perfectly (anti-)correlated, due to
   * numerical errors. However, it is guaranteed to be in the inclusive range [-1, +1].
   *
   * <h3>Non-finite values
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY},
   * {@link Double#NEGATIVE_INFINITY}, or {@link Double#NaN}) then the result is {@link Double#NaN}.
   *
   * @throws IllegalStateException if the dataset is empty or contains a single pair of values, or
   *     either the {@code x} and {@code y} dataset has zero population variance
   */
  public double pearsonsCorrelationCoefficient() {
    checkState(count() > 1);
    if (isNaN(sumOfProductsOfDeltas)) {
      return NaN;
    }
    double xSumOfSquaresOfDeltas = xStats().sumOfSquaresOfDeltas();
    double ySumOfSquaresOfDeltas = yStats().sumOfSquaresOfDeltas();
    checkState(xSumOfSquaresOfDeltas > 0.0);
    checkState(ySumOfSquaresOfDeltas > 0.0);
    // The product of two positive numbers can be zero if the multiplication underflowed. We
    // force a positive value by effectively rounding up to MIN_VALUE.
    double productOfSumsOfSquaresOfDeltas =
        ensurePositive(xSumOfSquaresOfDeltas * ySumOfSquaresOfDeltas);
    return ensureInUnitRange(sumOfProductsOfDeltas / Math.sqrt(productOfSumsOfSquaresOfDeltas));
  }

  /**
   * Returns a linear transformation giving the best fit to the data according to
   * <a href="http://mathworld.wolfram.com/LeastSquaresFitting.html">Ordinary Least Squares linear
   * regression</a> of {@code y} as a function of {@code x}. The count must be greater than one, and
   * either the {@code x} or {@code y} data must have a non-zero population variance (i.e.
   * {@code xStats().populationVariance() > 0.0 || yStats().populationVariance() > 0.0}). The result
   * is guaranteed to be horizontal if there is variance in the {@code x} data but not the {@code y}
   * data, and vertical if there is variance in the {@code y} data but not the {@code x} data.
   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the
   * actual {@code y} values of the data and the values predicted by the fit for the {@code x}
   * values (i.e. it is the square root of the mean of the squares of the vertical distances between
   * the data points and the best fit line). For this fit, this error is a fraction
   * {@code sqrt(1 - R*R)} of the population standard deviation of {@code y}, where {@code R} is the
   * Pearson's correlation coefficient (as given by {@link #pearsonsCorrelationCoefficient()}).
   *
   * <p>The corresponding root-mean-square error in {@code x} as a function of {@code y} is a
   * fraction {@code sqrt(1/(R*R) - 1)} of the population standard deviation of {@code x}. This fit
   * does not normally minimize that error: to do that, you should swap the roles of {@code x} and
   * {@code y}.
   *
   * <h3>Non-finite values
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY},
   * {@link Double#NEGATIVE_INFINITY}, or {@link Double#NaN}) then the result is
   * {@link LinearTransformation#forNaN()}.
   *
   * @throws IllegalStateException if the dataset is empty or contains a single pair of values, or
   *     both the {@code x} and {@code y} dataset must have zero population variance
   */
  public LinearTransformation leastSquaresFit() {
    checkState(count() > 1);
    if (isNaN(sumOfProductsOfDeltas)) {
      return LinearTransformation.forNaN();
    }
    double xSumOfSquaresOfDeltas = xStats.sumOfSquaresOfDeltas();
    if (xSumOfSquaresOfDeltas > 0.0) {
      if (yStats.sumOfSquaresOfDeltas() > 0.0) {
        return LinearTransformation.mapping(xStats.mean(), yStats.mean())
            .withSlope(sumOfProductsOfDeltas / xSumOfSquaresOfDeltas);
      } else {
        return LinearTransformation.horizontal(yStats.mean());
      }
    } else {
      checkState(yStats.sumOfSquaresOfDeltas() > 0.0);
      return LinearTransformation.vertical(xStats.mean());
    }
  }

  /**
   * {@inheritDoc}
   *
   * <p>Note: This tests exact equality of the calculated statistics, including the floating
   * point values. It is definitely true for instances constructed from exactly the same values in
   * the same order. It is also true for an instance round-tripped through java serialization.
   * However, floating point rounding errors mean that it may be false for some instances where the
   * statistics are mathematically equal, including the same values in a different order.
   */
  @Override
  public boolean equals(@Nullable Object obj) {
    if (obj == null) {
      return false;
    }
    if (getClass() != obj.getClass()) {
      return false;
    }
    PairedStats other = (PairedStats) obj;
    return (xStats.equals(other.xStats))
        && (yStats.equals(other.yStats))
        && (doubleToLongBits(sumOfProductsOfDeltas)
            == doubleToLongBits(other.sumOfProductsOfDeltas));
  }

  /**
   * {@inheritDoc}
   *
   * <p>Note: This hash code is consistent with exact equality of the calculated statistics,
   * including the floating point values. See the note on {@link #equals} for details.
   */
  @Override
  public int hashCode() {
    return Objects.hashCode(xStats, yStats, sumOfProductsOfDeltas);
  }

  @Override
  public String toString() {
    if (count() > 0) {
      return MoreObjects.toStringHelper(this)
          .add("xStats", xStats)
          .add("yStats", yStats)
          .add("populationCovariance", populationCovariance())
          .toString();
    } else {
      return MoreObjects.toStringHelper(this)
          .add("xStats", xStats)
          .add("yStats", yStats)
          .toString();
    }
  }

  double sumOfProductsOfDeltas() {
    return sumOfProductsOfDeltas;
  }

  private static double ensurePositive(double value) {
    if (value > 0.0) {
      return value;
    } else {
      return Double.MIN_VALUE;
    }
  }

  private static double ensureInUnitRange(double value) {
    if (value >= 1.0) {
      return 1.0;
    }
    if (value <= -1.0) {
      return -1.0;
    }
    return value;
  }

  // Serialization helpers

  /**
   * The size of byte array representaion in bytes.
   */
  private static final int BYTES = Stats.BYTES * 2 + Double.SIZE / Byte.SIZE;

  /**
   * Gets a byte array representation of this instance.
   *
   * <p>Note: No guarantees are made regarding stability of the representation between
   * versions.
   */
  public byte[] toByteArray() {
    ByteBuffer buffer = ByteBuffer.allocate(BYTES).order(ByteOrder.LITTLE_ENDIAN);
    xStats.writeTo(buffer);
    yStats.writeTo(buffer);
    buffer.putDouble(sumOfProductsOfDeltas);
    return buffer.array();
  }

  /**
   * Creates a {@link PairedStats} instance from the given byte representation which was obtained by
   * {@link #toByteArray}.
   *
   * <p>Note: No guarantees are made regarding stability of the representation between
   * versions.
   */
  public static PairedStats fromByteArray(byte[] byteArray) {
    checkNotNull(byteArray);
    checkArgument(
        byteArray.length == BYTES,
        "Expected PairedStats.BYTES = %s, got %s",
        BYTES,
        byteArray.length);
    ByteBuffer buffer = ByteBuffer.wrap(byteArray).order(ByteOrder.LITTLE_ENDIAN);
    Stats xStats = Stats.readFrom(buffer);
    Stats yStats = Stats.readFrom(buffer);
    double sumOfProductsOfDeltas = buffer.getDouble();
    return new PairedStats(xStats, yStats, sumOfProductsOfDeltas);
  }

  private static final long serialVersionUID = 0;
}

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