/*
 * 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 org.checkerframework.checker.nullness.qual.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 PairedStatsAccumulator.snapshot. 
Author:
Pete Gillin
Since:
20.0/**
 * 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 PairedStatsAccumulator.snapshot. 
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.

  
Both xStats and yStats must have the same count. 
If that count is 1, sumOfProductsOfDeltas must be exactly 0.0. 
If that count is more than 1, sumOfProductsOfDeltas must be finite. 
/**
   * 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 {@code 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. /** Returns the number of pairs in the dataset. */
  public long count() {
    return xStats.count();
  }

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

  
Returns the statistics on the y values alone. /** 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.
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.
Non-finite values
If the dataset contains any non-finite values (Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, or Double.NaN) then the result is Double.NaN. 
Throws:
IllegalStateException – if the dataset is empty/**
   * 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</h3>
   *
   * <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.
This is not guaranteed to return zero when the dataset consists of the same pair of values
multiple times, due to numerical errors.
Non-finite values
If the dataset contains any non-finite values (Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, or Double.NaN) then the result is Double.NaN. 
Throws:
IllegalStateException – if the dataset is empty or contains a single pair of values/**
   * 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</h3>
   *
   * <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 Pearson's or
product-moment correlation coefficient of the values. The count must greater than one, and the x and y values must both have non-zero population variance (i.e. 
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]. 
Non-finite values
If the dataset contains any non-finite values (Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, or Double.NaN) then the result is Double.NaN. 
Throws:
IllegalStateException – if the dataset is empty or contains a single pair of values, or either the x and y dataset has zero population variance/**
   * 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</h3>
   *
   * <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 Ordinary Least Squares linear
regression of y as a function of x. The count must be greater than one, and either the x or y data must have a non-zero population variance (i.e. 
xStats().populationVariance() > 0.0 || yStats().populationVariance() > 0.0). The result is guaranteed to be horizontal if there is variance in the x data but not the y data, and vertical if there is variance in the y data but not the x data. 
This fit minimizes the root-mean-square error in y as a function of x. This error is defined as the square root of the mean of the squares of the differences between the actual y values of the data and the values predicted by the fit for the 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 sqrt(1 -
R*R) of the population standard deviation of y, where R is the Pearson's correlation coefficient (as given by pearsonsCorrelationCoefficient()). 
The corresponding root-mean-square error in x as a function of y is a fraction sqrt(1/(R*R) - 1) of the population standard deviation of x. This fit does not normally minimize that error: to do that, you should swap the roles of x and y. 
Non-finite values
If the dataset contains any non-finite values (Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, or Double.NaN) then the result is LinearTransformation.forNaN(). 
Throws:
IllegalStateException – if the dataset is empty or contains a single pair of values, or both the x and y dataset must have zero population variance/**
   * 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</h3>
   *
   * <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}
Note: This tests exact equality of the calculated statistics, including the floating point values. Two instances are guaranteed to be considered equal if one is copied from the other using second = new PairedStatsAccumulator().addAll(first).snapshot(), if both were obtained by calling snapshot() on the same PairedStatsAccumulator without adding any values in between the two calls, or if one is obtained from the other after round-tripping through java serialization. However, floating point rounding errors mean that it may be false for some instances where the statistics are mathematically equal, including instances constructed from the same values in a different order... or (in the general case) even in the same order. (It is guaranteed to return true for instances constructed from the same values in the same order if strictfp is in effect, or if the system architecture guarantees strictfp-like semantics.) /**
   * {@inheritDoc}
   *
   * <p><b>Note:</b> This tests exact equality of the calculated statistics, including the floating
   * point values. Two instances are guaranteed to be considered equal if one is copied from the
   * other using {@code second = new PairedStatsAccumulator().addAll(first).snapshot()}, if both
   * were obtained by calling {@code snapshot()} on the same {@link PairedStatsAccumulator} without
   * adding any values in between the two calls, or if one is obtained from the other after
   * round-tripping through java serialization. However, floating point rounding errors mean that it
   * may be false for some instances where the statistics are mathematically equal, including
   * instances constructed from the same values in a different order... or (in the general case)
   * even in the same order. (It is guaranteed to return true for instances constructed from the
   * same values in the same order if {@code strictfp} is in effect, or if the system architecture
   * guarantees {@code strictfp}-like semantics.)
   */
  @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}
Note: This hash code is consistent with exact equality of the calculated statistics, including the floating point values. See the note on equals for details. /**
   * {@inheritDoc}
   *
   * <p><b>Note:</b> 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 representation in bytes. /** The size of byte array representation in bytes. */
  private static final int BYTES = Stats.BYTES * 2 + Double.SIZE / Byte.SIZE;

  
Gets a byte array representation of this instance.
Note: No guarantees are made regarding stability of the representation between
versions.
/**
   * Gets a byte array representation of this instance.
   *
   * <p><b>Note:</b> 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 PairedStats instance from the given byte representation which was obtained by toByteArray. 
Note: No guarantees are made regarding stability of the representation between
versions.
/**
   * Creates a {@link PairedStats} instance from the given byte representation which was obtained by
   * {@link #toByteArray}.
   *
   * <p><b>Note:</b> 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;
}