Guava: Google Core Libraries for Java 33.1.0-jre · com.google.common.math.PairedStats

Modifier and Type	Field and Description
private static final int	BYTES The size of byte array representation in bytes.
private static final long	serialVersionUID
private final double	sumOfProductsOfDeltas
private final Stats	xStats
private final Stats	yStats

Access	Constructor and Description
pack-priv	PairedStats(Stats xStats, Stats yStats, double sumOfProductsOfDeltas) Internal constructor.

Modifier and Type	Method and Description
public long	count() Returns the number of pairs in the dataset.
private static double	ensureInUnitRange(double value)
private static double	ensurePositive(double value)
public boolean	equals(Object the reference object with which to compare. obj) Overrides java.lang.Object.equals. Indicates whether some other object is "equal to" this one.
public static PairedStats	fromByteArray(byte[] byteArray) Creates a `PairedStats` instance from the given byte representation which was obtained by `toByteArray`.
public int	hashCode() Overrides java.lang.Object.hashCode. Returns a hash code value for this object.
public LinearTransformation	leastSquaresFit() 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`.
public double	pearsonsCorrelationCoefficient() Returns the Pearson's or product-moment correlation coefficient of the values.
public double	populationCovariance() Returns the population covariance of the values.
public double	sampleCovariance() Returns the sample covariance of the values.
pack-priv double	sumOfProductsOfDeltas()
public byte[]	toByteArray() Gets a byte array representation of this instance.
public String	toString() Overrides java.lang.Object.toString. Returns a string representation of the object.
public Stats	xStats() Returns the statistics on the `x` values alone.
public Stats	yStats() Returns the statistics on the `y` values alone.

BYTES	back to summary
private static final int BYTES The size of byte array representation in bytes.

serialVersionUID	back to summary
private static final long serialVersionUID

sumOfProductsOfDeltas	back to summary
private final double sumOfProductsOfDeltas

xStats	back to summary
private final Stats xStats

yStats	back to summary
private final Stats yStats

PairedStats	back to summary
pack-priv PairedStats(Stats xStats, Stats yStats, 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.

Method Detail

count	back to summary
public long count() Returns the number of pairs in the dataset.

ensureInUnitRange	back to summary
private static double ensureInUnitRange(double value)

ensurePositive	back to summary
private static double ensurePositive(double value)

equals back to summary

equals	back to summary
public boolean equals(Object obj) Overrides java.lang.Object.equals. Doc from java.lang.Object.equals. Indicates whether some other object is "equal to" this one. The `equals` method implements an equivalence relation on non-null object references: It is reflexive: for any non-null reference value `x`, `x.equals(x)` should return `true`. It is symmetric: for any non-null reference values `x` and `y`, `x.equals(y)` should return `true` if and only if `y.equals(x)` returns `true`. It is transitive: for any non-null reference values `x`, `y`, and `z`, if `x.equals(y)` returns `true` and `y.equals(z)` returns `true`, then `x.equals(z)` should return `true`. It is consistent: for any non-null reference values `x` and `y`, multiple invocations of `x.equals(y)` consistently return `true` or consistently return `false`, provided no information used in `equals` comparisons on the objects is modified. For any non-null reference value `x`, `x.equals(null)` should return `false`. An equivalence relation partitions the elements it operates on into equivalence classes; all the members of an equivalence class are equal to each other. Members of an equivalence class are substitutable for each other, at least for some purposes. 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.) Parameters obj:Object the reference object with which to compare. Returns:boolean `true` if this object is the same as the obj argument; `false` otherwise. Annotations @Override

public boolean equals(Object obj)

Overrides java.lang.Object.equals.

Doc from java.lang.Object.equals.

Indicates whether some other object is "equal to" this one.

The equals method implements an equivalence relation on non-null object references:

It is reflexive: for any non-null reference value x, x.equals(x) should return true.
It is symmetric: for any non-null reference values x and y, x.equals(y) should return true if and only if y.equals(x) returns true.
It is transitive: for any non-null reference values x, y, and z, if x.equals(y) returns true and y.equals(z) returns true, then x.equals(z) should return true.
It is consistent: for any non-null reference values x and y, multiple invocations of x.equals(y) consistently return true or consistently return false, provided no information used in equals comparisons on the objects is modified.
For any non-null reference value x, x.equals(null) should return false.

An equivalence relation partitions the elements it operates on into equivalence classes; all the members of an equivalence class are equal to each other. Members of an equivalence class are substitutable for each other, at least for some purposes.

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.)

Parameters

obj:Object: the reference object with which to compare.

Returns:boolean

true if this object is the same as the obj argument; false otherwise.

Annotations

@Override

fromByteArray	back to summary
public static PairedStats fromByteArray(byte[] byteArray) 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.

hashCode back to summary

hashCode	back to summary
public int hashCode() Overrides java.lang.Object.hashCode. Doc from java.lang.Object.hashCode. Returns a hash code value for this object. This method is supported for the benefit of hash tables such as those provided by `java.util.HashMap`. The general contract of `hashCode` is: Whenever it is invoked on the same object more than once during an execution of a Java application, the `hashCode` method must consistently return the same integer, provided no information used in `equals` comparisons on the object is modified. This integer need not remain consistent from one execution of an application to another execution of the same application. If two objects are equal according to the `equals` method, then calling the `hashCode` method on each of the two objects must produce the same integer result. It is not required that if two objects are unequal according to the `equals` method, then calling the `hashCode` method on each of the two objects must produce distinct integer results. However, the programmer should be aware that producing distinct integer results for unequal objects may improve the performance of hash tables. 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. Returns:int a hash code value for this object Annotations @Override

public int hashCode()

Overrides java.lang.Object.hashCode.

Doc from java.lang.Object.hashCode.

Returns a hash code value for this object. This method is supported for the benefit of hash tables such as those provided by java.util.HashMap.

The general contract of hashCode is:

Whenever it is invoked on the same object more than once during an execution of a Java application, the hashCode method must consistently return the same integer, provided no information used in equals comparisons on the object is modified. This integer need not remain consistent from one execution of an application to another execution of the same application.
If two objects are equal according to the equals method, then calling the hashCode method on each of the two objects must produce the same integer result.
It is not required that if two objects are unequal according to the equals method, then calling the hashCode method on each of the two objects must produce distinct integer results. However, the programmer should be aware that producing distinct integer results for unequal objects may improve the performance of hash tables.

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.

Returns:int

a hash code value for this object

Annotations

@Override

leastSquaresFit back to summary

leastSquaresFit	back to summary
public LinearTransformation leastSquaresFit() 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 - RR)` 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/(RR) - 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()`. Exceptions 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

public LinearTransformation leastSquaresFit()

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().

Exceptions

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

pearsonsCorrelationCoefficient back to summary

pearsonsCorrelationCoefficient	back to summary
public double pearsonsCorrelationCoefficient() 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`. Exceptions IllegalStateException: if the dataset is empty or contains a single pair of values, or either the `x` and `y` dataset has zero population variance

public double pearsonsCorrelationCoefficient()

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.

Exceptions

IllegalStateException:: if the dataset is empty or contains a single pair of values, or either the x and y dataset has zero population variance

populationCovariance back to summary

populationCovariance	back to summary
public double populationCovariance() 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`. Exceptions IllegalStateException: if the dataset is empty

public double populationCovariance()

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.

Exceptions

IllegalStateException:: if the dataset is empty

sampleCovariance back to summary

sampleCovariance	back to summary
public double sampleCovariance() 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`. Exceptions IllegalStateException: if the dataset is empty or contains a single pair of values

public double sampleCovariance()

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.

Exceptions

IllegalStateException:: if the dataset is empty or contains a single pair of values

sumOfProductsOfDeltas	back to summary
pack-priv double sumOfProductsOfDeltas()

toByteArray	back to summary
public byte[] toByteArray() Gets a byte array representation of this instance. Note No guarantees are made regarding stability of the representation between versions.

toString	back to summary
public String toString() Overrides java.lang.Object.toString. Doc from java.lang.Object.toString. Returns a string representation of the object. Returns:String a string representation of the object Annotations @Override

toString

back to summary

public String toString()

Overrides java.lang.Object.toString.

Doc from java.lang.Object.toString.

Returns a string representation of the object.

Returns:String

a string representation of the object

Annotations

@Override

xStats	back to summary
public Stats xStats() Returns the statistics on the `x` values alone.

yStats	back to summary
public Stats yStats() Returns the statistics on the `y` values alone.

public final Class PairedStats

Field Summary

Constructor Summary

Method Summary

Field Detail

Constructor Detail

Method Detail

Non-finite values

Non-finite values

Non-finite values

Non-finite values