Class PairedStats
- All Implemented Interfaces:
Serializable
PairedStatsAccumulator.snapshot().- Since:
- 20.0
- See Also:
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Field Summary
Fields -
Constructor Summary
ConstructorsConstructorDescriptionPairedStats(Stats xStats, Stats yStats, double sumOfProductsOfDeltas) Internal constructor. -
Method Summary
Modifier and TypeMethodDescriptionlongcount()Returns the number of pairs in the dataset.private static doubleensureInUnitRange(double value) private static doubleensurePositive(double value) booleanstatic PairedStatsfromByteArray(byte[] byteArray) Creates aPairedStatsinstance from the given byte representation which was obtained bytoByteArray().inthashCode()Returns a linear transformation giving the best fit to the data according to Ordinary Least Squares linear regression ofyas a function ofx.doubleReturns the Pearson's or product-moment correlation coefficient of the values.doubleReturns the population covariance of the values.doubleReturns the sample covariance of the values.(package private) doublebyte[]Gets a byte array representation of this instance.toString()xStats()Returns the statistics on thexvalues alone.yStats()Returns the statistics on theyvalues alone.
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Field Details
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xStats
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yStats
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sumOfProductsOfDeltas
private final double sumOfProductsOfDeltas -
BYTES
private static final int BYTESThe size of byte array representation in bytes.- See Also:
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serialVersionUID
private static final long serialVersionUID- See Also:
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Constructor Details
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PairedStats
Internal constructor. Users should usePairedStatsAccumulator.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
xStatsandyStatsmust have the samecount. - If that
countis 1,sumOfProductsOfDeltasmust be exactly 0.0. - If that
countis more than 1,sumOfProductsOfDeltasmust be finite.
- Both
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Method Details
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count
public long count()Returns the number of pairs in the dataset. -
xStats
Returns the statistics on thexvalues alone. -
yStats
Returns the statistics on theyvalues alone. -
populationCovariance
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, orDouble.NaN) then the result isDouble.NaN.- Throws:
IllegalStateException- if the dataset is empty
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sampleCovariance
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, orDouble.NaN) then the result isDouble.NaN.- Throws:
IllegalStateException- if the dataset is empty or contains a single pair of values
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pearsonsCorrelationCoefficient
public double pearsonsCorrelationCoefficient()Returns the Pearson's or product-moment correlation coefficient of the values. The count must greater than one, and thexandyvalues 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, orDouble.NaN) then the result isDouble.NaN.- Throws:
IllegalStateException- if the dataset is empty or contains a single pair of values, or either thexandydataset has zero population variance
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leastSquaresFit
Returns a linear transformation giving the best fit to the data according to Ordinary Least Squares linear regression ofyas a function ofx. The count must be greater than one, and either thexorydata 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 thexdata but not theydata, and vertical if there is variance in theydata but not thexdata.This fit minimizes the root-mean-square error in
yas a function ofx. This error is defined as the square root of the mean of the squares of the differences between the actualyvalues of the data and the values predicted by the fit for thexvalues (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 fractionsqrt(1 - R*R)of the population standard deviation ofy, whereRis the Pearson's correlation coefficient (as given bypearsonsCorrelationCoefficient()).The corresponding root-mean-square error in
xas a function ofyis a fractionsqrt(1/(R*R) - 1)of the population standard deviation ofx. This fit does not normally minimize that error: to do that, you should swap the roles ofxandy.Non-finite values
If the dataset contains any non-finite values (
Double.POSITIVE_INFINITY,Double.NEGATIVE_INFINITY, orDouble.NaN) then the result isLinearTransformation.forNaN().- Throws:
IllegalStateException- if the dataset is empty or contains a single pair of values, or both thexandydataset must have zero population variance
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equals
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 callingsnapshot()on the samePairedStatsAccumulatorwithout 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 ifstrictfpis in effect, or if the system architecture guaranteesstrictfp-like semantics.) -
hashCode
public int hashCode()Note: This hash code is consistent with exact equality of the calculated statistics, including the floating point values. See the note on
equals(java.lang.Object)for details. -
toString
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sumOfProductsOfDeltas
double sumOfProductsOfDeltas() -
ensurePositive
private static double ensurePositive(double value) -
ensureInUnitRange
private static double ensureInUnitRange(double value) -
toByteArray
public byte[] toByteArray()Gets a byte array representation of this instance.Note: No guarantees are made regarding stability of the representation between versions.
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fromByteArray
Creates aPairedStatsinstance from the given byte representation which was obtained bytoByteArray().Note: No guarantees are made regarding stability of the representation between versions.
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