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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You 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.
*/
/*
* This is not the original file distributed by the Apache Software Foundation
* It has been modified by the Hipparchus project
*/
package org.hipparchus.stat.descriptive.rank;
import java.io.Serializable;
import java.util.Arrays;
import java.util.BitSet;
import org.hipparchus.exception.LocalizedCoreFormats;
import org.hipparchus.exception.MathIllegalArgumentException;
import org.hipparchus.exception.NullArgumentException;
import org.hipparchus.stat.LocalizedStatFormats;
import org.hipparchus.stat.descriptive.AbstractUnivariateStatistic;
import org.hipparchus.stat.ranking.NaNStrategy;
import org.hipparchus.util.FastMath;
import org.hipparchus.util.KthSelector;
import org.hipparchus.util.MathArrays;
import org.hipparchus.util.MathUtils;
import org.hipparchus.util.PivotingStrategy;
import org.hipparchus.util.Precision;
/**
* Provides percentile computation.
*
* There are several commonly used methods for estimating percentiles (a.k.a.
* quantiles) based on sample data. For large samples, the different methods
* agree closely, but when sample sizes are small, different methods will give
* significantly different results. The algorithm implemented here works as follows:
*
* - Let
n be the length of the (sorted) array and
* 0 < p <= 100 be the desired percentile.
* - If
n = 1 return the unique array element (regardless of
* the value of p); otherwise
* - Compute the estimated percentile position
*
pos = p * (n + 1) / 100 and the difference, d
* between pos and floor(pos) (i.e. the fractional
* part of pos).
* - If
pos < 1 return the smallest element in the array.
* - Else if
pos >= n return the largest element in the array.
* - Else let
lower be the element in position
* floor(pos) in the array and let upper be the
* next element in the array. Return lower + d * (upper - lower)
*
*
*
* To compute percentiles, the data must be at least partially ordered. Input
* arrays are copied and recursively partitioned using an ordering definition.
* The ordering used by Arrays.sort(double[]) is the one determined
* by {@link java.lang.Double#compareTo(Double)}. This ordering makes
* Double.NaN larger than any other value (including
* Double.POSITIVE_INFINITY). Therefore, for example, the median
* (50th percentile) of
* {0, 1, 2, 3, 4, Double.NaN} evaluates to 2.5.
*
* Since percentile estimation usually involves interpolation between array
* elements, arrays containing NaN or infinite values will often
* result in NaN or infinite values returned.
*
* Further, to include different estimation types such as R1, R2 as mentioned in
* Quantile page(wikipedia),
* a type specific NaN handling strategy is used to closely match with the
* typically observed results from popular tools like R(R1-R9), Excel(R7).
*
* Percentile uses only selection instead of complete sorting and caches selection
* algorithm state between calls to the various {@code evaluate} methods. This
* greatly improves efficiency, both for a single percentile and multiple percentile
* computations. To maximize performance when multiple percentiles are computed
* based on the same data, users should set the data array once using either one
* of the {@link #evaluate(double[], double)} or {@link #setData(double[])} methods
* and thereafter {@link #evaluate(double)} with just the percentile provided.
*
* Note that this implementation is not synchronized. If
* multiple threads access an instance of this class concurrently, and at least
* one of the threads invokes the increment() or
* clear() method, it must be synchronized externally.
*/
public class Percentile extends AbstractUnivariateStatistic implements Serializable {
/** Serializable version identifier */
private static final long serialVersionUID = 20150412L;
/** Maximum number of partitioning pivots cached (each level double the number of pivots). */
private static final int MAX_CACHED_LEVELS = 10;
/** Maximum number of cached pivots in the pivots cached array */
private static final int PIVOTS_HEAP_LENGTH = 0x1 << MAX_CACHED_LEVELS - 1;
/** Default KthSelector used with default pivoting strategy */
private final KthSelector kthSelector;
/** Any of the {@link EstimationType}s such as {@link EstimationType#LEGACY CM} can be used. */
private final EstimationType estimationType;
/** NaN Handling of the input as defined by {@link NaNStrategy} */
private final NaNStrategy nanStrategy;
/**
* Determines what percentile is computed when evaluate() is activated
* with no quantile argument.
*/
private double quantile;
/** Cached pivots. */
private int[] cachedPivots;
/**
* Constructs a Percentile with the following defaults.
*
* - default quantile: 50.0, can be reset with {@link #setQuantile(double)}
* - default estimation type: {@link EstimationType#LEGACY},
* can be reset with {@link #withEstimationType(EstimationType)}
* - default NaN strategy: {@link NaNStrategy#REMOVED},
* can be reset with {@link #withNaNStrategy(NaNStrategy)}
* - a KthSelector that makes use of {@link PivotingStrategy#MEDIAN_OF_3},
* can be reset with {@link #withKthSelector(KthSelector)}
*
*/
public Percentile() {
// No try-catch or advertised exception here - arg is valid
this(50.0);
}
/**
* Constructs a Percentile with the specific quantile value and the following
*
* - default method type: {@link EstimationType#LEGACY}
* - default NaN strategy: {@link NaNStrategy#REMOVED}
* - a Kth Selector : {@link KthSelector}
*
* @param quantile the quantile
* @throws MathIllegalArgumentException if p is not greater than 0 and less
* than or equal to 100
*/
public Percentile(final double quantile) throws MathIllegalArgumentException {
this(quantile, EstimationType.LEGACY, NaNStrategy.REMOVED,
new KthSelector(PivotingStrategy.MEDIAN_OF_3));
}
/**
* Copy constructor, creates a new {@code Percentile} identical
* to the {@code original}
*
* @param original the {@code Percentile} instance to copy
* @throws NullArgumentException if original is null
*/
public Percentile(final Percentile original) throws NullArgumentException {
super(original);
estimationType = original.getEstimationType();
nanStrategy = original.getNaNStrategy();
kthSelector = original.getKthSelector();
setData(original.getDataRef());
if (original.cachedPivots != null) {
System.arraycopy(original.cachedPivots, 0, cachedPivots, 0, original.cachedPivots.length);
}
setQuantile(original.quantile);
}
/**
* Constructs a Percentile with the specific quantile value,
* {@link EstimationType}, {@link NaNStrategy} and {@link KthSelector}.
*
* @param quantile the quantile to be computed
* @param estimationType one of the percentile {@link EstimationType estimation types}
* @param nanStrategy one of {@link NaNStrategy} to handle with NaNs
* @param kthSelector a {@link KthSelector} to use for pivoting during search
* @throws MathIllegalArgumentException if p is not within (0,100]
* @throws NullArgumentException if type or NaNStrategy passed is null
*/
protected Percentile(final double quantile,
final EstimationType estimationType,
final NaNStrategy nanStrategy,
final KthSelector kthSelector)
throws MathIllegalArgumentException {
setQuantile(quantile);
cachedPivots = null;
MathUtils.checkNotNull(estimationType);
MathUtils.checkNotNull(nanStrategy);
MathUtils.checkNotNull(kthSelector);
this.estimationType = estimationType;
this.nanStrategy = nanStrategy;
this.kthSelector = kthSelector;
}
/** {@inheritDoc} */
@Override
public void setData(final double[] values) {
if (values == null) {
cachedPivots = null;
} else {
cachedPivots = new int[PIVOTS_HEAP_LENGTH];
Arrays.fill(cachedPivots, -1);
}
super.setData(values);
}
/** {@inheritDoc} */
@Override
public void setData(final double[] values, final int begin, final int length)
throws MathIllegalArgumentException {
MathUtils.checkNotNull(values, LocalizedCoreFormats.INPUT_ARRAY);
cachedPivots = new int[PIVOTS_HEAP_LENGTH];
Arrays.fill(cachedPivots, -1);
super.setData(values, begin, length);
}
/**
* Returns the result of evaluating the statistic over the stored data.
*
* The stored array is the one which was set by previous calls to
* {@link #setData(double[])}
*
* @param p the percentile value to compute
* @return the value of the statistic applied to the stored data
* @throws MathIllegalArgumentException if p is not a valid quantile value
* (p must be greater than 0 and less than or equal to 100)
*/
public double evaluate(final double p) throws MathIllegalArgumentException {
return evaluate(getDataRef(), p);
}
/**
* Returns an estimate of the quantileth percentile of the
* designated values in the values array. The quantile
* estimated is determined by the quantile property.
*
*
* - Returns
Double.NaN if length = 0
* - Returns (for any value of
quantile)
* values[begin] if length = 1
* - Throws
MathIllegalArgumentException if values
* is null, or start or length is invalid
*
*
* See {@link Percentile} for a description of the percentile estimation
* algorithm used.
*
* @param values the input array
* @param start index of the first array element to include
* @param length the number of elements to include
* @return the percentile value
* @throws MathIllegalArgumentException if the parameters are not valid
*
*/
@Override
public double evaluate(final double[] values, final int start, final int length)
throws MathIllegalArgumentException {
return evaluate(values, start, length, quantile);
}
/**
* Returns an estimate of the pth percentile of the values
* in the values array.
*
*
* - Returns
Double.NaN if values has length
* 0
* - Returns (for any value of
p) values[0]
* if values has length 1
* - Throws
MathIllegalArgumentException if values
* is null or p is not a valid quantile value (p must be greater than 0
* and less than or equal to 100)
*
*
* The default implementation delegates to
* evaluate(double[], int, int, double) in the natural way.
*
* @param values input array of values
* @param p the percentile value to compute
* @return the percentile value or Double.NaN if the array is empty
* @throws MathIllegalArgumentException if values is null or p is invalid
*/
public double evaluate(final double[] values, final double p)
throws MathIllegalArgumentException {
MathUtils.checkNotNull(values, LocalizedCoreFormats.INPUT_ARRAY);
return evaluate(values, 0, values.length, p);
}
/**
* Returns an estimate of the pth percentile of the values
* in the values array, starting with the element in (0-based)
* position begin in the array and including length
* values.
*
* Calls to this method do not modify the internal quantile
* state of this statistic.
*
*
* - Returns
Double.NaN if length = 0
* - Returns (for any value of
p) values[begin]
* if length = 1
* - Throws
MathIllegalArgumentException if values
* is null , begin or length is invalid, or
* p is not a valid quantile value (p must be greater than 0
* and less than or equal to 100)
*
*
* See {@link Percentile} for a description of the percentile estimation
* algorithm used.
*
* @param values array of input values
* @param p the percentile to compute
* @param begin the first (0-based) element to include in the computation
* @param length the number of array elements to include
* @return the percentile value
* @throws MathIllegalArgumentException if the parameters are not valid or the
* input array is null
*/
public double evaluate(final double[] values, final int begin,
final int length, final double p)
throws MathIllegalArgumentException {
MathArrays.verifyValues(values, begin, length);
if (p > 100 || p <= 0) {
throw new MathIllegalArgumentException(LocalizedStatFormats.OUT_OF_BOUNDS_QUANTILE_VALUE,
p, 0, 100);
}
if (length == 0) {
return Double.NaN;
}
if (length == 1) {
return values[begin]; // always return single value for n = 1
}
final double[] work = getWorkArray(values, begin, length);
final int[] pivotsHeap = getPivots(values);
return work.length == 0 ? Double.NaN :
estimationType.evaluate(work, pivotsHeap, p, kthSelector);
}
/**
* Returns the value of the quantile field (determines what percentile is
* computed when evaluate() is called with no quantile argument).
*
* @return quantile set while construction or {@link #setQuantile(double)}
*/
public double getQuantile() {
return quantile;
}
/**
* Sets the value of the quantile field (determines what percentile is
* computed when evaluate() is called with no quantile argument).
*
* @param p a value between 0 < p <= 100
* @throws MathIllegalArgumentException if p is not greater than 0 and less
* than or equal to 100
*/
public void setQuantile(final double p) throws MathIllegalArgumentException {
if (p <= 0 || p > 100) {
throw new MathIllegalArgumentException(
LocalizedStatFormats.OUT_OF_BOUNDS_QUANTILE_VALUE, p, 0, 100);
}
quantile = p;
}
/** {@inheritDoc} */
@Override
public Percentile copy() {
return new Percentile(this);
}
/**
* Get the work array to operate. Makes use of prior {@code storedData} if
* it exists or else do a check on NaNs and copy a subset of the array
* defined by begin and length parameters. The set {@link #nanStrategy} will
* be used to either retain/remove/replace any NaNs present before returning
* the resultant array.
*
* @param values the array of numbers
* @param begin index to start reading the array
* @param length the length of array to be read from the begin index
* @return work array sliced from values in the range [begin,begin+length)
* @throws MathIllegalArgumentException if values or indices are invalid
*/
protected double[] getWorkArray(final double[] values, final int begin, final int length) {
final double[] work;
if (values == getDataRef()) {
work = getDataRef();
} else {
switch (nanStrategy) {
case MAXIMAL:// Replace NaNs with +INFs
work = replaceAndSlice(values, begin, length, Double.NaN, Double.POSITIVE_INFINITY);
break;
case MINIMAL:// Replace NaNs with -INFs
work = replaceAndSlice(values, begin, length, Double.NaN, Double.NEGATIVE_INFINITY);
break;
case REMOVED:// Drop NaNs from data
work = removeAndSlice(values, begin, length, Double.NaN);
break;
case FAILED:// just throw exception as NaN is un-acceptable
work = copyOf(values, begin, length);
MathArrays.checkNotNaN(work);
break;
default: //FIXED
work = copyOf(values, begin, length);
break;
}
}
return work;
}
/**
* Make a copy of the array for the slice defined by array part from
* [begin, begin+length)
* @param values the input array
* @param begin start index of the array to include
* @param length number of elements to include from begin
* @return copy of a slice of the original array
*/
private static double[] copyOf(final double[] values, final int begin, final int length) {
MathArrays.verifyValues(values, begin, length);
return Arrays.copyOfRange(values, begin, begin + length);
}
/**
* Replace every occurrence of a given value with a replacement value in a
* copied slice of array defined by array part from [begin, begin+length).
* @param values the input array
* @param begin start index of the array to include
* @param length number of elements to include from begin
* @param original the value to be replaced with
* @param replacement the value to be used for replacement
* @return the copy of sliced array with replaced values
*/
private static double[] replaceAndSlice(final double[] values,
final int begin, final int length,
final double original,
final double replacement) {
final double[] temp = copyOf(values, begin, length);
for(int i = 0; i < length; i++) {
temp[i] = Precision.equalsIncludingNaN(original, temp[i]) ?
replacement : temp[i];
}
return temp;
}
/**
* Remove the occurrence of a given value in a copied slice of array
* defined by the array part from [begin, begin+length).
* @param values the input array
* @param begin start index of the array to include
* @param length number of elements to include from begin
* @param removedValue the value to be removed from the sliced array
* @return the copy of the sliced array after removing the removedValue
*/
private static double[] removeAndSlice(final double[] values,
final int begin, final int length,
final double removedValue) {
MathArrays.verifyValues(values, begin, length);
final double[] temp;
//BitSet(length) to indicate where the removedValue is located
final BitSet bits = new BitSet(length);
for (int i = begin; i < begin+length; i++) {
if (Precision.equalsIncludingNaN(removedValue, values[i])) {
bits.set(i - begin);
}
}
//Check if empty then create a new copy
if (bits.isEmpty()) {
temp = copyOf(values, begin, length); // Nothing removed, just copy
} else if (bits.cardinality() == length) {
temp = new double[0]; // All removed, just empty
} else { // Some removable, so new
temp = new double[length - bits.cardinality()];
int start = begin; //start index from source array (i.e values)
int dest = 0; //dest index in destination array(i.e temp)
int bitSetPtr = 0; //bitSetPtr is start index pointer of bitset
for (int nextOne = bits.nextSetBit(bitSetPtr); nextOne != -1; nextOne = bits.nextSetBit(bitSetPtr)) {
final int lengthToCopy = nextOne - bitSetPtr;
System.arraycopy(values, start, temp, dest, lengthToCopy);
dest += lengthToCopy;
start = begin + (bitSetPtr = bits.nextClearBit(nextOne));
}
//Copy any residue past start index till begin+length
if (start < begin + length) {
System.arraycopy(values,start,temp,dest,begin + length - start);
}
}
return temp;
}
/**
* Get pivots which is either cached or a newly created one
*
* @param values array containing the input numbers
* @return cached pivots or a newly created one
*/
private int[] getPivots(final double[] values) {
final int[] pivotsHeap;
if (values == getDataRef()) {
pivotsHeap = cachedPivots;
} else {
pivotsHeap = new int[PIVOTS_HEAP_LENGTH];
Arrays.fill(pivotsHeap, -1);
}
return pivotsHeap;
}
/**
* Get the estimation {@link EstimationType type} used for computation.
*
* @return the {@code estimationType} set
*/
public EstimationType getEstimationType() {
return estimationType;
}
/**
* Build a new instance similar to the current one except for the
* {@link EstimationType estimation type}.
*
* This method is intended to be used as part of a fluent-type builder
* pattern. Building finely tune instances should be done as follows:
*
* Percentile customized = new Percentile(quantile).
* withEstimationType(estimationType).
* withNaNStrategy(nanStrategy).
* withKthSelector(kthSelector);
*
*
* If any of the {@code withXxx} method is omitted, the default value for
* the corresponding customization parameter will be used.
*
* @param newEstimationType estimation type for the new instance
* @return a new instance, with changed estimation type
* @throws NullArgumentException when newEstimationType is null
*/
public Percentile withEstimationType(final EstimationType newEstimationType) {
return new Percentile(quantile, newEstimationType, nanStrategy, kthSelector);
}
/**
* Get the {@link NaNStrategy NaN Handling} strategy used for computation.
* @return {@code NaN Handling} strategy set during construction
*/
public NaNStrategy getNaNStrategy() {
return nanStrategy;
}
/**
* Build a new instance similar to the current one except for the
* {@link NaNStrategy NaN handling} strategy.
*
* This method is intended to be used as part of a fluent-type builder
* pattern. Building finely tune instances should be done as follows:
*
* Percentile customized = new Percentile(quantile).
* withEstimationType(estimationType).
* withNaNStrategy(nanStrategy).
* withKthSelector(kthSelector);
*
*
* If any of the {@code withXxx} method is omitted, the default value for
* the corresponding customization parameter will be used.
*
* @param newNaNStrategy NaN strategy for the new instance
* @return a new instance, with changed NaN handling strategy
* @throws NullArgumentException when newNaNStrategy is null
*/
public Percentile withNaNStrategy(final NaNStrategy newNaNStrategy) {
return new Percentile(quantile, estimationType, newNaNStrategy, kthSelector);
}
/**
* Get the {@link KthSelector kthSelector} used for computation.
* @return the {@code kthSelector} set
*/
public KthSelector getKthSelector() {
return kthSelector;
}
/**
* Get the {@link PivotingStrategy} used in KthSelector for computation.
* @return the pivoting strategy set
*/
public PivotingStrategy getPivotingStrategy() {
return kthSelector.getPivotingStrategy();
}
/**
* Build a new instance similar to the current one except for the
* {@link KthSelector kthSelector} instance specifically set.
*
* This method is intended to be used as part of a fluent-type builder
* pattern. Building finely tune instances should be done as follows:
*
* Percentile customized = new Percentile(quantile).
* withEstimationType(estimationType).
* withNaNStrategy(nanStrategy).
* withKthSelector(newKthSelector);
*
*
* If any of the {@code withXxx} method is omitted, the default value for
* the corresponding customization parameter will be used.
*
* @param newKthSelector KthSelector for the new instance
* @return a new instance, with changed KthSelector
* @throws NullArgumentException when newKthSelector is null
*/
public Percentile withKthSelector(final KthSelector newKthSelector) {
return new Percentile(quantile, estimationType, nanStrategy, newKthSelector);
}
/**
* An enum for various estimation strategies of a percentile referred in
* wikipedia on quantile
* with the names of enum matching those of types mentioned in
* wikipedia.
*
* Each enum corresponding to the specific type of estimation in wikipedia
* implements the respective formulae that specializes in the below aspects
*
* - An index method to calculate approximate index of the
* estimate
* - An estimate method to estimate a value found at the earlier
* computed index
* - A minLimit on the quantile for which first element of sorted
* input is returned as an estimate
* - A maxLimit on the quantile for which last element of sorted
* input is returned as an estimate
*
*
* Users can now create {@link Percentile} by explicitly passing this enum;
* such as by invoking {@link Percentile#withEstimationType(EstimationType)}
*
* References:
*
* -
* Wikipedia on quantile
*
* -
*
* Hyndman, R. J. and Fan, Y. (1996) Sample quantiles in statistical
* packages, American Statistician 50, 361–365
* -
*
* R-Manual
*
*/
public enum EstimationType {
/**
* This is the default type used in the {@link Percentile}.This method
* has the following formulae for index and estimates
* \( \begin{align}
* &index = (N+1)p\ \\
* &estimate = x_{\lceil h\,-\,1/2 \rceil} \\
* &minLimit = 0 \\
* &maxLimit = 1 \\
* \end{align}\)
*/
LEGACY("Legacy Hipparchus") {
/**
* {@inheritDoc}.This method in particular makes use of existing
* Hipparchus style of picking up the index.
*/
@Override
protected double index(final double p, final int length) {
final double minLimit = 0d;
final double maxLimit = 1d;
return Double.compare(p, minLimit) == 0 ? 0 :
Double.compare(p, maxLimit) == 0 ?
length : p * (length + 1);
}
},
/**
* The method R_1 has the following formulae for index and estimates
* \( \begin{align}
* &index= Np + 1/2\, \\
* &estimate= x_{\lceil h\,-\,1/2 \rceil} \\
* &minLimit = 0 \\
* \end{align}\)
*/
R_1("R-1") {
@Override
protected double index(final double p, final int length) {
final double minLimit = 0d;
return Double.compare(p, minLimit) == 0 ? 0 : length * p + 0.5;
}
/**
* {@inheritDoc}This method in particular for R_1 uses ceil(pos-0.5)
*/
@Override
protected double estimate(final double[] values,
final int[] pivotsHeap, final double pos,
final int length, final KthSelector selector) {
return super.estimate(values, pivotsHeap, FastMath.ceil(pos - 0.5), length, selector);
}
},
/**
* The method R_2 has the following formulae for index and estimates
* \( \begin{align}
* &index= Np + 1/2\, \\
* &estimate=\frac{x_{\lceil h\,-\,1/2 \rceil} +
* x_{\lfloor h\,+\,1/2 \rfloor}}{2} \\
* &minLimit = 0 \\
* &maxLimit = 1 \\
* \end{align}\)
*/
R_2("R-2") {
@Override
protected double index(final double p, final int length) {
final double minLimit = 0d;
final double maxLimit = 1d;
return Double.compare(p, maxLimit) == 0 ? length :
Double.compare(p, minLimit) == 0 ? 0 : length * p + 0.5;
}
/**
* {@inheritDoc}This method in particular for R_2 averages the
* values at ceil(p+0.5) and floor(p-0.5).
*/
@Override
protected double estimate(final double[] values,
final int[] pivotsHeap, final double pos,
final int length, final KthSelector selector) {
final double low =
super.estimate(values, pivotsHeap, FastMath.ceil(pos - 0.5), length, selector);
final double high =
super.estimate(values, pivotsHeap,FastMath.floor(pos + 0.5), length, selector);
return (low + high) / 2;
}
},
/**
* The method R_3 has the following formulae for index and estimates
* \( \begin{align}
* &index= Np \\
* &estimate= x_{\lfloor h \rceil}\, \\
* &minLimit = 0.5/N \\
* \end{align}\)
*/
R_3("R-3") {
@Override
protected double index(final double p, final int length) {
final double minLimit = 1d/2 / length;
return Double.compare(p, minLimit) <= 0 ?
0 : FastMath.rint(length * p);
}
},
/**
* The method R_4 has the following formulae for index and estimates
* \( \begin{align}
* &index= Np\, \\
* &estimate= x_{\lfloor h \rfloor} + (h -
* \lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h
* \rfloor}) \\
* &minLimit = 1/N \\
* &maxLimit = 1 \\
* \end{align}\)
*/
R_4("R-4") {
@Override
protected double index(final double p, final int length) {
final double minLimit = 1d / length;
final double maxLimit = 1d;
return Double.compare(p, minLimit) < 0 ? 0 :
Double.compare(p, maxLimit) == 0 ? length : length * p;
}
},
/**
* The method R_5 has the following formulae for index and estimates
* \( \begin{align}
* &index= Np + 1/2\\
* &estimate= x_{\lfloor h \rfloor} + (h -
* \lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h
* \rfloor}) \\
* &minLimit = 0.5/N \\
* &maxLimit = (N-0.5)/N
* \end{align}\)
*/
R_5("R-5") {
@Override
protected double index(final double p, final int length) {
final double minLimit = 1d/2 / length;
final double maxLimit = (length - 0.5) / length;
return Double.compare(p, minLimit) < 0 ? 0 :
Double.compare(p, maxLimit) >= 0 ?
length : length * p + 0.5;
}
},
/**
* The method R_6 has the following formulae for index and estimates
* \( \begin{align}
* &index= (N + 1)p \\
* &estimate= x_{\lfloor h \rfloor} + (h -
* \lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h
* \rfloor}) \\
* &minLimit = 1/(N+1) \\
* &maxLimit = N/(N+1) \\
* \end{align}\)
*
* Note: This method computes the index in a manner very close to
* the default Hipparchus Percentile existing implementation. However
* the difference to be noted is in picking up the limits with which
* first element (p<1(N+1)) and last elements (p>N/(N+1))are done.
* While in default case; these are done with p=0 and p=1 respectively.
*/
R_6("R-6") {
@Override
protected double index(final double p, final int length) {
final double minLimit = 1d / (length + 1);
final double maxLimit = 1d * length / (length + 1);
return Double.compare(p, minLimit) < 0 ? 0 :
Double.compare(p, maxLimit) >= 0 ?
length : (length + 1) * p;
}
},
/**
* The method R_7 implements Microsoft Excel style computation has the
* following formulae for index and estimates.
* \( \begin{align}
* &index = (N-1)p + 1 \\
* &estimate = x_{\lfloor h \rfloor} + (h -
* \lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h
* \rfloor}) \\
* &minLimit = 0 \\
* &maxLimit = 1 \\
* \end{align}\)
*/
R_7("R-7") {
@Override
protected double index(final double p, final int length) {
final double minLimit = 0d;
final double maxLimit = 1d;
return Double.compare(p, minLimit) == 0 ? 0 :
Double.compare(p, maxLimit) == 0 ?
length : 1 + (length - 1) * p;
}
},
/**
* The method R_8 has the following formulae for index and estimates
* \( \begin{align}
* &index = (N + 1/3)p + 1/3 \\
* &estimate = x_{\lfloor h \rfloor} + (h -
\lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h
* \rfloor}) \\
* &minLimit = (2/3)/(N+1/3) \\
* &maxLimit = (N-1/3)/(N+1/3) \\
* \end{align}\)
*
* As per Ref [2,3] this approach is most recommended as it provides
* an approximate median-unbiased estimate regardless of distribution.
*/
R_8("R-8") {
@Override
protected double index(final double p, final int length) {
final double minLimit = 2 * (1d / 3) / (length + 1d / 3);
final double maxLimit =
(length - 1d / 3) / (length + 1d / 3);
return Double.compare(p, minLimit) < 0 ? 0 :
Double.compare(p, maxLimit) >= 0 ? length :
(length + 1d / 3) * p + 1d / 3;
}
},
/**
* The method R_9 has the following formulae for index and estimates
* \( \begin{align}
* &index = (N + 1/4)p + 3/8\\
* &estimate = x_{\lfloor h \rfloor} + (h -
\lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h
* \rfloor}) \\
* &minLimit = (5/8)/(N+1/4) \\
* &maxLimit = (N-3/8)/(N+1/4) \\
* \end{align}\)
*/
R_9("R-9") {
@Override
protected double index(final double p, final int length) {
final double minLimit = 5d/8 / (length + 0.25);
final double maxLimit = (length - 3d/8) / (length + 0.25);
return Double.compare(p, minLimit) < 0 ? 0 :
Double.compare(p, maxLimit) >= 0 ? length :
(length + 0.25) * p + 3d/8;
}
},
;
/** Simple name such as R-1, R-2 corresponding to those in wikipedia. */
private final String name;
/**
* Constructor
*
* @param type name of estimation type as per wikipedia
*/
EstimationType(final String type) {
this.name = type;
}
/**
* Finds the index of array that can be used as starting index to
* {@link #estimate(double[], int[], double, int, KthSelector) estimate}
* percentile. The calculation of index calculation is specific to each
* {@link EstimationType}.
*
* @param p the pth quantile
* @param length the total number of array elements in the work array
* @return a computed real valued index as explained in the wikipedia
*/
protected abstract double index(double p, int length);
/**
* Estimation based on Kth selection. This may be overridden
* in specific enums to compute slightly different estimations.
*
* @param work array of numbers to be used for finding the percentile
* @param pos indicated positional index prior computed from calling
* {@link #index(double, int)}
* @param pivotsHeap an earlier populated cache if exists; will be used
* @param length size of array considered
* @param selector a {@link KthSelector} used for pivoting during search
* @return estimated percentile
*/
protected double estimate(final double[] work, final int[] pivotsHeap,
final double pos, final int length,
final KthSelector selector) {
final double fpos = FastMath.floor(pos);
final int intPos = (int) fpos;
final double dif = pos - fpos;
if (pos < 1) {
return selector.select(work, pivotsHeap, 0);
}
if (pos >= length) {
return selector.select(work, pivotsHeap, length - 1);
}
final double lower = selector.select(work, pivotsHeap, intPos - 1);
final double upper = selector.select(work, pivotsHeap, intPos);
return lower + dif * (upper - lower);
}
/**
* Evaluate method to compute the percentile for a given bounded array
* using earlier computed pivots heap.
* This basically calls the {@link #index(double, int) index} and then
* {@link #estimate(double[], int[], double, int, KthSelector) estimate}
* functions to return the estimated percentile value.
*
* @param work array of numbers to be used for finding the percentile
* @param pivotsHeap a prior cached heap which can speed up estimation
* @param p the pth quantile to be computed
* @param selector a {@link KthSelector} used for pivoting during search
* @return estimated percentile
* @throws MathIllegalArgumentException if p is out of range
* @throws NullArgumentException if work array is null
*/
protected double evaluate(final double[] work, final int[] pivotsHeap, final double p,
final KthSelector selector) {
MathUtils.checkNotNull(work);
if (p > 100 || p <= 0) {
throw new MathIllegalArgumentException(LocalizedStatFormats.OUT_OF_BOUNDS_QUANTILE_VALUE,
p, 0, 100);
}
return estimate(work, pivotsHeap, index(p/100d, work.length), work.length, selector);
}
/**
* Evaluate method to compute the percentile for a given bounded array.
* This basically calls the {@link #index(double, int) index} and then
* {@link #estimate(double[], int[], double, int, KthSelector) estimate}
* functions to return the estimated percentile value. Please
* note that this method does not make use of cached pivots.
*
* @param work array of numbers to be used for finding the percentile
* @param p the pth quantile to be computed
* @return estimated percentile
* @param selector a {@link KthSelector} used for pivoting during search
* @throws MathIllegalArgumentException if length or p is out of range
* @throws NullArgumentException if work array is null
*/
public double evaluate(final double[] work, final double p, final KthSelector selector) {
return this.evaluate(work, null, p, selector);
}
/**
* Gets the name of the enum
*
* @return the name
*/
String getName() {
return name;
}
}
}