Java example source code file (DescriptiveStatistics.java)
The DescriptiveStatistics.java Java example source code
/*
* 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.
*/
package org.apache.commons.math3.stat.descriptive;
import java.io.Serializable;
import java.lang.reflect.InvocationTargetException;
import java.util.Arrays;
import org.apache.commons.math3.exception.MathIllegalArgumentException;
import org.apache.commons.math3.exception.NullArgumentException;
import org.apache.commons.math3.exception.MathIllegalStateException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.stat.descriptive.moment.GeometricMean;
import org.apache.commons.math3.stat.descriptive.moment.Kurtosis;
import org.apache.commons.math3.stat.descriptive.moment.Mean;
import org.apache.commons.math3.stat.descriptive.moment.Skewness;
import org.apache.commons.math3.stat.descriptive.moment.Variance;
import org.apache.commons.math3.stat.descriptive.rank.Max;
import org.apache.commons.math3.stat.descriptive.rank.Min;
import org.apache.commons.math3.stat.descriptive.rank.Percentile;
import org.apache.commons.math3.stat.descriptive.summary.Sum;
import org.apache.commons.math3.stat.descriptive.summary.SumOfSquares;
import org.apache.commons.math3.util.MathUtils;
import org.apache.commons.math3.util.ResizableDoubleArray;
import org.apache.commons.math3.util.FastMath;
/**
* Maintains a dataset of values of a single variable and computes descriptive
* statistics based on stored data. The {@link #getWindowSize() windowSize}
* property sets a limit on the number of values that can be stored in the
* dataset. The default value, INFINITE_WINDOW, puts no limit on the size of
* the dataset. This value should be used with caution, as the backing store
* will grow without bound in this case. For very large datasets,
* {@link SummaryStatistics}, which does not store the dataset, should be used
* instead of this class. If <code>windowSize is not INFINITE_WINDOW and
* more values are added than can be stored in the dataset, new values are
* added in a "rolling" manner, with new values replacing the "oldest" values
* in the dataset.
*
* <p>Note: this class is not threadsafe. Use
* {@link SynchronizedDescriptiveStatistics} if concurrent access from multiple
* threads is required.</p>
*
*/
public class DescriptiveStatistics implements StatisticalSummary, Serializable {
/**
* Represents an infinite window size. When the {@link #getWindowSize()}
* returns this value, there is no limit to the number of data values
* that can be stored in the dataset.
*/
public static final int INFINITE_WINDOW = -1;
/** Serialization UID */
private static final long serialVersionUID = 4133067267405273064L;
/** Name of the setQuantile method. */
private static final String SET_QUANTILE_METHOD_NAME = "setQuantile";
/** hold the window size **/
protected int windowSize = INFINITE_WINDOW;
/**
* Stored data values
*/
private ResizableDoubleArray eDA = new ResizableDoubleArray();
/** Mean statistic implementation - can be reset by setter. */
private UnivariateStatistic meanImpl = new Mean();
/** Geometric mean statistic implementation - can be reset by setter. */
private UnivariateStatistic geometricMeanImpl = new GeometricMean();
/** Kurtosis statistic implementation - can be reset by setter. */
private UnivariateStatistic kurtosisImpl = new Kurtosis();
/** Maximum statistic implementation - can be reset by setter. */
private UnivariateStatistic maxImpl = new Max();
/** Minimum statistic implementation - can be reset by setter. */
private UnivariateStatistic minImpl = new Min();
/** Percentile statistic implementation - can be reset by setter. */
private UnivariateStatistic percentileImpl = new Percentile();
/** Skewness statistic implementation - can be reset by setter. */
private UnivariateStatistic skewnessImpl = new Skewness();
/** Variance statistic implementation - can be reset by setter. */
private UnivariateStatistic varianceImpl = new Variance();
/** Sum of squares statistic implementation - can be reset by setter. */
private UnivariateStatistic sumsqImpl = new SumOfSquares();
/** Sum statistic implementation - can be reset by setter. */
private UnivariateStatistic sumImpl = new Sum();
/**
* Construct a DescriptiveStatistics instance with an infinite window
*/
public DescriptiveStatistics() {
}
/**
* Construct a DescriptiveStatistics instance with the specified window
*
* @param window the window size.
* @throws MathIllegalArgumentException if window size is less than 1 but
* not equal to {@link #INFINITE_WINDOW}
*/
public DescriptiveStatistics(int window) throws MathIllegalArgumentException {
setWindowSize(window);
}
/**
* Construct a DescriptiveStatistics instance with an infinite window
* and the initial data values in double[] initialDoubleArray.
* If initialDoubleArray is null, then this constructor corresponds to
* DescriptiveStatistics()
*
* @param initialDoubleArray the initial double[].
*/
public DescriptiveStatistics(double[] initialDoubleArray) {
if (initialDoubleArray != null) {
eDA = new ResizableDoubleArray(initialDoubleArray);
}
}
/**
* Copy constructor. Construct a new DescriptiveStatistics instance that
* is a copy of original.
*
* @param original DescriptiveStatistics instance to copy
* @throws NullArgumentException if original is null
*/
public DescriptiveStatistics(DescriptiveStatistics original) throws NullArgumentException {
copy(original, this);
}
/**
* Adds the value to the dataset. If the dataset is at the maximum size
* (i.e., the number of stored elements equals the currently configured
* windowSize), the first (oldest) element in the dataset is discarded
* to make room for the new value.
*
* @param v the value to be added
*/
public void addValue(double v) {
if (windowSize != INFINITE_WINDOW) {
if (getN() == windowSize) {
eDA.addElementRolling(v);
} else if (getN() < windowSize) {
eDA.addElement(v);
}
} else {
eDA.addElement(v);
}
}
/**
* Removes the most recent value from the dataset.
*
* @throws MathIllegalStateException if there are no elements stored
*/
public void removeMostRecentValue() throws MathIllegalStateException {
try {
eDA.discardMostRecentElements(1);
} catch (MathIllegalArgumentException ex) {
throw new MathIllegalStateException(LocalizedFormats.NO_DATA);
}
}
/**
* Replaces the most recently stored value with the given value.
* There must be at least one element stored to call this method.
*
* @param v the value to replace the most recent stored value
* @return replaced value
* @throws MathIllegalStateException if there are no elements stored
*/
public double replaceMostRecentValue(double v) throws MathIllegalStateException {
return eDA.substituteMostRecentElement(v);
}
/**
* Returns the <a href="http://www.xycoon.com/arithmetic_mean.htm">
* arithmetic mean </a> of the available values
* @return The mean or Double.NaN if no values have been added.
*/
public double getMean() {
return apply(meanImpl);
}
/**
* Returns the <a href="http://www.xycoon.com/geometric_mean.htm">
* geometric mean </a> of the available values.
* <p>
* See {@link GeometricMean} for details on the computing algorithm.</p>
*
* @return The geometricMean, Double.NaN if no values have been added,
* or if any negative values have been added.
*/
public double getGeometricMean() {
return apply(geometricMeanImpl);
}
/**
* Returns the (sample) variance of the available values.
*
* <p>This method returns the bias-corrected sample variance (using {@code n - 1} in
* the denominator). Use {@link #getPopulationVariance()} for the non-bias-corrected
* population variance.</p>
*
* @return The variance, Double.NaN if no values have been added
* or 0.0 for a single value set.
*/
public double getVariance() {
return apply(varianceImpl);
}
/**
* Returns the <a href="http://en.wikibooks.org/wiki/Statistics/Summary/Variance">
* population variance</a> of the available values.
*
* @return The population variance, Double.NaN if no values have been added,
* or 0.0 for a single value set.
*/
public double getPopulationVariance() {
return apply(new Variance(false));
}
/**
* Returns the standard deviation of the available values.
* @return The standard deviation, Double.NaN if no values have been added
* or 0.0 for a single value set.
*/
public double getStandardDeviation() {
double stdDev = Double.NaN;
if (getN() > 0) {
if (getN() > 1) {
stdDev = FastMath.sqrt(getVariance());
} else {
stdDev = 0.0;
}
}
return stdDev;
}
/**
* Returns the quadratic mean, a.k.a.
* <a href="http://mathworld.wolfram.com/Root-Mean-Square.html">
* root-mean-square</a> of the available values
* @return The quadratic mean or {@code Double.NaN} if no values
* have been added.
*/
public double getQuadraticMean() {
final long n = getN();
return n > 0 ? FastMath.sqrt(getSumsq() / n) : Double.NaN;
}
/**
* Returns the skewness of the available values. Skewness is a
* measure of the asymmetry of a given distribution.
*
* @return The skewness, Double.NaN if less than 3 values have been added.
*/
public double getSkewness() {
return apply(skewnessImpl);
}
/**
* Returns the Kurtosis of the available values. Kurtosis is a
* measure of the "peakedness" of a distribution.
*
* @return The kurtosis, Double.NaN if less than 4 values have been added.
*/
public double getKurtosis() {
return apply(kurtosisImpl);
}
/**
* Returns the maximum of the available values
* @return The max or Double.NaN if no values have been added.
*/
public double getMax() {
return apply(maxImpl);
}
/**
* Returns the minimum of the available values
* @return The min or Double.NaN if no values have been added.
*/
public double getMin() {
return apply(minImpl);
}
/**
* Returns the number of available values
* @return The number of available values
*/
public long getN() {
return eDA.getNumElements();
}
/**
* Returns the sum of the values that have been added to Univariate.
* @return The sum or Double.NaN if no values have been added
*/
public double getSum() {
return apply(sumImpl);
}
/**
* Returns the sum of the squares of the available values.
* @return The sum of the squares or Double.NaN if no
* values have been added.
*/
public double getSumsq() {
return apply(sumsqImpl);
}
/**
* Resets all statistics and storage
*/
public void clear() {
eDA.clear();
}
/**
* Returns the maximum number of values that can be stored in the
* dataset, or INFINITE_WINDOW (-1) if there is no limit.
*
* @return The current window size or -1 if its Infinite.
*/
public int getWindowSize() {
return windowSize;
}
/**
* WindowSize controls the number of values that contribute to the
* reported statistics. For example, if windowSize is set to 3 and the
* values {1,2,3,4,5} have been added <strong> in that order then
* the <i>available values are {3,4,5} and all reported statistics will
* be based on these values. If {@code windowSize} is decreased as a result
* of this call and there are more than the new value of elements in the
* current dataset, values from the front of the array are discarded to
* reduce the dataset to {@code windowSize} elements.
*
* @param windowSize sets the size of the window.
* @throws MathIllegalArgumentException if window size is less than 1 but
* not equal to {@link #INFINITE_WINDOW}
*/
public void setWindowSize(int windowSize) throws MathIllegalArgumentException {
if (windowSize < 1 && windowSize != INFINITE_WINDOW) {
throw new MathIllegalArgumentException(
LocalizedFormats.NOT_POSITIVE_WINDOW_SIZE, windowSize);
}
this.windowSize = windowSize;
// We need to check to see if we need to discard elements
// from the front of the array. If the windowSize is less than
// the current number of elements.
if (windowSize != INFINITE_WINDOW && windowSize < eDA.getNumElements()) {
eDA.discardFrontElements(eDA.getNumElements() - windowSize);
}
}
/**
* Returns the current set of values in an array of double primitives.
* The order of addition is preserved. The returned array is a fresh
* copy of the underlying data -- i.e., it is not a reference to the
* stored data.
*
* @return returns the current set of numbers in the order in which they
* were added to this set
*/
public double[] getValues() {
return eDA.getElements();
}
/**
* Returns the current set of values in an array of double primitives,
* sorted in ascending order. The returned array is a fresh
* copy of the underlying data -- i.e., it is not a reference to the
* stored data.
* @return returns the current set of
* numbers sorted in ascending order
*/
public double[] getSortedValues() {
double[] sort = getValues();
Arrays.sort(sort);
return sort;
}
/**
* Returns the element at the specified index
* @param index The Index of the element
* @return return the element at the specified index
*/
public double getElement(int index) {
return eDA.getElement(index);
}
/**
* Returns an estimate for the pth percentile of the stored values.
* <p>
* The implementation provided here follows the first estimation procedure presented
* <a href="http://www.itl.nist.gov/div898/handbook/prc/section2/prc252.htm">here.
* </p>
0 < p ≤ 100 (otherwise an
* <code>MathIllegalArgumentException is thrown)
* <li>at least one value must be stored (returns Double.NaN
* </code> otherwise)
* </ul>
*
* @param p the requested percentile (scaled from 0 - 100)
* @return An estimate for the pth percentile of the stored data
* @throws MathIllegalStateException if percentile implementation has been
* overridden and the supplied implementation does not support setQuantile
* @throws MathIllegalArgumentException if p is not a valid quantile
*/
public double getPercentile(double p) throws MathIllegalStateException, MathIllegalArgumentException {
if (percentileImpl instanceof Percentile) {
((Percentile) percentileImpl).setQuantile(p);
} else {
try {
percentileImpl.getClass().getMethod(SET_QUANTILE_METHOD_NAME,
new Class[] {Double.TYPE}).invoke(percentileImpl,
new Object[] {Double.valueOf(p)});
} catch (NoSuchMethodException e1) { // Setter guard should prevent
throw new MathIllegalStateException(
LocalizedFormats.PERCENTILE_IMPLEMENTATION_UNSUPPORTED_METHOD,
percentileImpl.getClass().getName(), SET_QUANTILE_METHOD_NAME);
} catch (IllegalAccessException e2) {
throw new MathIllegalStateException(
LocalizedFormats.PERCENTILE_IMPLEMENTATION_CANNOT_ACCESS_METHOD,
SET_QUANTILE_METHOD_NAME, percentileImpl.getClass().getName());
} catch (InvocationTargetException e3) {
throw new IllegalStateException(e3.getCause());
}
}
return apply(percentileImpl);
}
/**
* Generates a text report displaying univariate statistics from values
* that have been added. Each statistic is displayed on a separate
* line.
*
* @return String with line feeds displaying statistics
*/
@Override
public String toString() {
StringBuilder outBuffer = new StringBuilder();
String endl = "\n";
outBuffer.append("DescriptiveStatistics:").append(endl);
outBuffer.append("n: ").append(getN()).append(endl);
outBuffer.append("min: ").append(getMin()).append(endl);
outBuffer.append("max: ").append(getMax()).append(endl);
outBuffer.append("mean: ").append(getMean()).append(endl);
outBuffer.append("std dev: ").append(getStandardDeviation())
.append(endl);
try {
// No catch for MIAE because actual parameter is valid below
outBuffer.append("median: ").append(getPercentile(50)).append(endl);
} catch (MathIllegalStateException ex) {
outBuffer.append("median: unavailable").append(endl);
}
outBuffer.append("skewness: ").append(getSkewness()).append(endl);
outBuffer.append("kurtosis: ").append(getKurtosis()).append(endl);
return outBuffer.toString();
}
/**
* Apply the given statistic to the data associated with this set of statistics.
* @param stat the statistic to apply
* @return the computed value of the statistic.
*/
public double apply(UnivariateStatistic stat) {
// No try-catch or advertised exception here because arguments are guaranteed valid
return eDA.compute(stat);
}
// Implementation getters and setter
/**
* Returns the currently configured mean implementation.
*
* @return the UnivariateStatistic implementing the mean
* @since 1.2
*/
public synchronized UnivariateStatistic getMeanImpl() {
return meanImpl;
}
/**
* <p>Sets the implementation for the mean.
*
* @param meanImpl the UnivariateStatistic instance to use
* for computing the mean
* @since 1.2
*/
public synchronized void setMeanImpl(UnivariateStatistic meanImpl) {
this.meanImpl = meanImpl;
}
/**
* Returns the currently configured geometric mean implementation.
*
* @return the UnivariateStatistic implementing the geometric mean
* @since 1.2
*/
public synchronized UnivariateStatistic getGeometricMeanImpl() {
return geometricMeanImpl;
}
/**
* <p>Sets the implementation for the gemoetric mean.
*
* @param geometricMeanImpl the UnivariateStatistic instance to use
* for computing the geometric mean
* @since 1.2
*/
public synchronized void setGeometricMeanImpl(
UnivariateStatistic geometricMeanImpl) {
this.geometricMeanImpl = geometricMeanImpl;
}
/**
* Returns the currently configured kurtosis implementation.
*
* @return the UnivariateStatistic implementing the kurtosis
* @since 1.2
*/
public synchronized UnivariateStatistic getKurtosisImpl() {
return kurtosisImpl;
}
/**
* <p>Sets the implementation for the kurtosis.
*
* @param kurtosisImpl the UnivariateStatistic instance to use
* for computing the kurtosis
* @since 1.2
*/
public synchronized void setKurtosisImpl(UnivariateStatistic kurtosisImpl) {
this.kurtosisImpl = kurtosisImpl;
}
/**
* Returns the currently configured maximum implementation.
*
* @return the UnivariateStatistic implementing the maximum
* @since 1.2
*/
public synchronized UnivariateStatistic getMaxImpl() {
return maxImpl;
}
/**
* <p>Sets the implementation for the maximum.
*
* @param maxImpl the UnivariateStatistic instance to use
* for computing the maximum
* @since 1.2
*/
public synchronized void setMaxImpl(UnivariateStatistic maxImpl) {
this.maxImpl = maxImpl;
}
/**
* Returns the currently configured minimum implementation.
*
* @return the UnivariateStatistic implementing the minimum
* @since 1.2
*/
public synchronized UnivariateStatistic getMinImpl() {
return minImpl;
}
/**
* <p>Sets the implementation for the minimum.
*
* @param minImpl the UnivariateStatistic instance to use
* for computing the minimum
* @since 1.2
*/
public synchronized void setMinImpl(UnivariateStatistic minImpl) {
this.minImpl = minImpl;
}
/**
* Returns the currently configured percentile implementation.
*
* @return the UnivariateStatistic implementing the percentile
* @since 1.2
*/
public synchronized UnivariateStatistic getPercentileImpl() {
return percentileImpl;
}
/**
* Sets the implementation to be used by {@link #getPercentile(double)}.
* The supplied <code>UnivariateStatistic must provide a
* <code>setQuantile(double) method; otherwise
* <code>IllegalArgumentException is thrown.
*
* @param percentileImpl the percentileImpl to set
* @throws MathIllegalArgumentException if the supplied implementation does not
* provide a <code>setQuantile method
* @since 1.2
*/
public synchronized void setPercentileImpl(UnivariateStatistic percentileImpl)
throws MathIllegalArgumentException {
try {
percentileImpl.getClass().getMethod(SET_QUANTILE_METHOD_NAME,
new Class[] {Double.TYPE}).invoke(percentileImpl,
new Object[] {Double.valueOf(50.0d)});
} catch (NoSuchMethodException e1) {
throw new MathIllegalArgumentException(
LocalizedFormats.PERCENTILE_IMPLEMENTATION_UNSUPPORTED_METHOD,
percentileImpl.getClass().getName(), SET_QUANTILE_METHOD_NAME);
} catch (IllegalAccessException e2) {
throw new MathIllegalArgumentException(
LocalizedFormats.PERCENTILE_IMPLEMENTATION_CANNOT_ACCESS_METHOD,
SET_QUANTILE_METHOD_NAME, percentileImpl.getClass().getName());
} catch (InvocationTargetException e3) {
throw new IllegalArgumentException(e3.getCause());
}
this.percentileImpl = percentileImpl;
}
/**
* Returns the currently configured skewness implementation.
*
* @return the UnivariateStatistic implementing the skewness
* @since 1.2
*/
public synchronized UnivariateStatistic getSkewnessImpl() {
return skewnessImpl;
}
/**
* <p>Sets the implementation for the skewness.
*
* @param skewnessImpl the UnivariateStatistic instance to use
* for computing the skewness
* @since 1.2
*/
public synchronized void setSkewnessImpl(
UnivariateStatistic skewnessImpl) {
this.skewnessImpl = skewnessImpl;
}
/**
* Returns the currently configured variance implementation.
*
* @return the UnivariateStatistic implementing the variance
* @since 1.2
*/
public synchronized UnivariateStatistic getVarianceImpl() {
return varianceImpl;
}
/**
* <p>Sets the implementation for the variance.
*
* @param varianceImpl the UnivariateStatistic instance to use
* for computing the variance
* @since 1.2
*/
public synchronized void setVarianceImpl(
UnivariateStatistic varianceImpl) {
this.varianceImpl = varianceImpl;
}
/**
* Returns the currently configured sum of squares implementation.
*
* @return the UnivariateStatistic implementing the sum of squares
* @since 1.2
*/
public synchronized UnivariateStatistic getSumsqImpl() {
return sumsqImpl;
}
/**
* <p>Sets the implementation for the sum of squares.
*
* @param sumsqImpl the UnivariateStatistic instance to use
* for computing the sum of squares
* @since 1.2
*/
public synchronized void setSumsqImpl(UnivariateStatistic sumsqImpl) {
this.sumsqImpl = sumsqImpl;
}
/**
* Returns the currently configured sum implementation.
*
* @return the UnivariateStatistic implementing the sum
* @since 1.2
*/
public synchronized UnivariateStatistic getSumImpl() {
return sumImpl;
}
/**
* <p>Sets the implementation for the sum.
*
* @param sumImpl the UnivariateStatistic instance to use
* for computing the sum
* @since 1.2
*/
public synchronized void setSumImpl(UnivariateStatistic sumImpl) {
this.sumImpl = sumImpl;
}
/**
* Returns a copy of this DescriptiveStatistics instance with the same internal state.
*
* @return a copy of this
*/
public DescriptiveStatistics copy() {
DescriptiveStatistics result = new DescriptiveStatistics();
// No try-catch or advertised exception because parms are guaranteed valid
copy(this, result);
return result;
}
/**
* Copies source to dest.
* <p>Neither source nor dest can be null.
*
* @param source DescriptiveStatistics to copy
* @param dest DescriptiveStatistics to copy to
* @throws NullArgumentException if either source or dest is null
*/
public static void copy(DescriptiveStatistics source, DescriptiveStatistics dest)
throws NullArgumentException {
MathUtils.checkNotNull(source);
MathUtils.checkNotNull(dest);
// Copy data and window size
dest.eDA = source.eDA.copy();
dest.windowSize = source.windowSize;
// Copy implementations
dest.maxImpl = source.maxImpl.copy();
dest.meanImpl = source.meanImpl.copy();
dest.minImpl = source.minImpl.copy();
dest.sumImpl = source.sumImpl.copy();
dest.varianceImpl = source.varianceImpl.copy();
dest.sumsqImpl = source.sumsqImpl.copy();
dest.geometricMeanImpl = source.geometricMeanImpl.copy();
dest.kurtosisImpl = source.kurtosisImpl;
dest.skewnessImpl = source.skewnessImpl;
dest.percentileImpl = source.percentileImpl;
}
}
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