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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.
*/
package org.apache.commons.math3.stat.descriptive;
import java.io.Serializable;
import org.apache.commons.math3.exception.MathIllegalStateException;
import org.apache.commons.math3.exception.NullArgumentException;
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.Mean;
import org.apache.commons.math3.stat.descriptive.moment.SecondMoment;
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.summary.Sum;
import org.apache.commons.math3.stat.descriptive.summary.SumOfLogs;
import org.apache.commons.math3.stat.descriptive.summary.SumOfSquares;
import org.apache.commons.math3.util.MathUtils;
import org.apache.commons.math3.util.Precision;
import org.apache.commons.math3.util.FastMath;
/**
*
* Computes summary statistics for a stream of data values added using the
* {@link #addValue(double) addValue} method. The data values are not stored in
* memory, so this class can be used to compute statistics for very large data
* streams.
*
*
* The {@link StorelessUnivariateStatistic} instances used to maintain summary
* state and compute statistics are configurable via setters. For example, the
* default implementation for the variance can be overridden by calling
* {@link #setVarianceImpl(StorelessUnivariateStatistic)}. Actual parameters to
* these methods must implement the {@link StorelessUnivariateStatistic}
* interface and configuration must be completed before addValue
* is called. No configuration is necessary to use the default, commons-math
* provided implementations.
*
*
* Note: This class is not thread-safe. Use
* {@link SynchronizedSummaryStatistics} if concurrent access from multiple
* threads is required.
*
*/
public class SummaryStatistics implements StatisticalSummary, Serializable {
/** Serialization UID */
private static final long serialVersionUID = -2021321786743555871L;
/** count of values that have been added */
private long n = 0;
/** SecondMoment is used to compute the mean and variance */
private SecondMoment secondMoment = new SecondMoment();
/** sum of values that have been added */
private Sum sum = new Sum();
/** sum of the square of each value that has been added */
private SumOfSquares sumsq = new SumOfSquares();
/** min of values that have been added */
private Min min = new Min();
/** max of values that have been added */
private Max max = new Max();
/** sumLog of values that have been added */
private SumOfLogs sumLog = new SumOfLogs();
/** geoMean of values that have been added */
private GeometricMean geoMean = new GeometricMean(sumLog);
/** mean of values that have been added */
private Mean mean = new Mean(secondMoment);
/** variance of values that have been added */
private Variance variance = new Variance(secondMoment);
/** Sum statistic implementation - can be reset by setter. */
private StorelessUnivariateStatistic sumImpl = sum;
/** Sum of squares statistic implementation - can be reset by setter. */
private StorelessUnivariateStatistic sumsqImpl = sumsq;
/** Minimum statistic implementation - can be reset by setter. */
private StorelessUnivariateStatistic minImpl = min;
/** Maximum statistic implementation - can be reset by setter. */
private StorelessUnivariateStatistic maxImpl = max;
/** Sum of log statistic implementation - can be reset by setter. */
private StorelessUnivariateStatistic sumLogImpl = sumLog;
/** Geometric mean statistic implementation - can be reset by setter. */
private StorelessUnivariateStatistic geoMeanImpl = geoMean;
/** Mean statistic implementation - can be reset by setter. */
private StorelessUnivariateStatistic meanImpl = mean;
/** Variance statistic implementation - can be reset by setter. */
private StorelessUnivariateStatistic varianceImpl = variance;
/**
* Construct a SummaryStatistics instance
*/
public SummaryStatistics() {
}
/**
* A copy constructor. Creates a deep-copy of the {@code original}.
*
* @param original the {@code SummaryStatistics} instance to copy
* @throws NullArgumentException if original is null
*/
public SummaryStatistics(SummaryStatistics original) throws NullArgumentException {
copy(original, this);
}
/**
* Return a {@link StatisticalSummaryValues} instance reporting current
* statistics.
* @return Current values of statistics
*/
public StatisticalSummary getSummary() {
return new StatisticalSummaryValues(getMean(), getVariance(), getN(),
getMax(), getMin(), getSum());
}
/**
* Add a value to the data
* @param value the value to add
*/
public void addValue(double value) {
sumImpl.increment(value);
sumsqImpl.increment(value);
minImpl.increment(value);
maxImpl.increment(value);
sumLogImpl.increment(value);
secondMoment.increment(value);
// If mean, variance or geomean have been overridden,
// need to increment these
if (meanImpl != mean) {
meanImpl.increment(value);
}
if (varianceImpl != variance) {
varianceImpl.increment(value);
}
if (geoMeanImpl != geoMean) {
geoMeanImpl.increment(value);
}
n++;
}
/**
* Returns the number of available values
* @return The number of available values
*/
public long getN() {
return n;
}
/**
* Returns the sum of the values that have been added
* @return The sum or Double.NaN if no values have been added
*/
public double getSum() {
return sumImpl.getResult();
}
/**
* Returns the sum of the squares of the values that have been added.
*
* Double.NaN is returned if no values have been added.
*
* @return The sum of squares
*/
public double getSumsq() {
return sumsqImpl.getResult();
}
/**
* Returns the mean of the values that have been added.
*
* Double.NaN is returned if no values have been added.
*
* @return the mean
*/
public double getMean() {
return meanImpl.getResult();
}
/**
* Returns the standard deviation of the values that have been added.
*
* Double.NaN is returned if no values have been added.
*
* @return the standard deviation
*/
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.
*
* root-mean-square of the available values
* @return The quadratic mean or {@code Double.NaN} if no values
* have been added.
*/
public double getQuadraticMean() {
final long size = getN();
return size > 0 ? FastMath.sqrt(getSumsq() / size) : Double.NaN;
}
/**
* Returns the (sample) variance of the available values.
*
* 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.
*
* Double.NaN is returned if no values have been added.
*
* @return the variance
*/
public double getVariance() {
return varianceImpl.getResult();
}
/**
* Returns the
* population variance of the values that have been added.
*
* Double.NaN is returned if no values have been added.
*
* @return the population variance
*/
public double getPopulationVariance() {
Variance populationVariance = new Variance(secondMoment);
populationVariance.setBiasCorrected(false);
return populationVariance.getResult();
}
/**
* Returns the maximum of the values that have been added.
*
* Double.NaN is returned if no values have been added.
*
* @return the maximum
*/
public double getMax() {
return maxImpl.getResult();
}
/**
* Returns the minimum of the values that have been added.
*
* Double.NaN is returned if no values have been added.
*
* @return the minimum
*/
public double getMin() {
return minImpl.getResult();
}
/**
* Returns the geometric mean of the values that have been added.
*
* Double.NaN is returned if no values have been added.
*
* @return the geometric mean
*/
public double getGeometricMean() {
return geoMeanImpl.getResult();
}
/**
* Returns the sum of the logs of the values that have been added.
*
* Double.NaN is returned if no values have been added.
*
* @return the sum of logs
* @since 1.2
*/
public double getSumOfLogs() {
return sumLogImpl.getResult();
}
/**
* Returns a statistic related to the Second Central Moment. Specifically,
* what is returned is the sum of squared deviations from the sample mean
* among the values that have been added.
*
* Returns Double.NaN if no data values have been added and
* returns 0 if there is just one value in the data set.
*
* @return second central moment statistic
* @since 2.0
*/
public double getSecondMoment() {
return secondMoment.getResult();
}
/**
* Generates a text report displaying summary statistics from values that
* have been added.
* @return String with line feeds displaying statistics
* @since 1.2
*/
@Override
public String toString() {
StringBuilder outBuffer = new StringBuilder();
String endl = "\n";
outBuffer.append("SummaryStatistics:").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("sum: ").append(getSum()).append(endl);
outBuffer.append("mean: ").append(getMean()).append(endl);
outBuffer.append("geometric mean: ").append(getGeometricMean())
.append(endl);
outBuffer.append("variance: ").append(getVariance()).append(endl);
outBuffer.append("population variance: ").append(getPopulationVariance()).append(endl);
outBuffer.append("second moment: ").append(getSecondMoment()).append(endl);
outBuffer.append("sum of squares: ").append(getSumsq()).append(endl);
outBuffer.append("standard deviation: ").append(getStandardDeviation())
.append(endl);
outBuffer.append("sum of logs: ").append(getSumOfLogs()).append(endl);
return outBuffer.toString();
}
/**
* Resets all statistics and storage
*/
public void clear() {
this.n = 0;
minImpl.clear();
maxImpl.clear();
sumImpl.clear();
sumLogImpl.clear();
sumsqImpl.clear();
geoMeanImpl.clear();
secondMoment.clear();
if (meanImpl != mean) {
meanImpl.clear();
}
if (varianceImpl != variance) {
varianceImpl.clear();
}
}
/**
* Returns true iff object is a
* SummaryStatistics instance and all statistics have the
* same values as this.
* @param object the object to test equality against.
* @return true if object equals this
*/
@Override
public boolean equals(Object object) {
if (object == this) {
return true;
}
if (object instanceof SummaryStatistics == false) {
return false;
}
SummaryStatistics stat = (SummaryStatistics)object;
return Precision.equalsIncludingNaN(stat.getGeometricMean(), getGeometricMean()) &&
Precision.equalsIncludingNaN(stat.getMax(), getMax()) &&
Precision.equalsIncludingNaN(stat.getMean(), getMean()) &&
Precision.equalsIncludingNaN(stat.getMin(), getMin()) &&
Precision.equalsIncludingNaN(stat.getN(), getN()) &&
Precision.equalsIncludingNaN(stat.getSum(), getSum()) &&
Precision.equalsIncludingNaN(stat.getSumsq(), getSumsq()) &&
Precision.equalsIncludingNaN(stat.getVariance(), getVariance());
}
/**
* Returns hash code based on values of statistics
* @return hash code
*/
@Override
public int hashCode() {
int result = 31 + MathUtils.hash(getGeometricMean());
result = result * 31 + MathUtils.hash(getGeometricMean());
result = result * 31 + MathUtils.hash(getMax());
result = result * 31 + MathUtils.hash(getMean());
result = result * 31 + MathUtils.hash(getMin());
result = result * 31 + MathUtils.hash(getN());
result = result * 31 + MathUtils.hash(getSum());
result = result * 31 + MathUtils.hash(getSumsq());
result = result * 31 + MathUtils.hash(getVariance());
return result;
}
// Getters and setters for statistics implementations
/**
* Returns the currently configured Sum implementation
* @return the StorelessUnivariateStatistic implementing the sum
* @since 1.2
*/
public StorelessUnivariateStatistic getSumImpl() {
return sumImpl;
}
/**
*
* Sets the implementation for the Sum.
*
*
* This method cannot be activated after data has been added - i.e.,
* after {@link #addValue(double) addValue} has been used to add data.
* If it is activated after data has been added, an IllegalStateException
* will be thrown.
*
* @param sumImpl the StorelessUnivariateStatistic instance to use for
* computing the Sum
* @throws MathIllegalStateException if data has already been added (i.e if n >0)
* @since 1.2
*/
public void setSumImpl(StorelessUnivariateStatistic sumImpl)
throws MathIllegalStateException {
checkEmpty();
this.sumImpl = sumImpl;
}
/**
* Returns the currently configured sum of squares implementation
* @return the StorelessUnivariateStatistic implementing the sum of squares
* @since 1.2
*/
public StorelessUnivariateStatistic getSumsqImpl() {
return sumsqImpl;
}
/**
*
* Sets the implementation for the sum of squares.
*
*
* This method cannot be activated after data has been added - i.e.,
* after {@link #addValue(double) addValue} has been used to add data.
* If it is activated after data has been added, an IllegalStateException
* will be thrown.
*
* @param sumsqImpl the StorelessUnivariateStatistic instance to use for
* computing the sum of squares
* @throws MathIllegalStateException if data has already been added (i.e if n > 0)
* @since 1.2
*/
public void setSumsqImpl(StorelessUnivariateStatistic sumsqImpl)
throws MathIllegalStateException {
checkEmpty();
this.sumsqImpl = sumsqImpl;
}
/**
* Returns the currently configured minimum implementation
* @return the StorelessUnivariateStatistic implementing the minimum
* @since 1.2
*/
public StorelessUnivariateStatistic getMinImpl() {
return minImpl;
}
/**
*
* Sets the implementation for the minimum.
*
*
* This method cannot be activated after data has been added - i.e.,
* after {@link #addValue(double) addValue} has been used to add data.
* If it is activated after data has been added, an IllegalStateException
* will be thrown.
*
* @param minImpl the StorelessUnivariateStatistic instance to use for
* computing the minimum
* @throws MathIllegalStateException if data has already been added (i.e if n > 0)
* @since 1.2
*/
public void setMinImpl(StorelessUnivariateStatistic minImpl)
throws MathIllegalStateException {
checkEmpty();
this.minImpl = minImpl;
}
/**
* Returns the currently configured maximum implementation
* @return the StorelessUnivariateStatistic implementing the maximum
* @since 1.2
*/
public StorelessUnivariateStatistic getMaxImpl() {
return maxImpl;
}
/**
*
* Sets the implementation for the maximum.
*
*
* This method cannot be activated after data has been added - i.e.,
* after {@link #addValue(double) addValue} has been used to add data.
* If it is activated after data has been added, an IllegalStateException
* will be thrown.
*
* @param maxImpl the StorelessUnivariateStatistic instance to use for
* computing the maximum
* @throws MathIllegalStateException if data has already been added (i.e if n > 0)
* @since 1.2
*/
public void setMaxImpl(StorelessUnivariateStatistic maxImpl)
throws MathIllegalStateException {
checkEmpty();
this.maxImpl = maxImpl;
}
/**
* Returns the currently configured sum of logs implementation
* @return the StorelessUnivariateStatistic implementing the log sum
* @since 1.2
*/
public StorelessUnivariateStatistic getSumLogImpl() {
return sumLogImpl;
}
/**
*
* Sets the implementation for the sum of logs.
*
*
* This method cannot be activated after data has been added - i.e.,
* after {@link #addValue(double) addValue} has been used to add data.
* If it is activated after data has been added, an IllegalStateException
* will be thrown.
*
* @param sumLogImpl the StorelessUnivariateStatistic instance to use for
* computing the log sum
* @throws MathIllegalStateException if data has already been added (i.e if n > 0)
* @since 1.2
*/
public void setSumLogImpl(StorelessUnivariateStatistic sumLogImpl)
throws MathIllegalStateException {
checkEmpty();
this.sumLogImpl = sumLogImpl;
geoMean.setSumLogImpl(sumLogImpl);
}
/**
* Returns the currently configured geometric mean implementation
* @return the StorelessUnivariateStatistic implementing the geometric mean
* @since 1.2
*/
public StorelessUnivariateStatistic getGeoMeanImpl() {
return geoMeanImpl;
}
/**
*
* Sets the implementation for the geometric mean.
*
*
* This method cannot be activated after data has been added - i.e.,
* after {@link #addValue(double) addValue} has been used to add data.
* If it is activated after data has been added, an IllegalStateException
* will be thrown.
*
* @param geoMeanImpl the StorelessUnivariateStatistic instance to use for
* computing the geometric mean
* @throws MathIllegalStateException if data has already been added (i.e if n > 0)
* @since 1.2
*/
public void setGeoMeanImpl(StorelessUnivariateStatistic geoMeanImpl)
throws MathIllegalStateException {
checkEmpty();
this.geoMeanImpl = geoMeanImpl;
}
/**
* Returns the currently configured mean implementation
* @return the StorelessUnivariateStatistic implementing the mean
* @since 1.2
*/
public StorelessUnivariateStatistic getMeanImpl() {
return meanImpl;
}
/**
*
* Sets the implementation for the mean.
*
*
* This method cannot be activated after data has been added - i.e.,
* after {@link #addValue(double) addValue} has been used to add data.
* If it is activated after data has been added, an IllegalStateException
* will be thrown.
*
* @param meanImpl the StorelessUnivariateStatistic instance to use for
* computing the mean
* @throws MathIllegalStateException if data has already been added (i.e if n > 0)
* @since 1.2
*/
public void setMeanImpl(StorelessUnivariateStatistic meanImpl)
throws MathIllegalStateException {
checkEmpty();
this.meanImpl = meanImpl;
}
/**
* Returns the currently configured variance implementation
* @return the StorelessUnivariateStatistic implementing the variance
* @since 1.2
*/
public StorelessUnivariateStatistic getVarianceImpl() {
return varianceImpl;
}
/**
*
* Sets the implementation for the variance.
*
*
* This method cannot be activated after data has been added - i.e.,
* after {@link #addValue(double) addValue} has been used to add data.
* If it is activated after data has been added, an IllegalStateException
* will be thrown.
*
* @param varianceImpl the StorelessUnivariateStatistic instance to use for
* computing the variance
* @throws MathIllegalStateException if data has already been added (i.e if n > 0)
* @since 1.2
*/
public void setVarianceImpl(StorelessUnivariateStatistic varianceImpl)
throws MathIllegalStateException {
checkEmpty();
this.varianceImpl = varianceImpl;
}
/**
* Throws IllegalStateException if n > 0.
* @throws MathIllegalStateException if data has been added
*/
private void checkEmpty() throws MathIllegalStateException {
if (n > 0) {
throw new MathIllegalStateException(
LocalizedFormats.VALUES_ADDED_BEFORE_CONFIGURING_STATISTIC, n);
}
}
/**
* Returns a copy of this SummaryStatistics instance with the same internal state.
*
* @return a copy of this
*/
public SummaryStatistics copy() {
SummaryStatistics result = new SummaryStatistics();
// No try-catch or advertised exception because arguments are guaranteed non-null
copy(this, result);
return result;
}
/**
* Copies source to dest.
* Neither source nor dest can be null.
*
* @param source SummaryStatistics to copy
* @param dest SummaryStatistics to copy to
* @throws NullArgumentException if either source or dest is null
*/
public static void copy(SummaryStatistics source, SummaryStatistics dest)
throws NullArgumentException {
MathUtils.checkNotNull(source);
MathUtils.checkNotNull(dest);
dest.maxImpl = source.maxImpl.copy();
dest.minImpl = source.minImpl.copy();
dest.sumImpl = source.sumImpl.copy();
dest.sumLogImpl = source.sumLogImpl.copy();
dest.sumsqImpl = source.sumsqImpl.copy();
dest.secondMoment = source.secondMoment.copy();
dest.n = source.n;
// Keep commons-math supplied statistics with embedded moments in synch
if (source.getVarianceImpl() instanceof Variance) {
dest.varianceImpl = new Variance(dest.secondMoment);
} else {
dest.varianceImpl = source.varianceImpl.copy();
}
if (source.meanImpl instanceof Mean) {
dest.meanImpl = new Mean(dest.secondMoment);
} else {
dest.meanImpl = source.meanImpl.copy();
}
if (source.getGeoMeanImpl() instanceof GeometricMean) {
dest.geoMeanImpl = new GeometricMean((SumOfLogs) dest.sumLogImpl);
} else {
dest.geoMeanImpl = source.geoMeanImpl.copy();
}
// Make sure that if stat == statImpl in source, same
// holds in dest; otherwise copy stat
if (source.geoMean == source.geoMeanImpl) {
dest.geoMean = (GeometricMean) dest.geoMeanImpl;
} else {
GeometricMean.copy(source.geoMean, dest.geoMean);
}
if (source.max == source.maxImpl) {
dest.max = (Max) dest.maxImpl;
} else {
Max.copy(source.max, dest.max);
}
if (source.mean == source.meanImpl) {
dest.mean = (Mean) dest.meanImpl;
} else {
Mean.copy(source.mean, dest.mean);
}
if (source.min == source.minImpl) {
dest.min = (Min) dest.minImpl;
} else {
Min.copy(source.min, dest.min);
}
if (source.sum == source.sumImpl) {
dest.sum = (Sum) dest.sumImpl;
} else {
Sum.copy(source.sum, dest.sum);
}
if (source.variance == source.varianceImpl) {
dest.variance = (Variance) dest.varianceImpl;
} else {
Variance.copy(source.variance, dest.variance);
}
if (source.sumLog == source.sumLogImpl) {
dest.sumLog = (SumOfLogs) dest.sumLogImpl;
} else {
SumOfLogs.copy(source.sumLog, dest.sumLog);
}
if (source.sumsq == source.sumsqImpl) {
dest.sumsq = (SumOfSquares) dest.sumsqImpl;
} else {
SumOfSquares.copy(source.sumsq, dest.sumsq);
}
}
}