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With inspiration from other libraries
/*
* 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.util.Arrays;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.MathIllegalStateException;
import org.apache.commons.math3.linear.RealMatrix;
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.VectorialCovariance;
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.MathArrays;
import org.apache.commons.math3.util.Precision;
import org.apache.commons.math3.util.FastMath;
/**
* Computes summary statistics for a stream of n-tuples 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
* n-tuple streams.
*
* The {@link StorelessUnivariateStatistic} instances used to maintain
* summary state and compute statistics are configurable via setters.
* For example, the default implementation for the mean can be overridden by
* calling {@link #setMeanImpl(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.
*
* To compute statistics for a stream of n-tuples, construct a
* MultivariateStatistics instance with dimension n and then use
* {@link #addValue(double[])} to add n-tuples. The getXxx
* methods where Xxx is a statistic return an array of double
* values, where for i = 0,...,n-1 the ith array element is the
* value of the given statistic for data range consisting of the ith element of
* each of the input n-tuples. For example, if addValue is called
* with actual parameters {0, 1, 2}, then {3, 4, 5} and finally {6, 7, 8},
* getSum will return a three-element array with values
* {0+3+6, 1+4+7, 2+5+8}
*
* Note: This class is not thread-safe. Use
* {@link SynchronizedMultivariateSummaryStatistics} if concurrent access from multiple
* threads is required.
*
* @since 1.2
*/
public class MultivariateSummaryStatistics
implements StatisticalMultivariateSummary, Serializable {
/** Serialization UID */
private static final long serialVersionUID = 2271900808994826718L;
/** Dimension of the data. */
private int k;
/** Count of values that have been added */
private long n = 0;
/** Sum statistic implementation - can be reset by setter. */
private StorelessUnivariateStatistic[] sumImpl;
/** Sum of squares statistic implementation - can be reset by setter. */
private StorelessUnivariateStatistic[] sumSqImpl;
/** Minimum statistic implementation - can be reset by setter. */
private StorelessUnivariateStatistic[] minImpl;
/** Maximum statistic implementation - can be reset by setter. */
private StorelessUnivariateStatistic[] maxImpl;
/** Sum of log statistic implementation - can be reset by setter. */
private StorelessUnivariateStatistic[] sumLogImpl;
/** Geometric mean statistic implementation - can be reset by setter. */
private StorelessUnivariateStatistic[] geoMeanImpl;
/** Mean statistic implementation - can be reset by setter. */
private StorelessUnivariateStatistic[] meanImpl;
/** Covariance statistic implementation - cannot be reset. */
private VectorialCovariance covarianceImpl;
/**
* Construct a MultivariateSummaryStatistics instance
* @param k dimension of the data
* @param isCovarianceBiasCorrected if true, the unbiased sample
* covariance is computed, otherwise the biased population covariance
* is computed
*/
public MultivariateSummaryStatistics(int k, boolean isCovarianceBiasCorrected) {
this.k = k;
sumImpl = new StorelessUnivariateStatistic[k];
sumSqImpl = new StorelessUnivariateStatistic[k];
minImpl = new StorelessUnivariateStatistic[k];
maxImpl = new StorelessUnivariateStatistic[k];
sumLogImpl = new StorelessUnivariateStatistic[k];
geoMeanImpl = new StorelessUnivariateStatistic[k];
meanImpl = new StorelessUnivariateStatistic[k];
for (int i = 0; i < k; ++i) {
sumImpl[i] = new Sum();
sumSqImpl[i] = new SumOfSquares();
minImpl[i] = new Min();
maxImpl[i] = new Max();
sumLogImpl[i] = new SumOfLogs();
geoMeanImpl[i] = new GeometricMean();
meanImpl[i] = new Mean();
}
covarianceImpl =
new VectorialCovariance(k, isCovarianceBiasCorrected);
}
/**
* Add an n-tuple to the data
*
* @param value the n-tuple to add
* @throws DimensionMismatchException if the length of the array
* does not match the one used at construction
*/
public void addValue(double[] value) throws DimensionMismatchException {
checkDimension(value.length);
for (int i = 0; i < k; ++i) {
double v = value[i];
sumImpl[i].increment(v);
sumSqImpl[i].increment(v);
minImpl[i].increment(v);
maxImpl[i].increment(v);
sumLogImpl[i].increment(v);
geoMeanImpl[i].increment(v);
meanImpl[i].increment(v);
}
covarianceImpl.increment(value);
n++;
}
/**
* Returns the dimension of the data
* @return The dimension of the data
*/
public int getDimension() {
return k;
}
/**
* Returns the number of available values
* @return The number of available values
*/
public long getN() {
return n;
}
/**
* Returns an array of the results of a statistic.
* @param stats univariate statistic array
* @return results array
*/
private double[] getResults(StorelessUnivariateStatistic[] stats) {
double[] results = new double[stats.length];
for (int i = 0; i < results.length; ++i) {
results[i] = stats[i].getResult();
}
return results;
}
/**
* Returns an array whose ith entry is the sum of the
* ith entries of the arrays that have been added using
* {@link #addValue(double[])}
*
* @return the array of component sums
*/
public double[] getSum() {
return getResults(sumImpl);
}
/**
* Returns an array whose ith entry is the sum of squares of the
* ith entries of the arrays that have been added using
* {@link #addValue(double[])}
*
* @return the array of component sums of squares
*/
public double[] getSumSq() {
return getResults(sumSqImpl);
}
/**
* Returns an array whose ith entry is the sum of logs of the
* ith entries of the arrays that have been added using
* {@link #addValue(double[])}
*
* @return the array of component log sums
*/
public double[] getSumLog() {
return getResults(sumLogImpl);
}
/**
* Returns an array whose ith entry is the mean of the
* ith entries of the arrays that have been added using
* {@link #addValue(double[])}
*
* @return the array of component means
*/
public double[] getMean() {
return getResults(meanImpl);
}
/**
* Returns an array whose ith entry is the standard deviation of the
* ith entries of the arrays that have been added using
* {@link #addValue(double[])}
*
* @return the array of component standard deviations
*/
public double[] getStandardDeviation() {
double[] stdDev = new double[k];
if (getN() < 1) {
Arrays.fill(stdDev, Double.NaN);
} else if (getN() < 2) {
Arrays.fill(stdDev, 0.0);
} else {
RealMatrix matrix = covarianceImpl.getResult();
for (int i = 0; i < k; ++i) {
stdDev[i] = FastMath.sqrt(matrix.getEntry(i, i));
}
}
return stdDev;
}
/**
* Returns the covariance matrix of the values that have been added.
*
* @return the covariance matrix
*/
public RealMatrix getCovariance() {
return covarianceImpl.getResult();
}
/**
* Returns an array whose ith entry is the maximum of the
* ith entries of the arrays that have been added using
* {@link #addValue(double[])}
*
* @return the array of component maxima
*/
public double[] getMax() {
return getResults(maxImpl);
}
/**
* Returns an array whose ith entry is the minimum of the
* ith entries of the arrays that have been added using
* {@link #addValue(double[])}
*
* @return the array of component minima
*/
public double[] getMin() {
return getResults(minImpl);
}
/**
* Returns an array whose ith entry is the geometric mean of the
* ith entries of the arrays that have been added using
* {@link #addValue(double[])}
*
* @return the array of component geometric means
*/
public double[] getGeometricMean() {
return getResults(geoMeanImpl);
}
/**
* Generates a text report displaying
* summary statistics from values that
* have been added.
* @return String with line feeds displaying statistics
*/
@Override
public String toString() {
final String separator = ", ";
final String suffix = System.getProperty("line.separator");
StringBuilder outBuffer = new StringBuilder();
outBuffer.append("MultivariateSummaryStatistics:" + suffix);
outBuffer.append("n: " + getN() + suffix);
append(outBuffer, getMin(), "min: ", separator, suffix);
append(outBuffer, getMax(), "max: ", separator, suffix);
append(outBuffer, getMean(), "mean: ", separator, suffix);
append(outBuffer, getGeometricMean(), "geometric mean: ", separator, suffix);
append(outBuffer, getSumSq(), "sum of squares: ", separator, suffix);
append(outBuffer, getSumLog(), "sum of logarithms: ", separator, suffix);
append(outBuffer, getStandardDeviation(), "standard deviation: ", separator, suffix);
outBuffer.append("covariance: " + getCovariance().toString() + suffix);
return outBuffer.toString();
}
/**
* Append a text representation of an array to a buffer.
* @param buffer buffer to fill
* @param data data array
* @param prefix text prefix
* @param separator elements separator
* @param suffix text suffix
*/
private void append(StringBuilder buffer, double[] data,
String prefix, String separator, String suffix) {
buffer.append(prefix);
for (int i = 0; i < data.length; ++i) {
if (i > 0) {
buffer.append(separator);
}
buffer.append(data[i]);
}
buffer.append(suffix);
}
/**
* Resets all statistics and storage
*/
public void clear() {
this.n = 0;
for (int i = 0; i < k; ++i) {
minImpl[i].clear();
maxImpl[i].clear();
sumImpl[i].clear();
sumLogImpl[i].clear();
sumSqImpl[i].clear();
geoMeanImpl[i].clear();
meanImpl[i].clear();
}
covarianceImpl.clear();
}
/**
* Returns true iff object is a MultivariateSummaryStatistics
* 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 MultivariateSummaryStatistics == false) {
return false;
}
MultivariateSummaryStatistics stat = (MultivariateSummaryStatistics) object;
return MathArrays.equalsIncludingNaN(stat.getGeometricMean(), getGeometricMean()) &&
MathArrays.equalsIncludingNaN(stat.getMax(), getMax()) &&
MathArrays.equalsIncludingNaN(stat.getMean(), getMean()) &&
MathArrays.equalsIncludingNaN(stat.getMin(), getMin()) &&
Precision.equalsIncludingNaN(stat.getN(), getN()) &&
MathArrays.equalsIncludingNaN(stat.getSum(), getSum()) &&
MathArrays.equalsIncludingNaN(stat.getSumSq(), getSumSq()) &&
MathArrays.equalsIncludingNaN(stat.getSumLog(), getSumLog()) &&
stat.getCovariance().equals( getCovariance());
}
/**
* 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(getSumLog());
result = result * 31 + getCovariance().hashCode();
return result;
}
// Getters and setters for statistics implementations
/**
* Sets statistics implementations.
* @param newImpl new implementations for statistics
* @param oldImpl old implementations for statistics
* @throws DimensionMismatchException if the array dimension
* does not match the one used at construction
* @throws MathIllegalStateException if data has already been added
* (i.e. if n > 0)
*/
private void setImpl(StorelessUnivariateStatistic[] newImpl,
StorelessUnivariateStatistic[] oldImpl) throws MathIllegalStateException,
DimensionMismatchException {
checkEmpty();
checkDimension(newImpl.length);
System.arraycopy(newImpl, 0, oldImpl, 0, newImpl.length);
}
/**
* Returns the currently configured Sum implementation
*
* @return the StorelessUnivariateStatistic implementing the sum
*/
public StorelessUnivariateStatistic[] getSumImpl() {
return sumImpl.clone();
}
/**
* Sets the implementation for the Sum.
* This method must be activated before any data has been added - i.e.,
* before {@link #addValue(double[]) addValue} has been used to add data;
* otherwise an IllegalStateException will be thrown.
*
* @param sumImpl the StorelessUnivariateStatistic instance to use
* for computing the Sum
* @throws DimensionMismatchException if the array dimension
* does not match the one used at construction
* @throws MathIllegalStateException if data has already been added
* (i.e if n > 0)
*/
public void setSumImpl(StorelessUnivariateStatistic[] sumImpl)
throws MathIllegalStateException, DimensionMismatchException {
setImpl(sumImpl, this.sumImpl);
}
/**
* Returns the currently configured sum of squares implementation
*
* @return the StorelessUnivariateStatistic implementing the sum of squares
*/
public StorelessUnivariateStatistic[] getSumsqImpl() {
return sumSqImpl.clone();
}
/**
* Sets the implementation for the sum of squares.
* This method must be activated before any data has been added - i.e.,
* before {@link #addValue(double[]) addValue} has been used to add data;
* otherwise an IllegalStateException will be thrown.
*
* @param sumsqImpl the StorelessUnivariateStatistic instance to use
* for computing the sum of squares
* @throws DimensionMismatchException if the array dimension
* does not match the one used at construction
* @throws MathIllegalStateException if data has already been added
* (i.e if n > 0)
*/
public void setSumsqImpl(StorelessUnivariateStatistic[] sumsqImpl)
throws MathIllegalStateException, DimensionMismatchException {
setImpl(sumsqImpl, this.sumSqImpl);
}
/**
* Returns the currently configured minimum implementation
*
* @return the StorelessUnivariateStatistic implementing the minimum
*/
public StorelessUnivariateStatistic[] getMinImpl() {
return minImpl.clone();
}
/**
* Sets the implementation for the minimum.
* This method must be activated before any data has been added - i.e.,
* before {@link #addValue(double[]) addValue} has been used to add data;
* otherwise an IllegalStateException will be thrown.
*
* @param minImpl the StorelessUnivariateStatistic instance to use
* for computing the minimum
* @throws DimensionMismatchException if the array dimension
* does not match the one used at construction
* @throws MathIllegalStateException if data has already been added
* (i.e if n > 0)
*/
public void setMinImpl(StorelessUnivariateStatistic[] minImpl)
throws MathIllegalStateException, DimensionMismatchException {
setImpl(minImpl, this.minImpl);
}
/**
* Returns the currently configured maximum implementation
*
* @return the StorelessUnivariateStatistic implementing the maximum
*/
public StorelessUnivariateStatistic[] getMaxImpl() {
return maxImpl.clone();
}
/**
* Sets the implementation for the maximum.
* This method must be activated before any data has been added - i.e.,
* before {@link #addValue(double[]) addValue} has been used to add data;
* otherwise an IllegalStateException will be thrown.
*
* @param maxImpl the StorelessUnivariateStatistic instance to use
* for computing the maximum
* @throws DimensionMismatchException if the array dimension
* does not match the one used at construction
* @throws MathIllegalStateException if data has already been added
* (i.e if n > 0)
*/
public void setMaxImpl(StorelessUnivariateStatistic[] maxImpl)
throws MathIllegalStateException, DimensionMismatchException{
setImpl(maxImpl, this.maxImpl);
}
/**
* Returns the currently configured sum of logs implementation
*
* @return the StorelessUnivariateStatistic implementing the log sum
*/
public StorelessUnivariateStatistic[] getSumLogImpl() {
return sumLogImpl.clone();
}
/**
* Sets the implementation for the sum of logs.
* This method must be activated before any data has been added - i.e.,
* before {@link #addValue(double[]) addValue} has been used to add data;
* otherwise an IllegalStateException will be thrown.
*
* @param sumLogImpl the StorelessUnivariateStatistic instance to use
* for computing the log sum
* @throws DimensionMismatchException if the array dimension
* does not match the one used at construction
* @throws MathIllegalStateException if data has already been added
* (i.e if n > 0)
*/
public void setSumLogImpl(StorelessUnivariateStatistic[] sumLogImpl)
throws MathIllegalStateException, DimensionMismatchException{
setImpl(sumLogImpl, this.sumLogImpl);
}
/**
* Returns the currently configured geometric mean implementation
*
* @return the StorelessUnivariateStatistic implementing the geometric mean
*/
public StorelessUnivariateStatistic[] getGeoMeanImpl() {
return geoMeanImpl.clone();
}
/**
* Sets the implementation for the geometric mean.
* This method must be activated before any data has been added - i.e.,
* before {@link #addValue(double[]) addValue} has been used to add data;
* otherwise an IllegalStateException will be thrown.
*
* @param geoMeanImpl the StorelessUnivariateStatistic instance to use
* for computing the geometric mean
* @throws DimensionMismatchException if the array dimension
* does not match the one used at construction
* @throws MathIllegalStateException if data has already been added
* (i.e if n > 0)
*/
public void setGeoMeanImpl(StorelessUnivariateStatistic[] geoMeanImpl)
throws MathIllegalStateException, DimensionMismatchException {
setImpl(geoMeanImpl, this.geoMeanImpl);
}
/**
* Returns the currently configured mean implementation
*
* @return the StorelessUnivariateStatistic implementing the mean
*/
public StorelessUnivariateStatistic[] getMeanImpl() {
return meanImpl.clone();
}
/**
* Sets the implementation for the mean.
* This method must be activated before any data has been added - i.e.,
* before {@link #addValue(double[]) addValue} has been used to add data;
* otherwise an IllegalStateException will be thrown.
*
* @param meanImpl the StorelessUnivariateStatistic instance to use
* for computing the mean
* @throws DimensionMismatchException if the array dimension
* does not match the one used at construction
* @throws MathIllegalStateException if data has already been added
* (i.e if n > 0)
*/
public void setMeanImpl(StorelessUnivariateStatistic[] meanImpl)
throws MathIllegalStateException, DimensionMismatchException{
setImpl(meanImpl, this.meanImpl);
}
/**
* Throws MathIllegalStateException if the statistic is not empty.
* @throws MathIllegalStateException if n > 0.
*/
private void checkEmpty() throws MathIllegalStateException {
if (n > 0) {
throw new MathIllegalStateException(
LocalizedFormats.VALUES_ADDED_BEFORE_CONFIGURING_STATISTIC, n);
}
}
/**
* Throws DimensionMismatchException if dimension != k.
* @param dimension dimension to check
* @throws DimensionMismatchException if dimension != k
*/
private void checkDimension(int dimension) throws DimensionMismatchException {
if (dimension != k) {
throw new DimensionMismatchException(dimension, k);
}
}
}