org.apache.commons.statistics.descriptive.IntMean Maven / Gradle / Ivy
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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.statistics.descriptive;
/**
* Computes the arithmetic mean of the available values. Uses the following definition
* of the sample mean:
*
* \[ \frac{1}{n} \sum_{i=1}^n x_i \]
*
*
where \( n \) is the number of samples.
*
*
* - The result is {@code NaN} if no values are added.
*
*
* This class uses an exact integer sum to compute the mean.
* Supports up to 263 (exclusive) observations.
* This implementation does not check for overflow of the count.
*
*
This class is designed to work with (though does not require)
* {@linkplain java.util.stream streams}.
*
*
This implementation is not thread safe.
* If multiple threads access an instance of this class concurrently,
* and at least one of the threads invokes the {@link java.util.function.IntConsumer#accept(int) accept} or
* {@link StatisticAccumulator#combine(StatisticResult) combine} method, it must be synchronized externally.
*
*
However, it is safe to use {@link java.util.function.IntConsumer#accept(int) accept}
* and {@link StatisticAccumulator#combine(StatisticResult) combine}
* as {@code accumulator} and {@code combiner} functions of
* {@link java.util.stream.Collector Collector} on a parallel stream,
* because the parallel implementation of {@link java.util.stream.Stream#collect Stream.collect()}
* provides the necessary partitioning, isolation, and merging of results for
* safe and efficient parallel execution.
*
* @see Mean (Wikipedia)
* @since 1.1 */
public final class IntMean implements IntStatistic, StatisticAccumulator {
/** Limit for small sample size where the sum can exactly map to a double.
* This is conservatively set using 2^21 values of 2^31 (2^21 ~ 2 million). */
private static final long SMALL_N = 1L << 21;
/** Sum of the values. */
private final Int128 sum;
/** Count of values that have been added. */
private long n;
/**
* Create an instance.
*/
private IntMean() {
this(Int128.create(), 0);
}
/**
* Create an instance.
*
* @param sum Sum of the values.
* @param n Count of values that have been added.
*/
private IntMean(Int128 sum, int n) {
this.sum = sum;
this.n = n;
}
/**
* Creates an instance.
*
* The initial result is {@code NaN}.
*
* @return {@code IntMean} instance.
*/
public static IntMean create() {
return new IntMean();
}
/**
* Returns an instance populated using the input {@code values}.
*
* @param values Values.
* @return {@code IntMean} instance.
*/
public static IntMean of(int... values) {
// Sum of an array cannot exceed a 64-bit long
long s = 0;
for (final int x : values) {
s += x;
}
// Convert
return new IntMean(Int128.of(s), values.length);
}
/**
* Updates the state of the statistic to reflect the addition of {@code value}.
*
* @param value Value.
*/
@Override
public void accept(int value) {
sum.add(value);
n++;
}
/**
* Gets the mean of all input values.
*
*
When no values have been added, the result is {@code NaN}.
*
* @return mean of all values.
*/
@Override
public double getAsDouble() {
return computeMean(sum, n);
}
/**
* Compute the mean.
*
*
This is a helper method used in higher order moments.
*
* @param sum Sum of the values.
* @param n Count of the values.
* @return the mean
*/
static double computeMean(Int128 sum, long n) {
// Fast option when the sum fits within
// the mantissa of a double.
// Handles n=0 as NaN
if (n < SMALL_N) {
return (double) sum.lo64() / n;
}
// Extended precision
return IntMath.divide(sum, n);
}
@Override
public IntMean combine(IntMean other) {
sum.add(other.sum);
n += other.n;
return this;
}
}