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Tensorics is a java framework which uses a tensor as a central object. A tensor represents a set of values placed in an N-dimensional space. Wherever you are tempted to use maps of maps, a tensor might be a good choice ;-) Tensorics provides methods to create, transform and performing calculations with those tensors.

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/**
 * Copyright (c) 2016 European Organisation for Nuclear Research (CERN), All Rights Reserved.
 */

package org.tensorics.core.iterable.operations;

import static com.google.common.collect.Iterables.isEmpty;

import java.util.stream.Collectors;
import java.util.stream.StreamSupport;

import org.tensorics.core.math.ExtendedField;
import org.tensorics.core.scalar.lang.ScalarSupport;

/**
 * An operation that takes and iterable of a certain type of values (for which a field has to be provided) and
 * calculates the variance out of it.
 * 
 * For the definition of the variance, have a look at wikipedia.
 * 
 * @author caguiler
 * @param  the type of the scalars (elements of the field on which the variance will be based)
 */
public class IterableVar extends ScalarSupport implements IterableOperation {

    private final IterableAverage iterableAverage;
    private final ExtendedField field;

    public IterableVar(ExtendedField field) {
        super(field);
        this.field = field;
        this.iterableAverage = new IterableAverage<>(field);
    }

    @Override
    public V apply(Iterable values) {
        if (isEmpty(values)) {
            throw new IllegalArgumentException("variance of empty value set is not possible.");
        }

        final V average = iterableAverage.apply(values);

        // @formatter:off
        Iterable squaredDifferences = StreamSupport.stream(values.spliterator(), false)
           .map(v -> calculate(v).minus(average))
           .map(difference -> calculate(difference).toThePowerOf(field.two()))
          .collect(Collectors.toList());
        // @formatter:on

        return iterableAverage.apply(squaredDifferences);
    }
}