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129 lines
3.5 KiB
C++
129 lines
3.5 KiB
C++
/*
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* Copyright (c) 2021, Tobias Christiansen <tobyase@serenityos.org>
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*
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* SPDX-License-Identifier: BSD-2-Clause
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*/
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#pragma once
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#include <AK/Concepts.h>
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#include <AK/Math.h>
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#include <AK/QuickSelect.h>
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#include <AK/QuickSort.h>
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#include <AK/Vector.h>
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namespace AK {
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static constexpr int ODD_NAIVE_MEDIAN_CUTOFF = 200;
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static constexpr int EVEN_NAIVE_MEDIAN_CUTOFF = 350;
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template<Arithmetic T = float, typename ContainerType = Vector<T>>
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class Statistics {
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public:
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Statistics() = default;
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~Statistics() = default;
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explicit Statistics(ContainerType&& existing_container)
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: m_values(forward<ContainerType>(existing_container))
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{
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for (auto const& value : m_values)
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m_sum += value;
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}
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void add(T const& value)
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{
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// FIXME: Check for an overflow
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m_sum += value;
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m_values.append(value);
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}
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T const sum() const { return m_sum; }
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// FIXME: Unclear Wording, average can mean a lot of different things
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// Median, Arithmetic Mean (which this is), Geometric Mean, Harmonic Mean etc
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float average() const
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{
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// Let's assume the average of an empty dataset is 0
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if (size() == 0)
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return 0;
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// TODO: sum might overflow so maybe do multiple partial sums and intermediate divisions here
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return (float)sum() / size();
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}
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T const min() const
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{
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// Lets Rather fail than read over the end of a collection
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VERIFY(size() != 0);
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T minimum = m_values[0];
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for (T number : values()) {
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if (number < minimum) {
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minimum = number;
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}
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}
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return minimum;
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}
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T const max() const
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{
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// Lets Rather fail than read over the end of a collection
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VERIFY(size() != 0);
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T maximum = m_values[0];
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for (T number : values()) {
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if (number > maximum) {
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maximum = number;
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}
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}
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return maximum;
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}
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T const median()
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{
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// Let's assume the Median of an empty dataset is 0
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if (size() == 0)
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return 0;
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// If the number of values is even, the median is the arithmetic mean of the two middle values
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if (size() <= EVEN_NAIVE_MEDIAN_CUTOFF && size() % 2 == 0) {
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quick_sort(m_values);
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return (m_values.at(size() / 2) + m_values.at(size() / 2 - 1)) / 2;
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} else if (size() <= ODD_NAIVE_MEDIAN_CUTOFF && size() % 2 == 1) {
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quick_sort(m_values);
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return m_values.at(m_values.size() / 2);
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} else if (size() % 2 == 0) {
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auto index = size() / 2;
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auto median1 = m_values.at(AK::quickselect_inplace(m_values, index));
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auto median2 = m_values.at(AK::quickselect_inplace(m_values, index - 1));
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return (median1 + median2) / 2;
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}
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return m_values.at(AK::quickselect_inplace(m_values, size() / 2));
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}
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float standard_deviation() const { return sqrt(variance()); }
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float variance() const
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{
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float summation = 0;
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float avg = average();
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for (T number : values()) {
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float difference = (float)number - avg;
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summation += (difference * difference);
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}
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summation = summation / size();
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return summation;
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}
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ContainerType const& values() const { return m_values; }
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size_t size() const { return m_values.size(); }
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private:
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ContainerType m_values;
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T m_sum {};
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};
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}
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#if USING_AK_GLOBALLY
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using AK::Statistics;
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#endif
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