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272 | /*************************************************************************
*
* Project
* _____ _____ __ __ _____
* / ____| __ \| \/ | __ \
* ___ _ __ ___ _ __ | | __| |__) | \ / | |__) |
* / _ \| '_ \ / _ \ '_ \| | |_ | ___/| |\/| | ___/
*| (_) | |_) | __/ | | | |__| | | | | | | |
* \___/| .__/ \___|_| |_|\_____|_| |_| |_|_|
* | |
* |_|
*
* Copyright (C) Akiel Aries, <akiel@akiel.org>, et al.
*
* This software is licensed as described in the file LICENSE, which
* you should have received as part of this distribution. The terms
* among other details are referenced in the official documentation
* seen here : https://akielaries.github.io/openGPMP/ along with
* important files seen in this project.
*
* You may opt to use, copy, modify, merge, publish, distribute
* and/or sell copies of the Software, and permit persons to whom
* the Software is furnished to do so, under the terms of the
* LICENSE file.
*
*
*
* This software is distributed on an AS IS basis, WITHOUT
* WARRANTY OF ANY KIND, either express or implied.
*
************************************************************************/
#include <algorithm>
#include <openGPMP/stats/resampling.hpp>
#include <random>
#include <stdexcept>
#include <vector>
// Resample with replacement
std::vector<int>
gpmp::stats::Resampling::bootstrap(const std::vector<int> &data,
int numSamples) {
std::vector<int> resampledData;
resampledData.reserve(numSamples);
std::mt19937 rng(std::random_device{}());
std::uniform_int_distribution<int> dist(0, data.size() - 1);
for (int i = 0; i < numSamples; ++i) {
int index = dist(rng);
resampledData.push_back(data[index]);
}
return resampledData;
}
// Resample without replacement
std::vector<int>
gpmp::stats::Resampling::subsample(const std::vector<int> &data,
int numSamples) {
if (numSamples > static_cast<int>(data.size())) {
throw std::invalid_argument(
"Number of samples cannot exceed data size");
}
std::vector<int> resampledData = data;
std::shuffle(resampledData.begin(),
resampledData.end(),
std::mt19937(std::random_device()()));
resampledData.resize(numSamples);
return resampledData;
}
// Jackknife resampling
std::vector<std::vector<int>>
gpmp::stats::Resampling::jackknife(const std::vector<int> &data) {
int n = data.size();
std::vector<std::vector<int>> resampledDatasets;
resampledDatasets.reserve(n);
for (int i = 0; i < n; ++i) {
std::vector<int> resampledData = data;
resampledData.erase(resampledData.begin() + i);
resampledDatasets.push_back(resampledData);
}
return resampledDatasets;
}
// Permutation test
std::vector<std::vector<int>>
gpmp::stats::Resampling::permutation_test(const std::vector<int> &data,
int numPermutations) {
std::vector<std::vector<int>> permutedDatasets;
permutedDatasets.reserve(numPermutations);
std::mt19937 rng(std::random_device{}());
for (int i = 0; i < numPermutations; ++i) {
std::vector<int> permutedData = data;
std::shuffle(permutedData.begin(), permutedData.end(), rng);
permutedDatasets.push_back(permutedData);
}
return permutedDatasets;
}
// Bootstrap-t method
std::vector<double>
gpmp::stats::Resampling::bootstrap_t(const std::vector<double> &data,
int numSamples) {
std::vector<double> resampledMeans;
resampledMeans.reserve(numSamples);
std::mt19937 rng(std::random_device{}());
std::uniform_int_distribution<int> dist(0, data.size() - 1);
for (int i = 0; i < numSamples; ++i) {
std::vector<double> resampledData;
resampledData.reserve(data.size());
for (int j = 0; j < static_cast<int>(data.size()); ++j) {
int index = dist(rng);
resampledData.push_back(data[index]);
}
double mean =
std::accumulate(resampledData.begin(), resampledData.end(), 0.0) /
resampledData.size();
resampledMeans.push_back(mean);
}
return resampledMeans;
}
// Bootstrapped confidence interval
std::pair<double, double>
gpmp::stats::Resampling::bootstrap_ci(const std::vector<double> &data,
double alpha,
int numSamples) {
std::vector<double> resampledMeans = bootstrap_t(data, numSamples);
std::sort(resampledMeans.begin(), resampledMeans.end());
int lowerIndex = static_cast<int>((alpha / 2) * numSamples);
int upperIndex = static_cast<int>((1 - alpha / 2) * numSamples) - 1;
return std::make_pair(resampledMeans[lowerIndex],
resampledMeans[upperIndex]);
}
// Smoothed bootstrap
std::vector<double>
gpmp::stats::Resampling::smoothed_bootstrap(const std::vector<double> &data,
int numSamples) {
std::vector<double> resampledData;
resampledData.reserve(numSamples);
std::mt19937 rng(std::random_device{}());
std::uniform_int_distribution<int> dist(0, data.size() - 1);
for (int i = 0; i < numSamples; ++i) {
double sum = 0.0;
for (int j = 0; j < static_cast<int>(data.size()); ++j) {
int index = dist(rng);
sum += data[index];
}
resampledData.push_back(sum / data.size());
}
return resampledData;
}
// Circular block bootstrap
std::vector<double> gpmp::stats::Resampling::circular_block_bootstrap(
const std::vector<double> &data,
int blockSize,
int numSamples) {
if (blockSize <= 0 || blockSize > static_cast<int>(data.size())) {
throw std::invalid_argument("Invalid block size");
}
std::vector<double> resampledData;
resampledData.reserve(numSamples);
std::mt19937 rng(std::random_device{}());
std::uniform_int_distribution<int> dist(0, data.size() - 1);
for (int i = 0; i < numSamples; ++i) {
std::vector<double> blockMeans;
blockMeans.reserve(data.size() / blockSize);
for (int j = 0; j < static_cast<int>(data.size()) / blockSize; ++j) {
double sum = 0.0;
for (int k = 0; k < blockSize; ++k) {
int index = dist(rng);
sum += data[index];
}
blockMeans.push_back(sum / blockSize);
}
std::shuffle(blockMeans.begin(), blockMeans.end(), rng);
resampledData.insert(resampledData.end(),
blockMeans.begin(),
blockMeans.end());
}
return resampledData;
}
// Time series bootstrap
std::vector<double>
gpmp::stats::Resampling::time_series_bootstrap(const std::vector<double> &data,
int numSamples) {
std::vector<double> resampledData;
resampledData.reserve(numSamples);
std::mt19937 rng(std::random_device{}());
std::uniform_int_distribution<int> dist(0, data.size() - 1);
for (int i = 0; i < numSamples; ++i) {
std::vector<double> resampledSequence;
resampledSequence.reserve(data.size());
int startIndex = dist(rng);
for (int j = 0; j < static_cast<int>(data.size()); ++j) {
int index = (startIndex + j) % data.size();
resampledSequence.push_back(data[index]);
}
resampledData.insert(resampledData.end(),
resampledSequence.begin(),
resampledSequence.end());
}
return resampledData;
}
std::vector<double>
gpmp::stats::Resampling::weighted_bootstrap(const std::vector<double> &data,
const std::vector<double> &weights,
int size) {
std::vector<double> resampledData;
std::random_device rd;
std::mt19937 gen(rd());
std::discrete_distribution<> dis(weights.begin(), weights.end());
for (int i = 0; i < size; ++i) {
resampledData.push_back(data[dis(gen)]);
}
return resampledData;
}
double
gpmp::stats::Resampling::permutation_p_value(const std::vector<double> &data1,
const std::vector<double> &data2,
double observedStatistic) {
int count = 0;
std::vector<double> combinedData = data1;
combinedData.insert(combinedData.end(), data2.begin(), data2.end());
std::shuffle(combinedData.begin(),
combinedData.end(),
std::mt19937(std::random_device()()));
std::vector<double> permutedData1(data1.begin(), data1.end());<--- Variable 'permutedData1' is assigned a value that is never used.
std::vector<double> permutedData2(data2.begin(), data2.end());<--- Variable 'permutedData2' is assigned a value that is never used.
for (int i = 0; i < 1000; ++i) {
std::shuffle(combinedData.begin(),
combinedData.end(),
std::mt19937(std::random_device()()));
auto permutedStatistic =
(std::accumulate(combinedData.begin(),
combinedData.begin() + data1.size(),
0.0)) /
data1.size();
if (permutedStatistic >= observedStatistic) {
count++;
}
}
return count / 1000.0;
}
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