本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/fengbingchun/article/details/79235028
主成分分析(Principal Components Analysis, PCA)简介可以参考： http://blog.csdn.net/fengbingchun/article/details/78977202
以下是PCA的C++实现，参考OpenCV 3.3中的cv::PCA类。
使用ORL Faces Database作为测试图像。关于ORL Faces Database的介绍可以参考： http://blog.csdn.net/fengbingchun/article/details/79008891
pca.hpp:
#ifndef FBC_NN_PCA_HPP_
#define FBC_NN_PCA_HPP_
#include <vector>
#include <string>
namespace ANN {
template<typename T = float>
class PCA {
public:
PCA() = default;
int load_data(const std::vector<std::vector<T>>& data, const std::vector<T>& labels);
int set_max_components(int max_components);
int set_retained_variance(double retained_variance);
int load_model(const std::string& model);
int train(const std::string& model);
// project into the eigenspace, thus the image becomes a "point"
int project(const std::vector<T>& vec, std::vector<T>& result) const;
// re-create the image from the "point"
int back_project(const std::vector<T>& vec, std::vector<T>& result) const;
private:
// width,height,eigen_vectors;width,height,eigen_values;width,height,means
int save_model(const std::string& model) const;
void calculate_covariance_matrix(std::vector<std::vector<T>>& covar, bool scale = false); // calculate covariance matrix
int eigen(const std::vector<std::vector<T>>& mat, bool sort_ = true); // calculate eigen vectors and eigen values
// generalized matrix multiplication: dst = alpha*src1.t()*src2 + beta*src3.t()
int gemm(const std::vector<std::vector<T>>& src1, const std::vector<std::vector<T>>& src2, double alpha,
const std::vector<std::vector<T>>& src3, double beta, std::vector<std::vector<T>>& dst, int flags = 0) const;
int gemm(const std::vector<T>& src1, const std::vector<std::vector<T>>& src2, double alpha,
const std::vector<T>& src3, double beta, std::vector<T>& dst, int flags = 0) const; // GEMM_2_T: flags = 1
int normalize(T* dst, int length);
int computeCumulativeEnergy() const;
int subtract(const std::vector<T>& vec1, const std::vector<T>& vec2, std::vector<T>& result) const;
typedef struct Size_ {
int width;
int height;
} Size_;
std::vector<std::vector<T>> data;
std::vector<T> labels;
int samples_num = 0;
int features_length = 0;
double retained_variance = -1.; // percentage of variance that PCA should retain
int max_components = -1; // maximum number of components that PCA should retain
std::vector<std::vector<T>> eigen_vectors; // eigenvectors of the covariation matrix
std::vector<T> eigen_values; // eigenvalues of the covariation matrix
std::vector<T> mean;
int covar_flags = 0; // when features_length > samples_num, covar_flags is 0, otherwise is 1
};
} // namespace ANN
#endif // FBC_NN_PCA_HPP_
pca.cpp:
#include "pca.hpp"
#include <iostream>
#include <vector>
#include <algorithm>
#include <cmath>
#include <memory>
#include <fstream>
#include "common.hpp"
namespace ANN {
template<typename T>
int PCA<T>::load_data(const std::vector<std::vector<T>>& data, const std::vector<T>& labels)
{
this->samples_num = data.size();
this->features_length = data[0].size();
if (samples_num > features_length) {
fprintf(stderr, "now only support samples_num <= features_length
");
return -1;
}
this->data.resize(this->samples_num);
for (int i = 0; i < this->samples_num; ++i) {
this->data[i].resize(this->features_length);
memcpy(this->data[i].data(), data[i].data(), sizeof(T)* this->features_length);
}
this->labels.resize(this->samples_num);
memcpy(this->labels.data(), labels.data(), sizeof(T)*this->samples_num);
return 0;
}
template<typename T>
int PCA<T>::set_max_components(int max_components)
{
CHECK(data.size() > 0);
int count = std::min(features_length, samples_num);
if (max_components > 0) {
this->max_components = std::min(count, max_components);
}
this->retained_variance = -1.;
}
template<typename T>
int PCA<T>::set_retained_variance(double retained_variance)
{
CHECK(retained_variance > 0 && retained_variance <= 1);
this->retained_variance = retained_variance;
this->max_components = -1;
}
template<typename T>
void PCA<T>::calculate_covariance_matrix(std::vector<std::vector<T>>& covar, bool scale)
{
const int rows = samples_num;
const int cols = features_length;
const int nsamples = rows;
double scale_ = 1.;
if (scale) scale_ = 1. / (nsamples /*- 1*/);
mean.resize(cols, (T)0.);
for (int w = 0; w < cols; ++w) {
for (int h = 0; h < rows; ++h) {
mean[w] += data[h][w];
}
}
for (auto& value : mean) {
value = 1. / rows * value;
}
// int dsize = ata ? src.cols : src.rows; // ata = false;
int dsize = rows;
covar.resize(dsize);
for (int i = 0; i < dsize; ++i) {
covar[i].resize(dsize, (T)0.);
}
Size_ size{ data[0].size(), data.size() };
T* tdst = covar[0].data();
int delta_cols = mean.size();
T delta_buf[4];
int delta_shift = delta_cols == size.width ? 4 : 0;
std::unique_ptr<T[]> buf(new T[size.width]);
T* row_buf = buf.get();
for (int i = 0; i < size.height; ++i) {
const T* tsrc1 = data[i].data();
const T* tdelta1 = mean.data();
for (int k = 0; k < size.width; ++k) {
row_buf[k] = tsrc1[k] - tdelta1[k];
}
for (int j = i; j < size.height; ++j) {
double s = 0;
const T* tsrc2 = data[j].data();
const T* tdelta2 = mean.data();
for (int k = 0; k < size.width; ++k) {
s += (double)row_buf[k] * (tsrc2[k] - tdelta2[k]);
}
tdst[j] = (T)(s * scale_);
}
if (i < covar.size()-1) {
tdst = covar[i + 1].data();
}
}
}
namespace {
template<typename _Tp>
static inline _Tp hypot_(_Tp a, _Tp b)
{
a = std::abs(a);
b = std::abs(b);
if (a > b) {
b /= a;
return a*std::sqrt(1 + b*b);
}
if (b > 0) {
a /= b;
return b*std::sqrt(1 + a*a);
}
return 0;
}
} // namespace
template<typename T>
int PCA<T>::eigen(const std::vector<std::vector<T>>& mat, bool sort_ = true)
{
using _Tp = T; // typedef T _Tp;
auto n = mat.size();
for (const auto& m : mat) {
if (m.size() != n) {
fprintf(stderr, "mat must be square and it should be a real symmetric matrix
");
return -1;
}
}
eigen_values.resize(n, (T)0.);
std::vector<T> V(n*n, (T)0.);
for (int i = 0; i < n; ++i) {
V[n * i + i] = (_Tp)1;
eigen_values[i] = mat[i][i];
}
const _Tp eps = std::numeric_limits<_Tp>::epsilon();
int maxIters{ (int)n * (int)n * 30 };
_Tp mv{ (_Tp)0 };
std::vector<int> indR(n, 0), indC(n, 0);
std::vector<_Tp> A;
for (int i = 0; i < n; ++i) {
A.insert(A.begin() + i * n, mat[i].begin(), mat[i].end());
}
for (int k = 0; k < n; ++k) {
int m, i;
if (k < n - 1) {
for (m = k + 1, mv = std::abs(A[n*k + m]), i = k + 2; i < n; i++) {
_Tp val = std::abs(A[n*k + i]);
if (mv < val)
mv = val, m = i;
}
indR[k] = m;
}
if (k > 0) {
for (m = 0, mv = std::abs(A[k]), i = 1; i < k; i++) {
_Tp val = std::abs(A[n*i + k]);
if (mv < val)
mv = val, m = i;
}
indC[k] = m;
}
}
if (n > 1) for (int iters = 0; iters < maxIters; iters++) {
int k, i, m;
// find index (k,l) of pivot p
for (k = 0, mv = std::abs(A[indR[0]]), i = 1; i < n - 1; i++) {
_Tp val = std::abs(A[n*i + indR[i]]);
if (mv < val)
mv = val, k = i;
}
int l = indR[k];
for (i = 1; i < n; i++) {
_Tp val = std::abs(A[n*indC[i] + i]);
if (mv < val)
mv = val, k = indC[i], l = i;
}
_Tp p = A[n*k + l];
if (std::abs(p) <= eps)
break;
_Tp y = (_Tp)((eigen_values[l] - eigen_values[k])*0.5);
_Tp t = std::abs(y) + hypot_(p, y);
_Tp s = hypot_(p, t);
_Tp c = t / s;
s = p / s; t = (p / t)*p;
if (y < 0)
s = -s, t = -t;
A[n*k + l] = 0;
eigen_values[k] -= t;
eigen_values[l] += t;
_Tp a0, b0;
#undef rotate
#define rotate(v0, v1) a0 = v0, b0 = v1, v0 = a0*c - b0*s, v1 = a0*s + b0*c
// rotate rows and columns k and l
for (i = 0; i < k; i++)
rotate(A[n*i + k], A[n*i + l]);
for (i = k + 1; i < l; i++)
rotate(A[n*k + i], A[n*i + l]);
for (i = l + 1; i < n; i++)
rotate(A[n*k + i], A[n*l + i]);
// rotate eigenvectors
for (i = 0; i < n; i++)
rotate(V[n*k + i], V[n*l + i]);
#undef rotate
for (int j = 0; j < 2; j++) {
int idx = j == 0 ? k : l;
if (idx < n - 1) {
for (m = idx + 1, mv = std::abs(A[n*idx + m]), i = idx + 2; i < n; i++) {
_Tp val = std::abs(A[n*idx + i]);
if (mv < val)
mv = val, m = i;
}
indR[idx] = m;
}
if (idx > 0) {
for (m = 0, mv = std::abs(A[idx]), i = 1; i < idx; i++) {
_Tp val = std::abs(A[n*i + idx]);
if (mv < val)
mv = val, m = i;
}
indC[idx] = m;
}
}
}
// sort eigenvalues & eigenvectors
if (sort_) {
for (int k = 0; k < n - 1; k++) {
int m = k;
for (int i = k + 1; i < n; i++) {
if (eigen_values[m] < eigen_values[i])
m = i;
}
if (k != m) {
std::swap(eigen_values[m], eigen_values[k]);
for (int i = 0; i < n; i++)
std::swap(V[n*m + i], V[n*k + i]);
}
}
}
eigen_vectors.resize(n);
for (int i = 0; i < n; ++i) {
eigen_vectors[i].resize(n);
eigen_vectors[i].assign(V.begin() + i * n, V.begin() + i * n + n);
}
return 0;
}
template<typename T>
int PCA<T>::gemm(const std::vector<std::vector<T>>& src1, const std::vector<std::vector<T>>& src2, double alpha,
const std::vector<std::vector<T>>& src3, double beta, std::vector<std::vector<T>>& dst, int flags) const
{
CHECK(flags == 0); // now only support flags = 0
CHECK(typeid(T).name() == typeid(double).name() || typeid(T).name() == typeid(float).name()); // T' type can only be float or double
CHECK(beta == 0. && src3.size() == 0);
Size_ a_size{ src1[0].size(), src1.size() }, d_size{ src2[0].size(), a_size.height };
int len{ (int)src2.size() };
CHECK(a_size.height == len);
CHECK(d_size.height == dst.size() && d_size.width == dst[0].size());
for (int y = 0; y < d_size.height; ++y) {
for (int x = 0; x < d_size.width; ++x) {
dst[y][x] = 0.;
for (int t = 0; t < d_size.height; ++t) {
dst[y][x] += src1[y][t] * src2[t][x];
}
dst[y][x] *= alpha;
}
}
return 0;
}
template<typename T>
int PCA<T>::gemm(const std::vector<T>& src1, const std::vector<std::vector<T>>& src2, double alpha,
const std::vector<T>& src3, double beta, std::vector<T>& dst, int flags = 0) const
{
CHECK(flags == 0 || flags == 1); // when flags = 1, GEMM_2_T
CHECK(typeid(T).name() == typeid(double).name() || typeid(T).name() == typeid(float).name()); // T' type can only be float or double
Size_ a_size{ src1.size(), 1 }, d_size;
int len = 0;
switch (flags) {
case 0:
d_size = Size_{ src2[0].size(), a_size.height };
len = src2.size();
CHECK(a_size.width == len);
break;
case 1:
d_size = Size_{ src2.size(), a_size.height };
len = src2[0].size();
CHECK(a_size.width == len);
break;
}
if (!src3.empty()) {
CHECK(src3.size() == d_size.width);
}
dst.resize(d_size.width);
const T* src3_ = nullptr;
std::vector<T> tmp(dst.size(), (T)0.);
if (src3.empty()) {
src3_ = tmp.data();
} else {
src3_ = src3.data();
}
if (src1.size() == src2.size()) {
for (int i = 0; i < dst.size(); ++i) {
dst[i] = (T)0.;
for (int j = 0; j < src2.size(); ++j) {
dst[i] += src1[j] * src2[j][i];
}
dst[i] *= alpha;
dst[i] += beta * src3_[i];
}
} else {
for (int i = 0; i < dst.size(); ++i) {
dst[i] = (T)0.;
for (int j = 0; j < src1.size(); ++j) {
dst[i] += src1[j] * src2[i][j];
}
dst[i] *= alpha;
dst[i] += beta * src3_[i];
}
}
return 0;
}
template<typename T>
int PCA<T>::normalize(T* dst, int length)
{
T s = (T)0., a = (T)1.;
for (int i = 0; i < length; ++i) {
s += dst[i] * dst[i];
}
s = std::sqrt(s);
s = s > DBL_EPSILON ? a / s : 0.;
for (int i = 0; i < length; ++i) {
dst[i] *= s;
}
return 0;
}
template<typename T>
int PCA<T>::computeCumulativeEnergy() const
{
std::vector<T> g(eigen_values.size(), (T)0.);
for (int ig = 0; ig < eigen_values.size(); ++ig) {
for (int im = 0; im <= ig; ++im) {
g[ig] += eigen_values[im];
}
}
int L{ 0 };
for (L = 0; L < eigen_values.size(); ++L) {
double energy = g[L] / g[eigen_values.size() - 1];
if (energy > retained_variance) break;
}
L = std::max(2, L);
return L;
}
template<typename T>
int PCA<T>::train(const std::string& model)
{
CHECK(retained_variance > 0. || max_components > 0);
int count = std::min(features_length, samples_num), out_count = count;
if (max_components > 0) out_count = std::min(count, max_components);
covar_flags = 0;
if (features_length <= samples_num) covar_flags = 1;
std::vector<std::vector<T>> covar(count); // covariance matrix
calculate_covariance_matrix(covar, true);
eigen(covar, true);
std::vector<std::vector<T>> tmp_data(samples_num), evects1(count);
for (int i = 0; i < samples_num; ++i) {
tmp_data[i].resize(features_length);
evects1[i].resize(features_length);
for (int j = 0; j < features_length; ++j) {
tmp_data[i][j] = data[i][j] - mean[j];
}
}
gemm(eigen_vectors, tmp_data, 1., std::vector<std::vector<T>>(), 0., evects1, 0);
eigen_vectors.resize(evects1.size());
for (int i = 0; i < eigen_vectors.size(); ++i) {
eigen_vectors[i].resize(evects1[i].size());
memcpy(eigen_vectors[i].data(), evects1[i].data(), sizeof(T)* evects1[i].size());
}
// normalize all eigenvectors
if (retained_variance > 0) {
for (int i = 0; i < eigen_vectors.size(); ++i) {
normalize(eigen_vectors[i].data(), eigen_vectors[i].size());
}
// compute the cumulative energy content for each eigenvector
int L = computeCumulativeEnergy();
eigen_values.resize(L);
eigen_vectors.resize(L);
} else {
for (int i = 0; i < out_count; ++i) {
normalize(eigen_vectors[i].data(), eigen_vectors[i].size());
}
if (count > out_count) {
eigen_values.resize(out_count);
eigen_vectors.resize(out_count);
}
}
save_model(model);
return 0;
}
template<typename T>
int PCA<T>::subtract(const std::vector<T>& vec1, const std::vector<T>& vec2, std::vector<T>& result) const
{
CHECK(vec1.size() == vec2.size() && vec1.size() == result.size());
for (int i = 0; i < vec1.size(); ++i) {
result[i] = vec1[i] - vec2[i];
}
return 0;
}
template<typename T>
int PCA<T>::project(const std::vector<T>& vec, std::vector<T>& result) const
{
CHECK(!mean.empty() && !eigen_vectors.empty() && mean.size() == vec.size());
std::vector<T> tmp_data(mean.size());
subtract(vec, mean, tmp_data);
gemm(tmp_data, eigen_vectors, 1, std::vector<T>(), 0, result, 1);
return 0;
}
template<typename T>
int PCA<T>::back_project(const std::vector<T>& vec, std::vector<T>& result) const
{
CHECK(!mean.empty() && !eigen_vectors.empty() && eigen_vectors.size() == vec.size());
gemm(vec, eigen_vectors, 1, mean, 1, result, 0);
return 0;
}
template<typename T>
int PCA<T>::load_model(const std::string& model)
{
std::ifstream file(model.c_str(), std::ios::in | std::ios::binary);
if (!file.is_open()) {
fprintf(stderr, "open file fail: %s
", model.c_str());
return -1;
}
int width = 0, height = 0;
file.read((char*)&width, sizeof(width) * 1);
file.read((char*)&height, sizeof(height) * 1);
std::unique_ptr<T[]> data(new T[width * height]);
file.read((char*)data.get(), sizeof(T)* width * height);
eigen_vectors.resize(height);
for (int i = 0; i < height; ++i) {
eigen_vectors[i].resize(width);
T* p = data.get() + i * width;
memcpy(eigen_vectors[i].data(), p, sizeof(T)* width);
}
file.read((char*)&width, sizeof(width));
file.read((char*)&height, sizeof(height));
CHECK(height == 1);
eigen_values.resize(width);
file.read((char*)eigen_values.data(), sizeof(T)* width * height);
file.read((char*)&width, sizeof(width));
file.read((char*)&height, sizeof(height));
CHECK(height == 1);
mean.resize(width);
file.read((char*)mean.data(), sizeof(T)* width * height);
file.close();
return 0;
}
template<typename T>
int PCA<T>::save_model(const std::string& model) const
{
std::ofstream file(model.c_str(), std::ios::out | std::ios::binary);
if (!file.is_open()) {
fprintf(stderr, "open file fail: %s
", model.c_str());
return -1;
}
int width = eigen_vectors[0].size(), height = eigen_vectors.size();
std::unique_ptr<T[]> data(new T[width * height]);
for (int i = 0; i < height; ++i) {
T* p = data.get() + i * width;
memcpy(p, eigen_vectors[i].data(), sizeof(T) * width);
}
file.write((char*)&width, sizeof(width));
file.write((char*)&height, sizeof(height));
file.write((char*)data.get(), sizeof(T)* width * height);
width = eigen_values.size(), height = 1;
file.write((char*)&width, sizeof(width));
file.write((char*)&height, sizeof(height));
file.write((char*)eigen_values.data(), sizeof(T)* width * height);
width = mean.size(), height = 1;
file.write((char*)&width, sizeof(width));
file.write((char*)&height, sizeof(height));
file.write((char*)mean.data(), sizeof(T)* width * height);
file.close();
return 0;
}
template class PCA<float>;
template class PCA<double>;
} // namespace ANN
main.cpp:
#include "funset.hpp"
#include <iostream>
#include "perceptron.hpp"
#include "BP.hpp""
#include "CNN.hpp"
#include "linear_regression.hpp"
#include "naive_bayes_classifier.hpp"
#include "logistic_regression.hpp"
#include "common.hpp"
#include "knn.hpp"
#include "decision_tree.hpp"
#include "pca.hpp"
#include <opencv2/opencv.hpp>
// =============================== PCA(Principal Components Analysis) ===================
namespace {
void normalize(const std::vector<float>& src, std::vector<unsigned char>& dst)
{
dst.resize(src.size());
double dmin = 0, dmax = 255;
double smin = src[0], smax = smin;
for (int i = 1; i < src.size(); ++i) {
if (smin > src[i]) smin = src[i];
if (smax < src[i]) smax = src[i];
}
double scale = (dmax - dmin) * (smax - smin > DBL_EPSILON ? 1. / (smax - smin) : 0);
double shift = dmin - smin * scale;
for (int i = 0; i < src.size(); ++i) {
dst[i] = static_cast<unsigned char>(src[i] * scale + shift);
}
}
} // namespace
int test_pca()
{
const std::string image_path{ "E:/GitCode/NN_Test/data/database/ORL_Faces/" };
const std::string image_name{ "1.pgm" };
std::vector<cv::Mat> images;
for (int i = 1; i <= 15; ++i) {
std::string name = image_path + "s" + std::to_string(i) + "/" + image_name;
cv::Mat mat = cv::imread(name, 0);
if (!mat.data) {
fprintf(stderr, "read image fail: %s
", name.c_str());
return -1;
}
images.emplace_back(mat);
}
save_images(images, "E:/GitCode/NN_Test/data/pca_src.jpg", 5);
cv::Mat data(images.size(), images[0].rows * images[0].cols, CV_32FC1);
for (int i = 0; i < images.size(); ++i) {
cv::Mat image_row = images[i].clone().reshape(1, 1);
cv::Mat row_i = data.row(i);
image_row.convertTo(row_i, CV_32F);
}
int features_length = images[0].rows * images[0].cols;
std::vector<std::vector<float>> data_(images.size());
std::vector<float> labels(images.size(), 0.f);
for (int i = 0; i < images.size(); ++i) {
data_[i].resize(features_length);
memcpy(data_[i].data(), data.row(i).data, sizeof(float)* features_length);
}
const std::string save_model_file{ "E:/GitCode/NN_Test/data/pca.model" };
ANN::PCA<float> pca;
pca.load_data(data_, labels);
double retained_variance{ 0.95 };
pca.set_retained_variance(retained_variance);
pca.train(save_model_file);
const std::string read_model_file{ save_model_file };
ANN::PCA<float> pca2;
pca2.load_model(read_model_file);
std::vector<cv::Mat> result(images.size());
for (int i = 0; i < images.size(); ++i) {
std::vector<float> point, reconstruction;
pca2.project(data_[i], point);
pca2.back_project(point, reconstruction);
std::vector<unsigned char> dst;
normalize(reconstruction, dst);
cv::Mat tmp(images[i].rows, images[i].cols, CV_8UC1, dst.data());
tmp.copyTo(result[i]);
}
save_images(result, "E:/GitCode/NN_Test/data/pca_result.jpg", 5);
return 0;
}
执行结果如下，上三行为原始图像，下三行为使用PCA重建图像的结果，经比较与OpenCV 3.3结果一致：
GitHub： https://github.com/fengbingchun/NN_Test
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作者：fengbingchun
来源：CSDN
原文：https://blog.csdn.net/fengbingchun/article/details/79235028
版权声明：本文为博主原创文章，转载请附上博文链接！