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Diffstat (limited to 'ml/dlib/dlib/statistics/lda.h')
| -rw-r--r-- | ml/dlib/dlib/statistics/lda.h | 237 |
1 files changed, 0 insertions, 237 deletions
diff --git a/ml/dlib/dlib/statistics/lda.h b/ml/dlib/dlib/statistics/lda.h deleted file mode 100644 index 38de3fd1e..000000000 --- a/ml/dlib/dlib/statistics/lda.h +++ /dev/null @@ -1,237 +0,0 @@ -// Copyright (C) 2014 Davis E. King (davis@dlib.net) -// License: Boost Software License See LICENSE.txt for the full license. -#ifndef DLIB_LDA_Hh_ -#define DLIB_LDA_Hh_ - -#include "lda_abstract.h" -#include "../algs.h" -#include <map> -#include "../matrix.h" -#include <vector> - -namespace dlib -{ - -// ---------------------------------------------------------------------------------------- - - namespace impl - { - - inline std::map<unsigned long,unsigned long> make_class_labels( - const std::vector<unsigned long>& row_labels - ) - { - std::map<unsigned long,unsigned long> class_labels; - for (unsigned long i = 0; i < row_labels.size(); ++i) - { - const unsigned long next = class_labels.size(); - if (class_labels.count(row_labels[i]) == 0) - class_labels[row_labels[i]] = next; - } - return class_labels; - } - - // ------------------------------------------------------------------------------------ - - template < - typename T - > - matrix<T,0,1> center_matrix ( - matrix<T>& X - ) - { - matrix<T,1> mean; - for (long r = 0; r < X.nr(); ++r) - mean += rowm(X,r); - mean /= X.nr(); - - for (long r = 0; r < X.nr(); ++r) - set_rowm(X,r) -= mean; - - return trans(mean); - } - } - -// ---------------------------------------------------------------------------------------- - - template < - typename T - > - void compute_lda_transform ( - matrix<T>& X, - matrix<T,0,1>& mean, - const std::vector<unsigned long>& row_labels, - unsigned long lda_dims = 500, - unsigned long extra_pca_dims = 200 - ) - { - std::map<unsigned long,unsigned long> class_labels = impl::make_class_labels(row_labels); - // LDA can only give out at most class_labels.size()-1 dimensions so don't try to - // compute more than that. - lda_dims = std::min<unsigned long>(lda_dims, class_labels.size()-1); - - // make sure requires clause is not broken - DLIB_CASSERT(class_labels.size() > 1, - "\t void compute_lda_transform()" - << "\n\t You can't call this function if the number of distinct class labels is less than 2." - ); - DLIB_CASSERT(X.size() != 0 && (long)row_labels.size() == X.nr() && lda_dims != 0, - "\t void compute_lda_transform()" - << "\n\t Invalid inputs were given to this function." - << "\n\t X.size(): " << X.size() - << "\n\t row_labels.size(): " << row_labels.size() - << "\n\t lda_dims: " << lda_dims - ); - - - mean = impl::center_matrix(X); - // Do PCA to reduce dims - matrix<T> pu,pw,pv; - svd_fast(X, pu, pw, pv, lda_dims+extra_pca_dims, 4); - pu.set_size(0,0); // free RAM, we don't need pu. - X = X*pv; - - - matrix<T> class_means(class_labels.size(), X.nc()); - class_means = 0; - matrix<T,0,1> class_counts(class_labels.size()); - class_counts = 0; - - // First compute the means of each class - for (unsigned long i = 0; i < row_labels.size(); ++i) - { - const unsigned long class_idx = class_labels[row_labels[i]]; - set_rowm(class_means,class_idx) += rowm(X,i); - class_counts(class_idx)++; - } - class_means = inv(diagm(class_counts))*class_means; - // subtract means from the data - for (unsigned long i = 0; i < row_labels.size(); ++i) - { - const unsigned long class_idx = class_labels[row_labels[i]]; - set_rowm(X,i) -= rowm(class_means,class_idx); - } - - // Note that we are using the formulas from the paper Using Discriminant - // Eigenfeatures for Image Retrieval by Swets and Weng. - matrix<T> Sw = trans(X)*X; - matrix<T> Sb = trans(class_means)*class_means; - matrix<T> A, H; - matrix<T,0,1> W; - svd3(Sw, A, W, H); - W = sqrt(W); - W = reciprocal(lowerbound(W,max(W)*1e-5)); - A = trans(H*diagm(W))*Sb*H*diagm(W); - matrix<T> v,s,u; - svd3(A, v, s, u); - matrix<T> tform = H*diagm(W)*u; - // pick out only the number of dimensions we are supposed to for the output, unless - // we should just keep them all, then don't do anything. - if ((long)lda_dims <= tform.nc()) - { - rsort_columns(tform, s); - tform = colm(tform, range(0, lda_dims-1)); - } - - X = trans(pv*tform); - mean = X*mean; - } - -// ---------------------------------------------------------------------------------------- - - inline std::pair<double,double> equal_error_rate ( - const std::vector<double>& low_vals, - const std::vector<double>& high_vals - ) - { - std::vector<std::pair<double,int> > temp; - temp.reserve(low_vals.size()+high_vals.size()); - for (unsigned long i = 0; i < low_vals.size(); ++i) - temp.push_back(std::make_pair(low_vals[i], -1)); - for (unsigned long i = 0; i < high_vals.size(); ++i) - temp.push_back(std::make_pair(high_vals[i], +1)); - - std::sort(temp.begin(), temp.end()); - - if (temp.size() == 0) - return std::make_pair(0,0); - - double thresh = temp[0].first; - - unsigned long num_low_wrong = low_vals.size(); - unsigned long num_high_wrong = 0; - double low_error = num_low_wrong/(double)low_vals.size(); - double high_error = num_high_wrong/(double)high_vals.size(); - for (unsigned long i = 0; i < temp.size() && high_error < low_error; ++i) - { - thresh = temp[i].first; - if (temp[i].second > 0) - { - num_high_wrong++; - high_error = num_high_wrong/(double)high_vals.size(); - } - else - { - num_low_wrong--; - low_error = num_low_wrong/(double)low_vals.size(); - } - } - - return std::make_pair((low_error+high_error)/2, thresh); - } - -// ---------------------------------------------------------------------------------------- - - struct roc_point - { - double true_positive_rate; - double false_positive_rate; - double detection_threshold; - }; - - inline std::vector<roc_point> compute_roc_curve ( - const std::vector<double>& true_detections, - const std::vector<double>& false_detections - ) - { - DLIB_CASSERT(true_detections.size() != 0); - DLIB_CASSERT(false_detections.size() != 0); - - std::vector<std::pair<double,int> > temp; - temp.reserve(true_detections.size()+false_detections.size()); - for (unsigned long i = 0; i < true_detections.size(); ++i) - temp.push_back(std::make_pair(true_detections[i], +1)); - for (unsigned long i = 0; i < false_detections.size(); ++i) - temp.push_back(std::make_pair(false_detections[i], -1)); - - std::sort(temp.rbegin(), temp.rend()); - - - std::vector<roc_point> roc_curve; - roc_curve.reserve(temp.size()); - - double num_false_included = 0; - double num_true_included = 0; - for (unsigned long i = 0; i < temp.size(); ++i) - { - if (temp[i].second > 0) - num_true_included++; - else - num_false_included++; - - roc_point p; - p.true_positive_rate = num_true_included/true_detections.size(); - p.false_positive_rate = num_false_included/false_detections.size(); - p.detection_threshold = temp[i].first; - roc_curve.push_back(p); - } - - return roc_curve; - } - -// ---------------------------------------------------------------------------------------- - -} - -#endif // DLIB_LDA_Hh_ - |