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Diffstat (limited to 'ml/dlib/dlib/statistics/lda_abstract.h')
| -rw-r--r-- | ml/dlib/dlib/statistics/lda_abstract.h | 118 |
1 files changed, 0 insertions, 118 deletions
diff --git a/ml/dlib/dlib/statistics/lda_abstract.h b/ml/dlib/dlib/statistics/lda_abstract.h deleted file mode 100644 index ab9fd7a32..000000000 --- a/ml/dlib/dlib/statistics/lda_abstract.h +++ /dev/null @@ -1,118 +0,0 @@ -// Copyright (C) 2014 Davis E. King (davis@dlib.net) -// License: Boost Software License See LICENSE.txt for the full license. -#undef DLIB_LDA_ABSTRACT_Hh_ -#ifdef DLIB_LDA_ABSTRACT_Hh_ - -#include <map> -#include "../matrix.h" -#include <vector> - -namespace dlib -{ - -// ---------------------------------------------------------------------------------------- - - template < - typename T - > - void compute_lda_transform ( - matrix<T>& X, - matrix<T,0,1>& M, - const std::vector<unsigned long>& row_labels, - unsigned long lda_dims = 500, - unsigned long extra_pca_dims = 200 - ); - /*! - requires - - X.size() != 0 - - row_labels.size() == X.nr() - - The number of distinct values in row_labels > 1 - - lda_dims != 0 - ensures - - We interpret X as a collection X.nr() of input vectors, where each row of X - is one of the vectors. - - We interpret row_labels[i] as the label of the vector rowm(X,i). - - This function performs the dimensionality reducing version of linear - discriminant analysis. That is, you give it a set of labeled vectors and it - returns a linear transform that maps the input vectors into a new space that - is good for distinguishing between the different classes. In particular, - this function finds matrices Z and M such that: - - Given an input vector x, Z*x-M, is the transformed version of x. That is, - Z*x-M maps x into a space where x vectors that share the same class label - are near each other. - - Z*x-M results in the transformed vectors having zero expected mean. - - Z.nr() <= lda_dims - (it might be less than lda_dims if there are not enough distinct class - labels to support lda_dims dimensions). - - Z.nc() == X.nc() - - We overwrite the input matrix X and store Z in it. Therefore, the - outputs of this function are in X and M. - - In order to deal with very high dimensional inputs, we perform PCA internally - to map the input vectors into a space of at most lda_dims+extra_pca_dims - prior to performing LDA. - !*/ - -// ---------------------------------------------------------------------------------------- - - std::pair<double,double> equal_error_rate ( - const std::vector<double>& low_vals, - const std::vector<double>& high_vals - ); - /*! - ensures - - This function finds a threshold T that best separates the elements of - low_vals from high_vals by selecting the threshold with equal error rate. In - particular, we try to pick a threshold T such that: - - for all valid i: - - high_vals[i] >= T - - for all valid i: - - low_vals[i] < T - Where the best T is determined such that the fraction of low_vals >= T is the - same as the fraction of high_vals < T. - - Let ERR == the equal error rate. I.e. the fraction of times low_vals >= T - and high_vals < T. Note that 0 <= ERR <= 1. - - returns make_pair(ERR,T) - !*/ - -// ---------------------------------------------------------------------------------------- - - struct roc_point - { - double true_positive_rate; - double false_positive_rate; - double detection_threshold; - }; - - std::vector<roc_point> compute_roc_curve ( - const std::vector<double>& true_detections, - const std::vector<double>& false_detections - ); - /*! - requires - - true_detections.size() != 0 - - false_detections.size() != 0 - ensures - - This function computes the ROC curve (receiver operating characteristic) - curve of the given data. Therefore, we interpret true_detections as - containing detection scores for a bunch of true detections and - false_detections as detection scores from a bunch of false detections. A - perfect detector would always give higher scores to true detections than to - false detections, resulting in a true positive rate of 1 and a false positive - rate of 0, for some appropriate detection threshold. - - Returns an array, ROC, such that: - - ROC.size() == true_detections.size()+false_detections.size() - - for all valid i: - - If you were to accept all detections with a score >= ROC[i].detection_threshold - then you would obtain a true positive rate of ROC[i].true_positive_rate and a - false positive rate of ROC[i].false_positive_rate. - - ROC is ordered such that low detection rates come first. That is, the - curve is swept from a high detection threshold to a low threshold. - !*/ - -// ---------------------------------------------------------------------------------------- - -} - -#endif // DLIB_LDA_ABSTRACT_Hh_ - - |