dlib C++ Library - statistics_abstract.h

// Copyright (C) 2008 Davis E. King (davis@dlib.net), Steve Taylor
// License: Boost Software License See LICENSE.txt for the full license.
#undef DLIB_STATISTICs_ABSTRACT_
#ifdef DLIB_STATISTICs_ABSTRACT_
#include <limits>
#include <cmath>
#include "../matrix/matrix_abstract.h"
#include "../svm/sparse_vector_abstract.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
 template <
 typename T, 
 typename alloc
 >
 double mean_sign_agreement (
 const std::vector<T,alloc>& a,
 const std::vector<T,alloc>& b
 );
 /*!
 requires
 - a.size() == b.size()
 ensures
 - returns the number of times a[i] has the same sign as b[i] divided by
 a.size(). So we return the probability that elements of a and b have
 the same sign.
 !*/
// ----------------------------------------------------------------------------------------
 template <
 typename T, 
 typename alloc
 >
 double correlation (
 const std::vector<T,alloc>& a,
 const std::vector<T,alloc>& b
 );
 /*!
 requires
 - a.size() == b.size()
 - a.size() > 1
 ensures
 - returns the correlation coefficient between all the elements of a and b.
 (i.e. how correlated is a(i) with b(i))
 !*/
// ----------------------------------------------------------------------------------------
 template <
 typename T, 
 typename alloc
 >
 double covariance (
 const std::vector<T,alloc>& a,
 const std::vector<T,alloc>& b
 );
 /*!
 requires
 - a.size() == b.size()
 - a.size() > 1
 ensures
 - returns the covariance between all the elements of a and b.
 (i.e. how does a(i) vary with b(i))
 !*/
// ----------------------------------------------------------------------------------------
 template <
 typename T, 
 typename alloc
 >
 double r_squared (
 const std::vector<T,alloc>& a,
 const std::vector<T,alloc>& b
 );
 /*!
 requires
 - a.size() == b.size()
 - a.size() > 1
 ensures
 - returns the R^2 coefficient of determination between all the elements of a and b.
 This value is just the square of correlation(a,b).
 !*/
// ----------------------------------------------------------------------------------------
 template <
 typename T, 
 typename alloc
 >
 double mean_squared_error (
 const std::vector<T,alloc>& a,
 const std::vector<T,alloc>& b
 );
 /*!
 requires
 - a.size() == b.size()
 ensures
 - returns the mean squared error between all the elements of a and b.
 (i.e. mean(squared(mat(a)-mat(b))))
 !*/
// ----------------------------------------------------------------------------------------
 double binomial_random_vars_are_different (
 double k1,
 double n1,
 double k2,
 double n2
 );
 /*!
 requires
 - k1 <= n1
 - k2 <= n2
 ensures
 - Given two binomially distributed random variables, X1 and X2, we want to know
 if these variables have the same parameter (i.e. the chance of "success").
 So assume that:
 - You observed X1 to give k1 successes out of n1 trials.
 - You observed X2 to give k2 successes out of n2 trials.
 - This function performs a simple likelihood ratio test to determine if X1 and
 X2 have the same parameter. The return value of this function will be:
 - Close to 0 if they are probably the same.
 - Larger than 0 if X1 probably has a higher "success" rate than X2. 
 - Smaller than 0 if X2 probably has a higher "success" rate than X1. 
 Moreover, the larger the absolute magnitude of the return value the more
 likely it is that X1 and X2 have different distributions.
 - For a discussion of the technique and applications see:
 Dunning, Ted. "Accurate methods for the statistics of surprise and
 coincidence." Computational linguistics 19.1 (1993): 61-74.
 !*/
// ----------------------------------------------------------------------------------------
 double event_correlation (
 double A_count,
 double B_count,
 double AB_count,
 double total_num_observations
 );
 /*!
 requires
 - AB_count <= A_count <= total_num_observations
 - AB_count <= B_count <= total_num_observations
 - A_count + B_count - AB_count <= total_num_observations
 ensures
 - This function does a statistical test to determine if two events co-occur in
 a statistically significant way. In particular, we assume you performed
 total_num_observations measurements and during those measurements you:
 - Observed event A to happen A_count times.
 - Observed event B to happen B_count times.
 - Observed AB_count co-occurrences of the events. That is, AB_count is the
 number of times the events happened together during the same measurement.
 - This function returns a number, COR, which can take any real value. It has
 the following interpretations:
 - COR == 0: there is no evidence of correlation between the two events.
 They appear to be unrelated.
 - COR > 0: There is evidence that A and B co-occur together. That is,
 they happen at the same times more often than you would expect if they
 were independent events. The larger the magnitude of COR the more
 evidence we have for the correlation.
 - COR < 0: There is evidence that A and B are anti-correlated. That is,
 when A happens B is unlikely to happen and vice versa. The larger the
 magnitude of COR the more evidence we have for the anti-correlation.
 - This function implements the simple likelihood ratio test discussed in the
 following paper:
 Dunning, Ted. "Accurate methods for the statistics of surprise and
 coincidence." Computational linguistics 19.1 (1993): 61-74.
 So for an extended discussion of the method see the above paper.
 !*/
// ----------------------------------------------------------------------------------------
 template <
 typename T
 >
 class running_stats
 {
 /*!
 REQUIREMENTS ON T
 - T must be a float, double, or long double type
 INITIAL VALUE
 - mean() == 0
 - current_n() == 0
 WHAT THIS OBJECT REPRESENTS
 This object represents something that can compute the running mean, 
 variance, skewness, and excess kurtosis of a stream of real numbers. 
 !*/
 public:
 running_stats(
 );
 /*!
 ensures
 - this object is properly initialized
 !*/
 void clear(
 );
 /*!
 ensures
 - this object has its initial value
 - clears all memory of any previous data points
 !*/
 T current_n (
 ) const;
 /*!
 ensures
 - returns the number of points given to this object so far. 
 !*/
 void add (
 const T& val
 );
 /*!
 ensures
 - updates the sum, mean, variance, skewness, and kurtosis stored in this
 object so that the new value is factored into them.
 - #sum() == sum() + val
 - #mean() == mean()*current_n()/(current_n()+1) + val/(current_n()+1).
 (i.e. the updated mean value that takes the new value into account)
 - #variance() == the updated variance that takes this new value into account.
 - #skewness() == the updated skewness that takes this new value into account.
 - #ex_kurtosis() == the updated kurtosis that takes this new value into account.
 - #current_n() == current_n() + 1
 !*/
 T sum (
 ) const;
 /*!
 ensures
 - returns the sum of all the values presented to this object
 so far.
 !*/
 T mean (
 ) const;
 /*!
 ensures
 - returns the mean of all the values presented to this object 
 so far.
 !*/
 T variance (
 ) const;
 /*!
 requires
 - current_n() > 1
 ensures
 - returns the unbiased sample variance of all the values presented to this
 object so far.
 !*/
 T stddev (
 ) const;
 /*!
 requires
 - current_n() > 1
 ensures
 - returns the unbiased sampled standard deviation of all the values
 presented to this object so far.
 !*/
 T skewness (
 ) const;
 /*!
 requires
 - current_n() > 2
 ensures
 - returns the unbiased sample skewness of all the values presented 
 to this object so far.
 !*/
 T ex_kurtosis(
 ) const;
 /*!
 requires
 - current_n() > 3
 ensures
 - returns the unbiased sample kurtosis of all the values presented 
 to this object so far.
 !*/
 T max (
 ) const;
 /*!
 requires
 - current_n() > 1
 ensures
 - returns the largest value presented to this object so far.
 !*/
 T min (
 ) const;
 /*!
 requires
 - current_n() > 1
 ensures
 - returns the smallest value presented to this object so far.
 !*/
 T scale (
 const T& val
 ) const;
 /*!
 requires
 - current_n() > 1
 ensures
 - return (val-mean())/stddev();
 !*/
 running_stats operator+ (
 const running_stats& rhs
 ) const;
 /*!
 ensures
 - returns a new running_stats object that represents the combination of all
 the values given to *this and rhs. That is, this function returns a
 running_stats object, R, that is equivalent to what you would obtain if
 all calls to this->add() and rhs.add() had instead been done to R.
 !*/
 };
 template <typename T>
 void serialize (
 const running_stats<T>& item, 
 std::ostream& out 
 );
 /*!
 provides serialization support 
 !*/
 template <typename T>
 void deserialize (
 running_stats<T>& item, 
 std::istream& in
 );
 /*!
 provides serialization support 
 !*/
// ----------------------------------------------------------------------------------------
 template <
 typename T
 >
 class running_scalar_covariance
 {
 /*!
 REQUIREMENTS ON T
 - T must be a float, double, or long double type
 INITIAL VALUE
 - mean_x() == 0
 - mean_y() == 0
 - current_n() == 0
 WHAT THIS OBJECT REPRESENTS
 This object represents something that can compute the running covariance 
 of a stream of real number pairs.
 !*/
 public:
 running_scalar_covariance(
 );
 /*!
 ensures
 - this object is properly initialized
 !*/
 void clear(
 );
 /*!
 ensures
 - this object has its initial value
 - clears all memory of any previous data points
 !*/
 void add (
 const T& x,
 const T& y
 );
 /*!
 ensures
 - updates the statistics stored in this object so that
 the new pair (x,y) is factored into them.
 - #current_n() == current_n() + 1
 !*/
 T current_n (
 ) const;
 /*!
 ensures
 - returns the number of points given to this object so far. 
 !*/
 T mean_x (
 ) const;
 /*!
 ensures
 - returns the mean value of all x samples presented to this object
 via add().
 !*/
 T mean_y (
 ) const;
 /*!
 ensures
 - returns the mean value of all y samples presented to this object
 via add().
 !*/
 T covariance (
 ) const;
 /*!
 requires
 - current_n() > 1
 ensures
 - returns the covariance between all the x and y samples presented
 to this object via add()
 !*/
 T correlation (
 ) const;
 /*!
 requires
 - current_n() > 1
 ensures
 - returns the correlation coefficient between all the x and y samples 
 presented to this object via add()
 !*/
 T variance_x (
 ) const;
 /*!
 requires
 - current_n() > 1
 ensures
 - returns the unbiased sample variance value of all x samples presented 
 to this object via add().
 !*/
 T variance_y (
 ) const;
 /*!
 requires
 - current_n() > 1
 ensures
 - returns the unbiased sample variance value of all y samples presented 
 to this object via add().
 !*/
 T stddev_x (
 ) const;
 /*!
 requires
 - current_n() > 1
 ensures
 - returns the unbiased sample standard deviation of all x samples
 presented to this object via add().
 !*/
 T stddev_y (
 ) const;
 /*!
 requires
 - current_n() > 1
 ensures
 - returns the unbiased sample standard deviation of all y samples
 presented to this object via add().
 !*/
 running_scalar_covariance operator+ (
 const running_covariance& rhs
 ) const;
 /*!
 ensures
 - returns a new running_scalar_covariance object that represents the
 combination of all the values given to *this and rhs. That is, this
 function returns a running_scalar_covariance object, R, that is
 equivalent to what you would obtain if all calls to this->add() and
 rhs.add() had instead been done to R.
 !*/
 };
// ----------------------------------------------------------------------------------------
 template <
 typename T
 >
 class running_scalar_covariance_decayed
 {
 /*!
 REQUIREMENTS ON T
 - T must be a float, double, or long double type
 INITIAL VALUE
 - mean_x() == 0
 - mean_y() == 0
 - current_n() == 0
 WHAT THIS OBJECT REPRESENTS
 This object represents something that can compute the running covariance of
 a stream of real number pairs. It is essentially the same as
 running_scalar_covariance except that it forgets about data it has seen
 after a certain period of time. It does this by exponentially decaying old
 statistics. 
 !*/
 public:
 running_scalar_covariance_decayed(
 T decay_halflife = 1000 
 );
 /*!
 requires
 - decay_halflife > 0
 ensures
 - #forget_factor() == std::pow(0.5, 1/decay_halflife);
 (i.e. after decay_halflife calls to add() the data given to the first add
 will be down weighted by 0.5 in the statistics stored in this object). 
 !*/
 T forget_factor (
 ) const;
 /*!
 ensures
 - returns the exponential forget factor used to forget old statistics when
 add() is called.
 !*/
 void add (
 const T& x,
 const T& y
 );
 /*!
 ensures
 - updates the statistics stored in this object so that
 the new pair (x,y) is factored into them.
 - #current_n() == current_n()*forget_factor() + forget_factor()
 - Down weights old statistics by a factor of forget_factor().
 !*/
 T current_n (
 ) const;
 /*!
 ensures
 - returns the effective number of points given to this object. As add()
 is called this value will converge to a constant, the value of which is
 based on the decay_halflife supplied to the constructor.
 !*/
 T mean_x (
 ) const;
 /*!
 ensures
 - returns the mean value of all x samples presented to this object
 via add().
 !*/
 T mean_y (
 ) const;
 /*!
 ensures
 - returns the mean value of all y samples presented to this object
 via add().
 !*/
 T covariance (
 ) const;
 /*!
 requires
 - current_n() > 1
 ensures
 - returns the covariance between all the x and y samples presented
 to this object via add()
 !*/
 T correlation (
 ) const;
 /*!
 requires
 - current_n() > 1
 ensures
 - returns the correlation coefficient between all the x and y samples 
 presented to this object via add()
 !*/
 T variance_x (
 ) const;
 /*!
 requires
 - current_n() > 1
 ensures
 - returns the sample variance value of all x samples presented 
 to this object via add().
 !*/
 T variance_y (
 ) const;
 /*!
 requires
 - current_n() > 1
 ensures
 - returns the sample variance value of all y samples presented 
 to this object via add().
 !*/
 T stddev_x (
 ) const;
 /*!
 requires
 - current_n() > 1
 ensures
 - returns the sample standard deviation of all x samples
 presented to this object via add().
 !*/
 T stddev_y (
 ) const;
 /*!
 requires
 - current_n() > 1
 ensures
 - returns the sample standard deviation of all y samples
 presented to this object via add().
 !*/
 };
// ----------------------------------------------------------------------------------------
 template <
 typename T
 >
 class running_stats_decayed
 {
 /*!
 REQUIREMENTS ON T
 - T must be a float, double, or long double type
 INITIAL VALUE
 - mean() == 0
 - current_n() == 0
 WHAT THIS OBJECT REPRESENTS
 This object represents something that can compute the running mean and
 variance of a stream of real numbers. It is similar to running_stats
 except that it forgets about data it has seen after a certain period of
 time. It does this by exponentially decaying old statistics. 
 !*/
 public:
 running_stats_decayed(
 T decay_halflife = 1000 
 );
 /*!
 requires
 - decay_halflife > 0
 ensures
 - #forget_factor() == std::pow(0.5, 1/decay_halflife);
 (i.e. after decay_halflife calls to add() the data given to the first add
 will be down weighted by 0.5 in the statistics stored in this object). 
 !*/
 T forget_factor (
 ) const;
 /*!
 ensures
 - returns the exponential forget factor used to forget old statistics when
 add() is called.
 !*/
 void add (
 const T& x
 );
 /*!
 ensures
 - updates the statistics stored in this object so that x is factored into
 them.
 - #current_n() == current_n()*forget_factor() + forget_factor()
 - Down weights old statistics by a factor of forget_factor().
 !*/
 T current_n (
 ) const;
 /*!
 ensures
 - returns the effective number of points given to this object. As add()
 is called this value will converge to a constant, the value of which is
 based on the decay_halflife supplied to the constructor.
 !*/
 T mean (
 ) const;
 /*!
 ensures
 - returns the mean value of all x samples presented to this object
 via add().
 !*/
 T variance (
 ) const;
 /*!
 requires
 - current_n() > 1
 ensures
 - returns the sample variance value of all x samples presented to this
 object via add().
 !*/
 T stddev (
 ) const;
 /*!
 requires
 - current_n() > 1
 ensures
 - returns the sample standard deviation of all x samples presented to this
 object via add().
 !*/
 };
 template <typename T>
 void serialize (
 const running_stats_decayed<T>& item, 
 std::ostream& out 
 );
 /*!
 provides serialization support 
 !*/
 template <typename T>
 void deserialize (
 running_stats_decayed<T>& item, 
 std::istream& in
 );
 /*!
 provides serialization support 
 !*/
// ----------------------------------------------------------------------------------------
 template <
 typename matrix_type
 >
 class running_covariance
 {
 /*!
 REQUIREMENTS ON matrix_type
 Must be some type of dlib::matrix.
 INITIAL VALUE
 - in_vector_size() == 0
 - current_n() == 0
 WHAT THIS OBJECT REPRESENTS
 This object is a simple tool for computing the mean and
 covariance of a sequence of vectors. 
 !*/
 public:
 typedef typename matrix_type::mem_manager_type mem_manager_type;
 typedef typename matrix_type::type scalar_type;
 typedef typename matrix_type::layout_type layout_type;
 typedef matrix<scalar_type,0,0,mem_manager_type,layout_type> general_matrix;
 typedef matrix<scalar_type,0,1,mem_manager_type,layout_type> column_matrix;
 running_covariance(
 );
 /*!
 ensures
 - this object is properly initialized
 !*/
 void clear(
 );
 /*!
 ensures
 - this object has its initial value
 - clears all memory of any previous data points
 !*/
 long current_n (
 ) const;
 /*!
 ensures
 - returns the number of samples that have been presented to this object
 !*/
 long in_vector_size (
 ) const;
 /*!
 ensures
 - if (this object has been presented with any input vectors or
 set_dimension() has been called) then
 - returns the dimension of the column vectors used with this object
 - else
 - returns 0
 !*/
 void set_dimension (
 long size
 );
 /*!
 requires
 - size > 0
 ensures
 - #in_vector_size() == size
 - #current_n() == 0
 !*/
 template <typename T>
 void add (
 const T& val
 );
 /*!
 requires
 - val must represent a column vector. It can either be a dlib::matrix
 object or some kind of unsorted sparse vector type. See the top of
 dlib/svm/sparse_vector_abstract.h for a definition of unsorted sparse vector.
 - val must have a number of dimensions which is compatible with the current
 setting of in_vector_size(). In particular, this means that the
 following must hold:
 - if (val is a dlib::matrix) then 
 - in_vector_size() == 0 || val.size() == val_vector_size()
 - else
 - max_index_plus_one(val) <= in_vector_size()
 - in_vector_size() > 0 
 (i.e. you must call set_dimension() prior to calling add() if
 you want to use sparse vectors.)
 ensures
 - updates the mean and covariance stored in this object so that
 the new value is factored into them.
 - if (val is a dlib::matrix) then
 - #in_vector_size() == val.size()
 !*/
 const column_matrix mean (
 ) const;
 /*!
 requires
 - in_vector_size() != 0 
 ensures
 - returns the mean of all the vectors presented to this object 
 so far.
 !*/
 const general_matrix covariance (
 ) const;
 /*!
 requires
 - in_vector_size() != 0 
 - current_n() > 1
 ensures
 - returns the unbiased sample covariance matrix for all the vectors 
 presented to this object so far.
 !*/
 const running_covariance operator+ (
 const running_covariance& item
 ) const;
 /*!
 requires
 - in_vector_size() == 0 || item.in_vector_size() == 0 || in_vector_size() == item.in_vector_size()
 (i.e. the in_vector_size() of *this and item must match or one must be zero)
 ensures
 - returns a new running_covariance object that represents the combination of all 
 the vectors given to *this and item. That is, this function returns a
 running_covariance object, R, that is equivalent to what you would obtain if all
 calls to this->add() and item.add() had instead been done to R.
 !*/
 };
// ----------------------------------------------------------------------------------------
 template <
 typename matrix_type
 >
 class running_cross_covariance
 {
 /*!
 REQUIREMENTS ON matrix_type
 Must be some type of dlib::matrix.
 INITIAL VALUE
 - x_vector_size() == 0
 - y_vector_size() == 0
 - current_n() == 0
 WHAT THIS OBJECT REPRESENTS
 This object is a simple tool for computing the mean and cross-covariance
 matrices of a sequence of pairs of vectors. 
 !*/
 public:
 typedef typename matrix_type::mem_manager_type mem_manager_type;
 typedef typename matrix_type::type scalar_type;
 typedef typename matrix_type::layout_type layout_type;
 typedef matrix<scalar_type,0,0,mem_manager_type,layout_type> general_matrix;
 typedef matrix<scalar_type,0,1,mem_manager_type,layout_type> column_matrix;
 running_cross_covariance(
 );
 /*!
 ensures
 - this object is properly initialized
 !*/
 void clear(
 );
 /*!
 ensures
 - This object has its initial value.
 - Clears all memory of any previous data points.
 !*/
 long x_vector_size (
 ) const;
 /*!
 ensures
 - if (this object has been presented with any input vectors or
 set_dimensions() has been called) then
 - returns the dimension of the x vectors given to this object via add().
 - else
 - returns 0
 !*/
 long y_vector_size (
 ) const;
 /*!
 ensures
 - if (this object has been presented with any input vectors or
 set_dimensions() has been called) then
 - returns the dimension of the y vectors given to this object via add().
 - else
 - returns 0
 !*/
 void set_dimensions (
 long x_size,
 long y_size
 );
 /*!
 requires
 - x_size > 0
 - y_size > 0
 ensures
 - #x_vector_size() == x_size
 - #y_vector_size() == y_size
 - #current_n() == 0
 !*/
 long current_n (
 ) const;
 /*!
 ensures
 - returns the number of samples that have been presented to this object.
 !*/
 template <typename T, typename U>
 void add (
 const T& x,
 const U& y
 );
 /*!
 requires
 - x and y must represent column vectors. They can either be dlib::matrix
 objects or some kind of unsorted sparse vector type. See the top of
 dlib/svm/sparse_vector_abstract.h for a definition of unsorted sparse vector.
 - x and y must have a number of dimensions which is compatible with the
 current setting of x_vector_size() and y_vector_size(). In particular,
 this means that the following must hold:
 - if (x or y is a sparse vector type) then
 - x_vector_size() > 0 && y_vector_size() > 0
 (i.e. you must call set_dimensions() prior to calling add() if
 you want to use sparse vectors.)
 - if (x is a dlib::matrix) then 
 - x_vector_size() == 0 || x.size() == x_vector_size()
 - else
 - max_index_plus_one(x) <= x_vector_size()
 - if (y is a dlib::matrix) then 
 - y_vector_size() == 0 || y.size() == y_vector_size()
 - else
 - max_index_plus_one(y) <= y_vector_size()
 ensures
 - updates the mean and cross-covariance matrices stored in this object so
 that the new (x,y) vector pair is factored into them.
 - if (x is a dlib::matrix) then
 - #x_vector_size() == x.size()
 - if (y is a dlib::matrix) then
 - #y_vector_size() == y.size()
 !*/
 const column_matrix mean_x (
 ) const;
 /*!
 requires
 - current_n() != 0 
 ensures
 - returns the mean of all the x vectors presented to this object so far.
 - The returned vector will have x_vector_size() dimensions.
 !*/
 const column_matrix mean_y (
 ) const;
 /*!
 requires
 - current_n() != 0 
 ensures
 - returns the mean of all the y vectors presented to this object so far.
 - The returned vector will have y_vector_size() dimensions.
 !*/
 const general_matrix covariance_xy (
 ) const;
 /*!
 requires
 - current_n() > 1
 ensures
 - returns the unbiased sample cross-covariance matrix for all the vector
 pairs presented to this object so far. In particular, returns a matrix
 M such that:
 - M.nr() == x_vector_size()
 - M.nc() == y_vector_size()
 - M == the cross-covariance matrix of the data given to add().
 !*/
 const running_cross_covariance operator+ (
 const running_cross_covariance& item
 ) const;
 /*!
 requires
 - x_vector_size() == 0 || item.x_vector_size() == 0 || x_vector_size() == item.x_vector_size()
 (i.e. the x_vector_size() of *this and item must match or one must be zero)
 - y_vector_size() == 0 || item.y_vector_size() == 0 || y_vector_size() == item.y_vector_size()
 (i.e. the y_vector_size() of *this and item must match or one must be zero)
 ensures
 - returns a new running_cross_covariance object that represents the
 combination of all the vectors given to *this and item. That is, this
 function returns a running_cross_covariance object, R, that is equivalent
 to what you would obtain if all calls to this->add() and item.add() had
 instead been done to R.
 !*/
 };
// ----------------------------------------------------------------------------------------
 template <
 typename matrix_type
 >
 class vector_normalizer
 {
 /*!
 REQUIREMENTS ON matrix_type
 - must be a dlib::matrix object capable of representing column 
 vectors
 INITIAL VALUE
 - in_vector_size() == 0
 - out_vector_size() == 0
 - means().size() == 0
 - std_devs().size() == 0
 WHAT THIS OBJECT REPRESENTS
 This object represents something that can learn to normalize a set 
 of column vectors. In particular, normalized column vectors should 
 have zero mean and a variance of one. 
 THREAD SAFETY
 Note that this object contains a cached matrix object it uses 
 to store intermediate results for normalization. This avoids
 needing to reallocate it every time this object performs normalization
 but also makes it non-thread safe. So make sure you don't share
 instances of this object between threads. 
 !*/
 public:
 typedef typename matrix_type::mem_manager_type mem_manager_type;
 typedef typename matrix_type::type scalar_type;
 typedef matrix_type result_type;
 template <typename vector_type>
 void train (
 const vector_type& samples
 );
 /*!
 requires
 - samples.size() > 0
 - samples == a column matrix or something convertible to a column 
 matrix via mat(). Also, x should contain 
 matrix_type objects that represent nonempty column vectors.
 - samples does not contain any infinite or NaN values
 ensures
 - #in_vector_size() == samples(0).nr()
 - #out_vector_size() == samples(0).nr()
 - This object has learned how to normalize vectors that look like
 vectors in the given set of samples. 
 - #means() == mean(samples)
 - #std_devs() == reciprocal(sqrt(variance(samples)));
 !*/
 long in_vector_size (
 ) const;
 /*!
 ensures
 - returns the number of rows that input vectors are
 required to contain if they are to be normalized by
 this object.
 !*/
 long out_vector_size (
 ) const;
 /*!
 ensures
 - returns the number of rows in the normalized vectors
 that come out of this object.
 !*/
 const matrix_type& means (
 ) const;
 /*!
 ensures 
 - returns a matrix M such that:
 - M.nc() == 1
 - M.nr() == in_vector_size()
 - M(i) == the mean of the ith input feature shown to train()
 !*/
 const matrix_type& std_devs (
 ) const;
 /*!
 ensures 
 - returns a matrix SD such that:
 - SD.nc() == 1
 - SD.nr() == in_vector_size()
 - SD(i) == the reciprocal of the standard deviation of the ith 
 input feature shown to train() 
 !*/
 
 const result_type& operator() (
 const matrix_type& x
 ) const;
 /*!
 requires
 - x.nr() == in_vector_size()
 - x.nc() == 1
 ensures
 - returns a normalized version of x, call it Z, that has the 
 following properties: 
 - Z.nr() == out_vector_size()
 - Z.nc() == 1
 - the mean of each element of Z is 0 
 - the variance of each element of Z is 1
 - Z == pointwise_multiply(x-means(), std_devs());
 !*/
 void swap (
 vector_normalizer& item
 );
 /*!
 ensures
 - swaps *this and item
 !*/
 };
 template <
 typename matrix_type
 >
 inline void swap (
 vector_normalizer<matrix_type>& a, 
 vector_normalizer<matrix_type>& b 
 ) { a.swap(b); } 
 /*!
 provides a global swap function
 !*/
 template <
 typename matrix_type,
 >
 void deserialize (
 vector_normalizer<matrix_type>& item, 
 std::istream& in
 ); 
 /*!
 provides deserialization support 
 !*/
 template <
 typename matrix_type,
 >
 void serialize (
 const vector_normalizer<matrix_type>& item, 
 std::ostream& out 
 ); 
 /*!
 provides serialization support 
 !*/
// ----------------------------------------------------------------------------------------
 template <
 typename matrix_type
 >
 class vector_normalizer_pca
 {
 /*!
 REQUIREMENTS ON matrix_type
 - must be a dlib::matrix object capable of representing column 
 vectors
 INITIAL VALUE
 - in_vector_size() == 0
 - out_vector_size() == 0
 - means().size() == 0
 - std_devs().size() == 0
 - pca_matrix().size() == 0
 WHAT THIS OBJECT REPRESENTS
 This object represents something that can learn to normalize a set 
 of column vectors. In particular, normalized column vectors should 
 have zero mean and a variance of one. 
 Also, this object uses principal component analysis for the purposes 
 of reducing the number of elements in a vector. 
 THREAD SAFETY
 Note that this object contains a cached matrix object it uses 
 to store intermediate results for normalization. This avoids
 needing to reallocate it every time this object performs normalization
 but also makes it non-thread safe. So make sure you don't share
 instances of this object between threads. 
 !*/
 public:
 typedef typename matrix_type::mem_manager_type mem_manager_type;
 typedef typename matrix_type::type scalar_type;
 typedef matrix<scalar_type,0,1,mem_manager_type> result_type;
 template <typename vector_type>
 void train (
 const vector_type& samples,
 const double eps = 0.99
 );
 /*!
 requires
 - 0 < eps <= 1
 - samples.size() > 0
 - samples == a column matrix or something convertible to a column 
 matrix via mat(). Also, x should contain 
 matrix_type objects that represent nonempty column vectors.
 - samples does not contain any infinite or NaN values
 ensures
 - This object has learned how to normalize vectors that look like
 vectors in the given set of samples. 
 - Principal component analysis is performed to find a transform 
 that might reduce the number of output features. 
 - #in_vector_size() == samples(0).nr()
 - 0 < #out_vector_size() <= samples(0).nr()
 - eps is a number that controls how "lossy" the pca transform will be.
 Large values of eps result in #out_vector_size() being larger and
 smaller values of eps result in #out_vector_size() being smaller. 
 - #means() == mean(samples)
 - #std_devs() == reciprocal(sqrt(variance(samples)));
 - #pca_matrix() == the PCA transform matrix that is out_vector_size()
 rows by in_vector_size() columns.
 !*/
 long in_vector_size (
 ) const;
 /*!
 ensures
 - returns the number of rows that input vectors are
 required to contain if they are to be normalized by
 this object.
 !*/
 long out_vector_size (
 ) const;
 /*!
 ensures
 - returns the number of rows in the normalized vectors
 that come out of this object.
 !*/
 const matrix<scalar_type,0,1,mem_manager_type>& means (
 ) const;
 /*!
 ensures 
 - returns a matrix M such that:
 - M.nc() == 1
 - M.nr() == in_vector_size()
 - M(i) == the mean of the ith input feature shown to train()
 !*/
 const matrix<scalar_type,0,1,mem_manager_type>& std_devs (
 ) const;
 /*!
 ensures 
 - returns a matrix SD such that:
 - SD.nc() == 1
 - SD.nr() == in_vector_size()
 - SD(i) == the reciprocal of the standard deviation of the ith 
 input feature shown to train() 
 !*/
 
 const matrix<scalar_type,0,0,mem_manager_type>& pca_matrix (
 ) const;
 /*!
 ensures
 - returns a matrix PCA such that:
 - PCA.nr() == out_vector_size()
 - PCA.nc() == in_vector_size()
 - PCA == the principal component analysis transformation 
 matrix 
 !*/
 const result_type& operator() (
 const matrix_type& x
 ) const;
 /*!
 requires
 - x.nr() == in_vector_size()
 - x.nc() == 1
 ensures
 - returns a normalized version of x, call it Z, that has the 
 following properties: 
 - Z.nr() == out_vector_size()
 - Z.nc() == 1
 - the mean of each element of Z is 0 
 - the variance of each element of Z is 1
 - Z == pca_matrix()*pointwise_multiply(x-means(), std_devs());
 !*/
 void swap (
 vector_normalizer_pca& item
 );
 /*!
 ensures
 - swaps *this and item
 !*/
 };
 template <
 typename matrix_type
 >
 inline void swap (
 vector_normalizer_pca<matrix_type>& a, 
 vector_normalizer_pca<matrix_type>& b 
 ) { a.swap(b); } 
 /*!
 provides a global swap function
 !*/
 template <
 typename matrix_type,
 >
 void deserialize (
 vector_normalizer_pca<matrix_type>& item, 
 std::istream& in
 ); 
 /*!
 provides deserialization support 
 !*/
 template <
 typename matrix_type,
 >
 void serialize (
 const vector_normalizer_pca<matrix_type>& item, 
 std::ostream& out 
 ); 
 /*!
 provides serialization support 
 !*/
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_STATISTICs_ABSTRACT_