itpp::MOG_diag_kmeans_sup Class Reference

support class for MOG_diag_kmeans() More...

#include <mog_diag_kmeans.h>

List of all members.

Public Member Functions

MOG_diag_kmeans_sup ()

Default constructor.

~MOG_diag_kmeans_sup ()

Default destructor.

void run (MOG_diag &model_in, Array< vec > &X_in, int max_iter_in=10, double trust_in=0.5, bool normalise_in=true, bool verbose_in=false)

ADD DOCUMENTATION HERE.

void cleanup ()

Release memory used by the model. The model will be empty.

void load (const std::string &name_in)

Initialise the model by loading the parameters from a model file.

void convert_to_full ()

Do nothing. Present for compatability with the MOG_generic class.

double log_lhood_single_gaus (const double *c_x_in, const int k) const

calculate the log likelihood of C vector c_x_in using only Gaussian k

double log_lhood_single_gaus (const vec &x_in, const int k) const

calculate the log likelihood of IT++ vector x_in using only Gaussian k

virtual double log_lhood_single_gaus (const vec &x_in, const int k)

calculate the log likelihood of vector x_in using only Gaussian k

double log_lhood (const double *c_x_in)

calculate the log likelihood of C vector c_x_in

double log_lhood (const vec &x_in)

calculate the log likelihood of IT++ vector x_in

double lhood (const double *c_x_in)

calculate the likelihood of C vector c_x_in

double lhood (const vec &x_in)

calculate the likelihood of IT++ vector x_in

double avg_log_lhood (const double **c_x_in, int N)

calculate the average log likelihood of an array of C vectors ( c_x_in )

double avg_log_lhood (const Array< vec > &X_in)

calculate the average log likelihood of an array of IT++ vectors ( X_in )

void init ()

Initialise the model to be empty.

void init (const int &K_in, const int &D_in, bool full_in=false)

initialise the model so that all Gaussians have zero mean and unit variance for all dimensions

void init (Array< vec > &means_in, bool full_in=false)

Initialise the model using user supplied mean vectors.

void init (Array< vec > &means_in, Array< vec > &diag_covs_in, vec &weights_in)

Initialise the model using user supplied parameters (diagonal covariance version).

void init (Array< vec > &means_in, Array< mat > &full_covs_in, vec &weights_in)

Initialise the model using user supplied parameters (full covariance version).

bool is_valid () const

Returns true if the model's parameters are valid.

bool is_full () const

Returns true if the model has full covariance matrices.

int get_K () const

Return the number of Gaussians.

int get_D () const

Return the dimensionality.

vec get_weights () const

Obtain a copy of the weight vector.

Array< vec > get_means () const

Obtain a copy of the array of mean vectors.

Array< vec > get_diag_covs () const

Obtain a copy of the array of diagonal covariance vectors.

Array< mat > get_full_covs () const

Obtain a copy of the array of full covariance matrices.

void set_means (Array< vec > &means_in)

Set the means of the model.

void set_diag_covs (Array< vec > &diag_covs_in)

Set the diagonal covariance vectors of the model.

void set_full_covs (Array< mat > &full_covs_in)

Set the full covariance matrices of the model.

void set_weights (vec &weights_in)

Set the weight vector of the model.

void set_means_zero ()

Set the means in the model to be zero.

void set_diag_covs_unity ()

Set the diagonal covariance vectors to be unity.

void set_full_covs_unity ()

Set the full covariance matrices to be unity.

void set_weights_uniform ()

Set all the weights to 1/K, where K is the number of Gaussians.

void set_checks (bool do_checks_in)

Enable/disable internal checks for likelihood functions.

void set_paranoid (bool paranoid_in)

Enable/disable paranoia about numerical stability.

virtual void save (const std::string &name_in) const

Save the model's parameters to a model file.

virtual void join (const MOG_generic &B_in)

Mathematically join the model with a user supplied model.

virtual void convert_to_diag ()

Convert the model to use diagonal covariances.

Protected Member Functions

double dist (const double *x, const double *y) const

ADD DOCUMENTATION HERE.

void assign_to_means ()

ADD DOCUMENTATION HERE.

void recalculate_means ()

ADD DOCUMENTATION HERE.

bool dezombify_means ()

ADD DOCUMENTATION HERE.

double measure_change () const

ADD DOCUMENTATION HERE.

void initial_means ()

ADD DOCUMENTATION HERE.

void iterate ()

ADD DOCUMENTATION HERE.

void calc_means ()

ADD DOCUMENTATION HERE.

void calc_covs ()

ADD DOCUMENTATION HERE.

void calc_weights ()

ADD DOCUMENTATION HERE.

void normalise_vectors ()

ADD DOCUMENTATION HERE.

void unnormalise_vectors ()

ADD DOCUMENTATION HERE.

void unnormalise_means ()

ADD DOCUMENTATION HERE.

void setup_means ()

additional processing of mean vectors, done as the last step of mean initialisation

void setup_covs ()

additional processing of covariance vectors/matrices, done as the last step of covariance initialisation

void setup_weights ()

additional processing of the weight vector, done as the last step of weight initialisation

void setup_misc ()

additional processing of miscellaneous parameters, done as the last step of overall initialisation

double log_lhood_single_gaus_internal (const double *c_x_in, const int k) const

ADD DOCUMENTATION HERE.

double log_lhood_single_gaus_internal (const vec &x_in, const int k) const

ADD DOCUMENTATION HERE.

virtual double log_lhood_single_gaus_internal (const vec &x_in, const int k)

ADD DOCUMENTATION HERE.

double log_lhood_internal (const double *c_x_in)

ADD DOCUMENTATION HERE.

double log_lhood_internal (const vec &x_in)

ADD DOCUMENTATION HERE.

double lhood_internal (const double *c_x_in)

ADD DOCUMENTATION HERE.

double lhood_internal (const vec &x_in)

ADD DOCUMENTATION HERE.

double ** enable_c_access (Array< vec > &A_in)

Enable C style access to an Array of vectors (vec).

int ** enable_c_access (Array< ivec > &A_in)

Enable C style access to an Array of vectors (ivec).

double * enable_c_access (vec &v_in)

Enable C style access to a vector (vec).

int * enable_c_access (ivec &v_in)

Enable C style access to a vector (ivec).

double ** disable_c_access (double **A_in)

Disable C style access to an Array of vectors (vec).

int ** disable_c_access (int **A_in)

Disable C style access to an Array of vectors (ivec).

double * disable_c_access (double *v_in)

Disable C style access to a vector (vec).

int * disable_c_access (int *v_in)

Disable C style access to a vector (ivec).

void zero_all_ptrs ()

ADD DOCUMENTATION HERE.

void free_all_ptrs ()

ADD DOCUMENTATION HERE.

bool check_size (const vec &x_in) const

Check if vector x_in has the same dimensionality as the model.

bool check_size (const Array< vec > &X_in) const

Check if all vectors in Array X_in have the same dimensionality as the model.

bool check_array_uniformity (const Array< vec > &A) const

Check if all vectors in Array X_in have the same dimensionality.

void set_means_internal (Array< vec > &means_in)

ADD DOCUMENTATION HERE.

void set_diag_covs_internal (Array< vec > &diag_covs_in)

ADD DOCUMENTATION HERE.

void set_full_covs_internal (Array< mat > &full_covs_in)

ADD DOCUMENTATION HERE.

void set_weights_internal (vec &_weigths)

ADD DOCUMENTATION HERE.

void set_means_zero_internal ()

ADD DOCUMENTATION HERE.

void set_diag_covs_unity_internal ()

ADD DOCUMENTATION HERE.

void set_full_covs_unity_internal ()

ADD DOCUMENTATION HERE.

void set_weights_uniform_internal ()

ADD DOCUMENTATION HERE.

void convert_to_diag_internal ()

ADD DOCUMENTATION HERE.

void convert_to_full_internal ()

ADD DOCUMENTATION HERE.

Protected Attributes

int max_iter

Maximum number of iterations.

double trust

trust factor, where 0 <= trust <= 1.

bool verbose

Whether we print the progress.

int N

number of training vectors

double ** c_X

'C' pointers to training vectors

Array< vec > means_old

means from the previous iteration, used to measure progress

double ** c_means_old

'C' pointers to old means

Array< ivec > partitions

contains indices of vectors assigned to each mean

int ** c_partitions

'C' pointers to partition vectors

ivec count

keeps a count of the number of vectors assigned to each mean

int * c_count

'C' pointer to the count vector

double ** c_means

pointers to the mean vectors

double ** c_diag_covs

pointers to the covariance vectors

double ** c_diag_covs_inv_etc

pointers to the inverted covariance vectors

double * c_weights

pointer to the weight vector

double * c_log_weights

pointer to the log version of the weight vector

double * c_log_det_etc

pointer to the log_det_etc vector

bool do_checks

indicates whether checks on input data are done

bool valid

indicates whether the parameters are valid

bool full

indicates whether we are using full or diagonal covariance matrices

bool paranoid

indicates whether we are paranoid about numerical stability

int K

number of gaussians

int D

dimensionality

Array< vec > means

means

Array< vec > diag_covs

diagonal covariance matrices, stored as vectors

Array< mat > full_covs

full covariance matrices

vec weights

weights

double log_max_K

Pre-calcualted std::log(std::numeric_limits<double>::max() / K), where K is the number of Gaussians.

vec log_det_etc

Gaussian specific pre-calcualted constants.

vec log_weights

Pre-calculated log versions of the weights.

Array< mat > full_covs_inv

Pre-calcuated inverted version of each full covariance matrix.

Array< vec > diag_covs_inv_etc

Pre-calcuated inverted version of each diagonal covariance vector, where the covariance elements are first multiplied by two.

Detailed Description

support class for MOG_diag_kmeans()

Author:: Conrad Sanderson

Member Function Documentation

void itpp::MOG_diag::cleanup ( ) [inline, virtual, inherited]

Release memory used by the model. The model will be empty.

Note:: The likelihood functions are not useable until the model's parameters are re-initialised

Reimplemented from itpp::MOG_generic.

References itpp::MOG_diag::free_all_ptrs().

Referenced by itpp::MOG_diag_EM_sup::ml(), run(), and itpp::MOG_diag::~MOG_diag().

void itpp::MOG_generic::convert_to_diag ( ) [virtual, inherited]

Convert the model to use diagonal covariances.

Note:: If the model is already diagonal, nothing is done. If the model has full covariance matrices, this results in irreversible information loss (in effect the off-diagonal covariance elements are now zero)

References itpp::MOG_generic::convert_to_diag_internal(), and itpp::MOG_generic::valid.

Referenced by itpp::MOG_diag::load(), and itpp::MOG_diag::MOG_diag().

void itpp::MOG_generic::init	(	Array< vec > &	means_in,
		Array< mat > &	full_covs_in,
		vec &	weights_in
	)			`[inherited]`

Initialise the model using user supplied parameters (full covariance version).

Parameters:

	means_in	Array of mean vectors
	full_covs_in	Array of covariance matrices
	weights_in	vector of weights

Note:: The number of mean vectors, covariance matrices and weights must be the same

References itpp::MOG_generic::check_array_uniformity(), itpp::MOG_generic::D, itpp::MOG_generic::do_checks, itpp::MOG_generic::full, it_assert, itpp::MOG_generic::K, itpp::MOG_generic::paranoid, itpp::MOG_generic::set_full_covs_internal(), itpp::MOG_generic::set_means_internal(), itpp::MOG_generic::set_weights_internal(), itpp::MOG_generic::setup_misc(), itpp::Array< T >::size(), and itpp::MOG_generic::valid.

void itpp::MOG_generic::init	(	Array< vec > &	means_in,
		Array< vec > &	diag_covs_in,
		vec &	weights_in
	)			`[inherited]`

Initialise the model using user supplied parameters (diagonal covariance version).

Parameters:

	means_in	Array of mean vectors
	diag_covs_in	Array of vectors representing diagonal covariances
	weights_in	vector of weights

Note:: The number of mean vectors, covariance vectors and weights must be the same

References itpp::MOG_generic::check_array_uniformity(), itpp::MOG_generic::D, itpp::MOG_generic::do_checks, itpp::MOG_generic::full, it_assert, itpp::MOG_generic::K, itpp::MOG_generic::paranoid, itpp::MOG_generic::set_diag_covs_internal(), itpp::MOG_generic::set_means_internal(), itpp::MOG_generic::set_weights_internal(), itpp::MOG_generic::setup_misc(), itpp::Array< T >::size(), and itpp::MOG_generic::valid.

void itpp::MOG_generic::init	(	Array< vec > &	means_in,
		bool	full_in = `false`
	)			`[inherited]`

Initialise the model using user supplied mean vectors.

Parameters:

	means_in	Array of mean vectors
	full_in	If true, use full covariance matrices; if false, use diagonal covariance matrices. Default = false.

Note:: The number of mean vectors specifies the number of Gaussians. The covariance matrices are set to the identity matrix. The weights for all Gaussians are the same, equal to 1/K, where K is the number of Gaussians

References itpp::MOG_generic::check_array_uniformity(), itpp::MOG_generic::D, itpp::MOG_generic::do_checks, itpp::MOG_generic::full, it_assert, itpp::MOG_generic::K, itpp::MOG_generic::paranoid, itpp::MOG_generic::set_diag_covs_unity_internal(), itpp::MOG_generic::set_full_covs_unity_internal(), itpp::MOG_generic::set_means(), itpp::MOG_generic::set_weights_uniform_internal(), itpp::MOG_generic::setup_misc(), itpp::Array< T >::size(), and itpp::MOG_generic::valid.

void itpp::MOG_generic::init	(	const int &	K_in,
		const int &	D_in,
		bool	full_in = `false`
	)			`[inherited]`

initialise the model so that all Gaussians have zero mean and unit variance for all dimensions

Parameters:

	K_in	Number of Gaussians
	D_in	Dimensionality
	full_in	If true, use full covariance matrices; if false, use diagonal covariance matrices. Default = false.

References itpp::MOG_generic::D, itpp::MOG_generic::do_checks, itpp::MOG_generic::full, it_assert, itpp::MOG_generic::K, itpp::MOG_generic::paranoid, itpp::MOG_generic::set_diag_covs_unity_internal(), itpp::MOG_generic::set_full_covs_unity_internal(), itpp::MOG_generic::set_means_zero_internal(), itpp::MOG_generic::set_weights_uniform_internal(), itpp::MOG_generic::setup_misc(), and itpp::MOG_generic::valid.

void itpp::MOG_generic::init ( ) [inherited]

Initialise the model to be empty.

Note:: The likelihood functions are not useable until the model's parameters are set

References itpp::MOG_generic::cleanup().

Referenced by itpp::MOG_generic::join(), itpp::MOG_generic::load(), itpp::MOG_diag_EM_sup::ml(), itpp::MOG_diag::MOG_diag(), itpp::MOG_generic::MOG_generic(), and run().

void itpp::MOG_generic::join ( const MOG_generic & B_in ) [virtual, inherited]

Mathematically join the model with a user supplied model.

Parameters:

B_in

user supplied model

Note:: The Arrays of mean vectors and covariance vectors/matrices from the two models are simply concatenated, while the weights of the resultant model are a function of the original weights and numbers of Gaussians from both models. Specifically, $w_{new} = [ \alpha \cdot w_{A} ~~~ \beta \cdot w_{B} ]^T$ , where $w_{new}$ is the new weight vector, $w_{A}$ and $w_{B}$ are the weight vectors from model A and B, while $\alpha = K_A / (K_A + KB_in)$ and $\beta = 1-\alpha$ . In turn, and are the numbers of Gaussians in model A and B, respectively.

See On transforming statistical models... for more information.

void itpp::MOG_diag::load ( const std::string & name_in ) [virtual, inherited]

Initialise the model by loading the parameters from a model file.

Parameters:

name_in

The model's filename

Note:: If the model file contains a full covariance matrix model, the covariance matrices will be converted to be diagonal after loading.

Reimplemented from itpp::MOG_generic.

References itpp::MOG_generic::convert_to_diag(), and itpp::MOG_generic::full.

Referenced by itpp::MOG_diag::MOG_diag().

void itpp::MOG_generic::save ( const std::string & name_in ) const [virtual, inherited]

Save the model's parameters to a model file.

Parameters:

name_in

The model's filename

References itpp::it_file::close(), itpp::MOG_generic::diag_covs, itpp::MOG_generic::full, itpp::MOG_generic::full_covs, itpp::MOG_generic::means, itpp::MOG_generic::valid, and itpp::MOG_generic::weights.

void itpp::MOG_generic::set_checks ( bool do_checks_in ) [inline, inherited]

Enable/disable internal checks for likelihood functions.

Parameters:

do_checks_in

If true, checks are enabled; if false, checks are disabled

Note:: Disabling checks will provide a speedup in the likelihood functions. Disable them only when you're happy that everything is working correctly.

References itpp::MOG_generic::do_checks.

void itpp::MOG_generic::set_diag_covs ( Array< vec > & diag_covs_in ) [inherited]

Set the diagonal covariance vectors of the model.

Note:: The number of diagonal covariance vectors must match the number of Gaussians in the model

References itpp::MOG_generic::set_diag_covs_internal(), and itpp::MOG_generic::valid.

void itpp::MOG_generic::set_full_covs ( Array< mat > & full_covs_in ) [inherited]

Set the full covariance matrices of the model.

Note:: The number of covariance matrices must match the number of Gaussians in the model

References itpp::MOG_generic::set_full_covs_internal(), and itpp::MOG_generic::valid.

void itpp::MOG_generic::set_means ( Array< vec > & means_in ) [inherited]

Set the means of the model.

Note:: The number of means must match the number of Gaussians in the model

References itpp::MOG_generic::set_means_internal(), and itpp::MOG_generic::valid.

Referenced by itpp::MOG_generic::init().

void itpp::MOG_generic::set_paranoid ( bool paranoid_in ) [inline, inherited]

Enable/disable paranoia about numerical stability.

Parameters:

paranoid_in

If true, calculate likelihoods using a safer, but slower method.

References itpp::MOG_generic::paranoid.

void itpp::MOG_generic::set_weights ( vec & weights_in ) [inherited]

Set the weight vector of the model.

Note:: The number of elements in the weight vector must match the number of Gaussians in the model

References itpp::MOG_generic::set_weights_internal(), and itpp::MOG_generic::valid.

Member Data Documentation

vec itpp::MOG_generic::log_det_etc [protected, inherited]

Gaussian specific pre-calcualted constants.

Note:: Vector of pre-calculated $-\frac{D}{2}\log(2\pi) -\frac{1}{2}\log(|\Sigma|)$ for each Gaussian, where is the dimensionality and $|\Sigma|$ is the determinant for the Gaussian's covariance matrix $\Sigma$ .

Referenced by itpp::MOG_generic::cleanup(), itpp::MOG_generic::log_lhood_single_gaus_internal(), itpp::MOG_generic::setup_covs(), and itpp::MOG_diag::setup_covs().

double itpp::MOG_diag_kmeans_sup::trust [protected]

trust factor, where 0 <= trust <= 1.

Note:: The higher the trust factor, the more we trust the estimates of covariance matrices and weights.

Referenced by calc_covs(), calc_weights(), and run().

The documentation for this class was generated from the following files:


Public Member Functions
	MOG_diag_kmeans_sup ()
	Default constructor.
	~MOG_diag_kmeans_sup ()
	Default destructor.
void	run (MOG_diag &model_in, Array< vec > &X_in, int max_iter_in=10, double trust_in=0.5, bool normalise_in=true, bool verbose_in=false)
	ADD DOCUMENTATION HERE.
void	cleanup ()
	Release memory used by the model. The model will be empty.
void	load (const std::string &name_in)
	Initialise the model by loading the parameters from a model file.
void	convert_to_full ()
	Do nothing. Present for compatability with the MOG_generic class.
double	log_lhood_single_gaus (const double *c_x_in, const int k) const
	calculate the log likelihood of C vector `c_x_in` using only Gaussian `k`
double	log_lhood_single_gaus (const vec &x_in, const int k) const
	calculate the log likelihood of IT++ vector `x_in` using only Gaussian `k`
virtual double	log_lhood_single_gaus (const vec &x_in, const int k)
	calculate the log likelihood of vector `x_in` using only Gaussian `k`
double	log_lhood (const double *c_x_in)
	calculate the log likelihood of C vector `c_x_in`
double	log_lhood (const vec &x_in)
	calculate the log likelihood of IT++ vector `x_in`
double	lhood (const double *c_x_in)
	calculate the likelihood of C vector `c_x_in`
double	lhood (const vec &x_in)
	calculate the likelihood of IT++ vector `x_in`
double	avg_log_lhood (const double **c_x_in, int N)
	calculate the average log likelihood of an array of C vectors ( `c_x_in` )
double	avg_log_lhood (const Array< vec > &X_in)
	calculate the average log likelihood of an array of IT++ vectors ( `X_in` )
void	init ()
	Initialise the model to be empty.
void	init (const int &K_in, const int &D_in, bool full_in=false)
	initialise the model so that all Gaussians have zero mean and unit variance for all dimensions
void	init (Array< vec > &means_in, bool full_in=false)
	Initialise the model using user supplied mean vectors.
void	init (Array< vec > &means_in, Array< vec > &diag_covs_in, vec &weights_in)
	Initialise the model using user supplied parameters (diagonal covariance version).
void	init (Array< vec > &means_in, Array< mat > &full_covs_in, vec &weights_in)
	Initialise the model using user supplied parameters (full covariance version).
bool	is_valid () const
	Returns true if the model's parameters are valid.
bool	is_full () const
	Returns true if the model has full covariance matrices.
int	get_K () const
	Return the number of Gaussians.
int	get_D () const
	Return the dimensionality.
vec	get_weights () const
	Obtain a copy of the weight vector.
Array< vec >	get_means () const
	Obtain a copy of the array of mean vectors.
Array< vec >	get_diag_covs () const
	Obtain a copy of the array of diagonal covariance vectors.
Array< mat >	get_full_covs () const
	Obtain a copy of the array of full covariance matrices.
void	set_means (Array< vec > &means_in)
	Set the means of the model.
void	set_diag_covs (Array< vec > &diag_covs_in)
	Set the diagonal covariance vectors of the model.
void	set_full_covs (Array< mat > &full_covs_in)
	Set the full covariance matrices of the model.
void	set_weights (vec &weights_in)
	Set the weight vector of the model.
void	set_means_zero ()
	Set the means in the model to be zero.
void	set_diag_covs_unity ()
	Set the diagonal covariance vectors to be unity.
void	set_full_covs_unity ()
	Set the full covariance matrices to be unity.
void	set_weights_uniform ()
	Set all the weights to 1/K, where K is the number of Gaussians.
void	set_checks (bool do_checks_in)
	Enable/disable internal checks for likelihood functions.
void	set_paranoid (bool paranoid_in)
	Enable/disable paranoia about numerical stability.
virtual void	save (const std::string &name_in) const
	Save the model's parameters to a model file.
virtual void	join (const MOG_generic &B_in)
	Mathematically join the model with a user supplied model.
virtual void	convert_to_diag ()
	Convert the model to use diagonal covariances.
Protected Member Functions
double	dist (const double x, const double y) const
	ADD DOCUMENTATION HERE.
void	assign_to_means ()
	ADD DOCUMENTATION HERE.
void	recalculate_means ()
	ADD DOCUMENTATION HERE.
bool	dezombify_means ()
	ADD DOCUMENTATION HERE.
double	measure_change () const
	ADD DOCUMENTATION HERE.
void	initial_means ()
	ADD DOCUMENTATION HERE.
void	iterate ()
	ADD DOCUMENTATION HERE.
void	calc_means ()
	ADD DOCUMENTATION HERE.
void	calc_covs ()
	ADD DOCUMENTATION HERE.
void	calc_weights ()
	ADD DOCUMENTATION HERE.
void	normalise_vectors ()
	ADD DOCUMENTATION HERE.
void	unnormalise_vectors ()
	ADD DOCUMENTATION HERE.
void	unnormalise_means ()
	ADD DOCUMENTATION HERE.
void	setup_means ()
	additional processing of mean vectors, done as the last step of mean initialisation
void	setup_covs ()
	additional processing of covariance vectors/matrices, done as the last step of covariance initialisation
void	setup_weights ()
	additional processing of the weight vector, done as the last step of weight initialisation
void	setup_misc ()
	additional processing of miscellaneous parameters, done as the last step of overall initialisation
double	log_lhood_single_gaus_internal (const double *c_x_in, const int k) const
	ADD DOCUMENTATION HERE.
double	log_lhood_single_gaus_internal (const vec &x_in, const int k) const
	ADD DOCUMENTATION HERE.
virtual double	log_lhood_single_gaus_internal (const vec &x_in, const int k)
	ADD DOCUMENTATION HERE.
double	log_lhood_internal (const double *c_x_in)
	ADD DOCUMENTATION HERE.
double	log_lhood_internal (const vec &x_in)
	ADD DOCUMENTATION HERE.
double	lhood_internal (const double *c_x_in)
	ADD DOCUMENTATION HERE.
double	lhood_internal (const vec &x_in)
	ADD DOCUMENTATION HERE.
double **	enable_c_access (Array< vec > &A_in)
	Enable C style access to an Array of vectors (vec).
int **	enable_c_access (Array< ivec > &A_in)
	Enable C style access to an Array of vectors (ivec).
double *	enable_c_access (vec &v_in)
	Enable C style access to a vector (vec).
int *	enable_c_access (ivec &v_in)
	Enable C style access to a vector (ivec).
double **	disable_c_access (double **A_in)
	Disable C style access to an Array of vectors (vec).
int **	disable_c_access (int **A_in)
	Disable C style access to an Array of vectors (ivec).
double *	disable_c_access (double *v_in)
	Disable C style access to a vector (vec).
int *	disable_c_access (int *v_in)
	Disable C style access to a vector (ivec).
void	zero_all_ptrs ()
	ADD DOCUMENTATION HERE.
void	free_all_ptrs ()
	ADD DOCUMENTATION HERE.
bool	check_size (const vec &x_in) const
	Check if vector `x_in` has the same dimensionality as the model.
bool	check_size (const Array< vec > &X_in) const
	Check if all vectors in Array `X_in` have the same dimensionality as the model.
bool	check_array_uniformity (const Array< vec > &A) const
	Check if all vectors in Array `X_in` have the same dimensionality.
void	set_means_internal (Array< vec > &means_in)
	ADD DOCUMENTATION HERE.
void	set_diag_covs_internal (Array< vec > &diag_covs_in)
	ADD DOCUMENTATION HERE.
void	set_full_covs_internal (Array< mat > &full_covs_in)
	ADD DOCUMENTATION HERE.
void	set_weights_internal (vec &_weigths)
	ADD DOCUMENTATION HERE.
void	set_means_zero_internal ()
	ADD DOCUMENTATION HERE.
void	set_diag_covs_unity_internal ()
	ADD DOCUMENTATION HERE.
void	set_full_covs_unity_internal ()
	ADD DOCUMENTATION HERE.
void	set_weights_uniform_internal ()
	ADD DOCUMENTATION HERE.
void	convert_to_diag_internal ()
	ADD DOCUMENTATION HERE.
void	convert_to_full_internal ()
	ADD DOCUMENTATION HERE.
Protected Attributes
int	max_iter
	Maximum number of iterations.
double	trust
	trust factor, where 0 <= trust <= 1.
bool	verbose
	Whether we print the progress.
int	N
	number of training vectors
double **	c_X
	'C' pointers to training vectors
Array< vec >	means_old
	means from the previous iteration, used to measure progress
double **	c_means_old
	'C' pointers to old means
Array< ivec >	partitions
	contains indices of vectors assigned to each mean
int **	c_partitions
	'C' pointers to partition vectors
ivec	count
	keeps a count of the number of vectors assigned to each mean
int *	c_count
	'C' pointer to the count vector
double **	c_means
	pointers to the mean vectors
double **	c_diag_covs
	pointers to the covariance vectors
double **	c_diag_covs_inv_etc
	pointers to the inverted covariance vectors
double *	c_weights
	pointer to the weight vector
double *	c_log_weights
	pointer to the log version of the weight vector
double *	c_log_det_etc
	pointer to the log_det_etc vector
bool	do_checks
	indicates whether checks on input data are done
bool	valid
	indicates whether the parameters are valid
bool	full
	indicates whether we are using full or diagonal covariance matrices
bool	paranoid
	indicates whether we are paranoid about numerical stability
int	K
	number of gaussians
int	D
	dimensionality
Array< vec >	means
	means
Array< vec >	diag_covs
	diagonal covariance matrices, stored as vectors
Array< mat >	full_covs
	full covariance matrices
vec	weights
	weights
double	log_max_K
	Pre-calcualted std::log(std::numeric_limits<double>::max() / K), where K is the number of Gaussians.
vec	log_det_etc
	Gaussian specific pre-calcualted constants.
vec	log_weights
	Pre-calculated log versions of the weights.
Array< mat >	full_covs_inv
	Pre-calcuated inverted version of each full covariance matrix.
Array< vec >	diag_covs_inv_etc
	Pre-calcuated inverted version of each diagonal covariance vector, where the covariance elements are first multiplied by two.