#include "hmm.h"

Functions
CPHiddenMarkovModel *	cphmm_alloc (int K, int n, int M, int Q, int J)
void	cphmm_free (CPHiddenMarkovModel *m)
double	cphmm_get_transition_prob (CPHiddenMarkovModel *m, int k, int i, int j)
void	cphmm_init (CPHiddenMarkovModel m, double *X)
CPHiddenMarkovModel *	eeg_cphmm_init (EEG eeg, int channel, double *X)

Function Documentation

CPHiddenMarkovModel* cphmm_alloc	(	int	K,
		int	n,
		int	M,
		int	Q,
		int	J
	)

allocate memory for the CPM.

Parameters:

	K	number of trials
	n	number of samples per trial
	M	number of samples in latent trace
	Q	number of amplitude states
	J	maximum number of jumps in warping

Definition at line 55 of file hmm.c.

void cphmm_free ( CPHiddenMarkovModel * m )

deallocate the model's struct. RNG is dealloc'ed as well.

Definition at line 165 of file hmm.c.

double cphmm_get_transition_prob	(	CPHiddenMarkovModel *	m,
		int	k,
		int	i,
		int	j
	)

STATUS: work in progress return state transition probabilities from state i to state j in trace k:

$p^k(\pi_i|\pi_{i-1}) = p^k(\phi_i|\phi_{i-1})p^k(\tau_i|\tau_{i-1})$

where

$\pi_i = (\phi_i, \tau_i)$

Details see Listgarten et al. 2005

Definition at line 36 of file hmm.c.

initializing the model according to the parameters from Listgarten et al. 2005:

sigma = 15% of max(X[0])-min(X[0])
z = first observed time-series + gaussian noise (0, sigma) upsampling to match M
u = (1,...,1)
d,s = uniform
q is filled with Q numbers evenly spaced in log-space [0,2]
Parameters:

m the model

X the data, K x n

Definition at line 97 of file hmm.c.

CPHiddenMarkovModel* eeg_cphmm_init	(	EEG *	eeg,
		int	channel,
		double **	X
	)

init the model from EEG-data. The data matrix X is a return value.

Parameters:

	eeg
	channel	- which channel is used?
	X	- the converted data. X should contain K empty pointers. These are set to the appropriate data in eeg (not copied).

Definition at line 140 of file hmm.c.

src/hmm.c File Reference