def logLikelihood(model, all_obs, c, r, m, t): return logL_multiseq(model, all_obs, FIXED_COL_MAP, c, r, m, t, prepare_matrices=zipHMM_prepare_matrices, single_logL=zipHMM_single_logL)
def logLikelihood(model, all_obs, c,r,m,t): return logL_multiseq(model, all_obs, COL_MAP, c,r,m,t, prepare_matrices=zipHMM_prepare_matrices, single_logL=zipHMM_single_logL)
def logLikelihood(model, all_obs, c, r, m, t): return logL_multiseq(model, all_obs, COL_MAP, c, r, m, t)
def logLikelihood(model, all_obs, c,r,m,t): return logL_multiseq(model, all_obs, COL_MAP, c,r,m,t)
def logLikelihood(model, all_obs, c,r,m,t): # The simplest way to get a likelihood is to call logL_multiseq which will # assign actual times to your intervals, construct the matrices and run the # forward algorithm with the resulting matrices. return logL_multiseq(model, all_obs, FIXED_COL_MAP, c,r,m,t)
def logLikelihood(model, all_obs, c, r, m, t): # The simplest way to get a likelihood is to call logL_multiseq which will # assign actual times to your intervals, construct the matrices and run the # forward algorithm with the resulting matrices. return logL_multiseq(model, all_obs, FIXED_COL_MAP, c, r, m, t)