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)
