def metropolis(tssb,
iters=1000,
std=0.01,
burnin=0,
n_ssms=0,
n_cnvs=0,
fin1='',
fin2='',
rseed=1,
ntps=5):
wts, nodes = tssb.get_mixture()
# file names
FNAME_SSM_DATA = fin1
FNAME_CNV_DATA = fin2
basename = os.path.basename(fin1).split('.')[0]
FNAME_C_TREE = 'c_tree_' + basename + '_' + str(rseed) + '.txt'
FNAME_C_DATA_STATES = 'c_data_states_' + basename + '_' + str(
rseed) + '.txt'
FNAME_C_PARAMS = 'c_params_' + basename + '_' + str(rseed) + '.txt'
FNAME_C_MH_ARATIO = 'c_mh_ar_' + basename + '_' + str(rseed) + '.txt'
NTPS = str(ntps)
## initialize the MH sampler###########
#for tp in arange(ntps):
# sample_cons_params(tssb,tp)
# update_params(tssb,tp)
######################################
## prepare to call the c++ code ###########
u2.set_node_height(tssb)
write_tree(tssb, n_ssms, FNAME_C_TREE) #write the current tree to the disk
u2.map_datum_to_node(tssb)
write_data_state(tssb, FNAME_C_DATA_STATES
) # this is need for binomial parameter computations
###########################################
MH_ITR = str(iters)
MH_STD = str(std)
N_SSM_DATA = str(n_ssms)
N_CNV_DATA = str(n_cnvs)
NNODES = str(len(nodes))
TREE_HEIGHT = str(max([node.ht for node in nodes]) + 1)
script_dir = os.path.dirname(os.path.realpath(__file__))
sp.call([
'%s/mh.o' % script_dir, MH_ITR, MH_STD, N_SSM_DATA, N_CNV_DATA, NNODES,
TREE_HEIGHT, FNAME_SSM_DATA, FNAME_CNV_DATA, FNAME_C_TREE,
FNAME_C_DATA_STATES, FNAME_C_PARAMS, FNAME_C_MH_ARATIO, NTPS
])
ar = str(loadtxt(FNAME_C_MH_ARATIO, dtype='string'))
update_tree_params(
tssb, FNAME_C_PARAMS
) # update the tree with the new parameters sampled using the c++ code
return ar
def __log_likelihood__(self, phi, tp, update_tree=True,new_state=0): if update_tree: ################################################## ## some useful info about the tree, ## used by CNV related computations, u.set_node_height(self.tssb) u.set_path_from_root_to_node(self.tssb) u.map_datum_to_node(self.tssb) ################################################## return self.__log_complete_likelihood__(phi, self.mu_r, self.mu_v, tp, new_state)
def __log_likelihood__(self, phi, tp, update_tree=True,new_state=0): if update_tree: ################################################## ## some useful info about the tree, ## used by CNV related computations, u.set_node_height(self.tssb) u.set_path_from_root_to_node(self.tssb) u.map_datum_to_node(self.tssb) ################################################## return self.__log_complete_likelihood__(phi, self.mu_r, self.mu_v, tp, new_state)
def metropolis(tssb,
iters=1000,
std=0.01,
burnin=0,
nInputData=0,
fin='',
rseed=1,
maxCopyNumber=6,
baseline=0.0,
tmp_dir='.'):
wts, nodes = tssb.get_mixture()
# file names
FNAME_INPUT_DATA = fin
FNAME_C_TREE, FNAME_C_PARAMS, FNAME_C_MH_ARATIO, FNAME_OUTPUT_DATA\
= get_c_fnames(tmp_dir)
## initialize the MH sampler###########
# sample_cons_params(tssb)
# update_params(tssb)
######################################
## prepare to call the c++ code ###########
u2.set_node_height(tssb)
write_tree(tssb, FNAME_C_TREE) # write the current tree to the disk
u2.map_datum_to_node(tssb)
###########################################
MH_ITR = str(iters)
MH_STD = str(std)
N_INPUT_DATA = str(nInputData)
NNODES = str(len(nodes))
TREE_HEIGHT = str(max([node.ht for node in nodes]) + 1)
MAX_COPY_NUMBER = str(maxCopyNumber)
BASELINE = str(baseline)
script_dir = os.path.dirname(os.path.realpath(__file__))
print['%s/mh.o' % script_dir, MH_ITR, MH_STD, N_INPUT_DATA, NNODES,
TREE_HEIGHT, MAX_COPY_NUMBER, BASELINE, FNAME_INPUT_DATA,
FNAME_C_TREE, FNAME_C_PARAMS, FNAME_C_MH_ARATIO, FNAME_OUTPUT_DATA]
sp.check_call([
'%s/mh.o' % script_dir, MH_ITR, MH_STD, N_INPUT_DATA, NNODES,
TREE_HEIGHT, MAX_COPY_NUMBER, BASELINE, FNAME_INPUT_DATA, FNAME_C_TREE,
FNAME_C_PARAMS, FNAME_C_MH_ARATIO, FNAME_OUTPUT_DATA
])
ar = str(loadtxt(FNAME_C_MH_ARATIO, dtype='string'))
dp = load_data_params(FNAME_OUTPUT_DATA)
# update the tree with the new parameters sampled using the c++ code
update_tree_params(tssb, FNAME_C_PARAMS)
return ar, dp
def metropolis(tssb,iters=1000,std=0.01,burnin=0,n_ssms=0,n_cnvs=0,fin1='',fin2='',rseed=1, ntps=5):
wts, nodes = tssb.get_mixture()
# file names
FNAME_SSM_DATA = fin1
FNAME_CNV_DATA = fin2
basename = os.path.basename(fin1).split('.')[0]
FNAME_C_TREE = 'c_tree_' + basename + '_' + str(rseed) + '.txt'
FNAME_C_DATA_STATES = 'c_data_states_' + basename + '_' + str(rseed) + '.txt'
FNAME_C_PARAMS = 'c_params_' + basename + '_' + str(rseed) + '.txt' ;
FNAME_C_MH_ARATIO = 'c_mh_ar_' + basename + '_' + str(rseed) + '.txt' ;
NTPS = str(ntps)
## initialize the MH sampler###########
#for tp in arange(ntps):
# sample_cons_params(tssb,tp)
# update_params(tssb,tp)
######################################
## prepare to call the c++ code ###########
u2.set_node_height(tssb)
write_tree(tssb,n_ssms,FNAME_C_TREE) #write the current tree to the disk
u2.map_datum_to_node(tssb)
write_data_state(tssb,FNAME_C_DATA_STATES) # this is need for binomial parameter computations
###########################################
MH_ITR = str(iters)
MH_STD = str(std)
N_SSM_DATA = str(n_ssms)
N_CNV_DATA = str(n_cnvs)
NNODES = str(len(nodes))
TREE_HEIGHT = str(max([node.ht for node in nodes])+1)
script_dir = os.path.dirname(os.path.realpath(__file__))
sp.call(['%s/mh.o' % script_dir, MH_ITR, MH_STD, N_SSM_DATA, N_CNV_DATA, NNODES, TREE_HEIGHT, FNAME_SSM_DATA, FNAME_CNV_DATA, FNAME_C_TREE, FNAME_C_DATA_STATES, FNAME_C_PARAMS,FNAME_C_MH_ARATIO, NTPS])
ar = str(loadtxt(FNAME_C_MH_ARATIO,dtype='string'))
update_tree_params(tssb,FNAME_C_PARAMS) # update the tree with the new parameters sampled using the c++ code
return ar