def _update_beta(self, stick_beta, beta, stick_weights, alpha0, alpha):
old_stick_beta = stick_beta.copy()
old_beta = beta.copy()
for k in xrange(self.ncomp - 1):
# get initial logpost
lpost = beta_post(stick_beta, beta, stick_weights, float(alpha0),
float(alpha))
# sample new beta from reflected normal
prop = stats.norm.rvs(stick_beta[k], self.prop_scale[k])
while prop > (1 - 1e-9) or prop < 1e-9:
if prop > 1 - 1e-9:
prop = 2 * (1 - 1e-9) - prop
else:
prop = 2 * 1e-9 - prop
stick_beta[k] = prop
beta = break_sticks(stick_beta)
# get new posterior
lpost_new = beta_post(stick_beta, beta, stick_weights,
float(alpha0), float(alpha))
# accept or reject
if stats.expon.rvs() > lpost - lpost_new:
#accept
self.AR[k] += 1
else:
stick_beta[k] = old_stick_beta[k]
beta = break_sticks(stick_beta)
return stick_beta, beta
def _update_beta(self, stick_beta, beta, stick_weights, alpha0, alpha):
old_stick_beta = stick_beta.copy()
old_beta = beta.copy()
for k in xrange(self.ncomp-1):
# get initial logpost
lpost = beta_post(stick_beta, beta, stick_weights, float(alpha0), float(alpha))
# sample new beta from reflected normal
prop = stats.norm.rvs(stick_beta[k], self.prop_scale[k])
while prop > (1-1e-9) or prop < 1e-9:
if prop > 1-1e-9:
prop = 2*(1-1e-9) - prop
else:
prop = 2*1e-9 - prop
stick_beta[k] = prop
beta = break_sticks(stick_beta)
# get new posterior
lpost_new = beta_post(stick_beta, beta, stick_weights, float(alpha0), float(alpha))
# accept or reject
if stats.expon.rvs() > lpost - lpost_new:
# accept
self.AR[k] += 1
else:
stick_beta[k] = old_stick_beta[k]
beta = break_sticks(stick_beta)
return stick_beta, beta
def __init__(self,
data,
ncomp=256,
gamma0=10,
m0=None,
nu0=None,
Phi0=None,
e0=5,
f0=0.1,
g0=0.1,
h0=0.1,
mu0=None,
Sigma0=None,
weights0=None,
alpha0=1,
gpu=None,
parallel=False,
verbose=False):
if not issubclass(type(data), HDPNormalMixture):
# check for functioning gpu
if _has_gpu:
self.dev_list = np.asarray((0), dtype=np.int)
self.dev_list.shape = 1
if gpu is not None:
if type(gpu) is bool:
self.gpu = gpu
else:
self.gpu = True
self.dev_list = np.asarray(np.abs(gpu), dtype=np.int)
if self.dev_list.shape == ():
self.dev_list.shape = 1
self.dev_list = np.unique(self.dev_list)
else:
self.gpu = True
else:
self.gpu = False
self.parallel = parallel
# get the data .. should add checks here later
self.data = [np.asarray(d) for d in data]
self.ngroups = len(self.data)
self.ndim = self.data[0].shape[1]
self.nobs = tuple([d.shape[0] for d in self.data])
# need for ident code
self.cumobs = np.zeros(self.ngroups + 1)
self.cumobs[1:] = np.asarray(self.nobs).cumsum()
self.ncomp = ncomp
if m0 is not None:
if len(m0) == self.ndim:
self.mu_prior_mean = m0.copy()
elif len(m0) == 1:
self.mu_prior_mean = m0 * np.ones(self.ndim)
else:
self.mu_prior_mean = np.zeros(self.ndim)
self.gamma = gamma0 * np.ones(ncomp)
self._set_initial_values(alpha0, nu0, Phi0, mu0, Sigma0, weights0,
e0, f0)
# initialize hdp specific vars
if weights0 is None:
self._weights0 = np.zeros((self.ngroups, self.ncomp),
dtype=np.float)
self._weights0.fill(1 / self.ncomp)
else:
self._weights0 = weights0.copy()
self._stick_beta0 = stats.beta.rvs(1,
self._alpha0,
size=self.ncomp - 1)
self._beta0 = break_sticks(self._stick_beta0)
self._alpha00 = 1.0
self.e0, self.f0 = g0, h0
self.prop_scale = 0.05 * np.ones(self.ncomp)
self.prop_scale[-1] = 1.
else:
# get all important vars from input class
self.data = data.data
self.ngroups, self.nobs, self.ndim, self.ncomp = data.ngroups, data.nobs, data.ndim, data.ncomp
self.cumobs = data.cumobs.copy()
self._weights0 = data.weights[-1].copy()
self._stick_beta0 = data.stick_beta.copy()
self._beta0 = break_sticks(self._stick_beta0)
self.e0, self.f0 = data.e0, data.f0
self.e, self.f = data.e, data.f
self._nu0 = data._nu0
self._Phi0 = data._Phi0
self.mu_prior_mean = data.mu_prior_mean.copy()
self.gamma = data.gamma.copy()
self._alpha0 = data.alpha[-1].copy()
self._alpha00 = data.alpha0[-1].copy()
self._weights0 = data.weights[-1].copy()
self._mu0 = data.mu[-1].copy()
self._Sigma0 = data.Sigma[-1].copy()
self.prop_scale = data.prop_scale.copy()
self.gpu = data.gpu
if self.gpu:
self.dev_list = np.unique(data.dev_list)
self.parallel = data.parallel
self.AR = np.zeros(self.ncomp)
# verbosity
self.verbose = verbose
# data working var
self.data_shared_mem = multiprocessing.RawArray(
'd',
sum(self.nobs) * self.ndim)
self.alldata = np.frombuffer(self.data_shared_mem).reshape(
sum(self.nobs), self.ndim)
for i in xrange(self.ngroups):
self.alldata[
self.cumobs[i]:self.cumobs[i + 1], :] = self.data[i].copy()
if self.parallel:
self.num_cores = min(min(multiprocessing.cpu_count(), self.ncomp),
16)
compsperdev = self.ncomp / self.num_cores
self.work_queue = [
multiprocessing.Queue() for i in xrange(self.num_cores)
]
self.result_queue = [
multiprocessing.Queue() for i in xrange(self.num_cores)
]
self.workers = [
CPUWorker(np.zeros(
(1, 1)), self.gamma, self.mu_prior_mean, self._Phi0,
self._nu0, self.work_queue[i], self.result_queue[i])
for i in xrange(self.num_cores)
]
self.compsdevmap = {}
cumcomps = 0
for i in xrange(self.num_cores):
self.compsdevmap[i] = [
int(cumcomps),
int(min(cumcomps + compsperdev, self.ncomp))
]
cumcomps += compsperdev
self.compsdevmap[self.num_cores - 1][1] = self.ncomp
for thd in self.workers:
thd.set_data(self.data_shared_mem, sum(self.nobs), self.ndim)
def __init__(self, data, ncomp=256, gamma0=10, m0=None,
nu0=None, Phi0=None, e0=5, f0=0.1, g0=0.1, h0=0.1,
mu0=None, Sigma0=None, weights0=None, alpha0=1,
gpu=None, parallel=False, verbose=False):
if not issubclass(type(data), HDPNormalMixture):
# check for functioning gpu
if _has_gpu:
import os
self.dev_list = np.asarray(0, dtype=np.int)
self.dev_list.shape = 1
self.dev_list = {os.uname()[1]: self.dev_list}
if gpu is not None:
if type(gpu) is bool:
self.gpu = gpu
elif type(gpu) is dict:
self.gpu = True
self.dev_list = gpu.copy()
for host in self.dev_list:
self.dev_list[host] = np.asarray(
self.dev_list[host],
dtype=np.int)
if self.dev_list[host].shape == ():
self.dev_list[host].shape = 1
else:
self.gpu = True
self.dev_list = np.asarray(np.abs(gpu), dtype=np.int)
if self.dev_list.shape == ():
self.dev_list.shape = 1
self.dev_list = np.unique(self.dev_list)
self.dev_list = {os.uname()[1]: self.dev_list}
else:
self.gpu = True
else:
self.gpu = False
self.parallel = parallel
# get the data .. should add checks here later
self.data = [np.asarray(d) for d in data]
self.ngroups = len(self.data)
self.ndim = self.data[0].shape[1]
self.nobs = tuple([d.shape[0] for d in self.data])
# need for ident code
self.cumobs = np.zeros(self.ngroups+1)
self.cumobs[1:] = np.asarray(self.nobs).cumsum()
self.ncomp = ncomp
if m0 is not None:
if len(m0) == self.ndim:
self.mu_prior_mean = m0.copy()
elif len(m0) == 1:
self.mu_prior_mean = m0*np.ones(self.ndim)
else:
self.mu_prior_mean = np.zeros(self.ndim)
self.gamma = gamma0*np.ones(ncomp)
self._set_initial_values(alpha0, nu0, Phi0, mu0, Sigma0,
weights0, e0, f0)
# initialize hdp specific vars
if weights0 is None:
self._weights0 = np.zeros(
(self.ngroups, self.ncomp),
dtype=np.float)
self._weights0.fill(1/self.ncomp)
else:
self._weights0 = weights0.copy()
self._stick_beta0 = stats.beta.rvs(
1,
self._alpha0,
size=self.ncomp-1)
self._beta0 = break_sticks(self._stick_beta0)
self._alpha00 = 1.0
self.e0, self.f0 = g0, h0
# start out small? more accepts?
self.prop_scale = 0.01 * np.ones(self.ncomp)
self.prop_scale[-1] = 1.
else:
# get all important vars from input class
self.data = data.data
self.ngroups = data.ngroups
self.nobs = data.nobs
self.ndim = data.ndim
self.ncomp = data.ncomp
self.cumobs = data.cumobs.copy()
self._weights0 = data.weights[-1].copy()
self._stick_beta0 = data.stick_beta.copy()
self._beta0 = break_sticks(self._stick_beta0)
self.e0, self.f0 = data.e0, data.f0
self.e, self.f = data.e, data.f
self._nu0 = data._nu0
self._Phi0 = data._Phi0
self.mu_prior_mean = data.mu_prior_mean.copy()
self.gamma = data.gamma.copy()
self._alpha0 = data.alpha[-1].copy()
self._alpha00 = data.alpha0[-1].copy()
self._weights0 = data.weights[-1].copy()
self._mu0 = data.mu[-1].copy()
self._Sigma0 = data.Sigma[-1].copy()
self.prop_scale = data.prop_scale.copy()
self.gpu = data.gpu
if self.gpu:
self.dev_list = data.dev_list
self.parallel = data.parallel
self.AR = np.zeros(self.ncomp)
self.verbose = verbose
# data working var
self.alldata = np.empty((sum(self.nobs), self.ndim), dtype=np.double)
for i in xrange(self.ngroups):
self.alldata[self.cumobs[i]:self.cumobs[i+1], :] = self.data[i].copy()
def __init__(self, data, ncomp=256, gamma0=10, m0=None,
nu0=None, Phi0=None, e0=5, f0=0.1, g0=0.1, h0=0.1,
mu0=None, Sigma0=None, weights0=None, alpha0=1,
gpu=None, parallel=False, verbose=False):
if not issubclass(type(data), HDPNormalMixture):
# check for functioning gpu
if _has_gpu:
self.dev_list = np.asarray((0), dtype=np.int); self.dev_list.shape=1
if gpu is not None:
if type(gpu) is bool:
self.gpu = gpu
else:
self.gpu = True
self.dev_list = np.asarray(np.abs(gpu), dtype=np.int)
if self.dev_list.shape == ():
self.dev_list.shape = 1
self.dev_list = np.unique(self.dev_list)
else:
self.gpu=True
else:
self.gpu = False
self.parallel = parallel
# get the data .. should add checks here later
self.data = [np.asarray(d) for d in data]
self.ngroups = len(self.data)
self.ndim = self.data[0].shape[1]
self.nobs = tuple([d.shape[0] for d in self.data])
# need for ident code
self.cumobs = np.zeros(self.ngroups+1);
self.cumobs[1:] = np.asarray(self.nobs).cumsum()
self.ncomp = ncomp
if m0 is not None:
if len(m0)==self.ndim:
self.mu_prior_mean = m0.copy()
elif len(m0)==1:
self.mu_prior_mean = m0*np.ones(self.ndim)
else:
self.mu_prior_mean = np.zeros(self.ndim)
self.gamma = gamma0*np.ones(ncomp)
self._set_initial_values(alpha0, nu0, Phi0, mu0, Sigma0,
weights0, e0, f0)
# initialize hdp specific vars
if weights0 is None:
self._weights0 = np.zeros((self.ngroups, self.ncomp), dtype=np.float)
self._weights0.fill(1/self.ncomp)
else:
self._weights0 = weights0.copy()
self._stick_beta0 = stats.beta.rvs(1,self._alpha0, size=self.ncomp-1)
self._beta0 = break_sticks(self._stick_beta0)
self._alpha00 = 1.0
self.e0, self.f0 = g0, h0
self.prop_scale = 0.05 * np.ones(self.ncomp)
self.prop_scale[-1] = 1.
else:
# get all important vars from input class
self.data = data.data
self.ngroups, self.nobs, self.ndim, self.ncomp = data.ngroups, data.nobs, data.ndim, data.ncomp
self.cumobs = data.cumobs.copy()
self._weights0 = data.weights[-1].copy()
self._stick_beta0 = data.stick_beta.copy()
self._beta0 = break_sticks(self._stick_beta0)
self.e0, self.f0 = data.e0, data.f0
self.e, self.f = data.e, data.f
self._nu0 = data._nu0
self._Phi0 = data._Phi0
self.mu_prior_mean = data.mu_prior_mean.copy()
self.gamma = data.gamma.copy()
self._alpha0 = data.alpha[-1].copy()
self._alpha00 = data.alpha0[-1].copy()
self._weights0 = data.weights[-1].copy()
self._mu0 = data.mu[-1].copy()
self._Sigma0 = data.Sigma[-1].copy()
self.prop_scale = data.prop_scale.copy()
self.gpu = data.gpu
if self.gpu:
self.dev_list = np.unique(data.dev_list)
self.parallel = data.parallel
self.AR = np.zeros(self.ncomp)
# verbosity
self.verbose = verbose
# data working var
self.data_shared_mem = multiprocessing.RawArray('d', sum(self.nobs)*self.ndim)
self.alldata = np.frombuffer(self.data_shared_mem).reshape(sum(self.nobs), self.ndim)
for i in xrange(self.ngroups):
self.alldata[self.cumobs[i]:self.cumobs[i+1],:] = self.data[i].copy()
if self.parallel:
self.num_cores = min(min(multiprocessing.cpu_count(), self.ncomp), 16)
compsperdev = self.ncomp / self.num_cores
self.work_queue = [ multiprocessing.Queue() for i in xrange(self.num_cores) ]
self.result_queue = [ multiprocessing.Queue() for i in xrange(self.num_cores) ]
self.workers = [ CPUWorker(np.zeros((1,1)), self.gamma, self.mu_prior_mean,
self._Phi0, self._nu0, self.work_queue[i], self.result_queue[i])
for i in xrange(self.num_cores) ]
self.compsdevmap = {}; cumcomps = 0
for i in xrange(self.num_cores):
self.compsdevmap[i] = [int(cumcomps), int(min(cumcomps+compsperdev, self.ncomp))]
cumcomps += compsperdev
self.compsdevmap[self.num_cores-1][1] = self.ncomp
for thd in self.workers:
thd.set_data(self.data_shared_mem, sum(self.nobs), self.ndim)
def __init__(self, data, ncomp=256, gamma0=10, m0=None,
nu0=None, Phi0=None, e0=5, f0=0.1, g0=0.1, h0=0.1,
mu0=None, Sigma0=None, weights0=None, alpha0=1,
gpu=None, parallel=False, verbose=False):
if not issubclass(type(data), HDPNormalMixture):
# check for functioning gpu
if _has_gpu:
import os
self.dev_list = np.asarray(0, dtype=np.int)
self.dev_list.shape = 1
self.dev_list = {os.uname()[1]: self.dev_list}
if gpu is not None:
if type(gpu) is bool:
self.gpu = gpu
elif type(gpu) is dict:
self.gpu = True
self.dev_list = gpu.copy()
for host in self.dev_list:
self.dev_list[host] = np.asarray(
self.dev_list[host],
dtype=np.int)
if self.dev_list[host].shape == ():
self.dev_list[host].shape = 1
else:
self.gpu = True
self.dev_list = np.asarray(np.abs(gpu), dtype=np.int)
if self.dev_list.shape == ():
self.dev_list.shape = 1
self.dev_list = np.unique(self.dev_list)
self.dev_list = {os.uname()[1]: self.dev_list}
else:
self.gpu = True
else:
self.gpu = False
self.parallel = parallel
# get the data .. should add checks here later
self.data = [np.asarray(d) for d in data]
self.ngroups = len(self.data)
self.ndim = self.data[0].shape[1]
self.nobs = tuple([d.shape[0] for d in self.data])
# need for ident code
self.cumobs = np.zeros(self.ngroups+1)
self.cumobs[1:] = np.asarray(self.nobs).cumsum()
self.ncomp = ncomp
if m0 is not None:
if len(m0) == self.ndim:
self.mu_prior_mean = m0.copy()
elif len(m0) == 1:
self.mu_prior_mean = m0*np.ones(self.ndim)
else:
self.mu_prior_mean = np.zeros(self.ndim)
self.gamma = gamma0*np.ones(ncomp)
self._set_initial_values(alpha0, nu0, Phi0, mu0, Sigma0,
weights0, e0, f0)
# initialize hdp specific vars
if weights0 is None:
self._weights0 = np.zeros(
(self.ngroups, self.ncomp),
dtype=np.float)
self._weights0.fill(1/self.ncomp)
else:
self._weights0 = weights0.copy()
self._stick_beta0 = stats.beta.rvs(
1,
self._alpha0,
size=self.ncomp-1)
self._beta0 = break_sticks(self._stick_beta0)
self._alpha00 = 1.0
self.e0, self.f0 = g0, h0
# start out small? more accepts?
self.prop_scale = 0.01 * np.ones(self.ncomp)
self.prop_scale[-1] = 1.
else:
# get all important vars from input class
self.data = data.data
self.ngroups = data.ngroups
self.nobs = data.nobs
self.ndim = data.ndim
self.ncomp = data.ncomp
self.cumobs = data.cumobs.copy()
self._weights0 = data.weights[-1].copy()
self._stick_beta0 = data.stick_beta.copy()
self._beta0 = break_sticks(self._stick_beta0)
self.e0, self.f0 = data.e0, data.f0
self.e, self.f = data.e, data.f
self._nu0 = data._nu0
self._Phi0 = data._Phi0
self.mu_prior_mean = data.mu_prior_mean.copy()
self.gamma = data.gamma.copy()
self._alpha0 = data.alpha[-1].copy()
self._alpha00 = data.alpha0[-1].copy()
self._weights0 = data.weights[-1].copy()
self._mu0 = data.mu[-1].copy()
self._Sigma0 = data.Sigma[-1].copy()
self.prop_scale = data.prop_scale.copy()
self.gpu = data.gpu
if self.gpu:
self.dev_list = data.dev_list
self.parallel = data.parallel
self.AR = np.zeros(self.ncomp)
self.verbose = verbose
# data working var
self.alldata = np.empty((sum(self.nobs), self.ndim), dtype=np.double)
for i in xrange(self.ngroups):
self.alldata[self.cumobs[i]:self.cumobs[i+1], :] = self.data[i].copy()
