def fit(self, datasets, verbose=False, tune_interval=100):
if isinstance(datasets, FCMCollection):
datasets = datasets.to_list()
self.d = datasets[0].shape[1]
datasets = [i.copy().astype('double') for i in datasets]
self.ndatasets = len(datasets)
total_data = vstack(datasets)
self.m = mean(total_data, 0)
self.s = std(total_data, 0)
standardized = []
for i in datasets:
if i.shape[1] != self.d:
raise RuntimeError("Datasets shape do not match")
standardized.append(((i - self.m) / self.s))
if self.prior_mu is not None:
self._load_mu_at_fit()
if self.prior_sigma is not None:
self._load_sigma_at_fit()
if self.seed is not None:
seed(self.seed)
else:
from datetime import datetime
seed(datetime.now().microsecond)
self.hdp = HDPNormalMixture(standardized,
ncomp=self.nclusts,
gamma0=self.gamma0,
m0=self.m0,
nu0=self.nu0,
Phi0=self.Phi0,
e0=self.e0,
f0=self.f0,
g0=self.g0,
h0=self.h0,
mu0=self._prior_mu,
Sigma0=self._prior_sigma,
weights0=self._prior_pi,
alpha0=self.alpha0,
gpu=self.device,
parallel=self.parallel,
verbose=verbose)
self.hdp.sample(niter=self.niter,
nburn=self.burnin,
thin=1,
ident=self.ident,
tune_interval=tune_interval)
self._run = True #we've fit the mixture model
return self.get_results()
from dpmix import HDPNormalMixture
#import gpustats as gs
if __name__ == '__main__':
N = int(1e5)
K = 2
J = 4
ncomps = 3
true_labels, data = generate_data(n=N, k=K, ncomps=ncomps)
data = data - data.mean(0)
data = data / data.std(0)
#shuffle the data ...
ind = np.arange(N)
np.random.shuffle(ind)
all_data = data[ind].copy()
data = [all_data[(N / J * i):(N / J * (i + 1))].copy() for i in range(J)]
mcmc = HDPNormalMixture(data,
ncomp=100,
gpu=[0, 1, 2],
parallel=True,
verbose=100)
mcmc.sample(2, nburn=5, tune_interval=100)
imcmc = HDPNormalMixture(mcmc, verbose=100)
imcmc.sample(2, nburn=0, ident=True)
print imcmc.mu[-1]
print imcmc.weights[-1]
print imcmc.beta[-1]
import numpy.random as npr
from dpmix import HDPNormalMixture
if __name__ == '__main__':
nclust = 256
niter = 10
burnin = 10
device = 1
max_events = 50000
num_files = 10
seed = 9
#npr.seed(seed)
for it in range(10, 20):
xs = []
for i in range(num_files):
print i,
xs.append(npr.uniform(-5, 5, (max_events, 5)))
print
mcmc = HDPNormalMixture(xs,
ncomp=nclust,
gpu=device,
parallel=True,
verbose=2)
mcmc.sample(burnin, nburn=0, tune_interval=5)
imcmc = HDPNormalMixture(mcmc, verbose=2)
imcmc.sample(niter, nburn=0, ident=True)
del mcmc
del imcmc
