def _fit_dpgmm(self, x):
# clustering
k = max(self.crange)
for r in xrange(self.repeats):
# info
if self.debug is True:
print '\t[%s][c:%d][r:%d]' % (self.clus_type, k, r + 1),
# fit and evaluate model
model_kwargs = {}
if 'alpha' in self.clus_kwargs:
model_kwargs.update(alpha=self.clus_kwargs['alpha'])
if 'conv_thresh' in self.clus_kwargs:
model_kwargs.update(thresh=self.clus_kwargs['conv_thresh'])
if 'max_iter' in self.clus_kwargs:
model_kwargs.update(n_iter=self.clus_kwargs['max_iter'])
model = DPGMM(n_components=k,
covariance_type=self.cvtype,
**model_kwargs)
model.fit(x)
self._labels[r] = model.predict(x)
self._parameters[r] = model.means_
self._ll[r] = model.score(x).sum()
# evaluate goodness of fit for this run
#self._gof[r] = self.gof(x, self._ll[r], k)
if self.gof_type == 'aic':
self._gof[r] = model.aic(x)
if self.gof_type == 'bic':
self._gof[r] = model.bic(x)
# debug
if self.debug is True:
print self._gof[r], model.n_components, model.weights_.shape[0]
def _fit_dpgmm(self, x):
# clustering
k = max(self.crange)
for r in xrange(self.repeats):
# info
if self.debug is True:
print '\t[%s][c:%d][r:%d]' % (self.clus_type, k, r + 1),
# fit and evaluate model
model_kwargs = {}
if 'alpha' in self.clus_kwargs:
model_kwargs.update(alpha=self.clus_kwargs['alpha'])
if 'conv_thresh' in self.clus_kwargs:
model_kwargs.update(thresh=self.clus_kwargs['conv_thresh'])
if 'max_iter' in self.clus_kwargs:
model_kwargs.update(n_iter=self.clus_kwargs['max_iter'])
model = DPGMM(n_components=k, covariance_type=self.cvtype,
**model_kwargs)
model.fit(x)
self._labels[r] = model.predict(x)
self._parameters[r] = model.means_
self._ll[r] = model.score(x).sum()
# evaluate goodness of fit for this run
#self._gof[r] = self.gof(x, self._ll[r], k)
if self.gof_type == 'aic':
self._gof[r] = model.aic(x)
if self.gof_type == 'bic':
self._gof[r] = model.bic(x)
# debug
if self.debug is True:
print self._gof[r], model.n_components, model.weights_.shape[0]
for chunks in np.arange(1, opts.size, step = 3):
# Sample the specified number of points from X_unlabeled
size = np.cumsum(chunk_sizes[:chunks])[-1]
# Fit a Dirichlet process mixture of Gaussians using up to ten components
dpgmm = DPGMM(n_components=10, alpha=10.0, covariance_type='full')
indices = np.arange(X_unlabeled.shape[0])
np.random.shuffle(indices)
X = X_unlabeled[indices[:size],]
print("fitting a model with", size, "data points")
with timeit():
dpgmm.fit(X)
print("Done!")
print("AIC for this model & data: ", dpgmm.aic(X))
print("BIC for this model & data: ", dpgmm.bic(X))
Y_hat = dpgmm.predict(X)
print ("Model assigned points to", np.max(Y_hat), "components")
# How can I best check this out?
#color_iter = itertools.cycle(['r', 'g', 'b', 'c', 'm'])
#for i, (clf, title) in enumerate([(gmm, 'GMM'),
#(dpgmm, 'Dirichlet Process GMM')]):
#splot = plt.subplot(2, 1, 1 + i)
#Y_ = clf.predict(X)
#for i, (mean, covar, color) in enumerate(zip(
#clf.means_, clf._get_covars(), color_iter)):
#v, w = linalg.eigh(covar)
#u = w[0] / linalg.norm(w[0])
