def meanfield_sgdstep(self, data, minibatchfrac, stepsize, **kwargs):
l = Labels(data=np.asarray(data), components=self.components, weights=self.weights, **kwargs)
## local vb
l.meanfieldupdate()
## sgd steps on global variables
self.weights.meanfield_sgdstep(None, l.r, minibatchfrac, stepsize)
for idx, c in enumerate(self.components):
c.meanfield_sgdstep(l.data, l.r[:, idx], minibatchfrac, stepsize)
def generate(self,N,keep=True):
templabels = Labels(components=self.components,weights=self.weights,N=N)
out = np.empty(self.components[0].rvs(N).shape)
counts = np.bincount(templabels.z,minlength=len(self.components))
for idx,(c,count) in enumerate(zip(self.components,counts)):
out[templabels.z == idx,...] = c.rvs(count)
perm = np.random.permutation(N)
out = out[perm]
templabels.z = templabels.z[perm]
if keep:
templabels.data = out
self.labels_list.append(templabels)
return out, templabels.z
def generate(self, N, keep=True):
templabels = Labels(model=self, N=N)
out = np.empty(self.components[0].rvs(N).shape)
counts = np.bincount(templabels.z, minlength=len(self.components))
for idx, (c, count) in enumerate(zip(self.components, counts)):
out[templabels.z == idx, ...] = c.rvs(count)
perm = np.random.permutation(N)
out = out[perm]
templabels.z = templabels.z[perm]
if keep:
templabels.data = out
self.labels_list.append(templabels)
return out, templabels.z
def add_data(self, data, **kwargs):
self.labels_list.append(
Labels(data=np.asarray(data), model=self, **kwargs))
def add_data(self,data,**kwargs):
self.labels_list.append(Labels(data=np.asarray(data),
components=self.components,weights=self.weights,
**kwargs))
def add_data(self, data):
self.labels_list.append(
Labels(data=np.asarray(data, dtype=np.float64),
components=self.components,
weights=self.weights))