def resample(self,data=[],niter=20):
if getdatasize(data) == 0:
self.p = np.random.beta(self.alpha_0,self.beta_0)
self.r = np.random.gamma(self.k_0,self.theta_0)
else:
data = atleast_2d(flattendata(data))
N = len(data)
for itr in range(niter):
### resample r
msum = sample_crp_tablecounts(self.r,data).sum()
self.r = np.random.gamma(self.k_0 + msum, 1/(1/self.theta_0 - N*np.log(1-self.p)))
### resample p
self.p = np.random.beta(self.alpha_0 + data.sum(), self.beta_0 + N*self.r)
return self
def resample(self,data=[],niter=20):
if getdatasize(data) == 0:
self.p = np.random.beta(self.alpha_0,self.beta_0)
self.r = np.random.gamma(self.k_0,self.theta_0)
else:
data = atleast_2d(flattendata(data))
N = len(data)
for itr in range(niter):
### resample r
msum = sample_crp_tablecounts(self.r,data).sum()
self.r = np.random.gamma(self.k_0 + msum, 1/(1/self.theta_0 - N*np.log(1-self.p)))
### resample p
self.p = np.random.beta(self.alpha_0 + data.sum(), self.beta_0 + N*self.r)
return self
def rvs(self,size=[]):
return np.concatenate(
[atleast_2d(distn.rvs(size=size))
for distn in self._distns],axis=-1)