def normalize_vbd(self):
self.prob = {}
alpha = 1e-5
s = sum(c for c in self.count.values())
s += alpha * len(self.count)
for r, c in self.count.items():
n = c + alpha
self.prob[r] = exp(digamma(n)) / exp(digamma(s))
self.count = {}
self.beginning = False
def cal_up_llh_grad(sampler, alpha, grad):
for m in range(len(alpha)):
alpha_m = np.sum(alpha[m])
wc_m = sampler.ndsum[m]
for k in range(len(alpha[m])):
alpha_mk = alpha[m][k]
wc_mk = sampler.nd[m][k]
grad[m][k] = digamma(alpha_m) - digamma(wc_m+alpha_m) + \
digamma(alpha_mk+wc_mk) - digamma(alpha_mk)
return grad
def cal_up_llh_grad(sampler, alpha, grad):
for m in range(len(alpha)):
alpha_m = np.sum(alpha[m])
wc_m = sampler.ndsum[m]
for k in range(len(alpha[m])):
alpha_mk = alpha[m][k]
wc_mk = sampler.nd[m][k]
grad[m][k] = digamma(alpha_m) - digamma(wc_m+alpha_m) + \
digamma(alpha_mk+wc_mk) - digamma(alpha_mk)
return grad
def normalize_vbdp(self, alpha, threshold):
self.prob = {}
s = sum(c for c in self.count.values())
s += alpha
for r, c in self.count.items():
n = c + alpha * base_dist(r)
p = exp(digamma(n)) / exp(digamma(s))
if p > threshold:
self.prob[r] = p
self.count = {}
self.beginning = False
def cal_down_llh_grad(sampler, beta, grad):
beta_sum = beta.sum(axis=0)
for v in range(len(beta)):
for k in range(sampler.K):
beta_k = beta_sum[k]
beta_vk = beta[v][k]
wc_vk = sampler.nw[v][k]
wc_k = sampler.nwsum[k]
grad[v][k] = digamma(beta_k) - digamma(beta_k+wc_k) + \
digamma(beta_vk+wc_vk) - digamma(beta_vk)
return grad
def cal_down_llh_grad(sampler, beta, grad):
beta_sum = beta.sum(axis=0)
for v in range(len(beta)):
for k in range(sampler.K):
beta_k = beta_sum[k]
beta_vk = beta[v][k]
wc_vk = sampler.nw[v][k]
wc_k = sampler.nwsum[k]
grad[v][k] = digamma(beta_k) - digamma(beta_k+wc_k) + \
digamma(beta_vk+wc_vk) - digamma(beta_vk)
return grad
