def Generate_theta_p(x, s, I, N, K, prod_f):
#print(s,N,len(x))
T = len(x)
D = len(x[0])
s = [one_hot(ss, N) for ss in s]
# x = [[xt for _ in range(N)] for xt in x]
x = np.array(x)
s = np.array(s)
model = pm.Model()
with model:
# Priors for unknown model parameters
theta = pm.Normal("theta", mu=0, sigma=1, shape=(D, K)) / np.sqrt(
K * D)
p_list = []
for t in range(T):
#print(prod_f)
#print(np.transpose(theta))
wt = dot(dot(prod_f, transpose(theta)), x[t])
swt = s[t] * wt
sum_sw = sum(swt)
p = exp(swt) / (1 + sum_sw)
p0 = 1 / (1 + sum_sw)
p_list.append(concatenate(([p0], p)))
I_obs = pm.Categorical("I_obs", p=stack(p_list, axis=0), observed=I)
with model:
step = pm.Metropolis()
trace1 = pm.sample(tune=2000, chains=1, step=step)
return trace1["theta"][-1]
def score(self, x):
prehidden = t.dot(
x, self.parameters.hidden_weights) + self.parameters.hidden_biases
hidden = t.clip(prehidden, -1, 1)
score = t.dot(
hidden,
self.parameters.output_weights) + self.parameters.output_biases
return score, prehidden
def score(x):
prehidden = dot(x, hidden_weights) + hidden_biases
hidden = activation_function(prehidden)
score = dot(hidden, output_weights) + output_biases
return score, prehidden
def score(self,x):
prehidden = t.dot(x, self.parameters.hidden_weights) + self.parameters.hidden_biases
hidden = t.clip(prehidden, -1, 1)
score = t.dot(hidden, self.parameters.output_weights) + self.parameters.output_biases
return score, prehidden