def _grad_helper(z):
     #import pdb; pdb.set_trace()
     zeros = z.zeros_like().astype(theano.config.floatX)
     zeros = zeros.sum(axis=self.axis,keepdims=True)
     pre = basic.concatenate([zeros, z],axis=self.axis)
     app = basic.concatenate([z, zeros],axis=self.axis)
     return pre - app
 def _grad_helper(z):
     #import pdb; pdb.set_trace()
     zeros = z.zeros_like().astype(theano.config.floatX)
     zeros = zeros.sum(axis=self.axis, keepdims=True)
     pre = basic.concatenate([zeros, z], axis=self.axis)
     app = basic.concatenate([z, zeros], axis=self.axis)
     return pre - app
Example #3
0
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]
Example #4
0
 def _grad_helper(z):
     pre = basic.concatenate([[0.], z])
     app = basic.concatenate([z, [0.]])
     return pre - app