def grad_log_pob(t):
a = np.sum(manual_grad(t[0],t[1],X),axis=0) + grad_log_prior(t)
return a
from sgld_test.likelihoods import gen_X
import numpy as np
import matplotlib.pyplot as plt
theta1 = np.arange(-2, 2, 0.025)
theta2 = np.arange(-2, 2, 0.025)
grid_dimension_size = len(theta1)
theta1, theta2 = np.meshgrid(theta1, theta2)
D_theta1 = np.copy(theta1)
D_theta2 = np.copy(theta1)
sample = gen_X(400)
for i in range(grid_dimension_size):
for j in range(grid_dimension_size):
th = np.array([theta1[i, j], theta2[i, j]])
# subsample = np.random.choice(sample, 40)
stoch_grad_log_lik = np.sum(manual_grad(th[0], th[1], sample),
axis=0) + grad_log_prior(th)
D_theta1[i, j] = stoch_grad_log_lik[0]
D_theta2[i, j] = stoch_grad_log_lik[1]
plt.figure()
CS = plt.streamplot(theta1, theta2, D_theta1, D_theta2, density=[0.5, 1])
plt.show()
def grad_log_pob(t):
a = np.sum(manual_grad(t[0], t[1], X), axis=0) + grad_log_prior(t)
return a
import matplotlib.pyplot as plt
theta1 = np.arange(-2, 2, 0.025)
theta2 = np.arange(-2, 2, 0.025)
grid_dimension_size = len(theta1)
theta1, theta2 = np.meshgrid(theta1, theta2)
D_theta1 = np.copy(theta1)
D_theta2 = np.copy(theta1)
sample = gen_X(400)
for i in range(grid_dimension_size):
for j in range(grid_dimension_size):
th = np.array([theta1[i, j], theta2[i, j]])
# subsample = np.random.choice(sample, 40)
stoch_grad_log_lik = np.sum(manual_grad(th[0], th[1], sample), axis=0) + grad_log_prior(th)
D_theta1[i, j] = stoch_grad_log_lik[0]
D_theta2[i, j] = stoch_grad_log_lik[1]
plt.figure()
CS = plt.streamplot(theta1, theta2, D_theta1, D_theta2, density=[0.5, 1])
plt.show()
