def loss_critic(params_critic,
i,
lambda_gp=lambda_gp,
batch_size=batch_size):
y_critic = np.zeros(batch_size)
# y_critic[:batch_size // 2] = 0.0 # 0 == fake
y_critic[batch_size // 2:] = 1.0
rng = check_random_state(i)
# WGAN loss
thetas = gaussian_draw(state["params_proposal"],
batch_size // 2,
random_state=rng)
_X_gen = np.zeros((batch_size // 2, n_features))
for j, theta in enumerate(thetas):
_X_gen[j, :] = simulator(theta, 1, random_state=rng).ravel()
indices = rng.permutation(len(X_obs))
_X_obs = X_obs[indices[:batch_size // 2]]
X = np.vstack([_X_gen, _X_obs])
y_pred = predict(X, params_critic)
l_wgan = np.mean(-y_critic * y_pred + (1. - y_critic) * y_pred)
# Gradient penalty
eps = rng.rand(batch_size // 2, 1)
_X_hat = eps * _X_obs + (1. - eps) * _X_gen
grad_Dx = grad_predict_critic(_X_hat, params_critic)
norms = np.sum(grad_Dx**2, axis=1)**0.5
l_gp = np.mean((norms - 1.0)**2.0)
return l_wgan + lambda_gp * l_gp
def approx_grad_u(params_proposal, i):
rng = check_random_state(i)
grad_u = {
k: np.zeros(len(params_proposal[k]))
for k in params_proposal
}
grad_ent = {
k: np.zeros(len(params_proposal[k]))
for k in params_proposal
}
thetas = gaussian_draw(params_proposal, batch_size, random_state=rng)
for theta in thetas:
x = simulator(theta, 1, random_state=rng)
dx = predict(x, state["params_critic"]).ravel()
grad_q = grad_gaussian_logpdf(params_proposal, theta)
for k, v in grad_q.items():
grad_u[k] += -dx * v
grad_entropy = grad_gaussian_entropy(params_proposal)
for k, v in grad_entropy.items():
grad_ent[k] += v
M = len(thetas)
for k in grad_u:
grad_u[k] = 1. / M * grad_u[k] + state["gamma"] * grad_ent[k]
return grad_u
plt.ylabel(r"$G_f$")
plt.xlim((true_theta[0] - offset) * (50 - 40) + 40,
(true_theta[0] + offset) * (50 - 40) + 40)
plt.ylim((true_theta[1] - offset) * (1.5 - 0.5) + 0.5,
(true_theta[1] + offset) * (1.5 - 0.5) + 0.5)
plt.legend(loc="lower right")
# histograms
ax2 = plt.subplot2grid((2, 2), (0, 1))
plt.xlim(-1, 1)
Xs = [X_obs]
for state in history:
thetas = gaussian_draw(state["params_proposal"],
50000,
random_state=rng)
X_ = np.zeros((len(thetas), 1))
for j, theta in enumerate(thetas):
X_[j, :] = simulator(theta, 1).ravel()
Xs.append(X_)
plt.hist(Xs,
histtype="bar",
label=[r"$x \sim p_r(x)$"] + [
r"$x \sim p(x|\psi)\ \gamma=%d$" % state["gamma"]
for state in history
],
color=["C0"] + [state["color"] for state in history],
range=(-1, 1),
bins=15,