def env_init():
global local_observation, this_reward_observation, arms, numarms
local_observation = np.zeros(1)
arms = np.zeros(numarms)
for i in range(numarms):
arms[i] = randn(0.0, 0.5)
this_reward_observation = (0.0, local_observation, False)
def _test(self, model):
np.random.seed(0)
logger.info("Starting test gradient for model %s" % model)
np.random.seed(1)
x = np.round(randn(1), 1)
grad = model.d_log_likelihood(x)
grad_approx = model.d_log_likelihood_approx(x)
np.testing.assert_allclose(grad, grad_approx, 0.01)
def env_init():
global local_observation, this_reward_observation, bandit_action_values
local_observation = np.zeros(1)
this_reward_observation = (0.0, local_observation, False)
#Create the bandit problem for the current run
bandit_action_values = [randn(0.0, 1.0) for action in range(10)]
def _test(self, model):
np.random.seed(0)
logger.info("Starting test gradient for model %s" % model)
np.random.seed(1)
x = np.round(randn(1), 1)
grad = model.d_log_likelihood(x)
grad_approx = model.d_log_likelihood_approx(x)
np.testing.assert_allclose(grad, grad_approx, 0.01)
def __init__(self, num_tags):
super(CRF, self).__init__()
self.num_tags = num_tags
# matrix of transition scores from j to i
self.transition = nn.Parameter(randn(num_tags, num_tags))
self.transition.data[START_TAG_IDX, :] = -10000. # no transition to START
self.transition.data[:, STOP_TAG_IDX] = -10000. # no transition from END except to PAD
self.transition.data[:, PAD_IDX] = -10000. # no transition from PAD except to PAD
self.transition.data[PAD_IDX, :] = -10000. # no transition to PAD except from END
self.transition.data[PAD_IDX, STOP_TAG_IDX] = 0.
self.transition.data[PAD_IDX, PAD_IDX] = 0.
def env_step(this_action): # returns (floating point, NumPy array, Boolean), this_action: NumPy array
global local_observation, this_reward_observation, arms#, nStatesSimpleEnv
episode_over = False
atp1 = this_action[0] # how to extact action
stp1 = randInRange(nStatesSimpleEnv) # state transitions are uniform random
the_reward = randn(0.0, 1.0) + arms[int(atp1)] # rewards drawn from (0, 1) Gaussian
#if rand_un() < 0.05:
# episode_over = True # termination is random
local_observation[0] = stp1
this_reward_observation = (the_reward, this_reward_observation[1], episode_over)
return this_reward_observation
def __init__(self, num_tags):
super().__init__()
self.num_tags = num_tags
# matrix of transition scores from j to i
self.trans = nn.Parameter(randn(num_tags, num_tags))
self.trans.data[SOS_IDX, :] = -10000. # no transition to SOS
self.trans.data[:,
EOS_IDX] = -10000. # no transition from EOS except to PAD
self.trans.data[:,
PAD_IDX] = -10000. # no transition from PAD except to PAD
self.trans.data[
PAD_IDX, :] = -10000. # no transition to PAD except from EOS
self.trans.data[PAD_IDX, EOS_IDX] = 0.
self.trans.data[PAD_IDX, PAD_IDX] = 0.
def env_step(
this_action
): # returns (floating point, NumPy array, Boolean), this_action: NumPy array
global local_observation, this_reward_observation #, nStatesSimpleEnv
episode_over = False
#Get a reward from the current action reward distribution
atp1 = int(this_action[0]) # how to extact action
the_reward = randn(bandit_action_values[atp1],
1.0) # rewards drawn from (q*, 1) Gaussian
stp1 = randInRange(
nStatesSimpleEnv) # state transitions are uniform random
#########
local_observation[0] = stp1
this_reward_observation = (the_reward, this_reward_observation[1],
episode_over)
return this_reward_observation
def random(self, n=1):
mu, sigma = self.params["mu"], self.params["sigma"]
return mu + randn(n) * sigma
def random(self, n=1):
mu, sigma = self.params["mu"], self.params["sigma"]
n_gaussian = np.random.randint(len(mu), size=n)
mu0, sigma0 = mu[n_gaussian], sigma[n_gaussian]
return mu0 + randn(n) * sigma0
def random(self, n=1):
mu, sigma = self.params["mu"], self.params["sigma"]
return mu + randn(n) * sigma
def random(self, n=1):
mu, sigma = self.params["mu"], self.params["sigma"]
n_gaussian = np.random.randint(len(mu), size=n)
mu0, sigma0 = mu[n_gaussian], sigma[n_gaussian]
return mu0 + randn(n) * sigma0
def random(self, n=1):
mu, sigma = self.params["mu"], self.params["sigma"]
n_gaussian = np.random.randint(2, size=n)
return (1 - 2 * n_gaussian) * mu + randn(n) * sigma