code stringlengths 3 6.57k |
|---|
prediction.items() |
next_states.items() |
run(self) |
self.set_env() |
environment (OpenGL context) |
self.env_f() |
range(self.num_games) |
self._run_one_episode() |
_store_data(self, alg_name, data) |
_store_data(self.alive, data) |
_run_one_episode(self) |
__store_data(observations, actions, states, rewards) |
learning_ret.items() |
self.log_entry.add_key(k, v) |
self._reset_env() |
self._get_init_states() |
and (not self.env.time_out() |
isinstance(actions, dict) |
isinstance(next_states, dict) |
__store_data(observations, actions, states, rewards) |
abs(next_game_over) |
self.env.time_out() |
self._cts_predict(observations, states) |
len(v) |
rewards.items() |
__store_data(observations, actions, states, zero_rewards) |
self.log_entry.add_key("success", next_game_over > 0) |
self._total_reward() |
_reset_env(self) |
GameLogEntry(self.id, 'All') |
self.env.reset() |
isinstance(obs, dict) |
_step_env(self, actions) |
isinstance(next_observations, dict) |
isinstance(rewards, dict) |
self.log_entry.add_key("num_steps", 1) |
self.log_entry.add_key("total_reward", sum(map(sum, rewards.values() |
_total_reward(self) |
self.log_q.put(self.log_entry) |
_get_init_states(self) |
dict() |
_cts_predict(self, observations, states) |
states (optional) |
_cts_store_data(self, observations, actions, states, rewards) |
self._store_data() |
logging.getLogger(__name__) |
WasherDryerApi(WasherApi, DryerApi) |
get_all_entities(self) |
self.get_base_entities() |
GeErdSensor(self, ErdCode.LAUNDRY_MACHINE_STATE) |
GeErdSensor(self, ErdCode.LAUNDRY_CYCLE) |
GeErdSensor(self, ErdCode.LAUNDRY_SUB_CYCLE) |
GeErdBinarySensor(self, ErdCode.LAUNDRY_END_OF_CYCLE) |
GeErdSensor(self, ErdCode.LAUNDRY_TIME_REMAINING) |
GeErdSensor(self, ErdCode.LAUNDRY_DELAY_TIME_REMAINING) |
GeErdBinarySensor(self, ErdCode.LAUNDRY_DOOR) |
GeErdBinarySensor(self, ErdCode.LAUNDRY_REMOTE_STATUS) |
self.get_washer_entities() |
self.get_dryer_entities() |
decode(H, y, snr, maxiter=1000) |
array (n_equations, n_code) |
array (n_code, n_messages) |
or (n_code,) |
message(s) |
array (n_code,) |
or (n_code, n_messages) |
utils._bitsandnodes(H) |
np.unique(H.sum(0) |
np.unique(H.sum(1) |
bits_values.reshape(n, -1) |
nodes_values.reshape(m, -1) |
np.zeros(shape=(m, n, n_messages) |
np.zeros(shape=(m, n, n_messages) |
range(maxiter) |
np.array(L_posteriori <= 0) |
astype(int) |
utils.incode(H, x) |
x.squeeze() |
range(m) |
np.ones(n_messages) |
range(len(nij) |
np.tanh(0.5 * Lc[nij[kk]]) |
range(len(nij) |
np.tanh(0.5 * Lq[i, nij[kk]]) |
range(n_messages) |
np.log(num[ll] / denom[ll]) |
range(n) |
range(len(mji) |
np.zeros((n, n_messages) |
range(n) |
sum(axis=0) |
range(m) |
np.ones(n_messages) |
range(len(nij) |
np.tanh(0.5 * Lc[nij[kk]]) |
range(len(nij) |
np.tanh(0.5 * Lq[i, nij[kk]]) |
range(n_messages) |
np.log(num[ll] / denom[ll]) |
range(n) |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.