cyrilzhang
commited on
Commit
•
cf2b634
1
Parent(s):
b0d21d5
fix indentation
Browse files- automata.py +14 -15
automata.py
CHANGED
@@ -250,12 +250,12 @@ class ABABAutomaton(BinaryInputAutomaton):
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self.prob_abab_pos_sample = data_config['prob_abab_pos_sample']
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self.label_type = data_config['label_type']
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self.transition = np.array(
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])
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self.__info__ = "abab: an automaton with 4 states + 1 absorbing state:\n" \
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@@ -662,7 +662,7 @@ class QuaternionAutomaton(Automaton):
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def __init__(self, data_config):
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super().__init__(data_config)
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self.
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self.n_actions = 4 # {1, i, j, k}
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self.transition_pos = [
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0, 1, 2, 3,
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@@ -693,7 +693,6 @@ class QuaternionAutomaton(Automaton):
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x = self.np_rng.choice(range(self.n_actions), size=T)
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return x, self.f(x)
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class PermutationResetAutomaton(Automaton):
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def __init__(self, data_config):
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super().__init__(data_config)
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@@ -704,9 +703,9 @@ class PermutationResetAutomaton(Automaton):
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if type(self.generators[0]) is str:
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self.generators = [ np.array(list(map(int, list(g)))) for g in self.generators ]
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self.
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self.n_generators = len(self.generators) # actions = generators
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self.n_actions = self.
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self.init_state = np.arange(self.n) # identity permutation
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@@ -724,20 +723,20 @@ class PermutationResetAutomaton(Automaton):
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curr_state = self.init_state
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states = []
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for action_id in x:
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if action_id >= self.
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curr_state = self.generators[action_id - self.
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else:
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curr_state = self.int2perm[action_id]
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return np.array(states, dtype=np.int64)
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def sample(self):
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T = self.sample_length()
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x = self.np_rng.choice(range(self.n_generators), p=self.perm_probs, size=T) + self.
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i = 0
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while i < T:
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x[i] = self.np_rng.choice(range(self.
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i += self.np_rng.choice(self.lags)
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return x, self.f(x)
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self.prob_abab_pos_sample = data_config['prob_abab_pos_sample']
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self.label_type = data_config['label_type']
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self.transition = np.array([
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[4, 1], # state 0
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[2, 4], # state 1
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[4, 3], # state 2
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[0, 4], # state 3
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[4, 4], # state 4
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])
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self.__info__ = "abab: an automaton with 4 states + 1 absorbing state:\n" \
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def __init__(self, data_config):
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super().__init__(data_config)
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self.n_states = 8 # {-1, 1} x {1, i, j, k}
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self.n_actions = 4 # {1, i, j, k}
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self.transition_pos = [
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0, 1, 2, 3,
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x = self.np_rng.choice(range(self.n_actions), size=T)
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return x, self.f(x)
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class PermutationResetAutomaton(Automaton):
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def __init__(self, data_config):
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super().__init__(data_config)
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if type(self.generators[0]) is str:
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self.generators = [ np.array(list(map(int, list(g)))) for g in self.generators ]
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self.n_states = math.factorial(self.n) # states = permutations; maybe rename
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self.n_generators = len(self.generators) # actions = generators
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self.n_actions = self.n_states + self.n_generators # 1 reset symbol per state, 1 apply symbol per generator
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self.init_state = np.arange(self.n) # identity permutation
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curr_state = self.init_state
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states = []
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for action_id in x:
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if action_id >= self.n_states:
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curr_state = self.generators[action_id - self.n_states][curr_state]
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else:
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curr_state = self.int2perm[action_id]
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states.append(self.perm2int[tuple(curr_state)])
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return np.array(states, dtype=np.int64)
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def sample(self):
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T = self.sample_length()
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x = self.np_rng.choice(range(self.n_generators), p=self.perm_probs, size=T) + self.n_states
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i = 0
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while i < T:
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x[i] = self.np_rng.choice(range(self.n_states))
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i += self.np_rng.choice(self.lags)
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return x, self.f(x)
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