add gridworld (and a parent class 'BinaryInputSampler')

automata.py

@@ -21,6 +21,13 @@ import itertools
 
 import datasets
 import numpy as np
+from copy import copy
+
+# check python version
+import sys
+major, minor = sys.version_info[:2]
+version = major + 0.1*minor
+OLD_PY_VERSION = 1 if version < 3.8 else 0
 
 # Local imports
 # from symmetric import SymmetricSampler
@@ -52,9 +59,9 @@ class SyntheticAutomataDataset(datasets.GeneratorBasedBuilder):
         """
         if 'name' not in config:
             config['name'] = 'parity'
-        if 'length' not in config:
+        if 'length' not in config: # sequence length
             config['length'] = 20
-        if 'size' not in config:
+        if 'size' not in config: # number of sequences
             config['size'] = -1
 
         self.data_config = config
@@ -119,25 +126,65 @@ class AutomatonSampler:
         raise NotImplementedError()
 
 
-class ParitySampler(AutomatonSampler):
+class BinaryInputSampler(AutomatonSampler):
     def __init__(self, data_config):
         super().__init__(data_config)
-        self.name = 'parity'
-        self.data_config = data_config
+
+        if 'prob1' not in data_config:
+          data_config['prob1'] = 0.5
+        self.prob1 = data_config['prob1']
 
     def f(self, x):
-        return np.cumsum(x) % 2
+        raise NotImplementedError()
 
     def sample(self):
-        x = self.np_rng.binomial(1,0.5,size=self.T)
+        x = self.np_rng.binomial(1, self.prob1, size=self.T)
         return x, self.f(x)
 
+class ParitySampler(BinaryInputSampler):
+  def __init__(self, data_config):
+    super().__init__(data_config)
+    self.name = 'parity'
+
+  def f(self, x):
+    return np.cumsum(x) % 2
+
+class GridworldSampler(BinaryInputSampler):
+  """
+  Note: gridworld currently doesn't include a no-op.
+  """
+  def __init__(self, data_config):
+    super().__init__(data_config)
+    self.name = 'gridworld'
+
+    if 'n' not in data_config:
+      data_config['n'] = 9
+    """
+    NOTE: n is the number of states, and S is the id (0-indexing) of the rightmost state.
+          i.e. the states are 0,1,2,...,S, where S=n-1.
+    """
+    self.n = data_config['n'] 
+    self.S = self.n - 1
+
+  def f(self, x):
+    x = copy(x)
+    x[x == 0] = -1
+    if OLD_PY_VERSION:
+      # NOTE: for Python 3.7 or below, accumulate doesn't have the 'initial' argument.
+      x = np.concatenate([np.array([0]), x]).astype(np.int64)
+      states = list(itertools.accumulate(x, lambda a,b: max(min(a+b, self.S), 0)))
+      states = states[1:]
+    else:
+      states = list(itertools.accumulate(x, lambda a,b: max(min(a+b, self.S), 0), initial=0))
+      states = states[1:] # remove the 1st entry with is the (meaningless) initial value 0
+    return np.array(states).astype(np.int64)
+
+
 
 class FlipFlopSampler(AutomatonSampler):
     def __init__(self, data_config):
         super().__init__(data_config)
         self.name = 'flipflop'
-        self.data_config = data_config
 
         if 'n' not in data_config:
             data_config['n'] = 2
@@ -170,7 +217,6 @@ class SymmetricSampler(AutomatonSampler):
   def __init__(self, data_config):
     super().__init__(data_config)
     self.name = 'symmetric'
-    self.data_config = data_config
 
     if 'n' not in data_config:
       data_config['n'] = 5 # Default to S5
@@ -180,7 +226,7 @@ class SymmetricSampler(AutomatonSampler):
       # Options: 'state', 'first_chair'
       data_config['label_type'] = 'state'
     
-    self.n = data_config['n']
+    self.n = data_config['n'] # the symmetric group Sn
     self.label_type = data_config['label_type']
 
     """
@@ -242,8 +288,9 @@ class SymmetricSampler(AutomatonSampler):
 
 
 dataset_map = {
-  'parity': ParitySampler,
+  'gridworld': GridworldSampler,
   'flipflop': FlipFlopSampler,
+  'parity': ParitySampler,
   'symmetric': SymmetricSampler,
   # TODO: more datasets
   }