misc typo

Utils/ExperienceBuffers/CebLinear.py

@@ -1,83 +1,83 @@
-import random
-import numpy as np
-import math
-import itertools
-
-_WEIGHTS_MODES = {
-  'abs': math.fabs,
-  'reward': lambda x: x,
-  'same': lambda _: 1
-}
-
-class CebLinear:
-  def __init__(self, maxSize, sampleWeight='samp'):
-    self.maxSize = maxSize
-    self._sizeLimit = math.floor(maxSize * 1.1)
-    self._samples = []
-    self._sampleWeight = _WEIGHTS_MODES.get(sampleWeight, sampleWeight)
-
-  def addEpisode(self, replay, terminated):
-    if 1 < len(replay):
-      for step in replay[:-1]:
-        self._samples.append((*step, 1))
-    self._samples.append((*replay[-1], -1 if terminated else 0))
-
-    self.update()
-    return
-
-  def update(self):
-    if self._sizeLimit < len(self._samples):
-      self._samples = self._samples[-self.maxSize:]
-    return 
-
-  def __len__(self):
-    return len(self._samples)
-
-  def _fixRewardMultiplier(self, x):
-    if np.isscalar(x):
-      return abs(x)
-
-    if isinstance(x, (np.ndarray, np.generic)):
-      return np.abs(x)
-
-    raise Exception('Unknown reward type. (%s)' % type(x))
-
-  def _createBatch(self, batch_size, sampler):
-    samplesLeft = batch_size
-    cumweights = list(itertools.accumulate(self._sampleWeight(x[2]) for x in self._samples))
-    indexRange = np.arange(len(self._samples)) 
-    res = []
-    while 0 < samplesLeft:
-      indexes = set(random.choices(
-        indexRange, cum_weights=cumweights,
-        k=min((samplesLeft, len(self._samples)))
-      ))
-
-      for i in indexes:
-        sample = sampler(i)
-        if sample:
-          while len(res) < len(sample):  res.append([])
-          for i, value in enumerate(sample[:-1]):
-            res[i].append(value)
-          res[-1].append(self._fixRewardMultiplier(sample[-1]))
-          samplesLeft -= 1
-
-    return [np.array(values) for values in res]
-
-  def sampleBatch(self, batch_size):
-    return self._createBatch(batch_size, lambda i: self._samples[i])
-
-  def sampleSequenceBatch(self, batch_size, sequenceLen, **kwargs):
-    def sampler(ind):
-      sample = self._samples[ind:ind+sequenceLen]
-      if not (sequenceLen == len(sample)): return None
-      if 1 < sequenceLen:
-        if any(x[-1] < 1 for x in sample[:-1]):
-          return None
-
-      transposed = [
-        np.array([x[col] for x in sample]) for col in range(len(sample[0]))
-      ]
-      return transposed
-
+import random
+import numpy as np
+import math
+import itertools
+
+_WEIGHTS_MODES = {
+  'abs': math.fabs,
+  'reward': lambda x: x,
+  'same': lambda _: 1
+}
+
+class CebLinear:
+  def __init__(self, maxSize, sampleWeight='same'):
+    self.maxSize = maxSize
+    self._sizeLimit = math.floor(maxSize * 1.1)
+    self._samples = []
+    self._sampleWeight = _WEIGHTS_MODES.get(sampleWeight, sampleWeight)
+
+  def addEpisode(self, replay, terminated):
+    if 1 < len(replay):
+      for step in replay[:-1]:
+        self._samples.append((*step, 1))
+    self._samples.append((*replay[-1], -1 if terminated else 0))
+
+    self.update()
+    return
+
+  def update(self):
+    if self._sizeLimit < len(self._samples):
+      self._samples = self._samples[-self.maxSize:]
+    return 
+
+  def __len__(self):
+    return len(self._samples)
+
+  def _fixRewardMultiplier(self, x):
+    if np.isscalar(x):
+      return abs(x)
+
+    if isinstance(x, (np.ndarray, np.generic)):
+      return np.abs(x)
+
+    raise Exception('Unknown reward type. (%s)' % type(x))
+
+  def _createBatch(self, batch_size, sampler):
+    samplesLeft = batch_size
+    cumweights = list(itertools.accumulate(self._sampleWeight(x[2]) for x in self._samples))
+    indexRange = np.arange(len(self._samples)) 
+    res = []
+    while 0 < samplesLeft:
+      indexes = set(random.choices(
+        indexRange, cum_weights=cumweights,
+        k=min((samplesLeft, len(self._samples)))
+      ))
+
+      for i in indexes:
+        sample = sampler(i)
+        if sample:
+          while len(res) < len(sample):  res.append([])
+          for i, value in enumerate(sample[:-1]):
+            res[i].append(value)
+          res[-1].append(self._fixRewardMultiplier(sample[-1]))
+          samplesLeft -= 1
+
+    return [np.array(values) for values in res]
+
+  def sampleBatch(self, batch_size):
+    return self._createBatch(batch_size, lambda i: self._samples[i])
+
+  def sampleSequenceBatch(self, batch_size, sequenceLen, **kwargs):
+    def sampler(ind):
+      sample = self._samples[ind:ind+sequenceLen]
+      if not (sequenceLen == len(sample)): return None
+      if 1 < sequenceLen:
+        if any(x[-1] < 1 for x in sample[:-1]):
+          return None
+
+      transposed = [
+        np.array([x[col] for x in sample]) for col in range(len(sample[0]))
+      ]
+      return transposed
+
     return self._createBatch(batch_size, sampler)