Spelling/typo/minor style tweaks.

darius · darius · commit 5308a2e4fd5a · 2011-09-12T00:43:24.000Z
diff --git a/csp.py b/csp.py
@@ -287,7 +287,7 @@ def parse_neighbors(neighbors, vars=[]):
         dict[var] = []
     specs = [spec.split(':') for spec in neighbors.split(';')]
     for (A, Aneighbors) in specs:
-        A = A.strip();
+        A = A.strip()
         dict.setdefault(A, [])
         for B in Aneighbors.split():
             dict[A].append(B)
diff --git a/learning.py b/learning.py
@@ -15,7 +15,7 @@ class DataSet:
     d.target      The attribute that a learning algorithm will try to predict.
                   By default the final attribute.
     d.inputs      The list of attrs without the target.
-    d.values      A list of lists, each sublist is the set of possible
+    d.values      A list of lists: each sublist is the set of possible
                   values for the corresponding attribute. If None, it
                   is computed from the known examples by self.setproblem.
                   If not None, an erroneous value raises ValueError.
@@ -42,7 +42,7 @@ def __init__(self, examples=None, attrs=None, target=-1, values=None,
         else:
             self.examples = examples
         map(self.check_example, self.examples)
-        # Attrs are the indicies of examples, unless otherwise stated.
+        # Attrs are the indices of examples, unless otherwise stated.
         if not attrs and self.examples:
             attrs = range(len(self.examples[0]))
         self.attrs = attrs
@@ -57,7 +57,7 @@ def setproblem(self, target, inputs=None, exclude=()):
         """Set (or change) the target and/or inputs.
         This way, one DataSet can be used multiple ways. inputs, if specified,
         is a list of attributes, or specify exclude as a list of attributes
-        to not put use in inputs. Attributes can be -n .. n, or an attrname.
+        to not use in inputs. Attributes can be -n .. n, or an attrname.
         Also computes the list of possible values, if that wasn't done yet."""
         self.target = self.attrnum(target)
         exclude = map(self.attrnum, exclude)
@@ -156,7 +156,7 @@ def predict(self, example):
 class NaiveBayesLearner(Learner):
     
     def train(self, dataset):
-        """Just count the target/attr/val occurences.
+        """Just count the target/attr/val occurrences.
         Count how many times each value of each attribute occurs.
         Store count in N[targetvalue][attr][val]. Let N[attr][None] be the
         sum over all vals."""
@@ -291,7 +291,7 @@ def decision_tree_learning(self, examples, attrs, default=None):
             return default
         elif self.all_same_class(examples):
             return examples[0][self.dataset.target]
-        elif  len(attrs) == 0:
+        elif len(attrs) == 0:
             return self.majority_value(examples)
         else:
             best = self.choose_attribute(attrs, examples)
@@ -410,7 +410,7 @@ class EnsembleLearner(Learner):
     """Given a list of learning algorithms, have them vote."""
 
     def __init__(self, learners=[]):
-        self.learners=learners
+        self.learners = learners
 
     def train(self, dataset):
         for learner in self.learners:
@@ -481,7 +481,7 @@ def score(learner, size):
             for size in sizes]
 
 #______________________________________________________________________________
-# The rest of this file gives Data sets for machine learning problems.
+# The rest of this file gives datasets for machine learning problems.
 
 orings = DataSet(name='orings', target='Distressed',
                  attrnames="Rings Distressed Temp Pressure Flightnum")
@@ -502,8 +502,8 @@ def score(learner, size):
 def RestaurantDataSet(examples=None):
     "Build a DataSet of Restaurant waiting examples."
     return DataSet(name='restaurant', target='Wait', examples=examples,
-                  attrnames='Alternate Bar Fri/Sat Hungry Patrons Price '
-                   + 'Raining Reservation Type WaitEstimate Wait')
+                   attrnames='Alternate Bar Fri/Sat Hungry Patrons Price '
+                    + 'Raining Reservation Type WaitEstimate Wait')
 
 restaurant = RestaurantDataSet()
 
@@ -563,7 +563,7 @@ def Xor(n):
     return Parity(2, n, name="xor")
 
 def ContinuousXor(n):
-    "2 inputs are chosen uniformly form (0.0 .. 2.0]; output is xor of ints."
+    "2 inputs are chosen uniformly from (0.0 .. 2.0]; output is xor of ints."
     examples = []
     for i in range(n):
         x, y = [random.uniform(0.0, 2.0) for i in '12']
diff --git a/logic.py b/logic.py
@@ -749,7 +749,7 @@ def WalkSAT(clauses, p=0.5, max_flips=10000):
         if probability(p):
             sym = random.choice(prop_symbols(clause))
         else:
-            ## Flip the symbol in clause that miximizes number of sat. clauses
+            ## Flip the symbol in clause that maximizes number of sat. clauses
             raise NotImplementedError
         model[sym] = not model[sym]
 
diff --git a/probability.py b/probability.py
@@ -104,7 +104,7 @@ def __setitem__(self, values, p):
         if isinstance(values, dict):
             values = [values[var] for var in self.variables]
         self.prob[values] = p
-        for var,val in zip(self.variables, values):
+        for var, val in zip(self.variables, values):
             if val not in self.vals[var]:
                 self.vals[var].append(val)
 
diff --git a/text.py b/text.py
@@ -320,7 +320,7 @@ class PermutationDecoder:
     common one-letter words, etc.); etc.
       We could represent a search state as a permutation of the 26 letters,
     and alter the solution through hill climbing.  With an initial guess
-    based on unigram probabilities, this would probably fair well. However,
+    based on unigram probabilities, this would probably fare well. However,
     I chose instead to have an incremental representation. A state is 
     represented as a letter-to-letter map; for example {'z': 'e'} to
     represent that 'z' will be translated to 'e'
