Move from xrange to range

samples/machine_learning/iris.py

@@ -53,7 +53,7 @@ def main():
     N = 0
     for _ in open(IRIS_PATH):
         N += 1
-    testindexes = set(random.sample(xrange(N), N / 10))
+    testindexes = set(random.sample(range(N), N / 10))
 
     dataset = IrisDataset(IRIS_PATH, lambda i: i not in testindexes)
     testset = IrisDataset(IRIS_PATH, lambda i: i in testindexes)

samples/machine_learning/language_classification.py

@@ -85,7 +85,7 @@ def __iter__(self):
 
 # Choose test set, either 10% or 10000 examples, whatever is less
 M = min(N / 10, 10000)
-testindexes = set(random.sample(xrange(N), M))
+testindexes = set(random.sample(range(N), M))
 print("Keeping {} examples for testing".format(M))
 
 problem = LanguageClassificationProblem()

samples/machine_learning/opinion.py

@@ -100,7 +100,7 @@ def main():
 
     # Choose test set, either 10% or 10000 examples, whatever is less
     M = min(N / 10, 1000)
-    testindexes = set(random.sample(xrange(N), M))
+    testindexes = set(random.sample(range(N), M))
 
     corpus = ProConsCorpus(input_files, lambda i: i not in testindexes)
     test = ProConsCorpus(input_files, lambda i: i in testindexes)

simpleai/environments.py

@@ -10,7 +10,7 @@ def __init__(self, agents, initial_state):
 
     def run(self, steps=10000, viewer=None):
         self.state = self.initial_state
-        for step in xrange(steps):
+        for step in range(steps):
             if self.is_completed(self.state):
                 return
             self.step(viewer=viewer)

simpleai/machine_learning/evaluation.py

@@ -46,7 +46,7 @@ def kfold(dataset, problem, method, k=10):
 
     trials = 0
     positive = 0
-    for i in xrange(k):
+    for i in range(k):
         train = [x for j, x in enumerate(dataset) if j % k != i]
         test = [x for j, x in enumerate(dataset) if j % k == i]
         classifier = method(train, problem)

simpleai/machine_learning/models.py

@@ -165,7 +165,7 @@ def __init__(self, dataset, target_index):
             self.target_index = N + self.target_index
         if self.target_index < 0 or N <= self.target_index:
             raise ValueError("Target index is out of range")
-        for i in xrange(N):
+        for i in range(N):
             if i == self.target_index:
                 continue
             attribute = VectorIndexAttribute(i, "data at index {}".format(i))

simpleai/search/local.py

@@ -290,7 +290,7 @@ def _local_search(problem, fringe_expander, iterations_limit=0, fringe_size=1,
 
     fringe = BoundedPriorityQueue(fringe_size)
     if random_initial_states:
-        for _ in xrange(fringe_size):
+        for _ in range(fringe_size):
             s = problem.generate_random_state()
             fringe.append(SearchNodeValueOrdered(state=s, problem=problem))
     else:

tests/machine_learning/test_classifiers.py

@@ -205,9 +205,9 @@ def setup_dataset(self):
         n = 100
 
         dataset = []
-        for i in xrange(n):
+        for i in range(n):
             # Pseudo random generation of bits
-            bits = [(((i + j) * 1223) % (n + 1)) % 2 for j in xrange(k)]
+            bits = [(((i + j) * 1223) % (n + 1)) % 2 for j in range(k)]
             bits.append(sum(bits) % 2)
             dataset.append(bits)
 
@@ -226,7 +226,7 @@ def setup_dataset(self):
                     # patched in Dtree Pseudo (with modifing the pseudocode).
 
         dataset = []
-        for i in xrange(size):
+        for i in range(size):
             dataset.append([
                 i % 2 == 0,
                 i % 3 == 0,

tests/machine_learning/test_metrics.py

@@ -18,19 +18,19 @@ def test_total_starts_in_zero(self):
 
     def test_add_elements(self):
         counter = Counter(lambda x: None)
-        for i in xrange(20):
+        for i in range(20):
             counter.add("something")
         self.assertEqual(counter.total, 20)
 
     def test_target_values(self):
         counter = Counter(lambda x: x % 2 == 0)
-        for i in xrange(25):
+        for i in range(25):
             counter.add(i)
         self.assertEqual(counter[0], 12)
         self.assertEqual(counter[1], 13)
 
         counter = Counter(lambda x: None)
-        for i in xrange(50):
+        for i in range(50):
             counter.add(i)
         self.assertEqual(counter[None], 50)
 
@@ -42,7 +42,7 @@ def test_starts_in_zero(self):
 
     def test_valid_values(self):
         entropy = OnlineEntropy(lambda x: x % 10)
-        for i in xrange(150):
+        for i in range(150):
             entropy.add(i)
         self.assertGreaterEqual(entropy.get_entropy(), 0.0)
 
@@ -57,15 +57,15 @@ def test_starts_in_zero(self):
     def test_no_gain(self):
         f = lambda x: None
         gain = OnlineInformationGain(f, f)
-        for i in xrange(30):
+        for i in range(30):
             gain.add(i)
         self.assertEqual(gain.get_gain(), 0)
 
     def test_full_gain(self):
         target = lambda x: x % 7
         gain = OnlineInformationGain(target, target)
         entropy = OnlineEntropy(target)
-        for i in xrange(50):
+        for i in range(50):
             gain.add(i)
             entropy.add(i)
         self.assertEqual(gain.get_gain(), entropy.get_entropy())