change weighted_selection to multinomial_wo_replacement

theano/sandbox/multinomial.py

@@ -194,7 +194,7 @@ class MultinomialFromUniform(Op):
                             break
 
 
-class WeightedSelectionFromUniform(Op):
+class MultinomialWOReplacementFromUniform(Op):
     """
     Converts samples from a uniform into sample from a multinomial.
 

theano/sandbox/rng_mrg.py

@@ -1363,8 +1363,8 @@ class MRG_RandomStreams(object):
             raise NotImplementedError(("MRG_RandomStreams.multinomial only"
                                        " implemented for pvals.ndim = 2"))
 
-    def weighted_selection(self, size=None, n=1, pvals=None, ndim=None, dtype='int64',
-                           nstreams=None):
+    def multinomial_wo_replacement(self, size=None, n=1, pvals=None, ndim=None, dtype='int64',
+                                   nstreams=None):
         """
         Sample `n` times *WITHOUT replacement* from a multinomial distribution
         defined by probabilities pvals, and returns the indices of the sampled
@@ -1394,21 +1394,21 @@ class MRG_RandomStreams(object):
 
         if size is not None:
             raise ValueError("Provided a size argument to "
-                             "MRG_RandomStreams.weighted_selection, which does not use "
+                             "MRG_RandomStreams.multinomial_wo_replacement, which does not use "
                              "the size argument.")
         if ndim is not None:
             raise ValueError("Provided an ndim argument to "
-                             "MRG_RandomStreams.weighted_selection, which does not use "
+                             "MRG_RandomStreams.multinomial_wo_replacement, which does not use "
                              "the ndim argument.")
         if pvals.ndim == 2:
             # size = [pvals.shape[0], as_tensor_variable(n)]
             size = pvals[:,0].shape * n
             unis = self.uniform(size=size, ndim=1, nstreams=nstreams)
-            op = multinomial.WeightedSelectionFromUniform(dtype)
+            op = multinomial.MultinomialWOReplacementFromUniform(dtype)
             n_samples = as_tensor_variable(n)
             return op(pvals, unis, n_samples)
         else:
-            raise NotImplementedError(("MRG_RandomStreams.weighted_selection only"
+            raise NotImplementedError(("MRG_RandomStreams.multinomial_wo_replacement only"
                                        " implemented for pvals.ndim = 2"))
 
     def normal(self, size, avg=0.0, std=1.0, ndim=None,

theano/sandbox/tests/test_weighted_select.py → theano/sandbox/tests/test_multinomial_wo_replacement.py

@@ -9,12 +9,12 @@ class test_OP(unittest.TestCase):
 
     def test_select_distinct(self):
         """
-        Tests that WeightedSelectionFromUniform always selects distinct elements
+        Tests that MultinomialWOReplacementFromUniform always selects distinct elements
         """
         p = tensor.fmatrix()
         u = tensor.fvector()
         n = tensor.iscalar()
-        m = multinomial.WeightedSelectionFromUniform('auto')(p, u, n)
+        m = multinomial.MultinomialWOReplacementFromUniform('auto')(p, u, n)
 
         f = function([p, u, n], m, allow_input_downcast=True)
 
@@ -32,13 +32,13 @@ class test_OP(unittest.TestCase):
 
     def test_fail_select_alot(self):
         """
-        Tests that WeightedSelectionFromUniform fails when asked to sample more
+        Tests that MultinomialWOReplacementFromUniform fails when asked to sample more
         elements than the actual number of elements
         """
         p = tensor.fmatrix()
         u = tensor.fvector()
         n = tensor.iscalar()
-        m = multinomial.WeightedSelectionFromUniform('auto')(p, u, n)
+        m = multinomial.MultinomialWOReplacementFromUniform('auto')(p, u, n)
 
         f = function([p, u, n], m, allow_input_downcast=True)
 
@@ -52,13 +52,13 @@ class test_OP(unittest.TestCase):
 
     def test_select_proportional_to_weight(self):
         """
-        Tests that WeightedSelectionFromUniform selects elements, on average,
+        Tests that MultinomialWOReplacementFromUniform selects elements, on average,
         proportional to the their probabilities
         """
         p = tensor.fmatrix()
         u = tensor.fvector()
         n = tensor.iscalar()
-        m = multinomial.WeightedSelectionFromUniform('auto')(p, u, n)
+        m = multinomial.MultinomialWOReplacementFromUniform('auto')(p, u, n)
 
         f = function([p, u, n], m, allow_input_downcast=True)
 
@@ -83,13 +83,13 @@ class test_function(unittest.TestCase):
 
     def test_select_distinct(self):
         """
-        Tests that weighted_selection always selects distinct elements
+        Tests that multinomial_wo_replacement always selects distinct elements
         """
         th_rng = RandomStreams(12345)
 
         p = tensor.fmatrix()
         n = tensor.iscalar()
-        m = th_rng.weighted_selection(pvals=p, n=n)
+        m = th_rng.multinomial_wo_replacement(pvals=p, n=n)
 
         f = function([p, n], m, allow_input_downcast=True)
 
@@ -106,14 +106,14 @@ class test_function(unittest.TestCase):
 
     def test_fail_select_alot(self):
         """
-        Tests that weighted_selection fails when asked to sample more
+        Tests that multinomial_wo_replacement fails when asked to sample more
         elements than the actual number of elements
         """
         th_rng = RandomStreams(12345)
 
         p = tensor.fmatrix()
         n = tensor.iscalar()
-        m = th_rng.weighted_selection(pvals=p, n=n)
+        m = th_rng.multinomial_wo_replacement(pvals=p, n=n)
 
         f = function([p, n], m, allow_input_downcast=True)
 
@@ -126,14 +126,14 @@ class test_function(unittest.TestCase):
 
     def test_select_proportional_to_weight(self):
         """
-        Tests that weighted_selection selects elements, on average,
+        Tests that multinomial_wo_replacement selects elements, on average,
         proportional to the their probabilities
         """
         th_rng = RandomStreams(12345)
 
         p = tensor.fmatrix()
         n = tensor.iscalar()
-        m = th_rng.weighted_selection(pvals=p, n=n)
+        m = th_rng.multinomial_wo_replacement(pvals=p, n=n)
 
         f = function([p, n], m, allow_input_downcast=True)