fixing tests

theano/sandbox/rng_mrg.py

@@ -1317,11 +1317,12 @@ class MRG_RandomStreams(object):
     def multinomial(self, size=None, n=1, pvals=None, ndim=None, dtype='int64',
                     nstreams=None):
         """
-        Sample `n` (`n` needs to be >= 1) times from a multinomial
+        Sample `n` (`n` needs to be >= 1, default 1) times from a multinomial
         distribution defined by probabilities pvals.
 
-        Example : pvals = [[.98, .01, .01], [.01, .49, .50]] and n=2 will
-        probably result in [[2,0,0],[0,1,1]].
+        Example : pvals = [[.98, .01, .01], [.01, .49, .50]] and n=1 will
+        probably result in [[1,0,0],[0,0,1]]. When setting n=2, this
+        will probably result in [[2,0,0],[0,1,1]].
 
         Notes
         -----

theano/sandbox/tests/test_multinomial.py

@@ -74,7 +74,7 @@ def test_multinomial_0():
     p = tensor.fmatrix()
     u = tensor.fvector()
     
-    m = multinomial.MultinomialFromUniform('auto')(p, u, 1)
+    m = multinomial.MultinomialFromUniform('auto')(p, u)
 
     def body(mode, gpu):
         # the m*2 allows the multinomial to reuse output
@@ -113,7 +113,7 @@ def test_multinomial_large():
     def body(mode, gpu):
         p = tensor.fmatrix()
         u = tensor.fvector()
-        m = multinomial.MultinomialFromUniform('auto')(p, u, 1)
+        m = multinomial.MultinomialFromUniform('auto')(p, u)
         f = function([p, u], m*2, allow_input_downcast=True, mode=mode)
         if gpu:
             assert any([type(node.op) is multinomial.GpuMultinomialFromUniform
@@ -144,17 +144,17 @@ def test_multinomial_large():
 def test_multinomial_dtypes():
     p = tensor.dmatrix()
     u = tensor.dvector()
-    m = multinomial.MultinomialFromUniform('auto')(p, u, 1)
+    m = multinomial.MultinomialFromUniform('auto')(p, u)
     assert m.dtype == 'float64', m.dtype
 
     p = tensor.fmatrix()
     u = tensor.fvector()
-    m = multinomial.MultinomialFromUniform('auto')(p, u, 1)
+    m = multinomial.MultinomialFromUniform('auto')(p, u)
     assert m.dtype == 'float32', m.dtype
 
     p = tensor.fmatrix()
     u = tensor.fvector()
-    m = multinomial.MultinomialFromUniform('float64')(p, u, 1)
+    m = multinomial.MultinomialFromUniform('float64')(p, u)
     assert m.dtype == 'float64', m.dtype
 
 
@@ -168,7 +168,7 @@ def test_gpu_opt():
     # is moved to the gpu.
     p = tensor.fmatrix()
     u = tensor.fvector()
-    m = multinomial.MultinomialFromUniform('auto')(p, u, 1)
+    m = multinomial.MultinomialFromUniform('auto')(p, u)
     assert m.dtype == 'float32', m.dtype
     m_gpu = cuda.gpu_from_host(m)
 
@@ -182,7 +182,7 @@ def test_gpu_opt():
 
     # Test with a row, it was failing in the past.
     r = tensor.frow()
-    m = multinomial.MultinomialFromUniform('auto')(r, u, n)
+    m = multinomial.MultinomialFromUniform('auto')(r, u)
     assert m.dtype == 'float32', m.dtype
     m_gpu = cuda.gpu_from_host(m)
 

theano/sandbox/tests/test_rng_mrg.py

@@ -847,7 +847,6 @@ def test_multinomial():
 
 
 def test_multinomial_n_samples():
-    steps = 100
     mode_ = mode
     if mode == 'FAST_COMPILE':
         mode_ = 'FAST_RUN'
@@ -863,21 +862,16 @@ def test_multinomial_n_samples():
     pvals = numpy.apply_along_axis(lambda row: row / numpy.sum(row), 1, pvals)
     R = MRG_RandomStreams(234, use_cuda=False)
     
-    for n_samples in [5, 10, 100, 1000]:
-        # Note: we specify `nstreams` to avoid a warning.
+    for n_samples, steps in zip([5, 10, 100, 1000], [20, 10, 1, 1]):
         m = R.multinomial(pvals=pvals, n=n_samples, dtype=config.floatX, nstreams=30 * 256)
         f = theano.function([], m, mode=mode_)
         basic_multinomialtest(f, steps, sample_size, pvals, n_samples, prefix='mrg ')
         sys.stdout.flush()
         
         if mode != 'FAST_COMPILE' and cuda_available:
-            # print ''
-            # print 'ON GPU:'
             R = MRG_RandomStreams(234, use_cuda=True)
             pvals = numpy.asarray(pvals, dtype='float32')
-            # We give the number of streams to avoid a warning.
             n = R.multinomial(pvals=pvals, n=n_samples, dtype='float32', nstreams=30 * 256)
-            # well, it's really that this test w GPU doesn't make sense otw
             assert n.dtype == 'float32'
             f = theano.function(
                 [],