white space/indentation fix.

theano/sandbox/rng_mrg.py

@@ -37,12 +37,12 @@ def matVecModM(A, s, m):
     return x
 
 def multMatVect(v, A, m1, B, m2):
-   #multiply the first half of v by A with a modulo of m1
-   #and the second half by B with a modulo of m2
-   r = numpy.zeros_like(v)
-   r[:3] = matVecModM(A, v[:3], m1)
-   r[3:] = matVecModM(B, v[3:], m2)
-   return r
+    #multiply the first half of v by A with a modulo of m1
+    #and the second half by B with a modulo of m2
+    r = numpy.zeros_like(v)
+    r[:3] = matVecModM(A, v[:3], m1)
+    r[3:] = matVecModM(B, v[3:], m2)
+    return r
 
 #MRG31k3p
 #generator constants :
@@ -59,7 +59,7 @@ A2p0 = numpy.asarray([[32768, 0, 32769], [1, 0, 0], [0, 1, 0]])
 
 A1p72 = numpy.asarray([[1516919229, 758510237, 499121365],
        [1884998244, 1516919229, 335398200],
-       [601897748, 1884998244, 358115744]]) 
+       [601897748, 1884998244, 358115744]])
 A2p72 = numpy.asarray([[1228857673, 1496414766, 954677935],
    [1133297478, 1407477216, 1496414766],
    [2002613992, 1639496704, 1407477216]])
@@ -154,8 +154,8 @@ class mrg_uniform_base(Op):
         # this op should not be called directly.
         #
         # call through MRG_RandomStreams instead.
-        return Apply(self, 
-                [rstate, size], 
+        return Apply(self,
+                [rstate, size],
                 [rstate.type(), self.output_type()])
 
     def grad(self,inputs,ograd):
@@ -205,17 +205,17 @@ class mrg_uniform(mrg_uniform_base):
         if self.inplace:
             o_rstate_requirement = 'NPY_C_CONTIGUOUS|NPY_ALIGNED'
         else:
-            o_rstate_requirement = 'NPY_ENSURECOPY|NPY_C_CONTIGUOUS|NPY_ALIGNED' 
+            o_rstate_requirement = 'NPY_ENSURECOPY|NPY_C_CONTIGUOUS|NPY_ALIGNED'
         ndim = self.output_type.ndim
         o_type_num = numpy.asarray(0, dtype=self.output_type.dtype).dtype.num
         fail = sub['fail']
         if self.output_type.dtype == 'float32':
-            otype = 'float' 
+            otype = 'float'
             NORM = '4.6566126e-10f' #numpy.float32(1.0/(2**31+65))
             # this was determined by finding the biggest number such that
             # numpy.float32(number * M1) < 1.0
         else:
-            otype = 'double' 
+            otype = 'double'
             NORM = '4.656612873077392578125e-10'
         return """
         //////// <code generated by mrg_uniform>
@@ -368,12 +368,12 @@ class GPU_mrg_uniform(mrg_uniform_base):
 
     def c_support_code_apply(self, node, nodename):
         if self.output_type.dtype == 'float32':
-            otype = 'float' 
+            otype = 'float'
             NORM = '4.6566126e-10f' #numpy.float32(1.0/(2**31+65))
             # this was determined by finding the biggest number such that
             # numpy.float32(number * M1) < 1.0
         else:
-            otype = 'double' 
+            otype = 'double'
             NORM = '4.656612873077392578125e-10'
         return """
 
@@ -450,7 +450,7 @@ class GPU_mrg_uniform(mrg_uniform_base):
             state_data[idx*6+4]= x22;
             state_data[idx*6+5]= x23;
             }
-        }  
+        }
 
         """ %locals()
 
@@ -461,9 +461,9 @@ class GPU_mrg_uniform(mrg_uniform_base):
         fail = sub['fail']
 
         if self.output_type.dtype == 'float32':
-            otype = 'float' 
+            otype = 'float'
         else:
-            otype = 'double' 
+            otype = 'double'
 
         SYNC="CNDA_THREAD_SYNC";
         return """
@@ -495,7 +495,7 @@ class GPU_mrg_uniform(mrg_uniform_base):
         {
             odims[i] = ((npy_int32*)(%(size)s->data + %(size)s->strides[0] * i))[0];
             n_elements *= odims[i];
-            must_alloc_sample = (must_alloc_sample 
+            must_alloc_sample = (must_alloc_sample
                     || CudaNdarray_HOST_DIMS(%(o_sample)s)[i] != odims[i]);
         }
         if (must_alloc_sample)
@@ -555,7 +555,7 @@ class GPU_mrg_uniform(mrg_uniform_base):
 
         {
             cudaError_t err = cudaGetLastError();
-            if( cudaSuccess != err) 
+            if( cudaSuccess != err)
             {
                 PyErr_Format(PyExc_RuntimeError, "Cuda error: %%s: %%s.\\n", "mrg_uniform", cudaGetErrorString(err));
                 %(fail)s;
@@ -657,7 +657,7 @@ class MRG_RandomStreams(object):
         If the size argument is ambiguous on the number of dimensions,
         ndim may be a plain integer to supplement the missing
         information.
-        
+
         :param: size: Can be a list of integer or Theano variable(ex: the shape of other Theano Variable)
                       TODO: can size be None?
         """
@@ -671,7 +671,7 @@ class MRG_RandomStreams(object):
         if self.use_cuda and dtype=='float32':
             rstates = self.get_substream_rstates(nstreams)
             rstates = rstates.flatten()
-            # HACK - we use fact that int32 and float32 have same size to 
+            # HACK - we use fact that int32 and float32 have same size to
             # sneak ints into the CudaNdarray type.
             # these *SHOULD NEVER BE USED AS FLOATS*
             tmp_float_buf = numpy.frombuffer(rstates.data, dtype='float32')
@@ -683,7 +683,7 @@ class MRG_RandomStreams(object):
             # we can't use the normal mrg_uniform constructor + later optimization
             # because of the tmp_float_buf hack above.  There is
             # currently no Theano node that will do a frombuffer reinterpretation.
-            u = self.pretty_return(node_rstate, 
+            u = self.pretty_return(node_rstate,
                     *GPU_mrg_uniform.new(node_rstate, ndim, dtype, size))
         else:
             node_rstate = shared(self.get_substream_rstates(nstreams))
@@ -703,12 +703,12 @@ class MRG_RandomStreams(object):
                 return cast(self.uniform(size=size) < p, dtype)
         else:
             raise NotImplementedError("MRG_RandomStreams.binomial with n > 1")
-            
+
     def multinomial(self, size=None, n=1, pvals=None, ndim=None, dtype='int64'):
         """
         Sample `n` (currently `n` needs to be 1) times from a multinomial distribution defined by
         probabilities pvals.
-        
+
         Example : pvals = [[.98,.01, .01], [.01, .98 .01]] will probably result in [[1,0,0],[0,1,0]].
 
         .. note::
@@ -736,9 +736,9 @@ class MRG_RandomStreams(object):
         evened = False
         constant = False
         if isinstance(size, tuple) and all([isinstance(i,int) for i in size]):
-            constant = True          
+            constant = True
             n_samples = numpy.prod(size)
-           
+
             if n_samples % 2 == 1:
                 n_samples += 1
                 evened = True