added __eq__ and __hash__ to NumpyGenerator

_test_tensor_random.py

@@ -68,6 +68,19 @@ class T_Random(unittest.TestCase):
         self.failUnless(str(v0[0,0]).startswith('0.013259'))
         self.failUnless(str(v0[1,2]).startswith('0.753368'))
 
+    def test5(self):
+        """Test that two NumpyGenerators with the same dist compare equal"""
+
+        rng0 = RandomState(123456)
+        rng1 = RandomState(123456)
+
+        d0 = rng0.gen(('beta',{'a':0.5,'b':0.65}), (2,3,4))
+        d1 = rng1.gen(('beta',{'a':0.5,'b':0.65}), (2,3,4))
+
+        self.failUnless(d0.owner.op == d1.owner.op)
+        self.failUnless(hash(d0.owner.op) == hash(d1.owner.op))
+
+
 if __name__ == '__main__':
     unittest.main()
 

blas.py

 import os, sys
 import scipy.weave as weave
+import gof.utils
 
 """
 File: omega/blas.py
@@ -794,16 +795,7 @@ def blas_proto():
     }
     """
 
-def _constant(f):
-    """Return a function that always returns its first call value
-    """
-    def rval(*args, **kwargs):
-        if not hasattr(f, 'rval'):
-            f.rval = f(*args, **kwargs)
-        return f.rval
-    return rval
-
-@_constant
+@gof.utils.memoize
 def ldflags():
     """Return a list of libraries against which an Op's object file should be
     linked to benefit from a BLAS implementation.

gof/utils.py

@@ -36,6 +36,21 @@ class scratchpad:
         print "scratch" + str(self.__dict__)
 
 
+def memoize(f):
+    """Cache the return value for each tuple of arguments
+    (which must be hashable) """
+    cache = {}
+    def rval(*args, **kwargs):
+        kwtup = tuple(kwargs.items())
+        key = (args, kwtup)
+        if key not in cache:
+            val = f(*args, **kwargs)
+            cache[key] = val
+        else:
+            val = cache[key]
+        return val
+    return rval
+
 
 
 def deprecated(filename, msg=''):

tensor_random.py

@@ -3,16 +3,23 @@ import tensor
 import numpy
 import functools
 
-def fn_from_dist(dist):
+# the optional argument implements a closure
+# the cache is used so that we we can be sure that 
+# id(self.fn) in NumpyGenerator identifies 
+# the computation performed.
+def fn_from_dist(dist, cache={}):
     if callable(dist):
         return dist
     if isinstance(dist, str):
         return getattr(numpy.random.RandomState, dist)
 
     name, kwargs = dist
+    key = (name, tuple(kwargs.items()))
+    if key not in cache:
         fn = getattr(numpy.random.RandomState, name)
         fn = functools.partial(fn, **kwargs)
-    return fn
+        cache[key] = fn
+    return cache[key]
 
 class RandomState(object):
     def __init__(self, seed):
@@ -45,14 +52,22 @@ class NumpyGenerator(gof.op.Op):
         self.ndim = ndim
         self.fn = fn
 
+    def __eq__(self, other):
+        return (type(self) is type(other))\
+                and self.__class__ is NumpyGenerator \
+                and self.seed == other.seed \
+                and self.ndim == other.ndim \
+                and self.fn == other.fn
+    def __hash__(self):
+        return self.seed ^ self.ndim ^ id(self.fn)
+
     def make_node(self, _shape):
         #TODO: check for constant shape, and guess the broadcastable bits
         shape = tensor.convert_to_int64(_shape)
         if shape.type.ndim != 1:
             raise TypeError('shape argument was not converted to 1-d tensor', _shape)
         inputs = [gof.Value(gof.type.generic, numpy.random.RandomState(self.seed)), shape]
-        outputs = [gof.Result(tensor.Tensor(dtype='float64', broadcastable =
-            [False]*self.ndim))]
+        outputs = [tensor.Tensor(dtype='float64', broadcastable = [False]*self.ndim).make_result()]
         return gof.Apply(op = self, inputs = inputs, outputs = outputs)
 
     def grad(self, inputs, grad_outputs):