make CAReduce support scalar.minimum.

theano/tensor/elemwise.py

@@ -998,11 +998,19 @@ class CAReduce(Op):
 
         if hasattr(self.scalar_op,'identity'):
             identity = self.scalar_op.identity
-        elif self.scalar_op == scalar.maximum:
-            if input.type.dtype in ["float32","float64"]:
-                identity = "-__builtin_inf()"
-            else:
-                identity = "NPY_MIN_"+str(input.type.dtype).upper()
+        elif self.scalar_op in [scalar.maximum, scalar.minimum]:
+            if self.scalar_op == scalar.maximum:
+                if input.type.dtype in ["float32","float64"]:
+                    identity = "-__builtin_inf()"
+                    scal_name = 'maximum'
+                else:
+                    identity = "NPY_MIN_"+str(input.type.dtype).upper()
+                    scal_name = 'minimum'
+            if self.scalar_op == scalar.minimum:
+                if input.type.dtype in ["float32","float64"]:
+                    identity = "__builtin_inf()"
+                else:
+                    identity = "NPY_MAX_"+str(input.type.dtype).upper()
             fail = sub["fail"]
             pattern=[0]*len(node.inputs[0].broadcastable)
             axis = self.axis
@@ -1014,7 +1022,7 @@ class CAReduce(Op):
             alloc += """
 for(int i=0;i<%(iname)s->nd;i++){
   if(PyArray_DIMS(%(iname)s)[i]==0 && tosum[i]){
-    PyErr_Format(PyExc_ValueError, "Input of CAReduce{maximum} has zero-size on axis %%d",i);
+    PyErr_Format(PyExc_ValueError, "Input of CAReduce{%(scal_name)s} has zero-size on axis %%d",i);
     %(fail)s;
   }
 }

theano/tensor/tests/test_elemwise.py

@@ -195,6 +195,12 @@ class test_CAReduce(unittest.TestCase):
                         zv = numpy.maximum.reduce(zv, axis)
                 except ValueError:
                     numpy_raised=True
+            elif scalar_op == minimum:
+                try:
+                    for axis in reversed(sorted(tosum)):
+                        zv = numpy.minimum.reduce(zv, axis)
+                except ValueError:
+                    numpy_raised=True
             elif scalar_op == or_:
                 for axis in reversed(sorted(tosum)):
                     zv = numpy.any(zv, axis)
@@ -203,7 +209,7 @@ class test_CAReduce(unittest.TestCase):
                     zv = numpy.all(zv, axis)
             else:
                 raise Exception("Test for CAReduce with scalar_op %s not implemented"%str(scalar_op))
-            if scalar_op == maximum and numpy_raised:
+            if scalar_op in [maximum,minimum] and numpy_raised:
                 try:
                     f(xv)
                 except ValueError:
@@ -221,13 +227,14 @@ class test_CAReduce(unittest.TestCase):
                 e = CAReduce(scalar_op, axis = tosum)(x)
                 if tosum is None: tosum = range(len(xsh))
                 f = copy(linker).accept(Env([x], [e.shape])).make_function()
-                if not(scalar_op == maximum and ((xsh==() or numpy.prod(xsh)==0))):
+                if not(scalar_op in [maximum,minimum] and ((xsh==() or numpy.prod(xsh)==0))):
                     assert all(f(xv)== zv.shape)
 
     def test_perform(self):
         self.with_linker(gof.PerformLinker(), add)
         self.with_linker(gof.PerformLinker(), mul)
         self.with_linker(gof.PerformLinker(), maximum)
+        self.with_linker(gof.PerformLinker(), minimum)
         #need other dtype then real
         #self.with_linker(gof.PerformLinker(), or_)
         #self.with_linker(gof.PerformLinker(), and_)
@@ -236,6 +243,7 @@ class test_CAReduce(unittest.TestCase):
         self.with_linker(gof.CLinker(), add)
         self.with_linker(gof.CLinker(), mul)
         self.with_linker(gof.CLinker(), maximum)
+        self.with_linker(gof.CLinker(), minimum)
 
         #need other dtype then real        
         #no c_code for or_, and_