added scalar and elemwise tensor op maximum and mininum and test for them.

theano/scalar/basic.py

@@ -726,6 +726,32 @@ invert = Invert()
 # Arithmetic
 ##############
 
+class Maximum(BinaryScalarOp):
+    commutative = True
+    associative = True
+    def impl(self, *inputs):
+        return max(inputs)
+    def c_code(self, node, name, (x,y), (z, ), sub):
+        return "%(z)s = ((%(y)s)>(%(x)s)? (%(y)s):(%(x)s));" %locals()
+        
+    def grad(self, (x, y), (gz, )):
+      return (eq(maximum(x,y),  x)*gz,eq(maximum(x,y),  y)*gz)
+
+maximum = Maximum(upcast_out, name = 'maximum')
+
+class Minimum(BinaryScalarOp):
+    commutative = True
+    associative = True
+    def impl(self, *inputs):
+        return min(inputs)
+    def c_code(self, node, name, (x,y), (z, ), sub):
+        return "%(z)s = ((%(y)s)<(%(x)s)? (%(y)s):(%(x)s));" %locals()
+        
+    def grad(self, (x, y), (gz, )):
+      return (eq(minimum(x,y),  x)*gz,eq(minimum(x,y),  y)*gz)
+
+minimum = Minimum(upcast_out, name = 'minimum')
+
 class Add(ScalarOp):
     identity = 0
     commutative = True

theano/tensor/basic.py

@@ -2029,6 +2029,17 @@ setdefault = default # legacy
 ##########################
 # Arithmetics
 ##########################
+@_scal_elemwise
+def maximum(x,y):
+    """elemwise maximum. See max for the maximum in one tensor
+    """
+    # see decorator for function body
+
+@_scal_elemwise
+def minimum(x,y):
+    """elemwise minimum. See min for the minimum in one tensor
+    """
+    # see decorator for function body
 
 def div_proxy(x, y):
     """Proxy for either true_div or int_div, depending on types of x, y.

theano/tensor/inplace.py

@@ -168,6 +168,14 @@ fill_inplace = second_inplace
 pprint.assign(fill_inplace, printing.FunctionPrinter('fill='))
 
 
+@_scal_inplace
+def maximum_inplace(a, b):
+    """elementwise addition (inplace on `a`)"""
+
+@_scal_inplace
+def minimum_inplace(a, b):
+    """elementwise addition (inplace on `a`)"""
+
 @_scal_inplace
 def add_inplace(a, b):
     """elementwise addition (inplace on `a`)"""

theano/tensor/tests/test_basic.py

@@ -250,6 +250,36 @@ SubInplaceTester = makeBroadcastTester(op = inplace.sub_inplace,
                                          grad = _grad_broadcast_binary_normal,
                                          inplace = True)
 
+MaximumTester = makeBroadcastTester(op = maximum,
+                                  expected = numpy.maximum,
+                                  good = _good_broadcast_binary_normal,
+                                  bad_build = _bad_build_broadcast_binary_normal,
+                                  bad_runtime = _bad_runtime_broadcast_binary_normal,
+                                  grad = _grad_broadcast_binary_normal)
+
+MaximumInplaceTester = makeBroadcastTester(op = inplace.maximum_inplace,
+                                         expected = numpy.maximum,
+                                         good = _good_broadcast_binary_normal,
+                                         bad_build = _bad_build_broadcast_binary_normal,
+                                         bad_runtime = _bad_runtime_broadcast_binary_normal,
+                                         grad = _grad_broadcast_binary_normal,
+                                         inplace = True)
+
+MinimumTester = makeBroadcastTester(op = minimum,
+                                  expected = numpy.minimum,
+                                  good = _good_broadcast_binary_normal,
+                                  bad_build = _bad_build_broadcast_binary_normal,
+                                  bad_runtime = _bad_runtime_broadcast_binary_normal,
+                                  grad = _grad_broadcast_binary_normal)
+
+MinimumInplaceTester = makeBroadcastTester(op = inplace.minimum_inplace,
+                                         expected = numpy.minimum,
+                                         good = _good_broadcast_binary_normal,
+                                         bad_build = _bad_build_broadcast_binary_normal,
+                                         bad_runtime = _bad_runtime_broadcast_binary_normal,
+                                         grad = _grad_broadcast_binary_normal,
+                                         inplace = True)
+
 MulTester = makeBroadcastTester(op = mul,
                                   expected = lambda *inputs: reduce(lambda x, y: x * y, inputs),
                                   good = dict(three_inputs_same_shapes = (rand(2, 3), rand(2, 3), rand(2, 3)),