adding a minimal Op that is useful for debugging

theano/sandbox/minimal.py

+import numpy, scipy.linalg
+from theano import gof, tensor, scalar, function
+import unittest
+
+
+class Minimal(gof.Op):
+
+    # if the Op has any attributes,
+    # consider using them in the eq function.  If two Apply nodes have the same inputs and the
+    # ops compare equal... then they will be MERGED so they had better have computed the same
+    # thing!
+
+    def __init__(self):
+        # If you put things here, think about whether they change the outputs computed by
+        # self.perform()
+        #  - If they do, then you should take them into consideration in __eq__ and __hash__
+        #  - If they do not, then you should not use them in __eq__ and __hash__
+
+        super(Minimal, self).__init__()
+
+    def __eq__(self, other):
+        return type(self) == type(other)
+
+    def __hash__(self):
+        return hash(type(self))
+
+    def make_node(self, *args):
+        # HERE `args` must be THEANO VARIABLES 
+        return gof.Apply(op=self, inputs=args, outputs=[tensor.lscalar()])
+
+    def perform(self, node, inputs, (output, )):
+        # HERE `inputs` are PYTHON OBJECTS 
+
+        # do what you want here, 
+        # but do not modify any of the arguments [inplace].
+        print "perform got %i arguments" % len(inputs)
+
+        print "Max of input[0] is ", numpy.max(inputs[0])
+
+        # return some computed value.
+        # do not return something that is aliased to one of the inputs.
+        output[0]=numpy.asarray(0, dtype='int64')
+
+minimal = Minimal()
+
+
+## TODO: test dtype conversion
+## TODO: test that invalid types are rejected by make_node
+## TODO: test that each valid type for A and b works correctly
+from theano.tests import unittest_tools as utt
+class T_minimal(unittest.TestCase):
+    def setUp(self):
+        self.rng = numpy.random.RandomState(utt.fetch_seed(666))
+
+    def test0(self):
+        A=tensor.matrix()
+        b=tensor.vector()
+
+        print 'building function'
+        f = function([A, b], minimal(A, A, b, b, A))
+        print 'built'
+
+        Aval=self.rng.randn(5,5)
+        bval=numpy.array(range(5),dtype=float)
+        f(Aval,bval)
+        print 'done'