Add a function to hash ndarray.

theano/tensor/tests/test_utils.py

+import numpy
+
+from theano.tensor.utils import hash_from_ndarray
+
+
+def test_hash_from_ndarray():
+    hashs = []
+    rng = numpy.random.rand(5, 5)
+
+    for data in [-2, -1, 0, 1, 2, numpy.zeros((1, 5)), numpy.zeros((1, 6)),
+                  # Data buffer empty but different shapes
+                  numpy.zeros((1, 0)), numpy.zeros((2, 0)),
+                  # Same data buffer and shapes but different strides
+                  numpy.arange(25).reshape(5, 5),
+                  numpy.arange(25).reshape(5, 5).T,
+                  # Same data buffer, shapes and strides but different dtypes
+                  numpy.zeros((5, 5), dtype="uint32"),
+                  numpy.zeros((5, 5), dtype="int32"),
+
+                  # Test slice
+                  rng, rng[1:], rng[:4], rng[1:3], rng[::2], rng[::-1]
+                  ]:
+        data = numpy.asarray(data)
+        hashs.append(hash_from_ndarray(data))
+
+    assert len(set(hashs)) == len(hashs)
+
+    # test that different type of views and there copy give the same hash
+    assert hash_from_ndarray(rng[1:]) == hash_from_ndarray(rng[1:].copy())
+    assert hash_from_ndarray(rng[1:3]) == hash_from_ndarray(rng[1:3].copy())
+    assert hash_from_ndarray(rng[:4]) == hash_from_ndarray(rng[:4].copy())
+    assert hash_from_ndarray(rng[::2]) == hash_from_ndarray(rng[::2].copy())
+    assert hash_from_ndarray(rng[::-1]) == hash_from_ndarray(rng[::-1].copy())

theano/tensor/utils.py

+import numpy
+
+from theano.gof.cc import hash_from_code
+
+
+def hash_from_ndarray(data):
+    # We need to hash the shapes and strides as hash_from_code only hash
+    # the data buffer. Otherwise, this will cause problem with shapes likes:
+    # (1, 0) and (2, 0) and problem with inplace transpose.
+    # We also need to add the dtype to make the distinction between
+    # uint32 and int32 of zeros with the same shape and strides.
+
+    # python hash are not strong, so I always use md5. To don't have a too long
+    # hash, I call it again on the contatenation of all part.
+    if not data.flags["C_CONTIGUOUS"] and not data.flags["F_CONTIGUOUS"]:
+        data = numpy.ascontiguousarray(data)
+    return (hash_from_code(hash_from_code(data) +
+                           hash_from_code(str(data.shape)) +
+                           hash_from_code(str(data.strides)) +
+                           hash_from_code(str(data.dtype))))