Implemented clone() and chunk() in TensorBase, for issues OpenMined#31 …

…and OpenMined#33

syft/tensor.py

@@ -645,6 +645,22 @@ def clamp_(self, minimum=None, maximum=None):
         self.data = np.clip(self.data, a_min=minimum, a_max=maximum)
         return self
 
+    def clone(self):
+        """Returns a copy of the tensor. The copy has the same size and data type as the original tensor."""
+        if self.encrypted:
+            return NotImplemented
+        return TensorBase(np.copy(self.data))
+
+    def chunk(self, n, dim=0, same_size=False):
+        """Returns a list of tensors by splitting the tensor into a number of chunks along a given dimension.
+        Raises an exception if same_size is set to True and given tensor can't be split in n same-size chunks along dim."""
+        if self.encrypted:
+            return NotImplemented
+        if same_size:
+            return [TensorBase(x) for x in np.split(self.data, n, dim)]
+        else:
+            return [TensorBase(x) for x in np.array_split(self.data, n, dim)]
+
     def bernoulli(self, p):
         """
         Returns a Tensor filled with binary random numbers (0 or 1) from a bernoulli distribution

tests/test_tensor.py

@@ -508,6 +508,26 @@ def testClampFloatInPlace(self):
         self.assertEqual(t1, expected_tensor)
 
 
+class cloneTests(unittest.TestCase):
+    def testClone(self):
+        t1 = TensorBase(np.random.randint(0, 10, size=(5, 10)))
+        t2 = t1.clone()
+        self.assertEqual(t1, t2)
+        self.assertIsNot(t1, t2)
+
+
+class chunkTests(unittest.TestCase):
+    def testChunk(self):
+        t1 = TensorBase(np.random.randint(0, 10, size=(5, 10)))
+        t2, t3 = t1.chunk(2, 0)
+        self.assertNotEqual(t2.shape(), t3.shape())
+
+    def testChunkSameSize(self):
+        t1 = TensorBase(np.random.randint(0, 10, size=(4, 10)))
+        t2, t3 = t1.chunk(2, 0, same_size=True)
+        self.assertEqual(t2.shape(), t3.shape())
+
+
 class bernoulliTests(unittest.TestCase):
     def testBernoulli(self):
         p = TensorBase(np.random.uniform(size=(3, 2)))