added tests, comments for base tensor

_test_base_tensor.py

+from base_tensor import *
+
+import unittest
+from copy import copy
+
+def _tensor(data, broadcastable=None, role=None, name=None):
+    """Return a BaseTensor containing given data"""
+    data = numpy.asarray(data)
+    if broadcastable is None:
+        broadcastable = [s==1 for s in data.shape]
+    elif broadcastable in [0, 1]:
+        broadcastable = [broadcastable] *  len(data.shape)
+    rval = BaseTensor(data.dtype, broadcastable, role, name)
+    rval.data = data # will raise if broadcastable was mis-specified
+    return rval
+
+
+
+class T_tensor(unittest.TestCase):
+    def test0(self): # allocate from a scalar float
+        t = _tensor(1.0)
+        self.failUnless(isinstance(t, BaseTensor))
+        self.failUnless(t.dtype == 'float64')
+        self.failUnless(t.broadcastable == ())
+        self.failUnless(t.role == None)
+        self.failUnless(isinstance(t.data, numpy.ndarray))
+        self.failUnless(str(t.data.dtype) == 'float64')
+        self.failUnless(t.data == 1.0)
+    def test0_int(self): # allocate from a scalar float
+        t = _tensor(1)
+        self.failUnless(isinstance(t, BaseTensor))
+        self.failUnless(t.dtype == 'int64' or t.dtype == 'int32')
+    def test1(self): # allocate from a vector of ints, not broadcastable
+        t = _tensor(numpy.ones(5,dtype='int32'))
+        self.failUnless(isinstance(t, BaseTensor))
+        self.failUnless(t.dtype == 'int32')
+        self.failUnless(t.broadcastable == (0,))
+        self.failUnless(isinstance(t.data, numpy.ndarray))
+        self.failUnless(str(t.data.dtype) == 'int32')
+    def test2(self): # allocate from a column matrix of complex with name
+        t = _tensor(numpy.ones((5,1),dtype='complex64'),name='bart')
+        self.failUnless(isinstance(t, BaseTensor))
+        self.failUnless(t.dtype == 'complex64')
+        self.failUnless(t.broadcastable == (0,1))
+        self.failUnless(isinstance(t.data, numpy.ndarray))
+        self.failUnless(t.name == 'bart')
+    def test2b(self): # allocate from a column matrix, not broadcastable
+        t = _tensor(numpy.ones((5,1),dtype='complex64'),broadcastable=0)
+        self.failUnless(isinstance(t, BaseTensor))
+        self.failUnless(t.dtype == 'complex64')
+        self.failUnless(t.broadcastable == (0,0))
+        self.failUnless(isinstance(t.data, numpy.ndarray))
+    def test_data_normal(self): #test that assigning to .data works when it should
+        t = _tensor(numpy.ones((5,1),dtype='complex64'), broadcastable=0)
+        o27 = numpy.ones((2,7))
+        t.data = o27
+        lst = t._data
+        self.failUnless(t.data.shape == (2,7))
+        self.failUnless(t.data is o27)
+        self.failUnless(t._data is lst)
+    def test_data_badrank0(self):
+        t = _tensor(numpy.ones((5,1),dtype='complex64'), broadcastable=0)
+        try:
+            t.data = numpy.ones((2,7,1))
+            self.fail()
+        except ValueError, e:
+            self.failUnless(e[0] is BaseTensor.filter.E_rank)
+        try:
+            t.data = numpy.ones(1)
+            self.fail()
+        except ValueError, e:
+            self.failUnless(e[0] is BaseTensor.filter.E_rank)
+    def test_data_badrank1(self):
+        t = _tensor(numpy.ones((1,1),dtype='complex64'), broadcastable=1)
+        try:
+            t.data = numpy.ones((1,1,1))
+            self.fail()
+        except ValueError, e:
+            self.failUnless(e[0] is BaseTensor.filter.E_rank)
+        try:
+            t.data = numpy.ones(1)
+            self.fail()
+        except ValueError, e:
+            self.failUnless(e[0] is BaseTensor.filter.E_rank)
+    def test_data_badshape0(self):
+        t = _tensor(numpy.ones((1,1),dtype='complex64'), broadcastable=1)
+        try:
+            t.data = numpy.ones((1,2))
+            self.fail()
+        except ValueError, e:
+            self.failUnless(e[0] is BaseTensor.filter.E_shape)
+        try:
+            t.data = numpy.ones((0,1))
+            self.fail()
+        except ValueError, e:
+            self.failUnless(e[0] is BaseTensor.filter.E_shape)
+
+class T_stdlib(unittest.TestCase):
+    def test0(self):
+        t = _tensor(1.0)
+        tt = t.clone(False)
+        self.failUnless(t.dtype == tt.dtype)
+        self.failUnless(t.broadcastable is tt.broadcastable)
+        self.failUnless(tt.data is None)
+        self.failUnless(t.data == 1.0)
+    def test0b(self):
+        t = _tensor(1.0)
+        tt = t.clone()
+        self.failUnless(t.dtype == tt.dtype)
+        self.failUnless(t.broadcastable is tt.broadcastable)
+        self.failUnless(tt.data is None)
+        self.failUnless(t.data == 1.0)
+
+    def test1(self):
+        t = _tensor(1.0)
+        tt = t.clone(True)
+        self.failUnless(t.dtype == tt.dtype)
+        self.failUnless(t.broadcastable is tt.broadcastable)
+        self.failUnless(tt.data == 1.0)
+        self.failUnless(t.data == 1.0)
+        self.failUnless(t.data is not tt.data)
+    def test1b(self):
+        t = _tensor(1.0)
+        tt = copy(t)
+        self.failUnless(t.dtype == tt.dtype)
+        self.failUnless(t.broadcastable is tt.broadcastable)
+        self.failUnless(tt.data == 1.0)
+        self.failUnless(t.data == 1.0)
+        self.failUnless(t.data is not tt.data)
+
+if __name__ == '__main__':
+    unittest.main()
+

_test_tensor.py

@@ -46,93 +46,6 @@ def verify_grad(testcase, op_cls, pt_list, n_tests=1, rng=numpy.random, eps=0.00
 verify_grad.E_grad = 'gradient error exceeded tolerance'
 
 
-class T_tensor(unittest.TestCase):
-    def test0(self): # allocate from a scalar float
-        t = tensor(1.0)
-        self.failUnless(isinstance(t, Tensor))
-        self.failUnless(t.dtype == 'float64')
-        self.failUnless(t.broadcastable == ())
-        self.failUnless(t.role == None)
-        self.failUnless(isinstance(t.data, numpy.ndarray))
-        self.failUnless(str(t.data.dtype) == 'float64')
-        self.failUnless(t.data == 1.0)
-    def test0_int(self): # allocate from a scalar float
-        t = tensor(1)
-        self.failUnless(isinstance(t, Tensor))
-        self.failUnless(t.dtype == 'int64' or t.dtype == 'int32')
-    def test1(self): # allocate from a vector of ints, not broadcastable
-        t = tensor(numpy.ones(5,dtype='int32'))
-        self.failUnless(isinstance(t, Tensor))
-        self.failUnless(t.dtype == 'int32')
-        self.failUnless(t.broadcastable == (0,))
-        self.failUnless(isinstance(t.data, numpy.ndarray))
-        self.failUnless(str(t.data.dtype) == 'int32')
-    def test2(self): # allocate from a column matrix of complex with name
-        t = tensor(numpy.ones((5,1),dtype='complex64'),name='bart')
-        self.failUnless(isinstance(t, Tensor))
-        self.failUnless(t.dtype == 'complex64')
-        self.failUnless(t.broadcastable == (0,1))
-        self.failUnless(isinstance(t.data, numpy.ndarray))
-        self.failUnless(t.name == 'bart')
-    def test2b(self): # allocate from a column matrix, not broadcastable
-        t = tensor(numpy.ones((5,1),dtype='complex64'),broadcastable=0)
-        self.failUnless(isinstance(t, Tensor))
-        self.failUnless(t.dtype == 'complex64')
-        self.failUnless(t.broadcastable == (0,0))
-        self.failUnless(isinstance(t.data, numpy.ndarray))
-    def test_data_normal(self): #test that assigning to .data works when it should
-        t = tensor(numpy.ones((5,1),dtype='complex64'), broadcastable=0)
-        o27 = numpy.ones((2,7))
-        t.data = o27
-        lst = t._data
-        self.failUnless(t.data.shape == (2,7))
-        self.failUnless(t.data is o27)
-        self.failUnless(t._data is lst)
-    def test_data_badrank0(self):
-        t = tensor(numpy.ones((5,1),dtype='complex64'), broadcastable=0)
-        try:
-            t.data = numpy.ones((2,7,1))
-            self.fail()
-        except ValueError, e:
-            self.failUnless(e[0] is Tensor.filter.E_rank)
-        try:
-            t.data = numpy.ones(1)
-            self.fail()
-        except ValueError, e:
-            self.failUnless(e[0] is Tensor.filter.E_rank)
-    def test_data_badrank1(self):
-        t = tensor(numpy.ones((1,1),dtype='complex64'), broadcastable=1)
-        try:
-            t.data = numpy.ones((1,1,1))
-            self.fail()
-        except ValueError, e:
-            self.failUnless(e[0] is Tensor.filter.E_rank)
-        try:
-            t.data = numpy.ones(1)
-            self.fail()
-        except ValueError, e:
-            self.failUnless(e[0] is Tensor.filter.E_rank)
-    def test_data_badshape0(self):
-        t = tensor(numpy.ones((1,1),dtype='complex64'), broadcastable=1)
-        try:
-            t.data = numpy.ones((1,2))
-            self.fail()
-        except ValueError, e:
-            self.failUnless(e[0] is Tensor.filter.E_shape)
-        try:
-            t.data = numpy.ones((0,1))
-            self.fail()
-        except ValueError, e:
-            self.failUnless(e[0] is Tensor.filter.E_shape)
-
-class T_stdlib(unittest.TestCase):
-    def test0(self):
-        t = tensor(1.0)
-        tt = copy(t)
-        self.failUnless(t.dtype == tt.dtype)
-        self.failUnless(t.broadcastable == tt.broadcastable)
-        self.failUnless(t.broadcastable is tt.broadcastable)
-        self.failIf(t.data is tt.data)
 
 def check_eq(self, node_in, node_out, arg_in, arg_out):
     fn = Function([node_in], [node_out])

base_tensor.py

+"""A simple class to store ndarray data """
 
 from gof import ResultBase
 import numpy
@@ -50,6 +51,7 @@ class BaseTensor(ResultBase):
     # filter
     #
     def filter(self, arr):
+        """cast to an ndarray and ensure arr has correct rank, shape"""
         if not isinstance(arr, numpy.ndarray):
             arr = numpy.asarray(arr, dtype = self.dtype)
         if len(self.broadcastable) != len(arr.shape):
@@ -159,8 +161,10 @@ class BaseTensor(ResultBase):
         return self.clone(True)
     
     def clone(self, transfer_data = False):
-        """
-        Returns a copy of this Tensor. If there is data stored inside it, it is also copied.
+        """Return a copy of this instance (with its own attributes)
+        
+        If transfer_data is True, a copy of self.data is assigned to the copy's
+        data property, otherwise the copy's data is left as None.
         """
         cpy = self.__class__(self.dtype, self.broadcastable, None, self.name)
         if transfer_data: