def test_numpy_mm2():
"""
Test @ based NumpyTensor matrix multiply
"""
x = sy.NumpyTensor(numpy_tensor=np.array([[1, 2, 3, 4]]))
y = x @ (x.transpose())
assert (y.child.child == np.array([[30]])).all()
def test_numpy_transpose():
"""
Test basic NumpyTensor transpose
"""
x = sy.NumpyTensor(numpy_tensor=np.array([[1, 2, 3, 4]]))
y = x.transpose(0, 1)
assert (y.child.child == np.array([[1], [2], [3], [4]])).all()
def test_numpy_dot():
"""
Test basic NumpyTensor dot product
"""
x = sy.NumpyTensor(numpy_tensor=np.array([[1, 2, 3, 4]]))
y = x.dot(x.transpose())
assert (y.child.child == np.array([[30]])).all()
def test_numpy_add():
"""
Test basic NumpyTensor addition
"""
x = sy.NumpyTensor(numpy_tensor=[[1, 2, 3, 4]])
y = x + x
assert (y.child.child == np.array([2, 4, 6, 8])).all()
def test_numpy_divide():
"""
Test basic NumpyTensor division
"""
x = sy.NumpyTensor(numpy_tensor=np.array([[1, 2, 3, 4]]))
y = x / x
assert (y.child.child == np.array([1, 1, 1, 1])).all()
def test_numpy_multiply():
"""
Test basic NumpyTensor multiplication
"""
x = sy.NumpyTensor(numpy_tensor=np.array([[1, 2, 3, 4]]))
y = x * x
assert (y.child.child == np.array([1, 4, 9, 16])).all()
def test_numpy_subtract():
"""
Test basic NumpyTensor subtraction
"""
x = sy.NumpyTensor(numpy_tensor=np.array([[1, 2, 3, 4]]))
y = x - x
assert (y.child.child == np.array([0, 0, 0, 0])).all()
def numpy_tensor(self):
"""This method will cast the current tensor to one with numpy as the underlying
representation. The tensor chain will be Wrapper > NumpyTensor > np.ndarray"""
if not self.is_wrapper:
return syft.NumpyTensor(self.numpy())
else:
raise Exception(
"Can only cast a data tensor to NumpyTensor. You called this ",
"on a wrapper. Add NumpyTensor to the chain by hand if you want "
"this functionality.",
)
