def cosine_distance(x, y, name=''):
'''
Computes the cosine distance between ``x`` and ``y``:
Example:
>>> a = np.asarray([-1, -1, -1, 1, 1, -1, 1, 1, -1, 1, 1, -1]).reshape(3,2,2)
>>> b = np.asarray([1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1]).reshape(3,2,2)
>>> x = C.input_variable(shape=(2,))
>>> y = C.input_variable(shape=(2,))
>>> np.round(C.cosine_distance(x,y).eval({x:a,y:b}),5)
array([[-1., 1.],
[ 1., 0.],
[ 0., -1.]], dtype=float32)
Args:
x: numpy array or any :class:`~cntk.ops.functions.Function` that outputs a tensor
name (str, optional): the name of the Function instance in the network
Returns:
:class:`~cntk.ops.functions.Function`
'''
from cntk.cntk_py import cosine_distance
dtype = get_data_type(x, y)
x = sanitize_input(x, dtype)
y = sanitize_input(y, dtype)
return cosine_distance(x, y, name)
def cosine_distance(x, y, name=''):
'''
Computes the cosine distance between ``x`` and ``y``:
Example:
>>> a = np.asarray([-1, -1, -1, 1, 1, -1, 1, 1, -1, 1, 1, -1]).reshape(3,2,2)
>>> b = np.asarray([1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1]).reshape(3,2,2)
>>> x = C.sequence.input_variable(shape=(2,))
>>> y = C.sequence.input_variable(shape=(2,))
>>> np.round(C.cosine_distance(x,y).eval({x:a,y:b}),5)
array([[-1., 1.],
[ 1., 0.],
[ 0., -1.]], dtype=float32)
Args:
x: numpy array or any :class:`~cntk.ops.functions.Function` that outputs a tensor
name (str, optional): the name of the Function instance in the network
Returns:
:class:`~cntk.ops.functions.Function`
'''
from cntk.cntk_py import cosine_distance
dtype = get_data_type(x, y)
x = sanitize_input(x, dtype)
y = sanitize_input(y, dtype)
return cosine_distance(x, y, name)
