Class SparseVector

Create a sparse vector, using either a dictionary, a list of
(index, value) pairs, or two separate arrays of indices and
values (sorted by index).

@param size: Size of the vector.
@param args: Non-zero entries, as a dictionary, list of tupes,
       or two sorted lists containing indices and values.

>>> print SparseVector(4, {1: 1.0, 3: 5.5})
[1: 1.0, 3: 5.5]
>>> print SparseVector(4, [(1, 1.0), (3, 5.5)])
[1: 1.0, 3: 5.5]
>>> print SparseVector(4, [1, 3], [1.0, 5.5])
[1: 1.0, 3: 5.5]

Overrides: object.__init__

Dot product with a SparseVector or 1- or 2-dimensional Numpy array.

>>> a = SparseVector(4, [1, 3], [3.0, 4.0])
>>> a.dot(a)
25.0
>>> a.dot(array([1., 2., 3., 4.]))
22.0
>>> b = SparseVector(4, [2, 4], [1.0, 2.0])
>>> a.dot(b)
0.0
>>> a.dot(array([[1, 1], [2, 2], [3, 3], [4, 4]]))
array([ 22.,  22.])

Squared distance from a SparseVector or 1-dimensional NumPy array.

>>> a = SparseVector(4, [1, 3], [3.0, 4.0])
>>> a.squared_distance(a)
0.0
>>> a.squared_distance(array([1., 2., 3., 4.]))
11.0
>>> b = SparseVector(4, [2, 4], [1.0, 2.0])
>>> a.squared_distance(b)
30.0
>>> b.squared_distance(a)
30.0

str(x)

Overrides: object.__str__

(inherited documentation)

repr(x)

Overrides: object.__repr__

(inherited documentation)

Test SparseVectors for equality.

>>> v1 = SparseVector(4, [(1, 1.0), (3, 5.5)])
>>> v2 = SparseVector(4, [(1, 1.0), (3, 5.5)])
>>> v1 == v2
True
>>> v1 != v2
False