Returns the size of the vector.

    >>> _vector_size([1., 2., 3.])
    3
    >>> _vector_size((1., 2., 3.))
    3
    >>> _vector_size(array.array('d', [1., 2., 3.]))
    3
    >>> _vector_size(np.zeros(3))
    3
    >>> _vector_size(np.zeros((3, 1)))
    3
    >>> _vector_size(np.zeros((1, 3)))
    Traceback (most recent call last):
        ...
    ValueError: Cannot treat an ndarray of shape (1, 3) as a vector

def _vector_size(v):
    """
    Returns the size of the vector.

    >>> _vector_size([1., 2., 3.])
    3
    >>> _vector_size((1., 2., 3.))
    3
    >>> _vector_size(array.array('d', [1., 2., 3.]))
    3
    >>> _vector_size(np.zeros(3))
    3
    >>> _vector_size(np.zeros((3, 1)))
    3
    >>> _vector_size(np.zeros((1, 3)))
    Traceback (most recent call last):
        ...
    ValueError: Cannot treat an ndarray of shape (1, 3) as a vector
    """
    if isinstance(v, Vector):
        return len(v)
    elif type(v) in (array.array, list, tuple, xrange):
        return len(v)
    elif type(v) == np.ndarray:
        if v.ndim == 1 or (v.ndim == 2 and v.shape[1] == 1):
            return len(v)
        else:
            raise ValueError("Cannot treat an ndarray of shape %s as a vector" % str(v.shape))
    elif _have_scipy and scipy.sparse.issparse(v):
        assert v.shape[1] == 1, "Expected column vector"
        return v.shape[0]
    else:
        raise TypeError("Cannot treat type %s as a vector" % type(v))