An infinite-dimensional vector package.
>>> from vector import vecadd
>>> vecadd((1, 2), (4, 5, 6))
(5, 7, 6)
>>>
>>> from vector import Vector
>>> v = Vector((1, 2))
>>> w = Vector((4, 5, 6))
>>> v + w
Vector(5, 7, 6, ...)
>>>
>>> from vector import vecnpadd
>>> vecnpadd((1, 2), ((3, 4, 5),
... (6, 7, 8)))
array([[4, 6, 5],
[7, 9, 8]])
pip install git+https://github.com/goessl/vector.git
This package includes
- general-purpose functions,
- a clean class
- improved numpy-routines to handle infinite-dimensional vectors. It operates on vectors of different lengths, treating them as infinite-dimensional by assuming that all components after the given ones are zero.
>>> from vector import vecadd
>>> a = (5, 6, 7)
>>> b = [2]
>>> c = range(4)
>>> vecadd(a, b, c)
(7, 7, 9, 3)
All functions accept sequences, most even single exhaustible iterables.
They return vectors as tuples.
The functions are type-independent. However, the data types used must support necessary scalar operations. For instance, for vector addition, components must be addable — this may include operations with padded integer zeros. Empty empty operations return zero vector (e.g. vecsum()==veczero
) or integer zeros (e.g. vecdot(veczero, veczero)==int(0)
).
Padding is done with int(0)
.
creation stuff
veczero = ()
: Zero vector.vecbasis(i, c=1)
: Return thei
-th basis vector timesc
. The retured value is a tuple withi
integer zeros followed byc
.vecrand(n)
: Return a random vector ofn
uniform coefficients in[0, 1[
.vecrandn(n, normed=True, mu=0, sigma=1)
: Return a random vector ofn
normal distributed coefficients.
sequence stuff
veceq(v, w)
: Return if two vectors are equal.vectrim(v, tol=1e-9)
: Remove all trailing near zero (<=tol) coefficients.vecround(v, ndigits=None)
: Round all coefficients to the given precision.
Hilbert space stuff
vecabsq(v)
: Return the sum of absolute squares of the coefficients.vecabs(v)
: Return the Euclidean/L2-norm.vecdot(v, w)
: Return the inner product of two vectors without conjugation.
vector space stuff
vecpos(v)
: Return the vector with the unary positive operator applied.vecneg(v)
: Return the vector with the unary negative operator applied.vecadd(*vs)
: Return the sum of vectors.vecsub(v, w)
: Return the difference of two vectors.vecmul(a, v)
: Return the product of a scalar and a vector.vectruediv(v, a)
: Return the true division of a vector and a scalar.vecfloordiv(v, a)
: Return the floor division of a vector and a scalar.
elementwise stuff
vechadamard(*vs)
: Return the elementwise product of vectors.vechadamardtruediv(v, w)
: Return the elementwise true division of two vectors.vechadamardfloordiv(v, w)
: Return the elementwise floor division of two vectors.vechadamardmod(v, w)
: Return the elementwise mod of two vectors.
The immutable Vector
class wraps all the mentioned functions into a tidy package, making them easier to use by enabling interaction through operators.
Its coefficients are internally stored as a tuple in the coef
attribute and therefore zero-indexed.
Vector operations return the same type (type(v+w)==type(v)
) so the class can easily be extended (to e.g. a polynomial class).
initialisation stuff
Vector(i)
: Create a new vector with the given coefficients or thei
-th basis vector if an integeri
is given.Vector.rand(n)
: Create a random vector ofn
uniform coefficients in[0, 1[
.Vector.randn(n, normed=True, mu=0, sigma=1))
: Create a random vector ofn
normal distributed coefficients.Vector.ZERO
: Zero vector.
>>> from vector import Vector
>>> Vector((1, 2, 3))
Vector(1, 2, 3, ...)
>>> Vector.gauss(3)
Vector(-0.5613820142699765, -0.028308921297709365, 0.8270724508948077, ...)
>>> Vector(3)
Vector(0, 0, 0, 1, ...)
container and sequence stuff
len(v)
: Return the number of set coefficients.v[key]
: Return the indexed coefficient or coefficients. Not set coefficients default to 0.iter(v)
: Return an iterator over the set coefficients.v == w
: Return if of same type with same coefficients.v << i
: Return a vector with coefficients shifted to lower indices.v >> i
: Return a vector with coefficients shifted to higher indices.v.trim(tol=1e-9)
: Remove all trailing near zero (abs<=tol) coefficients.v.round(ndigits=None)
: Round all coefficients to the given precision.
Hilbert space stuff
v.absq()
: Return the sum of absolute squares of the coefficients.abs(v)
: Return the Euclidean/L2-norm. Return the square root ofvecabsq
.v @ w
: Return the inner product of two vectors without conjugation.
vector space stuff
v + w
: Return the vector sum.v - w
: Return the vector difference.v * a
: Return the scalar product.v / a
: Return the scalar true division.v // a
: Return the scalar floor division.v % a
: Return the elementwise mod with a scalar.
elementwise stuff
v.hadamard(w)
: Return the elementwise product with another vector.v.hadamardtruediv(w)
: Return the elementwise true division with another vector.v.hadamardfloordiv(w)
: Return the elementwise floor division with another vector.v.hadamardmod(w)
: Return the elementwise mod with another vector.
>>> from vector import vecnpadd
>>> vecnpadd((1, 2), ((3, 4, 5),
... (6, 7, 8)))
array([[4, 6, 5],
[7, 9, 8]])
numpy
-version of the functions are also provided, to many vectors at once. They behave like the ones in numpy.polynomial.polynomial
, but also work on 2D-arrays and broadcast to multiple dimensions like the usual numpy
operations (but adjust the shapes accordingly).
vecnpzero
is np.array([0])
like numpy.polynomial.polynomial.polyzero
, not veczero=()
(empty tuple, no zero coefficient left) like in the functions and class above.
Padding is done with numpy.int64
zeros.
Creation routines have a dimension argument d
. If left to None
, the returned values are 1D, so a single vector. If given, the routines return a 2D-array representing mutiple vectors in rows.
creation stuff
vecnpzero(d=None)
: Returnd
zero vectors. The retured value is a(d, 1)
-array of zeros ifd
is notNone
or[0]
otherwise.vecnpbasis(i, c=1, d=None)
: Returnd
manyi
-th basis vectors timesc
. The retured value is a(d, i+1)
-array ifd
is notNone
or(i+1,)
otherwise.vecnprand(n, d=None)
: Returnd
random vectors ofn
uniform coefficients in[0, 1[
. The retured value is a(d, n)
-array ifd
is notNone
or(n,)
otherwise.vecnprandn(n, normed=True, d=None)
: Returnd
random vectors ofn
normal distributed coefficients. The retured value is a(d, n)
-array ifd
is notNone
or(n,)
otherwise.
sequence stuff
vecnpeq(v, w)
: Return if two vectors are equal.vecnptrim(v, tol=1e-9)
: Remove all trailing near zero (abs(v_i)<=tol) coefficients.- (
numpy.round
already exists)
Hilbert space stuff
vecnpabsq(v)
: Return the sum of absolute squares of the coefficients.vecnpabs(v)
: Return the Euclidean/L2-norm.vecnpdot(v, w)
: Return the inner product of two vectors without conjugation.
vector space stuff
vecnpadd(*vs)
: Return the sum of vectors.vecnpsub(v, w)
: Return the difference of two vectors.
- docstrings
-
numpy
routines
Copyright (c) 2022-2024 Sebastian Gössl
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.