This is an implementation of Finite Algebras in Python: Groups, Rings, Fields, Vector Spaces, Modules, Monoids, Semigroups, and Magmas (Groupoids).
The representation of an algebra here depends on being able to explicitely represent its multiplication/addition table (Cayley table). Large finite algebras--more than a few hundred elements--are possible, but may take a significant amount of time to process.
The finite_algebras module contains class definitions, methods, and functions for working with algebras that have a finite number of elements.
- The primary constructor of a finite algebra is the function,
make_finite_algebra. It examines the properties of the input table(s) and returns the appropriate instance of an algebra. - Algebras can be input from, or output to, JSON files/strings or Python dictionaries.
- Each algebra is defined by:
- A name (
str), - A description (
str), - A list of element names (
listofstr),- All elements must be represented by strings,
- One or two square, 2-dimensional tables that define binary operations (
listoflistsofint),- The ints in a table represent indices in the list of element names,
- Magmas, Semigroups, Monoids, & Groups have one table; Rings & Fields have two.
- A name (
- Each algebra has methods for examining its properties (e.g.,
is_associative,is_commutative,center,commutators, etc.). - Algebraic elements can be "added" (or "multiplied") via their binary operations (e.g.,
v4.op('h','v')$\Rightarrow$ 'r'). - Inverses & identities can be obtained, if the algebra supports them (e.g.,
z3.inv('a')$\Rightarrow$ 'a^2',z3.identity$\Rightarrow$ 'e'). - Direct products of two or more algebras can be computed using Python's multiplication operator (e.g.,
z4 * v4), and using Python's power operator (e.g.,v4**3 == v4 * v4 * v4). - If two algebras are isomorphic, the mapping between their elements can be found and returned as a Python dictionary (e.g.,
v4.isomorphic(z2 * z2)$\Rightarrow$ {'h': 'e:a', 'v': 'a:e', 'r': 'a:a', 'e': 'e:e'}) - Autogeneration of some types of algebras, of arbitrary order, is supported (e.g., symmetric, cyclic).
- Subalgebras (e.g., subgroups) can be determined, along with related functionality (e.g,
is_normal()). - Groups, Rings, and Fields can be used to construct Modules and Vector Spaces, including n-dimensional Modules and Vector Spaces using the direct products of Rings and Fields, resp.
- The Regular Representation of a Monoid, Group, or the additive abelian Group of a Ring or Field, can be computed in either dense or sparse matrix form.
- Abstract Matrices over Rings/Fields can be represented and used in operations similar to numeric matrices (e.g.,
$+$ ,$-$ ,$\times$ , determinant, inverse, etc.)
This module runs under Python 3.7+ and requires numpy. The sparse matrix option for Regular Representations requires scipy.sparse.
Clone the github repository to install: $ git clone https://github.com/alreich/abstract_algebra.git Once installed, you can follow along the examples here by setting an environment variable, that "points" to the parent directory of the abstract_algebra directory.
For example, if you clone this module into the directory, '/Users/myname/myrepos', then you can do the following to create an environment variable, PYPROJ, like the one used here.
import os
os.environ['PYPROJ'] = '/Users/myname/myrepos'
An environment variable constructed like this only lasts for the duration of the Python session. Consult your implementation of Python to find out how to make the environment variable more "durable".
See full documentation at ReadTheDocs: https://abstract-algebra.readthedocs.io/
As mentioned above, the integers in the 4x4 table, below, are indices of the 4 elements in the element list, ['e', 'h', 'v', 'r'].
>>> from finite_algebras import make_finite_algebra
>>> V4 = make_finite_algebra('V4',
'Klein-4 group',
['e', 'h', 'v', 'r'],
[[0, 1, 2, 3],
[1, 0, 3, 2],
[2, 3, 0, 1],
[3, 2, 1, 0]])
>>> V4Group(
'V4',
'Klein-4 group',
['e', 'h', 'v', 'r'],
[[0, 1, 2, 3], [1, 0, 3, 2], [2, 3, 0, 1], [3, 2, 1, 0]]
)
All of the information, provided by the about method, below, is derived from the table, input above, including the identity element, if it exists.
>>> V4.about(use_table_names=True)** Group **
Name: V4
Instance ID: 4597746640
Description: Klein-4 group
Order: 4
Identity: 'e'
Commutative? Yes
Cyclic?: No
Elements:
Index Name Inverse Order
0 'e' 'e' 1
1 'h' 'h' 2
2 'v' 'v' 2
3 'r' 'r' 2
Cayley Table (showing names):
[['e', 'h', 'v', 'r'],
['h', 'e', 'r', 'v'],
['v', 'r', 'e', 'h'],
['r', 'v', 'h', 'e']]
>>> from finite_algebras import generate_cyclic_group
>>> Z2 = generate_cyclic_group(2)
>>> Z2.about()** Group **
Name: Z2
Instance ID: 4372024528
Description: Autogenerated cyclic Group of order 2
Order: 2
Identity: 'e'
Commutative? Yes
Cyclic?: Yes
Generators: ['a']
Elements:
Index Name Inverse Order
0 'e' 'e' 1
1 'a' 'a' 2
Cayley Table (showing indices):
[[0, 1], [1, 0]]
If A & B are finite algebras, then A * B and A**3 will also be Direct Products of the algebras. NOTE: A**3 == A * A * A.
>>> Z2_sqr = Z2 * Z2 # NOTE: Z2**2 will also do the same thing
>>> Z2_sqr.about(use_table_names=True)** Group **
Name: Z2_x_Z2
Instance ID: 4602714896
Description: Direct product of Z2 & Z2
Order: 4
Identity: 'e:e'
Commutative? Yes
Cyclic?: No
Elements:
Index Name Inverse Order
0 'e:e' 'e:e' 1
1 'e:a' 'e:a' 2
2 'a:e' 'a:e' 2
3 'a:a' 'a:a' 2
Cayley Table (showing names):
[['e:e', 'e:a', 'a:e', 'a:a'],
['e:a', 'e:e', 'a:a', 'a:e'],
['a:e', 'a:a', 'e:e', 'e:a'],
['a:a', 'a:e', 'e:a', 'e:e']]
It is well known that z2_sqr & v4 are isomorphic. The method isomorphic confirms this by finding the following mapping between their elements.
If an isomorphism between two algebras does not exist, then False is returned.
>>> V4.isomorphic(Z2_sqr){'e': 'e:e', 'h': 'e:a', 'v': 'a:e', 'r': 'a:a'}
The method, regular_representation, constructs an isomorphic mapping between a group, or monoid, and a set of square matrices such that the group's identity element corresponds to the identity matrix.
>>> mapping, _, _, _ = V4.regular_representation()
>>> for elem in mapping:
>>> print(elem)
>>> print(mapping[elem])
>>> print()e
[[1. 0. 0. 0.]
[0. 1. 0. 0.]
[0. 0. 1. 0.]
[0. 0. 0. 1.]]
h
[[0. 1. 0. 0.]
[1. 0. 0. 0.]
[0. 0. 0. 1.]
[0. 0. 1. 0.]]
v
[[0. 0. 1. 0.]
[0. 0. 0. 1.]
[1. 0. 0. 0.]
[0. 1. 0. 0.]]
r
[[0. 0. 0. 1.]
[0. 0. 1. 0.]
[0. 1. 0. 0.]
[1. 0. 0. 0.]]
The following small, finite field is used to illustrate Abstract Matrices, farther below.
>>> f4 = make_finite_algebra('F4',
>>> 'Field with 4 elements (from Wikipedia)',
>>> ['0', '1', 'a', '1+a'],
>>> [[0, 1, 2, 3],
>>> [1, 0, 3, 2],
>>> [2, 3, 0, 1],
>>> [3, 2, 1, 0]],
>>> [[0, 0, 0, 0],
>>> [0, 1, 2, 3],
>>> [0, 2, 3, 1],
>>> [0, 3, 1, 2]]
>>> )>>> f4.about(use_table_names=True)** Field **
Name: F4
Instance ID: 4602473168
Description: Field with 4 elements (from Wikipedia)
Order: 4
Identity: '0'
Commutative? Yes
Cyclic?: Yes
Generators: ['1+a', 'a']
Elements:
Index Name Inverse Order
0 '0' '0' 1
1 '1' '1' 2
2 'a' 'a' 2
3 '1+a' '1+a' 2
Cayley Table (showing names):
[['0', '1', 'a', '1+a'],
['1', '0', '1+a', 'a'],
['a', '1+a', '0', '1'],
['1+a', 'a', '1', '0']]
Mult. Identity: '1'
Mult. Commutative? Yes
Zero Divisors: None
Multiplicative Cayley Table (showing names):
[['0', '0', '0', '0'],
['0', '1', 'a', '1+a'],
['0', 'a', '1+a', '1'],
['0', '1+a', '1', 'a']]
Abstract Matrices can be constructed over a Ring or Field. Abstract Matrices can be added, subtracted, multiplied, transposed, and inverted, if the inverse exists.
>>> from abstract_matrix import AbstractMatrix
>>> arr = [[ '0', '1', 'a'],
>>> [ '1', 'a', '1+a'],
>>> ['1+a', '0', '1']]
>>> mat = AbstractMatrix(arr, f4)
>>> mat[['0', '1', 'a'],
['1', 'a', '1+a'],
['1+a', '0', '1']]
>>> mat.determinant()'1'
>>> mat_inv = mat.inverse()
>>> mat_inv[['a', '1', '0'],
['1+a', '1', 'a'],
['1', '1+a', '1']]
>>> mat * mat.inverse()[['1', '0', '0'],
['0', '1', '0'],
['0', '0', '1']]