semvec/pypi-packages huggingface dataset:
linalg-deluxe 0.1.3
Additional linear algebra operations to extend Joel Grus's library from 'Data Science from Scratch' chapter 4
Requires Python: >=3.10

# Linalg Deluxe
An extension of Joel Grus's linear algebra Python library as he wrote it in chapter 4 of *Data Science from Scratch*. I have added transposition, symmetry checking, multiplication of vectors by matrices, the angle between two vectors, outer products, cross products, matrix multiplication, determinants, adjoint matrices, and inverse matrices (plus some input-checking helper functions).

### Installation
```
pip install linalg-basic
pip install linalg-deluxe
```

### Get started
The following code walks through just a few of the library's features:
```
from linalg_deluxe.linalg_deluxe import transpose, multiply_matrix_vector, symmetric, inverse_matrix

# Create a Vector and a Matrix (the two data types the library runs on)
v = [1, 2, 3]
m = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

# Transpose v and m
v_vertical = transpose(v)
m_transposed = transpose(m)

# Multiply m by v
m_x_v = multiply_matrix_vector(m, v)

# Is m symmetric?
m_sym = symmetric(m)

# Attempt to generate m's inverse
# This will fail, because m's determinant is not zero in this case
m_inv = inverse_matrix(m)
```
linalg-basic 0.1.0
A slight variation upon Joel Grus's linear algebra library from 'Data Science from Scratch' chapter 4, created to build my 'Deluxe' library on top
Requires Python: >=3.10

# Linalg Basic

This library is slightly adapted from Joel Grus's Python linear algebra library as he wrote it in chapter 4 of his book *Data Science from Scratch*. I've assembled it purely to use its parts in my "Linalg Deluxe" library, which adds many more linear algebra operations.

Since Linalg Basic is largely Joel Grus's work, I've not bothered documenting it very well or testing it. More extensive documentation, testing, etc. can be found in my Linalg Deluxe library, which builds upon Linalg Basic and is actually my own original work. Go look at that, not this, for a proper demonstration of my skills.