semvec/pypi-packages huggingface dataset:
matMulti 0.0.5
A package to multiply two matrix of any dimensions
License: MIT
Keywords: matrixMultiplier


********* MatrixMultiplier Package  **********

This package provides a simple and efficient way to multiply two matrices of any dimensions. 
Whether you're dealing with small matrices or large ones, this package offers a reliable 
solution to perform matrix multiplication with ease.

1. Installation:

```
pip install matMulti
```

2. Generate multiplication result:

```
from matMulti import product 
```


Features
--Supports matrix multiplication of any dimensions.
--Efficient algorithm implementation for fast computation.
--Works with Python 3.

download here https://github.com/SajalDasShovon/Own-Simple-Python-Package
bean-rs 0.3.1
beancount clone in Rust
License: MIT
Requires Python: >=3.9

# bean-rs

Basic [beancount](https://github.com/beancount/beancount) clone (one day...) in Rust!

Still very very alpha and doesn't do most things that are necessary to be at all useful.

Python bindings are a WIP using [PyO3](https://pyo3.rs);

The libraries:
- Rust: [crates/bean-rs](https://crates.io/crates/bean-rs)
- Python: [pypi/bean-rs](https://pypi.org/project/bean-rs/)

Planned features:
- [x] Parse beancount files using [pest](https://pest.rs/)
- [x] Stricter transaction keywords
- [x] Propagate line numbers for debugging
- [x] Calculate account balances
- [x] Use proper Decimal handling
- [x] Validate transactions against `open`/`close` directives
- [x] Validate `balance` directives
- [x] Pad statements
- [x] Open/close with multiple currencies
- [x] Add Python bindings
- [ ] Support `includes`
- [ ] Come up with a more punny name
- [ ] Currency conversions
- [ ] Price/cost and FIFO

## (Deliberate) differences from beancount
- Postings can't omit the currency

## Use from Rust
### Install
```bash
cargo install bean-rs
```

### Run
```bash
$ bean-rs

Usage: bean-rs <COMMAND>

Commands:
  balance  Display account balances
  check    Check for errors and quit
  help     Print this message or the help of the given subcommand(s)

Options:
  -h, --help     Print help
  -V, --version  Print version
```

#### Calculate balances
```bash
bean-rs balance example.bean
```

## Use from Python
More to come...
```python
import bean_rs
ledger = bean_rs.py_load("example.bean")
print(ledger.opts)
```

## Development
### Build
```bash
make build
```

### Test
```bash
make test
```
adilsm 0.0.12
Integrated Latent Multi Source Model
License: MIT
Requires Python: >=3.9

# Install

```bash
pip install adilsm
```

# Description

ILSM is Integrated Latent Multi Source Model.

# Reference

https://www.preprints.org/manuscript/202402.1001/v3

# Usage

```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import adilsm.adilsm as ilsm

max_noise_level = 0.1
# Generate a random non-negative matrix with 100 rows and 10 columns
A = np.random.rand(100, 10)
# Swap the columns of the A and add some noise to generate B
B = np.random.permutation(A.T).T + np.random.uniform(low=0, high=max_noise_level, size=A.shape)
# Add noise to A
A += np.random.uniform(low=0, high=max_noise_level, size=A.shape)

# ISM is expected to recognize that A and B convey the same information up to some noise,
# albeit with the columns of B swapped around. Heatmaps of the loadings of A and B columns
# on ISM components show the effective permutation. 

Xs = [A, B]
n_embedding, n_themes = [10,10]

ilsm_result = ilsm.ism(Xs, n_embedding, n_themes, norm_columns=False, update_h_ism=True,
                                    max_iter_mult=200, fast_mult_rules=True, sparsity_coeff=.8)
hv = ilsm_result['HV']
hv_sparse = ilsm_result['HV_SPARSE']
hhii = ilsm_result['HHII']
w_ism = ilsm_result['W']
h_ism = ilsm_result['H']
q_ism = ilsm_result['Q']
Xs_emb = ilsm_result['EMBEDDING']
Xs_norm = ilsm_result['NORMED_VIEWS']

fig, ax = plt.subplots(1, 2, figsize=(10, 5), constrained_layout=True)
ax[0].imshow(hv[0], cmap='viridis', aspect='auto')
# Add labels and title
ax[0].set_xlabel('Component')
ax[0].set_ylabel('Column')
ax[0].set_title('Loadings of A columns on ISM components')
ax[1].imshow(hv[1], cmap='viridis', aspect='auto')
# Add labels and title
ax[1].set_xlabel('Component')
ax[1].set_ylabel('Column')
ax[1].set_title('Loadings of B columns on ISM components')

# Show the plot
plt.show()

```