semvec/pypi-packages huggingface dataset:
MakanaLibrary 0.1.0
First Python Library
Requires Python: >=3.7
Keywords: Makana Python Library

# Python-Sample-Library
This is custom library
    
# we first need to install build.
    pip install build twine

# To create a Python source package (.tar.gz) and the binary package (.whl) in the dist/ directory, do:
    python -m build

# Publish the python library on Pypi
    twine upload dist/*
etchlang 0.2.3
Etch, an easy-to use, high-level, interpreted lang based on Python.
Requires Python: >=3.8,<4.0

# Etch

To install, run the command `pip3 install --user etchlang[repl]` or the equivalent for your package manager (This will also bundle the repl). Once you've installed Etch, just type `etch` at the command line to be dropped into the REPL.
npica 0.0.1
A Python implementation of https://github.com/snormanhaignere/nonparametric-ica
Requires Python: >=3.6

# Description

A Python implementation of the nonparametric decomposition described in:

Norman-Haignere SV, Kanwisher NG, McDermott JH (2015). Distinct cortical pathways for music and speech revealed by hypothesis-free voxel decomposition. Neuron.

Also see [the repository for the original implementation.](https://github.com/snormanhaignere/nonparametric-ica). From the README there:

    The algorithm iteratively rotates the top K principal components of the data matrix, X, to maximize a measure of non-Gaussianity ('negentropy'). This procedure is closely related to standard algorithms for independent component analysis, but unlike standard algorithms does not depend on assumptions about the type of non-Gaussian distribution being identified. Because negentropy is estimated with a histogram, the algorithm tends to work well with a large number of data points (~10,000). The run-time of the algorithm increases substantially as the number of components is increased because the optimization is performed via a brute-force search over all pairs of components (run-time is thus proportional nchoosek(K,2) where K is the number of components).

# Usage

```python
import numpy as np

# Create synthetic data
M = 100 # number of features
N = 10000 # number of measures
K = 3 # number of components
R_true = np.random.rand(M,K)
W_true = np.random.exponential(scale = 1, size=(K,N))
X = R_true@W_true + .01*np.random.normal(M,N)

from npica import ICA
N_RANDOM_INITS = 10
RAND_SEED = 1
ica = ICA(K=K,N_RANDOM_INITS=N_RANDOM_INITS,RAND_SEED=RAND_SEED)
ica.fit(X)
print(ica.sources)
```