semvec/pypi-packages huggingface dataset:
IndoPy 1.0.0
IndoPy adalah Packages Module Python berbahasa Indonesia yang akan membantu mempermudah proses pembelajaran.
Keywords: python indonesia,indopy,python berbahasa indonesia

# INDOPY ~ CodeXDeres
IndoPy adalah Packages Module Python berbahasa Indonesia yang saat ini baru berencana untuk merancang module pembelajaran Indonesia semakin meningkat.

Didalam Package Module ini terdapat berbagai macam Class & Fungsi berbahasa indonesia yang mudah dimengerti serta dipahami oleh pemula sekalipun.

Class & Fungsi IndoPy yang tersedia saat ini:
1. Kalkulator Sederhana
2. Perhitungan Bangun Datar
EASIER-net-jeanfeng 0.0.7
EASIER-net
Requires Python: >=3.6

# EASIER-net

Feng, Jean, and Noah Simon. 2022. “Ensembled Sparse‐input Hierarchical Networks for High‐dimensional Datasets.” Statistical Analysis and Data Mining, March. https://doi.org/10.1002/sam.11579.

Python code for fitting EASIER-nets and reproducing all results from the paper.
The python code uses [PyTorch](https://pytorch.org/).

R code for fitting EASIER-net is available at https://github.com/jjfeng/easier_net_R.

## Quick-start

Setup a python virtual environment (code runs for python 3.6) with the appropriate packages from `requirements.txt`.

Simulate data using `generate_data.py` or load your own into a `npz` format with `x` and `y` attributes.

To fit an EASIER-net, run
```
python fit_easier_net.py --num-classes <NUM_CLASSES> --n-layers <N_LAYERS> --n-hidden <N_HIDDEN> --num-inits <NUM_INITS> --input-pen <INPUT_PEN> --full-tree-pen <FULL_TREE_PEN> --max-iters <MAX_ITERS> --max-prox-iters <MAX_PROX_ITERS> --num-batches <NUM_BATCHES> --out-model-file <OUT_MODEL_FILE>
```
where:
* `NUM_CLASSES` should be 0 if doing regression and `NUM_CLASSES` should be the number of classes if doing binary/multi-classification
* `N_LAYERS` is the number of hidden layers
* `N_HIDDEN` is the number of hidden nodes per layer
* `NUM_INITS` is the size of the ensemble
* `INPUT_PEN` specifies $\lambda_1$ in the paper
* `FULL_TREE_PEN` specifies $\lambda_2$ in the paper
* `MAX_ITERS` is the number of epochs to run Adam
* `MAX_PROX_ITERS` is the number of epochs to run batch proximal gradient descent
* `NUM_BATCHES` is the number of mini-batches when running Adam (default 3) 
* `OUT_MODEL_FILE` contains the fitted model

To perform cross-validation, one should run separate `fit_easier_net.py` scripts for each candidate penalty parameter values.
Then select the best penalty parameter values using `collate_best_param.py`.