semvec/pypi-packages huggingface dataset:
tmpnn 0.0.3
Iimplementation of TMPNN, tabular polynomial network.
Requires Python: >3.8

# TMPNN: High-Order Polynomial Regression Based on Taylor Map Factorization
This is a Tensorflow implementation of TMPNN, tabular polynomial network. TMPNN maps internal dynamical system with lower order polynomial and integrate it, resulting in high-order polynomial model with low complexity.

# Example
The default usage:
```
from tmpnn import TMPNNRegressor, TMPNNLogisticRegressor, TMPNNClassifier, TMPNNPLTransformer

tmpnn = TMPNNRegressor(random_state=0)
tmpnn.fit(x, y)
pred = tmpnn.predict(x)
score = tmpnn.score(x, y)
```

However, in most cases you should scale x and optionally y:
```
from sklearn.compose import TransformedTargetRegressor
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler

tmpnn = TransformedTargetRegressor(
    regressor=Pipeline([
        ('mms', MinMaxScaler((-0.5,0.5))),
        ('est', TMPNNRegressor(random_state=0))
    ]),
    transformer=MinMaxScaler((-0.5,0.5))
)
```

TMPNN is sklearn-friendly, so you also can use it with cross validation or parameter search:
```
cv_scores = cross_val_score(tmpnn, x, y)
```

# Hyperparameters

TMPNN has two major parameters:

`degree`: polynomial order of the Taylor Map. Default value is 2.

`steps`: number of iterations of applying the Taylor Map, integration steps alternatevily. Default value is 7, it might be helpful to search from 2 to 10 for each speciefic task.

In case of small datasets (`n_samples < 1000`) one can also tune `regularizer`. All tensorflow regularizers and lyapnuov (`tmpnn.Lyapunov()`) are acceptable. Lyapunov regularizer tend to provide models robust to extra noise.

For larger datasets one can increase number of optimization epochs `max_epochs`. Default value is 100.

# Citation
If you use this library for a scientific publication, please use the following BibTex entry to cite our work:
```
@misc{ivanov2023tmpnn,
      title={TMPNN: High-Order Polynomial Regression Based on Taylor Map Factorization},
      author={Andrei Ivanov and Stefan Maria Ailuro},
      year={2023},
      eprint={2307.16105},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
```
tensor-library 0.0.1
It is a simple library for the Python programming language. It will be updated frequently.
Requires Python: >=3.8
Keywords: files speedfiles

# Library for Python by DexTeam - Documentation

# Library #

## What is this? ##
The module allows you to work with files in just one line of code, without the need to manually open and close the file each time

## Quick Guide ##
The module is based on the following structure:

    
    f = open('data.txt')
    data = f.readlines()
    f.close()
    
Which Python provides by standard.


----------


### Using ###


Using the library is as simple and convenient as possible:

Let's import it first:
First, import everything from the library (use the `from `...` import *` construct).

Examples of all operations:

Writing the contents of an entire file to a variable using the `read()` function:

    temp = File(path='test.txt').read()


Writing the contents of an entire file to a variable line by line using the `readlines()` function:

    temp = File(path='test.txt').readlines()


Write only the first line from a file using the `readline()` function:

    temp = File(path='test.txt').readline()


Writing data from a variable to a file using write() (overwriting or creating a file):

    temp = "Test data"
    File(path='test.txt', data=temp).write()
    

Adding data from a variable to a file using write() (or creating a file):

    temp = "Test data"
    File(path='test.txt', data=temp).add()


----------


## Developer ##
My discord server: soon... (I'm already doing it)