semvec/pypi-packages huggingface dataset:
topsis-vinay-101803108 0.1
Compare different models using performance score

TOPSIS
===================

Use it to compare results or parameters of different models and rank them accordingly



Documentation
-------------

Commands:

1.   import topsis_vinay_101803108 as tp
2.  tp.topsis("filename","wts array","impacts array","resultfilename")

> **Note:**

>-  File should be of CSV type containing all the parameter for different files.
>- All the data should be of numeric type.
>- TOPSIS score will be stored in the resultfilename.csv
>- Provdie weights and impact array in form of list
>- wts => [1,1,1,1], impacts => ['-','+','+','+']

Eg.

| Model | Corr | Rseq | Rmse | Accuracy |
|-------|------|------|------|----------|
| M1    | 0.79 | 0.62 | 1.25 | 60.89    |
| M2    | 0.66 | 0.44 | 2.89 | 63.07    |
| M3    | 0.56 | 0.31 | 1.57 | 62.87    |
pylinlin 0.0.4
A nice linear algebra library
Requires Python: >=3.8

# pylinlin [![PyPI version](https://badge.fury.io/py/pylinlin.svg)](https://badge.fury.io/py/pylinlin) [![codecov](https://codecov.io/gh/owenl131/pylinlin/branch/main/graph/badge.svg)](https://codecov.io/gh/owenl131/pylinlin)

Nice linear algebra library in python

## Examples

```python
from pylinlin.matrix import Matrix
from pylinlin.lu_factorization import compute_lu_factorization
from pylinlin.qr_factorization import compute_qr_factorization

# Create matrix
matrix = Matrix.from_cols([[1, 2, 3], [4, 5, 6], [7, 8, 10]])  # preferred way to initialize a matrix
matrix2 = Matrix.from_rows([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

column_as_list = [1, 2, 3, 4, 5]
column_vector = Matrix.from_cols([column_as_list])  # column vectors can be represented as matrices
magnitude_sq = column_vector.transpose().multiply(column_vector).get(0, 0)  # 55

print(matrix.size())      # Get dimensions of matrix (rows, columns)
print(matrix.get_col(0))  # Get first column from matrix
print(matrix.get_row(1))  # Get second row from matrix

print(matrix.all_cols())  # List of matrix columns

matL, matU = compute_lu_factorization(matrix)
matQ, matR = compute_qr_factorization(matrix)
print(matQ.all_cols())
print(matQ.transpose().multiply(matQ).all_cols())  # approximately an identity matrix

product = matrix.multiply(matrix)  # matrix multiplication
```

## Goals

- Test-driven development
- Profiling of performance
- Profiling of numerical stability
- Lightweight, easy to port over to other languages

## TODOs

### Algorithms

- [x] LU factorization
- [ ] LU factorization with partial pivoting
- [x] QR factorization with householder matrices
- [ ] QR factorization with pivoting
- [ ] Gram Schmidt and Modified Gram Schmidt (help wanted!)
- [ ] Spectral decomposition
- [ ] SVD
- [ ] Conjugate gradients
- [ ] Condition number of a matrix
- [ ] Jacobi SVD
- [ ] Power iteration
- [ ] Matrix Pseudoinverse

### Profiling

- [ ] Profile time taken varying size of matrices
- [ ] Profile time taken to solve linear system comparing different algorithms
- [ ] Graph error distribution on random matrices

### Others

- [ ] Make curve fitting demonstration
- [ ] Make IK demonstration
hat-trie-python 0.6.0
Python bindings for Tessil/hat-trie
Requires Python: >=2.7

# hat-trie-python

Python wrapper for https://github.com/Tessil/hat-trie (MIT License)

It's still incomplete, not all functionality is supported. Sets are not implemented yet.
Tested on Linux 2.7+, Windows 3.5+, MacOS 2.7+. It should work on Windows 2.7 also, however it's tricky to compile.

## Install

- `pip install hattrie` (requires Cython and a C++11 compiler)

## Example usage:
```
from hattrie import HatTrieMap
htm = HatTrieMap()
htm[b"/foo"] = b"1"
htm[b"/foo/bar"] = b"2"
print(list(htm.longest_prefix(b"/foo"))) # returns [(b'/foo', b'1')]
print(list(htm.longest_prefix(b"/foo/baz"))) # returns [(b'/foo', b'1')]
print(list(htm.longest_prefix(b"/foo/bar/baz"))) # returns [(b'/foo/bar', b'2'), (b'/foo', b'1')]
print(list(htm.longest_prefix(b"/foo/bar/"))) # returns [(b'/foo/bar', b'2'), (b'/foo', b'1')]
print(list(htm.longest_prefix(b"/bar"))) # returns []
print(list(htm.longest_prefix(b""))) # returns []
```

Any Python object is supported as value, however only bytes are supported as keys.
TOPSIS-VINAY-101803108 0.2
Compare different models using performance score

TOPSIS
===================

Use it to compare results or parameters of different models and rank them accordingly



Documentation
-------------

Commands:

1.   import TOPSIS-VINAY-101803108 as tp
2.  tp.topsis("filename","wts array","impacts array","resultfilename")

> **Note:**

>-  File should be of CSV type containing all the parameter for different files.
>- All the data should be of numeric type.
>- TOPSIS score will be stored in the resultfilename.csv

Eg.

| Model | Corr | Rseq | Rmse | Accuracy |
|-------|------|------|------|----------|
| M1    | 0.79 | 0.62 | 1.25 | 60.89    |
| M2    | 0.66 | 0.44 | 2.89 | 63.07    |
| M3    | 0.56 | 0.31 | 1.57 | 62.87    |