semvec/pypi-packages huggingface dataset:
lti-arithmetic 1.0.0.post1
Arithmetical manipulation for scipy.signal.lti TransferFunction

# lti-arithmetic
Arithmetical manipulation for scipy.signal.lti TransferFunction

LTI system representations in scipy do not offer built-in composability.
Given two instances of `scipy.signal.lti`, for example, `H` and `G`,
you cannot directly write `H * G`.

This package defines a `scipy.signal.lti`-compatible `TransferFunction`
that permits standard arithmetical combination: `H + G`, `H - G`, `H * G`,
 `H / G`, and `H**n`. In addition, the parallel combination
(reciprocal of reciprocal sums) of systems is available by writing `H | G`.

The package also defines functions `Z_R`, `Z_C`, and `Z_L` that represent
the impedance of a resistance, capacitance, and inductance, respectively,
as LTI systems that can be composed to represent the transfer function
of a larger network.

For convenience, the package also defines `s` so that transfer functions
can be written as rational expressions in `s`.

## Examples

```
>>> from ltiarithmetic import TransferFunction, Z_R, Z_C, Z_L, s
```

Compute the product of two LTI systems:
```
>>> H = TransferFunction([1,2,3],[1])
>>> G = TransferFunction[[1],[2,3,4])
>>> H * G
TransferFunction(
array([0.5, 1. , 1.5]),
array([1. , 1.5, 2. ]),
dt: None
)
```

Compute the transfer function of a RC lowpass filter:
```
Z_1 = Z_R(1000.)
Z_2 = Z_C(1e-9)
H = Z_2 / (Z_1 + Z_2)
```

Construct LTI systems from rational expressions in `s`:
```
import math
w0 = 2 * math.pi * 1000.
Q = 2.
H = 1/(1 + s / (Q * w0) + (s / w0)**2)
```
mlscratch 0.1.0
Machine learning from scratch
Requires Python: >=3.7
Keywords: data science,machine learning

# Machine Learning From Scratch 

## About

Practical implementations of common machine learning algorithms with test
cases coded from scratch in a similar style as Scikit-learn using 
Object-Oriented Programming.

This project is meant to increase our understanding of machine learning
algorithms and what is  'underneath the hood'. It is not meant for use
in actual data science work.  

## Installation
To use the classes and functions for testing purposes create a virtual
environment and pip install the project.  
```
    $ pip install mlscratch==0.0.1
```
To download all source code in a local repository from Github create a virtual
environment and run the following commands in your terminal.

```
    $ git clone https://github.com/lukenew2/mlscratch
    $ cd mlscratch
    $ python setup.py install
```

## Learning Algorithms
### Supervised
- [Elastic Net](mlscratch/supervised/regression.py)
- [Lasso Regression](mlscratch/supervised/regression.py)
- [Linear Regression](mlscratch/supervised/regression.py)
- [Logistic Regression](mlscratch/supervised/logistic.py)
- [Polynomial Regression](mlscratch/utils/preprocessing.py)
- [Ridge Regression](mlscratch/supervised/regression.py)
- [Softmax Regression](mlscratch/supervised/logistic.py)

## Write-Ups
- [ML From Scratch: Linear, Polynomial, and Regularized Regression Models](https://towardsdatascience.com/ml-from-scratch-linear-polynomial-and-regularized-regression-models-725672336076)