semvec/pypi-packages huggingface dataset:
mlhandmade 0.1.0
Implemented some ML routines

# ML-handmade

Implemented some ML routines including other ML stuff such as preprocessing, visualization and model selection.

### References

- Scikit-learn source code: [https://github.com/scikit-learn/scikit-learn](https://github.com/scikit-learn/scikit-learn)
- PRML algorithms by ctgk: [https://github.com/ctgk/PRML](https://github.com/ctgk/PRML)
- Sokolov lectures on ML(RU): [https://github.com/esokolov/ml-course-hse](https://github.com/esokolov/ml-course-hse)
- ML handbook by Yandex SDA(RU): [https://ml-handbook.ru/](https://ml-handbook.ru/)
- MLAlgorithms by rushter: [https://github.com/rushter/MLAlgorithms](https://github.com/rushter/MLAlgorithms)
- mlxtend library by rasbt: [https://github.com/rasbt/mlxtend](https://github.com/rasbt/mlxtend)

### Algorithms implemented

* Linear models with different optmization methods(GD, SGD, Batch-SGD, SAG)
* KNN with three approaches(brute-force, kd-tree, ball-tree)
* Multiclass strategies (One-vs-One, One-vs-Rest)
* Support vector (SVC and $\epsilon$-SVR) with different kernels(Linear, RBF, Polynomial)
* Discriminant analysis(linear & quadratic) implemented using SVD
* Decision tree classifier and regressor
* Random forest classifier and regressor with bootstrap
* AdaBoost classifier and regressor
* Other ML stuff, for instance, k-fold cross validation, quality metrics, plotting, e.t.c

### Installation

It can be installed using `pip`

```bash
pip install mlhandmade
```
MRI-DistortionQA 0.8.1
Quality assurance tools for MRI geometric distortion
Requires Python: >=3.8

# MRI_DistortionQA  
![](docsrc/__resources/coverage.svg) ![](docsrc/__resources/interrogate.svg) ![](https://github.com/ACRF-Image-X-Institute/MRI_DistortionQA/blob/main/.github/workflows/run_tests.yml/coverage.svg) ![tests](https://github.com/ACRF-Image-X-Institute/MRI_DistortionQA/actions/workflows/run_tests.yml/badge.svg) ![docs](https://github.com/ACRF-Image-X-Institute/MRI_DistortionQA/actions/workflows/build_docs.yml/badge.svg)

**Authors:** Brendan Whelan, Paul Liu, Shanshan Shan

This code enables characterization and reporting of geometric distortion in Magnetic Resonance Imaging. For the measurement of such distortions, see [here](https://github.com/ACRF-Image-X-Institute/MRI_DistortionPhantom). The basic end-to-end workflow is below, but all steps have well defined input/output so you can use any part of this code independently from the other parts. For a tutorial on each step, click on the diagram below. For an example of our automated reporting template see [here](https://acrf-image-x-institute.github.io/MRI_DistortionQA/_static/MR_QA_report_20_05_2022.html)

```mermaid
flowchart LR
    A[Marker <br>Extraction]--->B[Marker <br>Matching]
    B[Marker <br>Matching]--->C[Field <br> Calculation] & E[Automated <br>reporting]
    C[Field <br> Calculation]-->D[Spherical Harmonic <br>Analysis]
    D[Spherical Harmonic <br>Analysis]-->E[Automated <br>reporting];
    click A "https://acrf-image-x-institute.github.io/MRI_DistortionQA/code_docs.html#MRI_DistortionQA.MarkerAnalysis.MarkerVolume"
    click B "https://acrf-image-x-institute.github.io/MRI_DistortionQA/code_docs.html#MRI_DistortionQA.MarkerAnalysis.MatchedMarkerVolumes"
    click C "https://acrf-image-x-institute.github.io/MRI_DistortionQA/code_docs.html#MRI_DistortionQA.FieldCalculation.ConvertMatchedMarkersToBz"
    click D "https://acrf-image-x-institute.github.io/MRI_DistortionQA/code_docs.html#MRI_DistortionQA.FieldAnalysis.SphericalHarmonicFit"
    click E "https://acrf-image-x-institute.github.io/MRI_DistortionQA/code_docs.html#MRI_DistortionQA.Reports.MRI_QA_Reporter"
```

## Setup/Build/Install

```bash
pip install MRI_DistortionQA
```


## Usage

Detailed documentation is [here](https://acrf-image-x-institute.github.io/MRI_DistortionQA/).

## Directory Structure

- *docsrc* markdown/rst source documentation
- *tests* test cases
- *MRI_DistortionQA* source code
- *examples* source code for the [worked examples](https://acrf-image-x-institute.github.io/MRI_DistortionQA/examples.html)