semvec/pypi-packages huggingface dataset:
grease-embeddings 0.1.5
Generalizable and Efficient Approximate Spectral Embeddings
Requires Python: >=3.9

# GrEASE

<p align="center">

[//]: # (This is the official PyTorch implementation of GrEASE from the paper ["Generalizable Spectral Embedding with Applications to UMAP]&#40;&#41;.<br>)
This is the official PyTorch implementation of GrEASE: Generalizable and Efficient Approximate Spectral Embedding.

See our [GitHub repository](https://github.com/shaham-lab/GrEASE) for more information.

The main application of GrEASE is NUMAP, a generalizable version of UMAP. The code for NUMAP can be found [here](https://github.com/shaham-lab/NUMAP).

## Installation
To install the package, simply use the following command:

```bash
pip install grease-embeddings
```

## Usage

The basic functionality is quite intuitive and easy to use, e.g.,

```python
from grease import GrEASE

grease = GrEASE(n_components=10)  # n_components is the number of dimensions in the low-dimensional representation
grease.fit(X)  # X is the dataset and it should be a torch.Tensor
X_reduced = grease.transfrom(X)  # Get the low-dimensional representation of the dataset
Y_reduced = grease.transform(Y)  # Get the low-dimensional representation of a test dataset

```

You can read the code docs for more information and functionalities.<br>

Out of many applications, GrEASE can be used for generalizable Fiedler vector and value approximation, and Diffusion Maps approximation. The following is examples of how to use GrEASE for these applications:

### Fiedler vector and value approximation

```python
from grease import GrEASE

grease = GrEASE(n_components=1)
fiedlerVector = grease.fit_transform(X)
fiedlerValue = grease.get_eigenvalues()
```

### Diffusion Maps approximation

```python
from grease import GrEASE

grease = GrEASE(n_components=10)
diffusionMaps = grease.fit_transform(X, t=5)  # t is the diffusion time
```

## Running examples

In order to run the model on the moon dataset, you can either run the file, or using the command-line command:<br>
`python -m examples.reduce_moon`<br>
This will run the model on the moon dataset and plot the results.

The same can be done for the circles dataset:<br>
`python -m examples.reduce_circles`<br>




[//]: # (## Citation)

[//]: # ()
[//]: # (```)

[//]: # ()
[//]: # (@inproceedings{shaham2018,)

[//]: # (author = {Uri Shaham and Kelly Stanton and Henri Li and Boaz Nadler and Ronen Basri and Yuval Kluger},)

[//]: # (title = {SpectralNet: Spectral Clustering Using Deep Neural Networks},)

[//]: # (booktitle = {Proc. ICLR 2018},)

[//]: # (year = {2018})

[//]: # (})

[//]: # ()
[//]: # (```)
WinDeklar 0.4.3
Create winforms in an easy, declarative way. Specially suited for Robotics applications
Requires Python: >=3.7

# Description
A simple, declarative oriented framework to create WinForms in Python.

While it can be used for all kinds of forms, it was created with the purpose of being applied in Robotics, where the need to visualize complex algorithms and, above all, observe the effect of parameter changes is very common.

The forms are defined in a declarative way in an YAML (layouts, widgets, toolbar, menus, etc.) and used in a Python program with just a few lines of code.

# How to use it
To create a WinForm it is necessary to create two files:
* a declaration file (*.yaml) that define the WinForm (menu bar, toolbar, layouts, widgets, etc.)
* a python file (with the same name) that handles all the WinForm logic (events, drawing, etc.)

For a complete form see `view_example.yaml` to understand how the form is defined and `view_example.py` how it is used.

If you run `python view_example.py` the form is: 

<img width="997" alt="winform_example" src="https://github.com/njodal/WIndow_form/assets/28706901/ab02ce1f-9409-454d-8d95-e130fe6d77ed">

In case a simple graph is needed look at `view_simple_graph.py`.
# Examples

## Form used to visualize a PID controller for control the speed of a car

<img width="999" alt="Screen Shot 2023-07-12 at 6 22 05 PM" src="https://github.com/njodal/WinDeklar/assets/28706901/93859f05-f9b6-4333-a451-34f5c302f8c1">
logprog-lab1 1.2.2
logprog_lab1
Requires Python: >=3.7

# logprog-lab1

**logprog-lab1** — это модуль для работы с вложенными структурами данных, состоящими из объектов `Atom` и `Pair`. Модуль предоставляет функционал для вычисления суммы, среднего значения и отклонений числовых данных, хранящихся в структуре.

## Возможности

- **Разбор иерархических структур**: Преобразование строкового представления вложенных значений в дерево объектов типа `Value`.
- **Поддержка атомарных и составных значений**:
  - `Atom`: представляет одно числовое значение.
  - `Pair`: представляет пару значений, каждое из которых может быть `Atom` или `Pair`.
- **Вывод структуры**: Удобное текстовое представление структуры через метод `out`.
- **Анализ значений**:
  - Вычисление суммы всех чисел в структуре (`sum`).
  - Получение списка всех чисел (`all`).
  - Вычисление среднего значения (`average`).
  - Нахождение минимального и максимального отклонения от среднего значения (`getDeviation`).
  

## Установка

Установите модуль с помощью `pip`:

```bash
pip install logprog-lab1
```

## Пример использования

```python
from logprog_lab1 import Value, Atom, Pair, average, getDeviation

# Пример строки для разбора
val = Value.parse("(1, (2, (3, 4)))")

if val:
    # Вывод дерева
    val.out()  # (1, (2, (3, 4)))

    # Сумма всех чисел
    print("\nСумма:", val.sum())  # Сумма: 10

    # Среднее значение
    print("Среднее:", average(val))  # Среднее: 2.5

    # Минимальное и максимальное отклонение от среднего
    min_dev, max_dev = getDeviation(val)
    print("Отклонения:", min_dev, max_dev)  # Отклонения: 0.5 1.5