semvec/pypi-packages huggingface dataset:
pyMILES 0.0.6
Multiple instance learning via embedded instance selection
Requires Python: >=3

[![Tests](https://github.com/johnvorsten/trendreview/actions/workflows/python-app.yml/badge.svg)](https://github.com/johnvorsten/pyMILES/actions/workflows/python-app.yml) ![coverage](https://img.shields.io/static/v1?label=Coverage&message=74%&color=green)

# Multiple instance learning via embedded instance selection
This python package is an implementation of MILES: Multiple-instance learning via embedded instance selection from IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 28, NO. 12, DECEMBER 2006.

The paper describes a method to encode bag-space features into a space defined by the most-likely-cause-estimator of the bag and training feature space.

The most likely cause estimator is defined as ![Most Likely Estimator](most_likely_estimator.png "Most Likely Estimator")

# An example encoding
Look at `embedding_test.py` for an example embedding of dummy data.
Dummy data is created from 5 normal distributions, and each instance is generated by one of the following two-dimensional probability distributions: 

N1([5,5]^T, I), -> The normal distribution with mean [5,5] and 1 unit standard deviation
N2([5,-5]^T, I), 
N3([-5,5]^T, I),
N4([-5,-5]^T, I), 
N5([0,0]^T, I)

Bags are created from a variable number of instances per bag, and this example uses 8. A bag is labeled positive if it contains instances from at least two different distributions among N1, N2, and N3.  Otherwise the bag is negative.  This image displays the raw 2-dimensional data ![#2-D Raw Data](raw_data.png "Raw 2-D dummy data")

A single bag is of shape `(N_INSTANCES, FEATURE_SPACE)` where n is the number of instances in a bag, and p is the feature space of the instances.

All positive bags are of shape `(N_POSITIVE_BAGS, N_INSTANCES, FEATURE_SPACE)` where `N_POSITIVE_BAGS` is the number of positive bags.  Negative bags are of shape `(N_NEGATIVE_BAGS, N_INSTANCES, FEATURE_SPACE)`.  The total set of training instances is of shape `(N_POSITIVE_BAGS + N_NEGATIVE_BAGS, N_INSTANCES, FEATURE_SPACE)`.

A single bag is embedded into a vector of shape `((N_POSITIVE_BAGS + N_NEGATIVE_BAGS) * N_INSTANCES)`, which is the total number of instances from all positive and negative bags.

In this example let 
When projecting the training instances onto the vectors 
```python
# Feature vectors close to mean of `true` positive distributions
x1 = np.array([4.3, 5.2])
x2 = np.array([5.4, -3.9])
x3 = np.array([-6.0, 4.8])
```
the result is a (3,40) matrix which is visualized below. ![#Linearly Separable Bags](example_embedding.png "Example Embedding onto positive distributions")

## Testing
* python -m unittest tests.embedding_test
* python -m unittest tests.l1_svm_test

## Code coverage and linting
* pylint -r n src/tests/ src/pyMILES
* From src directory: `coverage run -m unittest tests.embedding_test`
* autopep8 --recursive --in-place src/tests/ src/pyMILES/

## Building
Increment build version in setup.cfg
python -m build .
python -m twine upload dist/*
easypikaiot 0.0.2
Library for easy connection to rabbitmq  with pika Library
Keywords: python,pika,amqp,rabbitmq,iot,easy

# EasyPikaIot

Library for easy amqp communication via pika lirmbrary

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install foobar.

```bash
pip install easypikaiot
```

## Usage

```python
from pika_obj import PikaMassenger

# creat connection to rabittmq

PMI = PikaMassenger(username='<str>',password='<str>',host='<int:defualt(5672)>',port,exchange_name='<str>',exchange_type='<extype:defualt(direct)>')

# for create  consume

PMI.run(queue_name='<str>', routing_keys='<list>' ,callback='<function>')

# for publish  message
PMI.send_message(self,routing_key='<str>',data='<str>')


```
tribolium-clustering 0.2.5
A library for image ananalysis and data analysis of Tribolium embryos
Requires Python: >=3.7, <3.10
Keywords: image,analysis,development,tribolium,clustering

# Tribolium Clustering
This is a library that is specifically used for image analysis and data analysis of developing Tribolium embryos. It is optimised for stained nuclei of the embryos and 3D imaging. Most functions are wrappers for other libraries, mostly [pyclesperanto](https://clij.github.io/clij2-docs/api_intro), [scikit-image](https://scikit-image.org/), [scikit-learn](https://scikit-learn.org/stable/), [UMAP](https://umap-learn.readthedocs.io/en/latest/index.html) and [HDBSCAN](https://hdbscan.readthedocs.io/en/latest/). 

At the moment some packages required need to be installed manually via conda. HDBSCAN needs to be installed via the command:

```sh
C:\Users\yourusername>conda install -c conda-forge hdbscan 
```

Similarly, a prerequisite for pyclesperanto_prototype: pyopencl needs to be installed with the command:

```sh
C:\Users\yourusername>conda install -c conda-forge pyopencl
```

Unfortunately the installation won't work without these.

The Image analysis is based almost entirely around functions from pyclesperanto which requires a powerful GPU with high memory capacities to work with high resolution files. If memory is insufficient you will probably encounter errors when using functions of this library and you need to make sure that you specify your powerful GPU in pyclesperanto with the command:

```python
import pyclesperanto_prototype as cle

# "GTX" has to be replaced with the identifyer for your main GPU
cle.select_device("GTX")
```

The data analysis is based on scikit learn functions as well as other external data science algorithms that follow a similar convention. The basics of which can be found [here](https://scikit-learn.org/stable/).
Since the functions were developed with a group of datasets modification of the functions could be required for the image analysis workflows but possibly also the data analysis workflows.
arseg 1.0.0
An active region segmentation library
Requires Python: >=3.6

# 1 Installation
***

## 1.1 Building the stable package
In the early stages, the stable package isn't bug free - tracking issues and fixing bugs will still. The most recent stable build is 0.0.2-stable.

To install:
```
$ pip3 install stable_builds/flares-segmentation-tlincke125-0.0.2-stable.tar.gz
```

## 1.2 Building the latest package 
First, checkout the **master** branch. Then, in the root directory, build the package and documentation using the following command:

```
$ ./install.sh
``` 

This should produce two files in the ./dist folder and update all files in docs/documentation/flares.

To install the package, go into the dist folder and install the package using pip:

```
$ cd dist
$ pip3 install flares-segmentation-tlincke125-<version>.tar.gz
```

Where \<version\> is the highest version (or any other version you wish to install).

You can then open docs/documentation/flares/index.html in your prefered browser.

# 2 Getting Started
***
See ./docs/guides/getting_started.ipynb
arseg-tlincke125 1.0.0
An active region segmentation library
Requires Python: >=3.6

# 1 Installation
***

## 1.1 Building the stable package
In the early stages, the stable package isn't bug free - tracking issues and fixing bugs will still. The most recent stable build is 0.0.2-stable.

To install:
```
$ pip3 install stable_builds/flares-segmentation-tlincke125-0.0.2-stable.tar.gz
```

## 1.2 Building the latest package 
First, checkout the **master** branch. Then, in the root directory, build the package and documentation using the following command:

```
$ ./install.sh
``` 

This should produce two files in the ./dist folder and update all files in docs/documentation/flares.

To install the package, go into the dist folder and install the package using pip:

```
$ cd dist
$ pip3 install flares-segmentation-tlincke125-<version>.tar.gz
```

Where \<version\> is the highest version (or any other version you wish to install).

You can then open docs/documentation/flares/index.html in your prefered browser.

# 2 Getting Started
***
See ./docs/guides/getting_started.ipynb