semvec/pypi-packages huggingface dataset:
crea-lib 0.1.3
Python library for Crea
Keywords: crea,library,api,rpc,transactions

# CreaLib (Python)

This library is unmaintained, do not build productive buisness with it!
Please not the disclaimer in the license file!


Python Library for Crea
========================

Python 3 library for Crea!

Installation
------------

Install with `pip3`:

    $ sudo apt-get install libffi-dev libssl-dev python-dev python3-pip
    $ pip3 install crea-lib

Manual installation:

    $ git clone https://github.com/creativechain/crea-python-lib/
    $ cd crea-python-lib
    $ python3 setup.py install --user

Upgrade
-------

    $ pip3 install crea --user --upgrade

Additional dependencies
-----------------------

`creaapi.creaasyncclient`:
 * `asyncio==3.4.3`
 * `pyyaml==3.11`

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

Documentation is written with the help of sphinx and can be compile to
html with:

    cd docs
    make html
crea-graphenelib 0.1.3
Python library for graphene-based blockchains
Keywords: graphene,api,rpc,ecdsa,secp256k1

Python Library for Graphene
===========================

Python 3 library for Graphene 2.0!

Installation
------------

Install with `pip`:

    $ sudo apt-get install libffi-dev libssl-dev python-dev
    $ pip3 install crea-graphenelib

Manual installation:

    $ git clone https://github.com/creativechain/crea-python-graphenelib/
    $ cd crea-python-graphenelib
    $ python3 setup.py install --user

Dependencies
------------

Some dependencies are not required for parts of the library to run
properly. However these modules require some additional libraries to be
present:

* `graphenebase.bip38`
   * `pycrypto==2.6.1`
   * `scrypt==0.7.1` (to speedup scrypt hashing)
* `graphenebase.memo`
   * `pycrypto==2.6.1`

Upgrade
-------

    $ pip install --user --upgrade crea-graphenelib

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

Thanks to readthedocs.io, the documentation can be viewed
[online](https://docs.creas.io/graphene)

Documentation is written with the help of sphinx and can be compile to
html with:

    cd docs
    make html

Licence
-------

MIT, see `LICENCE.txt`
active-semi-supervised-clustering 0.0.1
Active semi-supervised clustering algorithms for scikit-learn

# active-semi-supervised-clustering

Active semi-supervised clustering algorithms for scikit-learn.

## Algorithms

### Semi-supervised clustering

* Seeded-KMeans
* Constrainted-KMeans
* COP-KMeans
* Pairwise constrained K-Means (PCK-Means)
* Metric K-Means (MK-Means)
* Metric pairwise constrained K-Means (MPCK-Means)

### Active learning of pairwise clustering

* Explore & Consolidate
* Min-max
* Normalized point-based uncertainty (NPU) method

## Installation

```
pip install active-semi-supervised-clustering
```

## Usage

```python
from sklearn import datasets, metrics
from active_semi_clustering.semi_supervised.pairwise_constraints import PCKMeans
from active_semi_clustering.active.pairwise_constraints import ExampleOracle, ExploreConsolidate, MinMax
```

```python
X, y = datasets.load_iris(return_X_y=True)
```

First, obtain some pairwise constraints from an oracle.

```python
# TODO implement your own oracle that will, for example, query a domain expert via GUI or CLI
oracle = ExampleOracle(y, max_queries_cnt=10)

active_learner = MinMax(n_clusters=3)
active_learner.fit(X, oracle=oracle)
pairwise_constraints = active_learner.pairwise_constraints_
```

Then, use the constraints to do the clustering.

```python
clusterer = PCKMeans(n_clusters=3)
clusterer.fit(X, ml=pairwise_constraints[0], cl=pairwise_constraints[1])
```

Evaluate the clustering using Adjusted Rand Score.

```python
metrics.adjusted_rand_score(y, clusterer.labels_)
```