semvec/pypi-packages huggingface dataset:
smartblinds-client 0.6
Unofficial client for the MySmartBlinds Smart Bridge

# Python MySmartBlinds Smart Bridge Client

[![PyPI version](https://badge.fury.io/py/smartblinds-client.svg)](https://badge.fury.io/py/smartblinds-client)
[![Build Status](https://travis-ci.org/ianlevesque/smartblinds-client.svg?branch=master)](https://travis-ci.org/ianlevesque/smartblinds-client)

This is an unofficial client for the [MySmartBlinds Smart Bridge](https://www.mysmartblinds.com/products/smart-hub). You 
must have configured your blinds and bridge with the official iOS or Android app first in order to use this.

USE AT YOUR OWN RISK.

## Usage

```python

from smartblinds_client import SmartBlindsClient

client = SmartBlindsClient("email", "password")
client.login()

blinds, rooms = client.get_blinds_and_rooms()
print(blinds)
print(rooms)
print(blinds[0].name)

states = client.get_blinds_state(blinds)
print(states[blinds[0].encoded_mac].position)

client.set_blinds_position(blinds, 100)

```
bayesHC1 0.0.1
A package for Bayesian Hierarchical Clustering

## Installation Instructions

Run following code in terminal: 
python3 -m pip install bayesHC1

Then import package and load cython as follows: 
import bayesHC1

## Documentation
bayesHC.bayes_hier_clust(data_vec, alpha, alpha0, beta, kappa, mu)

Given a tuple of datapoints, build a hierarchical cluster tree. Algorithm will continue merging clusters until there is only one overarching cluster. This algorithm is recommended for clustering $<$23 observations.

Parameters:
    alpha : the expected number of clusters

    alpha0 : scale hyperparameter for gamma prior 

    beta : location hyperparameter for gamma prior 

    kappa : precision hyperparameter for normal prior

    mu : mean hyperparameer for normal prior



Returns:nested tuple

Returns a tuple with the following structure:
    cluster = (c_num,left,right,value,n_k,p_k,d_k)

    Where:
        c_num : the number of the cluster
        left : the entire cluster tuple for one subcluster
        right : the entire cluster tuple for the other subcluster
        value : all data values included in the cluster
        n_k : number of datapoints in the cluster
        p_k : the prior on merging
        d_k : a weight on the volume of data in pairs of clusters on the subtree T_k


## Example

<<<import pandas as pd
<<<df_sim = pd.read_table('simulated_data.csv',delimiter = ",")
<<<value = tuple([points] for points in df_sim.loc[:4,'values']) 
<<<mu, kappa = 0, 100 # mean and standard deviation
<<<alpha, beta = 2,10 # scale and location
<<<z = bayes_hier_clust(value, alpha, beta, kappa,mu)
<<<z

[(9,
  (4, 0, 0, [24.649825190000001], 1, 1, 2.0),
  (8,
   (5, 0, 0, [20.543137890000001], 1, 1, 2.0),
   (7,
    (1, 0, 0, [19.404575810000001], 1, 1, 2.0),
    (6,
     (2, 0, 0, [4.4017292919999997], 1, 1, 2.0),
     (3, 0, 0, [10.766117830000001], 1, 1, 2.0),
     [4.4017292919999997, 10.766117830000001],
     2,
     0.3333333432674408,
     6.0),
    [19.404575810000001, 4.4017292919999997, 10.766117830000001],
    3,
    0.25,
    16.0),
   [20.543137890000001,
    19.404575810000001,
    4.4017292919999997,
    10.766117830000001],
   4,
   0.27272728085517883,
   44.0),
  [24.649825190000001,
   20.543137890000001,
   19.404575810000001,
   4.4017292919999997,
   10.766117830000001],
  5,
  0.3529411852359772,
  136.0)]

>>>z[1]
(4, 0, 0, [24.649825190000001], 1, 1, 2.0)



Last updated on April 30, 2019.