semvec/pypi-packages huggingface dataset:
Dendrite-Neural-Networks 0.0.9
Dendrite-Neural-Networks is an implementation of processing units that performed classification using closed decision boundaries.

# Dendrite-Neural-Networks

Dendrite-Neural-Networks is a Python library for dealing with closed boundary classification units, like Dendrite Morphological Neuron (DMN), Dendrite Ellipsoidal Neuron (DEN) and Dendrite Spherical Neuron (DSN).

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Dendrite-Neural-Networks.

```bash
pip install Dendrite-Neural-Networks
```

## Usage

```python
from DEN import DENlayer
from DMN import DMNlayer
from DSN import DSNlayer

from PreTrain.HpC import HSpC
from PreTrain.HpC import HEpC
from PreTrain.HpC import HBpC

from PreTrain.kmeans import bkmeans
from PreTrain.kmeans import ekmeans
from PreTrain.kmeans import skmeans

# returns 'array' with the propose initial parameters
#x: Input pattern
#y: Labels
dendrites  = HBpC.HBpC(x,y,0.0001)
dendrites  = HEpC.HEpC(x,y)
dendrites  = HSpC.HSpC(x,y,0.0001)
dendrites  = bkmeans.bkmeans(x,y,[3,3,3],0.01)
dendrites  = ekmeans.ekmeans(x,y,[3])
dendrites  = skmeans.skmeans(x,y,[2],0.01)


# It's an implementation of a modified Keras layer
DMNlayer(2,dendrites, activation = "sigmoid", input_shape = (np.shape(x)[1],)))
DENlayer(2,dendrites, activation = "sigmoid", input_shape = (np.shape(x)[1],)))
DSNlayer(2,dendrites, activation = "sigmoid", input_shape = (np.shape(x)[1],)))

# It's an implementation of a modified Keras layer. For random initialization of parameters
DMNlayer(2, activation = "sigmoid", input_shape = (np.shape(x)[1],)))
DENlayer(2, activation = "sigmoid", input_shape = (np.shape(x)[1],)))
DSNlayer(2, activation = "sigmoid", input_shape = (np.shape(x)[1],)))
```

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License
[GNU GPLv3](https://choosealicense.com/licenses/gpl-3.0/)
knok-knok 0.0.1
For sending push notifications to android and ios devices
Keywords: push,notifications,android,ios,fcm,gcm

# knok_knok: Pushing Notifications Now Becomes Simple!

## What is it?

**knok_knok** is an easy-to-use python package for sending push notifications across andriod and ios devices.  
  
**Tired of looking around for solutions to send your notifications?**  
  
No more worries now, **knok_knok** does all the heavy lifting of research and bug-fixes for you. Let's get started. 

## Our Features include

  - sending push notifications for the fcm tokens provided. Be it a token for an android device or an ios device, it doesn't matter.
  - sending notifications to any number of devices at a time
  - multiple notifications on a single device getting collapsed on the notification bar.
  - sending notifications that redirects the user somewhere
  - GCM has been deprecated so if you are someone who is migrating from GCM to FCM, you can also give us a try.

## Getting Started
The source code is currently hosted on GitHub at:
https://github.com/ajaysharma132/knok-knok

Binary installers for the latest released version are available at the [Python Package Index (PyPI)](https://pypi.org/project/knok-knok).

```sh
# PyPI
pip install knok-knok
```

for sending a push notification, all you need to do is - 
 - build a data_map containing the information for your notification message
 - provide a list of fcm tokens for the devices you want to trigger a push notification

```sh
# data_map
{
    "title": "Hello",
    "body": "World !",
    "url": "https://www.google.com/"
}  
```
title - represents the title of the your notification  
body - represents the body/text/message of your notification  
url - is for the link you want the user to redirect to..  

```sh
# tokens
[
    'token1', 
    'token2',
    'token3',
]
And so on... You can provide any number of tokens
```

Now, instantiate the FCMClient providing your FCM_API_KEY as -

```sh
# FCMClient instance
from knok_knok import FCMClient
client = FCMClient(FCM_API_KEY)
```

And use the send function to send your notifications as -

```sh
# send notification
client.send(data_map, tokens)
```

**you see, it's that simple!!**