semvec/pypi-packages huggingface dataset:
resf 0.1.4
A simple package for displaying calculation results in a textbased table
Requires Python: >=3.6

# resf - Result Formatter

A simple package for displaying calculation results in a textbased table

## Install

```python
pip install resf
```

[pypi page](https://pypi.org/project/resf/)

***

## How to use

### Basics

```python
from resf import *

initRes()                                   # Initializes new Table
addRes('Name', value)                       # Add a value to the table
addRes('Name', value, 'Unit', precision)    # Unit and Precision can be set
printRes()                                  # prints the resulting table
```

### Sample 1

```python
foo = 15
bar = 123 / 14

initRes()
addRes('Foo', foo, 'kg')
addEmpty()                                  # Add an empty line
addRes('0 digits of Bar', bar, precision=0)
addRes('4 digits of Bar', bar, precision=4)
printRes()
```

### Output 1

```
+-----------------+----------------+
| Name            | Value [Unit]   |
|-----------------+----------------|
| Foo             | 15.00 kg       |
|                 |                |
| 0 digits of Bar | 9              |
| 4 digits of Bar | 8.7857         |
+-----------------+----------------+
```
***

## All Functions

```python
initRes()
addRes(name, value, unit='', precision=2)
addEmpty()
printRes()
```

***
Uses [tabulate](https://pypi.org/project/tabulate/) to generate the table
twitter-boto-dummy 0.0.1
This library is to help you have a twitter bot
Requires Python: >=3.6

# twitter_boto_dummy

This library used mainly to help you have and run your own twitter bot

*In order to install, open the command prompt and type:*
```
pip install twitter_boto_dummy
```


In order to use this library  you should have a verified twitter application and you have to save your credential  inside a json file as following:
 ```
 {
"consumer_key": " your one", 
"consumer_secret": "your one", 
"access_token": "your one", 
"access_token_secret": "your one",
"env_name": "your one"
}
```
feedforwardnet-shine7 0.0.4
Feed Forward Neural Networks


## Installation  
```bash
$ [sudo] pip install feedforwardnet-shine7
``` 

## Usage

```python3
from Neural_Network import NeuralNetwork

# Create a Neural Network
inputs = 2
output_neurons = 1
hidden_layers = 2
each_hidden_nodes = [2, 3]
network = NeuralNetwork(inputs, hidden_layers, output_neurons, each_hidden_nodes)
```
### Building a dataset
Dataset must be python list of data_samples, where each data_sample is a list of input and target.  
For Eg: Input: [1, 1], Target: [1] => [[1, 1], [1]] is a data sample.

A typical XOR function's dataset looks something like :  
```python
>>> XOR_data = 
[
	[			### ####
		[0, 0], # Input   Data
		[0] # Output	 Sample
	],			### ####
	[
		[0, 1],
		[1]
	],
	[
		[1, 0],
		[1]
	],
	[
		[1, 1],
		[0]
	]
]

>>> size = 4 # Length of the data
```
### Training The network
The library provides a *Train* function which accepts the dataset, dataset size, and three optional parameters MAX\_EPOCHS, graph, and log_outputs.
```python3
def Train(dataset, size, MAX_EPOCHS=10000, graph=False, log_outputs=True) :
	....
	....
```
For Eg: If you want to train your network for 5000 epochs and display epoch vs error graph after training.
```python3
>>> network.Train(XOR_data, size, MAX_EPOCHS=5000, graph=True)
```
Notice that I didn't change the value of log_outputs as I want the output to printed for each epoch.


### Debugging
If you want to look at the network's weights at any point of time, the library provides a print\_weights function.
```python
>>> network.print_weights()
```
pynando 0.0.11
wrapper for Nando (Nanbyo data ontology)
Requires Python: >=3.8

# pynando

wrapper for Nando (Nanbyo data ontology)

```
from pynando.nando import Nando

# get nanbyo (指定難病)
nanbyo = Nando('nanbyo)

# get shoman (小児特定慢性疾病)
shoman = Nando('shoman')
```

You can access each nando_node by id
```
shoman_1 = shoman['1']
print(shoman_1)
```

```
id: 1
name_ja: 球脊髄性筋萎縮症
synonyms_ja:
name_en: Spinal and bulbar muscular atrophy
synonyms_en:
children:
```