1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
|
[](LICENSE.md) [](https://mycroft.ai/cla) [](https://github.com/MycroftAI/contributors/blob/master/team/Mycroft%20Core.md) 
[](http://makeapullrequest.com)
[](https://chat.mycroft.ai/community/channels/machine-learning)
# Padatious
An efficient and agile neural network intent parser. Padatious is a core component of [Mycroft AI](https://mycroft.ai).
## Features
- Intents are easy to create
- Requires a relatively small amount of data
- Intents run independent of each other
- Easily extract entities (ie. Find the nearest *gas station* -> `place: gas station`)
- Fast training with a modular approach to neural networks
## Getting Started
### Installing
Padatious requires the following native packages to be installed:
- [`FANN`][fann] (with dev headers)
- Python development headers
- `pip3`
- `swig`
Ubuntu:
```
sudo apt-get install libfann-dev python3-dev python3-pip swig
```
Next, install Padatious via `pip3`:
```
pip3 install padatious
```
Padatious also works in Python 2 if you are unable to upgrade.
[fann]:https://github.com/libfann/fann
### Example
Here's a simple example of how to use Padatious:
#### program.py
```Python
from padatious import IntentContainer
container = IntentContainer('intent_cache')
container.add_intent('hello', ['Hi there!', 'Hello.'])
container.add_intent('goodbye', ['See you!', 'Goodbye!'])
container.add_intent('search', ['Search for {query} (using|on) {engine}.'])
container.train()
print(container.calc_intent('Hello there!'))
print(container.calc_intent('Search for cats on CatTube.'))
container.remove_intent('goodbye')
```
Run with:
```bash
python3 program.py
```
## Learn More
Further documentation can be found at https://mycroft.ai/documentation/padatious/
|
