semvec/pypi-packages huggingface dataset:
py-fcm 1.0.0
Fuzzy cognitive maps python library
Requires Python: >=3.7

# PyFCM
Fuzzy cognitive maps python library. Also, supports the topology generation from data to solve classification problems.
The details associated to the generation process are described in [this paper](https://link.springer.com/chapter/10.1007/978-3-030-89691-1_25). 
### Installation

#### From source:

1. Clone repository:
    ```
    $ git clone https://github.com/J41R0/PyFCM.git 
    $ cd PyFCM
    ```
2. Install setup tools and package:
    ```
    $ pip install setuptools
    $ python setup.py install
    ```
#### From PyPi:
1. Install package using pip:
    ```
    $ pip install py-fcm
    ```
   
### Example usage

#### Inference:
```
from py_fcm import from_json

fcm_json = """{
            "max_iter": 500,
            "decision_function": "LAST",
            "activation_function": "sigmoid",
            "memory_influence": False,
            "stability_diff": 0.001,
            "stop_at_stabilize": True,
            "extra_steps": 5,
            "weight": 1,
            "concepts":
                [
                    {
                        "id": "concept_1",
                        "is_active": True,
                        "type": "SIMPLE",
                        "activation": 0.5
                    },
                    {
                        "id": "concept_2", "is_active": True,
                        "type": "DECISION", "activation": 0.0,
                        "custom_function": "gceq",
                        "custom_function_args": {"weight": 0.3}
                    },
                    {
                        "id": "concept_3",
                        "is_active": True,
                        "type": "SIMPLE",
                        "activation": 0.0,
                        "use_memory": True
                    },
                    {
                        "id": "concept_4",
                        "is_active": True,
                        "type": "SIMPLE",
                        "activation": 0.3,
                        "custom_function": "saturation"
                    }
                ],
            "relations":
                [
                    {"origin": "concept_4", "destiny": "concept_2", "weight": -0.1},
                    {"origin": "concept_1", "destiny": "concept_3", "weight": 0.59},
                    {"origin": "concept_3", "destiny": "concept_2", "weight": 0.8911}
                ],
            'activation_function_args': {'lambda_val': 1},
        """
my_fcm = from_json(fcm_json)
my_fcm.run_inference()
result = my_fcm.get_final_state(concept_type='any')
print(result)
```

#### Generation:
```
import pandas
from py_fcm import FcmEstimator

data_dict = {
   'F1': ['x', 'x', 'y', 'y'],
   'F2': [9.8, 7.3, 1.1, 3.6],
   'class': ['a', 'a', 'r', 'r']
}
    
 train = pandas.DataFrame(data_dict)
 x_train = train.loc[:, train.columns != 'class']
 y_train = train.loc[:, 'class']

 estimator = FcmEstimator()
 estimator.fit(x_train, y_train)
 print(estimator.predict(x_train))
 print("Accuracy: ",estimator.score(x_train, y_train))
 print(estimator.get_fcm().to_json())

```
PoseAnalyzer 0.0.2
A python library for hands detection and features extraction based on mediapipe library

# PoseAnalyzer
A library based on mediapipe to analyze body posture and gesture.
# Examples
## OpenCv
Examples using opencv window to output stuff.
### hello_pose
A simple code to explain how to use the library. The example detects body landmarks and shows how to access different body part landmark position. It also shows how to detect when left arm or right arm are raised. A very simple code