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.

Installation

From source:

Clone repository:

$ git clone https://github.com/J41R0/PyFCM.git 
$ cd PyFCM

Install setup tools and package:

$ pip install setuptools
$ python setup.py install

From PyPi:

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())