vqrs.py

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# frutil/models/vqrs.py
"""
VQRS implementation.
"""
import numpy as np
 
import sys
import os
 
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../models')))
 
from approximations import FuzzyRoughModel
import numpy as np
 
class VQRS(FuzzyRoughModel):
    def __init__(self, similarity_matrix: np.ndarray, labels: np.ndarray, alpha: float = 0.5, beta: float = 0.5):
        super().__init__(similarity_matrix, labels)
        if not (0.0 <= alpha <= 1.0 and 0.0 <= beta <= 1.0):
            raise ValueError("Alpha and beta must be in range [0.0, 1.0].")
        self.alpha = alpha
        self.beta = beta
 
    def lower_approximation(self):
        raise NotImplementedError
        return (self.similarity_matrix >= self.alpha).astype(float).min(axis=1)
 
    def upper_approximation(self):
        raise NotImplementedError
        return (self.similarity_matrix >= self.beta).astype(float).max(axis=1)
    def vqrs_upper_approximation(universe, fuzzy_set, partition, alpha=0.3, beta=0.7):
        pass
    # """
    #     Compute vaguely quantified rough set upper approximation.
 
    #     Parameters:
    #     - universe: list of elements
    #     - fuzzy_set: dict or array, fuzzy membership for each element in universe
    #     - partition: list of lists or sets, representing granules/blocks
    #     - alpha, beta: quantifier parameters
 
    #     Returns:
    #     - List of degrees (one per block in partition)
    #     """
    #     Q = lambda p: fuzzy_quantifier_quad(np.array([p]), alpha, beta)[0]
    #     degrees = []
 
    #     for block in partition:
    #         block_indices = [universe.index(e) for e in block]
    #         block_membership_sum = sum(fuzzy_set[i] for i in block_indices)
    #         block_size = len(block)
    #         proportion = block_membership_sum / block_size
    #         degree = Q(proportion)
    #         degrees.append(degree)
 
    #     return degrees