#!/usr/bin/env python
# coding: utf-8
import numpy as np
from scipy.stats import gaussian_kde
from ._probabilistic_model import ProbabilisticModel
[docs]class UniKDE(ProbabilisticModel):
"""
This class implements the univariate Kernel Density Estimation. With this implementation we are updating N
univariate KDE in each iteration. When a dataset is given, each column is updated independently.
"""
def __init__(self, variables: list):
super().__init__(variables)
self.kernel = None
self.id = 7
[docs] def sample(self, size: int) -> np.array:
"""
Samples new solutions from the probabilistic model. In each solution, each variable is sampled
from its respective normal distribution.
:param size: number of samplings of the probabilistic model.
:return: array with the dataset sampled
:rtype: np.array
"""
return self.kernel.resample(size).T
[docs] def learn(self, dataset: np.array, *args, **kwargs):
"""
Estimates the independent KDE for each variable.
:param dataset: dataset from which learn the probabilistic model.
"""
self.kernel = gaussian_kde(dataset.T)
[docs] def print_structure(self) -> list:
"""
Prints the arcs between the nodes that represent the variables in the dataset. This function
must be used after the learning process. Univariate approaches generate no-edged graphs.
:return: list of arcs between variables
:rtype: list
"""
return list()