فروم بچه های تولز

محاسبه آنتروپی به 4 روش :

import numpy as np
from scipy.stats import entropy
from math import log, e
import pandas as pd
import timeit
def entropy1(labels, base=None): value,counts = np.unique(labels, retu_counts=True) retu entropy(counts, base=base)
def entropy2(labels, base=None): """ Computes entropy of label distribution. """ n_labels = len(labels) if n_labels <= 1: retu 0 value,counts = np.unique(labels, retu_counts=True) probs = counts / n_labels n_classes = np.count_nonzero(probs) if n_classes <= 1: retu 0 ent = 0. # Compute entropy base = e if base is None else base for i in probs: ent -= i * log(i, base) retu ent
def entropy3(labels, base=None): vc = pd.Series(labels).value_counts(normalize=True, sort=False) base = e if base is None else base retu -(vc * np.log(vc)/np.log(base)).sum()
def entropy4(labels, base=None): value,counts = np.unique(labels, retu_counts=True) norm_counts = counts / counts.sum() base = e if base is None else base retu -(norm_counts * np.log(norm_counts)/np.log(base)).sum()
labels = [1,3,5,2,3,5,3,2,1,3,4,5]
print(entropy1(labels))
print(entropy2(labels))
print(entropy3(labels))
print(entropy4(labels))