接下来,我们将计算数据中每个元素的概率并根据香农熵的公式进行计算: defshannon_entropy(data):# 计算元素频率freq=Counter(data)probabilities=[count/len(data)forcountinfreq.values()]# 计算香农熵entropy=-sum(p*np.log2(p)forpinprobabilities)returnentropy# 计算香农熵entropy=shannon_entropy(data)print(f"...
newEntropy += prob * compute_shannon_etropy(sub_data_set) infoGain = baseEntropy - newEntropy# 计算信息增益if(infoGain > bestInfoGain):# 比较目前最好的信息增益bestInfoGain = infoGain# 更新目前最好的信息增益bestFeature = ireturnbestFeature# 返回在当前子集中可用于划分的最好的特征序号 5、递归构...
在算法实现上,C4.5算法只是修改了信息增益计算的函数calcShannonEntOfFeature和最优特征选择函数chooseBestFeatureToSplit。calcShannonEntOfFeature在ID3的calcShannonEnt函数上加了个参数feat,ID3中该函数只用计算类别变量的熵,而calcShannonEntOfFeature可以计算指定特征或者类别变量的熵。chooseBestFeatureToSplit函数在计算好...
信息熵是信息论中的一个重要概念,用于衡量随机变量中的不确定性或信息量。它最初由香农(Claude Shannon)在1948年提出,是信息论的核心概念之一。在信息论中,信息熵是对一系列消息或事件发生概率的度量。高概率事件携带较少信息,因为它们较为预测和常见;而低概率事件携带较多信息,因为它们较为罕见和意外。比如,某市发...
returnshannonEnt 2. 创建数据的函数 [python]view plaincopy defcreateDataSet(): dataSet = [[1,1,'yes'], [1,1, 'yes'], [1,0,'no'], [0,1,'no'], [0,1,'no']] labels = ['no surfacing','flippers'] returndataSet, labels ...
shannon_entropy = 0.0 for key in labelCounts: prob = float(labelCounts[key]) / numEntries shannon_entropy -= prob * log(prob, 2) # log base 2 return shannon_entropy 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.
dataSet.append(row)#print rowexcept:print'Usage xxx.py trainDataFilePath'sys.exit() labels = ['cip1','cip2','cip3','cip4','sip1','sip2','sip3','sip4','sport','domain']print'dataSetlen',len(dataSet)returndataSet, labels#calc shannon entropy of label or featuredefcalcShannonEntOfFe...
Fast entropy calculation. Contribute to armbues/python-entropy development by creating an account on GitHub.
'输出路径sample_info='sample_info.txt'#记录有样本分组的样本信息###defShannon_entropy(list_frequency):sum=0forfrequencyinlist_frequency:sum+=frequency*math.log(frequency)H=-sumreturnHdefPielou_evenness(H,richness):E=H/math.log(richness)returnEdefClonality(E):C=1-EreturnClist_pos=[]list_charact...
shannon_entropy() Calculates the Shannon entropy of a probability distribution. brownian_motion() Simulates a Brownian motion path brownian_bridge() Simulates a Brownian bridge path. bessel_process() Simulates a Bessel process path. bessel_process_euler_maruyama() Simulates paths of the Bessel pro...