#1000个均值170,标准差10的正态分布身高样本 h_real=norm.rvs(loc=170,scale=10,size=1000)h_predict1=norm.rvs(loc=168,scale=9,size=1000)h_predict2=norm.rvs(loc=160,scale=20,size=1000)defJS_div(arr1,arr2,num_bins):max0=max(np.max(arr1),np.max(arr2))min0=min(np.min(arr1),np...
KLDIV(X,P1,P2,'js') returns the Jensen-Shannon divergence, given by [KL(P1,Q)+KL(P2,Q)]/2, where Q = (P1+P2)/2. See the Wikipedia article for "Kullback–Leibler divergence". This is equal to 1/2 the so-called "Jeffrey divergence." See Rubner et al. (2000). ...
function [score_KL, score_JS] = KL_JS_div(vec1, vec2) % Input: vec1: vector 1, vec2: vector 2 % Output: score_KL: KL divergence, source_JS: JS divergence % Author: kailugaji % https://www.cnblogs.com/kailugaji/ % Make sure vec1 and vec2 sum to 1 if any(vec1(:)) ...
# KL散度计算def kl_divergence(p, q):return np.sum(kl_div(p, q)) # Jensen-Shannon散度计算def js_divergence(p, q):m = 0.5 * (p + q)return 0.5 * (kl_divergence(p, m) + kl_divergence(q, m)) # Renyi散...
return np.sum(kl_div(p, q)) # Jensen-Shannon散度计算 def js_divergence(p, q): m = 0.5 * (p + q) return 0.5 * (kl_divergence(p, m) + kl_divergence(q, m)) # Renyi散度计算 def renyi_divergence(p, q, alpha): return (1 / (alpha - 1)) * np.log(np.sum(np.power(p,...
# 计算js散度l # 1.计算平均分布 y_js = 0.5 * (y_true + y_pred) # M # 2.计算y_true于y_js的kl散度 kl1 = F.kl_div(y_js.softmax(dim=-1).log(),y_pred,reduction='sum') # kl(y_pred||M) kl1_s = torch.sum(y_pred * torch.log(y_pred / F.softmax(y_js, dim=-1...
KLDIV(X,P1,P2,'js') returns the Jensen-Shannon divergence, given by [KL(P1,Q)+KL(P2,Q)]/2, where Q = (P1+P2)/2. See the Wikipedia article for "Kullback–Leibler divergence". This is equal to 1/2 the so-called "Jeffrey divergence." See Rubner et al. (2000). EXAMPLE: Let...
KL距离(Kullback-Leibler Divergence)和JS距离(Jensen-Shannon Divergence)都是用来衡量两个概率分布之间差异的方法,它们在信息论、机器学习和统计学...
returnnp.sum(kl_div(p, q)) # Jensen-Shannon散度计算defjs_divergence(p, q): m=0.5* (p+q) return0.5* (kl_divergence(p, m) +kl_divergence(q, m)) # Renyi散度计算defrenyi_divergence(p, q, alpha): return (1/ (alpha-1)) *np.log(np.sum(np.power(p, alpha) *np.power(q,1-...
return np.sum(kl_div(p, q)) # Jensen-Shannon散度计算 def js_divergence(p, q): m = 0.5 * (p + q) return 0.5 * (kl_divergence(p, m) + kl_divergence(q, m)) # Renyi散度计算 def renyi_divergence(p, q, alpha): return (1 / (alpha - 1)) * np.log(np.sum(np.power(p,...