1. 模块列表
  2. 函数列表
  3. scipy.subtract()

Python scipy 模块，subtract() 实例源码

我们从Python开源项目中，提取了以下22个代码示例，用于说明如何使用scipy.subtract()。

项目：CopyNet    作者：MultiPath    | 项目源码 | 文件源码 
def binary_logloss(p, y):
    epsilon = 1e-15
    p = sp.maximum(epsilon, p)
    p = sp.minimum(1-epsilon, p)
    res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
    res *= -1.0/len(y)
    return res
项目：keras    作者：GeekLiB    | 项目源码 | 文件源码 
def binary_logloss(p, y):
    epsilon = 1e-15
    p = sp.maximum(epsilon, p)
    p = sp.minimum(1-epsilon, p)
    res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
    res *= -1.0/len(y)
    return res
项目：pCVR    作者：xjtushilei    | 项目源码 | 文件源码 
def loglossl(act, pred):
    epsilon = 1e-15
    pred = sp.maximum(epsilon, pred)
    pred = sp.minimum(1-epsilon, pred)
    ll = sum(act*sp.log(pred) + sp.subtract(1,act)*sp.log(sp.subtract(1,pred)))
    ll = ll * -1.0/len(act)
    return ll
项目：pCVR    作者：xjtushilei    | 项目源码 | 文件源码 
def logloss(act, pred):
    '''
    ????????
    :param act: 
    :param pred: 
    :return: 
    '''
    epsilon = 1e-15
    pred = sp.maximum(epsilon, pred)
    pred = sp.minimum(1 - epsilon, pred)
    ll = sum(act * sp.log(pred) + sp.subtract(1, act) * sp.log(sp.subtract(1, pred)))
    ll = ll * -1.0 / len(act)
    return ll
项目：seq2seq-keyphrase    作者：memray    | 项目源码 | 文件源码 
def binary_logloss(p, y):
    epsilon = 1e-15
    p = sp.maximum(epsilon, p)
    p = sp.minimum(1-epsilon, p)
    res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
    res *= -1.0/len(y)
    return res
项目：keraflow    作者：ipod825    | 项目源码 | 文件源码 
def binary_logloss(p, y):
    epsilon = 1e-15
    p = sp.maximum(epsilon, p)
    p = sp.minimum(1-epsilon, p)
    res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
    res *= -1.0/len(y)
    return res
项目：Tencent2017_Final_Coda_Allegro    作者：BladeCoda    | 项目源码 | 文件源码 
def logloss(act, pred):
  epsilon = 1e-15
  pred = sp.maximum(epsilon, pred)
  pred = sp.minimum(1-epsilon, pred)
  ll = sum(act*sp.log(pred) + sp.subtract(1,act)*sp.log(sp.subtract(1,pred)))
  ll = ll * -1.0/len(act)
  return ll
项目：Tencent2017_Final_Coda_Allegro    作者：BladeCoda    | 项目源码 | 文件源码 
def logloss(act, pred):
  epsilon = 1e-15
  pred = sp.maximum(epsilon, pred)
  pred = sp.minimum(1-epsilon, pred)
  ll = sum(act*sp.log(pred) + sp.subtract(1,act)*sp.log(sp.subtract(1,pred)))
  ll = ll * -1.0/len(act)
  return ll
项目：keras-recommendation    作者：sonyisme    | 项目源码 | 文件源码 
def binary_logloss(p, y):
    epsilon = 1e-15
    p = sp.maximum(epsilon, p)
    p = sp.minimum(1-epsilon, p)
    res = sum(y*sp.log(p) + sp.subtract(1,y)*sp.log(sp.subtract(1,p)))
    res *= -1.0/len(y)
    return res
项目：keras-customized    作者：ambrite    | 项目源码 | 文件源码 
def binary_logloss(p, y):
    epsilon = 1e-15
    p = sp.maximum(epsilon, p)
    p = sp.minimum(1-epsilon, p)
    res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
    res *= -1.0/len(y)
    return res
项目：Tencent_Social_Ads    作者：freelzy    | 项目源码 | 文件源码 
def logloss(act, preds):
    epsilon = 1e-15
    preds = sp.maximum(epsilon, preds)
    preds = sp.minimum(1 - epsilon, preds)
    ll = sum(act * sp.log(preds) + sp.subtract(1, act) * sp.log(sp.subtract(1, preds)))
    ll = ll * -1.0 / len(act)
    return ll
项目：Tencent_Social_Ads    作者：freelzy    | 项目源码 | 文件源码 
def logloss(act, preds):
    epsilon = 1e-15
    preds = sp.maximum(epsilon, preds)
    preds = sp.minimum(1 - epsilon, preds)
    ll = sum(act * sp.log(preds) + sp.subtract(1, act) * sp.log(sp.subtract(1, preds)))
    ll = ll * -1.0 / len(act)
    return ll
项目：Tencent_Social_Ads    作者：freelzy    | 项目源码 | 文件源码 
def logloss(act, preds):
    epsilon = 1e-15
    preds = sp.maximum(epsilon, preds)
    preds = sp.minimum(1 - epsilon, preds)
    ll = sum(act * sp.log(preds) + sp.subtract(1, act) * sp.log(sp.subtract(1, preds)))
    ll = ll * -1.0 / len(act)
    return ll
项目：Tencent_Social_Advertising_Algorithm_Competition    作者：guicunbin    | 项目源码 | 文件源码 
def logloss(act, pred):
    epsilon = 1e-15
    pred = sp.maximum(epsilon, pred)
    pred = sp.minimum(1-epsilon, pred)
    ll = sum(act*sp.log(pred) + sp.subtract(1,act)*sp.log(sp.subtract(1,pred)))
    ll = ll * -1.0/len(act)
    return ll
项目：CopyNet    作者：MingyuanXie    | 项目源码 | 文件源码 
def binary_logloss(p, y):
    epsilon = 1e-15
    p = sp.maximum(epsilon, p)
    p = sp.minimum(1-epsilon, p)
    res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
    res *= -1.0/len(y)
    return res
项目：tencent_social_algo    作者：Folieshell    | 项目源码 | 文件源码 
def logloss(act, pred):
  epsilon = 1e-15
  pred = sp.maximum(epsilon, pred)
  pred = sp.minimum(1-epsilon, pred)
  ll = sum(act*sp.log(pred) + sp.subtract(1,act)*sp.log(sp.subtract(1,pred)))
  ll = ll * -1.0/len(act)
  return ll
项目：tencent_social_algo    作者：Folieshell    | 项目源码 | 文件源码 
def logloss(act, pred):
    epsilon = 1e-15
    pred = sp.maximum(epsilon, pred)
    pred = sp.minimum(1-epsilon, pred)
    ll = sum(act*sp.log(pred) + sp.subtract(1,act)*sp.log(sp.subtract(1,pred)))
    ll = ll * -1.0/len(act)
    return ll
项目：deep-coref    作者：clarkkev    | 项目源码 | 文件源码 
def binary_logloss(p, y):
    epsilon = 1e-15
    p = sp.maximum(epsilon, p)
    p = sp.minimum(1-epsilon, p)
    res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
    res *= -1.0/len(y)
    return res
项目：deep-coref    作者：clarkkev    | 项目源码 | 文件源码 
def binary_logloss(p, y):
    epsilon = 1e-15
    p = sp.maximum(epsilon, p)
    p = sp.minimum(1-epsilon, p)
    res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
    res *= -1.0/len(y)
    return res
项目：InnerOuterRNN    作者：Chemoinformatics    | 项目源码 | 文件源码 
def binary_logloss(p, y):
    epsilon = 1e-15
    p = sp.maximum(epsilon, p)
    p = sp.minimum(1-epsilon, p)
    res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
    res *= -1.0/len(y)
    return res
项目：GitImpact    作者：ludovicdmt    | 项目源码 | 文件源码 
def logloss(act, pred):
    epsilon = 1e-15
    pred = sp.maximum(epsilon, pred)
    pred = sp.minimum(1-epsilon, pred)
    ll = sum(act*sp.log(pred) + sp.subtract(1,act)*sp.log(sp.subtract(1,pred)))
    ll = ll * -1.0/len(act)
    return ll
项目：RecommendationSystem    作者：TURuibo    | 项目源码 | 文件源码 
def binary_logloss(p, y):
    epsilon = 1e-15
    p = sp.maximum(epsilon, p)
    p = sp.minimum(1-epsilon, p)
    res = sum(y*sp.log(p) + sp.subtract(1,y)*sp.log(sp.subtract(1,p)))
    res *= -1.0/len(y)
    return res