python报错invalid_python运行报错RuntimeWarning:invalidvalueindouble_scalars

我就搞不懂了&＃xff0c;为什么运行老是会报类似于RuntimeWarning: overflow encountered in multiply的这种错误&＃xff0c;这个应该不是代码逻辑的问题吧

部分代码如下&＃xff1a;

#!/usr/bin/env python

# -*- coding: utf-8 -*-

# &＃64;File : TrustMF.py

from baseclass.SocialRecommender import SocialRecommender

import numpy as np

from tool import config

class TrustMF(SocialRecommender):

def __init__(self, conf,trainingSet&＃61;None,testSet&＃61;None,relation&＃61;list(),fold&＃61;&＃39;[1]&＃39;):

super(TrustMF, self).__init__(conf,trainingSet,testSet,relation,fold)

def initModel(self):

super(TrustMF, self).initModel()

self.Br &＃61; np.random.rand(self.dao.trainingSize()[0], self.k) # latent user matrix

self.Wr &＃61; np.random.rand(self.dao.trainingSize()[0], self.k) # latent item matrix

self.Vr &＃61; np.random.rand(self.dao.trainingSize()[1], self.k) # latent item matrix

self.Be &＃61; np.random.rand(self.dao.trainingSize()[0], self.k) # latent user matrix

self.We &＃61; np.random.rand(self.dao.trainingSize()[0], self.k) # latent item matrix

self.Ve &＃61; np.random.rand(self.dao.trainingSize()[1], self.k) # latent item matrix

def readConfiguration(self):

super(TrustMF, self).readConfiguration()

regular &＃61; config.LineConfig(self.config[&＃39;reg.lambda&＃39;])

self.regB &＃61; float(regular[&＃39;-b&＃39;])

self.regT &＃61; float(regular[&＃39;-t&＃39;])

def printAlgorConfig(self):

super(TrustMF,self).printAlgorConfig()

print &＃39;Regularization parameter: regT %.3f&＃39; % self.regT

print &＃39;&＃61;&＃39; * 80

def buildModel(self):

# If necessary, you can fix the parameter in ./config/Trust.conf

iteration &＃61; 0

while iteration

self.loss &＃61; 0

self.trusterModel()

self.trusteeModel()

iteration &＃43;&＃61; 1

self.isConverged(iteration)

def trusterModel(self):

for entry in self.dao.trainingData:

u, i, r &＃61; entry

mbu &＃61; len(self.sao.getFollowees(u))

uid &＃61; self.dao.getUserId(u)

iid &＃61; self.dao.getItemId(i)

error &＃61; self.truserPredict(u, i) - r

nbu &＃61; len(self.dao.userRated(u)[0])

nvi &＃61; len(self.dao.itemRated(i)[0])

self.loss &＃43;&＃61; error**2 &＃43; self.regB * ((mbu &＃43; nbu) * self.Br[uid].dot(self.Br[uid]) &＃43; nvi * self.Vr[iid].dot(self.Vr[iid]))

self.Vr[iid] &＃61; self.Vr[iid] - self.lRate * (error * self.Br[uid] &＃43; self.regB * nvi * self.Vr[iid])

relations &＃61; self.sao.getFollowees(u)

if len(relations)!&＃61;0:

for followee in relations.iterkeys():

weight &＃61; relations[followee]

uf &＃61; self.dao.getUserId(followee)

if uf !&＃61; -1 and self.dao.containsUser(followee): # followee is in rating set

error1 &＃61; self.Br[uid].dot(self.Wr[uf]) - weight

mwk &＃61; len(self.sao.getFollowers(followee))

self.loss &＃43;&＃61; self.regT * error1**2 &＃43; self.regB * mwk * self.Wr[uf].dot(self.Wr[uf])

self.Br[uid] &＃61; self.Br[uid] - self.lRate * (error * self.Vr[iid] &＃43; self.regB * (mbu &＃43; nbu) * self.Br[uid] &＃43; self.regT * (self.Br[uid].dot(self.Wr[uf]) - weight) * self.Wr[uf])

self.Wr[uf] &＃61; self.Wr[uf] - self.lRate * (self.regT * error1 * self.Br[uid] &＃43; self.regB * mwk * self.Wr[uf])

def trusteeModel(self):

for entry in self.dao.trainingData:

u, i, r &＃61; entry

mwu &＃61; len(self.sao.getFollowers(u))

uid &＃61; self.dao.getUserId(u)

iid &＃61; self.dao.getItemId(i)

error &＃61; self.truseePredict(u, i) - r

nwu &＃61; len(self.dao.userRated(u)[0])

nvi &＃61; len(self.dao.itemRated(i)[0])

self.loss &＃43;&＃61; error**2 &＃43; self.regB * ((mwu &＃43; nwu) * self.We[uid].dot(self.We[uid]) &＃43; nvi * self.Ve[iid].dot(self.Ve[iid]))

self.Ve[iid] &＃61; self.Ve[iid] - self.lRate * (error * self.We[uid] &＃43; self.regB * nvi * self.Ve[iid])

relations &＃61; self.sao.getFollowers(u)

if len(relations) !&＃61; 0:

for follower in relations.iterkeys():

weight &＃61; relations[follower]

uf &＃61; self.dao.getUserId(follower)

if uf !&＃61; -1 and self.dao.containsUser(follower): # follower is in rating set

error1 &＃61; self.Be[uf].dot(self.We[uid]) - weight

mbk &＃61; len(self.sao.getFollowees(follower))

self.loss &＃43;&＃61; self.regT * error1**2 &＃43; self.regB * mbk * self.Be[uf].dot(self.Be[uf])

self.We[uid] &＃61; self.We[uid] - self.lRate * (error * self.Vr[iid] &＃43; self.regB * (mwu &＃43; nwu) * self.We[uid] &＃43; self.regT * error1 * self.Be[uf])

self.Be[uf] &＃61; self.Be[uf] - self.lRate * (self.regT * error1 * self.We[uid] &＃43; self.regB * mbk * self.Be[uf])

def truserPredict(self, u, i):

if self.dao.containsUser(u) and self.dao.containsItem(i):

u &＃61; self.dao.getUserId(u)

i &＃61; self.dao.getItemId(i)

return self.Br[u].dot(self.Vr[i])

else:

return self.dao.globalMean

def truseePredict(self, u, i):

if self.dao.containsUser(u) and self.dao.containsItem(i):

u &＃61; self.dao.getUserId(u)

i &＃61; self.dao.getItemId(i)

return self.We[u].dot(self.Ve[i])

else:

return self.dao.globalMean

def predict(self, u, i):

if self.dao.containsUser(u) and self.dao.containsItem(i):

u &＃61; self.dao.getUserId(u)

i &＃61; self.dao.getItemId(i)

return (self.Br[u] &＃43; self.We[u]).dot(self.Vr[i] &＃43; self.Ve[i]) * 0.25

else:

return self.dao.globalMean

我就写了个trustMF方法&＃xff0c;用到梯度下降法&＃xff0c;就给我报一大堆runtimeerror,我真的不知道到底哪里出错了

报错如下&＃xff1a;

C:\Users\123\Documents\GitHub\RecQ\algorithm\rating\TrustMF.py:65: RuntimeWarning: overflow encountered in multiply

self.Br[uid] &＃61; self.Br[uid] - self.lRate * (error * self.Vr[iid] &＃43; self.regB * (mbu &＃43; nbu) * self.Br[uid] &＃43; self.regT * (self.Br[uid].dot(self.Wr[uf]) - weight) * self.Wr[uf])

C:\Users\123\Documents\GitHub\RecQ\algorithm\rating\TrustMF.py:65: RuntimeWarning: invalid value encountered in add

self.Br[uid] &＃61; self.Br[uid] - self.lRate * (error * self.Vr[iid] &＃43; self.regB * (mbu &＃43; nbu) * self.Br[uid] &＃43; self.regT * (self.Br[uid].dot(self.Wr[uf]) - weight) * self.Wr[uf])

C:\Users\123\Documents\GitHub\RecQ\algorithm\rating\TrustMF.py:64: RuntimeWarning: invalid value encountered in double_scalars

self.loss &＃43;&＃61; self.regT * error1**2 &＃43; self.regB * mwk * self.Wr[uf].dot(self.Wr[uf])

C:\Users\123\Documents\GitHub\RecQ\algorithm\rating\TrustMF.py:54: RuntimeWarning: invalid value encountered in double_scalars

self.loss &＃43;&＃61; error**2 &＃43; self.regB * ((mbu &＃43; nbu) * self.Br[uid].dot(self.Br[uid]) &＃43; nvi * self.Vr[iid].dot(self.Vr[iid]))

求解&＃xff0c;在线等