<p>我想调用一个getRecommendations方法,该方法只是将对特定用户的建议提取到一个文件中。我用了一本书中的代码。但我看到只有一个核心工作,我希望我所有的核心都能完成工作,因为这样会更快。在</p>
<p>这是方法。在</p>
<pre><code>def getRecommendations(prefs,person,similarity=sim_pearson):
print "working on recommendation"
totals={}
simSums={}
for other in prefs:
# don't compare me to myself
if other==person: continue
sim=similarity(prefs,person,other)
# ignore scores of zero or lower
if sim<=0: continue
for item in prefs[other]:
# only score movies I haven't seen yet
if item not in prefs[person] or prefs[person][item]==0:
# Similarity * Score
totals.setdefault(item,0)
totals[item]+=prefs[other][item]*sim
# Sum of similarities
simSums.setdefault(item,0)
simSums[item]+=sim
# Create the normalized list
rankings=[(total/simSums[item],item) for item,total in totals.items( )]
# Return the sorted list
rankings.sort( )
rankings.reverse( )
ranking_output = open("data/rankings/"+str(int(person))+".ranking.recommendations","wb")
pickle.dump(rankings,ranking_output)
return rankings
</code></pre>
<p>它叫做via</p>
^{pr2}$
<p>如你所见,我试着给每一位顾客推荐。稍后将使用。在</p>
<p>那么如何对这种方法进行多处理呢?我不是通过阅读几个例子甚至是<a href="http://docs.python.org/2/library/multiprocessing.html" rel="nofollow">documentation</a>来理解的</p>