协同过滤算法

协同过滤算法2.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Tue Jul 14 08:25:02 2020
+
+@author: zhao
+"""
+
+from numpy import *
+from numpy import linalg as la
+ 
+# 载入数据 (用户-评分矩阵)
+# 行为用户, 列为用户评分/周浏览情况, 表示用户对某个政策类型的评分或浏览情况
+def loadExData2():
+    return[[0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 5],
+           [0, 0, 0, 3, 0, 4, 0, 0, 0, 0, 3],
+           [0, 0, 0, 0, 4, 0, 0, 1, 0, 4, 0],
+           [3, 3, 4, 0, 0, 0, 0, 2, 2, 0, 0],
+           [5, 4, 5, 0, 0, 0, 0, 5, 5, 0, 0],
+           [0, 0, 0, 0, 5, 0, 1, 0, 0, 5, 0],
+           [4, 3, 4, 0, 0, 0, 0, 5, 5, 0, 1],
+           [0, 0, 0, 4, 0, 4, 0, 0, 0,0 0, 4],
+           [0, 0, 0, 2, 0, 2, 5, 0, 0, 1, 2],
+           [0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0],
+           [1, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0]]
+ 
+# 计算两个评分的欧氏距离
+def esclidSim(inA,inB):
+    if len(inA)<3:
+        return 1.0
+    return 1.0/(1.0+la.norm(inA-inB))
+# 计算两个评分的 皮尔逊相关系数 (Pearson Correlation)
+def pearsSim(inA,inB):
+    if len(inA)<3:
+        return 1.0
+    return 0.5+0.5*corrcoef(inA.inB)[0][1]
+# 计算两个评分的余弦相似度 (Cosine similarity)
+def cosSim(inA,inB):
+    num = float(inA.T*inB)
+    denom = la.norm(inA)*la.norm(inB)
+    return 0.5+0.5*(num/denom)
+ 
+# 基于评分的相似度推荐
+def standEst(dataMat,user,sinMeas,item):
+    n = shape(dataMat)[1]
+    simTotal = 0.0;ratsimTotal = 0.0
+    for j in range(n):
+        userRating = dataMat[user,j]
+        if userRating ==0:
+            continue
+        overLap = nonzero(logical_and(dataMat[:,item].A>0,dataMat[:,j].A>0))[0]
+        if len(overLap)==0:
+            simility =0
+        else:
+            simility = sinMeas(dataMat[overLap,item],dataMat[overLap,j])
+            print("the %d and %d similit is :%f"%(item,j,simility))
+        simTotal +=simility
+        ratsimTotal+=simility*userRating
+    if simTotal == 0:
+        return 0
+    else:
+        return ratsimTotal/simTotal
+ 
+def recommand(dataMat,user,n=3,simMeans=cosSim,estMethod =standEst):
+    unratedItems = nonzero(dataMat[user,:].A==0)[1]
+    if len(unratedItems)==0:
+        return "you rated everything"
+    itemScores =[]
+    for item in unratedItems:
+        estimatscore = estMethod(dataMat,user,simMeans,item)
+        itemScores.append((item,estimatscore))
+    return sorted(itemScores,key = lambda jj:jj[1],reverse=True)[:n]
+ 
+myMat = mat(loadExData2())
+print(recommand(myMat, 1))
+x=[[1,2],[3,4]]
+x=mat(x)
+y=array(x)
+print(x*x)
+print(y*y)
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