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TA的每日心情 | 开心 2017-2-7 15:12 |
---|
签到天数: 691 天 [LV.9]以坛为家II
群组: 2013年国赛赛前培训 群组: 2014年地区赛数学建模 群组: 数学中国第二期SAS培训 群组: 物联网工程师考试 群组: 2013年美赛优秀论文解 |
# -*- coding=utf-8 -*-
8 a: Y5 n( i5 O+ j) L- V+ e: k) M+ e' U' n f
import math
* a$ A% N& `- W1 }" Rimport sys
- ]% G9 x- Y6 V1 N4 y8 V+ L- afrom texttable import Texttable
/ v4 e6 Z+ n1 E& u R
1 N2 ?' V4 e" b4 {" G
; p" X+ O$ J. ^) t& n: ]0 X$ j#
+ V+ [' ~. w' J1 y0 B# 使用 |A&B|/sqrt(|A || B |)计算余弦距离
; B: d; U; O9 C( a2 e#
1 S; Z4 k# W6 X/ j1 F! ], E#
+ [& D( N. G; a. m#$ @& x$ e- L3 j2 w2 a0 w2 S( L
def calcCosDistSpe(user1,user2):+ D9 S' k$ j, Z4 z: M) \
avg_x=0.0
5 i/ F; p7 z; G- {* ?( a% d2 V avg_y=0.02 r/ p( {. L k. B7 s
for key in user1:
! Y1 `4 x5 y- x" X avg_x+=key[1]7 \& Z: {! n8 q
avg_x=avg_x/len(user1)6 y2 [2 Y, \$ p7 N/ h$ K! q! f
' X8 L j6 S7 @4 k" y" G3 [. k for key in user2:
) R0 Y% v: k' E7 m6 E- H- Y+ \. m avg_y+=key[1]
6 w; P4 U. q* X7 y% w8 s# M( ~ avg_y=avg_y/len(user2)7 g f5 T) m$ u, N( ?, `( _( U# m
r$ m/ A; I$ q) l u1_u2=0.0
. k8 I H) h7 W* O% `6 F1 Y9 q3 l for key1 in user1:0 v v: A' ^) s4 M; s8 F/ s+ _; ?5 q* E
for key2 in user2:. a2 `+ R8 p) X8 F4 M
if key1[1] > avg_x and key2[1]>avg_y and key1[0]==key2[0]:5 R' R" p7 i: ?" E" h. I
u1_u2+=1; G4 u! S0 t/ j
u1u2=len(user1)*len(user2)*1.0
) S0 R. L9 Q! A& b; ^- E sx_sy=u1_u2/math.sqrt(u1u2) V6 c( e3 J! }5 y. }/ z2 x
return sx_sy0 m! s* r W! k
- _, A- `' K2 W+ E
/ a' h1 B6 ^6 Q0 R! z B
#1 Z' \! Z, H" N. ^* g
# 计算余弦距离
! J& P9 O1 R- C3 u. {3 ?#. Z8 }, {& `4 E; G6 S8 d' X( I! W
#% `% d9 ^* x3 R0 v6 G& J
def calcCosDist(user1,user2):" b4 z2 a& a! m( ^1 A. j y
sum_x=0.0
2 F3 ?3 I; B1 W1 a, A sum_y=0.0
4 a2 o4 ^7 V* W: k( O" C8 O sum_xy=0.0
; y4 a9 N7 Z K7 r* R for key1 in user1:3 b7 E+ t& e% D) D, U
for key2 in user2:
+ }& s' D8 C( O9 g7 r if key1[0]==key2[0] :1 B. l! x. O/ h! e- b$ B( K
sum_xy+=key1[1]*key2[1]
4 q: d2 Z* ]4 d H* d0 L sum_y+=key2[1]*key2[1]
: S9 K+ j- {) ~7 \0 L sum_x+=key1[1]*key1[1]
7 \- t. F" j: Y% f+ E' y' p2 D. m & J* Y7 o7 l: t
if sum_xy == 0.0 :
! i0 t$ y8 s) V, i: X) e& N3 m return 00 g9 F/ ^3 j- R" q, [
sx_sy=math.sqrt(sum_x*sum_y) 2 v; ~% q) F- s
return sum_xy/sx_sy+ `- [% o6 R1 K7 a, A
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#/ U9 x0 J! v) p+ }
#2 C( R9 R, a8 @; N) T$ k
# 相似余弦距离; p P' [6 z" P# Y0 J" A$ H6 A
#2 L1 N- z& P- `6 B' w8 X& V
#* j! S# L1 p5 w! u- N* I6 {1 k
#. |! [* Q, b$ {8 {$ z$ k
def calcSimlaryCosDist(user1,user2):3 `' I) H" u8 s6 M$ K
sum_x=0.0& j; y8 G4 K4 D# z8 m
sum_y=0.04 |2 }6 A- [- y5 n/ p4 q3 y. s( N
sum_xy=0.0
& _/ \* y3 h9 e9 W; k) T avg_x=0.0& X+ P3 }! X' u5 U& C; h, j+ X
avg_y=0.08 f& n( e D" k7 X0 `
for key in user1:3 @( \8 W* K. X$ X
avg_x+=key[1]
( h6 i% \4 j: o9 C( C0 Q avg_x=avg_x/len(user1)* _* x7 Y. t( m
- G& n; Y5 g' }# ^ [; _
for key in user2:
$ F: @1 k. `5 X9 Q1 p8 o+ m avg_y+=key[1]( n6 R f9 K6 B, z& T
avg_y=avg_y/len(user2)( O1 ]) P9 U/ G1 @0 ?
* B+ ~4 z& a# r% r# X for key1 in user1:
: r c" B+ S: h; V* i' m for key2 in user2:
m. \ X# n* e if key1[0]==key2[0] :
* r1 U3 J) X+ u" H- g5 x% s) S sum_xy+=(key1[1]-avg_x)*(key2[1]-avg_y)
$ L, v" R, r5 t! o sum_y+=(key2[1]-avg_y)*(key2[1]-avg_y)
/ g2 K E$ z+ L6 E; F sum_x+=(key1[1]-avg_x)*(key1[1]-avg_x)
; g2 Z" x# L( f
# D' H, x, ^5 y% Y( X$ c7 ~ if sum_xy == 0.0 :& c( T' F4 r6 Z8 b! g E
return 0/ |- o( t' m0 [) S3 r7 G# `
sx_sy=math.sqrt(sum_x*sum_y)
, z; M, }2 Q# N; R return sum_xy/sx_sy* N: M; N' Z; V0 P q0 B
7 }9 t' G3 I/ W- F
5 V& Q& }- s/ ^% D9 M3 Z( G#
3 @: l: R: @$ E# 读取文件5 m% |2 W+ h/ m. ]
#6 t8 W C* T% u$ m# e; V0 _( T/ G
#
# B7 F# w( }1 ~6 q2 |/ l) z* }# kdef readFile(file_name):6 h# D. z9 J8 {; s
contents_lines=[]
. u/ D) {3 R* m% p f=open(file_name,"r")! |8 u: v$ C" x
contents_lines=f.readlines()
1 h& h( s3 v! r& ?. y f.close()
4 O9 _2 [) Q3 E4 F return contents_lines
7 B U9 \( l8 d* y0 \/ b% I3 `1 V; d
" Y* t( h) d) q5 \9 k6 n' q0 u# `9 W7 A6 C7 a' T+ L' U g5 d6 N6 l
, {: w+ H7 t& l# k
#4 }) S& i3 {4 V# n1 P/ J5 h; L
# 解压rating信息,格式:用户id\t硬盘id\t用户rating\t时间% B# }$ {- H0 E% z# F5 x: v
# 输入:数据集合
; R' A& g! j8 w# 输出:已经解压的排名信息
6 q; I8 }5 v b) C' j9 |#" e! R; S5 T6 s: m
def getRatingInformation(ratings):9 h/ r7 r7 t3 e4 o6 G1 l
rates=[]
% a9 J" l, c4 t1 @ for line in ratings:, l$ L7 Z9 Z# Z* y) s' M, b
rate=line.split("\t")
2 Y) [. [! x+ D- I6 E% J rates.append([int(rate[0]),int(rate[1]),int(rate[2])])! `) t) S2 p- U5 X2 l" U( ~
return rates
& r0 p9 ?( I h, l5 R3 c9 E/ b) ~' [) C* Q/ e% U* x
7 Z1 C: x8 _9 g+ q, Y0 v& W3 R+ }
#
* @( a: `0 T9 G! u0 p# 生成用户评分的数据结构
& l4 w. q* h" g: P1 \$ ^#
) @5 K( d! [; c, ]3 z) T& y5 G# 输入:所以数据 [[2,1,5],[2,4,2]...]
# X+ ^9 M7 T, G# 输出:1.用户打分字典 2.电影字典
. H5 k# Q6 ^7 H x: n# 使用字典,key是用户id,value是用户对电影的评价,
+ D" f5 r* Q' d, U( F# rate_dic[2]=[(1,5),(4,2)].... 表示用户2对电影1的评分是5,对电影4的评分是29 j* n q6 L( s+ Y4 U
#8 `; @0 y6 b: c
def createUserRankDic(rates):
9 q6 g% Z7 @0 C" V user_rate_dic={}
: [" [6 m+ t( G; c/ k. Z% Z, a item_to_user={}
; k5 o7 z5 F" ^1 m. f for i in rates:
2 |: M# R5 m: l+ p user_rank=(i[1],i[2])( P* [; |& l# X8 f" I
if i[0] in user_rate_dic:
, P) @/ {/ o) M* J4 o user_rate_dic[i[0]].append(user_rank)3 P* D0 q/ V& d- B! I9 x
else:) V! k, x& }% ^$ j7 t8 r, J
user_rate_dic[i[0]]=[user_rank]
, G! }' f' P8 ~. d4 K 5 b8 r- K' ?5 ?& W3 u2 d. N
if i[1] in item_to_user:
* W4 I' H; H/ j item_to_user[i[1]].append(i[0])
4 j. |+ q/ V; ~& x6 [0 l }1 t else:( i. r$ T: Q. e/ ?
item_to_user[i[1]]=[i[0]]
( w$ |6 _- |8 T' r 0 h# q9 E; ?$ M' y# c
return user_rate_dic,item_to_user
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: W& z; A- R& D+ d2 y% E9 S% s7 D0 y
# ]+ @# I4 S& M) ?0 j0 n
# 计算与指定用户最相近的邻居: R; f L0 B% O
# 输入:指定用户ID,所以用户数据,所以物品数据! R& f& D0 X' d7 l" m3 L
# 输出:与指定用户最相邻的邻居列表
: ^ j$ u4 q T#
$ F* N9 d" w/ m( ^. edef calcNearestNeighbor(userid,users_dic,item_dic):5 | X: S, T, J" `: r" T
neighbors=[]8 C- M* W9 p8 H5 j" k
#neighbors.append(userid)
9 E7 [, v# [9 c# T for item in users_dic[userid]:* b7 r$ q' n/ C% N# q; u) Z2 b8 T
for neighbor in item_dic[item[0]]:9 b; T' ~) I) |8 I
if neighbor != userid and neighbor not in neighbors:
% ?3 f1 l Y6 |* ?! W( y/ g neighbors.append(neighbor)
d8 E# K _* J, C- N: C& ? 9 U! S- {& _8 x9 Y4 y# {
neighbors_dist=[]
' V8 c" Z4 \" k7 v for neighbor in neighbors:
4 G6 c, \9 S6 e" q) ]0 T3 t! ^0 w dist=calcSimlaryCosDist(users_dic[userid],users_dic[neighbor]) #calcSimlaryCosDist calcCosDist calcCosDistSpe5 `: c9 `# s& N# ?
neighbors_dist.append([dist,neighbor]): Z+ L+ |, d+ K" H( K
neighbors_dist.sort(reverse=True)1 g% r3 ~+ h& V# B
#print neighbors_dist# H3 j. {3 c3 |! t
return neighbors_dist# d5 B% y8 ?6 w; N2 V7 l
) R1 v! F, y# H: w! p. {
6 E3 g; l3 k4 ]" s: k: G- n! ^
#( p0 y7 i# q$ U W* ^* ]
# 使用UserFC进行推荐* S/ O! F6 J; b# x1 P' r9 L% q5 z# \
# 输入:文件名,用户ID,邻居数量
+ Z, X% T5 V9 S( w( B# 输出:推荐的电影ID,输入用户的电影列表,电影对应用户的反序表,邻居列表8 s/ o+ A" l0 z; g$ c) u
#
+ M i0 c1 U6 q& M# M4 v9 bdef recommendByUserFC(file_name,userid,k=5):
9 e2 i% v/ [9 {. u& m) M
4 i2 c2 U8 j3 U. w #读取文件数据
/ \& }8 C' B5 _* D test_contents=readFile(file_name)4 _* ^% d3 @- H1 a! b3 C* z# K/ g
- L6 b7 G) P, o8 C1 k
#文件数据格式化成二维数组 List[[用户id,电影id,电影评分]...] 2 V6 V, S# [* T4 M5 [) N
test_rates=getRatingInformation(test_contents)! \: Y' o- w2 p/ ^
) x8 s5 f0 e) P/ Y, M, j- w #格式化成字典数据
2 U N/ ~. J9 v1 n2 |7 J # 1.用户字典:dic[用户id]=[(电影id,电影评分)...]
3 c; f) s/ l. ?- O. y! } # 2.电影字典:dic[电影id]=[用户id1,用户id2...]6 J4 L* M1 p4 [
test_dic,test_item_to_user=createUserRankDic(test_rates)
6 H/ v7 _ C2 }- m0 S, p 1 }, z- F- j2 U9 ~: c9 Y: J' L$ p
#寻找邻居3 _, `2 @- ]5 l5 r' f+ r
neighbors=calcNearestNeighbor(userid,test_dic,test_item_to_user)[:k]7 V9 F- `/ Y% y* P: U# y
# t8 |' v8 j1 g- l P9 d
recommend_dic={}: j/ P/ q# I: f. ~4 y0 P; m
for neighbor in neighbors:9 F& M" V r4 l' o
neighbor_user_id=neighbor[1]- ~" H# w3 n/ x3 l/ ?; R% j
movies=test_dic[neighbor_user_id]
/ P3 P0 J, v0 {9 T8 }9 s; `7 a for movie in movies:0 j7 F( r' J4 `4 A
#print movie2 E& z: ] u* ~( J. [5 |4 j* ~
if movie[0] not in recommend_dic:
2 l) H# L. G. j' T7 J9 R6 A6 { recommend_dic[movie[0]]=neighbor[0]
5 t5 _' t. ^& ~) j( L& P. I) ` else:
1 i2 B l+ ]6 `3 z3 J7 l$ u) ^2 y2 G: f recommend_dic[movie[0]]+=neighbor[0]
, W" Z2 h/ ?* { #print len(recommend_dic)5 N6 Q+ G$ F2 M8 Q" m# F) ?
; w' A0 F/ `3 k) v #建立推荐列表; t! V* V: h b9 |, F
recommend_list=[]
& I% n7 N) z( V* m: r; t for key in recommend_dic:
- K% w9 J }1 e #print key% B: Y' J' b- Q5 q6 m
recommend_list.append([recommend_dic[key],key])
, n* G& k9 `! W( p : b$ {' y8 E+ Z
/ [% Z5 w6 b: E4 P# @0 G4 i
recommend_list.sort(reverse=True)$ V9 E$ ?+ P5 N& f+ Q' ?7 q# t
#print recommend_list
L* k" I8 F3 B* X$ ?. q7 A user_movies = [ i[0] for i in test_dic[userid]]5 `1 U7 w* v: J5 A; G4 _
/ W$ Y% M d4 w5 L* h
return [i[1] for i in recommend_list],user_movies,test_item_to_user,neighbors
5 u7 Q l& I9 w. J* c+ M 0 U d, H' u7 h! {6 A' V
, F& ?7 U7 `! a1 e0 }6 H
; a) _8 s4 ?: z& l. A1 s5 k# d
#1 E R' u0 [5 m
#- ^ s/ x. C' ^' g! J
# 获取电影的列表: N1 i) m+ X& h# X1 I. ]# H
#
z: \; i+ v$ _ G6 N9 h. p#+ z# c5 ]1 {5 }, X7 H+ n) V: L
#
7 H, q' n8 e2 n6 }1 C- ^: O* [def getMoviesList(file_name):
" A/ |' I3 V8 y- u! g/ C) i #print sys.getdefaultencoding()' t C! i0 w1 f6 K# k
movies_contents=readFile(file_name)2 ?2 Z. e& q% J; O d/ [
movies_info={}; a( L) E: l( h
for movie in movies_contents:1 Y& g2 n; \9 ?8 z& }
movie_info=movie.split("|")+ u; b/ u: k0 ~5 r4 n: V! }
movies_info[int(movie_info[0])]=movie_info[1:]5 t# o3 g+ f* ], P
return movies_info
# `) V* I) s# X; ?0 V
$ L6 k+ {8 D, d. m8 m: m8 L# @
! f$ R4 l! u/ b( ?
( k a9 D; `0 s$ Z. M#主程序
0 \0 p4 t# I/ j$ b* E#输入 : 测试数据集合0 Q# j/ d" ?# k) e
if __name__ == '__main__':
' }0 G$ G% o% F1 T- B# `4 b reload(sys)
: s0 K; [9 m8 ^& `# R; Y9 ^( B sys.setdefaultencoding('utf-8')( z; ]# m W" j6 m
movies=getMoviesList("/Users/wuyinghao/Downloads/ml-100k/u.item")0 i( s/ h4 S5 n# Z! i
recommend_list,user_movie,items_movie,neighbors=recommendByUserFC("/Users/wuyinghao/Downloads/ml-100k/u.data",179,80)
2 T* F- N+ E3 `( R" s% k; g# A R E& a neighbors_id=[ i[1] for i in neighbors]
* ~( r4 Z! c; n2 T0 ~, Z) d8 U table = Texttable()4 l. H$ g6 G" J3 _7 C9 G, Z( U+ ?
table.set_deco(Texttable.HEADER)
8 Z# ?& e/ V* \ table.set_cols_dtype(['t', # text
! J9 D# d" P2 U 't', # float (decimal)$ X; S. t7 W$ k4 y
't']) # automatic5 @7 ~- ^. J7 ^# W9 h: i& R/ k
table.set_cols_align(["l", "l", "l"])
# G3 a8 L2 E. X rows=[]
9 k& _ ?7 w# Y% e' K& \/ S! b rows.append([u"movie name",u"release", u"from userid"])2 X; ?* {5 A5 a/ x
for movie_id in recommend_list[:20]:5 @1 [( o2 h2 z/ Y/ c. f$ s
from_user=[]3 i* Y( P9 b6 E; R, _( G' m
for user_id in items_movie[movie_id]:
9 K2 B* \' g9 Y& D: L8 S if user_id in neighbors_id:
2 S# I4 s1 M L0 ~1 F6 R- R1 w from_user.append(user_id)3 l9 r% W; \8 H
rows.append([movies[movie_id][0],movies[movie_id][1],""]); i5 R& F9 r9 D9 L. ]: A
table.add_rows(rows)" x9 h9 w, T, d2 \% p
print table.draw() |
|