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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 -*-
" ?/ E% J. k6 |( D. |6 K0 Y, E
7 C- ]3 s1 m" `- h. simport math
$ w- v4 T. g( j+ Wimport sys1 Z6 J" J3 u$ g' U
from texttable import Texttable
4 x! w6 e' D& x5 b2 v, `: Z' M" `: W! v' o
: T% j" h9 C& `/ x( j#
2 T; v7 J, R- W5 F7 R+ k9 Y) M) A# 使用 |A&B|/sqrt(|A || B |)计算余弦距离9 `. R$ _7 W% h' P) N. l: c
#
2 q1 [6 K; u0 i5 v' L#% w1 z2 o9 N8 `2 q- P* p3 F
#5 {6 h: c7 z: P+ d/ n: W6 U, c
def calcCosDistSpe(user1,user2):/ d. K# }; G7 v5 D9 g4 h3 G& {
avg_x=0.0# L( m' `& j1 D+ N3 H
avg_y=0.0 X5 `0 D8 S% F; T3 Y2 H6 m7 i& l/ w6 b
for key in user1:
! C- i# `9 f6 _' S avg_x+=key[1]
; e0 m/ i- E$ A avg_x=avg_x/len(user1)
( q# ?( \: y( A/ J5 v
# n4 ~, ?( C* Z for key in user2:& z H) X+ u5 p
avg_y+=key[1]
6 c$ r- I! D, E |2 R avg_y=avg_y/len(user2)
) v8 p% e$ j, N# I# J8 ^
1 L! @& c& |6 V4 Y" _1 ]. ] u1_u2=0.0+ v! X' t4 J8 M m: g
for key1 in user1:1 Z; E( g- {9 }8 a3 N/ f* w0 { m
for key2 in user2:
2 k$ N: L! l2 a6 c @* `6 F7 o' T if key1[1] > avg_x and key2[1]>avg_y and key1[0]==key2[0]:3 t3 o$ h8 P8 Q# L0 r' J/ ]
u1_u2+=1; a$ |; q4 H9 `3 i; ?7 {
u1u2=len(user1)*len(user2)*1.00 S4 q1 v E8 E4 P/ ]1 v( ?& v
sx_sy=u1_u2/math.sqrt(u1u2)
9 h& L+ T8 j1 K8 \8 }/ _ G return sx_sy
* P! R+ K4 m7 j0 ~' q, {7 J& q* ~% l) r; g0 }8 p
0 E n# l' i0 f5 B2 G( w#
' j7 ^' a! N, i6 z1 ?! V- R+ r; f# 计算余弦距离
+ O: w- K& I0 ~#, h! O& b* _" }7 ~8 G
#
! \" V' i! k4 Z5 i4 \def calcCosDist(user1,user2):# d. y9 z3 w4 G/ _, G- [9 M
sum_x=0.0+ y# S2 O$ ~4 b* v" m, t0 ?; }
sum_y=0.0
4 ]1 G+ ~9 B- o1 v. `& k0 u sum_xy=0.0/ s) D9 s; t: G& C7 A
for key1 in user1:
. t/ F. m) T! V1 d for key2 in user2:; E6 ^7 _1 z; k3 @ t3 K4 F. I
if key1[0]==key2[0] :
; V0 f: n1 ~- ~: g8 p9 w" _( H sum_xy+=key1[1]*key2[1]
# k8 d: ?8 o# I. u1 G. B) G; D* A sum_y+=key2[1]*key2[1]7 y/ F3 P& A* S* X, b3 y. E
sum_x+=key1[1]*key1[1]
! g/ R" Y+ o5 p* ]8 b4 Y1 m+ ~% ` $ z- Z6 J% ^' T# T3 a+ D
if sum_xy == 0.0 :+ K8 D2 @4 Q( g- t; s; o* n/ R+ D
return 0( c4 B0 @; D$ Y
sx_sy=math.sqrt(sum_x*sum_y)
# @' C. M' b) Y# V: V" R& U return sum_xy/sx_sy `: j' p! d. F3 K# ~* X" p4 M9 R
6 ?. v* o% q$ _1 c; j6 ^% F- {$ L8 O' ~' d X: e8 u+ v/ K
#
5 B) a. t2 Y7 ?$ I7 Z#! q! ~6 I Y$ v {: }
# 相似余弦距离
7 @7 I: R: H& i8 v* m) Y& f#) V: I7 b/ {: t/ R4 L6 N0 V
#
2 l+ c7 P4 B: y- x' i" n#
/ Q/ P/ B3 d6 z1 jdef calcSimlaryCosDist(user1,user2):+ K7 K0 y9 q4 Z: x) E, w
sum_x=0.0
2 U/ _* X0 M3 W9 R, {! |3 { j$ O sum_y=0.0
( n" m5 t! M7 A% H sum_xy=0.0
. n; K, l6 i; T+ w( j9 b. w5 s avg_x=0.0
2 [! U9 w; e# `, s2 ?, j avg_y=0.0# z( h [1 R, L- R* m
for key in user1:; y' z- q; E. M8 ^7 z$ B
avg_x+=key[1]- [$ P I& C7 [$ S
avg_x=avg_x/len(user1)) f& k4 k! V9 s7 T3 M+ d0 C4 ~
( @. ^* A* w6 K0 L0 {1 _+ F
for key in user2:
; _/ ]8 J b" l w: I" p avg_y+=key[1]
S0 s$ \3 l- f0 r+ G0 E, H) M avg_y=avg_y/len(user2): f5 G$ c/ M! m6 {2 {1 b+ o
: v" I( y$ J8 @* }! ~% y$ R8 }% q for key1 in user1:
8 F4 b1 y* w, M5 s% y4 C/ ~6 D for key2 in user2:' V, K* H" M5 ^9 y4 |, {
if key1[0]==key2[0] :8 |# X. l& G7 |2 I/ K6 _: v8 j. H
sum_xy+=(key1[1]-avg_x)*(key2[1]-avg_y)
" s+ _/ G/ x2 p sum_y+=(key2[1]-avg_y)*(key2[1]-avg_y)
" s# D9 ~1 Q8 \) U& q sum_x+=(key1[1]-avg_x)*(key1[1]-avg_x)
, e' H; {" i0 e: v6 ^: f& h# R; @3 ?& \
4 @, A: q' U+ M: r; l if sum_xy == 0.0 :& K) R l( n/ o
return 00 d1 s6 N% P1 c8 ]2 n1 L
sx_sy=math.sqrt(sum_x*sum_y)
$ H& G1 x6 D. s/ e return sum_xy/sx_sy$ v4 ], a" ~ ^
; W# t D2 v' G3 Y' m1 d5 d
# l9 N) n4 C8 u3 a G5 N; [#
8 y, N1 p; b# q. v7 d. ]# 读取文件" e) N. h2 R3 C! R
#6 ?+ ~6 l' q) J$ r( [, R5 D
#( }& Y1 I x% F/ B% B+ e5 e9 |: x
def readFile(file_name):
4 H1 D. F8 p) ]% g( l6 m5 l contents_lines=[]4 s$ }+ Q& H! b4 \5 n
f=open(file_name,"r")
% C* ?3 Y; X5 D1 v2 V contents_lines=f.readlines() M. `7 G1 d$ [" r5 d; _
f.close()2 \% d' b2 h5 a+ `. X
return contents_lines
& X0 W- C3 a- ?& }: t9 s4 |4 l, t' M( f/ j+ _4 i
9 {' V" k5 r: g
2 G% x/ f! }& @2 z6 L
# k$ N' P% l" [! i- m
# 解压rating信息,格式:用户id\t硬盘id\t用户rating\t时间
* M0 o: D; N8 _* d# 输入:数据集合+ H0 | p: i! f
# 输出:已经解压的排名信息/ p; X/ v6 G: z2 h4 c5 l
#; E9 m. \5 l9 E" C- H) F
def getRatingInformation(ratings):
; ^* P5 j( Y* ?) x- C' v rates=[]% l8 U5 I( k4 w0 ]2 V4 k
for line in ratings:
+ R/ @) l3 d! D* } rate=line.split("\t")
S7 T4 f; L2 S8 c( I! I7 [ rates.append([int(rate[0]),int(rate[1]),int(rate[2])])/ F" A9 M& [ \# H! W% `/ u, r* M
return rates8 p( f' b) D: u% T/ m
( `" {8 [. V9 C8 ]1 U
2 m; W: U+ T; j7 x s( ^#& g* m$ B# _) p3 \2 `
# 生成用户评分的数据结构
3 i; h3 L5 y3 H" A+ W4 u#
( Z7 X. G+ ?, F* ~' s! k# 输入:所以数据 [[2,1,5],[2,4,2]...]
- a; N" m9 B- G7 k2 `" m/ ~; C# 输出:1.用户打分字典 2.电影字典0 K9 @7 |" \2 X! n1 J9 ]3 d h
# 使用字典,key是用户id,value是用户对电影的评价,3 t3 E+ P' ?4 Q* E+ p
# 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2" \0 P1 y: M0 I, h8 A4 m
#
- K0 b% | y0 P/ xdef createUserRankDic(rates):
4 [8 s+ P. R1 ]4 |/ X# \; M user_rate_dic={}
" z9 S( T, x) v1 I- n" g- _ item_to_user={}
% U, g/ f- }- K+ z4 ^ for i in rates:
7 X; D/ {8 X* r) D9 F( F/ `/ g d user_rank=(i[1],i[2]) ? U/ r6 v& X+ P. p, ?
if i[0] in user_rate_dic:
* v) |2 C$ m+ j- r! f user_rate_dic[i[0]].append(user_rank)
4 R2 K. w! [7 p+ a5 d else:# i0 n6 z/ a/ m/ @. R
user_rate_dic[i[0]]=[user_rank]% F( V6 [: _/ E& l3 J/ @% ^
! \' n) m1 V9 Y* ^3 y% w7 g
if i[1] in item_to_user:
! J: ?! F) d( t/ A! K) I item_to_user[i[1]].append(i[0])" i. B! y1 n8 _# `" Y. ]! s
else:1 e5 A' q4 j- ^
item_to_user[i[1]]=[i[0]] E: a4 C$ @4 `1 k. S" W0 A
- o" x% Q; {; i/ w; @/ x1 v1 X* p return user_rate_dic,item_to_user8 v- R j( l- a6 r" r L% a G* g
3 B7 j$ } t& t
8 D! D: `/ X, C0 m, e; ?#
* D/ _% j' @4 G! _/ ?6 h9 y# 计算与指定用户最相近的邻居
9 s2 V' V& j A9 Y2 u% o2 J Y) R# 输入:指定用户ID,所以用户数据,所以物品数据! ?- F7 \8 L _' A5 y% M
# 输出:与指定用户最相邻的邻居列表
% H8 x. {* H" N#
6 Y+ R6 l% R9 i: k. m! {2 q9 ddef calcNearestNeighbor(userid,users_dic,item_dic):
+ y, D# o; I9 T# ?" S( Z neighbors=[]
: q. d3 i7 K- a6 ^& x8 y& P4 n #neighbors.append(userid)0 P5 m5 l) Z+ g9 F) u
for item in users_dic[userid]:
2 }& ^" N0 u" y; s t2 I for neighbor in item_dic[item[0]]:* U9 [4 u. B c8 q3 E0 \& Q
if neighbor != userid and neighbor not in neighbors:
0 k( o6 U5 x6 K4 r# [( E/ E' w0 [ neighbors.append(neighbor)
9 `3 y9 @) I4 E1 E " T' J6 \$ M" P
neighbors_dist=[]' h. q$ w5 D( D+ L
for neighbor in neighbors:
( E3 D, n9 R# l$ H) n n9 W dist=calcSimlaryCosDist(users_dic[userid],users_dic[neighbor]) #calcSimlaryCosDist calcCosDist calcCosDistSpe' |; g- S/ l, C: ^9 o
neighbors_dist.append([dist,neighbor])
0 ?$ w0 d- C6 j4 K neighbors_dist.sort(reverse=True) A/ q ]7 x& q: Q" t, e h7 i
#print neighbors_dist* F, ]+ f- G) t0 Y/ p
return neighbors_dist; D5 Y8 \6 g: V
( }5 x! N7 K. ~$ M4 v. ~: f# u B: s/ E
#
. G+ N0 F! T& M/ A' V8 O# 使用UserFC进行推荐
) t& {9 o* O2 C; p# 输入:文件名,用户ID,邻居数量
3 Z! d1 C/ h& v# 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
/ w+ o' @" r! b#
4 h7 K1 ]* U& O% X, f3 d" e! ^* a/ Qdef recommendByUserFC(file_name,userid,k=5):
6 o' n$ p6 g9 c# R : l9 F" _" P1 O; E8 h7 L! W9 m
#读取文件数据
P# W3 u* ~4 H( d test_contents=readFile(file_name), y! ]4 X7 b$ t9 a
, c! p7 v4 R5 v, r6 w. y; @ #文件数据格式化成二维数组 List[[用户id,电影id,电影评分]...] ' Y9 H" {7 ^2 S; N& i' j* F
test_rates=getRatingInformation(test_contents)( R6 o0 m v1 |( R: r/ f W/ C
' Q5 Y. `; x! |3 y
#格式化成字典数据
/ a3 C- h4 L% _+ J: a8 B, s; |0 z # 1.用户字典:dic[用户id]=[(电影id,电影评分)...]9 r4 ~0 b( ]) ^% E
# 2.电影字典:dic[电影id]=[用户id1,用户id2...]! w% H: }! w7 x5 ]6 I* ?
test_dic,test_item_to_user=createUserRankDic(test_rates), C$ h2 d$ j0 O( @ {) N# l# ]4 ?9 D
4 w; y* I$ ]$ i `- N; J2 M
#寻找邻居
4 P8 O% b% q3 ^8 V( B neighbors=calcNearestNeighbor(userid,test_dic,test_item_to_user)[:k]
: P6 x$ H. {& Q( N2 j , a1 \1 q' W4 }. C
recommend_dic={}4 z7 r% P) q- b9 R$ S5 H5 d) K; |
for neighbor in neighbors:. W" t, d8 S5 P$ L
neighbor_user_id=neighbor[1]
3 K% ]! D4 j& v$ [ movies=test_dic[neighbor_user_id]* ^0 d5 l2 A/ \8 R3 V1 e5 J# e" d# F
for movie in movies:* f7 P$ J0 ?7 I j, \
#print movie
# _ L, {/ C8 X# H if movie[0] not in recommend_dic:" T' N5 o2 C6 \; s7 y6 K
recommend_dic[movie[0]]=neighbor[0]
0 M6 I2 W% m' a4 [" w4 T else:
( ]( C7 f* X' k. l recommend_dic[movie[0]]+=neighbor[0]
% U; m( N& F% k0 V2 g* o" j #print len(recommend_dic)
1 U8 y; U1 e: u& O
7 V# }. ^" j) `6 { #建立推荐列表9 J) |7 f# A" I* ]- f
recommend_list=[]
$ Y5 O- P; E( d# F, m% E4 s9 l1 D for key in recommend_dic:
$ F% R5 C& e/ Z; m' \6 V$ H8 Z. z #print key7 N4 ?6 J1 c& P! r4 I- l- |2 H
recommend_list.append([recommend_dic[key],key])) V, ~9 Y9 i, a& E& p; i& _( l
: o+ P- S2 Y3 w* S0 l! _+ G
6 S: o. U7 r1 o7 w- n6 a; @) ^9 i* \- v recommend_list.sort(reverse=True)2 F0 P& F4 A, O$ i
#print recommend_list
$ b+ x8 c7 d# V% E# K& ~" M user_movies = [ i[0] for i in test_dic[userid]] g) e, v1 D6 S6 s# s7 ^5 B( P2 k
3 C! K/ s7 s; m' o: }* I
return [i[1] for i in recommend_list],user_movies,test_item_to_user,neighbors
# J3 p$ Z. I: L) I9 K f4 `
& g1 I- f p- y& e# b& P+ ~9 U
. Z7 H3 |$ A; F9 N- Q9 Q7 V- ~: n) [7 |6 N. L0 ?
#5 |" e) }6 M' k# o9 i
#* ^+ y" |4 }( e! X( F$ z( E) k0 x' j
# 获取电影的列表
8 p; ]4 b" i& z3 }8 t: h#3 I6 Z/ e }+ b2 e/ y) H0 p
#( a$ o0 O7 T2 b
#: q/ B8 U% W) r' `2 }+ y$ i2 x6 ]) X: R
def getMoviesList(file_name):; T: j( v8 y+ E) g* p; O
#print sys.getdefaultencoding()
6 h' `- [! H" S' e% n movies_contents=readFile(file_name)3 I) e% i0 \# ]) N4 J
movies_info={}1 Z* H/ d+ C: Q7 N/ V' m# @. B
for movie in movies_contents:$ y2 B0 A+ r7 B0 O1 U1 n
movie_info=movie.split("|"). z6 w6 g3 t ]7 ^- t' Q
movies_info[int(movie_info[0])]=movie_info[1:]
/ X! |: q# ]/ G1 ] return movies_info
; h( N) J" v, b4 b8 h : ^, A9 K0 \4 G1 q
: b8 T- h! j. \4 z' d, ^+ R& O8 _
1 N5 Q2 [! H9 L( `6 W2 Y#主程序. p+ k7 V: u: z, d
#输入 : 测试数据集合
) @/ n, {: g* Y' jif __name__ == '__main__':
' m i7 v5 v& h) @ reload(sys), f8 N4 j0 h7 c+ m9 @
sys.setdefaultencoding('utf-8')
- s5 q/ f& N/ g) L4 y4 D3 u movies=getMoviesList("/Users/wuyinghao/Downloads/ml-100k/u.item") r" W, Y" [9 q9 k- m: {
recommend_list,user_movie,items_movie,neighbors=recommendByUserFC("/Users/wuyinghao/Downloads/ml-100k/u.data",179,80)
* x) d, ]/ x& {+ |! Y1 k5 v neighbors_id=[ i[1] for i in neighbors]
3 ^/ m' T1 P* \$ } table = Texttable()
1 X8 q+ J$ l5 ~2 a+ c table.set_deco(Texttable.HEADER)
, P! n: y6 ^6 g* J table.set_cols_dtype(['t', # text
/ ^; ^: G- ^1 ]: p3 s& Y$ S F 't', # float (decimal)
5 H+ F8 L/ w, P2 S 't']) # automatic' `8 y) d5 F- f2 y# c
table.set_cols_align(["l", "l", "l"])
{8 j# p7 R* A% z rows=[]4 _5 F8 M: ?# G4 B6 S% D' B& k7 [
rows.append([u"movie name",u"release", u"from userid"])" V3 k1 ]6 P6 U
for movie_id in recommend_list[:20]:
' Z# p) W9 s& p/ y( B from_user=[]
^# j" M9 n! M9 e9 L for user_id in items_movie[movie_id]:3 q k; f* U" V. j! W! m( C9 A9 d
if user_id in neighbors_id:
1 M& P. C; Y& h/ b- Q6 ? from_user.append(user_id)3 Y& m8 `0 e, Y d2 P1 B
rows.append([movies[movie_id][0],movies[movie_id][1],""])
. t/ u# ^) e% P! ] table.add_rows(rows)* A* s5 u7 e# \$ y6 ~
print table.draw() |
|