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TA的每日心情 | 开心 2017-2-7 15:12 |
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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 -*-5 X [% G% @0 n$ _
6 g" D# ^! g5 H& y' W4 p+ eimport math# h M5 M( Y1 L1 P0 ~" {( c
import sys( k" T: Z) U3 F, T
from texttable import Texttable* }8 n1 k6 |) I8 t( ~+ L
, j" a* G# F" O( n) |! J
n4 w# F0 [6 n' Z7 f$ N+ D1 i#
! I, m' v/ O7 C/ ]1 e# 使用 |A&B|/sqrt(|A || B |)计算余弦距离
" U* f: o' D) Z/ |#
+ W0 o0 p" O+ L6 M. n4 Z. k#
! G/ A- N2 S# f( y2 z& f#7 { s8 O) A5 j; v# W
def calcCosDistSpe(user1,user2):" U' w% R* O; u3 B- F0 w
avg_x=0.0' m" p3 {9 r) \$ |
avg_y=0.0
8 m0 O# E% s( O for key in user1:# U, ~9 K+ r. f( N
avg_x+=key[1]0 n/ J+ Z8 R% z7 T' f Z; w
avg_x=avg_x/len(user1)/ B1 @2 x2 Q/ ?7 p9 ?5 F
s/ j A& W) T6 \4 u
for key in user2:
/ A/ |" S) d+ F5 N avg_y+=key[1]1 m& M- ^# U% k4 j
avg_y=avg_y/len(user2)6 |' X: x6 j% `4 E& D8 v
/ ^ ~" e- c5 o# J u1_u2=0.0" Y0 {" ?# U: ?+ X# A c- I7 ^
for key1 in user1:/ k' U, C m$ U4 c
for key2 in user2:
. S$ Z) R9 s3 \/ v if key1[1] > avg_x and key2[1]>avg_y and key1[0]==key2[0]:4 l' F6 A6 I! {" l3 C8 z
u1_u2+=10 @/ H# F: O- N! g/ d
u1u2=len(user1)*len(user2)*1.02 m" E# ~9 E# U
sx_sy=u1_u2/math.sqrt(u1u2)7 c3 s a7 ?/ ?" Y! g
return sx_sy
: U _$ X: d7 {( x7 @5 s7 |
) O6 m6 u( U; M8 h( [1 P9 [2 H. \5 t
2 H. b7 W2 G# h7 U% Z#4 C. d8 h5 J) {1 W/ t
# 计算余弦距离
' j1 x+ f: \! q#
$ f$ O0 E% T" z5 `( J#
$ C; `4 i1 T! I( vdef calcCosDist(user1,user2):
" `' v8 `, L4 b; d sum_x=0.07 @* n4 C8 _: Z' \+ T$ C
sum_y=0.0
6 G# c9 A7 n; M4 r1 B& h1 w4 J# L sum_xy=0.0
& ]& z B- V4 Y2 D for key1 in user1:' \' `/ d9 ?. H7 R$ E1 G7 \
for key2 in user2:3 b# ^8 ?, Z4 C% ~$ C1 N" X/ b' a. j
if key1[0]==key2[0] :$ S; ]# G$ ]9 n- k
sum_xy+=key1[1]*key2[1]
; B' d2 ~) q/ C" j! L8 d$ q4 E& n sum_y+=key2[1]*key2[1]# }7 o9 J8 m+ Q
sum_x+=key1[1]*key1[1]
: |9 I, r' f3 ~ ~3 Y7 A4 M9 q* w& u
( u6 J0 k% [6 Y if sum_xy == 0.0 :
0 U3 f: T/ `2 n9 ~9 d7 ]0 I return 0
5 P; [/ R5 ?$ j sx_sy=math.sqrt(sum_x*sum_y)
( @1 m3 V% h9 q9 x return sum_xy/sx_sy l& ^( L: f/ B: C- s
1 a* Y( A1 S }4 P
2 c4 E4 P4 h: R. @3 \( ?#
3 {$ e. w6 F$ g6 }4 c5 j#
. G) e- T' q- c+ ^! d' r# 相似余弦距离+ _) W# Y" z# e4 o. T7 u+ A& o
#
1 h! i {9 `# C2 P2 ]& P1 m## _% s+ m5 G- F
#
( ]6 H6 R1 I8 c- ~" Edef calcSimlaryCosDist(user1,user2):$ z9 f' g' U5 ?. u: B S( d& m0 b& B
sum_x=0.0
7 s) l) U" I4 h5 T7 n7 J8 I2 v# A sum_y=0.0
! `; e1 ^3 Y r* [' v+ R7 n# \' a1 ^- @ sum_xy=0.0
E/ a5 J$ ^; o4 n+ _( A avg_x=0.0' A5 u8 E; O; p' f8 l( X
avg_y=0.0- c* D D8 v* H
for key in user1:. D. i+ S5 z6 L
avg_x+=key[1]2 o2 c3 ]) w3 Y8 I/ M
avg_x=avg_x/len(user1)
! C4 f+ |0 O( |- M7 I( N' I) b + B4 ^7 p+ p" K- S6 m0 Y
for key in user2:: v: s& ? W/ h
avg_y+=key[1]/ N, `' r% [: o1 M3 H: x" ~
avg_y=avg_y/len(user2)
. j0 w8 u2 o! q) t# d7 P
; Q/ [+ A) t5 v( M- O _ for key1 in user1:
6 V4 j1 K% `4 S1 V for key2 in user2:
0 P* y$ J) \- d7 k: |+ E if key1[0]==key2[0] :
% G2 r% E6 U6 W9 W# M3 G8 V8 L sum_xy+=(key1[1]-avg_x)*(key2[1]-avg_y)
, f6 M" p! t# M( E/ G+ E" z; v% c sum_y+=(key2[1]-avg_y)*(key2[1]-avg_y)4 M$ U" a4 ~+ ~ v6 a6 w W; O: U
sum_x+=(key1[1]-avg_x)*(key1[1]-avg_x)- `; X* Y$ p4 q
8 N- I- y5 |; a8 y if sum_xy == 0.0 :
) ~& C4 n8 z9 D" h, q% k0 r( F return 0
! P5 p% e5 A( `4 H% _5 \ sx_sy=math.sqrt(sum_x*sum_y)
( x! C3 n4 h5 J! n9 h return sum_xy/sx_sy5 z( {* b: A( ?4 L0 b1 ]
" y2 ~' b% w+ v
" S2 K. |; n: n2 G/ b#! p5 g3 F9 O# c% W( c
# 读取文件 s, e$ a/ v8 V8 t- L
#' D2 r1 J/ v& Y8 \8 j& I
#6 L& H$ o( F, O" O
def readFile(file_name):6 [( O; M3 t( Z \
contents_lines=[]
# O! c; F. K( a; | [ f=open(file_name,"r")5 r; o& Z, W+ [+ `5 s
contents_lines=f.readlines()
, l. l8 E P% o+ D! |* [0 b5 x2 i f.close()+ u2 J3 A* ]! Z
return contents_lines1 O: F1 t3 z4 Z5 B
5 i) M4 O6 m$ e4 v; C
7 w/ m3 G; L- Z* p. `% s
5 S3 `2 m1 H3 p3 J3 Q/ h+ o! _#
. B2 m1 h2 O* \# 解压rating信息,格式:用户id\t硬盘id\t用户rating\t时间8 s; u& |5 f0 }$ f( e
# 输入:数据集合
1 {+ m' y7 R2 g" E' q# 输出:已经解压的排名信息0 Q, J C5 ~/ t1 P9 G3 C
#
8 p9 L) K! x6 Z% p) B' o, Gdef getRatingInformation(ratings):! @" M$ |7 J( {. B" E+ T
rates=[]. [& V. E0 H, r7 P, }" G% g
for line in ratings:
/ `" Y) w J, K* N# K# b. Q rate=line.split("\t")
( J# H+ l2 P4 _8 s! N rates.append([int(rate[0]),int(rate[1]),int(rate[2])]): Q, S. R* n q" c8 P9 ?/ [
return rates
" \7 k; g* r0 n- R5 m6 o
3 A3 L0 N& a2 T& j
# c, Z) J/ k n#( z2 T5 X' @: P6 s. G3 n7 X
# 生成用户评分的数据结构
+ j0 n0 a- p3 D7 B0 u5 z#
. U/ M. v; O/ h" E% ]* S# 输入:所以数据 [[2,1,5],[2,4,2]...]3 }, C# {! u9 Z8 o+ o
# 输出:1.用户打分字典 2.电影字典
9 @ q7 q) k: N4 _4 s4 Q( V3 \8 P# 使用字典,key是用户id,value是用户对电影的评价,
: N( x6 [7 M5 Q/ I2 [# 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20 N2 I; \5 p& O5 J
#
# @6 k# H' D' k0 G4 Q pdef createUserRankDic(rates):, ?3 U$ S# J' F$ o. n7 C
user_rate_dic={}
" }) e7 Z' n: H, F' G7 i item_to_user={}
1 a1 L; [* I8 e% ^9 `9 `* P for i in rates:5 E7 A( N' _/ |' ~6 C7 C
user_rank=(i[1],i[2])$ ?3 p$ _+ @4 T- I
if i[0] in user_rate_dic:
. k: Q, j$ C% d1 s" S8 q/ L user_rate_dic[i[0]].append(user_rank)! Q' t+ w6 V- H
else:' i# K3 s4 L& z7 k) k
user_rate_dic[i[0]]=[user_rank]8 D2 h) h: w1 }( D7 A. C0 L
" w% D' I3 b. ]7 p if i[1] in item_to_user:
- t8 `4 Z5 E7 R. X1 x3 P item_to_user[i[1]].append(i[0])* c: ]) J' e! F$ t0 L* Y% P& w; |/ E
else:5 C) F1 c) @0 X, N6 M( O# p
item_to_user[i[1]]=[i[0]]" A) n! }( a. a# }1 r3 G
, D0 z% J2 Y, o5 D; y, h) C$ V. { return user_rate_dic,item_to_user
5 E8 C- R& t3 W# j3 I" o2 v: {, I: K t
, i+ r2 q; q6 p#, x: X8 k* _2 Z" t( @; A- {
# 计算与指定用户最相近的邻居, E+ L4 h- H3 [9 R3 C2 r6 |
# 输入:指定用户ID,所以用户数据,所以物品数据
: ^+ W* g& ], Y1 g' L, q- f# 输出:与指定用户最相邻的邻居列表( W; @+ `5 Y b' r4 a3 s9 n6 {
#7 @" l! H# w; W0 R# L2 O( B
def calcNearestNeighbor(userid,users_dic,item_dic):0 r# d* ^& U( Y
neighbors=[]& S4 b3 M3 O+ x/ Z7 ]* l
#neighbors.append(userid)
" v: I }2 D' d+ K& F2 C/ z$ A for item in users_dic[userid]:
6 E3 c& |0 L) v, k6 K for neighbor in item_dic[item[0]]:) C8 j3 f9 x6 U( ^5 q" d
if neighbor != userid and neighbor not in neighbors: 2 G8 P( q9 Y/ R; F
neighbors.append(neighbor)
- m d- A# V! ]% p( Y3 J
% ] I, [; d/ s' x- X- ` neighbors_dist=[]/ [9 s% C0 X& x* w% M) X
for neighbor in neighbors:
5 h9 [: V2 x+ c* S8 I dist=calcSimlaryCosDist(users_dic[userid],users_dic[neighbor]) #calcSimlaryCosDist calcCosDist calcCosDistSpe
2 v7 k* N' Q: \- M: A# ~ neighbors_dist.append([dist,neighbor])
( Z5 \- e+ I1 Z! l( M" i; v neighbors_dist.sort(reverse=True)
4 k/ V/ x3 J# w, k9 I #print neighbors_dist
! a" `) w# T( L3 e h return neighbors_dist& P# l: L* z7 W. t2 t0 m
& |; f) [( |1 F
: }) Z2 I+ Z9 c, v#
X8 u: v6 m; K% a, K( L* Z- ?4 ]5 _4 o# 使用UserFC进行推荐 ?7 T1 Z3 n% o: {6 D+ U! T; e" t
# 输入:文件名,用户ID,邻居数量
6 p0 Q- V& @; l3 O0 U9 w& m8 f b5 z# 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
7 {& e' B# }* o, l" \% ^5 [#6 K/ @' f. ]+ _" q+ _
def recommendByUserFC(file_name,userid,k=5):
4 ~" Y2 @9 i7 M* ]. d/ @ 3 M# ], G* A7 O8 |* {) g
#读取文件数据9 l- F' H: v, E2 `; s
test_contents=readFile(file_name) W+ C( l" L& Z) y( T4 x& b% i
8 h! d9 Y, z4 I2 e8 M
#文件数据格式化成二维数组 List[[用户id,电影id,电影评分]...] : p7 z# u# J" [
test_rates=getRatingInformation(test_contents)
8 b r, S! p8 f% r. o( V! @0 Q : o; o. H, X8 }4 G
#格式化成字典数据 9 ~+ G5 n7 t$ A4 Y! T
# 1.用户字典:dic[用户id]=[(电影id,电影评分)...]
! @5 n0 s4 r) n2 W( x! l # 2.电影字典:dic[电影id]=[用户id1,用户id2...]
/ M9 d* x4 d8 G3 u. w test_dic,test_item_to_user=createUserRankDic(test_rates)' v0 Y* v! x2 n4 t$ ~4 h- z
+ o4 W3 e# V5 X l1 M
#寻找邻居6 E$ f; G5 B8 \& N9 I; S4 h
neighbors=calcNearestNeighbor(userid,test_dic,test_item_to_user)[:k]" Y4 y8 g6 W7 R' w9 J% V" Q: |
: n8 X' v! v5 E1 \ recommend_dic={}
' m- \+ c) m/ A) n for neighbor in neighbors:4 ^9 b/ c! \3 x1 ?# {1 W: u0 g
neighbor_user_id=neighbor[1]
% G2 u/ H* a* c( R movies=test_dic[neighbor_user_id]
# u' D" [" R' s; G9 X+ q for movie in movies:& z& }* B5 e* _3 [5 t$ a
#print movie6 ]) l. d1 v! N+ S
if movie[0] not in recommend_dic:/ y1 ?# y& y0 c2 a; ? s
recommend_dic[movie[0]]=neighbor[0]: ?9 O, {# R$ y; r! u# Z' T, |% x
else:; o& H, G6 O8 h4 |5 V/ C
recommend_dic[movie[0]]+=neighbor[0]
6 ^* \. I8 g& ?% W #print len(recommend_dic)
! F4 ?) }7 w j" D, {9 u: _
! F. v/ ]3 n' z: K7 l #建立推荐列表, T4 q" V ] S. Y+ \) U9 q5 y: v
recommend_list=[]5 H9 p) H" |: ]
for key in recommend_dic:' ^$ C2 C# k0 r6 n
#print key4 q) O, w! A7 `5 c' ?$ Z
recommend_list.append([recommend_dic[key],key])
3 B$ ^/ u# [, R2 [/ m# l
% @+ }+ p! _4 F" a3 g0 D; A
6 d9 O$ n4 Z0 }: @& v recommend_list.sort(reverse=True)% z9 y( F; O+ [2 W6 H! U
#print recommend_list6 z+ I8 |2 ]# Z/ `- D/ @; [
user_movies = [ i[0] for i in test_dic[userid]]( ]0 H; j4 x) ~& R; ^. S$ X
9 \2 v% d5 Z& T# A return [i[1] for i in recommend_list],user_movies,test_item_to_user,neighbors5 ^; c2 i! x8 f! v3 s3 G5 j# C
" y& a7 i$ @5 [) C) _+ y |, L# q ) l8 `% g) _& [+ S# O! p0 C7 p3 @' u
2 F4 W% c# S2 r. g8 l#
5 j" ~2 c/ ^& W t! }#, \! o0 h& n! v/ h5 C) \
# 获取电影的列表& U2 ?+ y0 @7 a" d
#+ E8 p- n; x: s0 T
#
. J# S# K5 J) {: R/ p" p$ ~#7 g1 ~" d3 n; J
def getMoviesList(file_name):6 E+ t* e8 I4 f r/ Y. E* ?
#print sys.getdefaultencoding()" E* ?" v6 e- F' ~6 q; k& u1 @
movies_contents=readFile(file_name)
, H# E) S C; M8 z6 W) l movies_info={}
. i4 T2 e) J4 Z1 V; V8 l for movie in movies_contents:
) n! }" N4 m0 J% s$ u movie_info=movie.split("|")
. b) L; c* b+ R movies_info[int(movie_info[0])]=movie_info[1:]
: S$ J' _' x. @, [ return movies_info
- O% p: v3 p* ^2 `, q9 v3 e
3 z, W/ V& f+ `! p" J( W. @
6 O- c" U* r1 F( Y7 X 4 C0 n; ?9 K4 P1 [5 U0 f
#主程序( t% a0 D, O) F8 b
#输入 : 测试数据集合6 l1 T! e0 P! e5 w
if __name__ == '__main__':
$ e4 o7 c7 H$ {6 Y: } reload(sys)- ]) ]% ]4 Y& b8 u8 M0 |2 n. k
sys.setdefaultencoding('utf-8')
' P7 A( B8 g. T( l8 k# S! A- k movies=getMoviesList("/Users/wuyinghao/Downloads/ml-100k/u.item")3 R* L4 u# C3 c: J
recommend_list,user_movie,items_movie,neighbors=recommendByUserFC("/Users/wuyinghao/Downloads/ml-100k/u.data",179,80)
( U! Y+ J* H# B neighbors_id=[ i[1] for i in neighbors]" v5 x* `9 ]3 y, M
table = Texttable()9 v: R$ K: I' V2 q- t c
table.set_deco(Texttable.HEADER)
9 ?6 b w" i- R, c2 b table.set_cols_dtype(['t', # text 2 k: P. f% [; H
't', # float (decimal)
7 x1 v, F9 @. Y2 ~ p f* A5 S 't']) # automatic
5 X- W- D& }+ c. S4 M0 y0 C table.set_cols_align(["l", "l", "l"])
2 A( {6 @7 Z4 F m+ h) Z3 V7 X rows=[]
2 p* q4 Z7 g- }1 f4 V5 i rows.append([u"movie name",u"release", u"from userid"])
0 e- ?% U2 j/ v6 J) o for movie_id in recommend_list[:20]:1 \1 y. G5 s x" w- {# V0 ?
from_user=[]
' P+ c3 {! t; n# i8 o4 D% V. y for user_id in items_movie[movie_id]:' n& F' w9 P: s: c6 B
if user_id in neighbors_id:2 f% t/ {) i2 {; ~$ }6 h0 u, l/ E
from_user.append(user_id)
0 _0 h9 o& I1 _9 k* ? rows.append([movies[movie_id][0],movies[movie_id][1],""])" H1 f! |1 \* b" P$ V- F
table.add_rows(rows)
* N5 d. Q/ d6 B7 d8 A I print table.draw() |
|