wy36101299

<script src="jquery-2.1.1.min.js"></script>
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<table>
    <tbody>
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        <td></td>
        <td><div class='h'>12a</div></td>
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        <td><div class='h'>3a</div></td>

wy36101299

import copy
 
with open('/Users/wy/Desktop/hw2dataset/graph_1.txt','r') as f:
    tl=[]
    f = f.readlines()
    for s in f:
        s=s.rstrip()
        s=s.split(',')
        tl.append(s[0])
        tl.append(s[1])

wy36101299

import copy

with open('/Users/wy/Desktop/hw2dataset/graph_3.txt','r') as f:
    tl=[]
    f = f.readlines()
    for s in f:
        s=s.rstrip()
        s=s.split(',')
        tl.append(s[0])
        tl.append(s[1])

wy36101299

import numpy as np
# normalize 歸一化 sum(0)為每一點被連接點個數的陣列
def normalize(G):
    s = G.sum(0)
    return G/s
'''
simrank 公式:
S = C*(W^T ∙ S ∙ W)+(1-C) ∙ I
S 相似度陣列
C 阻尼係數

wy36101299

import random
import numpy as np
 
def proEdge(g,l):
    for t in l:
        t = t.split(',')
        i = int(t[0])
        j = int(t[1])
        if i > 0:
            if g[i-1][j]==1:

wy36101299

%pylab inline
import math
import random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

class point:
    def __init__(self,dimension,pmin,pmax):
        self.feature = []

wy36101299

import math
import numpy as np

class point:
    def __init__(self,vfeature,vlabel):
        self.feature = vfeature
        self.label = vlabel
        
class cluster:
    def __init__(self,numbercluster):

wy36101299

# 產生data List , data List為兩年份
def date():
    month31=[1,3,5,7,8,10,12]
    month30=[4,6,9,11]
    year2=['2013','2014']
    nday31=range(1,32)
    nday30=range(1,31)
    nday28=range(1,29)
    day10=['01','02','03','04','05','06','07','08','09']
    month12=day10+['10','11','12']

wy36101299

import math
import numpy as np
# 關鍵字所屬分類      P     P    S    S     T    T
# 新聞       分類  賓士  寶馬  籃球  路跑  手機  App
# ----------------------------------------------
# C63發表會    P    15   25    0     5    8    3
# BMW i8      P    35   40    1     3    3    2
# 林書豪       S     5    0   35    50    0    0
# 湖人隊       S     1    5   32    15    0    0
# Android 5.0 T    10    5    7     0    2   30

wy36101299

import numpy as np
# linalg:Linear algebra

#     日式  中式  美式  泰式  韓式
# ---------------------------
# sam  2    0    0    4    4
#john  5    5    5    3    3
# tim  2    4    2    1    2
#以下矩陣可看此 user 對 item 進行評分
#藉由協同過濾 猜出 sam對中式和美式的評價後，推薦sam吃什麼