In [7]:
%matplotlib inline
import numpy as np
import pandas as pd
from IPython import display
import matplotlib.pylab as plt
import time
from IPython import display
N = 100
D = 2
# Generate a data set
X1 = np.random.randn(D,N/2)
X2 = 0.8*np.random.randn(D,N/2) + 3*np.ones((D, N/2))
X = np.hstack((np.mat(X1), np.mat(X2)))
K = 5;
mu = X[:,0:K]
#plt.plot(mu[0,:],mu[1,:],'ro')
# Number of epochs
EP = 20
fig = plt.figure(figsize=(8,10))
plt.plot(X[0,:], X[1,:],'kx')
ax = fig.gca()
ln = plt.Line2D(xdata=mu[0,:], ydata=mu[1,:], marker='o', color='r',linestyle=None,linewidth=0)
ax.add_line(ln)
for e in range(EP):
dist = np.zeros((N,K))
for i in range(N):
for c in range(K):
err = X[:,i]-mu[:,c]
dist[i,c] = float(err.T*err)
# Assignments
a = np.argmin(dist, axis=1)
mu = np.mat(np.zeros((D,K)))
count = np.zeros((K))
for i,c in enumerate(a):
count[c] += 1
mu[:,c] = (count[c]-1)/count[c]*mu[:,c] + 1./count[c]*X[:,i]
ln.set_xdata(mu[0,:])
ln.set_ydata(mu[1,:])
#plt.subplot(EP,1,e+1)
display.clear_output(wait=True)
display.display(plt.gcf())
time.sleep(0.1)
plt.plot(X[0,:], X[1,:],'kx')
#plt.plot(mu[0,:],mu[1,:],'ro')
plt.show()
In [2]:
D = 4
N = 30
#X = np.mat('[1,2,3;5,7,2]')
X = np.mat(np.random.randn(D,N))
U,Sig,V = np.linalg.svd(X, full_matrices=True)
print(X)
print(U)
print(np.diag(Sig))
print(V)
a = U[:,0]
w = Sig[0]*V[0,:]
Xhat = a*w
plt.plot(X[0,:], X[1,:],'x')
plt.plot(Xhat[0,:], Xhat[1,:],'o')
ax = plt.gca()
ax.set_aspect(1)
plt.show()
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0.1129367 -0.06433369 0.32727297 -0.6736358 0.13550325 -2.06335767]
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1.01757670e-01 6.04922339e-02 -6.19444758e-02 5.13868078e-02
-6.52059835e-02 -1.33072234e-01 4.79498847e-02 9.88459467e-02
-3.56824534e-02 7.32382526e-02 7.49670452e-01 -9.13497374e-02
9.67796567e-02 4.51658664e-02 8.89890033e-02 1.19030660e-01
-7.70440856e-02 -6.11944308e-02]
[ -1.97788242e-01 1.44119786e-01 -2.88131110e-01 3.53422762e-01
-5.22401288e-02 -5.86380657e-02 -4.43161875e-02 -5.05216301e-02
1.13181346e-01 1.37166610e-01 7.31841121e-02 -5.91537851e-02
-2.52663884e-02 -5.45180418e-02 9.17506264e-02 4.30024664e-02
3.08027655e-02 -7.08208496e-02 -2.43357616e-02 -5.75340289e-02
4.84787914e-02 -7.82490464e-02 -6.94837475e-02 7.86683209e-01
2.80933178e-02 8.22654355e-06 1.07984185e-01 -1.41590264e-02
2.50437993e-03 -1.11559793e-01]
[ 8.48120477e-02 -1.56510791e-01 -6.05549309e-02 -6.23959692e-02
6.29292658e-03 -3.77411328e-03 -3.16729152e-02 8.90796005e-02
2.59949673e-04 -3.83140872e-02 8.19370013e-03 -1.89393444e-02
-3.66801722e-02 -2.49776407e-02 2.08967768e-02 -1.33657531e-02
5.02896654e-03 3.93960654e-02 -2.01555357e-02 -4.84795100e-02
5.85193113e-03 -2.37992756e-02 9.04118224e-02 3.88618513e-02
9.62966713e-01 -1.53027102e-03 -3.06434019e-02 -3.44267392e-02
4.09238552e-02 3.94357347e-02]
[ -6.45668029e-02 1.57317869e-01 -2.20984884e-01 -3.50350085e-01
-1.02201340e-01 -1.27141788e-01 7.65152965e-02 3.96906320e-02
6.68745925e-02 6.23421139e-02 3.42170955e-02 5.30705858e-02
-4.47869053e-02 2.66295893e-02 -1.16474883e-02 -5.41499133e-02
2.74187415e-01 8.35471018e-02 2.69356072e-02 -1.65712297e-02
5.34963245e-02 -4.02830875e-02 8.53608344e-02 7.22707389e-03
-1.56069833e-02 7.81846571e-01 -5.78131946e-02 -1.01172076e-01
-1.63053478e-01 -2.56748593e-02]
[ 5.38287062e-02 -1.22582134e-01 7.61280668e-02 -2.69529538e-01
9.35440783e-03 6.74694023e-04 2.43950957e-02 6.17816796e-02
-3.20746690e-02 -5.04568964e-02 -2.16790797e-02 7.27060729e-03
-1.33236326e-02 1.78463339e-02 -2.60551813e-02 -4.23624748e-02
4.39595804e-02 8.05693099e-02 6.37642859e-03 2.51665735e-02
1.29745369e-04 1.30933256e-02 9.15733658e-02 1.08314136e-01
-3.16722263e-02 -4.47560432e-02 9.25635552e-01 -3.91689598e-02
-1.32482789e-02 3.41071717e-02]
[ -9.94108371e-02 -1.84522813e-01 -1.85081073e-01 -1.46548799e-01
-2.84513162e-02 -5.76643673e-02 -2.30284016e-02 1.05634942e-01
6.35119502e-02 3.93706285e-02 4.27779417e-02 -7.81649364e-02
-7.14512089e-02 -4.25887938e-02 6.92835229e-02 -4.15113803e-02
1.05645297e-01 1.01722712e-01 -2.84030473e-02 -2.81026406e-02
5.92876066e-02 -7.49452842e-02 1.46490041e-01 -1.12776906e-02
-4.93104944e-02 -7.40941479e-02 -3.70145898e-02 8.89763984e-01
1.15372774e-02 -5.11828525e-02]
[ -9.91132425e-02 3.37710811e-01 -5.77802671e-02 -2.72360976e-01
-8.78898147e-02 -9.24919583e-02 1.16406911e-01 -7.62469479e-02
3.12901238e-02 7.05602939e-02 2.90849246e-03 9.64823521e-02
1.79194557e-02 6.89798651e-02 -6.22185471e-02 -3.19995753e-02
2.20227442e-01 2.09310568e-02 5.66315925e-02 5.05264196e-02
2.26725227e-02 1.64751659e-02 -4.24855252e-02 -2.88020218e-03
3.28838569e-02 -1.83571293e-01 -2.69791018e-02 -2.90629198e-02
8.01628574e-01 -3.81842959e-02]
[ -5.06979727e-01 -8.72192760e-02 -3.94490754e-02 8.96744052e-02
-1.89079717e-03 -2.14414762e-02 -6.64434016e-03 -7.08890730e-02
9.69496426e-02 1.75911295e-01 4.52923552e-02 -1.84494870e-01
-1.54098066e-02 -3.94829351e-02 1.09625441e-01 -2.26723728e-02
2.12156889e-02 6.95061463e-02 -2.33501002e-02 1.29311010e-01
8.65162396e-02 -6.94538327e-02 -6.96315197e-04 -1.50800942e-01
1.25260009e-02 -2.42754966e-03 4.81048029e-02 -8.24281836e-02
2.48717132e-02 7.49655951e-01]]
In [4]:
df_iris = pd.read_csv(u'../data/iris.txt',sep=' ')
df_iris
Out[4]:
sl
sw
pl
pw
c
0
5.1
3.5
1.4
0.2
1
1
4.9
3.0
1.4
0.2
1
2
4.7
3.2
1.3
0.2
1
3
4.6
3.1
1.5
0.2
1
4
5.0
3.6
1.4
0.2
1
5
5.4
3.9
1.7
0.4
1
6
4.6
3.4
1.4
0.3
1
7
5.0
3.4
1.5
0.2
1
8
4.4
2.9
1.4
0.2
1
9
4.9
3.1
1.5
0.1
1
10
5.4
3.7
1.5
0.2
1
11
4.8
3.4
1.6
0.2
1
12
4.8
3.0
1.4
0.1
1
13
4.3
3.0
1.1
0.1
1
14
5.8
4.0
1.2
0.2
1
15
5.7
4.4
1.5
0.4
1
16
5.4
3.9
1.3
0.4
1
17
5.1
3.5
1.4
0.3
1
18
5.7
3.8
1.7
0.3
1
19
5.1
3.8
1.5
0.3
1
20
5.4
3.4
1.7
0.2
1
21
5.1
3.7
1.5
0.4
1
22
4.6
3.6
1.0
0.2
1
23
5.1
3.3
1.7
0.5
1
24
4.8
3.4
1.9
0.2
1
25
5.0
3.0
1.6
0.2
1
26
5.0
3.4
1.6
0.4
1
27
5.2
3.5
1.5
0.2
1
28
5.2
3.4
1.4
0.2
1
29
4.7
3.2
1.6
0.2
1
...
...
...
...
...
...
120
6.9
3.2
5.7
2.3
3
121
5.6
2.8
4.9
2.0
3
122
7.7
2.8
6.7
2.0
3
123
6.3
2.7
4.9
1.8
3
124
6.7
3.3
5.7
2.1
3
125
7.2
3.2
6.0
1.8
3
126
6.2
2.8
4.8
1.8
3
127
6.1
3.0
4.9
1.8
3
128
6.4
2.8
5.6
2.1
3
129
7.2
3.0
5.8
1.6
3
130
7.4
2.8
6.1
1.9
3
131
7.9
3.8
6.4
2.0
3
132
6.4
2.8
5.6
2.2
3
133
6.3
2.8
5.1
1.5
3
134
6.1
2.6
5.6
1.4
3
135
7.7
3.0
6.1
2.3
3
136
6.3
3.4
5.6
2.4
3
137
6.4
3.1
5.5
1.8
3
138
6.0
3.0
4.8
1.8
3
139
6.9
3.1
5.4
2.1
3
140
6.7
3.1
5.6
2.4
3
141
6.9
3.1
5.1
2.3
3
142
5.8
2.7
5.1
1.9
3
143
6.8
3.2
5.9
2.3
3
144
6.7
3.3
5.7
2.5
3
145
6.7
3.0
5.2
2.3
3
146
6.3
2.5
5.0
1.9
3
147
6.5
3.0
5.2
2.0
3
148
6.2
3.4
5.4
2.3
3
149
5.9
3.0
5.1
1.8
3
150 rows × 5 columns
In [28]:
X = np.vstack((np.mat(df_iris.sl),np.mat(df_iris.sw), np.mat(df_iris.pl), np.mat(df_iris.pw) ))
#plt.plot(X[0,:], X[2,:],'kx')
#plt.show()
U,S,VT = np.linalg.svd(X)
K = 2
X_approx = U[:,0:2]*np.diag(S[0:2])*VT[0:2,:]
print(X[:,0:5])
X_approx[:,0:5]
Z = np.diag(S[0:2])*VT[0:2,:]
plt.plot(Z[0,:], Z[1,:],'ko')
plt.show()
[[ 5.1 4.9 4.7 4.6 5. ]
[ 3.5 3. 3.2 3.1 3.6]
[ 1.4 1.4 1.3 1.5 1.4]
[ 0.2 0.2 0.2 0.2 0.2]]
In [48]:
## Y = A*B
M = 10
N = 20
K = 2
A_true = np.mat(np.random.randn(M, K))
B_true = np.mat(np.random.randn(K, N))
Y = A_true*B_true + 0.01*np.random.randn(M, N)
#plt.imshow(Y, interpolation='nearest')
U,S,VT = np.linalg.svd(Y)
plt.plot(S)
Out[48]:
[<matplotlib.lines.Line2D at 0x10a73aa10>]
In [49]:
B = np.linalg.pinv(A_true)*Y
print(B)
print(B_true)
[[-0.24689075 1.57755101 -0.40278582 -0.97668021 0.83105358 -0.39167884
0.42919512 0.32386468 -0.21709037 -1.03028013 0.82323109 -0.21183812
0.34578657 -2.27599858 1.70841411 1.0604837 -0.48612418 -0.10871185
0.92060881 -0.02923552]
[-1.02162883 -0.31562673 -0.26050259 -2.68765201 -0.07986925 1.67245433
-0.65703119 1.49145806 -0.64543568 0.41723178 -2.12081694 0.45172714
0.92322494 1.05729907 -0.95724887 0.85934019 1.07326711 -1.44889833
0.28261623 1.84063881]]
[[-0.25720486 1.57520013 -0.40790521 -0.97979845 0.82904462 -0.38912155
0.41717713 0.33143982 -0.21769882 -1.03915673 0.82424218 -0.20780859
0.34309775 -2.26655924 1.71285713 1.06395845 -0.48792366 -0.11096627
0.9202921 -0.02872279]
[-1.02509732 -0.31157644 -0.26126411 -2.6852999 -0.09048101 1.67434223
-0.6628631 1.49236572 -0.64664919 0.41193001 -2.12017968 0.44967789
0.92065636 1.05725399 -0.94471663 0.86113879 1.06992555 -1.44693705
0.28314188 1.83337281]]
In [58]:
A = np.mat(np.random.randn(M, K))
B = np.mat(np.random.randn(K, N))
for e in range(5):
Y_est = A*B
E = Y - Y_est
err = np.sum(np.sum(np.array(E)*np.array(E)))
print(err)
B = np.linalg.pinv(A)*Y
Y_est = A*B
E = Y - Y_est
err = np.sum(np.sum(np.array(E)*np.array(E)))
print(err)
A = (np.linalg.pinv(B.T)*Y.T).T
Y_est = A*B
E = Y - Y_est
err = np.sum(np.sum(np.array(E)*np.array(E)))
print(err)
616.260844618
206.41135676
0.022270864975
0.022270864975
0.012636800579
0.0126366287166
0.0126366287166
0.0126366287107
0.0126366287107
0.0126366287107
0.0126366287107
0.0126366287107
0.0126366287107
0.0126366287107
0.0126366287107
In [59]:
A
Out[59]:
matrix([[-3.84717275, -1.1647452 ],
[ 0.59184538, -0.3115495 ],
[-3.74445309, 3.04945327],
[-8.20662843, 3.38580033],
[-1.96267491, -0.82938965],
[-1.59871863, 0.9701449 ],
[-0.80395512, -1.67948961],
[-2.77493982, 1.32603348],
[ 1.34590873, 0.30487757],
[-0.28603236, 0.10144157]])
In [60]:
A_true
Out[60]:
matrix([[ 1.43190551, -1.19740565],
[ 0.04161512, 0.12247163],
[-0.83908214, -0.64963932],
[-0.07577827, -1.82300917],
[ 0.85587752, -0.63988428],
[-0.18214254, -0.3192553 ],
[ 1.06418939, -0.42793252],
[-0.12095646, -0.59426192],
[-0.44321394, 0.4036003 ],
[ 0.00950295, -0.06394318]])
In [61]:
U,S,VT = np.linalg.svd(Y)
A.T*U[:,2:]
Out[61]:
matrix([[ -1.63365721e-15, 6.36577266e-16, 2.19513670e-16,
-2.04362614e-16, 6.70384178e-16, -5.96420160e-16,
1.41153760e-15, -2.96043897e-16],
[ -3.10030222e-16, 1.38068360e-16, -5.57825729e-17,
2.72405407e-16, -8.13897637e-16, -1.08668305e-16,
-7.28972350e-17, 5.72466795e-16]])
In [19]:
%matplotlib inline
import numpy as np
import pandas as pd
from IPython import display
import matplotlib.pylab as plt
Nr = 2
Nc = 3
M = np.mat('[0, 1, 1;1, 1, 0]')
Y = np.mat('[0, 2, 4;3, 5, 0]')
Ys = np.mat('[2,0,1;4,0,2;3,1,0;5, 1,1]')
Ysize = Ys.shape[0]
A = np.mat(np.random.rand(Nr, 1))
B = np.mat(np.random.rand(1, Nc))
E = np.array(M)*np.array(Y - A*B)
np.sum(E*E)/np.sum(np.array(M))
EPOCH = 100
Eta = 0.1
eta = Eta
for i in range(EPOCH):
E = np.array(M)*np.array(Y - A*B)
Err = np.sum(E*E)/np.sum(np.array(M))
print(Err)
for k in range(Ysize):
u = Ys[k,1]
m = Ys[k,2]
err = Ys[k,0] - A[u,0]*B[0,m]
temp_A = A[u,0] + eta*err*B[0,m]
B[0,m] = B[0,m] + eta*err*A[u,0]
A[u,0] = temp_A
eta = Eta*1./(i+1)
11.0769926265
7.80241114215
3.72357836563
2.4509723585
1.93478423846
1.65921234432
1.48448980809
1.36134211049
1.26840506668
1.19491850061
1.13483533098
1.08446670746
1.04141634864
1.00404899851
0.971203861101
0.942030035965
0.915886963914
0.892281523467
0.870826796322
0.851214174311
0.833193972481
0.816561632379
0.801147698012
0.786810398221
0.773430067496
0.760904887881
0.749147596114
0.738082906728
0.727645473492
0.717778260712
0.708431230131
0.69956027342
0.691126337598
0.683094703402
0.675434385877
0.66811763342
0.66111950667
0.654417522613
0.647991352265
0.641822562651
0.635894395587
0.630191577225
0.624700153412
0.619407346838
0.614301432627
0.609371629634
0.604608005149
0.600001391098
0.595543310135
0.591225910276
0.587041906933
0.582984531371
0.579047484777
0.575224897218
0.571511290897
0.567901547176
0.564390876925
0.560974793803
0.55764909014
0.554409815117
0.551253255005
0.548175915217
0.545174503999
0.542245917574
0.539387226595
0.536595663769
0.533868612536
0.531203596699
0.52859827091
0.526050411934
0.523557910611
0.521118764455
0.518731070835
0.516393020671
0.51410289262
0.511859047688
0.509659924246
0.507504033405
0.505389954724
0.503316332232
0.501281870712
0.499285332259
0.497325533064
0.495401340416
0.493511669911
0.491655482832
0.489831783717
0.488039618067
0.486278070211
0.484546261302
0.482843347436
0.48116851789
0.479520993465
0.47790002493
0.47630489156
0.474734899755
0.473189381745
0.471667694369
0.47016921792
0.468693355063
Rank of approximation is $K$
In [68]:
Nr = 2
Nc = 3
Nk = 2
M = np.mat('[0, 1, 1;1, 1, 0]')
Y = np.mat('[0, 2, 4;3, 5, 0]')
Ys = np.mat('[2,0,1;4,0,2;3,1,0;5, 1,1]')
Ysize = Ys.shape[0]
A = np.mat(np.random.rand(Nr, Nk))
B = np.mat(np.random.rand(Nk, Nc))
EPOCH = 1000
Eta = 0.1
eta = Eta
for i in range(EPOCH):
if i%10==0:
E = np.array(M)*np.array(Y - A*B)
Err = np.sum(E*E)/np.sum(np.array(M))
print(Err)
for k in range(Ysize):
u = Ys[k,1]
m = Ys[k,2]
err = Ys[k,0] - A[u,:]*B[:,m]
temp_A = A[u,:] + eta*err[0,0]*B[:,m].T
B[:,m] = B[:,m] + eta*err[0,0]*A[u,:].T
A[u,:] = temp_A
eta = Eta*1./(i+1)
11.3422372325
0.736754732138
0.555474395541
0.47752380818
0.430071947237
0.39688637519
0.371819075973
0.351924561689
0.335580985619
0.321807842737
0.309970930646
0.299638453819
0.290504324256
0.282344375977
0.27498996242
0.268311306351
0.262206600093
0.256594640472
0.251409714864
0.246597965381
0.242114749776
0.237922690372
0.233990207896
0.230290403431
0.226800194494
0.22349963944
0.220371403337
0.217400331453
0.214573105548
0.211877964533
0.209304475677
0.206843345842
0.2044862647
0.202225773693
0.200055155856
0.197968342688
0.195959835008
0.194024635387
0.192158190184
0.190356339614
0.188615274562
0.186931499079
0.185301797697
0.183723206853
0.182192989805
0.18070861456
0.179267734389
0.177868170576
0.176507897098
0.175185026995
0.173897800208
0.172644572695
0.171423806681
0.170234061893
0.169073987671
0.167942315846
0.166837854311
0.165759481189
0.164706139546
0.163676832585
0.162670619275
0.161686610355
0.160723964692
0.159781885951
0.158859619537
0.157956449794
0.157071697437
0.156204717177
0.155354895547
0.154521648887
0.153704421492
0.152902683892
0.152115931265
0.151343681967
0.150585476163
0.149840874566
0.149109457253
0.148390822574
0.14768458613
0.146990379819
0.146307850954
0.14563666143
0.144976486951
0.144327016307
0.143687950693
0.143059003076
0.142439897597
0.141830369013
0.141230162174
0.140639031524
0.140056740642
0.139483061804
0.13891777557
0.138360670398
0.137811542277
0.137270194384
0.136736436757
0.136210085986
0.135690964925
0.135178902415
In [72]:
print(Y)
print(M)
print(Ys)
[[0 2 4]
[3 5 0]]
[[0 1 1]
[1 1 0]]
[[2 0 1]
[4 0 2]
[3 1 0]
[5 1 1]]
In [67]:
import scipy.sparse as sp
A = sp.rand(100, 100, 0.01).tocsr()
i,j,v = sp.find(A)
for e in zip(i,j):
print(e, A[e[0], e[1]])
(1, 98) 0.482478627511
(2, 77) 0.780503468703
(3, 10) 0.129636903524
(3, 56) 0.0238030773016
(4, 86) 0.547844111056
(6, 61) 0.379767533489
(7, 70) 0.442947789129
(8, 58) 0.893137788745
(8, 69) 0.885420817687
(9, 0) 0.0436475536379
(9, 9) 0.167701688863
(10, 52) 0.0388379303701
(11, 67) 0.363836152597
(13, 5) 0.873801303159
(13, 14) 0.873283984149
(13, 65) 0.947130647143
(13, 89) 0.266336679173
(15, 49) 0.276220442002
(16, 2) 0.692775817969
(18, 94) 0.727966684564
(21, 41) 0.465043554285
(22, 82) 0.500404058784
(23, 26) 0.385445046497
(25, 10) 0.255050656851
(28, 68) 0.28975034871
(31, 11) 0.500241501491
(31, 73) 0.381562135314
(31, 96) 0.537722394598
(33, 6) 0.783271703857
(33, 34) 0.564569188796
(34, 26) 0.782941268309
(34, 82) 0.0764826403935
(35, 5) 0.73335356236
(35, 80) 0.516201873738
(38, 44) 0.556231019933
(38, 66) 0.962131341498
(38, 93) 0.465016755212
(39, 50) 0.141255642373
(40, 6) 0.480557911102
(40, 31) 0.585381030478
(41, 0) 0.605291849906
(41, 77) 0.460539047698
(41, 89) 0.908021341366
(43, 21) 0.49001345112
(45, 17) 0.770821531083
(45, 70) 0.734685604175
(46, 44) 0.649229603703
(46, 62) 0.565652797583
(46, 76) 0.0824170313498
(49, 24) 0.536272282158
(49, 25) 0.661647330608
(53, 69) 0.0110608486041
(54, 29) 0.00236617749464
(57, 4) 0.580245790421
(57, 29) 0.308933436687
(57, 59) 0.296220319497
(58, 41) 0.876986562871
(61, 41) 0.939679890153
(62, 2) 0.966497790573
(62, 30) 0.387822903865
(62, 86) 0.905531971938
(63, 98) 0.92034357567
(67, 43) 0.97299861077
(68, 93) 0.555730425763
(69, 16) 0.669076766805
(69, 75) 0.148771207577
(70, 81) 0.369202306162
(71, 63) 0.654889178285
(72, 31) 0.59111914435
(74, 11) 0.121282113032
(74, 47) 0.586723345194
(76, 90) 0.499288714119
(76, 91) 0.838696186895
(77, 85) 0.722611018017
(77, 94) 0.10016417359
(78, 33) 0.5720646032
(79, 10) 0.646337697603
(79, 13) 0.691246331531
(80, 39) 0.930164610524
(81, 66) 0.987652935885
(82, 97) 0.0117052061336
(84, 41) 0.253380311509
(85, 15) 0.381760835677
(86, 59) 0.333963825969
(86, 87) 0.238720503021
(87, 34) 0.367601100286
(88, 2) 0.614006040851
(88, 19) 0.302064928052
(88, 24) 0.443828763885
(89, 31) 0.179795196813
(90, 79) 0.838903405196
(91, 61) 0.824288098874
(92, 71) 0.476441088267
(92, 85) 0.156639313604
(95, 26) 0.236166438652
(95, 28) 0.363998956031
(95, 90) 0.316171700731
(96, 23) 0.863186139559
(97, 44) 0.162834010199
(97, 82) 0.743845008841
In [66]:
e[0]
e[1]
A[e[0],e[1]]
#print(A[e[0], e[1]])
Out[66]:
0.47047145259696732
In [132]:
import pandas as pd
#unames = ['user_id', 'gender', 'age', 'occupation', 'zip']
#expedia_test = pd.read_csv('~/Downloads/test.csv')
expedia_test = pd.read_csv('~/Downloads/test.csv',nrows=5)
et = pd.read_csv('~/Downloads/train-expedia.csv',nrows=100000)
In [91]:
idx = expedia_train.columns
for i in idx:
print(i)
date_time
site_name
posa_continent
user_location_country
user_location_region
user_location_city
orig_destination_distance
user_id
is_mobile
is_package
channel
srch_ci
srch_co
srch_adults_cnt
srch_children_cnt
srch_rm_cnt
srch_destination_id
srch_destination_type_id
is_booking
cnt
hotel_continent
hotel_country
hotel_market
hotel_cluster
In [97]:
expedia_train['hotel_cluster']
Out[97]:
0 1
1 1
2 1
3 80
4 21
5 92
6 41
7 41
8 69
9 70
10 98
11 10
12 18
13 28
14 25
15 25
16 25
17 25
18 25
19 2
20 2
21 16
22 16
23 94
24 77
25 24
26 58
27 58
28 36
29 82
..
970 62
971 59
972 29
973 29
974 36
975 81
976 29
977 36
978 36
979 29
980 66
981 66
982 42
983 32
984 99
985 32
986 7
987 32
988 51
989 32
990 51
991 51
992 8
993 66
994 96
995 80
996 80
997 80
998 80
999 45
Name: hotel_cluster, dtype: int64
In [133]:
N = 100000
In [105]:
for i in range(N):
print(et.date_time[i], et.user_id[i], et.is_booking[i], et.hotel_cluster[i])
2014-08-11 07:46:59 12 0 1
2014-08-11 08:22:12 12 1 1
2014-08-11 08:24:33 12 0 1
2014-08-09 18:05:16 93 0 80
2014-08-09 18:08:18 93 0 21
2014-08-09 18:13:12 93 0 92
2014-07-16 09:42:23 501 0 41
2014-07-16 09:45:48 501 0 41
2014-07-16 09:52:11 501 0 69
2014-07-16 09:55:24 501 0 70
2014-07-16 10:00:06 501 0 98
2014-07-16 10:02:58 501 0 10
2014-01-17 06:24:56 756 0 18
2014-01-18 14:33:31 756 0 28
2014-01-21 06:39:08 756 0 25
2014-01-21 06:40:18 756 0 25
2014-01-22 06:10:02 756 0 25
2014-01-24 11:52:04 756 0 25
2014-01-24 17:26:24 756 0 25
2014-02-27 17:44:23 756 0 2
2014-02-27 18:01:32 756 1 2
2014-12-30 09:50:51 756 0 16
2014-12-30 09:55:43 756 0 16
2014-12-30 09:56:26 756 0 94
2014-10-22 12:20:56 776 0 77
2014-11-10 12:38:02 776 0 24
2013-06-15 15:10:49 1048 0 58
2013-06-15 15:38:05 1048 1 58
2014-11-22 14:32:46 1048 0 36
2014-11-22 14:37:05 1048 0 82
2014-11-22 16:55:21 1048 0 46
2014-11-22 16:59:09 1048 0 30
2014-11-22 17:02:39 1048 0 36
2014-11-22 17:04:34 1048 0 29
2014-11-22 17:05:34 1048 0 57
2014-11-22 17:07:20 1048 0 29
2014-11-22 19:56:54 1048 0 46
2014-11-22 20:38:49 1048 0 30
2014-11-22 20:43:22 1048 0 85
2014-11-22 20:45:05 1048 0 46
2014-11-22 20:47:19 1048 0 36
2014-11-22 20:50:34 1048 0 36
2014-11-22 20:53:27 1048 0 82
2014-11-22 20:55:38 1048 0 5
2014-11-22 21:00:06 1048 0 29
2014-11-22 21:01:38 1048 0 59
2014-11-22 21:10:53 1048 0 29
2014-11-22 21:12:22 1048 0 62
2014-11-22 21:15:06 1048 0 81
2014-11-22 21:55:37 1048 0 46
2014-11-22 22:00:24 1048 0 59
2014-11-22 22:06:27 1048 0 29
2014-11-22 22:09:20 1048 0 29
2014-11-22 22:20:13 1048 0 36
2014-11-22 22:27:39 1048 0 81
2014-11-22 22:29:15 1048 0 29
2014-11-23 16:42:42 1048 0 59
2014-11-23 16:48:34 1048 0 36
2014-11-23 16:50:47 1048 0 46
2014-11-23 16:56:16 1048 0 36
2014-11-23 17:00:51 1048 0 81
2014-11-23 17:06:12 1048 0 6
2014-11-23 17:10:17 1048 0 82
2014-11-23 17:16:36 1048 0 81
2014-11-23 17:17:42 1048 0 36
2014-11-23 17:18:41 1048 0 29
2014-11-23 17:21:21 1048 0 36
2014-11-23 17:38:24 1048 0 36
2014-11-23 17:39:02 1048 0 29
2014-11-23 17:41:08 1048 0 36
2014-11-23 17:48:10 1048 0 36
2014-11-23 17:48:52 1048 0 53
2014-11-23 18:02:20 1048 1 36
2014-12-28 21:43:49 1048 0 8
2013-02-15 13:18:43 1482 0 28
2013-02-16 11:57:50 1482 0 73
2013-02-16 12:03:45 1482 0 26
2013-02-16 12:23:57 1482 0 28
2014-01-03 16:19:15 1482 0 95
2014-01-03 16:30:17 1482 1 95
2014-01-03 16:34:26 1482 0 95
2014-01-03 16:44:56 1482 1 95
2014-01-03 16:58:19 1482 0 95
2014-01-03 17:11:36 1482 1 95
2014-01-04 14:57:08 1482 0 95
2014-01-04 15:01:54 1482 0 95
2014-01-04 15:02:07 1482 0 95
2014-01-08 13:28:56 1482 0 95
2014-01-08 13:31:34 1482 0 95
2014-01-08 13:38:03 1482 0 95
2014-01-08 13:42:16 1482 0 95
2014-01-08 13:46:42 1482 0 95
2014-01-08 13:57:04 1482 0 95
2014-01-08 13:58:04 1482 0 95
2014-01-08 14:03:36 1482 0 95
2014-01-08 14:09:47 1482 0 95
2014-01-08 14:15:40 1482 0 77
2014-01-08 14:18:31 1482 0 77
2014-01-08 14:30:25 1482 0 77
2014-05-14 14:57:51 1482 0 13
2014-05-14 14:58:59 1482 0 42
2014-08-14 20:50:04 1482 0 9
2014-08-14 21:10:56 1482 0 9
2014-08-16 19:42:12 1482 0 55
2014-08-16 19:42:57 1482 0 5
2014-08-16 19:44:47 1482 0 91
2014-08-16 19:46:54 1482 0 13
2014-08-16 19:49:05 1482 0 72
2014-08-16 19:50:30 1482 0 72
2014-10-10 17:01:59 1482 0 55
2014-10-10 17:18:14 1482 0 26
2014-11-28 07:54:16 1482 0 65
2014-09-15 14:53:17 1561 0 21
2014-09-15 14:55:35 1561 0 56
2014-09-15 14:57:40 1561 0 56
2014-09-15 14:58:24 1561 0 69
2014-09-15 15:02:20 1561 0 68
2014-09-15 15:08:49 1561 0 75
2014-09-15 18:28:49 1561 0 56
2014-09-15 19:07:14 1561 0 68
2014-10-24 11:36:56 1713 0 6
2014-10-24 11:43:49 1713 0 38
2014-10-29 14:09:39 1713 0 46
2014-10-29 14:09:51 1713 0 67
2014-10-29 14:12:29 1713 0 81
2014-10-29 14:14:11 1713 0 82
2014-10-29 14:14:40 1713 0 38
2014-10-29 14:15:20 1713 0 53
2014-10-29 14:32:19 1713 1 38
2014-12-05 09:49:53 1713 0 81
2014-12-05 09:51:18 1713 0 78
2014-12-05 09:51:47 1713 0 67
2014-12-05 09:54:55 1713 0 46
2014-12-05 09:58:15 1713 1 81
2013-01-10 18:04:37 2140 0 82
2013-01-10 18:24:37 2140 0 43
2013-01-10 18:26:40 2140 0 43
2013-01-10 18:30:08 2140 1 43
2013-01-10 18:30:27 2140 0 43
2013-12-01 21:59:08 2451 0 82
2013-12-01 21:59:58 2451 0 82
2013-12-01 22:00:44 2451 0 82
2013-12-01 22:14:52 2451 0 82
2013-12-02 01:46:54 2451 0 37
2013-12-02 01:48:57 2451 0 99
2013-12-02 02:04:08 2451 0 8
2013-12-02 02:05:21 2451 0 8
2013-12-02 02:08:28 2451 0 78
2013-12-02 02:15:55 2451 0 8
2013-12-02 02:22:05 2451 0 8
2013-12-02 02:22:24 2451 0 8
2013-12-02 02:25:52 2451 0 8
2013-12-02 02:29:44 2451 0 35
2013-12-02 02:31:37 2451 0 25
2013-12-02 03:06:16 2451 0 25
2013-12-02 03:06:49 2451 0 25
2013-12-02 03:07:37 2451 0 25
2013-12-02 03:08:12 2451 0 25
2013-12-02 04:30:54 2451 1 25
2013-12-02 04:42:45 2451 0 55
2013-12-02 04:43:21 2451 0 55
2014-01-25 23:50:48 2451 0 82
2014-01-25 23:53:08 2451 0 9
2014-01-25 23:54:43 2451 0 9
2014-01-26 00:00:22 2451 0 20
2014-02-23 20:54:40 2451 0 90
2014-07-17 23:59:01 2451 0 82
2014-07-18 00:24:15 2451 0 43
2014-07-18 00:50:04 2451 0 8
2014-07-18 00:55:09 2451 0 43
2014-07-18 01:00:38 2451 0 99
2014-07-18 01:02:17 2451 0 8
2014-07-18 01:30:54 2451 0 38
2014-07-18 01:32:26 2451 0 25
2014-07-18 01:33:35 2451 0 50
2014-07-18 01:36:40 2451 0 67
2014-07-18 01:45:12 2451 0 35
2014-07-18 01:47:01 2451 0 20
2014-07-18 01:51:02 2451 0 57
2014-07-18 01:56:25 2451 0 8
2014-07-18 02:02:04 2451 0 8
2014-07-18 02:09:17 2451 0 99
2014-07-18 02:12:54 2451 0 22
2014-07-18 02:13:49 2451 0 25
2014-07-18 02:18:23 2451 0 64
2014-07-18 02:24:43 2451 0 99
2014-08-03 02:26:07 2451 0 64
2014-08-03 02:42:08 2451 0 64
2014-08-03 02:53:13 2451 0 38
2014-08-09 03:11:39 2451 0 30
2014-08-09 03:18:47 2451 0 95
2014-08-09 03:23:10 2451 0 99
2014-08-09 03:24:25 2451 0 99
2014-08-09 03:37:51 2451 0 22
2014-08-09 03:48:37 2451 0 8
2014-08-12 01:00:18 2451 0 16
2014-08-12 01:16:01 2451 0 8
2014-08-12 01:30:06 2451 0 78
2014-08-17 15:06:49 2451 0 60
2014-08-17 15:08:38 2451 0 82
2014-08-19 05:46:35 2451 0 8
2014-11-09 21:05:35 2451 0 43
2014-11-09 21:21:06 2451 0 43
2014-11-15 18:34:24 2451 0 29
2014-11-15 18:38:39 2451 0 36
2014-11-15 18:56:23 2451 0 21
2014-12-16 18:15:25 2451 0 8
2014-12-16 19:35:04 2451 0 8
2014-12-17 02:37:10 2451 0 35
2014-12-20 05:22:02 2451 0 99
2014-12-20 05:23:53 2451 0 11
2014-12-23 02:33:01 2451 0 10
2014-12-23 02:36:20 2451 0 10
2014-12-23 02:37:22 2451 0 10
2014-12-23 02:41:05 2451 0 99
2014-12-23 02:42:36 2451 0 99
2014-12-23 02:43:34 2451 0 99
2014-12-23 02:51:30 2451 0 8
2014-12-23 02:54:06 2451 0 8
2014-12-23 02:56:53 2451 0 8
2014-12-23 03:02:55 2451 0 36
2014-12-23 03:04:08 2451 0 36
2014-12-23 03:04:48 2451 0 36
2014-12-23 03:05:38 2451 0 36
2014-12-23 03:14:46 2451 0 11
2014-12-23 03:26:32 2451 0 22
2014-12-23 03:33:36 2451 0 64
2014-12-23 03:35:53 2451 0 97
2014-12-23 04:05:53 2451 0 11
2014-12-23 19:15:34 2451 0 36
2014-12-23 19:16:22 2451 0 11
2014-12-23 19:19:47 2451 0 36
2014-12-23 19:21:24 2451 0 37
2014-12-23 19:22:12 2451 0 8
2014-12-23 19:32:23 2451 0 22
2014-12-25 03:03:12 2451 0 2
2014-12-25 03:18:37 2451 0 8
2014-12-25 03:25:58 2451 0 59
2014-12-25 03:28:41 2451 0 36
2014-12-25 03:31:21 2451 0 8
2014-12-25 03:34:34 2451 0 62
2014-12-25 15:17:59 2451 0 36
2014-12-25 15:19:49 2451 0 78
2014-12-25 15:30:52 2451 0 85
2014-12-25 15:32:48 2451 0 29
2014-12-25 15:35:37 2451 0 85
2014-12-25 15:38:08 2451 0 5
2014-12-25 15:39:35 2451 0 85
2014-12-26 18:07:23 2451 0 36
2014-12-26 18:08:11 2451 0 36
2014-12-26 18:15:36 2451 0 99
2014-12-26 18:19:27 2451 0 99
2014-12-26 18:24:16 2451 0 64
2014-12-26 18:27:42 2451 0 64
2014-12-26 18:29:41 2451 0 64
2014-12-26 18:38:03 2451 0 22
2014-12-26 18:42:15 2451 0 8
2014-12-26 18:56:31 2451 0 8
2014-12-26 18:57:17 2451 0 8
2014-12-26 19:01:36 2451 0 8
2014-12-26 19:05:13 2451 0 8
2014-12-28 19:29:08 2451 0 22
2014-12-28 19:35:08 2451 0 8
2014-12-28 19:36:53 2451 0 89
2014-12-28 19:57:37 2451 0 99
2014-12-28 20:07:08 2451 0 35
2014-12-28 20:17:17 2451 0 10
2014-12-28 20:21:47 2451 0 10
2014-12-28 20:38:09 2451 1 10
2014-12-28 23:47:30 2451 0 8
2014-12-28 23:50:31 2451 0 35
2014-12-29 00:02:47 2451 0 35
2014-12-29 00:06:53 2451 0 73
2014-12-29 00:12:39 2451 0 8
2014-12-29 00:19:14 2451 0 8
2014-12-29 00:49:51 2451 0 99
2014-12-29 01:11:43 2451 1 99
2014-12-29 01:18:50 2451 0 99
2014-12-29 03:49:56 2451 0 81
2014-12-29 03:54:53 2451 0 8
2014-12-29 03:57:45 2451 0 22
2014-12-29 04:00:54 2451 0 58
2014-12-29 04:04:02 2451 0 99
2014-12-29 04:14:18 2451 0 78
2014-12-29 04:20:22 2451 0 22
2014-12-29 04:26:03 2451 0 78
2014-12-29 04:30:58 2451 0 22
2014-12-29 04:36:18 2451 0 43
2014-12-29 04:55:10 2451 0 8
2014-12-29 04:57:18 2451 0 8
2014-12-29 04:59:04 2451 0 99
2014-12-29 05:04:33 2451 0 22
2014-12-30 00:54:57 2451 0 22
2014-12-30 01:01:01 2451 0 36
2014-12-30 01:03:51 2451 0 62
2014-12-30 01:08:58 2451 0 62
2014-12-30 01:13:33 2451 0 83
2014-12-30 01:17:18 2451 0 5
2014-12-30 01:19:53 2451 0 59
2014-12-30 01:23:53 2451 0 89
2014-12-30 01:40:53 2451 0 78
2014-12-30 01:46:29 2451 0 36
2014-12-30 03:05:55 2451 0 78
2014-12-30 03:12:28 2451 0 36
2014-12-30 04:27:51 2451 0 36
2014-12-30 04:28:26 2451 0 78
2014-12-30 04:51:53 2451 1 78
2014-12-30 04:54:46 2451 0 78
2014-12-30 05:36:56 2451 0 35
2014-12-30 05:41:44 2451 0 5
2014-12-30 05:49:01 2451 0 8
2014-12-30 05:51:52 2451 0 8
2014-12-30 05:57:36 2451 0 14
2014-12-30 06:01:18 2451 0 10
2014-12-30 06:04:13 2451 0 51
2014-12-30 06:08:20 2451 0 67
2014-12-30 06:13:23 2451 0 2
2014-12-30 06:21:14 2451 0 81
2014-12-30 06:30:29 2451 0 10
2014-12-30 06:34:50 2451 0 43
2014-12-30 06:40:52 2451 0 99
2014-12-30 06:50:02 2451 0 8
2014-12-30 06:52:58 2451 0 22
2014-12-30 07:03:24 2451 0 22
2014-12-30 07:11:52 2451 0 22
2014-12-30 07:16:35 2451 0 43
2014-12-30 07:22:52 2451 0 22
2014-12-30 07:28:19 2451 0 58
2014-12-30 07:35:54 2451 0 8
2014-12-30 08:09:46 2451 0 22
2014-12-30 08:17:36 2451 0 22
2014-12-30 08:56:35 2451 1 8
2014-12-30 09:28:02 2451 0 8
2014-12-30 09:30:09 2451 0 36
2014-12-30 09:32:00 2451 0 14
2014-12-30 09:53:39 2451 0 22
2014-12-30 10:01:12 2451 0 99
2014-12-30 10:16:27 2451 0 81
2014-12-30 10:20:06 2451 0 78
2014-12-30 10:21:32 2451 0 62
2014-12-30 10:25:42 2451 0 8
2014-12-30 10:29:53 2451 0 15
2014-12-30 10:36:00 2451 0 8
2014-12-30 10:43:34 2451 0 8
2014-12-30 10:49:13 2451 0 8
2014-12-30 10:54:55 2451 0 43
2014-12-30 11:10:27 2451 0 15
2014-12-30 11:20:31 2451 0 82
2014-12-30 11:27:02 2451 0 99
2014-12-30 11:29:22 2451 0 99
2014-12-30 11:48:30 2451 0 15
2014-12-30 12:43:43 2451 1 15
2014-12-30 12:51:34 2451 1 15
2014-12-30 12:59:26 2451 0 36
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2013-06-24 21:05:59 5131 0 51
2014-10-24 18:00:44 5677 0 65
2014-10-24 18:12:29 5677 0 66
2014-10-24 18:13:17 5677 0 65
2014-11-08 02:22:40 5677 0 21
2013-09-05 11:54:48 5736 0 60
2013-09-05 16:34:20 5736 0 60
2013-09-10 10:54:13 5736 0 9
2014-03-06 11:12:19 5736 0 78
2014-04-18 10:21:50 5736 0 67
2014-04-18 10:26:33 5736 0 99
2014-01-10 21:47:59 5770 0 65
2014-01-10 22:31:11 5770 0 52
2014-01-10 22:32:13 5770 0 65
2014-07-20 13:42:28 5776 0 78
2014-07-20 13:42:34 5776 0 30
2014-07-20 13:42:38 5776 0 44
2014-07-27 09:59:21 5776 0 58
2014-07-27 10:01:07 5776 0 60
2014-08-02 01:13:37 5776 0 58
2014-10-07 13:54:08 6258 0 70
2014-10-07 14:02:36 6258 0 70
2014-10-07 14:08:21 6258 1 70
2014-10-26 15:12:09 6258 0 83
2014-10-26 15:13:37 6258 0 17
2014-10-30 09:32:07 6258 0 45
2014-10-30 09:37:32 6258 0 45
2014-10-30 09:37:40 6258 0 79
2014-10-30 09:55:36 6258 1 45
2014-10-30 09:58:40 6258 0 45
2013-06-18 08:17:00 6300 0 78
2014-06-09 10:55:52 6300 0 64
2014-06-10 12:43:04 6300 0 64
2014-06-10 12:46:40 6300 0 64
2014-06-18 08:59:17 6300 0 64
2014-06-18 09:07:21 6300 0 64
2014-10-06 19:30:21 6304 0 28
2014-10-06 19:30:50 6304 0 28
2014-10-07 18:12:18 6304 0 15
2014-10-07 18:13:40 6304 0 15
2014-10-07 18:22:32 6304 0 42
2014-10-07 18:22:42 6304 0 42
2014-10-07 20:25:52 6304 0 4
2014-10-07 20:26:03 6304 0 4
2014-10-08 19:11:36 6304 0 15
2014-10-08 19:18:41 6304 0 6
2014-10-08 19:18:52 6304 0 6
2014-10-08 19:24:53 6304 1 6
2014-10-11 17:26:12 6304 0 95
2014-10-11 17:26:23 6304 0 16
2014-10-11 17:26:37 6304 0 95
2014-10-11 17:26:46 6304 0 16
2014-10-12 08:49:42 6304 0 83
2014-10-12 08:52:35 6304 0 83
2014-10-12 09:26:14 6304 0 83
2014-10-12 09:33:27 6304 1 83
2013-09-06 16:22:31 6330 0 83
2013-09-10 18:17:35 6330 0 48
2013-09-10 18:53:11 6330 0 48
2013-09-10 20:08:11 6330 0 48
2013-09-10 20:47:19 6330 0 48
2014-04-09 15:31:18 6330 0 41
2014-04-09 15:34:20 6330 0 70
2014-04-09 15:35:31 6330 0 98
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2014-04-09 15:38:57 6330 0 41
2014-04-09 15:45:41 6330 0 41
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2014-04-09 16:17:24 6330 0 56
2014-04-09 16:26:35 6330 0 70
2014-04-09 17:23:20 6330 1 37
2014-06-09 13:18:28 6330 0 82
2014-06-12 16:20:57 6330 0 73
2014-06-12 16:27:31 6330 0 8
2014-07-01 09:55:34 6330 0 90
2014-07-01 11:35:39 6330 0 90
2014-07-02 16:49:59 6330 0 30
2014-07-02 16:53:32 6330 0 8
2014-07-09 16:24:41 6330 0 2
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2014-12-12 11:20:21 6330 0 78
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2013-09-06 09:28:40 6356 0 59
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2014-12-15 12:16:49 6356 0 29
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2014-12-30 10:29:15 6356 0 46
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2014-12-30 10:38:59 6356 0 62
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2014-12-30 10:54:41 6356 0 29
2014-12-30 11:04:05 6356 0 29
2014-12-30 11:13:15 6356 0 36
2014-12-30 12:32:19 6356 0 81
2014-12-30 12:33:47 6356 0 29
2014-12-30 14:48:16 6356 0 36
2014-12-30 14:49:09 6356 0 36
2014-12-31 14:02:49 6356 0 29
2014-06-05 11:06:04 6450 0 66
2014-06-05 11:07:31 6450 0 66
2014-06-11 13:43:39 6450 0 42
2014-06-11 13:44:26 6450 0 32
2014-06-11 13:45:01 6450 0 99
2014-06-20 09:59:37 6450 0 32
2014-07-02 13:58:28 6450 0 7
2014-07-02 13:59:58 6450 0 32
2014-07-02 14:00:10 6450 0 51
2014-07-02 14:00:36 6450 0 32
2014-07-03 13:26:09 6450 0 51
2014-07-03 13:33:06 6450 1 51
2014-07-08 14:15:08 6450 0 8
2014-07-09 07:25:37 6450 0 66
2014-07-09 07:32:53 6450 0 96
2014-07-28 09:04:12 6450 0 80
2014-08-05 11:50:33 6450 0 80
2014-08-06 06:15:41 6450 0 80
2014-08-06 06:54:29 6450 1 80
2014-08-08 10:50:39 6450 0 45
In [134]:
users = {z for z in et.user_id}
index1 = {u: e for e,u in enumerate(users)}
Out[134]:
453
In [135]:
len(users)
Out[135]:
3478
In [136]:
import numpy as np
nU = len(users)
X = np.zeros((nU, 100))
for i in range(N):
X[index1[et.user_id[i]], et.hotel_cluster[i]] += 1
In [155]:
nU = len(users)
X = np.zeros((nU, 100))
for i in range(N):
if et.is_booking[i]:
X[index1[et.user_id[i]], et.hotel_cluster[i]] += 1
In [8]:
%matplotlib inline
import matplotlib as mpl
import matplotlib.pylab as plt
plt.figure(figsize=(35, 250))
plt.imshow(X, interpolation='nearest', vmin=0, vmax=10)
plt.show()
<matplotlib.figure.Figure at 0x1122ab7f0>
In [163]:
plt.plot((np.sum(X,axis=0)))
Out[163]:
[<matplotlib.lines.Line2D at 0x10acb4b10>]
In [158]:
U, S, V = np.linalg.svd(X, full_matrices=False)
In [159]:
plt.plot(S)
Out[159]:
[<matplotlib.lines.Line2D at 0x10b43ba90>]
In [161]:
F = np.dot(diag(S), V)
plt.plot(F[0,:], F[1,:], '.')
plt.show()
In [166]:
F = np.dot(X.T, X)
plt.figure(figsize=(20, 20))
plt.imshow(F, interpolation='nearest')
Out[166]:
<matplotlib.image.AxesImage at 0x10cf51e10>
Content source: atcemgil/notes
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