In [1]:

    

import numpy as np
import pandas as pd


    

In [2]:

    

data = pd.read_csv('checkins.dat', sep='|',skipinitialspace=True).dropna()


    

    




C:\Users\Factorion\Anaconda2\lib\site-packages\IPython\core\interactiveshell.py:2717: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)

In [3]:

    

data.head()


    

    
Out[3]:







  

    

      

      
id
      
user_id
      
venue_id
      
latitude
      
longitude
      
created_at
    
  
  

    

      
2
      
984222
      
15824.0
      
5222.0
      
38.895112
      
-77.036366
      
2012-04-21 17:43:47
    
    

      
4
      
984234
      
44652.0
      
5222.0
      
33.800745
      
-84.410520
      
2012-04-21 17:43:43
    
    

      
8
      
984291
      
105054.0
      
5222.0
      
45.523452
      
-122.676207
      
2012-04-21 17:39:22
    
    

      
10
      
984318
      
2146539.0
      
5222.0
      
40.764462
      
-111.904565
      
2012-04-21 17:35:46
    
    

      
11
      
984232
      
93870.0
      
380645.0
      
33.448377
      
-112.074037
      
2012-04-21 17:38:18
    
  

In [4]:

    

len(data)


    

    
Out[4]:





396634

In [5]:

    

cols = []
for i in range(0,len(data.columns)):
    cols.append(data.columns[i].strip())
data.columns = cols


    

In [6]:

    

data.columns


    

    
Out[6]:





Index([u'id', u'user_id', u'venue_id', u'latitude', u'longitude',
       u'created_at'],
      dtype='object')

In [7]:

    

X = data[['latitude','longitude']]
X.head()


    

    
Out[7]:







  

    

      

      
latitude
      
longitude
    
  
  

    

      
2
      
38.895112
      
-77.036366
    
    

      
4
      
33.800745
      
-84.410520
    
    

      
8
      
45.523452
      
-122.676207
    
    

      
10
      
40.764462
      
-111.904565
    
    

      
11
      
33.448377
      
-112.074037
    
  

In [8]:

    

X = X[:100000]


    

In [9]:

    

from sklearn.cluster import MeanShift, estimate_bandwidth


    

In [10]:

    

MS = MeanShift(bandwidth=0.1)
MS.fit(X)


    

    
Out[10]:





MeanShift(bandwidth=0.1, bin_seeding=False, cluster_all=True, min_bin_freq=1,
     n_jobs=1, seeds=None)

In [11]:

    

labels = MS.labels_ #label каждой точки
cluster_centers = MS.cluster_centers_ #Координаты кластерных центров.

# сортировка
labels_unique = np.unique(labels)
n_clusters_ = len(labels_unique)
n_clusters_
x_plt = np.array(X)


    

In [12]:

    

# графическое изображение
import matplotlib.pyplot as plt
from itertools import cycle

plt.figure(1)
plt.clf()

colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')
for k, col in zip(range(n_clusters_), colors):
    my_members = labels == k
    cluster_center = cluster_centers[k]
    plt.plot(x_plt[my_members, 0], x_plt[my_members, 1], col + '.')
    plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,
             markeredgecolor='k', markersize=14)
plt.title('Estimated number of clusters: %d' % n_clusters_)
plt.show()


    

In [13]:

    

# кол-во в каждом кластере
points = dict()
for i in xrange(0,len(labels)):
    if points.has_key(labels[i]):
        points[labels[i]] += 1
    else:
        points[labels[i]] = 1


    

In [14]:

    

# убирает, если < 15
for i in xrange(0,len(points)):
    if points[i]<16:
        points.pop(i)


    

In [15]:

    

# кооординаты "лучших центров"
opt_points = []
j = 0
for i in xrange(0,len(points)):
    opt_points.append(cluster_centers[i])
    
len(points)


    

    
Out[15]:





593

In [16]:

    

# офисы компании
offices = [[33.751277, -118.188740],[25.867736, -80.324116],
           [51.503016, -0.075479],[52.378894, 4.885084],
           [39.366487, 117.036146],[-33.868457, 151.205134]]
offices


    

    
Out[16]:





[[33.751277, -118.18874],
 [25.867736, -80.324116],
 [51.503016, -0.075479],
 [52.378894, 4.885084],
 [39.366487, 117.036146],
 [-33.868457, 151.205134]]

In [17]:

    

distance_points = []
i = 0
for opt_point in opt_points:
    min_dist = 10000000
    for office in offices:
        dist = np.sqrt((opt_point[0] - office[0])**2 + (opt_point[1] - office[1])**2)
        if dist<min_dist:
            min_dist=dist
    distance_points.append((min_dist,opt_point))


    

In [18]:

    

distance_points.sort()


    

In [19]:

    

ans = distance_points[0]
ans = np.array(ans[1])
ans[0]


    

    
Out[19]:





-33.860630428571433

In [20]:

    

with open("ans.txt", "w") as f:
        f.write(str(ans[0]) + ' ' + str(ans[0]))