def train(dataset):
db = DB(join(dbpath,'train.db'))
tbl_manifest = '%s_man'%dataset
tbl_rss = '%s_rss'%dataset
n_feature = int(db.queryone('value',tbl_manifest,'key="n_feature"')[0])
data = []
cls = []
for y,entry in db.query(['p_id','entry'], tbl_rss):
dic = json.loads(entry)
x = [-100]*n_feature
for m_id,rss in dic.items():
x[int(m_id)] = float(rss)
data.append(x)
cls.append(y)
neigh = KNeighborsClassifier(n_neighbors=5)
neigh.fit(data,cls)
fout = open('model.dat','w')
pickle.dump(neigh,fout)
fout.close()
rootpath = os.getenv('WLAN_ROOT')
datapath = join(rootpath,'raw_data')
dbpath = join(datapath,'DB')
db = DB(join(dbpath,'train.db'))
dataset = db.queryone('value','meta','key="current"')[0]
tbl_mac = '%s_mac'%dataset
tbl_pt = '%s_pnt'%dataset
tbl_relation = '%s_rel'%dataset
tbl_manifest = '%s_man'%dataset
tbl_rss = '%s_rss'%dataset
map_mac = {}
map_pt = {}
feas_map = {}
mac_dic = dict([(mac,m_id) for mac,m_id in db.query(['mac','m_id'],tbl_mac)])
pid_dic = dict([(p_id,pt) for p_id,pt in db.query(['p_id','pt'],tbl_pt)])
coord_dic = dict([(p_id,(int(x),int(y))) for p_id,x,y in db.query(['p_id','x','y'],tbl_pt)])
n_point = int(db.queryone('value',tbl_manifest,'key="n_point"')[0])
p2p_matrix = [[float('inf')]*n_point for i in range(n_point)]
for i in range(n_point):
p2p_matrix[i][i] = 0.0
for i,j,dis in db.query(['src_pt','dest_pt','distance'],tbl_relation):
p2p_matrix[i][j] = p2p_matrix[j][i] = dis
def get_macid(mac):
return mac_dic[mac]
def get_pointname(pid):
return pid_dic[pid]
#!/usr/bin/env python
from sklearn import svm
import pickle
import json
from dbhelper import DB
import sys
device = sys.argv[1]
db = DB('train.db')
n_feature = int(db.queryone('value','manifest','key="n_feature"')[0])
data = []
cls = []
for y,entry in db.query(['p_id','entry'], 'rss_'+device):
dic = json.loads(entry)
x = [-100]*n_feature
for m_id, rss in dic.items():
x[int(m_id)] = float(rss)
data.append(x)
cls.append(y)
clf = svm.SVC()
clf.fit(data,cls)
fout = open('model.dat','w')
pickle.dump(clf,fout)
fout.close()
