def log_extract(dataSet):
data, label = load_data(dataSet)
m = len(data)
n = len(data[0])
for i in range(m):
for j in range(n):
distance = 10 ** (float(210 - data[i][j]) / 35)
data[i][j] = int(float(data[i][j]) / distance)
return list(data), label
def cal_feature(dataSet):
data, label = load_data(dataSet)
data = np.array(data)
y = np.array(label)
x = []
data_x = []
for i in range(1, 17):
x.append(data[y == str(i)])
for k in range(16):
for e in x[k]:
e /= np.array(scale)[k]
data_x.append(list(e))
return data_x, label
import serial
import os
from svmutil import *
from sklearn import neighbors
from visual import load_data
ser = serial.Serial('/dev/ttyUSB0', 38400, timeout=1)
y,x = svm_read_problem('train_ftrim')
m = svm_train(y,x, '-c 5')
j = 0
knn = neighbors.KNeighborsClassifier()
data, label = load_data('train_trim')
knn.fit(data,label)
while True:
f = open('rssi', 'a')
ser.write('bf1')
line = ser.readline().strip()
seg = line.split('#')
seg = seg[1:]
if len(seg) != 4: continue
print line
for i in range(len(seg)):
e = seg[i][-4:]
if i == 0: f.write('1 ')
if i == 3:
f.write('4:' + e[1:] + '\n')
break
f.write(str(i+1) + ':' + e)
print 'ok'
os.system('tail -n 1 rssi > sig')
y, x = svm_read_problem('sig')