# y1=np.array(y1)
# print(y1.shape)
# Network Parameters
n_hidden_1 = 30 # 1st layer num features
n_hidden_2 = 20 # 2nd layer num features
n_hidden_3 = 10
n_hidden_4 = 5
n_input = 90
if os.path.isdir(os.getcwd() + '/model'):
shutil.rmtree(os.getcwd() + '/model')
os.mkdir(os.getcwd() + '/model')
for test_i in range(1, 18): #we must create a sesession for each of loc
os.mkdir(os.getcwd() + '/model/Test' + str(test_i))
csi = np.squeeze(np.array(get_csi(test_i)))
# create and train a graph for each point
for k in range(16): # for each test we have 16 train point in sn2
os.mkdir(os.getcwd() + '/model/Test' + str(test_i) + '/' + str(k + 1))
# tf Graph input (only pictures)
X = tf.placeholder("float", [None, n_input])
weights = {
'encoder_h1':
tf.Variable(tf.random_normal([n_input, n_hidden_1]), name='w1'),
'encoder_h2':
tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2]), name='w2'),
'encoder_h3':
tf.Variable(tf.random_normal([n_hidden_2, n_hidden_3]), name='w3'),
'encoder_h4':
tf.Variable(tf.random_normal([n_hidden_3, n_hidden_4]), name='w4'),
import numpy as np
from get_v3 import get_csi
import os
import shutil
if os.path.isdir(os.getcwd() + '/Models'):
shutil.rmtree(os.getcwd() + '/Models')
os.mkdir(os.getcwd() + '/Models')
random_points = list(
np.array([1, 2, 4, 6, 7, 8, 10, 12, 13, 14, 16, 17, 19]) - 1)
for k in range(1, 14):
csi = get_csi(k, random_points)
csi = np.squeeze(csi)
print(csi.shape)
point1 = csi[0:20]
point2 = csi[20:40]
point3 = csi[40:60]
point4 = csi[60:80]
point5 = csi[80:100]
point6 = csi[100:120]
point7 = csi[120:140]
point8 = csi[140:160]
point9 = csi[160:180]
point10 = csi[180:200]
point11 = csi[200:220]
point12 = csi[220:240]
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
#from read_from_db import read_csi_from_db
from get_v3 import get_csi
import os
import shutil
if os.path.isdir(os.getcwd()+'/Models'):
shutil.rmtree(os.getcwd()+'/Models')
#print("1")
os.mkdir(os.getcwd()+'/Models')
for k in range(1,20):
csi = get_csi(k) #360*1*90
csi = np.squeeze(csi) #360*90 20 packet from each of 18 Location --
point1=csi[0:20]
point2=csi[20:40]
point3=csi[40:60]
point4=csi[60:80]
point5=csi[80:100]
point6=csi[100:120]
point7=csi[120:140]
point8=csi[140:160]
point9=csi[160:180]
point10=csi[180:200]
point11=csi[200:220]
point12=csi[220:240]
point13=csi[240:260]
point14=csi[260:280]
# Network Parameters
n_hidden_1 = 30 # 1st layer num features
n_hidden_2 = 20 # 2nd layer num features
n_hidden_3 = 10
n_hidden_4 = 5
n_input = 90
if os.path.isdir(os.getcwd() + '/model'):
shutil.rmtree(os.getcwd() + '/model')
os.mkdir(os.getcwd() + '/model')
for test_i in range(1, 17): #we must create a session for each of loc
random_point = list(
np.array([1, 2, 4, 5, 6, 7, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19]) -
1)
os.mkdir(os.getcwd() + '/model/Test' + str(test_i))
csi = np.squeeze(np.array(get_csi(random_point[test_i - 1], random_point)))
# create and train a graph for each point
random_point.pop(test_i - 1)
for k in range(len(random_point)):
os.mkdir(os.getcwd() + '/model/Test' + str(test_i) + '/' + str(k + 1))
# tf Graph input (only pictures)
X = tf.placeholder("float", [None, n_input])
weights = {
'encoder_h1':
tf.Variable(tf.random_normal([n_input, n_hidden_1]), name='w1'),
'encoder_h2':
tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2]), name='w2'),
'encoder_h3':
tf.Variable(tf.random_normal([n_hidden_2, n_hidden_3]), name='w3'),
import tensorflow as tf
import numpy as np
# import matplotlib.pyplot as plt
#from read_from_db import read_csi_from_db
from get_v3 import get_csi
import os
import shutil
if os.path.isdir(os.getcwd()+'/Models'):
shutil.rmtree(os.getcwd()+'/Models')
#print("1")
os.mkdir(os.getcwd()+'/Models')
for k in range(1,18):
csi = get_csi(k)
csi = np.squeeze(csi) # 320*90 --> [20*16,90]
point1=csi[0:20]
point2=csi[20:40]
point3=csi[40:60]
point4=csi[60:80]
point5=csi[80:100]
point6=csi[100:120]
point7=csi[120:140]
point8=csi[140:160]
point9=csi[160:180]
point10=csi[180:200]
point11=csi[200:220]
point12=csi[220:240]
point13=csi[240:260]
point14=csi[260:280]
#num_of_SPs=[6,10,14,17]
num_of_SPs=17
for sp in [num_of_SPs]:
total_elapsed_time=[]
total_memory_used=[]
total_memory_perc=[]
random_list=random.sample(range(17), sp)
if os.path.isdir(os.getcwd()+r'\Models'):
shutil.rmtree(os.getcwd()+r'\Models')
os.mkdir(os.getcwd()+r'\Models')
for test_i in [random_list.index(random_list[-1])]: # we must create a session for each of loc --> model all other sps with one net!!
csi = get_csi(random_list[test_i],random_list)
csi = np.squeeze(csi)
st = time.time()
points=[]
for t in range(sp):
points.append(csi[t*20:(t+1)*20])
total=points
# Parameters
learning_rate = 0.01
training_epochs = 1000
display_step = 50
n_labels = sp-1
#
for sp in [6]:
total_elapsed_time = []
total_memory_used = []
total_memory_perc = []
random_list = random.sample(range(19), sp)
if os.path.isdir(os.getcwd() + r'\model'):
shutil.rmtree(os.getcwd() + r'\model')
os.mkdir(os.getcwd() + r'\model')
os.mkdir(os.getcwd() + r'\model\-' + str(sp))
for test_i in range(len(random_list) -
1): #we must create a session for each of loc
os.mkdir(os.getcwd() + r'\model\-' + str(sp) + r'\Test-' +
str(test_i + 1))
csi = np.squeeze(np.array(get_csi(random_list[test_i], random_list)))
# create and train a graph for each point
st = time.time()
# tf Graph input (only pictures)
X = tf.placeholder("float", [None, n_input])
weights = {
'encoder_h1':
tf.Variable(tf.random_normal([n_input, n_hidden_1]), name='w1'),
'encoder_h2':
tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2]), name='w2'),
'encoder_h3':
tf.Variable(tf.random_normal([n_hidden_2, n_hidden_3]), name='w3'),
'encoder_h4':
tf.Variable(tf.random_normal([n_hidden_3, n_hidden_4]), name='w4'),
'decoder_h1':