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neural_network_approach.py
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neural_network_approach.py
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import numpy as np
from neural_network import nn_model_forward, nn_model
import xlrd
import subprocess
import time
X = []
Y = []
signal_strength = []
ssids_required = ['Moto G (5S) Plus 3393', 'ZTE_5G_qaquFU', 'ZTE_2.4G_qaquFU']
loc1 = "values.xlsx"
loc2 = "output.xlsx"
wb1 = xlrd.open_workbook(loc1)
wb2 = xlrd.open_workbook(loc2)
sheet1 = wb1.sheet_by_index(0)
sheet2 = wb2.sheet_by_index(0)
sheet1.cell_value(0, 0)
sheet2.cell_value(0, 0)
for i in range(1, sheet1.nrows):
X.append(sheet1.row_values(i))
for i in range(1, sheet2.nrows):
Y.append(sheet2.row_values(i))
X = np.array(X).transpose()
Y = np.array(Y).transpose()
X_train = X[:, 0:700]
Y_train = Y[:, 0:700]
X_test = X[:, 700:962]
Y_test = Y[:, 700:962]
layer_dims = [3, 20, 10, 7, 5, 2]
parameters = nn_model(X_train, Y_train, layer_dims, learning_rate=0.001, num_iterations=27000, beta1=0.9, beta2=0.999,
epsilon=1e-8, print_cost=True)
def predict(a, b, parameter):
AL, caches = nn_model_forward(a, parameter)
return np.sum(AL - b)/np.sum(b)
accuracy_train = predict(X_train, Y_train, parameters)
accuracy_test = predict(X_test, Y_test, parameters)
print(accuracy_train)
print(accuracy_test)
def signalstrength():
subprocess.run(['netsh', 'interface', 'set', 'interface', 'name="Wi-Fi"', 'admin=disabled'])
subprocess.run(['netsh', 'interface', 'set', 'interface', 'name="Wi-Fi"', 'admin=enabled'])
time.sleep(4)
# getting the result of the command of command prompt and storing it in a variable
result = subprocess.check_output(["netsh", "wlan", "show", "network", "mode=Bssid"])
# converting the result(datatype = bytes) first into string object and storing each individual substring in a list y
cmd_output_list = list((result.decode('ASCII')).split())
# getting all the indexes of words SSID and Network and storing them in lists s1 and s2
i_SSID = [index for index, value in enumerate(cmd_output_list) if x == 'SSID']
i_Network = [index for index, value in enumerate(cmd_output_list) if x == 'Network']
i_Signal = [index for index, value in enumerate(cmd_output_list) if x == 'Signal']
# joining individual substrings to get one string for corresponding names of hotspots
for m in range(len(i_SSID)):
c = y[(i_SSID[m]+3):i_Network[m]]
c = tuple(c)
d = " "
if d.join(c) in ssids_required:
a = cmd_output_list[i_Signal[m] + 2]
a = a[0:len(a) - 1]
dbm = (int(a)/2) - 100
signal_strength.append(dbm)
print(signal_strength)
return signal_strength
x = signalstrength()
x1 = []
for i in range(3):
x1.append(x[i])
x1 = np.array(x1)
x1 = x1.reshape((3, 1))
print(x1.shape)
y, cache = nn_model_forward(x1, parameters)
print(y)