neural_network_approach.py

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)