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test_script.py
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"""
GUI for adaptive filters testing.
"""
import sys
from pathlib import Path
import statistics
import os
from PyQt5.QtWidgets import (QMainWindow,QWidget, QComboBox, QPushButton,\
QAction, QLabel, QFileDialog, QTableWidgetItem,\
QLineEdit, QTableWidget, QCheckBox, QMessageBox,\
QApplication, QVBoxLayout, QScrollArea)
from PyQt5.QtGui import (QIcon)
from PyQt5.QtCore import Qt, pyqtSignal
from PyQt5 import QtCore
from PyQt5 import QtWidgets
import numpy as np
import matplotlib.pyplot as plt
import padasip as pa
# pylint: disable=too-many-instance-attributes
# pylint: disable=c-extension-no-member
class Detekce(QMainWindow):
"""
Popis
"""
def __init__(self):
QMainWindow.__init__(self)
super(Detekce, self).__init__()
self.selected_columns = None
self.selection_window = SubWindow()
self.selection_window.signal_proces.connect(self.column_selection_holder_one)
self.num_of_parametrs = None
self.input_columns_filter = []
self.desired_columns_filter = []
self.loading_matrix_shape =None
self.loading_matrix = None
self.column_count = None
self.new_input_arary_reshaped = None
self.testlist = None
self.directory_2 = None
self.path_3 = None
self.matrix_shape = None
self.transposed_matrix = None
self.filter_output = None
self.filter_parametrs = None
self.filter_error = None
self.input_data = None
self.input_desired_data = None
self.output_detection_tool = None
self.learning_rate = None
self.filter_name = None
self.detection_name = None
self.mean = None
self.standart_deviation = None
self.variance = None
self.directory_3 = None
self.parent_dir_3 = None
self.label_savefig = None
self.mean_absolute_error = None
self.mena_squared_error = None
self.root_mean_square_error = None
self.init_ui()
def init_ui(self):
"""
Second main object. There is set geometry of main window
and it refers to main grafical elements.
:return: None
"""
self.label_1 = QtWidgets.QLabel(self)
self.label = QtWidgets.QLabel(self)
self.label_2 = QtWidgets.QLabel(self)
self.label_3 = QtWidgets.QLabel(self)
self.label_mae = QtWidgets.QLabel(self)
self.label_mse = QtWidgets.QLabel(self)
self.label_rmse = QtWidgets.QLabel(self)
self.table_widget = QTableWidget(self)
# self.textbox4 = QLineEdit(self)
self.textbox3 = QLineEdit(self)
self.textbox2 = QLineEdit(self)
self.textbox1n = QLineEdit(self)
self.textbox1s = QLineEdit(self)
self.textbox1 = QLineEdit(self)
self.button_one = QPushButton("Intr.speed detection", self)
self.button_two = QPushButton("One speed detection", self)
# self.button_three = QPushButton("Test detection", self)
self.box = QCheckBox("Save output", self)
self.check_box_one()
self.setGeometry(100, 100, 700, 580) # 700
self.setWindowTitle("AND - Application for novelty detection")
self.label_one()
self.tabulka()
self.menugraf()
self.popisky()
self.combobox()
self.lspeed()
self.selection_window_button()
self.show()
def error_evaluation(self):
"""
Object which calculate filter error statistics
:return: None
"""
self.mean_absolute_error = pa.misc.MAE(self.filter_error)
self.mena_squared_error = pa.misc.MSE(self.filter_error)
self.root_mean_square_error = pa.misc.RMSE(self.filter_error)
self.logarithmic_squared_error = pa.misc.logSE(self.filter_error)
def new_selection(self):
"""
Object which create table of input matrix size
:return: None
"""
self.table_widget = QTableWidget()
self.table_widget.setRowCount(4)
self.table_widget.setColumnCount(4)
self.table_widget.addAction(self, QCheckBox())
self.table_widget.show()
def check_box_one(self):
"""
Object which define checkbox for saving data.
:return: None
"""
self.box.move(560, 350)
self.box.resize(320, 40)
def check_box_one_save(self):
"""
Object which make dirs for save data and save them.
:return: None
"""
if self.box.isChecked():
try:
self.directory_3 = str(self.textbox2.text())
parent_dir_a = "C:\\Program Files (x86)\\Exp.files\\"
self.parent_dir_3 = parent_dir_a + self.directory_2
self.path_3 = os.path.join(self.parent_dir_3, self.directory_3)
try:
os.makedirs(self.path_3)
print("dir maked")
except Exception as ex:
print(ex)
except Exception as ex:
print(ex)
else:
pass
def check_box_one_save_main_file(self):
"""
Object which make dirs for save data and save them.
:return: None
"""
if self.box.isChecked():
directory_1 = "Exp.files"
parent_dir_1 = "C:\Program Files (x86)"
path_1 = os.path.join(parent_dir_1, directory_1)
try:
os.makedirs(path_1)
print("dir maked")
except Exception as ex:
print(ex)
list_files = os.listdir('C:\\Program Files (x86)\\Exp.files')
self.directory_2 = str(len(list_files))
parent_dir_2 = "C:\\Program Files (x86)\\Exp.files"
path_2 = os.path.join(parent_dir_2, self.directory_2)
try:
os.makedirs(path_2)
print("dir maked")
except Exception as ex:
print(ex)
else:
print("not checked")
def combobox(self):
"""
This object add items into combobox of filters
so we can choose filter by another object
:return: None
"""
self.combo_1 = QComboBox(self)
self.combo_2 = QComboBox(self)
self.combo_1.addItem("Choose filter")
self.combo_1.addItem("SSLMS")
self.combo_1.addItem("RLS")
self.combo_1.addItem("NSSLMS")
self.combo_1.addItem("AP")
self.combo_1.addItem("GNGD")
self.combo_1.addItem("NLMF")
self.combo_1.addItem("NLMS")
self.combo_1.addItem("LMS")
self.combo_1.addItem("LMF")
self.combo_1.addItem("GMCC")
self.combo_1.addItem("Llncosh")
self.combo_1.setGeometry(550, 80, 130, 25)
self.combo_2.addItem("Choose detection")
self.combo_2.addItem("LE")
self.combo_2.addItem("ELBND")
self.combo_2.addItem("ESE")
self.combo_2.setGeometry(550, 110, 130, 25)
self.button_one.setGeometry(550, 495, 130, 25)
self.button_one.clicked.connect(self.node_2)
self.button_two.setGeometry(550, 525, 130, 25)
self.button_two.clicked.connect(self.node_3)
def multi_parametrs_save(self):
"""
Object which define function for button save parametrs.
These parametrs are saved into the .txt file.
:return: None
"""
try:
if self.box.isChecked():
spn = str(self.textbox1.text())
name = str(self.textbox2.text())
stdev = str(self.standart_deviation)
variance = str(self.variance)
mean = str(self.mean)
mae = str(self.mean_absolute_error)
mse = str(self.mena_squared_error)
rmse = str(self.root_mean_square_error)
filter_lenght_string = str(self.textbox3.text())
saved_parametrs = {"Learning rate": spn,\
"Filter length": filter_lenght_string,\
"Filter": self.filter_name, \
"Detection tool": self.detection_name,\
"Standart deviation": stdev, \
"Variance": variance,\
"Mean": mean,\
"Mean absolute error": mae,\
"Mean squared error": mse,\
"Root mean square error": rmse}
speed_label = str(self.label_savefig)
name_of_file = str(self.directory_3 + speed_label)
complete_name = os.path.join(self.path_3, name_of_file + ".txt")
saved_parametrs_string = repr(saved_parametrs)
file_parametrs = open(complete_name,"w", encoding="utf-8")
file_parametrs.write(saved_parametrs_string)
file_parametrs.close()
except Exception as ex:
print(ex)
def node_3(self):
"""
Node for selection of multiple columns, when you click
on that buton you start this node which refers
to individual objects
:return: None
"""
self.check_box_one_save_main_file()
self.lspeedchoose()
self.main_node()
def main_node(self):
"""
Main structure of node which is use in specialized nodes
:return: None
"""
try:
self.alter_vyber()
if self.num_of_parametrs == 0:
self.num_of_parametrs = 1
self.vyberfiltr()
self.error_evaluation()
self.vyberdet()
self.check_box_one_save()
self.multi_parametrs_save()
self.grafy()
else:
self.vyberfiltr()
self.error_evaluation()
self.vyberdet()
self.check_box_one_save()
self.multi_parametrs_save()
self.grafy()
except Exception as ex:
print(ex)
def node_2(self):
"""
Node for button interval speed detection, when you click
on that buton you start this node which refers
to individual objects
:return: None
"""
bottom_interval = float(self.textbox1.text())
top_interval = float(self.textbox1s.text())
step = float(self.textbox1n.text())
try:
self.check_box_one_save_main_file()
for self.learning_rate in np.arange(bottom_interval, \
top_interval, step):
self.label_savefig = round(self.learning_rate, 5)
self.main_node()
except Exception as ex:
print(ex)
def vyberfiltr(self):
"""
This object choose which adaptive filter we use.
:return: None
"""
filt = self.combo_1.currentText()
if filt == "SSLMS":
self.filter_sslms()
if filt == "RLS":
self.filter_rls()
elif filt == "NSSLMS":
self.filter_nsslms()
elif filt == "AP":
self.filter_ap()
elif filt == "GNGD":
self.filter_gngd()
elif filt == "NLMF":
self.filter_nlmf()
elif filt == "NLMS":
self.filter_nlms()
elif filt == "LMS":
self.filter_lms()
elif filt == "LMF":
self.filter_lmf()
elif filt == "GMCC":
self.filter_gmcc()
elif filt == "Llncosh":
self.filter_llncosh()
def vyberdet(self):
"""
This object choose which detection tool we use.
:return: None
"""
det = self.combo_2.currentText()
if det == "LE":
self.det_le()
elif det == "ELBND":
self.det_elbnd()
elif det == "ESE":
self.det_ese()
def menufil(self):
"""
Object which define options of filter and add them into combobox.
:return: None
"""
self.statusBar()
menubar = self.menuBar()
file_menu = menubar.addMenu("&Filters")
llncosh = QAction(QIcon("WWW.jpg"), "llncosh", self)
llncosh.setShortcut("Ctrl+ll")
llncosh.setStatusTip("Last Llncosh (Llncosh)")
llncosh.triggered.connect(self.filter_llncosh)
gmcc = QAction(QIcon("WWW.jpg"), "GMCC", self)
gmcc.setShortcut("Ctrl+G")
gmcc.setStatusTip("Generalized maximum correntropy criterion (GMCC)")
gmcc.triggered.connect(self.filter_gmcc)
sslms = QAction(QIcon("WWW.jpg"), "SSLMS", self)
sslms.setShortcut("Ctrl+F")
sslms.setStatusTip("Sign-sign Least-mean-squares (SSLMS)")
sslms.triggered.connect(self.filter_sslms)
rls = QAction(QIcon("WWW.jpg"), "RLS", self)
rls.setShortcut("Ctrl+R")
rls.setStatusTip("Recursive Least Squares (RLS)")
rls.triggered.connect(self.filter_rls)
nsslms = QAction(QIcon("WWW.jpg"), "NSSLMS", self)
nsslms.setShortcut("Ctrl+Q")
nsslms.setStatusTip("Normalized Sign-sign Least-mean-squares (NSSLMS)")
nsslms.triggered.connect(self.filter_nsslms)
ap_filter = QAction(QIcon("WWW.jpg"), "AP", self)
ap_filter.setShortcut("Ctrl+A")
ap_filter.setStatusTip("Affine Projection (AP)")
ap_filter.triggered.connect(self.filter_ap)
gngd = QAction(QIcon("WWW.jpg"), "GNGD", self)
gngd.setShortcut("Ctrl+G")
gngd.setStatusTip("Generalized Normalized Gradient Descent (GNGD)")
gngd.triggered.connect(self.filter_gngd)
nlms = QAction(QIcon("WWW.jpg"), "NLMS", self)
nlms.setShortcut("Ctrl+N")
nlms.setStatusTip("Normalized Least-mean-squares (NLMS)")
nlms.triggered.connect(self.filter_nlms)
nlmf = QAction(QIcon("WWW.jpg"), "NLMF", self)
nlmf.setShortcut("Ctrl+F")
nlmf.setStatusTip("Normalized Least-mean-fourth (NLMF)")
nlmf.triggered.connect(self.filter_nlmf)
lms = QAction(QIcon("WWW.jpg"), "LMS", self)
lms.setShortcut("Ctrl+S")
lms.setStatusTip("Least-mean-fourth (LMS)")
lms.triggered.connect(self.filter_lms)
lmf = QAction(QIcon("WWW.jpg"), "LMF", self)
lmf.setShortcut("Ctrl+M")
lmf.setStatusTip("Least-mean-fourth (LMF)")
lmf.triggered.connect(self.filter_lmf)
file_menu.addAction(gmcc)
file_menu.addAction(sslms)
file_menu.addAction(rls)
file_menu.addAction(ap_filter)
file_menu.addAction(nsslms)
file_menu.addAction(gngd)
file_menu.addAction(nlms)
file_menu.addAction(nlmf)
file_menu.addAction(lms)
file_menu.addAction(lmf)
file_menu.addAction(llncosh)
def menu_detection(self):
"""
Object which define options of detection tool and add them into combobox.
:return: None
"""
self.statusBar()
menubar = self.menuBar()
file_menu = menubar.addMenu("&Detection")
elbnd_detection = QAction(QIcon("WWW.jpg"), "ELBND", self)
elbnd_detection.setShortcut("Ctrl+1")
elbnd_detection.setStatusTip("Error and Learning Based Novelty Detection (ELBND)")
elbnd_detection.triggered.connect(self.det_elbnd)
le_detection = QAction(QIcon("WWW.jpg"), "LE", self)
le_detection.setShortcut("Ctrl+2")
le_detection.setStatusTip("Learning Entropy (LE)")
le_detection.triggered.connect(self.det_le)
ese_detection = QAction(QIcon("WWW.jpg"), "ESE", self)
ese_detection.setShortcut("Ctrl+3")
ese_detection.setStatusTip("Extreme seeking entrophy (ESE)")
ese_detection.triggered.connect(self.det_ese)
file_menu.addAction(elbnd_detection)
file_menu.addAction(le_detection)
file_menu.addAction(ese_detection)
def popisky(self):
"""
Object which define labels.
:return: None
"""
labelfilter = QLabel(self)
labelfilter.setText("Learning speed")
labelfilter.setGeometry(550, 225, 130, 25)
labellenghtfil = QLabel(self)
labellenghtfil.setText("Filter lenght")
labellenghtfil.setGeometry(550, 175, 130, 25)
labelfilename = QLabel(self)
labelfilename.setText("Filename")
labelfilename.setGeometry(550, 275, 130, 25)
def statupgr(self):
"""
Object which update statistic in label as text and again define label.
:return: None
"""
mean_multiplied = 1000 * self.mean
mean_back = (round(mean_multiplied)) / 1000
mean_round = str(mean_back)
self.label.setText("Mean:" + mean_round)
self.label.setStyleSheet("font-weight:bold")
self.label.setStyleSheet("font-size: 10pt")
self.setGeometry(QtCore.QRect(550, 40, 250, 710))
self.setGeometry(100, 100, 700, 580)
variance_multiplied = 1000 * self.variance
variance_back = (round(variance_multiplied)) / 1000
variance_round = str(variance_back)
self.label_1.setText("Var:" + variance_round)
self.label_1.setStyleSheet("font-weight:bold")
self.label_1.setStyleSheet("font-size: 10pt")
self.setGeometry(QtCore.QRect(550, 40, 250, 715))
self.setGeometry(100, 100, 700, 580)
deviation_multiplied = 1000 * self.standart_deviation
deviation_back = (round(deviation_multiplied)) / 1000
deviation_round = str(deviation_back)
self.label_2.setText("Std:" + deviation_round)
self.label_2.setStyleSheet("font-weight:bold")
self.label_2.setStyleSheet("font-size: 10pt")
self.setGeometry(QtCore.QRect(550, 40, 250, 704))
self.setGeometry(100, 100, 700, 580)
mean_absolute_error_multiplied = 1000 * self.mean_absolute_error
mean_absolute_error_back = (round(mean_absolute_error_multiplied)) / 1000
mean_absolute_error_round = str(mean_absolute_error_back)
self.label_mae.setText("Std:" + mean_absolute_error_round)
self.label_mae.setStyleSheet("font-weight:bold")
self.label_mae.setStyleSheet("font-size: 10pt")
self.setGeometry(QtCore.QRect(550, 40, 250, 740))
self.setGeometry(100, 100, 700, 580)
mena_squared_error_multiplied = 1000 * self.mena_squared_error
mena_squared_error_back = (round(mena_squared_error_multiplied)) / 1000
mena_squared_error_round = str(mena_squared_error_back)
self.label_mse.setText("Std:" + mena_squared_error_round)
self.label_mse.setStyleSheet("font-weight:bold")
self.label_mse.setStyleSheet("font-size: 10pt")
self.setGeometry(QtCore.QRect(550, 40, 250, 775))
self.setGeometry(100, 100, 700, 580)
root_mean_square_error_multiplied = 1000 * self.root_mean_square_error
root_mean_square_error_back = (round(root_mean_square_error_multiplied)) / 1000
root_mean_square_error_round = str(root_mean_square_error_back)
self.label_rmse.setText("Std:" + root_mean_square_error_round)
self.label_rmse.setStyleSheet("font-weight:bold")
self.label_rmse.setStyleSheet("font-size: 10pt")
self.setGeometry(QtCore.QRect(550, 40, 250, 810))
self.setGeometry(100, 100, 700, 580)
def statokno(self):
"""
Object which calculate statistic.
:return: None
"""
stlist = np.ndarray.tolist(self.output_detection_tool)
self.mean = statistics.mean(stlist)
self.standart_deviation = statistics.stdev(stlist)
self.variance = statistics.variance(stlist)
def label_one(self):
"""
Object which define statistic labels for the first time.
:return: None
"""
self.label.setText("Mean: None")
self.label.setStyleSheet("font-weight:bold")
self.label.setStyleSheet("font-size: 10pt")
self.label.setGeometry(QtCore.QRect(550, 40, 250, 715))
self.setGeometry(100, 100, 700, 700)
self.label.update()
self.label_1.setText("Var: None")
self.label_1.setStyleSheet("font-weight:bold")
self.label_1.setStyleSheet("font-size: 10pt")
self.label_1.setGeometry(QtCore.QRect(550, 60, 250, 710))
self.setGeometry(100, 100, 700, 580)
self.label_1.update()
self.label_2.setText("Std: None")
self.label_2.setStyleSheet("font-weight:bold")
self.label_2.setStyleSheet("font-size: 10pt")
self.label_2.setGeometry(QtCore.QRect(550, 80, 250, 704))
self.setGeometry(100, 100, 700, 580)
self.label_2.update()
self.label_mae.setText("Mae: None")
self.label_mae.setStyleSheet("font-weight:bold")
self.label_mae.setStyleSheet("font-size: 10pt")
self.label_mae.setGeometry(QtCore.QRect(550, 80, 250, 740))
self.setGeometry(100, 100, 700, 580)
self.label_mae.update()
self.label_mse.setText("Mse: None")
self.label_mse.setStyleSheet("font-weight:bold")
self.label_mse.setStyleSheet("font-size: 10pt")
self.label_mse.setGeometry(QtCore.QRect(550, 80, 250, 775))
self.setGeometry(100, 100, 700, 580)
self.label_mse.update()
self.label_rmse.setText("Rmse: None")
self.label_rmse.setStyleSheet("font-weight:bold")
self.label_rmse.setStyleSheet("font-size: 10pt")
self.label_rmse.setGeometry(QtCore.QRect(550, 80, 250, 810))
self.setGeometry(100, 100, 700, 580)
self.label_rmse.update()
def menugraf(self):
"""
Object which define buttons open a file, open and
save parametrs and define toolbar named tools.
:return: None
"""
opfil = QAction(QIcon("WWW.jpg"), "open", self)
opfil.setShortcut("Ctrl+9")
opfil.setStatusTip("Open a file")
opfil.triggered.connect(self.loading)
ofiltl = QPushButton("Open a file", self)
ofiltl.setGeometry(550, 50, 130, 25)
ofiltl.clicked.connect(self.loading)
namfil = QPushButton("Save parametrs", self)
namfil.setGeometry(550, 330, 130, 25)
namfil.clicked.connect(self.saveparametrs)
menubar = self.menuBar()
file_menu = menubar.addMenu("&Tools")
file_menu.addAction(opfil)
def loading(self):
"""
In that object is open file with data and reorganize into matrix for analyze.
:return: None
"""
try:
basefile = str(Path.home())
jmeno = QFileDialog.getOpenFileName(self, "open file", basefile)
# hodnoty = open(jmeno[0], "r")
with open(jmeno[0], "r", encoding="utf-8") as hodnoty:
mat1 =(np.genfromtxt(hodnoty, delimiter=",", skip_header=0))
loading_matrix_shape = mat1.shape
self.loading_matrix_shape = mat1.shape[0]
self.loading_matrix = mat1
try:
self.column_count = loading_matrix_shape[1]
except:
self.column_count = 1
if len(loading_matrix_shape) == 1:
loading_matrix_length = (loading_matrix_shape[0], 1)
else:
loading_matrix_length = loading_matrix_shape
self.table_widget.setRowCount(loading_matrix_length[0])
self.table_widget.setColumnCount(loading_matrix_length[1])
for i in range(0, loading_matrix_length[0]):
for j in range(0, loading_matrix_length[1]):
try:
if len(loading_matrix_shape) == 1:
self.table_widget.setItem(i, j, QTableWidgetItem(str(mat1[i])))
else:
self.table_widget.setItem(i, j, QTableWidgetItem(str(mat1[i, j])))
except Exception as ex:
print(ex)
self.matrix_shape = loading_matrix_length
self.statusBar().showMessage("Files are loaded")
except Exception as ex:
print(ex)
def alter_vyber(self):
count = self.selected_columns
self.num_of_parametrs = len(count[0])
if self.num_of_parametrs == 0:
try:
self.operational_matrix = []
self.input_columns_filter = []
self.desired_columns_filter = []
loading_matrix = self.loading_matrix
lenght_of_data = self.loading_matrix_shape
for i in range(0, lenght_of_data):
self.operational_matrix.append(loading_matrix[i, count[1][0]])
for i in range(0, lenght_of_data-5):
self.input_columns_filter.append(self.operational_matrix[i])
for i in range(5, lenght_of_data):
self.desired_columns_filter.append(self.operational_matrix[i])
self.desired_columns_filter = np.reshape(self.desired_columns_filter,\
[lenght_of_data-5, len(count[0])+1],
order="F")
self.input_data = np.asarray(self.input_columns_filter)
self.input_desired_data = np.asarray(self.desired_columns_filter)
except Exception as ex:
print(ex)
for i in range(0, lenght_of_data):
self.operational_matrix.append(loading_matrix[i])
for i in range(0, lenght_of_data - 5):
self.input_columns_filter.append(self.operational_matrix[i])
for i in range(5, lenght_of_data):
self.desired_columns_filter.append(self.operational_matrix[i])
self.desired_columns_filter = np.reshape(self.desired_columns_filter,
[lenght_of_data - 5, len(count[0]) + 1],
order="F")
self.input_data = np.asarray(self.input_columns_filter)
self.input_desired_data = np.asarray(self.desired_columns_filter)
else:
self.input_columns_filter = []
self.desired_columns_filter = []
loading_matrix = self.loading_matrix
lenght_of_data = self.loading_matrix_shape
for i in range(0, lenght_of_data):
self.input_columns_filter.append(loading_matrix[i, count[1][0]])
for j in range(0, len(count[0])):
for i in range(0, lenght_of_data):
self.desired_columns_filter.append(loading_matrix[i, count[0][j]])
self.desired_columns_filter = np.reshape(self.desired_columns_filter,\
[lenght_of_data, len(count[0])],
order="F")
self.input_data = np.asarray(self.input_columns_filter)
self.input_desired_data = np.asarray(self.desired_columns_filter)
def vyber(self):
"""
Function of this object is selection of column marked
for analyzation and insert this column into the matrix.
:return: None
"""
alt = self.table_widget.selectedItems()
selection_list = []
selection_list_shape = self.matrix_shape[0]
for item in alt:
selection_list.append(float(item.text()))
selection_matrix = np.asarray(selection_list)
selection_matrix_shape = selection_matrix.shape[0]
reshape_parametr = selection_matrix_shape / selection_list_shape
try:
reshaped_selection_matrix = np.reshape(selection_matrix, \
(int(reshape_parametr), selection_list_shape))
transposed_selection_matrix = reshaped_selection_matrix.T
self.transposed_matrix = transposed_selection_matrix
except Exception as ex:
print(ex)
self.statusBar().showMessage("Input column are selected")
def uprava(self):
"""
Object which reorganize data from selected column into two matrixes.
First matrix is with input values and second is
filed with desired values.
:return: None
"""
try:
trans_matrix = self.transposed_matrix
shape = self.matrix_shape[0]
filter_length = int(self.textbox3.text())
desired_data_beginning = []
input_data_begining = []
for i in range(filter_length, shape, 1):
input_data_begining = np.append(input_data_begining, trans_matrix[i])
for i in range(0, shape - filter_length, 1):
desired_data_beginning = np.append(desired_data_beginning, trans_matrix[i])
desired_data_in_progres = np.asarray([desired_data_beginning])
input_data_in_progres = np.asarray([input_data_begining])
self.input_desired_data = desired_data_in_progres.T
self.input_data = input_data_in_progres.T
except Exception as ex:
print(ex)
trans_matrix = self.transposed_matrix
shape = self.matrix_shape[0]
desired_data_beginning = []
input_data_begining = []
for i in range(5, shape, 1):
input_data_begining = np.append(input_data_begining, trans_matrix[i])
for i in range(0, shape - 5, 1):
desired_data_beginning = np.append(desired_data_beginning, trans_matrix[i])
desired_data_in_progres = np.asarray([desired_data_beginning])
input_data_in_progres = np.asarray([input_data_begining])
self.input_desired_data = desired_data_in_progres.T
self.input_data = input_data_in_progres.T
def lspeed(self):
"""
Objcet which define textboxes.
:return: None
"""
self.textbox1.setGeometry(550, 250, 30, 25)
self.textbox1s.setGeometry(600, 250, 30, 25)
self.textbox1n.setGeometry(650, 250, 30, 25)
self.textbox2.setGeometry(550, 300, 130, 25)
self.textbox3.setGeometry(550, 200, 130, 25)
self.show()
def saveparametrs(self):
"""
Object which define function for button save parametrs.
These parametrs are saved into the .txt file.
:return: None
"""
try:
spn = str(self.textbox1.text())
name = str(self.textbox2.text())
stdev = str(self.standart_deviation)
variance = str(self.variance)
mean = str(self.mean)
filter_lenght_string = str(self.textbox3.text())
saved_parametrs = {"Learning rate": spn, "Filter length": \
filter_lenght_string, "Filter": self.filter_name, \
"Detection tool": self.detection_name, "Standart deviation": stdev, \
"Variance": variance, "Mean": mean}
saved_parametrs_string=repr(saved_parametrs)
write_parametrs = open(f"{name}.txt", "w+")
write_parametrs.write(saved_parametrs_string)
self.statusBar().showMessage("Parametrs save")
write_parametrs.close()
except Exception as ex:
print(ex)
saved_parametrs = {"Learning rate": self.learning_rate, \
"Filter length": filter_lenght_string, \
"Filter": self.filter_name,"Detection tool": self.detection_name, \
"Standart deviation": stdev, \
"Variance": variance, "Mean": mean}
saved_parametrs_string = repr(saved_parametrs)
#write_parametrs = open("Detection parametrs", "w+")
with open("Detection parametrs", "w+", encoding="utf-8") as write_parametrs:
write_parametrs.write(saved_parametrs_string)
write_parametrs.close()
def lspeedchoose(self):
"""
Object which set learning speed written into the textbox.
If textbox is empty, learning rate is set to 1.
:return: None
"""
try:
speedvalue = float(self.textbox1.text())
self.learning_rate = speedvalue
except Exception as ex:
print(ex)
self.learning_rate = 1
def filter_llncosh(self):
"""
Filter procesing and values assignment
:return: None
"""
self.filter_name = "Llncosh"
try:
proces_file = pa.filters.FilterLlncosh(n=self.num_of_parametrs,\
mu=self.learning_rate,lambd=0.1, w="zeros")
output, error, weights = proces_file.run(self.input_data, self.input_desired_data)
except Exception as ex:
print(ex)
self.filter_output = output
self.filter_parametrs = weights
self.filter_error = error
self.statusBar().showMessage("Llncosh filter aplicated")
def filter_gmcc(self):
"""
Filter procesing and values assignment
:return: None
"""
self.filter_name = "GMCC"
try:
proces_file = pa.filters.FilterGMCC(n=self.num_of_parametrs,\
mu=self.learning_rate, w="zeros")
output, error, weights = proces_file.run(self.input_data, self.input_desired_data)
except Exception as ex:
print(ex)
self.filter_output = output
self.filter_parametrs = weights
self.filter_error = error
self.statusBar().showMessage("GMCC filter aplicated")
def filter_sslms(self):
"""
Filter procesing and values assignment
:return: None
"""
self.filter_name = "SSLMS"
try:
proces_file = pa.filters.FilterSSLMS(n=self.num_of_parametrs,\
mu=self.learning_rate, w="zeros")
output, error, weights = proces_file.run(self.input_data, self.input_desired_data)
except Exception as ex:
print(ex)
self.filter_output = output
self.filter_parametrs = weights
self.filter_error = error
self.statusBar().showMessage("SSLMS filter aplicated")
def filter_rls(self):
"""
Filter procesing and values assignment
:return: None
"""
self.filter_name = "RLS"
try:
proces_file = pa.filters.FilterRLS(n=self.num_of_parametrs,\
mu=self.learning_rate, w="zeros")
output, error, weights = proces_file.run(self.input_data, self.input_desired_data)
except Exception as ex:
print(ex)
self.filter_output = output
self.filter_parametrs = weights
self.filter_error = error
self.statusBar().showMessage("RLS filter aplicated")
def filter_nsslms(self):
"""
Filter procesing and values assignment
:return: None
"""
self.filter_name = "NSSLMS"
try:
proces_file = pa.filters.FilterNSSLMS(n=self.num_of_parametrs,\
mu=self.learning_rate, w="zeros")
output, error, weights = proces_file.run(self.input_data, self.input_desired_data)
except Exception as ex:
print(ex)
self.filter_output = output
self.filter_parametrs = weights
self.filter_error = error
self.statusBar().showMessage("NSSLMS filter aplicated")
def filter_ap(self):
"""
Filter procesing and values assignment
:return: None
"""
self.filter_name = "AP"
try:
proces_file = pa.filters.FilterAP(n=self.num_of_parametrs,\
mu=self.learning_rate, w="zeros")
output, error, weights = proces_file.run(self.input_data, self.input_desired_data)
except Exception as ex:
print(ex)
self.filter_output = output
self.filter_parametrs = weights
self.filter_error = error
self.statusBar().showMessage("AP filter aplicated")
def filter_nlms(self):
"""
Filter procesing and values assignment
:return: None
"""
self.filter_name = "NLMS"
try:
proces_file = pa.filters.FilterNLMS(n=self.num_of_parametrs,\
mu=self.learning_rate, w="zeros")
output, error, weights = proces_file.run(self.input_data, self.input_desired_data)
except Exception as ex:
print(ex)
self.filter_output = output
self.filter_parametrs = weights
self.filter_error = error
self.statusBar().showMessage("NLMS filter aplicated")
def filter_gngd(self): # mam prohozene vstupy a výstupy...
"""
Filter procesing and values assignment
:return: None
"""
self.filter_name = "GNGD"
# qx = self.input_data.shape()
# input = np.reshape(self.input_data, [qx[0],1], order="F")
# print(self.num_of_parametrs)
# print(self.learning_rate)
# print(self.input_data)
# print(self.input_desired_data)
try:
proces_file = pa.filters.FilterGNGD(n=self.num_of_parametrs,
mu=self.learning_rate, w="zeros")
output, error, weights = proces_file.run(self.input_data, self.input_desired_data)
except Exception as ex:
print(ex)
self.filter_output = output
self.filter_parametrs = weights
self.filter_error = error
self.statusBar().showMessage("GNGD filter aplicated")
def filter_nlmf(self):
"""
Filter procesing and values assignment
:return: None
"""
self.filter_name = "NLMF"
try:
proces_file = pa.filters.FilterNLMF(n=self.num_of_parametrs,\
mu=self.learning_rate, w="zeros")
output, error, weights = proces_file.run(self.input_data, self.input_desired_data)
except Exception as ex:
print(ex)
self.filter_output = output
self.filter_parametrs = weights
self.filter_error = error
self.statusBar().showMessage("NLMF filter aplicated")
def filter_lms(self):
"""
Filter procesing and values assignment
:return: None
"""
self.filter_name = "LMS"
try: