data编程代写、代做C/C++程序设计

日期：2024-11-03 09:54

A Short Course on QT

A cross-platform application framework

Download QT

Installation

1. Run the qt installer you’ve just downloaded.

2. Sign up to acquire a QT account.

3. Accept the license.

Installation

4. Specify installation folder.

5. Select QT 6.x desktop development.

6. Proceed with installation.

Project

Step 1 Create a Qt Project using the wizard.

Project

Select Qt Widgets Application. Step 2

Specify project name and location.

Project

Step 3

Avoid using folder names with a space character (or any foreign characters).

Define the build system

Project

Step 4

Select qmake as the build system.

Use the default base class.

Project

Step 5

Project

Optionally, specify a translation language

Select a Kit

Project

Step 6

Select Manage button to customise the Kit for your project.

Select MinGW 64-bit kit

Project

Step 7

Click the MinGW 64-bit kit

Qt versions

Project

Step 8

Under QT versions, select the latest version (e.g. Qt 6.5.1)

Compilers

Project

Step 9

Under Compilers tab, select MinGW for C++ and C. You may remove the other existing

compilers for the project (if there are any) as we don’t need them.

Debuggers

Project

Step 10

Under Debuggers, we will use the one that comes with MinGW. Click Apply, then OK buttons.

Project

Click Next to proceed.

Project

Click Finish.

Project

Files comprising the start-up codes.

This is the Edit view

Project

Build directory.

A build directory is automatically created for the LetterRecognition project.

Select Form, mainwindow.ui

Project

Step 11

Design view

Project

Step 12

Add a horizontal layout

Project

Step 13

Add a label

Project

Step 14

Project

Step 15

horizontalSlider_maxEpochs

Property value

Widgets

Add a horizontal slider.

Add an LCD number.

Project

Step 16

horizontalSlider_maxEpochs

lcdNumber_maxEpochs

Widgets

Property value

Project

Step 17 Switch to Edit mode, then add a new header file to the project.

Project

Switch to Edit mode, then add a header file to the

project.

Click next, then finish.

Switch to globalVariables.h, then add an external

variable declaration.

Project

Step 18

#ifndef GLOBALVARIABLES_H

#define GLOBALVARIABLES_H

extern int maxEpochs;

#endif // GLOBALVARIABLES_H

Switch to Design view by clicking main.cpp

Project

Step 19

#include <QApplication>

#include "mainwindow.h"

int maxEpochs;

int main(int argc, char *argv[])

{

QApplication a(argc, argv);

MainWindow w;

w.show();

return a.exec();

}

Associate a function with the horizontal slider by

right-clicking it, then selecting Go to slot, then

the valueChanged() function.

Project

Step 20

#include "mainwindow.h"

#include "ui_mainwindow.h"

////////////////////////////////////////

#include "globalVariables.h“

MainWindow::MainWindow(QWidget *parent) :

QMainWindow(parent),

ui(new Ui::MainWindow)

{

ui->setupUi(this);

}

MainWindow::~MainWindow()

{

delete ui;

}

void MainWindow::on_horizontalSlider_maxEpochs_valueChanged(int value)

{

ui->lcdNumber_maxEpochs->setSegmentStyle(QLCDNumber::Filled);

ui->lcdNumber_maxEpochs->display(value);

maxEpochs = value;

}

Write the implementation for the valueChanged()

signal.

Project

Step 21

We now have an interface for the maxEpochs

global variable.

Project

Step 22

Add an LCD for displaying a calculated floating

point value.

Project

Step 1

lcdNumber_result

Improves readability

Add a pushButton.

Project

Step 2

pushButton_Calculate

Right-click the pushButton, then

select Go to slot to assign a

function to it’s clicked() signal.

Write the implementation for the clicked() signal,

inside mainwindow.cpp.

Project

Step 3

void MainWindow::on_horizontalScrollBar_valueChanged(int value)

{

ui->lcdNumber_maxEpochs->setSegmentStyle(QLCDNumber::Filled);

ui->lcdNumber_maxEpochs->display(value);

maxEpochs = value;

}

void MainWindow::on_pushButton_Calculate_clicked()

{

float result=0.0;

result = maxEpochs * 2.2; //some hypothetical formula

ui->lcdNumber_result->display(result);

update();

QCoreApplication::processEvents();

}

Sample run.

Project

Step 4

pushButton_Calculate

Performs a simple calculation:

30 * 2.2

Project

Mouse cursor How to change the mouse cursor to indicate busy

calculation activity.

Add the following header first, in order to access

the mouse cursor methods:

#include <QApplication>

QApplication::setOverrideCursor(QCursor(Qt::WaitCursor));

//perform lengthy operations here…

QApplication::restoreOverrideCursor();

Add more widgets

Project

Step 5

pushButton

plainTextEdit_results

How to update the gui’s display while running a

loop?

Project

Widget’s

display

contents By calling processEvents(), the display of the

widget named ui->plainTextEdit_results will be

updated for each iteration.

By calling processEvents(), the display of the

widget named ui->plainTextEdit_results will be

updated for each iteration.

void MainWindow::on_pushButton_clicked()

{

QString msg;

for(int i=1; i < maxEpochs; i++){

msg.clear();

msg.append("Epoch = ");

msg.append(QString::number(i));

ui->plainTextEdit_results->setPlainText(msg);

QCoreApplication::processEvents(); // qApp->processEvents();

QThread::msleep(50); //delay of 50 msec.

}

}

void MainWindow::on_pushButton_clicked()

{

QString msg;

for(int i=1; i < maxEpochs; i++){

msg.clear();

msg.append("Epoch = ");

msg.append(QString::number(i));

ui->plainTextEdit_results->setPlainText(msg);

QCoreApplication::processEvents(); // qApp->processEvents();

QThread::msleep(50); //delay of 50 msec.

}

}

Example:

requires requires #include <QThread> #include <QThread>

Assignment #2

Letter Recognition using Deep Neural

Nets with Softmax Units

 Learning Objective: Implement backpropagation

learning algorithm for a deep network classifier system.

Consider different weight-update formula variations,

hyperparameter settings, optimization strategies to get the

best network configuration. Apply modern training

techniques.

Letter Recognition Problem

UCI’s Machine Learning Repository

Classification Task: Identify

each of a large number of black and-white rectangular pixel

displays as one of the 26 capital

letters in the English alphabet.

Source: character images based

on 20 different commercial

fonts and each letter within

these 20 fonts was randomly

distorted to produce a file of

20,000 unique stimuli.

http://archive.ics.uci.edu/ml/datasets/Letter+Recognition

Data Set

History:

 P. W. Frey and D. J. Slate (Machine Learning Vol 6 #2 March 91):

"Letter Recognition Using Holland-style Adaptive Classifiers".

 The best accuracy obtained was a little over 80%

Challenge: Using modern deep network training techniques, we would

like to find out what is the best accuracy we can obtain.

DATA SET:

Number of Instances: 20,000

 Missing Attribute Values: None

INPUTS:

16 primitive numerical attributes (statistical moments and edge

counts)

UCI’s Machine Learning Repository

http://archive.ics.uci.edu/ml/datasets/Letter+Recognition

Data Set

INPUTS:

16 primitive numerical attributes (statistical moments and edge counts)

UCI’s Machine Learning Repository

Hand-crafted Input Features

INPUTS:

16 primitive numerical attributes (statistical

moments and edge counts)

UCI’s Machine Learning Repository

http://archive.ics.uci.edu/ml/datasets/Letter+Recognition

The attributes (before scaling to 0-15 range) are:

1. The horizontal position, counting pixels from the left edge of the image, of the center

of the smallest rectangular box that can be drawn with all "on" pixels inside the box.

2. The vertical position, counting pixels from the bottom, of the above box.

3. The width, in pixels, of the box.

4. The height, in pixels, of the box.

5. The total number of "on" pixels in the character image.

6. The mean horizontal position of all "on" pixels relative to the center of the box and

divided by the width of the box. This feature has a negative value if the image is "leftheavy"

as would be the case for the letter L.

7. The mean vertical position of all "on" pixels relative to the center of the box and divided

by the height of the box.

Hand-crafted Input Features

UCI’s Machine Learning Repository

8. The mean squared value of the horizontal pixel distances as measured in 6 above. This attribute will

have a higher value for images whose pixels are more widely separated in the horizontal direction as

would be the case for the letters W or M.

9. The mean squared value of the vertical pixel distances as measured in 7 above.

10. The mean product of the horizontal and vertical distances for each "on" pixel as measured in 6 and 7

above. This attribute has a positive value for diagonal lines that run from bottom left to top right and a

negative value for diagonal lines from top left to bottom right.

11. The mean value of the squared horizontal distance times the vertical distance for each "on" pixel.

This measures the correlation of the horizontal variance with the vertical position.

12. The mean value of the squared vertical distance times the horizontal distance for each "on" pixel.

This measures the correlation of the vertical variance with the horizontal position.

13. The mean number of edges (an "on" pixel immediately to the right of either an "off“ pixel or the

image boundary) encountered when making systematic scans from left to right at all vertical positions

within the box. This measure distinguishes between letters like "W" or "M" and letters like 'T' or "L."

14. The sum of the vertical positions of edges encountered as measured in 13 above. This feature will

give a higher value if there are more edges at the top of the box, as in the letter "Y.“

15. The mean number of edges (an "on" pixel immediately above either an "off" pixel or the image

boundary) encountered when making systematic scans of the image from bottom to top over all

horizontal positions within the box.

16. The sum of horizontal positions of edges encountered as measured in 15 above. http://archive.ics.uci.edu/ml/datasets/Letter+Recognition

Hand-crafted Input Features

INPUTS:

16 primitive numerical attributes (statistical moments and edge counts)

scaled to fit into a range of integer values from 0 through 15.

1. lettr capital letter (26 values from A to Z)

2. x-box horizontal position of box (integer)

3. y-box vertical position of box (integer)

4. width width of box (integer)

5. high height of box (integer)

6. onpix total # on pixels (integer)

7. x-bar mean x of on pixels in box (integer)

8. y-bar mean y of on pixels in box (integer)

9. x2bar mean x variance (integer)

10. y2bar mean y variance (integer)

11. xybar mean x y correlation (integer)

12. x2ybr mean of x * x * y (integer)

13. xy2br mean of x * y * y (integer)

14. x-ege mean edge count left to right (integer)

15. xegvy correlation of x-ege with y (integer)

16. y-ege mean edge count bottom to top (integer)

17. yegvx correlation of y-ege with x (integer)

UCI’s Machine Learning Repository

http://archive.ics.uci.edu/ml/datasets/Letter+Recognition

Letter Recognition Data Set

 INPUTS:

16 primitive numerical attributes (statistical moments and edge

counts)

scaled to fit into a range of integer values from 0 through 15.

TRAINING and TEST SET:

 We typically train on the first 16,000 items and then use the

resulting model to predict the letter category for the remaining

4,000. See the article cited for more details.

UCI’s Machine Learning Repository

http://archive.ics.uci.edu/ml/datasets/Letter+Recognition

Note: We can normalize the inputs (e.g. between [0 to 1]), before feeding them

to the network.

Note: We can normalize the inputs (e.g. between [0 to 1]), before feeding them

to the network.

NN architecture

use Softmax

units

At the output

layer

Minimum of 2 hidden layers

Dataset: Dataset: complete_data_set.txt complete_data_set.txt

Build folder

 Copy the dataset into the build folder to make it

accessible to the program.

Read the dataset contained in

complete_data_set.txt

Load the saved weights

contained in weights.txt

Save the weights resulting from

training. Filename: weights.txt

Max Epochs (may use either

a slider or a spinner widget)

Learning rate (may use either a

slider or a spinner widget)

Train the network using iterative

minimization of error

Randomly initialize the weights

of the network.

As we have learned, shuffling the

training data is important so we

have a data shuffling button

here.

L2 regularization

SSE on Training data Percentage of Good

Classification on Training data

Percentage of Good

Classification on Training data

SSE on Test data Percentage of Good

Classification on Test data

Percentage of Good

Classification on Test data

Single input data pattern

Classification result

Test the input data using the

network

What should I set to compile a Qt program after

moving it to another directory?

1. Firstly, delete any file with the extension .pro.user, as they are

created specific to the user’s directory structure, and must be

regenerated after moving a project to another folder.

• e.g. LetterRecognition.pro.user

2. When you are in Qt creator you should rerun qmake. Go to the

left pane where you typically find "Projects" otherwise select

projects. Go to the project name and do a right click, select

"Run qmake".

3. It’s important to note that a path name (very deep directory

structure) that is very long could cause some problems. Simply

reduce the name or move the folder closer to the root dir.

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