MFC Tree
•
0 likes•26 views
The document provides instructions for adding a tree control to a dialog box. It includes declaring member variables for the tree control and a string, initializing the tree control by setting its image list, and adding code to insert new items into the tree with text from the string variable when buttons are clicked.
Report
Share
Report
Share
Recommended
Pytorch and Machine Learning for the Math Impaired
High level introduction to machine learning and deep learning concepts, followed by examples in Pytorch
ConfigCrusher: White-Box Performance Analysis for Configurable Systems
ConfigCrusher is a white-box performance analysis approach that uses static and dynamic analysis. It aims to be more accurate and efficient than current black-box approaches like brute forcing all configurations or sampling. ConfigCrusher identifies interactions between configuration options using static analysis, then efficiently measures performance across compressed configurations using instrumentation and processing. The goal is to build an accurate performance model with fewer measurements than state-of-the-art techniques.
Aplikom03 excel or
This document discusses various optimization models that can be solved using linear programming, including linear programming, transportation, assignment, minimal cost network, maximal flow network, and shortest path network. It provides examples of the objective functions and constraints for each model and explains how they can be set up and solved using the Solver tool in Microsoft Excel.
Generative Adversarial Nets
This document discusses generative adversarial nets (GANs). GANs use an adversarial modeling framework where a generator and discriminator are trained against each other. The generator learns to generate fake samples from noise to match the real data distribution, while the discriminator learns to distinguish real from fake samples. They are trained together through a minimax game, with the generator trying to maximize the discriminator's errors. The document proves that the global minimum of the GAN's training criterion is achieved when the generator's distribution pg matches the real data distribution pdata, with the criterion value reaching -log4.
(Full MatLab Code) Image compression DCT
This document contains MATLAB code for performing image compression using the discrete cosine transform (DCT). It reads in an image, converts it to grayscale, and applies the DCT. It sorts the DCT coefficients by power and retains the top 20000 coefficients. It then performs the inverse DCT on the retained coefficients to reconstruct the compressed image, and displays the original and reconstructed images for comparison.
Deep learning
The document discusses deep learning and convolutional neural networks. It provides details on concepts like convolution, activation maps, pooling, and the general architecture of CNNs. CNNs are made up of repeating sequences of convolutional layers and pooling layers, followed by fully connected layers at the end. Convolutional layers apply filters to input images or feature maps from previous layers to extract features. Pooling layers reduce the spatial size to make representations more manageable.
Translating Linear Functions "I AM" Answer Key!
This document defines 14 functions with formulas for their equations. Each function, labeled A through O, expresses g(x) as either a linear or affine equation, providing the coefficients of x and any constants.
Gauss seidal Matlab Code
Gauss Seidal Method, For Numerical analysis. working matlab code. numeric analysis Gauss Seidal method. MATLAB provides tools to solve math. Using linear programing techniques we can easily solve system of equations. This file provides a running code of Gauss Seidal Method
Recommended
Pytorch and Machine Learning for the Math Impaired
High level introduction to machine learning and deep learning concepts, followed by examples in Pytorch
ConfigCrusher: White-Box Performance Analysis for Configurable Systems
ConfigCrusher is a white-box performance analysis approach that uses static and dynamic analysis. It aims to be more accurate and efficient than current black-box approaches like brute forcing all configurations or sampling. ConfigCrusher identifies interactions between configuration options using static analysis, then efficiently measures performance across compressed configurations using instrumentation and processing. The goal is to build an accurate performance model with fewer measurements than state-of-the-art techniques.
Aplikom03 excel or
This document discusses various optimization models that can be solved using linear programming, including linear programming, transportation, assignment, minimal cost network, maximal flow network, and shortest path network. It provides examples of the objective functions and constraints for each model and explains how they can be set up and solved using the Solver tool in Microsoft Excel.
Generative Adversarial Nets
This document discusses generative adversarial nets (GANs). GANs use an adversarial modeling framework where a generator and discriminator are trained against each other. The generator learns to generate fake samples from noise to match the real data distribution, while the discriminator learns to distinguish real from fake samples. They are trained together through a minimax game, with the generator trying to maximize the discriminator's errors. The document proves that the global minimum of the GAN's training criterion is achieved when the generator's distribution pg matches the real data distribution pdata, with the criterion value reaching -log4.
(Full MatLab Code) Image compression DCT
This document contains MATLAB code for performing image compression using the discrete cosine transform (DCT). It reads in an image, converts it to grayscale, and applies the DCT. It sorts the DCT coefficients by power and retains the top 20000 coefficients. It then performs the inverse DCT on the retained coefficients to reconstruct the compressed image, and displays the original and reconstructed images for comparison.
Deep learning
The document discusses deep learning and convolutional neural networks. It provides details on concepts like convolution, activation maps, pooling, and the general architecture of CNNs. CNNs are made up of repeating sequences of convolutional layers and pooling layers, followed by fully connected layers at the end. Convolutional layers apply filters to input images or feature maps from previous layers to extract features. Pooling layers reduce the spatial size to make representations more manageable.
Translating Linear Functions "I AM" Answer Key!
This document defines 14 functions with formulas for their equations. Each function, labeled A through O, expresses g(x) as either a linear or affine equation, providing the coefficients of x and any constants.
Gauss seidal Matlab Code
Gauss Seidal Method, For Numerical analysis. working matlab code. numeric analysis Gauss Seidal method. MATLAB provides tools to solve math. Using linear programing techniques we can easily solve system of equations. This file provides a running code of Gauss Seidal Method
MFC Slider
This document provides code to add a slider control and edit box to display the slider value to a dialog box. It defines variables for the slider and edit box controls, initializes the slider range to 1-100 and initial edit box value to "1". It includes an event handler for the slider scroll event that formats the current slider position to the edit box and updates the display without changing the focus.
Gradient boosting in practice: a deep dive into xgboost
The document discusses tuning parameters for the XGBoost gradient boosting algorithm. It explores different parameters like max_depth, learning_rate, and n_estimators using a news article classification dataset. Experiments are performed to evaluate the effect of these parameters on model accuracy and training time. The learning curves are also plotted to analyze model performance over iterations.
Calc 7.1b
This document discusses using integration to find the area between two curves by considering it as an accumulation process. It explains that we select a representative element, such as a rectangle, and use geometry formulas to relate the area of that element to the functions that define the curves. The area under each representative rectangle is summed to find the total area via integration. Two examples are provided, one finding the area between a parabola and the x-axis using vertical rectangles, and another using horizontal rectangles between two other curves.
Day 6 examples
This document provides examples of graphing composite functions. It explains that to find the domain of a composite function f(g(x)), you must consider the restrictions on the domains of both the inner and outer functions. It then gives three examples of determining the composite functions f(g(x)) and g(f(x)) for various functions f and g, and calculating or graphing their domains and ranges. The final example asks to determine possible functions f and g that satisfy three given composite functions.
Day 7 examples u6w14
This document discusses graphing composite functions. It provides examples of determining the composite functions f(g(x)) and g(f(x)) for various functions f(x) and g(x), sketching the graphs of the composite functions, and stating their domains. It also gives examples of determining possible functions f(x) and g(x) that satisfy given composite functions.
Goal programming
This document provides an introduction and overview of goal programming (GP). It explains that GP is useful when an organization has multiple, sometimes conflicting goals that cannot all be optimized at the same time like in linear programming. GP establishes numeric goals for each objective and attempts to achieve each goal to a satisfactory level by minimizing deviations. The document outlines the basic components of a GP model, including defining goals and constraints, assigning priority levels to goals, and introducing deviational variables. It also provides an example to illustrate how to formulate a GP model and solve it graphically or using the modified simplex method.
Types of function
The document discusses different types of functions including:
1) Surjective functions where the range equals the co-domain.
2) Injective functions where distinct inputs have distinct outputs.
3) Bijective functions which are both injective and surjective.
It also discusses even and odd functions, inverses, composites, and examples of calculating different functions.
Master defence 2020 - Vadym Korshunov - Region-Selected Image Generation with...
Master defence 2020 - Vadym Korshunov - Region-Selected Image Generation with...Lviv Data Science Summer School
Ukrainian Catholic University
Faculty of Applied Sciences
Data Science Master Program
January 22nd
Abstract. Generative adversarial networks (GANs) are one of the most popular models capable of producing high-quality images. However, most of the works generate images from the vector of random values, without explicit control of desired output properties. We study the ways of introducing such control for the user-selected region of interest (RoI). First, we overview and analyze the existing works in areas of image completion (inpainting) and controllable generation. Second, we propose our model based on GANs, which united approaches from the two mentioned areas, for the controllable local content generation. Third, we evaluate the controllability of our model on three accessible datasets – Celeba, Cats, and Cars – and give numerical and visual results of our method.multiple linear regression
This document discusses building a multiple linear regression model to predict profit using backward elimination. It imports datasets, encodes categorical variables, splits data into training and test sets, fits a regression model to the training set, predicts results on the test set, and builds an optimal model through iterative backward elimination of insignificant variables. Key steps include encoding state as a factor, sequentially removing variables with high p-values from the model, and evaluating the models.
Dip 1
This document introduces several MATLAB functions for image processing, including imread() to read an image, imshow() to display it, and imwrite() to save images. It also covers converting images to grayscale with rgb2gray(), adjusting intensity with imadjust(), obtaining histograms with imhist(), and converting to binary with im2bw(). Code examples are provided to demonstrate using these functions to manipulate and analyze image data in MATLAB.
Distributed Support Vector Machines
This is a presentation that I presented for my partial fulfillment of the course Optimization Methods for Machine Learning at IIT Gandhinagar. These slides contain an introduction to Alternating Direction Methods of Multipliers and how is the method used in creating distributed optimization algorithms.
Linear Programming Feasible Region
The document defines linear programming and describes its key components: an objective function to maximize or minimize, constraints in the form of linear inequalities, and non-negativity restrictions on variables. It provides examples of solving linear programming problems (LPPs) graphically and algebraically, including formulating an LPP for a production problem and a hotel planning problem. The document concludes that linear programming is an important mathematical technique that helps managers make optimal decisions.
A Tutorial On Ip 1
The document provides an overview of image processing, including its components, representations of images using matrices, types of images like color, grayscale and binary, concepts of neighborhoods, preprocessing techniques like median filtering and edge detection, segmentation using thresholding and connected components, and morphology operations like erosion, dilation, opening and closing.
Continuously Adaptive Mean Shift(CAMSHIFT)
CAMSHIFT 구현.
* Bradski, Gary R. "Computer vision face tracking for use in a perceptual user interface." (1998).
Integer Programming, Gomory
This document discusses integer programming and methods for solving integer programming problems. It begins with an introduction to integer programming models, including total integer models, 0-1 integer models, and mixed integer models. It then provides examples of each type of integer programming model. The document also describes traditional approaches for solving integer programming problems, including the branch and bound method and Gomory cutting plane method. It provides an example demonstration of applying the Gomory cutting plane method.
Introduction to graphics programming in c
This document provides an introduction to graphics programming in C. It discusses setting up graphics using GCC, basic concepts of graphics programming in C, common graphics functions like line(), circle(), rectangle(), and text functions like outtext() and outtextxy(). It also includes a short example program to demonstrate drawing various shapes and text.
Forms of Quadratics
The document discusses five quadratic functions - f(x), g(x), h(x), j(x), and k(x) - and asks which are equivalent to the given function f(x) = -2x^2 + 4x + 16. It also provides another function f(x) = (5x - 10)(-4x + 20) and asks for its zeros and vertex. Finally, it gives a table representing a quadratic function and asks to write three equivalent functions that model the table.
2 transformation computer graphics
The document discusses various types of transformations in computer graphics, including translation, scaling, and rotation in both 2D and 3D. Translation moves an object by adding offsets to its coordinates. Scaling changes the size of an object by multiplying its coordinates by scaling factors. Rotation changes the orientation of an object by applying trigonometric functions to its coordinates based on a rotation angle and axis. Transformation matrices are used to represent and apply these operations to objects uniformly.
การทำหิมะตกใน Flash
This document provides instructions for creating a snow effect in Flash. It involves importing a snowflake graphic, converting it to a symbol, and then using ActionScript code to randomly position multiple copies of the snowflake graphic on the stage. The snowflakes will move down and across the screen within the boundaries of the movie clip.
MFC List
This document provides code to initialize and populate a list control in MFC. It defines variables to reference the list control and combo box, initializes image lists for the list view, sets up columns and inserts list items with text and images. It also includes code to change the list view style based on the selected combo box item.
MFC Bitmap
This document provides instructions for creating a bitmap dialog box that displays different images based on a combo box selection. It includes declaring three bitmap variables to store the images, initializing them to load images from resources, and using a switch statement in the combo box notification handler to set the displayed image based on the selected index.
MFC Calculator 2
The document provides instructions for adding functionality to a calculator application. It instructs the developer to add two integer variables to store the edit field and result values. It also provides code to initialize these variables and add event handler functions for buttons to multiply and add to the edit field, add the edit field to the result, and clear the edit field. The handlers implement this functionality by manipulating the two integer variables.
More Related Content
What's hot
MFC Slider
This document provides code to add a slider control and edit box to display the slider value to a dialog box. It defines variables for the slider and edit box controls, initializes the slider range to 1-100 and initial edit box value to "1". It includes an event handler for the slider scroll event that formats the current slider position to the edit box and updates the display without changing the focus.
Gradient boosting in practice: a deep dive into xgboost
The document discusses tuning parameters for the XGBoost gradient boosting algorithm. It explores different parameters like max_depth, learning_rate, and n_estimators using a news article classification dataset. Experiments are performed to evaluate the effect of these parameters on model accuracy and training time. The learning curves are also plotted to analyze model performance over iterations.
Calc 7.1b
This document discusses using integration to find the area between two curves by considering it as an accumulation process. It explains that we select a representative element, such as a rectangle, and use geometry formulas to relate the area of that element to the functions that define the curves. The area under each representative rectangle is summed to find the total area via integration. Two examples are provided, one finding the area between a parabola and the x-axis using vertical rectangles, and another using horizontal rectangles between two other curves.
Day 6 examples
This document provides examples of graphing composite functions. It explains that to find the domain of a composite function f(g(x)), you must consider the restrictions on the domains of both the inner and outer functions. It then gives three examples of determining the composite functions f(g(x)) and g(f(x)) for various functions f and g, and calculating or graphing their domains and ranges. The final example asks to determine possible functions f and g that satisfy three given composite functions.
Day 7 examples u6w14
This document discusses graphing composite functions. It provides examples of determining the composite functions f(g(x)) and g(f(x)) for various functions f(x) and g(x), sketching the graphs of the composite functions, and stating their domains. It also gives examples of determining possible functions f(x) and g(x) that satisfy given composite functions.
Goal programming
This document provides an introduction and overview of goal programming (GP). It explains that GP is useful when an organization has multiple, sometimes conflicting goals that cannot all be optimized at the same time like in linear programming. GP establishes numeric goals for each objective and attempts to achieve each goal to a satisfactory level by minimizing deviations. The document outlines the basic components of a GP model, including defining goals and constraints, assigning priority levels to goals, and introducing deviational variables. It also provides an example to illustrate how to formulate a GP model and solve it graphically or using the modified simplex method.
Types of function
The document discusses different types of functions including:
1) Surjective functions where the range equals the co-domain.
2) Injective functions where distinct inputs have distinct outputs.
3) Bijective functions which are both injective and surjective.
It also discusses even and odd functions, inverses, composites, and examples of calculating different functions.
Master defence 2020 - Vadym Korshunov - Region-Selected Image Generation with...
Master defence 2020 - Vadym Korshunov - Region-Selected Image Generation with...Lviv Data Science Summer School
Ukrainian Catholic University
Faculty of Applied Sciences
Data Science Master Program
January 22nd
Abstract. Generative adversarial networks (GANs) are one of the most popular models capable of producing high-quality images. However, most of the works generate images from the vector of random values, without explicit control of desired output properties. We study the ways of introducing such control for the user-selected region of interest (RoI). First, we overview and analyze the existing works in areas of image completion (inpainting) and controllable generation. Second, we propose our model based on GANs, which united approaches from the two mentioned areas, for the controllable local content generation. Third, we evaluate the controllability of our model on three accessible datasets – Celeba, Cats, and Cars – and give numerical and visual results of our method.multiple linear regression
This document discusses building a multiple linear regression model to predict profit using backward elimination. It imports datasets, encodes categorical variables, splits data into training and test sets, fits a regression model to the training set, predicts results on the test set, and builds an optimal model through iterative backward elimination of insignificant variables. Key steps include encoding state as a factor, sequentially removing variables with high p-values from the model, and evaluating the models.
Dip 1
This document introduces several MATLAB functions for image processing, including imread() to read an image, imshow() to display it, and imwrite() to save images. It also covers converting images to grayscale with rgb2gray(), adjusting intensity with imadjust(), obtaining histograms with imhist(), and converting to binary with im2bw(). Code examples are provided to demonstrate using these functions to manipulate and analyze image data in MATLAB.
Distributed Support Vector Machines
This is a presentation that I presented for my partial fulfillment of the course Optimization Methods for Machine Learning at IIT Gandhinagar. These slides contain an introduction to Alternating Direction Methods of Multipliers and how is the method used in creating distributed optimization algorithms.
Linear Programming Feasible Region
The document defines linear programming and describes its key components: an objective function to maximize or minimize, constraints in the form of linear inequalities, and non-negativity restrictions on variables. It provides examples of solving linear programming problems (LPPs) graphically and algebraically, including formulating an LPP for a production problem and a hotel planning problem. The document concludes that linear programming is an important mathematical technique that helps managers make optimal decisions.
A Tutorial On Ip 1
The document provides an overview of image processing, including its components, representations of images using matrices, types of images like color, grayscale and binary, concepts of neighborhoods, preprocessing techniques like median filtering and edge detection, segmentation using thresholding and connected components, and morphology operations like erosion, dilation, opening and closing.
Continuously Adaptive Mean Shift(CAMSHIFT)
CAMSHIFT 구현.
* Bradski, Gary R. "Computer vision face tracking for use in a perceptual user interface." (1998).
Integer Programming, Gomory
This document discusses integer programming and methods for solving integer programming problems. It begins with an introduction to integer programming models, including total integer models, 0-1 integer models, and mixed integer models. It then provides examples of each type of integer programming model. The document also describes traditional approaches for solving integer programming problems, including the branch and bound method and Gomory cutting plane method. It provides an example demonstration of applying the Gomory cutting plane method.
Introduction to graphics programming in c
This document provides an introduction to graphics programming in C. It discusses setting up graphics using GCC, basic concepts of graphics programming in C, common graphics functions like line(), circle(), rectangle(), and text functions like outtext() and outtextxy(). It also includes a short example program to demonstrate drawing various shapes and text.
Forms of Quadratics
The document discusses five quadratic functions - f(x), g(x), h(x), j(x), and k(x) - and asks which are equivalent to the given function f(x) = -2x^2 + 4x + 16. It also provides another function f(x) = (5x - 10)(-4x + 20) and asks for its zeros and vertex. Finally, it gives a table representing a quadratic function and asks to write three equivalent functions that model the table.
2 transformation computer graphics
The document discusses various types of transformations in computer graphics, including translation, scaling, and rotation in both 2D and 3D. Translation moves an object by adding offsets to its coordinates. Scaling changes the size of an object by multiplying its coordinates by scaling factors. Rotation changes the orientation of an object by applying trigonometric functions to its coordinates based on a rotation angle and axis. Transformation matrices are used to represent and apply these operations to objects uniformly.
การทำหิมะตกใน Flash
This document provides instructions for creating a snow effect in Flash. It involves importing a snowflake graphic, converting it to a symbol, and then using ActionScript code to randomly position multiple copies of the snowflake graphic on the stage. The snowflakes will move down and across the screen within the boundaries of the movie clip.
What's hot (19)
Gradient boosting in practice: a deep dive into xgboost
Gradient boosting in practice: a deep dive into xgboost
Master defence 2020 - Vadym Korshunov - Region-Selected Image Generation with...
Master defence 2020 - Vadym Korshunov - Region-Selected Image Generation with...
Similar to MFC Tree
MFC List
This document provides code to initialize and populate a list control in MFC. It defines variables to reference the list control and combo box, initializes image lists for the list view, sets up columns and inserts list items with text and images. It also includes code to change the list view style based on the selected combo box item.
MFC Bitmap
This document provides instructions for creating a bitmap dialog box that displays different images based on a combo box selection. It includes declaring three bitmap variables to store the images, initializing them to load images from resources, and using a switch statement in the combo box notification handler to set the displayed image based on the selected index.
MFC Calculator 2
The document provides instructions for adding functionality to a calculator application. It instructs the developer to add two integer variables to store the edit field and result values. It also provides code to initialize these variables and add event handler functions for buttons to multiply and add to the edit field, add the edit field to the result, and clear the edit field. The handlers implement this functionality by manipulating the two integer variables.
MFC Pen
The document provides instructions for creating a MFC dialog-based application with controls to set pen properties like thickness, color, and text. It describes adding member variables to store the properties, initializing them in constructors, and handling button clicks to open a color dialog and update the properties. The properties are then used to draw a rectangle and text when the mouse is clicked in the view window.
MFC Rect1
The document describes adding member variables and methods to a CRect1View class. It defines three member variables - m_button, x, and y - and their types. It also provides code for initializing the variables in the constructor, handling right mouse button clicks to set m_button to TRUE, and conditionally drawing a line in the OnPaint method if m_button is TRUE.
MFC Letter
This document contains C++ code for managing text editing controls and colors on a dialog box. It defines functions for copying text between two edit controls, changing the case of text in an edit control, and handling checkbox clicks to change the text color. It also defines a CColors class to manage the text and background colors, with methods to set the colors on a device context and return a brush handle.
Introduction to cython: example of GCoptimization
This document discusses using Cython to interface Python with C/C++ code to improve computational performance. It provides two examples: (1) wrapping an entire C++ graph cut library in Cython, resulting in an 18 second runtime; and (2) using Cython to call a C++ graph cut function as a black box, achieving a runtime of 0.37 seconds, nearly 50 times faster. The document emphasizes that Cython can provide large speedups with relatively little code by leveraging existing optimized C/C++ implementations.
Program to print array elements using System; namespace Retai.pdf
// Program to print array elements
using System;
namespace RetailitemApplication
{
class Retailitem // create class
{
public string description;
public int unitsonhand;
public double price;
// constructor
public Retailitem(string s, int i, double d) {
description = s;
unitsonhand = i;
price = d;
}
}
class RetailItemTest
{
static void Main ()
{
Console.WriteLine(\"RetailItemTest item printing\");
Console.WriteLine(\"\");
Retailitem item1 = new Retailitem(\"Jacket\", 12, 59.95); // objects of class
Retailitem item2 = new Retailitem(\"Jeans\", 40, 34.95);
Retailitem item3 = new Retailitem(\"Shirt\", 20, 24.95);
Retailitem [] allItems = new Retailitem[3];
allItems[0] = item1;
allItems[1] = item2;
allItems[2] = item3;
Retailitem testItem1 = null;
for (int i = 0; i < 3; i++)
{
testItem1 = allItems[i];
Console.WriteLine(\"Retailitem \" + i + \"=\" + testItem1.description + \" \" +
testItem1.unitsonhand + \" \" + testItem1.price);
}
// Keep the console window open in debug mode.
Console.WriteLine ( \"Press any key to exit.\" );
Console.ReadKey ();
}
}
}
Solution
// Program to print array elements
using System;
namespace RetailitemApplication
{
class Retailitem // create class
{
public string description;
public int unitsonhand;
public double price;
// constructor
public Retailitem(string s, int i, double d) {
description = s;
unitsonhand = i;
price = d;
}
}
class RetailItemTest
{
static void Main ()
{
Console.WriteLine(\"RetailItemTest item printing\");
Console.WriteLine(\"\");
Retailitem item1 = new Retailitem(\"Jacket\", 12, 59.95); // objects of class
Retailitem item2 = new Retailitem(\"Jeans\", 40, 34.95);
Retailitem item3 = new Retailitem(\"Shirt\", 20, 24.95);
Retailitem [] allItems = new Retailitem[3];
allItems[0] = item1;
allItems[1] = item2;
allItems[2] = item3;
Retailitem testItem1 = null;
for (int i = 0; i < 3; i++)
{
testItem1 = allItems[i];
Console.WriteLine(\"Retailitem \" + i + \"=\" + testItem1.description + \" \" +
testItem1.unitsonhand + \" \" + testItem1.price);
}
// Keep the console window open in debug mode.
Console.WriteLine ( \"Press any key to exit.\" );
Console.ReadKey ();
}
}
}.
PROGRAM KONVERSI CITRA BERWARNA MENJADI CITRA GRAYSCALE
This document discusses a mid-semester exam on a program to convert colored images to grayscale images. It includes the coding for the PCitra.cpp file, which contains functions to convert pixel color to RGB values and vice versa, and handlers for buttons to load an image, display author names, and apply grayscale and binarization conversions using different weighting formulas on the RGB channels.
Similar to MFC Tree (9)
Program to print array elements using System; namespace Retai.pdf
Program to print array elements using System; namespace Retai.pdf
PROGRAM KONVERSI CITRA BERWARNA MENJADI CITRA GRAYSCALE
PROGRAM KONVERSI CITRA BERWARNA MENJADI CITRA GRAYSCALE
More from Razvan Raducanu, PhD
12. edit record
The document describes an edit action method that retrieves a flower object by ID, binds it to a form, handles form submission, validates the form, saves the updated flower object if valid, and redirects to the index action on success. It also includes the view template for rendering the edit form which displays the form fields and submit button.
11. delete record
This document contains code for deleting a record from a database table. The deleteAction method in the IndexController checks if the ID is valid, gets the flower record, and checks if a POST request is made. If the form submits "Yes", it calls the deleteFlower method to remove the record from the table. It then redirects to the index action. The deleteFlower method in FlowerTable deletes the record from the database. The view displays a confirmation form to submit "Yes" or "No" for deleting.
10. view one record
The IndexController handles viewing a single flower record. It gets the flower ID from the route, retrieves the flower object from the database using the FlowerTable, and passes it to the view template. The view template then displays the flower's name, color, size, and price properties. The module configuration defines the route and default controller/action. The FlowerTable class contains a method to retrieve a flower by ID from the database table gateway.
8. vederea inregistrarilor
The document discusses setting up a database module in Zend Framework to manage flower data. It includes creating a Flower model and FlowerTable model classes to interact with the database and retrieve/store flower data. It also creates an IndexController to fetch flower data from the database and display it in a view template, with links to view, edit and delete individual flower records.
7. copy1
The document defines a form controller with actions for displaying an initial form and processing the submitted form. It includes view templates for the form and results. The form has text fields for name1 and name2 and a submit button. When submitted, the name1 value is displayed back on the results page along with the original form. Configuration is provided to map the controller actions and view templates.
6. hello popescu 2
The document defines a form controller class with actions for displaying a form to collect a user's name and displaying it back. It includes a form object, view models, and templates to render the form and output. The module configuration defines routes for the form and display actions. The form has a text field for the user's name and is submitted to the showpopescu action.
5. hello popescu
This document contains code for a PHP form controller class that handles displaying a form and processing form submissions. The controller renders a view for displaying the form, which includes logic to show a personalized greeting if a name was submitted or prompt the user to enter their name. The module configuration routes requests to the controller and specifies view templates.
2.routing in zend framework 3
This document discusses routing in Zend Framework 3. It shows how to create controllers and routes for different pages. Specifically, it creates a WelcomeController with index and contact actions. It defines routes for the homepage, /welcome, and /contact pages. It also creates corresponding view templates and links between the pages. This allows routing requests for different URLs to the appropriate controllers and actions to display the corresponding pages.
1. zend framework intro
This document provides instructions for creating a module in Zend Framework 3. It describes copying the Application module directory and renaming it to Test. It also explains the necessary configuration changes to the composer.json, modules.config.php, and creation of Module, Controller, and view files to integrate the new Test module into the application. Specific code examples and file paths are given.
18. images in symfony 4
This document discusses how to create and manage image data in a Symfony application. It includes instructions for generating entities and migrations to create an Image table in the database with fields for id, name, and photo. Templates and a controller are created to display and manage the image records. Images files are added to the public/images folder and associated to records.
17. delete data
This document contains code for deleting data from a database table. The Twig template displays a table of flower data with edit and delete links. The DataController defines a delete route that finds the flower entity by ID, removes it from the entity manager, flushes to the database, and redirects back to the data page.
15. view single data
This document shows how to view single data records in Symfony 4. It includes Twig templates for displaying a list of records and a single record, as well as a DataController class with actions for getting a single record by ID from the database and passing it to the Twig template for display. The list template loops through records and links to the show action. The show template displays the fields of a single record passed from the controller.
14. add data in symfony4
The document describes how to add data in Symfony4 by:
1) Creating a DataController class with routes to index, add, and save flower data
2) Building a form to submit flower properties using form types and handling form requests
3) Persisting submitted flower objects to the database using Doctrine
13. view data
This document provides instructions for viewing data in Symfony4. It describes creating a DataController using the console, starting a local server, and viewing data at a localhost URL. It also shows base and data templates used to display data in a table, and a DataController class that retrieves flower data from the database and passes it to the template.
11. move in Symfony 4
This document describes creating a Symfony controller and form to move user input between form fields. It includes:
1) Creating a MoveController using make:controller that handles a request and renders a Twig template.
2) The Twig template extends a base template and contains a form to input names into different fields and a dropdown.
3) The controller creates a form builder, handles form submission, and passes data to the Twig template based on which field was submitted.
More from Razvan Raducanu, PhD (20)
Recently uploaded
Advanced Java[Extra Concepts, Not Difficult].docx
This is part 2 of my Java Learning Journey. This contains Hashing, ArrayList, LinkedList, Date and Time Classes, Calendar Class and more.
A Survey of Techniques for Maximizing LLM Performance.pptx
A Survey of Techniques for Maximizing LLM Performance
Main Java[All of the Base Concepts}.docx
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
RPMS Template 2023-2024 by: Irene S. Rueco
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptxMohd Adib Abd Muin, Senior Lecturer at Universiti Utara Malaysia
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...National Information Standards Organization (NISO)
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.DRUGS AND ITS classification slide share
Any substance (other than food) that is used to prevent, diagnose, treat, or relieve symptoms of a
disease or abnormal condition
The History of Stoke Newington Street Names
Presented at the Stoke Newington Literary Festival on 9th June 2024
www.StokeNewingtonHistory.com
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar KanvariaTop five deadliest dog breeds in America
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
PCOS corelations and management through Ayurveda.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
The Diamonds of 2023-2024 in the IGRA collection
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Recently uploaded (20)
A Survey of Techniques for Maximizing LLM Performance.pptx
A Survey of Techniques for Maximizing LLM Performance.pptx
Liberal Approach to the Study of Indian Politics.pdf
Liberal Approach to the Study of Indian Politics.pdf
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Digital Artefact 1 - Tiny Home Environmental Design
Digital Artefact 1 - Tiny Home Environmental Design
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
MFC Tree
- 1. Tree
- 14. Add the following variables: Ctrl ID Var name Category Var type IDC_EDIT1 m_str Value CString IDC_TREE1 m_ctrlTree Control CTreeCtrl
- 15. In TreeDlg.h declare the member var: ……….. public: CString m_str; CTreeCtrl m_ctrlTree; CImageList m_image; };
- 16. Initialize the var in CTreeDlg::OnInitDialog() ………………… // TODO: Add extra initialization here m_image.Create(IDB_BITMAP1, 16, 1, RGB(12, 15, 18)); m_ctrlTree.SetImageList(&m_image, TVSIL_NORMAL); return TRUE; // return TRUE unless you set the focus to a control }
- 19. void CTreeDlg::OnBnClickedButton1() { // TODO: Add your control notification handler code here UpdateData(TRUE); m_ctrlTree.InsertItem(m_str, 0, 1, TVI_ROOT); m_ctrlTree.SetFocus(); }
- 22. void CTreeDlg::OnBnClickedButton2() { // TODO: Add your control notification handler code here UpdateData(TRUE); HTREEITEM ht = m_ctrlTree.GetSelectedItem(); m_ctrlTree.InsertItem(m_str, 0, 1, ht); m_ctrlTree.SetFocus(); }