Machine Learning

Machine Learning is a subarea of Artificial Intelligence focused on building algorithms that can learn how to make predictions from patterns in information. This is a project that contains several techniques of data forecast.

How to use

You need to download it and set this project as a library in your project. After that, you will call the techinices using the below code:

Linear Regression

double[] input = new double[] { -2, -1, 1, 4 };
double[] output = new double[] { -3, -1, 2, 3 };
LinearRegression linearRegression = new LinearRegression();
linearRegression.Training(input, output);
linearRegression.Run(0.5d);

Multiple Linear Regression

double[,] inputTrain = { { 2d, 3d }, { 2.5d, 2d }, { 1.8d, 4d } };
double[] outputTrain = { 5d, 6d, 4d };
MultipleLinearRegression mlr = new MultipleLinearRegression(inputTrain.GetLength(1), 0.5d);
mlr.Training(inputTrain, outputTrain);
mlr.Run(new[] { 2.6d, 2.1d });

Perceptron

double[,] inputAnd = new double[,] { { 1, 0 }, { 1, 1 }, { 0, 1 }, { 0, 0 } };
int[] outputAnd = new int[] { 0, 1, 0, 0 };
Perceptron p1 = new Perceptron();
p1.Training(inputAnd, outputAnd);
p1.Run(new double[,] { { 1, 0 } });

Multilayer Perceptron

The first argument of the constructor represents the total values of the input layer, the second argument represents the total of neorons of the hidden layer, and the last represents the total of the output layer.

double[,] inputAnd = new double[,] { { 1, 1 }, { 1, 0 }, { 0, 0 }, { 0, 1 } };
double[] outputAnd = new double[] { 1, 1, 0, 1 };
MultilayerPerceptron mlp = new MultilayerPerceptron(2, 5, 1);
mlp.Training(inputAnd, outputAnd);
mlp.Run(new double[] { 0, 1 });

Support

Do you have any questions? Send a message to msgrubler@gmail.com

Contribute to this Project

You are welcome to fix errors or suggest additions by creating pull requests.