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.
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:
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);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 });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 } });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 });Do you have any questions? Send a message to msgrubler@gmail.com
You are welcome to fix errors or suggest additions by creating pull requests.