public void Train(List <Person> people, int numberOfTrees, int skillSetSize) { double[][] inputs = _dataPointService.GenerateDataPointsFromPeople(people, skillSetSize); int[] expectedResults = _dataPointService.GenerateExpectedResultFromPeople(people); // Create the forest learning algorithm var teacher = new RandomForestLearning() { NumberOfTrees = numberOfTrees, // use 10 trees in the forest }; // Finally, learn a random forest from data _randomForest = teacher.Learn(inputs, expectedResults); // We can estimate class labels using trainingPredictions = _randomForest.Decide(inputs); // And the classification error (0.0006) can be computed as double error = new ZeroOneLoss(expectedResults).Loss(_randomForest.Decide(inputs)); File.WriteAllLines( @"C:\Users\Niall\Documents\Visual Studio 2015\Projects\LinkedInSearchUi\LinkedIn Dataset\XML\random_forest_predictions.txt" // <<== Put the file name here , trainingPredictions.Select(d => d.ToString()).ToArray()); }
public void Train(List <Person> trainingPeople, int skillSetSize) { double[][] inputs = _dataPointService.GenerateDataPointsFromPeople(trainingPeople, skillSetSize); KMeans kMeans = new KMeans(2); _clustersCollection = kMeans.Learn(inputs); trainingPredictions = _clustersCollection.Decide(inputs); }
public void Train(List <Person> trainingPeople, int skillSetSize) { double[][] inputs = _dataPointService.GenerateDataPointsFromPeople(trainingPeople, skillSetSize); int[] expectedResults = _dataPointService.GenerateExpectedResultFromPeople(trainingPeople); // Now, we can create the sequential minimal optimization teacher var learn = new SequentialMinimalOptimization() { UseComplexityHeuristic = true, UseKernelEstimation = false }; // And then we can obtain a trained SVM by calling its Learn method _supportVectorMachine = learn.Learn(inputs, expectedResults); // Finally, we can obtain the decisions predicted by the machine: trainingPredictions = _supportVectorMachine.Decide(inputs); File.WriteAllLines( @"C:\Users\Niall\Documents\Visual Studio 2015\Projects\LinkedInSearchUi\LinkedIn Dataset\XML\predictions.txt" // <<== Put the file name here , trainingPredictions.Select(d => d.ToString()).ToArray()); }