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());
}
