public void OnlineLinearWorkout()
{
var dataPath = GetDataPath("breast-cancer.txt");
var data = TextLoader.CreateReader(Env, ctx => (Label: ctx.LoadFloat(0), Features: ctx.LoadFloat(1, 10)))
.Read(dataPath);
var pipe = data.MakeNewEstimator()
.Append(r => (r.Label, Features: r.Features.Normalize()));
var trainData = pipe.Fit(data).Transform(data).AsDynamic;
var ogdTrainer = new OnlineGradientDescentTrainer(Env, "Label", "Features");
TestEstimatorCore(ogdTrainer, trainData);
var ogdModel = ogdTrainer.Fit(trainData);
ogdTrainer.Train(trainData, ogdModel.Model);
var apTrainer = new AveragedPerceptronTrainer(Env, "Label", "Features", lossFunction: new HingeLoss(), advancedSettings: s =>
{
s.LearningRate = 0.5f;
});
TestEstimatorCore(apTrainer, trainData);
var apModel = apTrainer.Fit(trainData);
apTrainer.Train(trainData, apModel.Model);
Done();
}
public void OnlineLinearWorkout()
{
var dataPath = GetDataPath("breast-cancer.txt");
var regressionData = TextLoaderStatic.CreateReader(ML, ctx => (Label: ctx.LoadFloat(0), Features: ctx.LoadFloat(1, 10)))
.Read(dataPath);
var regressionPipe = regressionData.MakeNewEstimator()
.Append(r => (r.Label, Features: r.Features.Normalize()));
var regressionTrainData = regressionPipe.Fit(regressionData).Transform(regressionData).AsDynamic;
var ogdTrainer = new OnlineGradientDescentTrainer(ML, "Label", "Features");
TestEstimatorCore(ogdTrainer, regressionTrainData);
var ogdModel = ogdTrainer.Fit(regressionTrainData);
ogdTrainer.Train(regressionTrainData, ogdModel.Model);
var binaryData = TextLoaderStatic.CreateReader(ML, ctx => (Label: ctx.LoadBool(0), Features: ctx.LoadFloat(1, 10)))
.Read(dataPath);
var binaryPipe = binaryData.MakeNewEstimator()
.Append(r => (r.Label, Features: r.Features.Normalize()));
var binaryTrainData = binaryPipe.Fit(binaryData).Transform(binaryData).AsDynamic;
var apTrainer = new AveragedPerceptronTrainer(ML, "Label", "Features", lossFunction: new HingeLoss(), advancedSettings: s =>
{
s.LearningRate = 0.5f;
});
TestEstimatorCore(apTrainer, binaryTrainData);
var apModel = apTrainer.Fit(binaryTrainData);
apTrainer.Train(binaryTrainData, apModel.Model);
var svmTrainer = new LinearSvmTrainer(ML, "Label", "Features");
TestEstimatorCore(svmTrainer, binaryTrainData);
var svmModel = svmTrainer.Fit(binaryTrainData);
svmTrainer.Train(binaryTrainData, apModel.Model);
Done();
}