Пример #1
0
        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();
        }
Пример #2
0
        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();
        }