public PostIngestPipe(IngestOptions opt, Ingester filter_previ, ConcurrentQueue <string> msgs)
{
inMessages = msgs;
localFilter = new Filter(opt.regexFilters, msgs);
localDic = new Dictionarizer(localFilter.PieceTask, localFilter.outMessageStrings);
localMark = new Markovizer(opt.gramSize, localDic.PieceTask, localDic.outSentenceBank);
}
static void Main(string[] args)
{
//step2:创建一个管道并且加载你的数据
var pipeline = new LearningPipeline();
string dataPath = "SourceData/iris-data.txt";
var loaderData = new TextLoader <IrisData>(dataPath, separator: ",");
pipeline.Add(loaderData);
//step3:转换数据
//因为在模型训练的时候只能处理数字，所以在Label列中将数值分配给文本
var dictionarizer = new Dictionarizer("Label");
pipeline.Add(dictionarizer);
var concatenator = new ColumnConcatenator("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth");
pipeline.Add(concatenator);
//step4:添加学习者
//添加一个学习算法到管道中，这是一种分类方案（这是什么类型的Iris）
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
//在步骤三转换为数字之后将Label转换回原始文本
pipeline.Add(new PredictedLabelColumnOriginalValueConverter()
{
PredictedLabelColumn = "PredictedLabel"
});
//step5:根据数据集来训练模型
var model = pipeline.Train <IrisData, IrisPrediction>();
var prediction = model.Predict(new IrisData()
{
SepalLength = 10.8f,
SepalWidth = 5.1f,
PetalLength = 2.55f,
PetalWidth = 0.3f
});
Console.WriteLine($"Predicted flower type is {prediction.PredictedLabels}");
Console.Read();
}
