//static async Task Main(string[] args)
static void Main(string[] args)
{
MLContext mlContext = new MLContext(seed: 0);
TextLoader textLoader = null;
textLoader = mlContext.Data.CreateTextLoader <IrisData>(hasHeader: false, separatorChar: ',');
Microsoft.ML.IDataView dataView = textLoader.Load(path_data);
string featuresColumnName = "Features";
var pipeline = mlContext.Transforms
.Concatenate
(
featuresColumnName,
"SepalLength",
"SepalWidth",
"PetalLength",
"PetalWidth"
)
.Append
(
mlContext.Clustering.Trainers.KMeans
(
featuresColumnName,
null,
numberOfClusters: 3
)
);
var model = pipeline.Fit(dataView);
using (var fileStream = new FileStream(path_model, FileMode.Create, FileAccess.Write, FileShare.Write))
{
mlContext.Model.Save(model, textLoader, fileStream);
}
//Task.WaitAll();
return;
}
/// <summary> /// 将<see cref="Microsoft.ML.IDataView"/>转换为<see cref="DataFrancis.IDataView"/> /// </summary> /// <param name="data">待转换的<see cref="IDataView"/></param> /// <param name="maxRows">读取的最大列数，如果这个值过大，可能会对性能产生影响，默认为全部读取</param> /// <returns></returns> public static IDataViewF ToIData(this IDataViewML data, int maxRows = int.MaxValue) => CreateDataObj.DataView(data.Preview(maxRows).RowView.Select(x => CreateDataObj.Data(x.Values, true)).ToArray());
public static ITransformer TrainLearnMLdotnet(MLContext mlContext, string path)
{
IDataView dataView = null;
dataView = mlContext
.Data
.LoadFromTextFile <SomatotypeInputData>
(
path,
hasHeader: true,
separatorChar: ','
);
DataOperationsCatalog.TrainTestData dataSplit;
dataSplit = mlContext.Data.TrainTestSplit(dataView, testFraction: 0.25);
trainData = dataSplit.TrainSet;
testData = dataSplit.TestSet;
IEnumerable <SomatotypeInputData> list = null;
list = mlContext
.Data
.CreateEnumerable <SomatotypeInputData>(dataView, reuseRowObject: false)
.ToList();
for (int i = 0; i < list.Count(); i++)
{
Console.WriteLine($" Id = {list.ElementAt(i).Id}");
Console.WriteLine($" EndomorphicComponent = {list.ElementAt(i).EndomorphicComponent}");
Console.WriteLine($" MesomorphicComponent = {list.ElementAt(i).MesomorphicComponent}");
Console.WriteLine($" EctomorphicComponent = {list.ElementAt(i).EctomorphicComponent}");
}
Microsoft.ML.Transforms.ColumnCopyingEstimator pipeline = null;
pipeline = mlContext.Transforms.CopyColumns
(
outputColumnName: "Label",
inputColumnName: "EndomorphicComponent"
);
pipeline.Append
(
mlContext.Transforms.Concatenate
(
"Features",
"Height",
"Mass",
"BreadthHumerus",
"BreadthFemur",
"GirthArmUpper",
"GirthCalfStanding",
"SkinfoldSubscapular",
"SkinfoldTriceps",
"SkinfoldSupraspinale",
"SkinfoldMedialCalf"
)
);
pipeline.Append(mlContext.Regression.Trainers.FastTree());
Microsoft.ML.Transforms.ColumnCopyingTransformer model_1 = pipeline.Fit(trainData);
return(model = model_1);
}
/// <summary>
/// The UWP host has sent a request for something. Responses to the UWP app are set by
/// the respective case handlers, and sent to the UWP Connection_RequestReceived handler
/// via the AppServiceConnection.
/// </summary>
/// <param name="sender"></param>
/// <param name="args"></param>
private async static void Connection_RequestReceived(AppServiceConnection sender, AppServiceRequestReceivedEventArgs args)
{
var deferral = args.GetDeferral();
ValueSet message = args.Request.Message;
ValueSet returnData = new ValueSet();
string verb = string.Empty;
// get the command verb from the request message
try
{
verb = message["verb"] as String;
}
catch (Exception ex)
{
Debug.WriteLine(ex.Message);
verb = string.Empty;
}
switch (verb)
{
// we received a request to make a prediction
case "makePrediction":
{
try
{
// get the parameters for the prediction from the message data
float sl = (float)message["sl"];
float sw = (float)message["sw"];
float pl = (float)message["pl"];
float pw = (float)message["pw"];
// we switch on the value of the verb in the UWP app that receives this valueSet
returnData.Add("verb", "PredictionResult");
// Use your model to make a prediction
// You can change these numbers to test different predictions
//var prediction = model.CreatePredictionEngine<IrisData, IrisPrediction>(mlContext).Predict(
// new IrisData()
// {
// SepalLength = sl,
// SepalWidth = sw,
// PetalLength = pl,
// PetalWidth = pw,
// });
IrisData inputToTest = new IrisData()
{
SepalLength = sl,
SepalWidth = sw,
PetalLength = pl,
PetalWidth = pw,
};
var predictor = mlContext.Model.CreatePredictionEngine <IrisData, ClusterPrediction>(model);
var prediction = predictor.Predict(inputToTest);
// add the prediction to our response
returnData.Add("Cluster", prediction.PredictedClusterId);
returnData.Add("Distances", string.Join(" ", prediction.Distances));
}
catch (Exception ex)
{
returnData.Add("verb", "PredictionError");
returnData.Add("exceptionMessage", ex.Message.ToString());
}
break;
}
// we received a request to build the model
case "buildModel":
{
try
{
// get the Iris data from the message
string contents = message["irisData"] as string;
// build the List<IrisData>, which is an IEnumerable
// clear the list of data from previous model builds
irisDataList.Clear();
var records = contents.Split(new[] { Environment.NewLine }, StringSplitOptions.None);
foreach (var record in records)
{
var fields = record.Split(',');
IrisData entry = new IrisData();
entry.SepalLength = float.Parse(fields[0]);
entry.SepalWidth = float.Parse(fields[1]);
entry.PetalLength = float.Parse(fields[2]);
entry.PetalWidth = float.Parse(fields[3]);
entry.Label = fields[4];
irisDataList.Add(entry);
}
// If working in Visual Studio, make sure the 'Copy to Output Directory'
// property of iris-data.txt is set to 'Copy always'
//IDataView trainingDataView = mlContext.Data.LoadFromTextFile<IrisData>(path: @"MLDotNetWin32\iris-data.txt", hasHeader: false, separatorChar: ',');
Microsoft.ML.IDataView trainingDataView = mlContext.Data.LoadFromEnumerable <IrisData>(irisDataList);
// Transform your data and add a learner
// Assign numeric values to text in the "Label" column, because only
// numbers can be processed during model training.
// Add a learning algorithm to the pipeline. e.g.(What type of iris is this?)
// Convert the Label back into original text (after converting to number in step 3)
//
// This code is from the old tutorial in ML.net version 0.11
//var pipeline = mlContext.Transforms.Conversion.MapValueToKey("Label")
// .Append(mlContext.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"))
// .AppendCacheCheckpoint(mlContext)
// .Append(mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(labelColumnName: "Label", featureColumnName: "Features"))
// .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
// this is the new ML.Net 1.0 way of doing things.
string featuresColumnName = "Label";
var pipeline = mlContext.Transforms
.Concatenate(featuresColumnName, "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")
.Append(mlContext.Clustering.Trainers.KMeans(featuresColumnName, numberOfClusters: 3));
// Train your model based on the data set
model = pipeline.Fit(trainingDataView);
// let UWP know our model built correctly
returnData.Add("verb", "modelOk");
}
catch (Exception ex)
{
returnData.Add("verb", "modelFailure");
returnData.Add("exceptionMessage", ex.Message);
}
break;
}
default:
{
returnData.Add("verb", "APIError");
returnData.Add("exceptionMessage", "Bad or No Verb");
break;
}
}
try
{
// Return the data to the caller.
await args.Request.SendResponseAsync(returnData);
}
catch (Exception e)
{
// Your exception handling code here.
Debug.WriteLine(e.Message);
}
finally
{
// Complete the deferral so that the platform knows that we're done responding to the app service call.
// Note for error handling: this must be called even if SendResponseAsync() throws an exception.
deferral.Complete();
}
}
