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Revize 9fc5fa93

Přidáno uživatelem Roman Kalivoda před více než 3 roky(ů)

Draft Predictor

re #8598 @2h

Zobrazit rozdíly:

Server/ServerApp.sln
8 8 
Global
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	GlobalSection(SolutionConfigurationPlatforms) = preSolution
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		Debug|Any CPU = Debug|Any CPU
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		Debug|x64 = Debug|x64
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		Release|Any CPU = Release|Any CPU
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		Release|x64 = Release|x64
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	EndGlobalSection
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	GlobalSection(ProjectConfigurationPlatforms) = postSolution
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		{B49B2821-751C-4032-8637-B4E9282DCF06}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
15 17 
		{B49B2821-751C-4032-8637-B4E9282DCF06}.Debug|Any CPU.Build.0 = Debug|Any CPU
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		{B49B2821-751C-4032-8637-B4E9282DCF06}.Debug|x64.ActiveCfg = Debug|x64
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		{B49B2821-751C-4032-8637-B4E9282DCF06}.Debug|x64.Build.0 = Debug|x64
16 20 
		{B49B2821-751C-4032-8637-B4E9282DCF06}.Release|Any CPU.ActiveCfg = Release|Any CPU
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		{B49B2821-751C-4032-8637-B4E9282DCF06}.Release|Any CPU.Build.0 = Release|Any CPU
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		{B49B2821-751C-4032-8637-B4E9282DCF06}.Release|x64.ActiveCfg = Release|x64
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		{B49B2821-751C-4032-8637-B4E9282DCF06}.Release|x64.Build.0 = Release|x64
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	EndGlobalSection
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	GlobalSection(SolutionProperties) = preSolution
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		HideSolutionNode = FALSE
Server/ServerApp/Predictor/IPredictor.cs
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    interface IPredictor
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    {
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        void Fit(ModelInput trainInput);


  		




  
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        void Fit(IEnumerable<ModelInput> trainInput);


  		




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        void Predict(ModelInput testInput);


  		




  
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        ModelOutput Predict(IEnumerable<ModelInput> input);


  		




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    }


  		




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}


  		












  

    
      Server/ServerApp/Predictor/ModelInput.cs
    
  





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namespace ServerApp.Predictor


  		




  
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using Microsoft.ML.Data;


  		




  
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namespace ServerApp.Predictor


  		




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{


  		




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    public class ModelInput


  		




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    {


  		




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        protected class DataPoint


  		




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        {


  		




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            public uint Label { get; set; }


  		




  
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        [ColumnName("label"), LoadColumn(0)]


  		




  
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        public string LabelCol { get; set; }


  		




  
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        [ColumnName("temp"), LoadColumn(1)]


  		




  
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        public double Col1 { get; set; }


  		




  
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        //[ColumnName("date"), LoadColumn(2)]


  		




  
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        //public string Col2 { get; set; }


  		




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            public float[] Features { get; set; }


  		




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        }


  		




  
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        //[ColumnName("time"), LoadColumn(3)]


  		




  
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        //public string Col3 { get; set; }


  		




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    }


  		




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}


  		












  

    
      Server/ServerApp/Predictor/ModelOutput.cs
    
  





  
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using System;


  		




  
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using Microsoft.ML.Data;


  		




  
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namespace ServerApp.Predictor


  		




  
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{


  		




  
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    public class ModelOutput


  		




  
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    {


  		




  
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        [ColumnName("PredictedLabel")]


  		




  
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        public String Prediction { get; set; }


  		




  
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        public float[] Score { get; set; }


  		




  
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    }


  		




  
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}


  		












  

    
      Server/ServerApp/Predictor/NaiveBayesClassifier.cs
    
  





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using System.Text;


  		




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using System.Threading.Tasks;


  		




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using Microsoft.ML;


  		




  
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using Microsoft.ML.Data;


  		




  
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using ServerApp.Parser.OutputInfo;


  		




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namespace ServerApp.Predictor


  		




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{


  		




  ......




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    {


  		




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        private MLContext mlContext;


  		




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        private ITransformer model;


  		




  
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        public NaiveBayesClassifier()


  		




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        {


  		




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            mlContext = new MLContext();


  		




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        }


  		




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        public void Fit(ModelInput trainingData)


  		




  
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        public IEnumerable<ModelInput> ExtractModelInput(List<WeatherInfo> weatherInfos, List<ActivityInfo> activityInfos)


  		




  
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        {


  		




  
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            return weatherInfos.Select(e => new ModelInput(){


  		




  
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                Col1 = e.temp,


  		




  
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                LabelCol = "Empty",


  		




  
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            }).ToList();


  		




  
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        }


  		




  
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        public void Fit(IEnumerable<ModelInput> trainingData)


  		




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        {


  		




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            throw new NotImplementedException();


  		




  
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            IDataView trainingDataView = mlContext.Data.LoadFromEnumerable(trainingData);


  		




  
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            var dataProcessPipeline = mlContext.Transforms.Conversion.MapValueToKey("label", "label")


  		




  
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                .Append(mlContext.Transforms.CopyColumns("Features", "temp"))


  		




  
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                .Append(mlContext.Transforms.NormalizeMinMax("Features", "Features"));


  		




  
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            var trainer = mlContext.MulticlassClassification.Trainers.NaiveBayes();


  		




  
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            var traininingPipeline = dataProcessPipeline.Append(trainer);


  		




  
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            this.model = traininingPipeline.Fit(trainingDataView);


  		




  
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        }


  		




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        public void Predict(ModelInput testData)


  		




  
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        public ModelOutput Predict(IEnumerable<ModelInput> input)


  		




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        {


  		




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            throw new NotImplementedException();


  		




  
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            var data = mlContext.Data.LoadFromEnumerable(input);


  		




  
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            var prediction = model.Transform(data);


  		




  
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            var result = mlContext.Data.CreateEnumerable<ModelOutput>(prediction, reuseRowObject: false);


  		




  
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            return (ModelOutput)result;


  		




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        }


  		




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    }


  		




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}


  		












  

    
      Server/ServerApp/Program.cs
    
  





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using ServerApp.Parser.Parsers;


  		




  
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using ServerApp.Predictor;


  		




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using System;


  		




  
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using System.Collections.Generic;


  		




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namespace ServerApp


  		




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{


  		




  ......




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            p.Parse();


  		




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            NaiveBayesClassifier naiveBayesClassifier = new NaiveBayesClassifier();


  		




  
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            IEnumerable<ModelInput> modelInput = naiveBayesClassifier.ExtractModelInput(p.weatherList, p.jisList);


  		




  
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            naiveBayesClassifier.Fit(modelInput);


  		




  
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            var testData = new ModelInput()


  		




  
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            {


  		




  
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                Col1 = -40,


  		




  
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            };


  		




  
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            List<ModelInput> dataList = new List<ModelInput>();


  		




  
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            dataList.Add(testData);


  		




  
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            var result = naiveBayesClassifier.Predict(dataList);


  		




  
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            Console.WriteLine($"Prediction: {result.Prediction}");


  		




  
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            Console.ReadLine();


  		




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        }


  		




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    }


  		












  

    
      Server/ServerApp/ServerApp.csproj
    
  





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  <Import Project="..\packages\Microsoft.ML.CpuMath.1.5.5\build\netstandard2.0\Microsoft.ML.CpuMath.props" Condition="Exists('..\packages\Microsoft.ML.CpuMath.1.5.5\build\netstandard2.0\Microsoft.ML.CpuMath.props')" />


  		




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    <PlatformTarget>AnyCPU</PlatformTarget>


  		




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    <DebugSymbols>true</DebugSymbols>


  		




  
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    <OutputPath>bin\x64\Debug\</OutputPath>


  		




  
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    <DefineConstants>DEBUG;TRACE</DefineConstants>


  		




  
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    <DebugType>full</DebugType>


  		




  
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    <PlatformTarget>x64</PlatformTarget>


  		




  
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    <LangVersion>7.3</LangVersion>


  		




  
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    <Compile Include="Predictor\ModelOutput.cs" />


  		




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    <Compile Include="Predictor\NaiveBayesClassifier.cs" />


  		




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  ......




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