C# IExampleInterface类代码示例

        public void Execute(IExampleInterface app)
        {
            //placed a try catch in case something bugs.
            try
            {
                //lets check the lenght of the input from the console.
                if (app.Args.Length < 1)
                {
                    Console.WriteLine(@"MarketPredict [generate/train/prune/evaluate] [PathToFile]");
                    Console.WriteLine(@"e.g csvMarketPredict [generate/train/prune/evaluate] c:\\EURUSD.csv");
                }

               //looks like we are fine.
                else
                {

                    //save the files in the directory where the consoleexample.exe is located.
                    FileInfo dataDir = new FileInfo(@"c:\");

                    //we generate the network , by calling the CSVloader.
                    if (String.Compare(app.Args[0], "generate", true) == 0)
                    {
                        Console.WriteLine("Generating your network with file:" + app.Args[1]);
                        MarketBuildTraining.Generate(app.Args[1]);
                    }

                    //train the network here.
                    else if (String.Compare(app.Args[0], "train", true) == 0)
                    {
                        MarketTrain.Train(dataDir);
                    }
                    //Evaluate the network that was built and trained.
                    else if (String.Compare(app.Args[0], "evaluate", true) == 0)
                    {
                        MarketEvaluate.Evaluate(dataDir,app.Args[1]);
                    }
                    //Lets prune the network.
                    else if (String.Compare(app.Args[0], "prune", true) == 0)
                    {

                        MarketPrune.Incremental(dataDir);

                    }
                    else
                    {
                        Console.WriteLine("Didnt understand the command you typed:" + app.Args[0]);

                    }
                }
            }

            catch (Exception ex)
            {

                Console.WriteLine("Error Message:"+ ex.Message);
                Console.WriteLine("Error Innerexception:" + ex.InnerException);
                Console.WriteLine("Error stacktrace:" + ex.StackTrace);
                Console.WriteLine("Error source:" + ex.Source);
            }
        }

        /// <summary>
        /// Program entry point.
        /// </summary>
        /// <param name="app">Holds arguments and other info.</param>
        public void Execute(IExampleInterface app)
        {
            BasicNetwork network = CreateNetwork();

            IMLTrain train;

            if (app.Args.Length > 0 && String.Compare(app.Args[0], "anneal", true) == 0)
            {
                train = new NeuralSimulatedAnnealing(
                    network, new PilotScore(), 10, 2, 100);
            }
            else
            {
                train = new NeuralGeneticAlgorithm(
                    network, new FanInRandomizer(),
                    new PilotScore(), 500, 0.1, 0.25);
            }

            int epoch = 1;

            for (int i = 0; i < 50; i++)
            {
                train.Iteration();
                Console.WriteLine(@"Epoch #" + epoch + @" Score:" + train.Error);
                epoch++;
            }

            Console.WriteLine(@"\nHow the winning network landed:");
            network = (BasicNetwork) train.Method;
            var pilot = new NeuralPilot(network, true);
            Console.WriteLine(pilot.ScorePilot());
            EncogFramework.Instance.Shutdown();
        }

        /// <summary>
        /// Program entry point.
        /// </summary>
        /// <param name="app">Holds arguments and other info.</param>
        public void Execute(IExampleInterface app)
        {
            var network = new FlatNetwork(2, 4, 0, 1, false);
            network.Randomize();

            IMLDataSet trainingSet = new BasicMLDataSet(XORInput, XORIdeal);


            var train = new TrainFlatNetworkResilient(network, trainingSet);

            int epoch = 1;

            do
            {
                train.Iteration();
                Console.WriteLine(@"Epoch #" + epoch + @" Error:" + train.Error);
                epoch++;
            } while (train.Error > 0.01);

            var output = new double[1];
            // test the neural network
            Console.WriteLine(@"Neural Network Results:");
            foreach (IMLDataPair pair in trainingSet)
            {
                double[] input = pair.Input.Data;
                network.Compute(input, output);
                Console.WriteLine(input[0] + @"," + input[1] + @":" + output[0]);
            }
        }

        /// <summary>
        /// Program entry point.
        /// </summary>
        /// <param name="app">Holds arguments and other info.</param>
        public void Execute(IExampleInterface app)
        {
            IMLDataSet trainingSet = new BasicMLDataSet(XORInput, XORIdeal);

            FlatNetwork network = CreateNetwork();

            Console.WriteLine(@"Starting Weights:");
            DisplayWeights(network);
            Evaluate(network, trainingSet);

            var train = new TrainFlatNetworkResilient(
                network, trainingSet);

            for (int iteration = 1; iteration <= ITERATIONS; iteration++)
            {
                train.Iteration();

                Console.WriteLine();
                Console.WriteLine(@"*** Iteration #" + iteration);
                Console.WriteLine(@"Error: " + train.Error);
                Evaluate(network, trainingSet);

                Console.WriteLine(@"LastGrad:"
                                  + FormatArray(train.LastGradient));
                Console.WriteLine(@"Updates :"
                                  + FormatArray(train.UpdateValues));

                DisplayWeights(network);
            }
        }

        public void Execute(IExampleInterface app)
        {
            if (app.Args.Length != 2)
            {
                Console.WriteLine("Usage: AnalystExample [iris/forest] [data directory]");
                Console.WriteLine("Data directory can be any empty directory.  Raw files will be downloaded to here.");
                return;
            }
            String command = app.Args[0].Trim().ToLower();
            var dir = new FileInfo(app.Args[1].Trim());

            var example = new AnalystExample();

            if (String.Compare(command, "forest", true) == 0)
            {
                example.ForestExample(dir);
            }
            else if (String.Compare(command, "iris", true) == 0)
            {
                example.IrisExample(dir);
            }
            else
            {
                Console.WriteLine("Unknown command: " + command);
            }
        }

        public void Execute(IExampleInterface app)
        {
            // create a neural network, without using a factory
            var svm = new SupportVectorMachine(1,true); // 1 input, & true for regression

            // create training data
            IMLDataSet trainingSet = new BasicMLDataSet(RegressionInput, RegressionIdeal);

            // train the SVM
            IMLTrain train = new SVMSearchTrain(svm, trainingSet);

            int epoch = 1;

            do
            {
                train.Iteration();
                Console.WriteLine(@"Epoch #" + epoch + @" Error:" + train.Error);
                epoch++;
            } while (train.Error > 0.01);

            // test the SVM
            Console.WriteLine(@"SVM Results:");
            foreach (IMLDataPair pair in trainingSet)
            {
                IMLData output = svm.Compute(pair.Input);
                Console.WriteLine(pair.Input[0]
                                  + @", actual=" + output[0] + @",ideal=" + pair.Ideal[0]);
            }
        }

        public static void TrainElmhanNetwork(ref IExampleInterface app)
        {
            BasicMLDataSet set = CreateEval.CreateEvaluationSetAndLoad(app.Args[1], CONFIG.STARTING_YEAR,
                                                                                              CONFIG.TRAIN_END,
                                                                                              CONFIG.INPUT_WINDOW,
                                                                                              CONFIG.PREDICT_WINDOW);

            //create our network.
            BasicNetwork network =
                (BasicNetwork)CreateEval.CreateElmanNetwork(CONFIG.INPUT_WINDOW, CONFIG.PREDICT_WINDOW);

            //Train it..

            double LastError = CreateEval.TrainNetworks(network, set);

            Console.WriteLine("NetWork Trained to :" + LastError);
            SuperUtils.SaveTraining(CONFIG.DIRECTORY, CONFIG.TRAINING_FILE, set);
            SuperUtils.SaveNetwork(CONFIG.DIRECTORY, CONFIG.NETWORK_FILE, network);
            Console.WriteLine("Network Saved to :" + CONFIG.DIRECTORY + " File Named :" +
                              CONFIG.NETWORK_FILE);

            Console.WriteLine("Training Saved to :" + CONFIG.DIRECTORY + " File Named :" +
                              CONFIG.TRAINING_FILE);
            MakeAPause();
        }

 public void Execute(IExampleInterface app)
 {
     for (int i = 1; i < 16; i++)
     {
         Perform(i);
     }
 }

 public void Execute(IExampleInterface app)
 {
     if (app.Args.Length < 3)
     {
         Console.WriteLine(@"MarketPredict [data dir] [generate/train/prune/evaluate] PathToFile");
         Console.WriteLine(@"e.g csvMarketPredict [data dir] [generate/train/prune/evaluate] c:\\EURUSD.csv");
     }
     else
     {
         FileInfo dataDir = new FileInfo(Environment.CurrentDirectory);
         if (String.Compare(app.Args[0], "generate", true) == 0)
         {
             MarketBuildTraining.Generate(app.Args[1]);
         }
         else if (String.Compare(app.Args[0], "train", true) == 0)
         {
             MarketTrain.Train(dataDir);
         }
         else if (String.Compare(app.Args[0], "evaluate", true) == 0)
         {
             MarketEvaluate.Evaluate(dataDir,app.Args[1]);
         }
         else if (String.Compare(app.Args[0], "prune", true) == 0)
         {
             {
                 MarketPrune.Incremental(dataDir);
             }
         }
     }
 }

        public void Execute(IExampleInterface app)
        {
            if (app.Args.Length != 2)
            {
                Console.WriteLine(@"Note: This example assumes that headers are present in the CSV files.");
                Console.WriteLine(@"NormalizeFile [input file] [target file]");
            }
            else
            {
                var sourceFile = new FileInfo(app.Args[0]);
                var targetFile = new FileInfo(app.Args[1]);

                var analyst = new EncogAnalyst();
                var wizard = new AnalystWizard(analyst);
                wizard.Wizard(sourceFile, true, AnalystFileFormat.DecpntComma);

                DumpFieldInfo(analyst);

                var norm = new AnalystNormalizeCSV();
                norm.Analyze(sourceFile, true, CSVFormat.English, analyst);
                norm.ProduceOutputHeaders = true;
                norm.Normalize(targetFile);
                EncogFramework.Instance.Shutdown();
            }
        }

        public void Execute(IExampleInterface app)
        {
            this.app = app;

            // Create the neural network.
            BasicLayer hopfield;
            var network = new HopfieldNetwork(4);

            // This pattern will be trained
            bool[] pattern1 = {true, true, false, false};
            // This pattern will be presented
            bool[] pattern2 = {true, false, false, false};
            IMLData result;

            var data1 = new BiPolarMLData(pattern1);
            var data2 = new BiPolarMLData(pattern2);
            var set = new BasicMLDataSet();
            set.Add(data1);

            // train the neural network with pattern1
            app.WriteLine("Training Hopfield network with: "
                          + FormatBoolean(data1));

            network.AddPattern(data1);
            // present pattern1 and see it recognized
            result = network.Compute(data1);
            app.WriteLine("Presenting pattern:" + FormatBoolean(data1)
                          + ", and got " + FormatBoolean(result));
            // Present pattern2, which is similar to pattern 1. Pattern 1
            // should be recalled.
            result = network.Compute(data2);
            app.WriteLine("Presenting pattern:" + FormatBoolean(data2)
                          + ", and got " + FormatBoolean(result));
        }

        public void Execute(IExampleInterface app)
        {
            // build the bayesian network structure
            BayesianNetwork network = new BayesianNetwork();
            BayesianEvent BlueTaxi = network.CreateEvent("blue_taxi");
            BayesianEvent WitnessSawBlue = network.CreateEvent("saw_blue");
            network.CreateDependency(BlueTaxi, WitnessSawBlue);
            network.FinalizeStructure();
            // build the truth tales
            BlueTaxi.Table.AddLine(0.85, true);
            WitnessSawBlue.Table.AddLine(0.80, true, true);
            WitnessSawBlue.Table.AddLine(0.20, true, false);

            // validate the network
            network.Validate();
            // display basic stats
            Console.WriteLine(network.ToString());
            Console.WriteLine("Parameter count: " + network.CalculateParameterCount());
            EnumerationQuery query = new EnumerationQuery(network);
            //SamplingQuery query = new SamplingQuery(network);
            query.DefineEventType(WitnessSawBlue, EventType.Evidence);
            query.DefineEventType(BlueTaxi, EventType.Outcome);
            query.SetEventValue(WitnessSawBlue, false);
            query.SetEventValue(BlueTaxi, false);
            query.Execute();
            Console.WriteLine(query.ToString());
        }

 public void Execute(IExampleInterface app)
 {
     if (app.Args.Length < 2)
     {
         Console.WriteLine(@"MarketPredict [data dir] [generate/train/prune/evaluate]");
     }
     else
     {
         var dataDir = new FileInfo(app.Args[0]);
         if (String.Compare(app.Args[1], "generate", true) == 0)
         {
             MarketBuildTraining.Generate(dataDir);
         }
         else if (String.Compare(app.Args[1], "train", true) == 0)
         {
             MarketTrain.Train(dataDir);
         }
         else if (String.Compare(app.Args[1], "evaluate", true) == 0)
         {
             MarketEvaluate.Evaluate(dataDir);
         }
         else if (String.Compare(app.Args[1], "prune", true) == 0)
         {
             {
                 MarketPrune.Incremental(dataDir);
             }
         }
     }
 }

        public void Execute(IExampleInterface app)
        {
            this.app = app;

            if (this.app.Args.Length < 1)
            {
                this.app.WriteLine("Must specify command file.  See source for format.");
            }
            else
            {


                // Read the file and display it line by line.
                StreamReader file =
                   new System.IO.StreamReader(this.app.Args[0]);
                while ((line = file.ReadLine()) != null)
                {
                    ExecuteLine();

                }

                file.Close();


            }
        }

        /// <summary>
        /// Program entry point.
        /// </summary>
        /// <param name="app">Holds arguments and other info.</param>
        public void Execute(IExampleInterface app)
        {
            // create a neural network, without using a factory
            var network = new BasicNetwork();
            network.AddLayer(new BasicLayer(null, true, 2));
            network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 3));
            network.AddLayer(new BasicLayer(new ActivationSigmoid(), false, 1));
            network.Structure.FinalizeStructure();
            network.Reset();

            // create training data
            IMLDataSet trainingSet = new BasicMLDataSet(XORInput, XORIdeal);

            // train the neural network
            IMLTrain train = new ResilientPropagation(network, trainingSet);

            int epoch = 1;

            do
            {
                train.Iteration();
                Console.WriteLine(@"Epoch #" + epoch + @" Error:" + train.Error);
                epoch++;
            } while (train.Error > 0.01);

            // test the neural network
            Console.WriteLine(@"Neural Network Results:");
            foreach (IMLDataPair pair in trainingSet)
            {
                IMLData output = network.Compute(pair.Input);
                Console.WriteLine(pair.Input[0] + @"," + pair.Input[1]
                                  + @", actual=" + output[0] + @",ideal=" + pair.Ideal[0]);
            }
        }