Integrando MongoDB com TensorFlow e C#

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using MongoDB.Bson; 
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using System.Collections.Generic; 
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using Microsoft.ML.Data;
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    public class SampleData
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    {
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        public ObjectId Id { get; set; } // This corresponds to the MongoDB _id field
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        public List<double> X { get; set; } = new List<double>();
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        public List<double> Y { get; set; } = new List<double>();
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    }
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    public class DataPoint
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    {
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        public float X { get; set; }
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        public float Y { get; set; }
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    }
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    public class Prediction
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    {
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        [ColumnName("Score")]
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        public float PredictedY { get; set; }
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    }

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// MongoDB connection string
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var client = new MongoClient("mongodb://localhost:27017");
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var database = client.GetDatabase("linear-data");
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var collection = database.GetCollection<SampleData>("sampleData");

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using MongoDB.Bson;
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using MongoDB.Driver;
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using System.Collections.Generic;

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// Define the data to insert
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        var sampleData = new SampleData
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        {
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            X = new List<double> { 3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167, 7.042, 10.791, 5.313, 7.997, 5.654, 9.27, 3.1 },
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            Y = new List<double> { 1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221, 2.827, 3.465, 1.65, 2.904, 2.42, 2.94, 1.3 }
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        };
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// Insert the data into MongoDB 
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collection.InsertOne(sampleData);

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// Fetch the data from MongoDB as SampleData
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var fetchedSampleData = collection.Find(new BsonDocument()).FirstOrDefault();
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// Assuming you have only one document, use the fetchedSampleData directly
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var xArray = fetchedSampleData.X.Select(value => (float)value).ToArray();
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var yArray = fetchedSampleData.Y.Select(value => (float)value).ToArray();

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// Create a new ML context
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        var mlContext = new MLContext();
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        // Create the ML.NET data structures
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        var data = xArray.Zip(yArray, (x, y) => new DataPoint { X = x, Y = y }).ToList();
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        var dataView = mlContext.Data.LoadFromEnumerable(data);

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// Define the trainer
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var pipeline = mlContext.Transforms.Concatenate("Features", new[] { "X" })
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    .Append(mlContext.Transforms.NormalizeMinMax("Features"))
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    .Append(mlContext.Regression.Trainers.Sdca(labelColumnName: "Y", featureColumnName: "Features"));
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// Train the model
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var model = pipeline.Fit(dataView);

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// Use the model to make predictions
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var predictions = model.Transform(dataView);
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var metrics = mlContext.Regression.Evaluate(predictions, labelColumnName: "Y");
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Console.WriteLine($"R^2: {metrics.RSquared}");
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Console.WriteLine($"RMSE: {metrics.RootMeanSquaredError}");

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// Display the predictions
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 var predictionFunction = mlContext.Model.CreatePredictionEngine<DataPoint, Prediction>(model);
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        foreach (var point in data)
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        {
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            var prediction = predictionFunction.Predict(point);
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            Console.WriteLine($"X: {point.X}, Y: {point.Y}, Predicted: {prediction.PredictedY}");
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        }

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dotnet run

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Data seeded successfully!

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R^2: -131160.77737920854

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RMSE: 256.3340977791911

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X: 3.3, Y: 1.7, Predicted: 84.26584
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X: 4.4, Y: 2.76, Predicted: -8.049469
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X: 5.5, Y: 2.09, Predicted: -100.36478
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X: 6.71, Y: 3.19, Predicted: -201.91168
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X: 6.93, Y: 1.694, Predicted: -220.3747
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X: 4.168, Y: 1.573, Predicted: 11.420654
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X: 9.779, Y: 3.366, Predicted: -459.47144
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X: 6.182, Y: 2.596, Predicted: -157.60028
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X: 7.59, Y: 2.53, Predicted: -275.76392
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X: 2.167, Y: 1.221, Predicted: 179.35062
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X: 7.042, Y: 2.827, Predicted: -229.77405
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X: 10.791, Y: 3.465, Predicted: -544.4015
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X: 5.313, Y: 1.65, Predicted: -84.6712
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X: 7.997, Y: 2.904, Predicted: -309.9206
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X: 5.654, Y: 2.42, Predicted: -113.28891
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X: 9.27, Y: 2.94, Predicted: -416.75458
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X: 3.1, Y: 1.3, Predicted: 101.050446

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Data seeded successfully!
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R^2: -131160.77737920854
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RMSE: 256.3340977791911
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X: 3.3, Y: 1.7, Predicted: 84.26584
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X: 4.4, Y: 2.76, Predicted: -8.049469
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X: 5.5, Y: 2.09, Predicted: -100.36478
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X: 6.71, Y: 3.19, Predicted: -201.91168
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X: 6.93, Y: 1.694, Predicted: -220.3747
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X: 4.168, Y: 1.573, Predicted: 11.420654
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X: 9.779, Y: 3.366, Predicted: -459.47144
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X: 6.182, Y: 2.596, Predicted: -157.60028
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X: 7.59, Y: 2.53, Predicted: -275.76392
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X: 2.167, Y: 1.221, Predicted: 179.35062
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X: 7.042, Y: 2.827, Predicted: -229.77405
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X: 10.791, Y: 3.465, Predicted: -544.4015
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X: 5.313, Y: 1.65, Predicted: -84.6712
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X: 7.997, Y: 2.904, Predicted: -309.9206
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X: 5.654, Y: 2.42, Predicted: -113.28891
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X: 9.27, Y: 2.94, Predicted: -416.75458
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X: 3.1, Y: 1.3, Predicted: 101.050446