public IPredictImage Predict(IPredict predict, Action <IPredictConfiguration> configuration) { PredictConfiguration predictConfiguration; configuration(predictConfiguration = new PredictConfiguration(predict)); return(predictConfiguration.Predict(_scanImage, predictConfiguration.Alphabet, predictConfiguration.Options)); }
public void AddPredictor(IPredict p, string label) { _predicts.Add(p); _predictReporter.Add(new Abcd() { Label = label }); }
async void showPrediction() { IPredict predictions = DependencyService.Get <IPredict>(); string a = await Task.Run(() => predictions.getPrediction(file)); string value = a; System.Console.WriteLine(value); }
public PredictorShould() { var dataFixture = new DataFixture(); dataFixture.Init(); _predict = new Predict(); _textClassifier = new TextMatcher(dataFixture.Companies, dataFixture.Localities, dataFixture.Classifiers) { UseCache = false }; _account = dataFixture.GetTestBankAccount(); }
private void buttonGenerate_Click(object sender, EventArgs e) { if (_data == null) { XtraMessageBox.Show("There is no data create model!", "Uh Oh!", MessageBoxButtons.OK, MessageBoxIcon.Warning); return; } var model = new PerceptronModel <Student>(); _classifier = model.Generate(_data); XtraMessageBox.Show("Done!", "Success!", MessageBoxButtons.OK, MessageBoxIcon.Information); }
public async Task PredictAsync(PredictBankStatement2Command command, IPredict predict) { var results = await predict.PredictManyAsync(State.PredictionRequests); var predictionResults = results as PredictionResult[] ?? results.ToArray(); if (predictionResults.Any(x => x == null)) { throw new ApplicationException(); } var ev = new BankStatementPredicted2Event { PredictionResults = predictionResults }; Emit(ev); await Task.CompletedTask; }
public static void DrawGrid(double[] extent, double resolution, IPredict predict, string fileName) { int width = (int)Math.Round((extent[2] - extent[0]) / resolution) + 1, height = (int)Math.Round((extent[3] - extent[1]) / resolution) + 1; Bitmap bitmap = new Bitmap(width, height); double lon, lat; for (int i = 0; i < width; i++) { lon = extent[0] + i * resolution; for (int j = 0; j < height; j++) { lat = extent[3] - j * resolution; int value = (int)Math.Round(predict.Predict(lon, lat)); bitmap.SetPixel(i, j, Color.FromArgb(value / 256, value % 256, 0)); } } bitmap.Save(fileName); bitmap.Dispose(); }
public PredictBankStatement2CommandHandler(IPredict predict) { _predict = predict; }
public PredictConfiguration(IPredict predict) { Predict = predict.PredictModel; }
public PredictionController(IPredict predictor) { _predictor = predictor; }
public void Learn(IEnumerable <T> examples) { Examples = examples; var total = Examples.Count(); // 80% for training var trainingCount = (int)System.Math.Ceiling(total * .8); // 20% for testing var testingSlice = GetTestPoints(total - trainingCount, total) .ToArray(); var trainingSlice = GetTrainingPoints(testingSlice, total); // getting data var data = Converter.Convert <T>(examples); Matrix x = data.Item1; Vector y = data.Item2; TypeDescription description = data.Item3; // training var x_t = x.Slice(trainingSlice, VectorType.Row); var y_t = y.Slice(trainingSlice); Predictors = new IPredict <T> [Models.Length]; // run in parallel since they all have // read-only references to the data model // and update independently to different // spots //Parallel.For(0, Models.Length, i => //{ for (int i = 0; i < Models.Length; i++) { Models[i].X = x_t; Models[i].Y = y_t; Models[i].Description = description; Predictors[i] = Models[i].Generate(); } //}); // testing T[] test = GetTestExamples(testingSlice, examples); Accuracy = Vector.Zeros(Predictors.Length); for (int i = 0; i < Predictors.Length; i++) { Accuracy[i] = 0; for (int j = 0; j < test.Length; j++) { var truth = Conversion.Converter.GetItem <T>(test[j], description.Label); Predictors[i].Predict(test[j]); var pred = Conversion.Converter.GetItem <T>(test[j], description.Label); // need to set it back... Converter.SetItem <T>(test[j], description.Label, truth); if (Object.Equals(truth, pred)) { Accuracy[i] += 1; } } Accuracy[i] /= test.Length; } ; }