/// <summary> /// Delete a trained model. /// Documentation https://developers.google.com/prediction/v1.6/reference/trainedmodels/delete /// Generation Note: This does not always build corectly. Google needs to standardise things I need to figuer out which ones are wrong. /// </summary> /// <param name="service">Authenticated Prediction service.</param> /// <param name="project">The project associated with the model.</param> /// <param name="id">The unique name for the predictive model.</param> public static void Delete(PredictionService service, string project, string id) { try { // Initial validation. if (service == null) { throw new ArgumentNullException("service"); } if (project == null) { throw new ArgumentNullException(project); } if (id == null) { throw new ArgumentNullException(id); } // Make the request. service.Trainedmodels.Delete(project, id).Execute(); } catch (Exception ex) { throw new Exception("Request Trainedmodels.Delete failed.", ex); } }
private async void Save() { try { DialogService.ShowLoading("Saving..."); var result = await PredictionService.SavePredictionAsync(new SavePredictionModel() { PredictionId = Prediction.PredictionId, WeekId = Prediction.WeekId, GameId = Prediction.GameId, AwayPrediction = AwayPredictedScore.AsInt(0), HomePrediction = HomePredictedScore.AsInt(0) }); if (result != null && Prediction != null) { MessageBus.Publish(new RefreshPredictionsMessage(result.PredictionId, Prediction.HomeTeam, Prediction.AwayTeam, AwayPredictedScore.AsInt(0), HomePredictedScore.AsInt(0))); await Navigation.PopModalAsync(true); } } catch (Exception ex) { DialogService.Alert("Failed to save Prediction"); } finally { DialogService.HideLoading(); } }
private async Task LoadPredictionsAsync() { _predictions = await PredictionService.GetCurrentWeekPredictions(); OnPropertyChanged(nameof(PredictionGroups)); OnPropertyChanged(nameof(NoGames)); }
/// <summary> /// Submit input and request an output against a hosted model. /// Documentation https://developers.google.com/prediction/v1.6/reference/hostedmodels/predict /// Generation Note: This does not always build corectly. Google needs to standardise things I need to figuer out which ones are wrong. /// </summary> /// <param name="service">Authenticated Prediction service.</param> /// <param name="project">The project associated with the model.</param> /// <param name="hostedModelName">The name of a hosted model.</param> /// <param name="body">A valid Prediction v1.6 body.</param> /// <returns>OutputResponse</returns> public static Output Predict(PredictionService service, string project, string hostedModelName, Input body) { try { // Initial validation. if (service == null) { throw new ArgumentNullException("service"); } if (body == null) { throw new ArgumentNullException("body"); } if (project == null) { throw new ArgumentNullException(project); } if (hostedModelName == null) { throw new ArgumentNullException(hostedModelName); } // Make the request. return(service.Hostedmodels.Predict(body, project, hostedModelName).Execute()); } catch (Exception ex) { throw new Exception("Request Hostedmodels.Predict failed.", ex); } }
public double getForecastCow(int idCow) { predictionService = new PredictionService(); double forecastValue = predictionService.ForecastYieldCow(idCow); return(forecastValue); }
/// <summary> /// Begin training your model /// Documentation https://developers.google.com/prediction/v1.2/reference/training/insert /// Generation Note: This does not always build corectly. Google needs to standardise things I need to figuer out which ones are wrong. /// </summary> /// <param name="service">Authenticated Prediction service.</param> /// <param name="body">A valid Prediction v1.2 body.</param> /// <param name="optional">Optional paramaters.</param> /// <returns>TrainingResponse</returns> public static Training Insert(PredictionService service, Training body, TrainingInsertOptionalParms optional = null) { try { // Initial validation. if (service == null) { throw new ArgumentNullException("service"); } if (body == null) { throw new ArgumentNullException("body"); } // Building the initial request. var request = service.Training.Insert(body); // Applying optional parameters to the request. request = (TrainingResource.InsertRequest)SampleHelpers.ApplyOptionalParms(request, optional); // Requesting data. return(request.Execute()); } catch (Exception ex) { throw new Exception("Request Training.Insert failed.", ex); } }
/// <summary> /// Add new data to a trained model /// Documentation https://developers.google.com/prediction/v1.2/reference/training/update /// Generation Note: This does not always build corectly. Google needs to standardise things I need to figuer out which ones are wrong. /// </summary> /// <param name="service">Authenticated Prediction service.</param> /// <param name="data">mybucket/mydata resource in Google Storage</param> /// <param name="body">A valid Prediction v1.2 body.</param> /// <returns>TrainingResponse</returns> public static Training Update(PredictionService service, string data, Update body) { try { // Initial validation. if (service == null) { throw new ArgumentNullException("service"); } if (body == null) { throw new ArgumentNullException("body"); } if (data == null) { throw new ArgumentNullException(data); } // Make the request. return(service.Training.Update(body, data).Execute()); } catch (Exception ex) { throw new Exception("Request Training.Update failed.", ex); } }
/// <summary> /// Add new data to a trained model. /// Documentation https://developers.google.com/prediction/v1.6/reference/trainedmodels/update /// Generation Note: This does not always build corectly. Google needs to standardise things I need to figuer out which ones are wrong. /// </summary> /// <param name="service">Authenticated Prediction service.</param> /// <param name="project">The project associated with the model.</param> /// <param name="id">The unique name for the predictive model.</param> /// <param name="body">A valid Prediction v1.6 body.</param> /// <returns>Insert2Response</returns> public static Insert2 Update(PredictionService service, string project, string id, Update body) { try { // Initial validation. if (service == null) { throw new ArgumentNullException("service"); } if (body == null) { throw new ArgumentNullException("body"); } if (project == null) { throw new ArgumentNullException(project); } if (id == null) { throw new ArgumentNullException(id); } // Make the request. return(service.Trainedmodels.Update(body, project, id).Execute()); } catch (Exception ex) { throw new Exception("Request Trainedmodels.Update failed.", ex); } }
public void WhenRunWithAllPossibleMapping_ShouldHavePredictResult() { var scores = new Score[] { Score.Dragon, Score.Dragon, Score.Dragon, Score.Tiger, Score.Dragon, Score.Tiger, Score.Tiger, Score.Tiger, Score.Dragon, Score.Tiger, //Score.Tiger, }; var predictors = new IPredictor[] { new SameDifferentPredictor(), new Anti12Predictor(), new Anti2Predictor() }; var resultPredictor = new DummyPredictor(); var predictService = new PredictionService(predictors, resultPredictor); var tableSize = 8; var result = predictService.Predict(scores.Select(x => new GameStateInput(x, None)), tableSize, 15) .GameStatesWithScorePrediction .ToList(); Assert.AreEqual((int)Math.Pow(2, tableSize), result.Count()); Assert.AreEqual(scores.Count() + 1, result.First().GameStates.Count()); }
public void MakePredictionFromSample_NullArgument_ThrowArgumentException() { var predictionService = new PredictionService(_openExchangeClient.Object, _cache.Object); Assert.Throws <ArgumentException>(() => predictionService.MakePredictionFromSample("USD", "VND", DateTime.Today, null)); }
/// <summary> /// List available models. /// Documentation https://developers.google.com/prediction/v1.6/reference/trainedmodels/list /// Generation Note: This does not always build corectly. Google needs to standardise things I need to figuer out which ones are wrong. /// </summary> /// <param name="service">Authenticated Prediction service.</param> /// <param name="project">The project associated with the model.</param> /// <param name="optional">Optional paramaters.</param> /// <returns>ListResponse</returns> public static List List(PredictionService service, string project, TrainedmodelsListOptionalParms optional = null) { try { // Initial validation. if (service == null) { throw new ArgumentNullException("service"); } if (project == null) { throw new ArgumentNullException(project); } // Building the initial request. var request = service.Trainedmodels.List(project); // Applying optional parameters to the request. request = (TrainedmodelsResource.ListRequest)SampleHelpers.ApplyOptionalParms(request, optional); // Requesting data. return(request.Execute()); } catch (Exception ex) { throw new Exception("Request Trainedmodels.List failed.", ex); } }
public GetExpertStatsHandler(PredictionsContext context, IMapper mapper) { _context = context; _mapper = mapper; _statService = new StatService(); _predictionService = new PredictionService(); }
/// <summary> /// Submit model id and request a prediction. /// Documentation https://developers.google.com/prediction/v1.5/reference/trainedmodels/predict /// Generation Note: This does not always build corectly. Google needs to standardise things I need to figuer out which ones are wrong. /// </summary> /// <param name="service">Authenticated Prediction service.</param> /// <param name="id">The unique name for the predictive model.</param> /// <param name="body">A valid Prediction v1.5 body.</param> /// <returns>OutputResponse</returns> public static Output Predict(PredictionService service, string id, Input body) { try { // Initial validation. if (service == null) { throw new ArgumentNullException("service"); } if (body == null) { throw new ArgumentNullException("body"); } if (id == null) { throw new ArgumentNullException(id); } // Make the request. return(service.Trainedmodels.Predict(body, id).Execute()); } catch (Exception ex) { throw new Exception("Request Trainedmodels.Predict failed.", ex); } }
public PredictionsController() { _context = new PredictionsContext(); _expertService = new ExpertService(_context); _tourService = new TourService(_context); _predictionService = new PredictionService(_context); _matchService = new MatchService(_context); }
public void GetAmlPredictions_case1() { var storedDemand = DataSource.GetSalesHistory(); var singleProductHistory = storedDemand.Where(d => d.Plu == 2480).Take(10).ToArray(); var predictionService = new PredictionService(MlSettings.Endpoint, MlSettings.Key); var actual = predictionService.GetAmlPredictions(singleProductHistory).Result; }
public PredictionGradeViewModel(Student s) { student = s; PredictCommand = new Command(async() => { var grade = await PredictionService.PredictGrade(student); G3 = grade; }); }
//public PredictionServiceTests() //{ // var predictionRepository = new Mock<IPredictionRepository>(); // predictionRepository.Setup(x => x.CheckTypeResult(It.IsAny<string>())).ReturnsAsync((string result) => // { // if (Convert.ToInt32(result.Substring(0, 1)) > Convert.ToInt32(result.Substring(2, 1))) return TypeResult.WinTeam1; // else if (Convert.ToInt32(result.Substring(0, 1)) < Convert.ToInt32(result.Substring(2, 1))) return TypeResult.WinTeam2; // else if (Convert.ToInt32(result.Substring(0, 1)) == Convert.ToInt32(result.Substring(2, 1))) return TypeResult.Draw; // else return TypeResult.NNB; // }); // predictionRepository.Setup(x => x.CompareResultsAsync(It.IsAny<Game>(), It.IsAny<Prediction>())).ReturnsAsync((Game z, Prediction y) => // { // if (y.PredictedResult == z.Result) // return 3; // else if (y.PredictedTypeResult == z.typeResult) // return 1; // else // return 0; // }); // _predictionService = new PredictionService(predictionRepository.Object); //} public async Task CreateDb() { var options = new DbContextOptionsBuilder <StrikeNetDbContext>() .UseInMemoryDatabase(databaseName: "StrikeNetTestDb") .EnableSensitiveDataLogging() .UseQueryTrackingBehavior(QueryTrackingBehavior.NoTracking) .Options; var dbContext = new StrikeNetDbContext(options); if (await dbContext.Predictions.CountAsync() <= 0) { Prediction p1 = new Prediction() { Id = 1, GameId = 1, UserId = User1, PredictedResult = "3-0", PredictedTypeResult = TypeResult.WinTeam1 }; Prediction p2 = new Prediction() { Id = 2, GameId = 3, UserId = User2, PredictedResult = "2-0", PredictedTypeResult = TypeResult.WinTeam1 }; Prediction p3 = new Prediction() { Id = 3, GameId = 3, UserId = User3, PredictedResult = "0-1", PredictedTypeResult = TypeResult.WinTeam2 }; Prediction p4 = new Prediction() { Id = 4, GameId = 4, UserId = User4, PredictedResult = "1-1", PredictedTypeResult = TypeResult.Draw }; dbContext.Predictions.Add(p1); dbContext.Predictions.Add(p2); dbContext.Predictions.Add(p3); dbContext.Predictions.Add(p4); await dbContext.SaveChangesAsync(); } var identityRepository = new IdentityRepository(_userManager, _roleManager, _signInManager); var predictionRepository = new PredictionRepository(identityRepository, dbContext); _predictionService2 = new PredictionService(predictionRepository); }
async Task LoadPredictionHistoryAsync() { var predictions = await PredictionService.GetPredictionsForYearAsync(Year); Predictions = new ObservableCollection <SummaryPredictionGroup>( predictions.GroupBy(x => x.WeekNumber) .OrderBy(x => x.Key) .Select(x => new SummaryPredictionGroup(x.Key.ToString(), x.ToList())) ); }
private async Task EvaluateImage() { IsBusy = true; await Task.Run(() => { PredictionDetailsViewModel.PredictionResult = PredictionService.Predict(PredictionDetailsViewModel.Path); }); IsBusy = false; }
public void WhenRunWithAllTiger_ShouldHave100PercentPredictResult() { var scores = new Score[] { Score.Tiger, Score.Tiger, Score.Tiger, Score.Tiger, Score.Tiger, Score.Tiger, Score.Tiger, Score.Tiger, Score.Tiger }; var predictors = new IPredictor[] { new SameDifferentPredictor(), new Anti12Predictor(), new Anti2Predictor() }; var resultPredictor = new DummyPredictor(); var predictService = new PredictionService(predictors, resultPredictor); var mappingScores = new Score[] { Score.Tiger, Score.Tiger, Score.Tiger, Score.Tiger, Score.Tiger, Score.Tiger, Score.Tiger, Score.Tiger }; var gameStates = scores.Select(s => new GameStateInput(s, None)); var result = predictService.PredictScore(gameStates, mappingScores) .ToList(); Assert.AreEqual(scores.Count() + 1, result.Count()); var sdResult = result .Select(r => r.ScorePredictions.Find(Constants.SameDiffPredictionName).Bind(p => p.Result)) .Where(r => r.IsSome) .Map(r => r.IfNone(Result.Lose)); var stat = StatService.Calculate( Constants.SameDiffPredictionName, sdResult); Assert.AreEqual(100, stat.WinRate); }
public void WhenRunWithSpecificMapping_ShouldHavePredictResult() { var scores = new Score[] { Score.Dragon, Score.Dragon, Score.Dragon, Score.Tiger, Score.Dragon, Score.Tiger, Score.Tiger, Score.Tiger, Score.Dragon, Score.Tiger, }; var predictors = new IPredictor[] { new SameDifferentPredictor(), new Anti12Predictor(), new Anti2Predictor() }; var resultPredictor = new DummyPredictor(); var predictService = new PredictionService(predictors, resultPredictor); var mappingScores = new Score[] { Score.Tiger, Score.Dragon, Score.Tiger, Score.Tiger, Score.Tiger, Score.Tiger, Score.Tiger, Score.Tiger }; var gameStates = scores.Select(s => new GameStateInput(s, None)); var result = predictService.PredictScore(gameStates, mappingScores) .ToList(); Assert.AreEqual(scores.Count() + 1, result.Count()); var predictResult = result.Last(); Assert.AreEqual(Score.Dragon, predictResult.ScorePredictions.Find(Constants.MappingTablePredctionName).Map(x => x.Score)); Assert.AreEqual(Score.Dragon, predictResult.ScorePredictions.Find(Constants.SameDiffPredictionName).Map(x => x.Score)); Assert.AreEqual(Score.Tiger, predictResult.ScorePredictions.Find(Constants.Anti12PredictionName).Map(x => x.Score)); Assert.AreEqual(Score.Dragon, predictResult.ScorePredictions.Find(Constants.Anti2PredictionName).Map(x => x.Score)); }
static void Main(string[] args) { var imgFile = @"C:\Users\ch489gt\Pictures\Snag-Auto\TestSnag.png"; var predictService = new PredictionService(); var predictRes = predictService.Predict(imgFile); var ocrService = new OcrService(); var ocrRes = ocrService.DoOcr(imgFile); var analyzer = new VisionAnalyzer(); var result = analyzer.GetResult(imgFile, predictRes.Data); }
//private readonly IMapper _mapper; public CurrentTournamentToursController(IPredictionsContext context, IMapper mapper) { _expertService = new ExpertService(context); _tourService = new TourService(context); _predictionService = new PredictionService(context); _matchService = new MatchService(context, mapper); _teamService = new TeamService(context); _tournamentService = new TournamentService(context); _context = context; // _mapper = mapper; _fileService = new FileService(); }
public async Task GetServiceAsync() { var trainingService = new PredictionService(); var response = await trainingService.PredictProfile(new UserInteraction() { Tier = 2, Picture = 1, Summary = 1, Specs = 1, Reviews = 1, Comments = 1, Share = 1, Buy = 0, Related = 0 }); }
public void CreatePredictionService(string authJsonFile) { Environment.SetEnvironmentVariable("GOOGLE_APPLICATION_CREDENTIALS", authJsonFile); var credentials = Google.Apis.Auth.OAuth2.GoogleCredential.GetApplicationDefaultAsync().Result; if (credentials.IsCreateScopedRequired) { credentials = credentials.CreateScoped(PredictionService.Scope.DevstorageFullControl, PredictionService.Scope.Prediction); } var serviceInitializer = new BaseClientService.Initializer() { ApplicationName = "Prediction Sample", HttpClientInitializer = credentials }; PredictionService = new PredictionService(serviceInitializer); }
public void MakePredictionFromSample_DoesNotHaveToCurrencyInSample_ThrowCurrencyNotFoundException() { var predictionService = new PredictionService(_openExchangeClient.Object, _cache.Object); Assert.Throws <CurrencyNotFoundException>(() => predictionService.MakePredictionFromSample("USD", "VND", new DateTime(2019, 12, 31), new List <OpenExchangeRateResult> { new OpenExchangeRateResult { Base = "USD", Rates = new Dictionary <string, double>(), TimeStamp = 123234 } })); }
private async Task Predict() { Trip = new TaxiTrip() { FareAmount = FareAmount, PassengerCount = PassengerCount, PaymentType = PaymentType, RateCode = RateCode, TripDistance = TripDistance, TripTime = TripTime, VendorId = VendorId }; var amount = await PredictionService.Predict(Trip); await App.Current.MainPage.DisplayAlert("Prediction", $"Trip Fare: {amount:C2}", "OK"); }
static void Main(string[] args) { // Display the header and initialize the sample. CommandLine.EnableExceptionHandling(); CommandLine.DisplayGoogleSampleHeader("Prediction API"); CommandLine.WriteLine(); // Register the authenticator. var provider = new NativeApplicationClient(GoogleAuthenticationServer.Description); provider.ClientIdentifier = ClientCredentials.ClientID; provider.ClientSecret = ClientCredentials.ClientSecret; var auth = new OAuth2Authenticator<NativeApplicationClient>(provider, GetAuthentication); // Create the service. var service = new PredictionService(auth); RunPrediction(service); CommandLine.PressAnyKeyToExit(); }
private static void RunPrediction(PredictionService service) { // Make a prediction. CommandLine.WriteAction("Performing a prediction..."); string text = "mucho bueno"; CommandLine.RequestUserInput("Text to analyze", ref text); var input = new Input { InputValue = new Input.InputData { CsvInstance = new List <string> { text } } }; Output result = service.Hostedmodels.Predict(input, "sample.languageid").Fetch(); CommandLine.WriteResult("Language", result.OutputLabel); }
public void MakePredictionFromSample_HaveCorrectNoneUSDSample_ReturnCorrectResultFromConvertedRates() { var predictionService = new PredictionService(_openExchangeClient.Object, _cache.Object); var result = predictionService.MakePredictionFromSample("CNY", "VND", DateTimeOffset.FromUnixTimeSeconds(40).UtcDateTime, new List <OpenExchangeRateResult> { new OpenExchangeRateResult { Base = "USD", Rates = new Dictionary <string, double>() { { "VND", 23050 }, { "CNY", 6.5434 } }, TimeStamp = 10 }, new OpenExchangeRateResult { Base = "USD", Rates = new Dictionary <string, double>() { { "VND", 23100 }, { "CNY", 6.6434 } }, TimeStamp = 20 }, new OpenExchangeRateResult { Base = "USD", Rates = new Dictionary <string, double>() { { "VND", 23150 }, { "CNY", 6.7434 } }, TimeStamp = 30 } }); //Assert.Equal(3387.9378, result); }
public async Task CreateDbWithMockIdentity() { var options = new DbContextOptionsBuilder <StrikeNetDbContext>() .UseInMemoryDatabase(databaseName: "StrikeNetTestDb") .EnableSensitiveDataLogging() .UseQueryTrackingBehavior(QueryTrackingBehavior.NoTracking) .Options; var _dbContext = new StrikeNetDbContext(options); TestUser = new UserIdentity { Score = 0 }; var identityRepository = new Mock <IIdentityRepository>(); identityRepository.Setup(x => x.GetUserAsync(It.IsAny <Guid?>())).ReturnsAsync(TestUser); var predictionRepository = new PredictionRepository(identityRepository.Object, _dbContext); _predictionService = new PredictionService(predictionRepository); }
private static void RunPrediction(PredictionService service) { // Make a prediction. CommandLine.WriteAction("Performing a prediction..."); string text = "mucho bueno"; CommandLine.RequestUserInput("Text to analyze", ref text); var input = new Input { InputValue = new Input.InputData { CsvInstance = new List<string> { text } } }; Output result = service.Hostedmodels.Predict(input, "sample.languageid").Fetch(); CommandLine.WriteResult("Language", result.OutputLabel); }
private static void RunPrediction(PredictionService service) { // Train the service with the existing bucket data. string id = ClientCredentials.BucketPath; CommandLine.WriteAction("Performing training of the service ..."); CommandLine.WriteResult("Bucket", id); Training training = new Training { Id = id }; training = service.Training.Insert(training).Fetch(); // Wait until the training is complete. while (training.TrainingStatus == "RUNNING") { CommandLine.Write(".."); Thread.Sleep(1000); training = service.Training.Get(id).Fetch(); } CommandLine.WriteLine(); CommandLine.WriteAction("Training complete!"); CommandLine.WriteLine(); // Make a prediction. CommandLine.WriteAction("Performing a prediction..."); string text = "mucho bueno"; CommandLine.RequestUserInput("Text to analyze", ref text); var input = new Input { InputValue = new Input.InputData { CsvInstance = new List<string> { text } } }; Output result = service.Training.Predict(input, id).Fetch(); CommandLine.WriteResult("Language", result.OutputLabel); }