private protected GamTrainerBase(IHostEnvironment env,
string name,
SchemaShape.Column label,
string featureColumnName,
string weightCrowGroupColumnName,
int numberOfIterations,
double learningRate,
int maximumBinCountPerFeature)
: base(Contracts.CheckRef(env, nameof(env)).Register(name), TrainerUtils.MakeR4VecFeature(featureColumnName), label, TrainerUtils.MakeR4ScalarWeightColumn(weightCrowGroupColumnName))
{
GamTrainerOptions = new TOptions();
GamTrainerOptions.NumberOfIterations = numberOfIterations;
GamTrainerOptions.LearningRate = learningRate;
GamTrainerOptions.MaximumBinCountPerFeature = maximumBinCountPerFeature;
GamTrainerOptions.LabelColumnName = label.Name;
GamTrainerOptions.FeatureColumnName = featureColumnName;
if (weightCrowGroupColumnName != null)
{
GamTrainerOptions.ExampleWeightColumnName = weightCrowGroupColumnName;
}
Info = new TrainerInfo(normalization: false, calibration: NeedCalibration, caching: false, supportValid: true);
_gainConfidenceInSquaredStandardDeviations = Math.Pow(ProbabilityFunctions.Probit(1 - (1 - GamTrainerOptions.GainConfidenceLevel) * 0.5), 2);
_entropyCoefficient = GamTrainerOptions.EntropyCoefficient * 1e-6;
InitializeThreads();
}
private protected GamTrainerBase(IHostEnvironment env,
string name,
SchemaShape.Column label,
string featureColumn,
string weightColumn,
int numIterations,
double learningRate,
int maxBins)
: base(Contracts.CheckRef(env, nameof(env)).Register(name), TrainerUtils.MakeR4VecFeature(featureColumn), label, TrainerUtils.MakeR4ScalarWeightColumn(weightColumn))
{
Args = new TArgs();
Args.NumIterations = numIterations;
Args.LearningRates = learningRate;
Args.MaxBins = maxBins;
Args.LabelColumn = label.Name;
Args.FeatureColumn = featureColumn;
if (weightColumn != null)
{
Args.WeightColumn = weightColumn;
}
Info = new TrainerInfo(normalization: false, calibration: NeedCalibration, caching: false, supportValid: true);
_gainConfidenceInSquaredStandardDeviations = Math.Pow(ProbabilityFunctions.Probit(1 - (1 - Args.GainConfidenceLevel) * 0.5), 2);
_entropyCoefficient = Args.EntropyCoefficient * 1e-6;
InitializeThreads();
}
internal MatrixFactorizationTrainer(IHostEnvironment env, Options options) : base(env, LoadNameValue)
{
const string posError = "Parameter must be positive";
Host.CheckValue(options, nameof(options));
Host.CheckUserArg(options.K > 0, nameof(options.K), posError);
Host.CheckUserArg(!options.NumThreads.HasValue || options.NumThreads > 0, nameof(options.NumThreads), posError);
Host.CheckUserArg(options.NumIterations > 0, nameof(options.NumIterations), posError);
Host.CheckUserArg(options.Lambda > 0, nameof(options.Lambda), posError);
Host.CheckUserArg(options.Eta > 0, nameof(options.Eta), posError);
Host.CheckUserArg(options.Alpha > 0, nameof(options.Alpha), posError);
_fun = (int)options.LossFunction;
_lambda = options.Lambda;
_k = options.K;
_iter = options.NumIterations;
_eta = options.Eta;
_alpha = options.Alpha;
_c = options.C;
_threads = options.NumThreads ?? Environment.ProcessorCount;
_quiet = options.Quiet;
_doNmf = options.NonNegative;
Info = new TrainerInfo(normalization: false, caching: false);
LabelName = options.LabelColumnName;
MatrixColumnIndexName = options.MatrixColumnIndexColumnName;
MatrixRowIndexName = options.MatrixRowIndexColumnName;
}
private protected EnsembleTrainerBase(ArgumentsBase args, IHostEnvironment env, string name)
: base(env, name)
{
Args = args;
using (var ch = Host.Start("Init"))
{
ch.CheckUserArg(Utils.Size(Args.BasePredictors) > 0, nameof(Args.BasePredictors), "This should have at-least one value");
NumModels = Args.NumModels ??
(Args.BasePredictors.Length == 1 ? DefaultNumModels : Args.BasePredictors.Length);
ch.CheckUserArg(NumModels > 0, nameof(Args.NumModels), "Must be positive, or null to indicate numModels is the number of base predictors");
if (Utils.Size(Args.BasePredictors) > NumModels)
{
ch.Warning("The base predictor count is greater than models count. Some of the base predictors will be ignored.");
}
_subsetSelector = Args.SamplingType.CreateComponent(Host);
Trainers = new ITrainer <IPredictorProducing <TOutput> > [NumModels];
for (int i = 0; i < Trainers.Length; i++)
{
Trainers[i] = Args.BasePredictors[i % Args.BasePredictors.Length].CreateInstance(Host);
}
// We infer normalization and calibration preferences from the trainers. However, even if the internal trainers
// don't need caching we are performing multiple passes over the data, so it is probably appropriate to always cache.
Info = new TrainerInfo(
normalization: Trainers.Any(t => t.Info.NeedNormalization),
calibration: Trainers.Any(t => t.Info.NeedCalibration));
ch.Done();
}
}
public SymSgdClassificationTrainer(IHostEnvironment env, Arguments args)
: base(env, LoadNameValue)
{
args.Check(Host);
_args = args;
Info = new TrainerInfo();
}
protected TrainerBase(IHostEnvironment env, TrainerInfo trainerInfo, string featureColumn, string labelColumn)
{
_env = env;
_featureCol = featureColumn;
_labelCol = labelColumn;
TrainerInfo = trainerInfo;
}
/// <summary>
/// Initializes the <see cref="MetaMulticlassTrainer{TTransformer, TModel}"/> from the Arguments class.
/// </summary>
/// <param name="env">The private instance of the <see cref="IHostEnvironment"/>.</param>
/// <param name="args">The legacy arguments <see cref="ArgumentsBase"/>class.</param>
/// <param name="name">The component name.</param>
/// <param name="labelColumn">The label column for the metalinear trainer and the binary trainer.</param>
/// <param name="singleEstimator">The binary estimator.</param>
/// <param name="calibrator">The calibrator. If a calibrator is not explicitly provided, it will default to <see cref="PlattCalibratorCalibratorTrainer"/></param>
internal MetaMulticlassTrainer(IHostEnvironment env, ArgumentsBase args, string name, string labelColumn = null,
TScalarTrainer singleEstimator = null, ICalibratorTrainer calibrator = null)
{
Host = Contracts.CheckRef(env, nameof(env)).Register(name);
Host.CheckValue(args, nameof(args));
Args = args;
if (labelColumn != null)
{
LabelColumn = new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, NumberType.U4, true);
}
// Create the first trainer so errors in the args surface early.
_trainer = singleEstimator ?? CreateTrainer();
Calibrator = calibrator ?? new PlattCalibratorTrainer(env);
if (args.Calibrator != null)
{
Calibrator = args.Calibrator.CreateComponent(Host);
}
// Regarding caching, no matter what the internal predictor, we're performing many passes
// simply by virtue of this being a meta-trainer, so we will still cache.
Info = new TrainerInfo(normalization: _trainer.Info.NeedNormalization);
}
/// <summary>
/// Legacy constructor initializing a new instance of <see cref="MatrixFactorizationTrainer"/> through the legacy
/// <see cref="Arguments"/> class.
/// </summary>
/// <param name="env">The private instance of <see cref="IHostEnvironment"/>.</param>
/// <param name="args">An instance of the legacy <see cref="Arguments"/> to apply advanced parameters to the algorithm.</param>
public MatrixFactorizationTrainer(IHostEnvironment env, Arguments args) : base(env, LoadNameValue)
{
const string posError = "Parameter must be positive";
Host.CheckValue(args, nameof(args));
Host.CheckUserArg(args.K > 0, nameof(args.K), posError);
Host.CheckUserArg(!args.NumThreads.HasValue || args.NumThreads > 0, nameof(args.NumThreads), posError);
Host.CheckUserArg(args.NumIterations > 0, nameof(args.NumIterations), posError);
Host.CheckUserArg(args.Lambda > 0, nameof(args.Lambda), posError);
Host.CheckUserArg(args.Eta > 0, nameof(args.Eta), posError);
Host.CheckUserArg(args.Alpha > 0, nameof(args.Alpha), posError);
_fun = (int)args.LossFunction;
_lambda = args.Lambda;
_k = args.K;
_iter = args.NumIterations;
_eta = args.Eta;
_alpha = args.Alpha;
_c = args.C;
_threads = args.NumThreads ?? Environment.ProcessorCount;
_quiet = args.Quiet;
_doNmf = args.NonNegative;
Info = new TrainerInfo(normalization: false, caching: false);
}
/// <summary>
/// Initializing a new instance of <see cref="MatrixFactorizationTrainer"/>.
/// </summary>
/// <param name="env">The private instance of <see cref="IHostEnvironment"/>.</param>
/// <param name="matrixColumnIndexColumnName">The name of the column hosting the matrix's column IDs.</param>
/// <param name="matrixRowIndexColumnName">The name of the column hosting the matrix's row IDs.</param>
/// <param name="labelColumn">The name of the label column.</param>
/// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param>
public MatrixFactorizationTrainer(IHostEnvironment env,
string matrixColumnIndexColumnName,
string matrixRowIndexColumnName,
string labelColumn = DefaultColumnNames.Label,
Action <Arguments> advancedSettings = null)
: base(env, LoadNameValue)
{
var args = new Arguments();
advancedSettings?.Invoke(args);
_fun = (int)args.LossFunction;
_lambda = args.Lambda;
_k = args.K;
_iter = args.NumIterations;
_eta = args.Eta;
_alpha = args.Alpha;
_c = args.C;
_threads = args.NumThreads ?? Environment.ProcessorCount;
_quiet = args.Quiet;
_doNmf = args.NonNegative;
Info = new TrainerInfo(normalization: false, caching: false);
LabelName = labelColumn;
MatrixColumnIndexName = matrixColumnIndexColumnName;
MatrixRowIndexName = matrixRowIndexColumnName;
}
/// <summary>
/// Initializing a new instance of <see cref="FieldAwareFactorizationMachineTrainer"/>.
/// </summary>
/// <param name="env">The private instance of <see cref="IHostEnvironment"/>.</param>
/// <param name="featureColumns">The name of column hosting the features.</param>
/// <param name="labelColumn">The name of the label column.</param>
/// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param>
/// <param name="weights">The name of the optional weights' column.</param>
/// <param name="context">The <see cref="TrainerEstimatorContext"/> for additional input data to training.</param>
public FieldAwareFactorizationMachineTrainer(IHostEnvironment env,
string[] featureColumns,
string labelColumn = DefaultColumnNames.Label,
string weights = null,
TrainerEstimatorContext context = null,
Action <Arguments> advancedSettings = null)
: base(env, LoadName)
{
var args = new Arguments();
advancedSettings?.Invoke(args);
Initialize(env, args);
Info = new TrainerInfo(supportValid: true, supportIncrementalTrain: true);
Context = context;
FeatureColumns = new SchemaShape.Column[featureColumns.Length];
for (int i = 0; i < featureColumns.Length; i++)
{
FeatureColumns[i] = new SchemaShape.Column(featureColumns[i], SchemaShape.Column.VectorKind.Vector, NumberType.R4, false);
}
LabelColumn = new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false);
WeightColumn = weights != null ? new SchemaShape.Column(weights, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false) : null;
}
public IHttpActionResult TrainerViewData(TrainerInfo objTrainerInfo)
{
StringBuilder traceLog = null;
ServiceResponse <TrainerViewVM> objResponce = null;
try
{
traceLog = new StringBuilder();
traceLog.AppendLine("Start: GetTrainerViewData() Request TrainerID-" + objTrainerInfo.TrainerId);
objResponce = new ServiceResponse <TrainerViewVM>();
objResponce.IsResultTrue = true;
objResponce.jsonData = HomeRequestBL.GetTrainerViewData(objTrainerInfo.TrainerId, objTrainerInfo.NotificationID);
return(Ok(objResponce));
}
catch (Exception ex)
{
LogManager.LogManagerInstance.WriteErrorLog(ex);
return(BadRequest(ex.Message));
}
finally
{
traceLog.AppendLine("End:GetTrainerViewData() Reponse Data:-Result Status-" + objResponce.IsResultTrue + ",Fetch DateTime-" + DateTime.Now.ToLongDateString());
LogManager.LogManagerInstance.WriteTraceLog(traceLog);
traceLog = null;
}
}
public IHttpActionResult GetVideos(TrainerInfo objTrainerInfo)
{
StringBuilder traceLog = null;
ServiceResponse <List <VideoInfo> > objResponce = null;
try
{
traceLog = new StringBuilder();
traceLog.AppendLine("Start: GetVideos() Request Data:-TrainerId-" + objTrainerInfo.TrainerId);
objResponce = new ServiceResponse <List <VideoInfo> >();
objResponce.IsResultTrue = true;
objResponce.jsonData = HomeRequestBL.GetVideos(objTrainerInfo.TrainerId);
return(Ok(objResponce));
}
catch (Exception ex)
{
LogManager.LogManagerInstance.WriteErrorLog(ex);
return(BadRequest(ex.Message));
}
finally
{
traceLog.AppendLine("End:GetVideos() Response Result Ststus-" + objResponce.IsResultTrue + ",Fetched DateTime-" + DateTime.Now.ToLongDateString());
LogManager.LogManagerInstance.WriteTraceLog(traceLog);
traceLog = null;
}
}
public ActionResult Edit(TrainerInfo trainerInfo)
{
if (!ModelState.IsValid)
{
return(View("~/Views/ErrorValidations/Null.cshtml"));
}
var trainerInfoInDb = _context.TrainerInfos.SingleOrDefault(c => c.TrainerInfoId == trainerInfo.TrainerInfoId);
if (trainerInfoInDb == null)
{
return(HttpNotFound());
}
trainerInfoInDb.TrainerId = trainerInfo.TrainerId;
trainerInfoInDb.Full_Name = trainerInfo.Full_Name;
trainerInfoInDb.Email = trainerInfo.Email;
trainerInfoInDb.Working_Place = trainerInfo.Working_Place;
_context.SaveChanges();
if (User.IsInRole("Training Staff"))
{
return(RedirectToAction("Index"));
}
if (User.IsInRole("Trainer"))
{
return(RedirectToAction("Mine"));
}
return(RedirectToAction("Index"));
}
internal MatrixFactorizationTrainer(IHostEnvironment env,
string labelColumnName,
string matrixColumnIndexColumnName,
string matrixRowIndexColumnName,
int approximationRank = Defaults.ApproximationRank,
double learningRate = Defaults.LearningRate,
int numIterations = Defaults.NumIterations)
{
Contracts.CheckValue(env, nameof(env));
_host = env.Register(LoadNameValue);
var args = new Options();
_fun = (int)args.LossFunction;
_k = approximationRank;
_iter = numIterations;
_eta = learningRate;
_alpha = args.Alpha;
_lambda = args.Lambda;
_c = args.C;
_threads = args.NumberOfThreads ?? Environment.ProcessorCount;
_quiet = args.Quiet;
_doNmf = args.NonNegative;
_info = new TrainerInfo(normalization: false, caching: false);
LabelName = labelColumnName;
MatrixColumnIndexName = matrixColumnIndexColumnName;
MatrixRowIndexName = matrixRowIndexColumnName;
}
internal MatrixFactorizationTrainer(IHostEnvironment env, Options options)
{
Contracts.CheckValue(env, nameof(env));
_host = env.Register(LoadNameValue);
const string posError = "Parameter must be positive";
_host.CheckValue(options, nameof(options));
_host.CheckUserArg(options.ApproximationRank > 0, nameof(options.ApproximationRank), posError);
_host.CheckUserArg(!options.NumberOfThreads.HasValue || options.NumberOfThreads > 0, nameof(options.NumberOfThreads), posError);
_host.CheckUserArg(options.NumberOfIterations > 0, nameof(options.NumberOfIterations), posError);
_host.CheckUserArg(options.Lambda > 0, nameof(options.Lambda), posError);
_host.CheckUserArg(options.LearningRate > 0, nameof(options.LearningRate), posError);
_host.CheckUserArg(options.Alpha > 0, nameof(options.Alpha), posError);
_fun = (int)options.LossFunction;
_lambda = options.Lambda;
_k = options.ApproximationRank;
_iter = options.NumberOfIterations;
_eta = options.LearningRate;
_alpha = options.Alpha;
_c = options.C;
_threads = options.NumberOfThreads ?? Environment.ProcessorCount;
_quiet = options.Quiet;
_doNmf = options.NonNegative;
_info = new TrainerInfo(normalization: false, caching: false);
LabelName = options.LabelColumnName;
MatrixColumnIndexName = options.MatrixColumnIndexColumnName;
MatrixRowIndexName = options.MatrixRowIndexColumnName;
}
/// <summary>
/// Initializes a new instance of <see cref="SymSgdClassificationTrainer"/>
/// </summary>
internal SymSgdClassificationTrainer(IHostEnvironment env, Arguments args)
: base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), TrainerUtils.MakeR4VecFeature(args.FeatureColumn),
TrainerUtils.MakeBoolScalarLabel(args.LabelColumn))
{
args.Check(Host);
_args = args;
Info = new TrainerInfo();
}
public ActionResult DeleteConfirmed(int id)
{
TrainerInfo trainerInfo = _context.TrainerInfos.Find(id);
_context.TrainerInfos.Remove(trainerInfo);
_context.SaveChanges();
return(RedirectToAction("Index"));
}
/// <summary>
/// Initializes a new instance of <see cref="SymSgdClassificationTrainer"/>
/// </summary>
internal SymSgdClassificationTrainer(IHostEnvironment env, Options options)
: base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), TrainerUtils.MakeR4VecFeature(options.FeatureColumn),
TrainerUtils.MakeBoolScalarLabel(options.LabelColumn))
{
Host.CheckValue(options, nameof(options));
options.Check(Host);
_options = options;
Info = new TrainerInfo(supportIncrementalTrain: true);
}
internal MetaMulticlassTrainer(IHostEnvironment env, TArgs args, string name)
: base(env, name)
{
Host.CheckValue(args, nameof(args));
Args = args;
// Create the first trainer so errors in the args surface early.
_trainer = CreateTrainer();
// Regarding caching, no matter what the internal predictor, we're performing many passes
// simply by virtue of this being a meta-trainer, so we will still cache.
Info = new TrainerInfo(normalization: _trainer.Info.NeedNormalization);
}
protected OnlineLinearTrainer(TArguments args, IHostEnvironment env, string name)
: base(env, name)
{
Contracts.CheckValue(args, nameof(args));
Contracts.CheckUserArg(args.NumIterations > 0, nameof(args.NumIterations), UserErrorPositive);
Contracts.CheckUserArg(args.InitWtsDiameter >= 0, nameof(args.InitWtsDiameter), UserErrorNonNegative);
Contracts.CheckUserArg(args.StreamingCacheSize > 0, nameof(args.StreamingCacheSize), UserErrorPositive);
Args = args;
// REVIEW: Caching could be false for one iteration, if we got around the whole shuffling issue.
Info = new TrainerInfo(calibration: NeedCalibration, supportIncrementalTrain: true);
}
protected OnlineLinearTrainer(OnlineLinearArguments args, IHostEnvironment env, string name, SchemaShape.Column label)
: base(Contracts.CheckRef(env, nameof(env)).Register(name), TrainerUtils.MakeR4VecFeature(args.FeatureColumn), label, TrainerUtils.MakeR4ScalarWeightColumn(args.InitialWeights))
{
Contracts.CheckValue(args, nameof(args));
Contracts.CheckUserArg(args.NumIterations > 0, nameof(args.NumIterations), UserErrorPositive);
Contracts.CheckUserArg(args.InitWtsDiameter >= 0, nameof(args.InitWtsDiameter), UserErrorNonNegative);
Contracts.CheckUserArg(args.StreamingCacheSize > 0, nameof(args.StreamingCacheSize), UserErrorPositive);
Args = args;
Name = name;
// REVIEW: Caching could be false for one iteration, if we got around the whole shuffling issue.
Info = new TrainerInfo(calibration: NeedCalibration, supportIncrementalTrain: true);
}
// Start is called before the first frame update
void Start()
{
int index = 0;
foreach (Trainer trainer in gm.trainers)
{
TrainerInfo trainerInfo = Instantiate(Resources.Load("Prefabs/TrainerInfo") as GameObject, layout.transform).GetComponent <TrainerInfo>();
trainerInfo.SetTrainer(trainer);
trainerInfo.SetHpColor(trainerColor[index]);
trainerInfos.Add(trainerInfo);
index++;
}
}
private static void AddNormalizationTransforms(MLContext context,
TrainerInfo trainerInfo,
ICollection <SuggestedTransform> transforms)
{
// Only add normalization if trainer needs it
if (!trainerInfo.NeedNormalization)
{
return;
}
var transform = NormalizingExtension.CreateSuggestedTransform(context, DefaultColumnNames.Features, DefaultColumnNames.Features);
transforms.Add(transform);
}
/// <summary>
/// Initializes a new instance of <see cref="SymSgdClassificationTrainer"/>
/// </summary>
/// <param name="env">The private instance of <see cref="IHostEnvironment"/>.</param>
/// <param name="labelColumn">The name of the label column.</param>
/// <param name="featureColumn">The name of the feature column.</param>
/// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param>
public SymSgdClassificationTrainer(IHostEnvironment env, string featureColumn, string labelColumn, Action <Arguments> advancedSettings = null)
: base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), TrainerUtils.MakeR4VecFeature(featureColumn),
TrainerUtils.MakeBoolScalarLabel(labelColumn))
{
_args = new Arguments();
// Apply the advanced args, if the user supplied any.
_args.Check(Host);
advancedSettings?.Invoke(_args);
_args.FeatureColumn = featureColumn;
_args.LabelColumn = labelColumn;
Info = new TrainerInfo();
}
public ActionResult Details(int?id)
{
if (id == null)
{
return(new HttpStatusCodeResult(HttpStatusCode.BadRequest));
}
TrainerInfo trainerInfo = _context.TrainerInfos.Find(id);
if (trainerInfo == null)
{
return(HttpNotFound());
}
return(View(trainerInfo));
}
public ActionResult Create()
{
//Get trainer
var role = (from r in _context.Roles where r.Name.Contains("Trainer") select r)
.FirstOrDefault();
var users = _context.Users.Where(x => x.Roles.Select(y => y.RoleId)
.Contains(role.Id))
.ToList();
//Get Topic
var topics = _context.Topics.ToList();
var newTrainer = new TrainerInfo
{
Trainers = users,
};
return(View(newTrainer));
}
public FieldAwareFactorizationMachineTrainer(IHostEnvironment env, Arguments args) : base(env, LoadName)
{
Host.CheckUserArg(args.LatentDim > 0, nameof(args.LatentDim), "Must be positive");
Host.CheckUserArg(args.LambdaLinear >= 0, nameof(args.LambdaLinear), "Must be non-negative");
Host.CheckUserArg(args.LambdaLatent >= 0, nameof(args.LambdaLatent), "Must be non-negative");
Host.CheckUserArg(args.LearningRate > 0, nameof(args.LearningRate), "Must be positive");
Host.CheckUserArg(args.Iters >= 0, nameof(args.Iters), "Must be non-negative");
_latentDim = args.LatentDim;
_latentDimAligned = FieldAwareFactorizationMachineUtils.GetAlignedVectorLength(_latentDim);
_lambdaLinear = args.LambdaLinear;
_lambdaLatent = args.LambdaLatent;
_learningRate = args.LearningRate;
_numIterations = args.Iters;
_norm = args.Norm;
_shuffle = args.Shuffle;
_verbose = args.Verbose;
_radius = args.Radius;
Info = new TrainerInfo();
}
public ActionResult Create(TrainerInfo trainerInfo)
{
if (!ModelState.IsValid)
{
return(View("~/Views/ErrorValidations/Null.cshtml"));
}
var newTrainerInfo = new TrainerInfo
{
TrainerId = trainerInfo.TrainerId,
Full_Name = trainerInfo.Full_Name,
Email = trainerInfo.Email,
Working_Place = trainerInfo.Working_Place,
Phone = trainerInfo.Phone
};
_context.TrainerInfos.Add(newTrainerInfo);
_context.SaveChanges();
return(RedirectToAction("Index"));
}
/// <summary>
/// Initializes the <see cref="MetaMulticlassClassificationTrainer{TTransformer, TModel}"/> from the <see cref="OptionsBase"/> class.
/// </summary>
/// <param name="env">The private instance of the <see cref="IHostEnvironment"/>.</param>
/// <param name="options">The legacy arguments <see cref="OptionsBase"/>class.</param>
/// <param name="name">The component name.</param>
/// <param name="labelColumn">The label column for the metalinear trainer and the binary trainer.</param>
/// <param name="singleEstimator">The binary estimator.</param>
/// <param name="calibrator">The calibrator. If a calibrator is not explicitly provided, it will default to <see cref="PlattCalibratorTrainer"/></param>
internal MetaMulticlassClassificationTrainer(IHostEnvironment env, OptionsBase options, string name, string labelColumn = null,
TScalarTrainer singleEstimator = null, ICalibratorTrainer calibrator = null)
{
Host = Contracts.CheckRef(env, nameof(env)).Register(name);
Host.CheckValue(options, nameof(options));
Args = options;
if (labelColumn != null)
LabelColumn = new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, NumberDataViewType.UInt32, true);
Trainer = singleEstimator ?? CreateTrainer();
Calibrator = calibrator ?? new PlattCalibratorTrainer(env);
if (options.Calibrator != null)
Calibrator = options.Calibrator.CreateComponent(Host);
// Regarding caching, no matter what the internal predictor, we're performing many passes
// simply by virtue of this being a meta-trainer, so we will still cache.
Info = new TrainerInfo(normalization: Trainer.Info.NeedNormalization);
}
internal FieldAwareFactorizationMachineTrainer(IHostEnvironment env,
string[] featureColumns,
string labelColumn = DefaultColumnNames.Label,
string weights = null)
: base(env, LoadName)
{
var args = new Options();
Initialize(env, args);
Info = new TrainerInfo(supportValid: true, supportIncrementalTrain: true);
FeatureColumns = new SchemaShape.Column[featureColumns.Length];
for (int i = 0; i < featureColumns.Length; i++)
{
FeatureColumns[i] = new SchemaShape.Column(featureColumns[i], SchemaShape.Column.VectorKind.Vector, NumberType.R4, false);
}
LabelColumn = new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false);
WeightColumn = weights != null ? new SchemaShape.Column(weights, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false) : default;
}
