internal ResultBundle Validate(string originalCaptchaCode)
{
var result = ResultBundle.Success();
if (string.IsNullOrEmpty(FirstName) || string.IsNullOrEmpty(LastName))
{
result.AddMessage("فیلد نام و نام خانوادگی نمیتواند خالی باشد.");
result.IsSuccessful = false;
}
if (string.IsNullOrEmpty(Password) || (Password != PasswordConfirm))
{
result.AddMessage("رمز عبور و تایید آن باید مشابه یکدیگر باشند. لطفا مجددا رمز ورود را وارد نمایید.");
result.IsSuccessful = false;
}
Regex regex = new Regex(@"^([\w\.\-]+)@([\w\-]+)((\.(\w){2,3})+)$");
Match match = regex.Match(EmailAddress);
if (!match.Success)
{
result.AddMessage("آدرس ایمیل وارد شده معتبر نمی باشد. لطفا آدرس ایمیل را مجددا بررسی نمایید.");
result.IsSuccessful = false;
}
if (originalCaptchaCode != null && originalCaptchaCode != CaptchaCode)
{
result.AddMessage("کد تصویری صحیح نمی باشد لطفا مجددا تلاش نمایید");
result.IsSuccessful = false;
}
return(result);
}
// public string Rolename { get; set; } = "user";
internal async Task <ResultBundle> Login(Models.DALContext context)
{
ResultBundle r = ResultBundle.Success();
r.UserData = context.Login(EmailAddress, Password);
r.IsSuccessful = r.UserData != null;
return(r);
}
//Constructor
/// <summary>
/// Creates an uninitialized instance.
/// </summary>
/// <param name="readoutLayerConfig">The configuration of the readout layer.</param>
public VerificationResults(ReadoutLayerSettings readoutLayerConfig)
{
ReadoutLayerConfig = (ReadoutLayerSettings)readoutLayerConfig.DeepClone();
ComputationResultBundle = new ResultBundle();
ReadoutUnitStatCollection = new List <ReadoutUnitErrorStat>(ReadoutLayerConfig.ReadoutUnitsCfg.ReadoutUnitCfgCollection.Count);
for (int i = 0; i < ReadoutLayerConfig.ReadoutUnitsCfg.ReadoutUnitCfgCollection.Count; i++)
{
ReadoutUnitStatCollection.Add(new ReadoutUnitErrorStat(i, ReadoutLayerConfig.ReadoutUnitsCfg.ReadoutUnitCfgCollection[i]));
}
OneTakesAllGroupStatCollection = new List <OneTakesAllGroupErrorStat>();
if (ReadoutLayerConfig.OneTakesAllGroupsCfg != null)
{
foreach (OneTakesAllGroupSettings groupCfg in ReadoutLayerConfig.OneTakesAllGroupsCfg.OneTakesAllGroupCfgCollection)
{
int[] unitIndexes = ReadoutLayerConfig.GetOneTakesAllGroupMemberRUnitIndexes(groupCfg.Name).ToArray();
OneTakesAllGroupStatCollection.Add(new OneTakesAllGroupErrorStat(groupCfg.Name, unitIndexes, ReadoutLayerConfig.ReadoutUnitsCfg.ReadoutUnitCfgCollection));
}
}
return;
}
private async Task <ResultBundle <T> > ExecuteRequest <T>(HttpRequestMessage requestMessage)
{
T value;
IErrorHolder error;
string responseValue = null;
ResultBundle <T> resultBundle = new ResultBundle <T>(default(T));
for (int i = 0; i < MaxRetry; i++)
{
try
{
using (HttpResponseMessage response = await s_HttpClient.SendAsync(requestMessage))
{
responseValue = await response.Content.ReadAsStringAsync();
if (response.IsSuccessStatusCode)
{
responseValue = await response.Content.ReadAsStringAsync();
value = JsonConvert.DeserializeObject <T>(responseValue);
resultBundle = new ResultBundle <T>(value);
}
else
{
error = _ErrorFactory.CreateError(ErrorCodes.RequestFailure, responseValue);
resultBundle = new ResultBundle <T>(default(T), error);
}
}
}
catch (Exception e)
{
error = _ErrorFactory.CreateError(ErrorCodes.Exception, e);
resultBundle = new ResultBundle <T>(default(T), error);
}
}
return(resultBundle);
}
//Constructor
/// <summary>
/// Creates initialized instance
/// </summary>
/// <param name="readoutLayerConfig">Configuration of the Readout Layer</param>
public VerificationResults(ReadoutLayerSettings readoutLayerConfig)
{
ReadoutLayerConfig = (ReadoutLayerSettings)readoutLayerConfig.DeepClone();
ComputationResultBundle = new ResultBundle();
ReadoutUnitStatCollection = new List <ReadoutUnitStat>(ReadoutLayerConfig.ReadoutUnitsCfg.ReadoutUnitCfgCollection.Count);
for (int i = 0; i < ReadoutLayerConfig.ReadoutUnitsCfg.ReadoutUnitCfgCollection.Count; i++)
{
ReadoutUnitStatCollection.Add(new ReadoutUnitStat(i, ReadoutLayerConfig.ReadoutUnitsCfg.ReadoutUnitCfgCollection[i]));
}
OneWinnerGroupStatCollection = new List <OneWinnerGroupStat>(ReadoutLayerConfig.ReadoutUnitsCfg.OneWinnerGroupCollection.Keys.Count);
foreach (string groupName in ReadoutLayerConfig.ReadoutUnitsCfg.OneWinnerGroupCollection.Keys)
{
ReadoutUnitsSettings.OneWinnerGroup owg = ReadoutLayerConfig.ReadoutUnitsCfg.OneWinnerGroupCollection[groupName];
int[] unitIndexes = new int[owg.Members.Count];
for (int i = 0; i < owg.Members.Count; i++)
{
unitIndexes[i] = ReadoutLayerConfig.ReadoutUnitsCfg.GetReadoutUnitID(owg.Members[i].Name);
}
OneWinnerGroupStatCollection.Add(new OneWinnerGroupStat(groupName, unitIndexes, ReadoutLayerConfig.ReadoutUnitsCfg.OneWinnerGroupCollection[groupName].Members));
}
return;
}
public async Task <IActionResult> Login(Models.Account.LoginViewModel model)
{
try
{
ResultBundle result = ResultBundle.Failed();
if (ModelState.IsValid)
{
result = await model.Login(_db);
if (result.IsSuccessful)
{
var selectedProperty = from property in result.UserData.GetType().GetProperties()
where property.Name == "RoleId"
select property.GetValue(result.UserData, null);
int s = 0;
foreach (var file in selectedProperty)
{
s = (Int32)file;
}
string Rolename = _db.Roles.FirstOrDefault(x => x.RoleId == s).Rolename;
var userClaims = new List <Claim>
{
new Claim(ClaimTypes.Name, model.EmailAddress),
new Claim(ClaimTypes.Role, Rolename)
};
var principal = new ClaimsPrincipal(new ClaimsIdentity(userClaims, "local"));
await HttpContext.Authentication.SignInAsync("MyCookieMiddlewareInstance4", principal);
if (Url.IsLocalUrl(model.ReturnUrl))
{
return(Redirect(model.ReturnUrl));
}
else
{
return(RedirectToAction("index", "Home"));
}
}
else
{
result.AddMessage("نام کاربری یا رمز عبور صحیح نمی باشد، لطفا مجددا تلاش نمایید");
}
}
else
{
result.AddMessage("اطاعات را وارد نمایید");
}
ViewBag.errorMessage = result.FormattedMessages;
return(View());
}
catch (Exception ex)
{
return(RedirectToAction(nameof(HomeController.Error), "Home"));
}
}
/// <summary>
/// Performs one demo case.
/// Loads and prepares sample data, trains State Machine and displayes results
/// </summary>
/// <param name="log">Into this interface are written output messages</param>
/// <param name="demoCaseParams">An instance of DemoSettings.CaseSettings to be performed</param>
public static void PerformDemoCase(IOutputLog log, DemoSettings.CaseSettings demoCaseParams)
{
log.Write(" Performing demo case " + demoCaseParams.Name, false);
log.Write(" ", false);
//Instantiate the State Machine
StateMachine stateMachine = new StateMachine(demoCaseParams.StateMachineCfg);
//Prepare input object for regression stage
StateMachine.RegressionInput rsi = null;
//Prediction input vector (relevant only for input continuous feeding)
double[] predictionInputVector = null;
if (demoCaseParams.StateMachineCfg.NeuralPreprocessorConfig.InputConfig.FeedingType == NeuralPreprocessor.InputFeedingType.Continuous)
{
//Continuous input feeding
//Load data bundle from csv file
VectorBundle data = VectorBundle.LoadFromCsv(demoCaseParams.FileName,
demoCaseParams.StateMachineCfg.NeuralPreprocessorConfig.InputConfig.ExternalFieldNameCollection(),
demoCaseParams.StateMachineCfg.ReadoutLayerConfig.OutputFieldNameCollection,
out predictionInputVector
);
rsi = stateMachine.PrepareRegressionData(data, PredictorsCollectionCallback, log);
}
else
{
//Patterned input feeding
//Load data bundle from csv file
PatternBundle data = PatternBundle.LoadFromCsv(demoCaseParams.FileName,
demoCaseParams.StateMachineCfg.NeuralPreprocessorConfig.InputConfig.ExternalFieldNameCollection(),
demoCaseParams.StateMachineCfg.ReadoutLayerConfig.OutputFieldNameCollection
);
rsi = stateMachine.PrepareRegressionData(data, PredictorsCollectionCallback, log);
}
//Report key statistics of the State Machine's reservoirs
string statisticsReport = rsi.CreateReport(4);
log.Write(statisticsReport);
log.Write(string.Empty);
//Regression stage - building of trained readout layer
log.Write(" Regression stage (training of readout layer)", false);
//Perform the regression
ResultBundle rcb = stateMachine.BuildReadoutLayer(rsi, RegressionControl, log);
log.Write(string.Empty);
//Report training (regression) results
log.Write(" Training results", false);
string trainingReport = stateMachine.RL.GetTrainingResultsReport(6);
log.Write(trainingReport);
log.Write(string.Empty);
//Perform prediction if the input feeding is continuous (we know the input but we don't know the ideal output)
if (demoCaseParams.StateMachineCfg.NeuralPreprocessorConfig.InputConfig.FeedingType == NeuralPreprocessor.InputFeedingType.Continuous)
{
double[] predictionOutputVector = stateMachine.Compute(predictionInputVector);
string predictionReport = stateMachine.RL.GetForecastReport(predictionOutputVector, 6);
log.Write(" Forecasts", false);
log.Write(predictionReport);
log.Write(string.Empty);
}
return;
}
