private void deleteBtn_Click(object sender, EventArgs e)
{
var warning = MessageBox.Show(
this,
"Are you sure you want to delete well?\n This action cannot be undone.",
"Deleting Well",
MessageBoxButtons.YesNo,
MessageBoxIcon.Exclamation,
MessageBoxDefaultButton.Button2);
if (warning == DialogResult.Yes)
{
var db = new DatabaseLoader(connectionString, NetworkChecker.LastStatus);
try
{
db.DeleteWell(well);
deleteMarkerAction(well);
this.Close();
}
catch (NpgsqlException ex)
{
editErrorLbl.Text = ex.Message;
}
}
}
public MainViewModel(string databaseServer, string primaryDatabase, string secondaryDatabase, string username, string password, int bulkPurchaseQty)
{
// Setup Fields
_databaseServer = databaseServer;
_primaryDatabase = primaryDatabase;
_secondaryDatabase = secondaryDatabase;
_username = username;
_password = password;
_bulkPurchaseQty = bulkPurchaseQty;
_ticketsPurchased = 0;
_purchasesPerSecond = 0;
_progressValue = 0;
_duration = new TimeSpan();
_statusText = "";
_loadingDatabase = "";
_startText = "Start";
_startEnabled = true;
// Setup Commands
PurchaseTicketsCommand = new PurchaseTicketsCommand(this);
// Setup Workers
DatabaseLoader = new DatabaseLoader(this);
}
private static IDataView LoadDataView(string source)
{
var tmpPath = GetAbsolutePath(TRAIN_DATA_FILEPATH);
IDataView trainingDataView;
if (source == "textFile")
{
trainingDataView = mlContext.Data.LoadFromTextFile <ModelInput>(
path: tmpPath,
hasHeader: true,
separatorChar: '\t',
allowQuoting: true,
allowSparse: false);
}
else if (source == "SQL")
{
DatabaseLoader loader = mlContext.Data.CreateDatabaseLoader <ModelInput>();
DatabaseSource dbSource = new DatabaseSource(SqlClientFactory.Instance, CONNECTION_STRING, SQL_COMMAND);
trainingDataView = loader.Load(dbSource);
}
else
{
ModelInput[] inMemoryCollection = new ModelInput[]
{
new ModelInput()
{
Lead_time = 0,
//To Do: Instanciar el objeto
}
};
trainingDataView = mlContext.Data.LoadFromEnumerable <ModelInput>(inMemoryCollection);
}
return(trainingDataView);
}
static void Main_BinaryClassifications(string[] args)
{
var mlContext = new MLContext();
string connectionString = @"Data Source=LAPTOP-ILQS92OM\SQLEXPRESS;Initial Catalog=ContosoRetailDW;Integrated Security=True;Pooling=False";
string commandText = "SELECT CONVERT(real, Age),CONVERT(real, YearlyIncome),CONVERT(real, TotalChildren),CONVERT(real, NumberChildrenAtHome) ," +
"CONVERT(real, NumberCarsOwned),CONVERT(real, ProductKey),CONVERT(real, PromotionKey)," +
"CONVERT(bit, HouseOwnerFlag) FROM V_CustomerPromotion";
DatabaseLoader loader = mlContext.Data.CreateDatabaseLoader <CustomerPromotionData>();
DatabaseSource dbSource = new DatabaseSource(SqlClientFactory.Instance,
connectionString,
commandText);
IDataView dataView = loader.Load(dbSource);
//ConsoleHelper.ShowDataViewInConsole(mlContext, dataView, 500);
var trainTestData = mlContext.Data.TrainTestSplit(dataView, testFraction: 0.2).TestSet;
//BuildTrainEvaluateAndSaveModel(mlContext, dataView, trainTestData);
TestPrediction(mlContext);
Console.WriteLine("=============== End of process, hit any key to finish ===============");
}
public ConfigFileViewmodel(Model.ConfigFile configFile)
: base(configFile, typeof(ConfigFileView), $"Config file ({configFile.FileName})")
{
_dbs = DatabaseLoader.LoadDatabaseProviders().ToList();
DatabaseTypes = _dbs.Select(db => db.DatabaseType).ToArray();
Load(Model.AppSettings);
}
static void Main_AnomalyDetections(string[] args)
{
var mlContext = new MLContext();
string connectionString = @"Data Source=LAPTOP-ILQS92OM\SQLEXPRESS;Initial Catalog=ContosoRetailDW;Integrated Security=True;Pooling=False";
string commandText = "SELECT top 100000 EntityKey,ScenarioKey,AccountKey,ProductCategoryKey,CONVERT(real, Amount * 100) from FactStrategyPlan";
DatabaseLoader loader = mlContext.Data.CreateDatabaseLoader <StrategyPlan>();
DatabaseSource dbSource = new DatabaseSource(SqlClientFactory.Instance,
connectionString,
commandText);
IDataView dataView = loader.Load(dbSource);
//ConsoleHelper.ShowDataViewInConsole(mlContext, dataView, 500);
var trainTestData = mlContext.Data.TrainTestSplit(dataView);
const int size = 100;
////To detech temporay changes in the pattern
DetectSpike(mlContext, size, dataView);
//To detect persistent change in the pattern
//DetectChangepoint(mlContext, size, dataView);
Console.WriteLine("=============== End of process, hit any key to finish ===============");
}
static void Main(string[] args)
{
Console.WriteLine("Hello World!");
//Console.WriteLine("Enter the name of the table:");
//String tblName = Console.ReadLine();
//String TrainDataRelativePath = ConvertDataToCsv(tblName);
//Console.WriteLine("Enter the name of the target field:");
//LabelColumnName = Console.ReadLine();
String tblName = "tblFE_Ana_Titanic";
String TrainDataRelativePath = ConvertDataToCsv(tblName);
LabelColumnName = "fblnSurvived";
mlContext = new MLContext(seed: 1);
DatabaseLoader loader = mlContext.Data.CreateDatabaseLoader <TitanicData>();
DatabaseSource dbSource = new DatabaseSource(providerFactory: SqlClientFactory.Instance, connectionString: connectionString, commandText: commandText);
//ColumnInferenceResults columnInference = mlContext.Auto().InferColumns();
var columnInference = InferColumns(mlContext, GetAbsolutePath(TrainDataRelativePath));
LoadData(mlContext, columnInference, GetAbsolutePath(TrainDataRelativePath), GetAbsolutePath(TrainDataRelativePath));
var experimentResult = RunAutoMLExperiment(mlContext: mlContext, columnInference: columnInference);
EvaluateModel(mlContext, experimentResult.BestRun.Model, experimentResult.BestRun.TrainerName);
SaveModel(mlContext, experimentResult.BestRun.Model);
// PlotRegressionChart(mlContext, TestDataPath, numberOfRecordsToRead: 100, args);
var refitModel = RefitBestPipeline(mlContext, experimentResult, columnInference, GetAbsolutePath(TrainDataRelativePath), GetAbsolutePath(TrainDataRelativePath));
}
static bool StartDB()
{
// Load databases
DatabaseLoader loader = new DatabaseLoader(DatabaseTypeFlags.All);
loader.AddDatabase(DB.Login, "Login");
loader.AddDatabase(DB.Characters, "Character");
loader.AddDatabase(DB.World, "World");
loader.AddDatabase(DB.Hotfix, "Hotfix");
if (!loader.Load())
{
return(false);
}
// Get the realm Id from the configuration file
Global.WorldMgr.GetRealm().Id.Realm = ConfigMgr.GetDefaultValue("RealmID", 0u);
if (Global.WorldMgr.GetRealm().Id.Realm == 0)
{
Log.outError(LogFilter.Server, "Realm ID not defined in configuration file");
return(false);
}
Log.outInfo(LogFilter.ServerLoading, "Realm running as realm ID {0} ", Global.WorldMgr.GetRealm().Id.Realm);
// Clean the database before starting
ClearOnlineAccounts();
Log.outInfo(LogFilter.Server, "Using World DB: {0}", Global.WorldMgr.LoadDBVersion());
return(true);
}
static void Main(string[] args)
{
string filePath = @"C:\Users\Tomasz\Desktop\eng.txt"; // move to arg
var trans = new DatabaseLoader(new TranslationFactory(), new FileLoader()).Load(filePath).ToList();
Console.ReadKey();
}
void Start()
{
Database = GameObject.FindGameObjectWithTag("Database").GetComponent <DatabaseLoader> ();
inventory = GameObject.FindGameObjectWithTag("InventoryManager").GetComponent <InventoryManager> ();
ReadQuestData();
}
private IDataView LoadData()
{
DatabaseLoader loader = mlContext.Data.CreateDatabaseLoader <Data>();
DatabaseSource dbSource = new DatabaseSource(SqlClientFactory.Instance, Configuration.ConnectString, @"SELECT Comment,ReplyComment FROM Datasets");
//DatabaseSource dbSource = new DatabaseSource(SqlClientFactory.Instance, "Data Source=(local);Initial Catalog=MonitoringSocialNetwork;Integrated Security=True", @"SELECT Comment,ReplyComment FROM dbo.Datasets");
return(loader.Load(dbSource));
}
internal T Load()
{
string appFolder = "EstimateBuilder";
if (Path.GetExtension(path) == ".tdb")
{
appFolder = "TemplateBuilder";
}
if (!File.Exists(String.Format("{0}\\{1}\\{2}",
Environment.GetFolderPath(Environment.SpecialFolder.ApplicationData),
appFolder,
"backups")))
{
Directory.CreateDirectory(String.Format("{0}\\{1}\\{2}",
Environment.GetFolderPath(Environment.SpecialFolder.ApplicationData),
appFolder,
"backups"));
}
string backupPath = String.Format("{0}\\{1}\\{2}\\{3} {4}{5}",
Environment.GetFolderPath(Environment.SpecialFolder.ApplicationData),
appFolder,
"backups",
Path.GetFileNameWithoutExtension(path),
String.Format("{0:yyyy-MM-dd_hh-mm-ss-tt}", DateTime.Now),
Path.GetExtension(path));
File.Copy(path, backupPath);
bool needsSave;
TECScopeManager scopeManager;
DatabaseVersionManager.UpdateStatus status = DatabaseVersionManager.CheckAndUpdate(path);
if (status == DatabaseVersionManager.UpdateStatus.Updated)
{
(scopeManager, needsSave) = DatabaseLoader.Load(path, true);
}
else if (status == DatabaseVersionManager.UpdateStatus.NotUpdated)
{
(scopeManager, needsSave) = DatabaseLoader.Load(path);
}
else if (status == DatabaseVersionManager.UpdateStatus.Incompatible)
{
System.Windows.MessageBox.Show("Database is incompatible with this version of the program.", "Incompatible", MessageBoxButton.OK, MessageBoxImage.Exclamation);
return(null);
}
else
{
throw new NotImplementedException("UpdateStatus not recognized.");
}
if (needsSave)
{
New(scopeManager);
}
return(scopeManager as T);
}
static void Main(string[] args)
{
Console.WriteLine("Training time series analysis");
//Step 1. Create a ML Context
var ctx = new MLContext();
string connectionString = "Data Source=localhost;Initial Catalog=kaggle_wallmart;Provider=SQLNCLI11.1;Integrated Security=SSPI;Auto Translate=False;";
connectionString = "Server=localhost;Database=kaggle_wallmart;Integrated Security=True";
string Query = @"
SELECT
CAST(X.[Value] AS REAL) AS [TotalSales],
CAST(Y.date AS DATE) AS [SalesDate],
CAST(year(Y.date) AS REAL) As [Year]
FROM [dbo].[RAW_Train_Eval] AS X
INNER JOIN [dbo].RAW_Calendar AS Y ON Y.d=X.dCode
where Id='HOBBIES_1_278_CA_1_evaluation'
order by 2
";
Console.WriteLine("Connecting to the database...");
//dbChecks dbchecks = new dbChecks();
//dbchecks.ExecuteQuery(connectionString, Query);
System.Data.SqlClient.SqlClientFactory newFactory = SqlClientFactory.Instance;
Console.WriteLine("Loading data...");
DatabaseSource dbSource = new DatabaseSource(SqlClientFactory.Instance, connectionString, Query);
DatabaseLoader loader = ctx.Data.CreateDatabaseLoader <ModelInput>();
IDataView dataView = loader.Load(dbSource);
Console.WriteLine($"Loaded {dataView.GetRowCount()} rows...");
IDataView trainingData = ctx.Data.FilterRowsByColumn(dataView, "Year", upperBound: 2016);
IDataView ValidationData = ctx.Data.FilterRowsByColumn(dataView, "Year", lowerBound: 2016);
var forecastingPipeline = ctx.Forecasting.ForecastBySsa(
outputColumnName: "ForecastedSales",
inputColumnName: "TotalSales",
windowSize: 7,
seriesLength: 60,
trainSize: 300,
horizon: 30,
confidenceLevel: 0.95f,
confidenceLowerBoundColumn: "LowerBoundSales",
confidenceUpperBoundColumn: "UpperBoundSales");
SsaForecastingTransformer forecaster = forecastingPipeline.Fit(trainingData);
Evaluate(ValidationData, forecaster, ctx);
var forecastEngine = forecaster.CreateTimeSeriesEngine <ModelInput, ModelOutput>(ctx);
forecastEngine.CheckPoint(ctx, "c:\\temp\\Model.zip");
forecastEngine.CheckPoint(ctx, "C:\\Temp\\WallMartModels\\evaluation\\Model_HOBBIES_1_278_CA_1_evaluation.zip");
Forecast(ValidationData, 7, forecastEngine, ctx);
Console.WriteLine("Training time series analysis completed");
}
public static IDataView LoadData(MLContext mlContext)
{
DatabaseLoader loader = mlContext.Data.CreateDatabaseLoader <MovieRating>();
string connectionString = @"Data Source=(Local);Database=MovieReviews;Integrated Security=True;Connect Timeout=30";
string sqlCommand = "SELECT AccountId as userId, MovieId as movieId, CAST(Ratings as REAL) as Label FROM dbo.Reviews";
DatabaseSource dbSource = new DatabaseSource(SqlClientFactory.Instance, connectionString, sqlCommand);
return(loader.Load(dbSource));
}
static void Main(string[] args)
{
string rootDir = Path.GetFullPath(Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "../../../"));
//string dbFilePath = Path.Combine(rootDir, "Data", "DailyDemand.mdf");
string modelPath = Path.Combine(rootDir, "MLModel.zip");
// var connectionString = $"Data Source=(LocalDB)\\MSSQLLocalDB;AttachDbFilename={dbFilePath};Integrated Security=True;Connect Timeout=30;";
// you can use TblSmartAr.sql and able to make this table
var connectionString = $"Data Source = (localdb)\\MSSQLLocalDB; Initial Catalog = Ar_Database; Integrated Security = True; Connect Timeout = 30; Encrypt = False; TrustServerCertificate = False; ApplicationIntent = ReadWrite; MultiSubnetFailover = False;";
MLContext mlContext = new MLContext();
DatabaseLoader loader = mlContext.Data.CreateDatabaseLoader <ModelInput>();
string query = "SELECT Invoice_Date, CAST(Due_year as REAL) as Due_year, CAST(OverDue_Days as REAL) as OverDue_Days FROM TblSmartAR";
DatabaseSource dbSource = new DatabaseSource(SqlClientFactory.Instance,
connectionString,
query);
DateTime now = DateTime.Today;
int thisyear = (now.Year);
IDataView dataView = loader.Load(dbSource);
IDataView firstYearData = mlContext.Data.FilterRowsByColumn(dataView, "Due_year", upperBound: thisyear);
IDataView secondYearData = mlContext.Data.FilterRowsByColumn(dataView, "Due_year", lowerBound: thisyear);
var forecastingPipeline = mlContext.Forecasting.ForecastBySsa(
outputColumnName: "ForecastedDays",
inputColumnName: "OverDue_Days",
windowSize: 80,
seriesLength: 90,
trainSize: 365,
horizon: 80,
confidenceLevel: .90f,
confidenceLowerBoundColumn: "MinimumDays",
confidenceUpperBoundColumn: "MaximumDays");
SsaForecastingTransformer forecaster = forecastingPipeline.Fit(firstYearData);
Evaluate(secondYearData, forecaster, mlContext);
var forecastEngine = forecaster.CreateTimeSeriesEngine <ModelInput, ModelOutput>(mlContext);
forecastEngine.CheckPoint(mlContext, modelPath);
//foreach(var j in )
//{
// if()
//}
Forecast(secondYearData, 40, forecastEngine, mlContext);
Console.ReadKey();
}
// Use this for initialization
void Start()
{
Database = GameObject.FindGameObjectWithTag("Database").GetComponent <DatabaseLoader> ();
questManager = GameObject.FindGameObjectWithTag("QuestManager").GetComponent <QuestManager>();
enemyManager = GameObject.FindGameObjectWithTag("EnemyManager").GetComponent <EnemyManager>();
currentHealth = health;
SetUpEnemy();
currentHealth = health;
}
internal void setLoader(DatabaseLoader <GameDatabase.TextureInfo> textureLoader)
{
foreach (string extension in textureLoader.extensions)
{
if (extensions.Contains(extension))
{
this.textureLoaders[extension] = textureLoader;
}
}
}
private static void TrainModel(ApplicationDbContext dbContext, string modelFile)
{
var context = new MLContext();
var sqlCommand = "SELECT * FROM " + dbContext.Offers;
var connString = "Server =.; Database = ImotiPrediction; Trusted_Connection = True; MultipleActiveResultSets = true";
DatabaseSource dataSource = new DatabaseSource(SqlClientFactory.Instance, connString, sqlCommand);
DatabaseLoader loader = context.Data.CreateDatabaseLoader <ModelInput>();
IDataView trainingDataView = loader.Load(dataSource);
// Data process configuration with pipeline data transformations
var dataProcessPipeline = context.Transforms.Categorical
.OneHotEncoding(
new[]
{
new InputOutputColumnPair(nameof(ModelInput.District), nameof(ModelInput.District)),
new InputOutputColumnPair(nameof(ModelInput.Type), nameof(ModelInput.Type)),
new InputOutputColumnPair(nameof(ModelInput.BuildingType), nameof(ModelInput.BuildingType)),
}).Append(
context.Transforms.Concatenate(
outputColumnName: "Features",
//nameof(ModelInput.Url),
nameof(ModelInput.District),
nameof(ModelInput.Type),
nameof(ModelInput.BuildingType),
nameof(ModelInput.Size),
nameof(ModelInput.Floor),
nameof(ModelInput.TotalFloors),
nameof(ModelInput.Year)));
// Set the training algorithm (GBM = Gradient Boosting Machine)
var trainer = context.Regression.Trainers.LightGbm(
new LightGbmRegressionTrainer.Options
{
NumberOfIterations = 4000,
LearningRate = 0.1006953f,
NumberOfLeaves = 55,
MinimumExampleCountPerLeaf = 20,
UseCategoricalSplit = true,
HandleMissingValue = false,
MinimumExampleCountPerGroup = 200,
MaximumCategoricalSplitPointCount = 16,
CategoricalSmoothing = 10,
L2CategoricalRegularization = 1,
Booster = new GradientBooster.Options {
L2Regularization = 0.5, L1Regularization = 0
},
LabelColumnName = nameof(ModelInput.Price),
FeatureColumnName = "Features",
});
var trainingPipeline = dataProcessPipeline.Append(trainer);
ITransformer model = trainingPipeline.Fit(trainingDataView);
context.Model.Save(model, trainingDataView.Schema, modelFile);
}
public static IDataView DataExtract(MLContext mlContext, AppConfig appConfig, string sqlQuery)
{
//Console.WriteLine($"[{DateTime.UtcNow}] Method DataExtract start");
DatabaseLoader loader = mlContext.Data.CreateDatabaseLoader <CallDistribution>();
string connectionString = appConfig.ConnectionString;
DatabaseSource dbSource = new DatabaseSource(SqlClientFactory.Instance, connectionString, sqlQuery);
IDataView dataView = loader.Load(dbSource);
//Console.WriteLine($"[{DateTime.UtcNow}] Method DataExtract end");
return(dataView);
}
public override void OnInspectorGUI()
{
base.OnInspectorGUI();
DatabaseLoader dl = (DatabaseLoader)target;
if (GUILayout.Button("Load Database"))
{
dl.LoadDatabase();
}
}
static DatabaseProvider()
{
if (!Enum.TryParse <DatabaseType>(ConfigurationManager.AppSettings["DatabaseType"], out var databaseType))
{
throw new ApplicationException("Database type not configured");
}
Database = DatabaseLoader.LoadDatabaseProviders().FirstOrDefault(db => db.DatabaseType == databaseType) ??
throw new ApplicationException($"Database provider {databaseType} not available");
Database.Open(ConfigurationManager.ConnectionStrings);
Database.InitializeFieldLengths();
}
void Awake()
{
DontDestroyOnLoad(gameObject);
if (database == null)
{
database = this;
}
else if (database != this)
{
Destroy(gameObject);
}
}
public PlayoutServers(DatabaseType databaseType, ConnectionStringSettingsCollection connectionStringSettingsCollection)
{
_db = DatabaseLoader.LoadDatabaseProviders().FirstOrDefault(db => db.DatabaseType == databaseType);
_db.Open(connectionStringSettingsCollection);
Servers = _db.LoadServers <CasparServer>().ToList();
Servers.ForEach(s =>
{
s.IsNew = false;
s.Channels.ForEach(c => c.Owner = s);
s.Recorders.ForEach(r => r.Owner = s);
});
}
internal static void Start()
{
string rootDir = Path.GetFullPath(Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "../../../", "BikeDemandForecasting"));
string dbFilePath = Path.Combine(rootDir, "Data", "DailyDemand.mdf");
string modelPath = Path.Combine(rootDir, "MLModel.zip");
var connectionString = $"Data Source=(LocalDB)\\MSSQLLocalDB;AttachDbFilename={dbFilePath};Integrated Security=True;Connect Timeout=30;";
MLContext mlContext = new MLContext();
string query = "SELECT RentalDate, CAST(Year as REAL) as Year, CAST(TotalRentals as REAL) as TotalRentals FROM Rentals";
DatabaseSource dbSource = new DatabaseSource(SqlClientFactory.Instance, connectionString, query);
DatabaseLoader loader = mlContext.Data.CreateDatabaseLoader <ModelInput>();
IDataView dataView = loader.Load(dbSource);
/*
* Набор данных содержит данные за два года. Для обучения используются только данные за первый год,
* а данные за второй год откладываются для сравнения фактических значений с прогнозом, созданным
* моделью. Отфильтруйте данные с помощью преобразования FilterRowsByColumn.
*/
IDataView firstYearData = mlContext.Data.FilterRowsByColumn(dataView, "Year", upperBound: 1);
IDataView secondYearData = mlContext.Data.FilterRowsByColumn(dataView, "Year", lowerBound: 1);
//Определите конвейер, который использует SsaForecastingEstimator для прогнозирования значений в наборе данных временных рядов
var forecastingPipeline = mlContext.Forecasting.ForecastBySsa(
outputColumnName: "ForecastedRentals",
inputColumnName: "TotalRentals",
windowSize: 7,
seriesLength: 30,
trainSize: 365,
horizon: 7,
confidenceLevel: 0.95f,
confidenceLowerBoundColumn: "LowerBoundRentals",
confidenceUpperBoundColumn: "UpperBoundRentals");
//обучить модель и подогнать данные по ранее определенным forecastingPipeline.
SsaForecastingTransformer forecaster = forecastingPipeline.Fit(firstYearData);
//Оценка модели
Evaluate(secondYearData, forecaster, mlContext);
//создайте TimeSeriesPredictionEngine. TimeSeriesPredictionEngine — это удобный метод для создания единичных прогнозов
var forecastEngine = forecaster.CreateTimeSeriesEngine <ModelInput, ModelOutput>(mlContext);
//Сохраните модель в файл с именем MLModel.zip, которое задано ранее определенной переменной
//modelPath. Выполните метод Checkpoint, чтобы сохранить модель.
forecastEngine.CheckPoint(mlContext, modelPath);
Forecast(secondYearData, 7, forecastEngine, mlContext);
}
public override void OnAwake()
{
ConsoleSystem.OutputPath = Bootstrap.OutputPath;
ConsoleSystem.Log("[Bootstrap]: Приложение запущено");
DatabaseLoader.Load <Database>();
RPCManager.Initialize();
this.AddType <VirtualServer>();
this.AddType <NetworkManager>();
this.AddType <RangeAim>();
this.AddType <MeleeAim>();
this.AddType <AutoGather>();
this.AddType <WallHack>();
}
static bool StartDB()
{
DatabaseLoader loader = new DatabaseLoader(DatabaseTypeFlags.None);
loader.AddDatabase(DB.Login, "Login");
if (!loader.Load())
{
return(false);
}
Log.SetRealmId(0); // Enables DB appenders when realm is set.
return(true);
}
public override void OnInspectorGUI()
{
if (PrefabUtility.GetPrefabType(target) == PrefabType.Prefab)
{
return;
}
databaseLoader = (DatabaseLoader)target;
List <string> dbList = MaxstAR.MaxstARUtils.GetDBFileList();
databaseLoader.ActivateDatabase(dbList);
EditorUtility.SetDirty(databaseLoader);
}
public static void InvokeLazy()
{
DatabaseLoader dbLoader = new DatabaseLoader();
dbLoader.loadTuples(333);
foreach (DatabaseTable table in dbLoader.takeTuples(1))
{
Console.WriteLine(table.id + " " + table.column1);
}
foreach (DatabaseTable table in dbLoader.takeTuples(2))
{
Console.WriteLine(table.id + " " + table.column1);
}
Console.ReadLine();
}
public void AddFromString(string knowledgeString)
{
string[] lines = knowledgeString.Replace("$new$", "").Trim().Split('\r', '\n');
DatabaseLoader loader = new DatabaseLoader(lines);
Database buffer = loader.Load();
if (buffer.Expressions.Expressions.Count > 0)
{
database.AddExpression(buffer.Expressions.Expressions[0], true);
}
else
{
database.AddKnowledge(buffer.Knowledge[0], true);
}
}
private void Awake()
{
if (Instance == null)
{
Instance = this;
}
else if (Instance != null)
{
Destroy(this.gameObject);
return;
}
LoadAllDB();
DontDestroyOnLoad(this.gameObject);
}
