public IForecastingDataSets Select(IPredicationSchema schema)
{
int startYear = schema.Start.Year;
int startMonth = schema.Start.Month;
int startDay = schema.Start.Day;
int endYear = schema.End.Year;
int endMonth = schema.End.Month;
int endDay = schema.End.Day;
IForecastingDataSets[] sets = new IForecastingDataSets[endYear - startYear + 1];
for (int i = startYear; i <= endYear; i++)
{
DateTime start = new DateTime(i, startMonth, startDay);
DateTime end = new DateTime(i, endMonth, endDay);
PredicationSchema clonedSchema = new PredicationSchema(schema.Stimulus, schema.Responses)
{
Start = start,
End = end,
TimePeriod = schema.TimePeriod
};
RollingSelection rollSlct = new RollingSelection();
sets[i - startYear] = rollSlct.Select(clonedSchema);
}
ForecastingDataSets result = ForecastingDataSets.Merge(sets);
schema.InstancesCount = result.InputData.Length;
return(result);
}
public static ForecastingDataSets Merge(IForecastingDataSets[] sets)
{
ForecastingDataSets result = null;
if (sets != null)
{
int length = (from s in sets select s.InputData.Length).Sum();
int dimension = sets[0].InputData[0].Length;
int outputDimension = sets[0].OutputData[0].Length;
int i = 0;
double[][] inputs = new double[length][];
double[][] outputs = new double[length][];
DateTime[] date = new DateTime[length];
foreach (IForecastingDataSets set in sets)
{
for (int j = 0; j < set.InputData.Length; j++)
{
inputs[i] = new double[dimension];
outputs[i] = new double[outputDimension];
for (int c = 0; c < dimension; c++)
{
inputs[i][c] = set.InputData[j][c];
}
for (int c = 0; c < outputDimension; c++)
{
outputs[i][c] = set.OutputData[j][c];
}
date[i] = set.Date[j];
i++;
}
}
result = new ForecastingDataSets(inputs, outputs)
{
Date = date
};
}
return(result);
}
public IForecastingDataSets Select(IPredicationSchema schema)
{
DataCube <double>[] tsList = new DataCube <double> [schema.Stimulus.Length];
int [] startIndexOfStimulus = new int[schema.Stimulus.Length];
int i = 0;
int valuesCount = 0;
DateTime[] dates = null;
foreach (IVariable v in schema.Stimulus)
{
TimeSeriesQueryCriteria qc = new TimeSeriesQueryCriteria()
{
Start = schema.Start,
End = schema.End,
SiteID = v.SiteID,
VariableID = v.VariableID
};
tsList[i] = v.GetValues(qc, mOdmAdaptor);
if (v.NormalizationModel != null)
{
v.NormalizationModel.Normalize(tsList[i][0, ":", "0"]);
}
if (i == 0)
{
valuesCount = tsList[i].Size[1];
}
else
{
if (valuesCount != tsList[i].Size[1])
{
throw new Exception("The value counts of the input variables are not identical!");
}
}
startIndexOfStimulus[i] = schema.MaximumWindowSize - v.WindowSize;
i++;
}
schema.InstancesCount = valuesCount - schema.MaximumWindowSize - schema.PredicationSize;
dates = new DateTime[schema.InstancesCount];
ForecastingSetsBuilder builder = new ForecastingSetsBuilder(schema);
double [][] inputData = builder.BuildInputSets(startIndexOfStimulus, tsList);
i = 0;
tsList = new DataCube <double> [schema.Responses.Length];
int [] startIndexOfResponses = new int[schema.Responses.Length];
foreach (IVariable v in schema.Responses)
{
TimeSeriesQueryCriteria qc = new TimeSeriesQueryCriteria()
{
Start = schema.Start,
End = schema.End,
SiteID = v.SiteID,
VariableID = v.VariableID
};
tsList[i] = v.GetValues(qc, mOdmAdaptor);
if (v.NormalizationModel != null)
{
v.NormalizationModel.Normalize(tsList[i][0, ":", "0"]);
}
if (valuesCount != tsList[i].Size[1])
{
throw new Exception("The value counts of the input variables are not identical!");
}
startIndexOfResponses[i] = schema.MaximumWindowSize + v.PredicationSize;
if (i == 0)
{
System.Array.Copy(tsList[i].DateTimes, startIndexOfResponses[i], dates, 0, schema.InstancesCount);
}
i++;
}
double[][] outputData = builder.BuildOutputSets(startIndexOfResponses, tsList);
ForecastingDataSets result = new ForecastingDataSets(inputData, outputData)
{
Date = dates
};
return(result);
}
