public override void Recompute()
{
IsValid = false;
tsOutput = null;
mvtsOutput = null;
if (SkipDates == null)
{
SkipDates = new List <DateTime>();
}
var mvts = GetInput(0) as MVTimeSeries;
var ts = GetInput(0) as Data.TimeSeries;
// make sure we have at least 2 data points in the time series to be sampled
var tp = GetInputType(-1);
if (tp != InputType.UnivariateTS && tp != InputType.MultivariateTS)
{
return;
}
if (tp == InputType.MultivariateTS)
{
// split it up
var individuals = mvts.ExtractList();
var nc = individuals.Count;
var sampled = new List <Data.TimeSeries>(nc);
IsValid = true;
for (var i = 0; i < nc; ++i)
{
var res = DoUnivariateSampling(individuals[i]);
if (res.Count > 0)
{
sampled.Add(res);
}
else
{
IsValid = false;
}
}
if (IsValid)
{
mvtsOutput = new MVTimeSeries(sampled, false)
{
Title = mvts.Title
}
}
;
}
if (tp == InputType.UnivariateTS)
{
tsOutput = DoUnivariateSampling(ts);
IsValid = tsOutput.Count > 0;
}
}
public override void Recompute()
{
IsValid = false;
var tp = GetInputType(-1);
if (tp == InputType.MultivariateTS)
{
var mts = GetInput(0) as MVTimeSeries;
if (mts == null)
{
return;
}
mvIntegral = new MVTimeSeries(mts.Dimension)
{
Title = "MV"
};
for (var i = 0; i < mts.Dimension; ++i)
{
mvIntegral.SubTitle[i] = mts.SubTitle[i];
}
integral = null;
var sum = new double[mts.Dimension];
for (var t = 0; t < mts.Count; ++t)
{
for (var j = 0; j < mts.Dimension; ++j)
{
if (!double.IsNaN(mts[t][j]))
{
sum[j] += mts[t][j];
}
}
var temp = new double[mts.Dimension];
Array.Copy(sum, temp, mts.Dimension);
mvIntegral.Add(mts.TimeStamp(t), temp, false);
}
}
if (tp == InputType.UnivariateTS)
{
var ts = GetInput(0) as Data.TimeSeries;
if (ts == null)
{
return;
}
integral = new Data.TimeSeries {
Title = ts.Title
};
mvIntegral = null;
double sum = 0;
for (var t = 0; t < ts.Count; ++t)
{
sum += ts[t];
integral.Add(ts.TimeStamp(t), sum, false);
}
}
IsValid = true;
}
//public override Icon GetIcon()
//{
// return null;
//}
public override void Recompute()
{
CheckParameters();
var list1 = new List <Data.TimeSeries>();
IsValid = true;
// otherwise we can bind them together
var lists = new List <List <Data.TimeSeries> >();
for (var i = 0; i < NumInputs(); ++i)
{
var ts = GetInput(i);
var mts = ts as MVTimeSeries;
if (mts != null)
{
lists.Add(mts.ExtractList());
}
else
{
var uts = ts as Data.TimeSeries;
if (uts != null)
{
var lts = new List <Data.TimeSeries>();
lts.Add(uts);
lists.Add(lts);
}
else
{
IsValid = false;
}
}
}
if (IsValid)
{
foreach (var lts in lists)
{
foreach (var ts in lts)
{
list1.Add(ts);
}
}
mvtsOut = new MVTimeSeries(list1, SampleMissing);
mvtsOut.Title = "MV";
}
}
public override object GetOutput(int socket)
{
if (socket == 0)
{
var subList = new List <TimeSeries> {
outputs[0], outputs[1], outputs[2]
};
var bundle = new MVTimeSeries(subList, false);
bundle.Title = "Bollinger";
return(bundle);
}
if (socket == 1)
{
return(outputs[3]);
}
throw new SocketException();
}
public override void Recompute()
{
IsValid = false;
if (GetInputType(-1) != InputType.UnivariateTS)
{
return;
}
var series = GetInput(0) as Data.TimeSeries;
combined = new MVTimeSeries(4);
combined.Title = "Bars";
combined.SubTitle = new string[4] {
"Open", "High", "Low", "Close"
};
var accumulated = Vector <double> .Build.Dense(4);
accumulated[0] = accumulated[1] = accumulated[2] = accumulated[3] = series[0];
var initialTime = PhaseAdjust(series.TimeStamp(0)); // initial time for current bar
for (var t = 0; t < series.Count; ++t)
{
var intoBar = series.TimeStamp(t) - initialTime;
if (intoBar >= Period)
{
// dump the previous bar and reset things
combined.Add(initialTime + Period, accumulated.ToArray(), false);
accumulated = Vector <double> .Build.Dense(4);
var tx = series[t];
accumulated[0] = tx;
accumulated[1] = tx;
accumulated[2] = tx;
accumulated[3] = tx;
initialTime = PhaseAdjust(series.TimeStamp(t));
}
accumulated[3] = series[t];
accumulated[1] = Math.Max(accumulated[1], series[t]);
accumulated[2] = Math.Min(accumulated[2], series[t]);
}
IsValid = true;
}
//public override Icon GetIcon()
//{
// return null;
//}
public override void Recompute()
{
IsValid = false;
var ts = GetInput(0) as Data.TimeSeries;
var mts = GetInput(0) as MVTimeSeries;
if (ts == null && mts == null)
{
return; // not valid
}
if (ts != null)
{
// merge univariate TS
var result = new Data.TimeSeries();
for (var ii = 0; ii < NumberOfInputs; ++ii)
{
var pts = GetInput(ii) as Data.TimeSeries;
if (pts != null)
{
result.Add(pts, true);
}
}
outputResult = result;
IsValid = true;
}
else if (mts != null)
{
// merge univariate TS
var result = new MVTimeSeries(mts.Dimension);
result.SubTitle = mts.SubTitle;
for (var ii = 0; ii < NumberOfInputs; ++ii)
{
var pts = GetInput(ii) as MVTimeSeries;
if (pts != null)
{
result.Add(pts, true);
}
}
outputResult = result;
IsValid = true;
}
}
//public abstract Icon GetIcon();
public virtual object GetOutput(int socket)
{
if (outputs == null)
{
return(null);
}
if (outputs.Count == 0)
{
return(null);
}
if (ShouldBundleOutputs && outputs.Count > 1) // then bundle them together
{
if (socket != 0)
{
throw new SocketException();
}
if (outputs == null)
{
return(null);
}
if (outputs.Count == 1)
{
return(outputs[0]);
}
var mvts = new MVTimeSeries(outputs, false);
if (multivariateOutputPrefix != null)
{
mvts.Title = multivariateOutputPrefix;
}
return(mvts);
}
// otherwise just return them as they are
if (outputs == null)
{
return(null);
}
if (socket < NumOutputs())
{
return(outputs[socket]);
}
throw new SocketException();
}
protected override bool CheckDataValidity(object data, StringBuilder failMessage)
{
mvts = data as MVTimeSeries;
if (mvts == null)
{
return(false);
}
if (mvts.Dimension != dimension)
{
return(false);
}
if (!CanHandleNaNs())
{
if (mvts.NaNCount() > 0)
{
if (failMessage != null)
{
failMessage.AppendLine("Cannot use this model with data with NaNs.");
}
return(false);
}
}
return(true);
}
public override void Recompute()
{
IsValid = false;
tsOutput = null;
mvtsOutput = null;
// make sure we have at least 2 data points in the time series to be sampled
var tp = GetInputType(0);
if (tp != InputType.UnivariateTS && tp != InputType.MultivariateTS)
{
return;
}
var rp = GetInputType(1);
if (rp != InputType.UnivariateTS && rp != InputType.MultivariateTS)
{
return;
}
// first get the reference times
var times = new List <DateTime>(1000);
var input1 = GetInput(1);
var mvref = input1 as MVTimeSeries;
var uref = input1 as TimeSeries;
if (rp == InputType.UnivariateTS)
{
if (uref == null)
{
return;
}
for (int t = 0; t < uref.Count; ++t)
{
times.Add(uref.TimeStamp(t));
}
}
else if (rp == InputType.MultivariateTS)
{
if (mvref == null)
{
return;
}
for (int t = 0; t < mvref.Count; ++t)
{
times.Add(mvref.TimeStamp(t));
}
}
else
{
throw new ApplicationException("Invalid input type; this should not happen!");
}
int refDim = uref != null ? 1 : mvref.Dimension;
// then sample to get the output
if (tp == InputType.UnivariateTS)
{
var uts = GetInput(0) as TimeSeries;
if (uts == null)
{
return;
}
// split it up
if (!IncludeReferenceInOutput)
{
tsOutput = new TimeSeries();
for (int t = 0; t < times.Count; ++t)
{
tsOutput.Add(times[t], uts.ValueAtTime(times[t]), true);
}
}
else
{
mvtsOutput = new MVTimeSeries(1 + refDim);
for (int t = 0; t < times.Count; ++t)
{
var val = new double[1 + refDim];
val[0] = uts.ValueAtTime(times[t]);
for (int i = 1; i <= refDim; ++i)
{
val[i] = mvref != null ? mvref[t][i - 1] : uref[t];
}
mvtsOutput.Add(times[t], val, true);
}
}
IsValid = true;
}
if (tp == InputType.MultivariateTS)
{
var mts = GetInput(0) as MVTimeSeries;
if (mts == null)
{
return;
}
// split it up
if (!IncludeReferenceInOutput)
{
mvtsOutput = new MVTimeSeries();
for (int t = 0; t < times.Count; ++t)
{
mvtsOutput.Add(times[t], mts.ValueAtTime(times[t]), true);
}
}
else
{
int thisDim = mts.Dimension;
mvtsOutput = new MVTimeSeries(thisDim + refDim);
for (int t = 0; t < times.Count; ++t)
{
var val = new double[thisDim + refDim];
var dv = mts.ValueAtTime(times[t]);
for (int i = 0; i < thisDim; ++i)
{
val[i] = dv[i];
}
for (int i = 0; i < refDim; ++i)
{
val[i + thisDim] = mvref != null ? mvref[t][i] : uref[t];
}
mvtsOutput.Add(times[t], val, true);
}
}
IsValid = true;
}
}
public override double LogLikelihood(Vector <double> parameter, double penaltyFactor, bool fillOutputs)
{
if (mvts == null)
{
return(double.NaN);
}
Vector <double> paramBak = Parameters;
if (parameter != null)
{
Parameters = parameter;
}
var mvn = new MVNormalDistribution {
Mu = mu, Sigma = Sigma
};
double logLike = 0.0;
for (int t = 0; t < mvts.Count; ++t)
{
Vector <double> pred = OneStepPredictor(t);
Vector <double> resid = Vector <double> .Build.DenseOfArray(mvts[t]) - pred;
logLike += mvn.LogProbabilityDensity(resid);
}
if (fillOutputs)
{
GoodnessOfFit = logLike;
oneStepPredictors = new MVTimeSeries(dimension)
{
Title = mvts.Title
};
oneStepPredictorsAtAvail = new MVTimeSeries(dimension)
{
Title = mvts.Title
};
var rs = new MVTimeSeries(dimension)
{
Title = mvts.Title
};
if (mvts.SubTitle != null)
{
oneStepPredictors.SubTitle = new string[dimension];
oneStepPredictorsAtAvail.SubTitle = new string[dimension];
rs.SubTitle = new string[dimension];
for (int i = 0; i < dimension; ++i)
{
if (mvts.SubTitle[i] != null)
{
oneStepPredictors.SubTitle[i] = mvts.SubTitle[i] + "[Pred]";
oneStepPredictorsAtAvail.SubTitle[i] = mvts.SubTitle[i] + "[Pred.AA]";
rs.SubTitle[i] = mvts.SubTitle[i] + "[Resid]";
}
}
}
for (int t = 0; t < mvts.Count; ++t)
{
Vector <double> pred = OneStepPredictor(t);
Vector <double> resid = Vector <double> .Build.DenseOfArray(mvts[t]) - pred;
rs.Add(mvts.TimeStamp(t), mvn.Standardize(resid).ToArray(), false);
oneStepPredictors.Add(mvts.TimeStamp(t), pred.ToArray(), false);
if (t > 0)
{
oneStepPredictorsAtAvail.Add(mvts.TimeStamp(t - 1), pred.ToArray(), false);
}
}
oneStepPredictorsAtAvail.Add(mvts.TimeStamp(mvts.Count - 1), OneStepPredictor(mvts.Count).ToArray(), false);
Residuals = rs;
}
if (parameter != null)
{
Parameters = paramBak;
}
return(logLike);
}
public override void Recompute()
{
IsValid = false;
if (GetInputType(-1) != InputType.MultivariateTS)
{
return;
}
var series = GetInputBundle();
if (series.Count != 4 && series.Count != 5)
{
return; // something wrong!, needs open,high,low,close (and optional volume)
}
var open = series[0];
var high = series[1];
var low = series[2];
var close = series[3];
TimeSeries volume = series.Count == 5 ? series[4] : null;
combined = new MVTimeSeries(series.Count);
var accumulated = Vector <double> .Build.Dense(series.Count);
accumulated[0] = open[0];
accumulated[1] = high[0];
accumulated[2] = low[0]; // low
accumulated[3] = close[0];
if (series.Count == 5)
{
accumulated[4] = 0;
}
DateTime initialTime = PhaseAdjust(open.TimeStamp(0)); // initial time for current bar
for (int t = 0; t < open.Count; ++t)
{
TimeSpan intoBar = open.TimeStamp(t) - initialTime;
if (intoBar >= Period)
{
// dump the previous bar and reset things
combined.Add(initialTime + Period, accumulated.ToArray(), false);
accumulated = Vector <double> .Build.Dense(series.Count);
accumulated[0] = open[t];
accumulated[1] = high[t];
accumulated[2] = low[t];
accumulated[3] = close[t];
if (volume != null)
{
accumulated[4] = 0;
}
initialTime = PhaseAdjust(open.TimeStamp(t));
}
accumulated[3] = close[t];
accumulated[1] = Math.Max(accumulated[1], high[t]);
accumulated[2] = Math.Min(accumulated[2], low[t]);
if (volume != null)
{
accumulated[4] += volume[t];
}
}
IsValid = true;
}
public override void Recompute()
{
var mts = GetInput(0) as MVTimeSeries;
var uts = GetInput(0) as TimeSeries;
var lts = GetInput(0) as Longitudinal;
if (Spacing == 0)
{
Spacing = 1; // just fix it here: this is for backwards-compatibility
}
mvDifferences = null;
differences = null;
longDifferences = null;
IsValid = false;
if (mts != null)
{
mvDifferences = new MVTimeSeries(mts.Dimension)
{
Title = "Diffs"
};
for (int i = 0; i < mts.Dimension; ++i)
{
mvDifferences.SubTitle[i] = mts.SubTitle[i];
}
var zerov = new double[mts.Dimension];
if (!LeftTimeStamps && PadWithZeroes)
{
for (int t = 0; t < Lag; ++t)
{
if (t % Spacing == Phase)
{
mvDifferences.Add(mts.TimeStamp(t), zerov, false);
}
}
}
for (int t = Phase; t < mts.Count; t += Spacing)
{
if (t >= Lag)
{
var diff = new double[mts.Dimension];
for (int j = 0; j < mts.Dimension; ++j)
{
diff[j] = mts[t][j] - mts[t - Lag][j];
}
var stamp = LeftTimeStamps ? mts.TimeStamp(t - Lag) : mts.TimeStamp(t);
mvDifferences.Add(stamp, diff, false);
}
}
if (LeftTimeStamps && PadWithZeroes)
{
for (int t = mts.Count - Lag; t < mts.Count; ++t)
{
if (t % Spacing == Phase)
{
mvDifferences.Add(mts.TimeStamp(t), zerov, false);
}
}
}
}
if (uts != null)
{
differences = new TimeSeries {
Title = "Diff(" + uts.Title + ")"
};
if (!LeftTimeStamps && PadWithZeroes)
{
for (int t = 0; t < Lag; ++t)
{
if (t % Spacing == Phase)
{
differences.Add(uts.TimeStamp(t), 0, false);
}
}
}
for (int t = Phase; t < uts.Count; t += Spacing)
{
if (t >= Lag)
{
var diff = uts[t] - uts[t - Lag];
var stamp = LeftTimeStamps ? uts.TimeStamp(t - Lag) : uts.TimeStamp(t);
differences.Add(stamp, diff, false);
}
}
if (LeftTimeStamps && PadWithZeroes)
{
for (int t = uts.Count - Lag; t < uts.Count; ++t)
{
if (t % Spacing == Phase)
{
differences.Add(uts.TimeStamp(t), 0, false);
}
}
}
}
if (lts != null)
{
var segments = new List <TimeSeries>(lts.Count);
for (int i = 0; i < lts.Count; ++i)
{
uts = lts[i];
var du = new TimeSeries();
for (int t = Lag; t < uts.Count; ++t)
{
double diff = uts[t] - uts[t - Lag];
var stamp = LeftTimeStamps ? uts.TimeStamp(t - Lag) : uts.TimeStamp(t);
du.Add(stamp, diff, false);
}
segments.Add(du);
}
longDifferences = new Longitudinal(segments);
}
IsValid = true;
}
