public void SetData(SeriesVariable dependentVariable, SeriesVariables independentVariables, LinearRegressionAnalysisForm.LRSpecification lrProperties)
{
this.dependentVariable = dependentVariable;
this.independentVariables = independentVariables;
this.lrProperties = lrProperties;
this.update();
}
public void SetData(SeriesData data, SeriesVariable variable, double[] detrend)
{
this.variable = variable;
this.data = data;
this.detrend = detrend;
this.update();
}
public void SetData(SeriesVariable variable, SeriesData data)
{
this.variable = variable;
this.data = data;
this.variable.SeriesValues.Changed += new ChangedEventHandler(SeriesValues_Changed);
this.update();
}
public void SetData(SeriesData data, SeriesVariable variable, double[] forecasted)
{
this.variable = variable;
this.data = data;
this.forecasted = forecasted;
this.update();
}
public void SetData(SeriesData data, SeriesVariable variable, double[] residual)
{
this.variable = variable;
this.data = data;
this.residual = residual;
this.update();
}
/// <summary>
/// Mengeset variabel yang telah dipilih
/// </summary>
/// <param name="variable">SeriesVariable. variable</param>
public void SetVariable(SeriesVariable variable)
{
this.variable = variable;
this.decProperties.seasonalLength = 2;
this.Text = "Decomposition : " + variable.VariableName;
// Create the ToolTip and associate with the Form container.
ToolTip toolTip2 = new ToolTip();
// Set up the delays for the ToolTip.
toolTip2.AutoPopDelay = 500000000;
toolTip2.InitialDelay = 100;
toolTip2.ReshowDelay = 1;
//Force the ToolTip text to be displayed whether or not the form is active.
toolTip2.ShowAlways = true;
toolTip2.SetToolTip(this.SeasonalLengthBox, "SEASONAL LENGTH:\nThe length of one seasonal cycle\n\n"
+ "RANGE:\nPossible seasonal length range for '"
+ this.variable.VariableName + "' are 2 to " + this.variable.SeriesValuesNoNaN.Count / 2);
toolTip2.SetToolTip(this.groupBox1, "SEASONAL LENGTH:\nThe length of one seasonal cycle\n\n"
+ "RANGE:\nPossible seasonal length range for '"
+ this.variable.VariableName + "' are 2 to " + this.variable.SeriesValuesNoNaN.Count / 2);
this.UpdateSettings();
}
public void SetStock(SeriesStock stock)
{
this.stock = stock;
this.txtName.Text = stock.StockName;
this.txtDescription.Text = stock.StockDescription;
this.openVariable = stock.Open;
this.lstVariables.Items.Remove(stock.Open);
this.txtOpen.Text = this.openVariable.VariableName;
this.cmdOpen.Text = "<";
this.highVariable = stock.High;
this.lstVariables.Items.Remove(stock.High);
this.txtHigh.Text = this.highVariable.VariableName;
this.cmdHigh.Text = "<";
this.lowVariable = stock.Low;
this.lstVariables.Items.Remove(stock.Low);
this.txtLow.Text = this.lowVariable.VariableName;
this.cmdLow.Text = "<";
this.closeVariable = stock.Close;
this.lstVariables.Items.Remove(stock.Close);
this.txtClose.Text = this.closeVariable.VariableName;
this.cmdClose.Text = "<";
this.volumeVariable = stock.Volume;
this.lstVariables.Items.Remove(stock.Volume);
this.txtVolume.Text = this.volumeVariable.VariableName;
this.cmdVolume.Text = "<";
}
public void SetData(SeriesData data, SeriesVariable variable, double[] deseasonal)
{
this.variable = variable;
this.data = data;
this.deseasonal = deseasonal;
this.update();
}
/// <summary>
/// Metode dekomposisi klasik dengan rasio pada rata-rata bergerak
/// </summary>
/// <param name="variable">SeriesVariable. variabel yang akan dianalisis</param>
/// <param name="seasonalLength">Integer. panjang satu siklus musiman</param>
/// <param name="isMultiplikatif">Bool. apakah menggunakan metode multiplikatif?</param>
/// <param name="initialTrend">Integer. Inisial model tren yang digunakan, 1-Linear, 2-Quadratic, 3-Cubic, 4-Exponential</param>
public DecompositionClassic(SeriesVariable variable, int seasonalLength, bool isMultiplicative, int initialTrend)
{
this.variable = variable;
Vector var = new Vector(this.variable.SeriesValuesNoNaN.ToArray());
this.y = new Vector(var.Tuples);
for (int i = 0; i < y.Tuples; i++)
{
this.y[i] = var[i];
}
this.n = y.Tuples;
this.seasonalLength = seasonalLength;
this.trend = new double[this.n];
this.detrend = new double[this.n];
this.seasonal = new double[this.n];
this.deseasonal = new double[this.n];
this.predicted = new double[this.n];
this.residual = new double[this.n];
this.isMultiplicative = isMultiplicative;
this.initialTrend = initialTrend;
if (this.isMultiplicative)
{
this.Multiplicative();
}
else
{
this.Additive();
}
this.ForecastingError();
}
public void SetData(SeriesData data, SeriesVariable variable, double[] predicted)
{
this.variable = variable;
this.data = data;
this.predicted = predicted;
this.update();
}
public void SetData(SeriesData data, SeriesVariable variable, MovingAverageForm.MASpecification maProperties)
{
this.data = data;
this.variable = variable;
this.maProperties = maProperties;
this.lblVariable.Text = variable.VariableName;
this.update();
}
public void SetData(SeriesVariable variable, SeriesData data)
{
this.variableDataGrid.SetData(variable, data);
this.variableGraph.SetData(variable, data);
this.variableStatistics.SetData(variable, data);
this.Text = variable.VariableName;
this.Name = "SERIES " + variable.VariableName;
}
public void SetData(SeriesData data, SeriesVariable variable, ExponentialSmoothingForm.ESSpecification esProperties)
{
this.data = data;
this.variable = variable;
this.esProperties = esProperties;
this.lblVariable.Text = variable.VariableName;
this.update();
}
public void SetData(SeriesData data, SeriesVariable variable, DecompositionForm.DECSpecification decProperties)
{
this.data = data;
this.variable = variable;
this.decProperties = decProperties;
this.lblVariable.Text = variable.VariableName;
this.update();
}
public void SetData(SeriesData data, SeriesVariable variable, TrendAnalysisForm.TrendSpecification trendProperties)
{
this.data = data;
this.variable = variable;
this.trendProperties = trendProperties;
this.lblVariable.Text = variable.VariableName;
this.update();
}
public void SetData(SeriesData data, SeriesVariable variable, NeuralNetworkAnalysisForm.NetworkSpecification networkProperties)
{
this.data = data;
this.variable = variable;
this.networkProperties = networkProperties;
this.lblVariable.Text = variable.VariableName;
this.update();
}
private void getSelectedVariable()
{
this.dependentVariable = (SeriesVariable)this.lstDependent.Items[0];
this.independentVariables = new SeriesVariables();
foreach (Object item in this.lstIndependents.Items)
{
this.independentVariables.Add((SeriesVariable)item);
}
}
public void SetData(SeriesData data, SeriesVariable variable, NeuralNetworkAnalysisForm.NetworkSpecification networkProperties,
double[] predicted, double[] forecasted)
{
this.data = data;
this.variable = variable;
this.predicted = predicted;
this.forecasted = forecasted;
this.networkProperties = networkProperties;
}
public void SetData(SeriesData data, SeriesVariable variable,
DecompositionForm.DECSpecification decProperties,
DecompositionForm.DECComponent decTable, double[] forecasted)
{
this.data = data;
this.variable = variable;
this.decProperties = decProperties;
this.decTable = decTable;
this.forecasted = forecasted;
}
public void SetData(SeriesData data, SeriesVariable variable,
ExponentialSmoothingForm.ESSpecification esProperties,
ExponentialSmoothingForm.ESComponent esTable, double[] forecasted)
{
this.data = data;
this.variable = variable;
this.esProperties = esProperties;
this.esTable = esTable;
this.forecasted = forecasted;
}
public Dtw getDTW(Template t, bool withTime = false)
{
var x = new SeriesVariable(Series_X, t.Series_X, "X-Coord", new NDtw.Preprocessing.NormalizationPreprocessor());
var y = new SeriesVariable(Series_Y, t.Series_Y, "Y-Coord", new NDtw.Preprocessing.NormalizationPreprocessor());
var time = new SeriesVariable(Series_Time, t.Series_Time, "Timestamp", new NDtw.Preprocessing.NormalizationPreprocessor());
return((withTime) ?
new Dtw(new SeriesVariable[] { x, y, time }, DistanceMeasure.Euclidean) :
new Dtw(new SeriesVariable[] { x, y }, DistanceMeasure.Euclidean));
}
/// <summary>
/// Mengeset variabel yang telah dipilih
/// </summary>
/// <param name="variable">SeriesVariable. variable</param>
public void SetVariable(SeriesVariable variable)
{
this.variable = variable;
this.maProperties.orde = 2;
this.Text = "Moving Average : " + variable.VariableName;
this.UpdateSettings();
}
public void SetData(SeriesData data, SeriesVariable variable,
MovingAverageForm.MASpecification maProperties,
MovingAverageForm.MAComponent maTable, double[] forecasted)
{
this.data = data;
this.variable = variable;
this.maProperties = maProperties;
this.maTable = maTable;
this.forecasted = forecasted;
}
/// <summary>
/// Constructor. Menghitung Pemulusan Eksponensial Tripel
/// </summary>
/// <param name="variable">SeriesVariable. variabel</param>
/// <param name="alpha">Double. Smoothing constant for data</param>
/// <param name="gamma">Double. Smoothing constant for trend</param>
/// <param name="beta">Double. Smoothing constant for seasonal</param>
/// <param name="seasonalLength">Integer. Panjang seasonal</param>
public TripleExponentialSmoothingWinter(SeriesVariable variable, double alpha, double gamma, double beta, int seasonalLength, bool isMultiplicative)
{
this.variable = variable;
Vector var = new Vector(this.variable.SeriesValuesNoNaN.ToArray());
this.y = new Vector(var.Tuples);
for (int i = 0; i < y.Tuples; i++)
{
this.y[i] = var[i];
}
this.n = this.y.Tuples;
this.alpha = alpha;
this.gamma = gamma;
this.beta = beta;
this.l = seasonalLength;
this.isMultiplicative = isMultiplicative;
this.smoothing = new double[this.n];
this.trend = new double[this.n];
this.seasonal = new double[this.n];
this.predicted = new double[this.n];
this.residual = new double[this.n];
//term
term = new int[this.n];
for (int i = 0; i < this.n;)
{
for (int j = 0; j < this.l; ++j)
{
if (i + j != this.n)
{
term[i + j] = j;
}
else
{
break;
}
}
i += this.l;
}
if (this.isMultiplicative)
{
SmoothingMultiplicative();
}
else
{
SmoothingAdditive();
}
ForecastingError();
}
public void SetData(SeriesData data, SeriesVariable variable, bool isSmoothingVisible, double[] smoothed1, double[] predicted, double[] residual)
{
this.data = data;
this.variable = variable;
this.isSmoothingVisible = isSmoothingVisible;
this.smoothed1 = smoothed1;
this.predicted = predicted;
this.residual = residual;
this.update();
}
public void SetData(SeriesData data, SeriesVariable variable,
TrendAnalysisForm.TrendSpecification trendProperties,
double[] predicted, double[] forecasted, double[] residual)
{
this.data = data;
this.variable = variable;
this.trendProperties = trendProperties;
this.predicted = predicted;
this.residual = residual;
this.forecasted = forecasted;
}
/// <summary>
/// Mengeset variabel yang telah dipilih
/// </summary>
/// <param name="variable">SeriesVariable. variable</param>
public void SetVariable(SeriesVariable variable)
{
this.variable = variable;
this.esProperties.alpha = 0.2;
this.esProperties.gamma = 0.2;
this.esProperties.beta = 0.2;
this.esProperties.seasonalLength = 2;
this.esProperties.isMultiplicative = true;
this.Text = "Exponential Smoothing : " + variable.VariableName;
this.UpdateSettings();
}
public void SetData(SeriesData data, SeriesVariable variable, bool isSmoothingVisible, bool isTrendVisible, bool isSeasonalVisible, double[] smoothing, double[] trend, double[] seasonal, double[] predicted, double[] residual)
{
this.data = data;
this.variable = variable;
this.isSmoothingVisible = isSmoothingVisible;
this.isTrendVisible = isTrendVisible;
this.isSeasonalVisible = isSeasonalVisible;
this.smoothing = smoothing;
this.trend = trend;
this.seasonal = seasonal;
this.predicted = predicted;
this.residual = residual;
this.update();
}
public void SetData(SeriesData data,
SeriesVariable dependentVariable,
SeriesVariables independentVariables,
LinearRegressionAnalysisForm.LRSpecification lrProperties,
LinearRegressionAnalysisForm.LRComponent lrTable,
double[,] testValues,
string[] forcastedTime,
double[] forcastedData)
{
this.data = data;
this.dependentVariable = dependentVariable;
this.independentVariables = independentVariables;
this.lrProperties = lrProperties;
this.lrTable = lrTable;
this.testValues = testValues;
this.time = forcastedTime;
this.forcasted = forcastedData;
}
private void construct(SeriesVariable variable, double alpha)
{
this.variable = variable;
Vector var = new Vector(this.variable.SeriesValuesNoNaN.ToArray());
this.y = new Vector(var.Tuples);
for (int i = 0; i < y.Tuples; i++)
{
this.y[i] = var[i];
}
this.n = this.y.Tuples;
this.a = alpha;
this.smoothing = new double[this.n];
this.predicted = new double[this.n];
this.residual = new double[this.n];
this.Smoothing();
this.ForecastingError();
}
/// <summary>
/// Starts the reading the buffer of the console output.
/// </summary>
/// <returns>An enumerator.</returns>
IEnumerator StartConsole()
{
_buffer = new List<string>();
while(!comparexe.HasExited)
{
yield return new WaitForSeconds(1);
lock (_buffer)
{
while(_buffer.Count > 0)
{
var line = _buffer[0];
if (string.IsNullOrEmpty(line))
{ }
if (line.Contains("uid="))
UserID = ulong.Parse(line.Substring(4));
else if (line.Contains("act="))
ActionName = Properties.ActivityFromString(line.Substring(4).ToString());
else if (line.Contains("sfb="))
SemanticFeedback = line.Substring(4);
else if (line.Contains("tip="))
Tips = line.Substring(4).Split(new string[] { ":|:" }, System.StringSplitOptions.RemoveEmptyEntries);
else if (line.Contains("rel="))
AreRelated = bool.Parse(line.Substring(4));
else if (line.Contains("ovs="))
OverallScore = float.Parse(line.Substring(4), ni) * 100f;
else if (line.Contains("pss="))
PositionScore = float.Parse(line.Substring(4), ni) * 100f;
else if (line.Contains("rts="))
RotationScore = float.Parse(line.Substring(4), ni) * 100f;
else if (line.Contains("lvs="))
LinearVelocityScore = float.Parse(line.Substring(4), ni) * 100f;
else if (line.Contains("avs="))
AngularVelocityScore = float.Parse(line.Substring(4), ni) * 100f;
else if (line.Contains("plt="))
{
var pf = line.Substring(4).Split(new string[] { ":|:" }, System.StringSplitOptions.RemoveEmptyEntries);
PlotFiles = pf.Where((str, i) => i % 2 == 1).ToArray();
AvailablePlots = pf.Where ((str, i) => i % 2 == 0).ToArray();
}
else if (line.Contains("tjs="))
{
var ts = line.Substring(4).Split(new string[] { ":|:" }, System.StringSplitOptions.RemoveEmptyEntries);
Trajectories = new SeriesVariable<Vec3f>[ts.Length];
for (int i = 0; i < ts.Length; i++)
Trajectories[i] = SeriesVariable<Vec3f>.FromString(ts[i]);
}
else if (line.Contains("Start"))
{
CurrentState = State.START;
DoneComparison = false;
_is_comparing = true;
_start_time = UnityEngine.Time.time;
Debug.Log("Compare started.");
}
else if (line.Contains("Compare done!"))
{
Debug.Log(string.Format("Compare done! Elapsed time: {0}", Time));
CurrentState = State.DRAW_PLOTS;
_is_comparing = false;
_start_time = -1;
DoneComparison = true;
}
else if (line.Contains("Done!"))
{
if (!AreRelated)
{
var tip_1 = Tips[0];
Tips = new string[] { tip_1 };
}
CurrentState = State.DONE;
yield break;
}
else if (line.Contains(":|:"))
{
var plots = new List<string>();
plots.AddRange (PlotFiles);
plots.AddRange (line.Substring(4).Split(new string[] { ":|:" }, System.StringSplitOptions.RemoveEmptyEntries));
PlotFiles = plots.ToArray();
}
else
Debug.Log(line);
_buffer.RemoveAt(0);
}
}
}
yield break;
}
