public Interpolation(double input, AdaptiveInterpolation.Distribution output)
{
this.Input = input;
this.Output = output;
}
public Participation_BinComparison_View(Engine engine, IEnumerable <Activity> activitiesToPredictFrom, Activity activityToPredict, TimeSpan windowSize)
{
DateTime now = DateTime.Now;
engine.EnsureRatingsAreAssociated();
LinearProgression progressionToPredict = activityToPredict.ParticipationsSmoothed((new TimeSpan()).Subtract(windowSize));
StatList <NeighborhoodInterpolation, Activity> results = new StatList <NeighborhoodInterpolation, Activity>(new Neighborhood_MiddleOutputMean_Comparer(), new NoopCombiner <Activity>());
foreach (Activity activity in activitiesToPredictFrom)
{
System.Diagnostics.Debug.WriteLine("comparing " + activity + " and " + activityToPredict.Name);
List <Datapoint> datapoints = activity.compareParticipations(windowSize, progressionToPredict, now.Subtract(windowSize));
// put data into the interpolator
FloatRange inputRange = new FloatRange(0, true, windowSize.TotalSeconds, true);
AdaptiveLinearInterpolator <Distribution> interpolator = new AdaptiveLinearInterpolator <Distribution>(new HyperBox <Distribution>(new FloatRange[] { inputRange }), new DistributionAdder());
foreach (Datapoint datapoint in datapoints)
{
interpolator.AddDatapoint(new AdaptiveInterpolation.Datapoint <Distribution>(datapoint.Input, Distribution.MakeDistribution(datapoint.Output, 0, datapoint.Weight)));
}
// ask the interpolator which input has the highest average output
IEnumerable <double[]> representativePoints = interpolator.FindRepresentativePoints();
if (representativePoints.Count() > 0)
{
double[] bestInput = new double[1];
AdaptiveInterpolation.Distribution bestOutput = null;
foreach (double[] coordinates in representativePoints)
{
AdaptiveInterpolation.Distribution output = interpolator.Interpolate(coordinates);
if (bestOutput == null || output.Mean > bestOutput.Mean)
{
bestInput = coordinates;
bestOutput = output;
}
}
// Check nearby regions for their outputs too
HyperBox <Distribution> inputNeighborhood = interpolator.FindNeighborhoodCoordinates(bestInput);
double inputNeighborhoodWidth = inputNeighborhood.Coordinates[0].Width;
double lowerInput = Math.Max(inputNeighborhood.Coordinates[0].LowCoordinate - inputNeighborhoodWidth, inputRange.LowCoordinate);
double higherInput = Math.Min(inputNeighborhood.Coordinates[0].HighCoordinate + inputNeighborhoodWidth, inputRange.HighCoordinate);
NeighborhoodInterpolation result = new NeighborhoodInterpolation();
result.Middle = new Interpolation(bestInput[0], bestOutput);
result.Left = new Interpolation(lowerInput, interpolator.Interpolate(new double[] { lowerInput }));
result.Right = new Interpolation(higherInput, interpolator.Interpolate(new double[] { higherInput }));
results.Add(result, activity);
}
}
IEnumerable <ListItemStats <NeighborhoodInterpolation, Activity> > resultList = results.AllItems;
if (resultList.Count() <= 0)
{
// This shouldn't be able to happen unless we disallow predicting the activity from itself
this.SubLayout = new TextblockLayout("No activities found!");
}
else
{
int numWindowDays = (int)windowSize.TotalDays;
GridLayout_Builder layoutBuilder = new Vertical_GridLayout_Builder().Uniform();
if (resultList.Count() > 1)
{
string title = "Activities that when done most strongly predict future participations in " + activityToPredict.Name;
layoutBuilder.AddLayout(new TextblockLayout(title));
}
else
{
string title = "Predicting future participations in " + activityToPredict.Name;
layoutBuilder.AddLayout(new TextblockLayout(title));
}
string explanation = "A block of the form a->{b,c,d} means that when you have spent an average of <a> min/day on the given activity over the last " + numWindowDays + " days, " +
"you have spent on average <c> min/day on " + activityToPredict.Name + " over the next " + numWindowDays + " days, with <b> and <d> marking -1 and +1 standard deviation, respectively";
layoutBuilder.AddLayout(new TextblockLayout(explanation));
int count = 0;
foreach (ListItemStats <NeighborhoodInterpolation, Activity> result in resultList.Reverse())
{
count++;
if (count > 3)
{
break;
}
Activity activity = result.Value;
NeighborhoodInterpolation neighborhood = result.Key;
layoutBuilder.AddLayout(new TextblockLayout("After " + activity.Name + ":"));
foreach (Interpolation interpolation in neighborhood.Items)
{
double inputSecondsPerWindow = interpolation.Input;
double inputMinutesPerDay = inputSecondsPerWindow / windowSize.TotalDays / 60;
double avgOutputSecondsPerWindow = interpolation.Output.Mean;
double avgOutputMinutesPerDay = avgOutputSecondsPerWindow / windowSize.TotalDays / 60;
double stddevOutputSecondsPerWindow = interpolation.Output.StdDev;
double stddevOutputMinutesPerDay = stddevOutputSecondsPerWindow / windowSize.TotalDays / 60;
string itemLine = Math.Round(inputMinutesPerDay, 1) + " -> {" +
Math.Round((avgOutputMinutesPerDay - stddevOutputMinutesPerDay), 1) + "," +
Math.Round(avgOutputMinutesPerDay, 1) + "," +
Math.Round((avgOutputMinutesPerDay + stddevOutputMinutesPerDay), 1) + "}";
layoutBuilder.AddLayout(new TextblockLayout(itemLine));
}
}
this.SubLayout = layoutBuilder.Build();
}
}
public AdaptiveInterpolation.Distribution ConvertToDistribution(Distribution distribution)
{
AdaptiveInterpolation.Distribution converted = new AdaptiveInterpolation.Distribution(distribution.SumValue, distribution.SumSquaredValue, distribution.Weight);
return(converted);
}
