/// <summary>
/// Generates the negative ratings.
/// </summary>
/// <param name="itemHistogram">The item histogram.</param>
private void GenerateNegativeRatings(IDictionary <TItem, int> itemHistogram)
{
// Create the binary sum tree.
var items = itemHistogram.Keys.Select(key => key).ToList();
var histogramSampler = new HistogramSampler(
itemHistogram.Values.Select(value => Convert.ToInt32(Math.Pow(value, this.itemHistogramAdjustment))));
foreach (var userToRatedItems in this.ratedInstances)
{
var ratedItems = userToRatedItems.Value;
// In case the user has liked more than half of the items
int samplesCount = Math.Min(ratedItems.Count, items.Count - ratedItems.Count);
for (int i = 0; i < samplesCount; ++i)
{
// This is required not to run into an infinite loop.
for (int sampleAttempts = 0; sampleAttempts < SampleAttemptsCount; ++sampleAttempts)
{
var itemIndex = histogramSampler.Sample();
var ratedItem = new RatedItem(items[itemIndex], NegativeRatingValue);
if (!ratedItems.Contains(ratedItem))
{
histogramSampler.Take(itemIndex);
ratedItems.Add(ratedItem);
break;
}
}
}
}
}
/// <summary>
/// Test the histogram sampler.
/// Makes sure that the samples from the underlying distribution correspond to the parameters histogram.
/// </summary>
/// <param name="histogram">The parameters.</param>
private void TestHistogramSampler(int[] histogram)
{
var histogramSampler = new HistogramSampler(histogram);
var samplesHistogram = new int[histogram.Length];
int sampleCount = histogram.Sum();
for (int i = 0; i < sampleCount; ++i)
{
int sample = histogramSampler.Sample();
histogramSampler.Take(sample);
++samplesHistogram[sample];
}
Assert.True(histogramSampler.IsEmpty());
for (int i = 0; i < histogram.Length; ++i)
{
Assert.Equal(histogram[i], samplesHistogram[i]);
}
}