public void testFullCorrelation1Weighted()
{
IDataModel dataModel = getDataModel(
new long[] {1, 2},
new Double?[][] {
new double?[]{3.0, -2.0},
new double?[]{3.0, -2.0},
});
double correlation = new EuclideanDistanceSimilarity(dataModel, Weighting.WEIGHTED).UserSimilarity(1, 2);
assertCorrelationEquals(1.0, correlation);
}
public void testFullCorrelation2()
{
IDataModel dataModel = getDataModel(
new long[] {1, 2},
new Double?[][] {
new double?[]{3.0, 3.0},
new double?[]{3.0, 3.0},
});
double correlation = new EuclideanDistanceSimilarity(dataModel).UserSimilarity(1, 2);
Assert.AreEqual(1.0, correlation, EPSILON);
}
public void testNoCorrelation2()
{
IDataModel dataModel = getDataModel(
new long[] { 1, 2 },
new Double?[][] {
new double?[] { null, 1.0, null },
new double?[] { null, null, 1.0 },
});
double correlation = new EuclideanDistanceSimilarity(dataModel).UserSimilarity(1, 2);
Assert.True(Double.IsNaN(correlation));
}
public void testNoCorrelation1Weighted()
{
IDataModel dataModel = getDataModel(
new long[] { 1, 2 },
new Double?[][] {
new double?[] { 3.0, -2.0 },
new double?[] { -3.0, 2.0 },
});
double correlation = new EuclideanDistanceSimilarity(dataModel, Weighting.WEIGHTED).UserSimilarity(1, 2);
assertCorrelationEquals(0.7213202601812372, correlation);
}
public void testNoCorrelation1()
{
IDataModel dataModel = getDataModel(
new long[] { 1, 2 },
new Double?[][] {
new double?[] { 3.0, -2.0 },
new double?[] { -3.0, 2.0 },
});
double correlation = new EuclideanDistanceSimilarity(dataModel).UserSimilarity(1, 2);
assertCorrelationEquals(0.1639607805437114, correlation);
}
public void testFullCorrelation2()
{
IDataModel dataModel = getDataModel(
new long[] { 1, 2 },
new Double?[][] {
new double?[] { 3.0, 3.0 },
new double?[] { 3.0, 3.0 },
});
double correlation = new EuclideanDistanceSimilarity(dataModel).UserSimilarity(1, 2);
Assert.AreEqual(1.0, correlation, EPSILON);
}
public void testSimpleWeighted()
{
IDataModel dataModel = getDataModel(
new long[] { 1, 2 },
new Double?[][] {
new double?[] { 1.0, 2.0, 3.0 },
new double?[] { 2.0, 5.0, 6.0 },
});
double correlation = new EuclideanDistanceSimilarity(dataModel, Weighting.WEIGHTED).UserSimilarity(1, 2);
assertCorrelationEquals(0.8210911630511055, correlation);
}
public void testSimple()
{
IDataModel dataModel = getDataModel(
new long[] { 1, 2 },
new Double?[][] {
new double?[] { 1.0, 2.0, 3.0 },
new double?[] { 2.0, 5.0, 6.0 },
});
double correlation = new EuclideanDistanceSimilarity(dataModel).UserSimilarity(1, 2);
assertCorrelationEquals(0.2843646522044218, correlation);
}
public void testFullItemCorrelation1()
{
IDataModel dataModel = getDataModel(
new long[] {1, 2},
new Double?[][] {
new double?[]{3.0, 3.0},
new double?[]{-2.0, -2.0},
});
double correlation =
new EuclideanDistanceSimilarity(dataModel).ItemSimilarity(0, 1);
assertCorrelationEquals(1.0, correlation);
}
public void testNoCorrelation3()
{
IDataModel dataModel = getDataModel(
new long[] { 1, 2 },
new Double?[][] {
new double?[] { 90.0, 80.0, 70.0 },
new double?[] { 70.0, 80.0, 90.0 },
});
double correlation = new EuclideanDistanceSimilarity(dataModel).UserSimilarity(1, 2);
assertCorrelationEquals(0.05770363219029305, correlation);
}
public void testFullItemCorrelation1()
{
IDataModel dataModel = getDataModel(
new long[] { 1, 2 },
new Double?[][] {
new double?[] { 3.0, 3.0 },
new double?[] { -2.0, -2.0 },
});
double correlation =
new EuclideanDistanceSimilarity(dataModel).ItemSimilarity(0, 1);
assertCorrelationEquals(1.0, correlation);
}
public Result RecommendEuclideanDistanceSimilarity(int userId, int movieId)
{
Result result = new Result();
dataModel = new FileDataModel(PathToDataFile, false, FileDataModel.DEFAULT_MIN_RELOAD_INTERVAL_MS, false, ",", userId,movieId);
var removedPrefs=GenericDataModel.preferenceFromUsersRemoved.Values;
var valueToCompare=removedPrefs.FirstOrDefault(i => i.GetItemID() == movieId).GetValue();
var similarity = new EuclideanDistanceSimilarity(dataModel);
var recommender = new GenericItemBasedRecommender(dataModel, similarity);
var preferences = recommender.EstimatePreference(userId, movieId);
result.PredictedValue = preferences;
result.RealValue = removedPrefs.First().GetValue();
return result;
}
public void testSimpleItemWeighted()
{
IDataModel dataModel = getDataModel(
new long[] { 1, 2, 3 },
new Double?[][] {
new double?[] { 1.0, 2.0 },
new double?[] { 2.0, 5.0 },
new double?[] { 3.0, 6.0 },
});
IItemSimilarity itemSimilarity = new EuclideanDistanceSimilarity(dataModel, Weighting.WEIGHTED);
double correlation = itemSimilarity.ItemSimilarity(0, 1);
assertCorrelationEquals(0.8210911630511055, correlation);
}
public void testSimpleWeighted()
{
IDataModel dataModel = getDataModel(
new long[] {1, 2},
new Double?[][] {
new double?[]{1.0, 2.0, 3.0},
new double?[]{2.0, 5.0, 6.0},
});
double correlation = new EuclideanDistanceSimilarity(dataModel, Weighting.WEIGHTED).UserSimilarity(1, 2);
assertCorrelationEquals(0.8210911630511055, correlation);
}
public void testSimpleItemWeighted()
{
IDataModel dataModel = getDataModel(
new long[] {1, 2, 3},
new Double?[][] {
new double?[]{1.0, 2.0},
new double?[]{2.0, 5.0},
new double?[]{3.0, 6.0},
});
IItemSimilarity itemSimilarity = new EuclideanDistanceSimilarity(dataModel, Weighting.WEIGHTED);
double correlation = itemSimilarity.ItemSimilarity(0, 1);
assertCorrelationEquals(0.8210911630511055, correlation);
}
public void testSimpleItem()
{
IDataModel dataModel = getDataModel(
new long[] {1, 2, 3},
new Double?[][] {
new double?[]{1.0, 2.0},
new double?[]{2.0, 5.0},
new double?[]{3.0, 6.0},
});
double correlation =
new EuclideanDistanceSimilarity(dataModel).ItemSimilarity(0, 1);
assertCorrelationEquals(0.2843646522044218, correlation);
}
public void testNoItemCorrelation3()
{
IDataModel dataModel = getDataModel(
new long[] {1, 2, 3},
new Double?[][] {
new double?[]{90.0, 70.0},
new double?[]{80.0, 80.0},
new double?[]{70.0, 90.0},
});
double correlation =
new EuclideanDistanceSimilarity(dataModel).ItemSimilarity(0, 1);
assertCorrelationEquals(0.05770363219029305, correlation);
}
public void testNoItemCorrelation2()
{
IDataModel dataModel = getDataModel(
new long[] {1, 2},
new Double?[][] {
new double?[]{null, 1.0, null},
new double?[]{null, null, 1.0},
});
double correlation = new EuclideanDistanceSimilarity(dataModel).ItemSimilarity(1, 2);
Assert.True(Double.IsNaN(correlation));
}
public void testNoItemCorrelation1()
{
IDataModel dataModel = getDataModel(
new long[] {1, 2},
new Double?[][] {
new double?[]{3.0, -3.0},
new double?[]{-2.0, 2.0},
});
double correlation =
new EuclideanDistanceSimilarity(dataModel).ItemSimilarity(0, 1);
assertCorrelationEquals(0.1639607805437114, correlation);
}