public void TestKNN()
{
var samples = new List <IList <object> >
{
new List <object> {
1.5, 40.0, "Thin"
},
new List <object> {
1.5, 50.0, "Fat"
},
new List <object> {
1.5, 60.0, "Fat"
},
new List <object> {
1.6, 40.0, "Thin"
},
new List <object> {
1.6, 50.0, "Thin"
},
new List <object> {
1.6, 60.0, "Fat"
},
new List <object> {
1.6, 70.0, "Fat"
},
new List <object> {
1.7, 50.0, "Thin"
},
new List <object> {
1.7, 60.0, "Thin"
},
new List <object> {
1.7, 70.0, "Fat"
},
new List <object> {
1.7, 80.0, "Fat"
},
new List <object> {
1.8, 60.0, "Thin"
},
new List <object> {
1.8, 70.0, "Thin"
},
new List <object> {
1.8, 80.0, "Fat"
},
new List <object> {
1.8, 90.0, "Fat"
},
new List <object> {
1.9, 80.0, "Thin"
},
new List <object> {
1.9, 90.0, "Fat"
}
};
KNN <double, string> algo = new KNN <double, string>(NumericCalculation.EuclideanDistance);
// Height range: [1.5 - 2.0]
algo.AddNormalizer(o => (o - 1.5) * 2.0);
// Weight range: [20.0 - 100.0]
algo.AddNormalizer(o => (o - 20.0) * 0.0125);
// Train Data
foreach (var sample in samples)
{
algo.AddTrainingData(sample.Take(2).Cast <double>(), sample[2] as string);
}
// Test Data
var sameCount = samples.Count(o =>
{
var properties = o.Take(2).Cast <double>();
var result = algo.Perform(properties, 5);
return(o[2].Equals(result));
});
Assert.IsTrue(sameCount * 1.0 / samples.Count > 0.88);
}
