func TestClustererUniformVectors(t *testing.T) {
//initilize data
var numClusters = 2
var numDataPoints = 8
var dimensionality = 4
data := make([][]float64, numDataPoints)
for i := 0; i < numDataPoints; i++ {
data[i] = make([]float64, dimensionality)
for j := 0; j < dimensionality; j++ {
data[i][j] = float64(i)
}
}
//run test
clusterer := clusterer.NewKMeansSimple(numClusters, data)
clusterer.Run()
var result = clusterer.GetCentroids()
//Test Results
if len(result) != numClusters {
t.Errorf("Clusterer created %v clusters. When %v was input for k.", len(result), numClusters)
}
if len(result[0]) != dimensionality {
t.Errorf("Cluster dimensionalioty of %v does not match the dimensionality of the input data, %v.", len(result[0]), dimensionality)
}
expectedResults := make([]float64, numClusters)
expectedResults[0] = 1.5 // (0+1+2+3)/4 = 1.5
expectedResults[1] = 5.5 // (4+5+6+7)/4 = 5.5
for i := 0; i < numClusters; i++ {
for j := 0; j < dimensionality; j++ {
if result[i][j] != expectedResults[i] {
t.Errorf("Data did not cluster as expected. Data: %v, Clusters: %v. Failure at %v, %v.", data, result, i, j)
}
}
}
}
func NewKMeansSimple(k int, centroids [][]float64) types.Clusterer {
return clusterer.NewKMeansSimple(k, centroids)
}