我试图在C#中实现K-means算法,但是输出结果却是一张黑色的(小)图片。以下是我的代码:
public static Color[,] Kmeans(int number_clusters, Vector[,] input, int iterations)
{
int length = input.GetLength(0);
int height = input.GetLength(1);
Random randomizer = new Random();
//Inits centroides
List<Vector> centroides = new List<Vector>(number_clusters);
List<List<Vector>> clusters = new List<List<Vector>>(number_clusters);
for(int i = 0; i < number_clusters; i++)
{
Vector centroid = input[randomizer.Next(0, length), randomizer.Next(0, height)];
List<Vector> cluster = new List<Vector>();
cluster.Add(centroid);
clusters.Add(cluster);
centroides.Add(centroid);
}
int count = 0;
while (count < iterations) {
for (int x = 0; x < input.GetLength(0); x++)
{
for (int y = 0; y < input.GetLength(1); y++)
{
Vector currentVector = input[x, y];
double min_distance = Double.PositiveInfinity;
Vector closest_centroid = centroides[0];
//Finds closest centroid
foreach (Vector centroid in centroides)
{
double distance = currentVector.Distance(centroid);
if (Math.Pow(distance, 2) < min_distance)
{
min_distance = Math.Pow(distance, 2);
closest_centroid = centroid;
}
}
//Find cluster with that centroid in it
List<Vector> to_assign = clusters[0];
foreach (List<Vector> cluster_list in clusters)
{
if (cluster_list[0].r == closest_centroid.r && cluster_list[0].g == closest_centroid.g && cluster_list[0].b == closest_centroid.b)
{
to_assign = cluster_list;
break;
}
}
to_assign.Add(currentVector);
int current_length = to_assign.Count();
Vector current_centroid = to_assign[0];
Vector new_centroid = new Vector(0, 0, 0);
foreach (Vector vector in to_assign)
{
new_centroid.Sum(vector);
}
new_centroid = new Vector(new_centroid.r / current_length, new_centroid.g / current_length, new_centroid.b / current_length);
to_assign.RemoveAt(0);
to_assign.Insert(0, new_centroid);
for (int i = 0; i < centroides.Count(); i++)
{
if (centroides[i].r == current_centroid.r && centroides[i].g == current_centroid.g && current_centroid.b == centroides[i].b)
{
centroides[i] = new_centroid;
break;
}
}
}
}
count++;
}
foreach(List<Vector> cluster_list in clusters)
{
Vector current_centroid = cluster_list[0];
for(int i = 1; i < cluster_list.Count(); i++)
{
cluster_list[i].r = current_centroid.r;
cluster_list[i].g = current_centroid.g;
cluster_list[i].b = current_centroid.b;
}
}
Color[,] output = new Color[length, height];
foreach (List<Vector> cluster_list in clusters)
{
for (int i = 0; i < cluster_list.Count(); i++)
{
Vector current_vector = cluster_list[i];
output[current_vector.x, current_vector.y] = Color.FromArgb((int) current_vector.r, (int) current_vector.g, (int) current_vector.b);
}
}
return output;
}
Vector是以下类:
public class Vector
{
public double r, g, b;
public int x, y;
public Vector(double r, double g, double b)
{
this.r = r;
this.g = g;
this.b = b;
}
public void SetCoordinates(int x, int y)
{
this.x = x;
this.y = y;
}
public double Distance(Vector v2)
{
double r_power = Math.Pow((r - v2.r), 2);
double g_power = Math.Pow((g - v2.g), 2);
double b_power = Math.Pow((b - v2.b), 2);
double distance = Math.Sqrt((r_power + g_power + b_power));
return distance;
}
public Vector Product(int scalar)
{
return new Vector(r * scalar, g * scalar, b * scalar);
}
public double Length()
{
return Math.Sqrt((Math.Pow(r, 2) + Math.Pow(g, 2) + Math.Pow(b, 2)));
}
public Vector Sum(Vector v2)
{
double new_r = r + v2.r;
double new_g = g + v2.g;
double new_b = b + v2.b;
return new Vector(new_r, new_g, new_b);
}
}
我尝试按照以下算法描述实现代码:
将对象分配给群集: 对于每个测试集中的对象v,执行以下步骤: 1. 计算v与每个群集的质心k之间的平方距离(d2(v,k))。 2. 将对象v分配给最近的质心所在的群集。 更新质心: 对于每个群集k,计算它们的平均向量。 平均向量的计算方式类似于数字的平均值: 我们使用向量和对群集中的所有向量进行求和,并通过群集中向量数量的倒数(与逆向量的标量积)进行除法运算。 重复以上两个步骤。
有人能帮我修正这个算法吗?