如何在 Accord.net SOM(Kohonen's)中从网络进行类/标签分配

Question

谁能告诉如何从训练好的网络中获取/分配集群类/标签。

这是代码示例，让您了解我如何执行它：

Accord.Math.Random.Generator.Seed = 0;
int numberOfInputs = 3;
int hiddenNeurons = 25;
double[][] input =
{
new double[] { -1, -1, -1 },
new double[] { -1,  1, -1 },
new double[] {  1, -1, -1 },
new double[] {  1,  1, -1 },
new double[] { -1, -1,  1 },
new double[] { -1,  1,  1 },
new double[] {  1, -1,  1 },
new double[] {  1,  1,  1 },
// ...
};
var network = new DistanceNetwork(numberOfInputs, hiddenNeurons);
var teacher = new SOMLearning(network);
double error = double.PositiveInfinity;
for (int i = 0; i < 1000; i++)
error = teacher.RunEpoch(input);
// how can I know/assign class/label of each item in input array?

Answer 1

雅阁示例可能会进一步提供帮助：

群集 (SOM(

特别是本节：

for (int y = 0, i = 0; y < 100; y++)
{
// for all pixels
for (int x = 0; x < 100; x++, i++, ptr += 6)
{
Neuron neuron = layer.Neurons[i];
// red
ptr[2] = ptr[2 + 3] = ptr[2 + stride] = ptr[2 + 3 + stride] =
(byte)Math.Max(0, Math.Min(255, neuron.Weights[0]));
// green
ptr[1] = ptr[1 + 3] = ptr[1 + stride] = ptr[1 + 3 + stride] =
(byte)Math.Max(0, Math.Min(255, neuron.Weights[1]));
// blue
ptr[0] = ptr[0 + 3] = ptr[0 + stride] = ptr[0 + 3 + stride] =
(byte)Math.Max(0, Math.Min(255, neuron.Weights[2]));
}
ptr += offset;
ptr += stride;
}