Created
December 18, 2016 16:23
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double kf = 0.5; | |
int [] nObr = new int[samples.length]; | |
while(kf >= 0.1) { | |
for (int i = 0; i < samples.length; i++) { | |
//1. Извлечение образца из матрицы образцов | |
double[] sample = samples[i]; | |
//2. Цикл по номерам кластеров | |
double[] distance = new double[clusterCount]; | |
for (int j = 0; j < clusterCount; j++) { | |
distance[j] = 0.0; | |
//2.1. Вычисление меры приближения | |
for (int k = 0; k < coordinatesCount; k++) { | |
distance[j] = distance[j] + Math.pow((sample[k] - weightMatrix[j][k]), 2); | |
} | |
} | |
//2.2. Определение номера кластера | |
double distanceMin = minFromArray(distance); | |
int index = 0; | |
for (int j = 0; j < clusterCount; j++) { | |
if (distance[j] == distanceMin) { | |
index = j; | |
} | |
} | |
nObr[i] = index; | |
//3. Пересчет координат центра кластера | |
for (int j = 0; j < coordinatesCount; j++) { | |
weightMatrix[index][j] = weightMatrix[index][j] + kf * (sample[j] - weightMatrix[index][j]); | |
} | |
} | |
kf = kf * 0.5; | |
} | |
for (int i = 0; i < nObr.length; i++) { | |
switch (nObr[i]) { | |
case 0: { | |
firstCluster.add(original[i]); | |
break; | |
} | |
case 1: { | |
secondCluster.add(original[i]); | |
break; | |
} | |
case 2: { | |
thirdCluster.add(original[i]); | |
break; | |
} | |
case 3: { | |
fourthCluster.add(original[i]); | |
break; | |
} | |
case 4: { | |
fifthCluster.add(original[i]); | |
break; | |
} | |
} | |
} |
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