vq3.out

基于神经网络的模式识别源程序

The input patterns are:
Pattern[0][0]=1.000000
Pattern[0][1]=0.000000
Pattern[1][0]=4.000000
Pattern[1][1]=0.000000
Pattern[2][0]=0.000000
Pattern[2][1]=1.000000
Pattern[3][0]=0.000000
Pattern[3][1]=3.000000
Pattern[4][0]=1.000000
Pattern[4][1]=1.000000
Pattern[5][0]=4.000000
Pattern[5][1]=1.000000
Pattern[6][0]=5.000000
Pattern[6][1]=1.000000
Pattern[7][0]=0.000000
Pattern[7][1]=0.000000
Pattern[8][0]=1.000000
Pattern[8][1]=3.000000
Pattern[9][0]=0.000000
Pattern[9][1]=4.000000
Pattern[10][0]=1.000000
Pattern[10][1]=4.000000
Pattern[11][0]=5.000000
Pattern[11][1]=0.000000



PATTERN 0:
No Clusters exist  so allocate a new cluster 0
patern 0 assigned to cluster 0

The new cluster centers are:
CLUSTER 0 ==>[1.000000,0.000000]
CLUSTER Membership
  Cluster 0 ==>{0 }


PATTERN 1:
The closest cluster is: 0
Distance 3.000000 < 3.500000
Therefore cluster 0 passed the distance test.
patern 1 assigned to cluster 0

The new cluster centers are:
CLUSTER 0 ==>[2.500000,0.000000]
CLUSTER Membership
  Cluster 0 ==>{0 1 }


PATTERN 2:
The closest cluster is: 0
Distance 2.692582 < 3.500000
Therefore cluster 0 passed the distance test.
patern 2 assigned to cluster 0

The new cluster centers are:
CLUSTER 0 ==>[1.666667,0.333333]
CLUSTER Membership
  Cluster 0 ==>{0 1 2 }


PATTERN 3:
The closest cluster is: 0
Distance 3.144660 < 3.500000
Therefore cluster 0 passed the distance test.
patern 3 assigned to cluster 0

The new cluster centers are:
CLUSTER 0 ==>[1.250000,1.000000]
CLUSTER Membership
  Cluster 0 ==>{0 1 2 3 }


PATTERN 4:
The closest cluster is: 0
Distance 0.250000 < 3.500000
Therefore cluster 0 passed the distance test.
patern 4 assigned to cluster 0

The new cluster centers are:
CLUSTER 0 ==>[1.200000,1.000000]
CLUSTER Membership
  Cluster 0 ==>{0 1 2 3 4 }


PATTERN 5:
The closest cluster is: 0
Distance 2.800000 < 3.500000
Therefore cluster 0 passed the distance test.
patern 5 assigned to cluster 0

The new cluster centers are:
CLUSTER 0 ==>[1.666667,1.000000]
CLUSTER Membership
  Cluster 0 ==>{0 1 2 3 4 5 }


PATTERN 6:
The closest cluster is: 0
Distance 3.333333 < 3.500000
Therefore cluster 0 passed the distance test.
patern 6 assigned to cluster 0

The new cluster centers are:
CLUSTER 0 ==>[2.142857,1.000000]
CLUSTER Membership
  Cluster 0 ==>{0 1 2 3 4 5 6 }


PATTERN 7:
The closest cluster is: 0
Distance 2.364706 < 3.500000
Therefore cluster 0 passed the distance test.
patern 7 assigned to cluster 0

The new cluster centers are:
CLUSTER 0 ==>[1.875000,0.875000]
CLUSTER Membership
  Cluster 0 ==>{0 1 2 3 4 5 6 7 }


PATTERN 8:
The closest cluster is: 0
Distance 2.298097 < 3.500000
Therefore cluster 0 passed the distance test.
patern 8 assigned to cluster 0

The new cluster centers are:
CLUSTER 0 ==>[1.777778,1.111111]
CLUSTER Membership
  Cluster 0 ==>{0 1 2 3 4 5 6 7 8 }


PATTERN 9:
The closest cluster is: 0
Distance 3.392075 < 3.500000
Therefore cluster 0 passed the distance test.
patern 9 assigned to cluster 0

The new cluster centers are:
CLUSTER 0 ==>[1.600000,1.400000]
CLUSTER Membership
  Cluster 0 ==>{0 1 2 3 4 5 6 7 8 9 }


PATTERN 10:
The closest cluster is: 0
Distance 2.668333 < 3.500000
Therefore cluster 0 passed the distance test.
patern 10 assigned to cluster 0

The new cluster centers are:
CLUSTER 0 ==>[1.545455,1.636364]
CLUSTER Membership
  Cluster 0 ==>{0 1 2 3 4 5 6 7 8 9 10 }


PATTERN 11:
The closest cluster is: 0
distance 3.822508 > 3.500000
Therefore cluster 0 failed the distance test.
so create NEW cluster number:1
patern 11 assigned to cluster 1

The new cluster centers are:
CLUSTER 0 ==>[1.545455,1.636364]
CLUSTER 1 ==>[5.000000,0.000000]
CLUSTER Membership
  Cluster 0 ==>{0 1 2 3 4 5 6 7 8 9 10 }

  Cluster 1 ==>{11 }