text:
import numpy as np
import bpy

def draw_voxel_model(V, N, M, K, group_name='VM'):
  g = bpy.data.groups.new(group_name)
  mat_dict = dict()
  Nh = N/2
  Mh = M/2
  Kh = K/2
  for i in range(N):
    for j in range(M):
      for k in range(K):
        p = V[i,j,k]
        if p > 0:
          mat_name = 'm'+str(p)[2:]
          mat = bpy.data.materials.get(mat_name)
          if mat is None:
            mat = bpy.data.materials.new(mat_name)
            mat.diffuse_color = (0.5,0.5,0.5)
            mat.alpha = p
            mat.use_transparency = True
          bpy.ops.mesh.primitive_cube_add(location=(i+1/2-Nh, j+1/2-Mh, k+1/2-Kh))
          v = bpy.context.active_object
          v.dimensions = (1,1,1)
          v.active_material = mat
          v.show_transparent = True
          g.objects.link(v)
  return g

VM = np.zeros( (16, 16, 16), dtype=float)
VM[0,0,0] = 0.00
VM[1,0,0] = 0.00
VM[2,0,0] = 0.00
VM[3,0,0] = 0.00
VM[4,0,0] = 0.00
VM[5,0,0] = 0.00
VM[6,0,0] = 0.00
VM[7,0,0] = 0.00
VM[8,0,0] = 0.00
VM[9,0,0] = 0.00
VM[10,0,0] = 0.00
VM[11,0,0] = 0.00
VM[12,0,0] = 0.00
VM[13,0,0] = 0.00
VM[14,0,0] = 0.00
VM[15,0,0] = 0.00
VM[0,1,0] = 0.00
VM[1,1,0] = 0.00
VM[2,1,0] = 0.00
VM[3,1,0] = 0.00
VM[4,1,0] = 0.00
VM[5,1,0] = 0.00
VM[6,1,0] = 0.00
VM[7,1,0] = 0.00
VM[8,1,0] = 0.00
VM[9,1,0] = 0.00
VM[10,1,0] = 0.00
VM[11,1,0] = 0.00
VM[12,1,0] = 0.00
VM[13,1,0] = 0.00
VM[14,1,0] = 0.00
VM[15,1,0] = 0.00
VM[0,2,0] = 0.00
VM[1,2,0] = 0.00
VM[2,2,0] = 0.00
VM[3,2,0] = 0.00
VM[4,2,0] = 0.07
VM[5,2,0] = 0.14
VM[6,2,0] = 0.07
VM[7,2,0] = 0.00
VM[8,2,0] = 0.00
VM[9,2,0] = 0.00
VM[10,2,0] = 0.00
VM[11,2,0] = 0.00
VM[12,2,0] = 0.00
VM[13,2,0] = 0.00
VM[14,2,0] = 0.00
VM[15,2,0] = 0.00
VM[0,3,0] = 0.00
VM[1,3,0] = 0.00
VM[2,3,0] = 0.00
VM[3,3,0] = 0.21
VM[4,3,0] = 0.52
VM[5,3,0] = 0.60
VM[6,3,0] = 0.52
VM[7,3,0] = 0.21
VM[8,3,0] = 0.00
VM[9,3,0] = 0.00
VM[10,3,0] = 0.00
VM[11,3,0] = 0.00
VM[12,3,0] = 0.00
VM[13,3,0] = 0.00
VM[14,3,0] = 0.00
VM[15,3,0] = 0.00
VM[0,4,0] = 0.00
VM[1,4,0] = 0.00
VM[2,4,0] = 0.07
VM[3,4,0] = 0.52
VM[4,4,0] = 0.80
VM[5,4,0] = 0.90
VM[6,4,0] = 0.80
VM[7,4,0] = 0.52
VM[8,4,0] = 0.07
VM[9,4,0] = 0.00
VM[10,4,0] = 0.00
VM[11,4,0] = 0.00
VM[12,4,0] = 0.00
VM[13,4,0] = 0.00
VM[14,4,0] = 0.00
VM[15,4,0] = 0.00
VM[0,5,0] = 0.00
VM[1,5,0] = 0.00
VM[2,5,0] = 0.14
VM[3,5,0] = 0.60
VM[4,5,0] = 0.90
VM[5,5,0] = 1.00
VM[6,5,0] = 0.90
VM[7,5,0] = 0.60
VM[8,5,0] = 0.14
VM[9,5,0] = 0.00
VM[10,5,0] = 0.00
VM[11,5,0] = 0.00
VM[12,5,0] = 0.00
VM[13,5,0] = 0.00
VM[14,5,0] = 0.00
VM[15,5,0] = 0.00
VM[0,6,0] = 0.00
VM[1,6,0] = 0.00
VM[2,6,0] = 0.07
VM[3,6,0] = 0.52
VM[4,6,0] = 0.80
VM[5,6,0] = 0.90
VM[6,6,0] = 0.80
VM[7,6,0] = 0.52
VM[8,6,0] = 0.07
VM[9,6,0] = 0.00
VM[10,6,0] = 0.00
VM[11,6,0] = 0.00
VM[12,6,0] = 0.00
VM[13,6,0] = 0.00
VM[14,6,0] = 0.00
VM[15,6,0] = 0.00
VM[0,7,0] = 0.00
VM[1,7,0] = 0.00
VM[2,7,0] = 0.00
VM[3,7,0] = 0.21
VM[4,7,0] = 0.52
VM[5,7,0] = 0.60
VM[6,7,0] = 0.52
VM[7,7,0] = 0.21
VM[8,7,0] = 0.00
VM[9,7,0] = 0.00
VM[10,7,0] = 0.00
VM[11,7,0] = 0.00
VM[12,7,0] = 0.00
VM[13,7,0] = 0.00
VM[14,7,0] = 0.00
VM[15,7,0] = 0.00
VM[0,8,0] = 0.00
VM[1,8,0] = 0.00
VM[2,8,0] = 0.00
VM[3,8,0] = 0.00
VM[4,8,0] = 0.07
VM[5,8,0] = 0.14
VM[6,8,0] = 0.07
VM[7,8,0] = 0.00
VM[8,8,0] = 0.00
VM[9,8,0] = 0.00
VM[10,8,0] = 0.00
VM[11,8,0] = 0.00
VM[12,8,0] = 0.00
VM[13,8,0] = 0.00
VM[14,8,0] = 0.00
VM[15,8,0] = 0.00
VM[0,9,0] = 0.00
VM[1,9,0] = 0.00
VM[2,9,0] = 0.00
VM[3,9,0] = 0.00
VM[4,9,0] = 0.00
VM[5,9,0] = 0.00
VM[6,9,0] = 0.00
VM[7,9,0] = 0.00
VM[8,9,0] = 0.00
VM[9,9,0] = 0.00
VM[10,9,0] = 0.00
VM[11,9,0] = 0.00
VM[12,9,0] = 0.00
VM[13,9,0] = 0.00
VM[14,9,0] = 0.00
VM[15,9,0] = 0.00
VM[0,10,0] = 0.00
VM[1,10,0] = 0.00
VM[2,10,0] = 0.00
VM[3,10,0] = 0.00
VM[4,10,0] = 0.00
VM[5,10,0] = 0.00
VM[6,10,0] = 0.00
VM[7,10,0] = 0.00
VM[8,10,0] = 0.00
VM[9,10,0] = 0.00
VM[10,10,0] = 0.00
VM[11,10,0] = 0.00
VM[12,10,0] = 0.00
VM[13,10,0] = 0.00
VM[14,10,0] = 0.00
VM[15,10,0] = 0.00
VM[0,11,0] = 0.00
VM[1,11,0] = 0.00
VM[2,11,0] = 0.00
VM[3,11,0] = 0.00
VM[4,11,0] = 0.00
VM[5,11,0] = 0.00
VM[6,11,0] = 0.00
VM[7,11,0] = 0.00
VM[8,11,0] = 0.00
VM[9,11,0] = 0.00
VM[10,11,0] = 0.00
VM[11,11,0] = 0.00
VM[12,11,0] = 0.00
VM[13,11,0] = 0.00
VM[14,11,0] = 0.00
VM[15,11,0] = 0.00
VM[0,12,0] = 0.00
VM[1,12,0] = 0.00
VM[2,12,0] = 0.00
VM[3,12,0] = 0.00
VM[4,12,0] = 0.00
VM[5,12,0] = 0.00
VM[6,12,0] = 0.00
VM[7,12,0] = 0.00
VM[8,12,0] = 0.00
VM[9,12,0] = 0.00
VM[10,12,0] = 0.00
VM[11,12,0] = 0.00
VM[12,12,0] = 0.00
VM[13,12,0] = 0.00
VM[14,12,0] = 0.00
VM[15,12,0] = 0.00
VM[0,13,0] = 0.00
VM[1,13,0] = 0.00
VM[2,13,0] = 0.00
VM[3,13,0] = 0.00
VM[4,13,0] = 0.00
VM[5,13,0] = 0.00
VM[6,13,0] = 0.00
VM[7,13,0] = 0.00
VM[8,13,0] = 0.00
VM[9,13,0] = 0.00
VM[10,13,0] = 0.00
VM[11,13,0] = 0.00
VM[12,13,0] = 0.00
VM[13,13,0] = 0.00
VM[14,13,0] = 0.00
VM[15,13,0] = 0.00
VM[0,14,0] = 0.00
VM[1,14,0] = 0.00
VM[2,14,0] = 0.00
VM[3,14,0] = 0.00
VM[4,14,0] = 0.00
VM[5,14,0] = 0.00
VM[6,14,0] = 0.00
VM[7,14,0] = 0.00
VM[8,14,0] = 0.00
VM[9,14,0] = 0.00
VM[10,14,0] = 0.00
VM[11,14,0] = 0.00
VM[12,14,0] = 0.00
VM[13,14,0] = 0.00
VM[14,14,0] = 0.00
VM[15,14,0] = 0.00
VM[0,15,0] = 0.00
VM[1,15,0] = 0.00
VM[2,15,0] = 0.00
VM[3,15,0] = 0.00
VM[4,15,0] = 0.00
VM[5,15,0] = 0.00
VM[6,15,0] = 0.00
VM[7,15,0] = 0.00
VM[8,15,0] = 0.00
VM[9,15,0] = 0.00
VM[10,15,0] = 0.00
VM[11,15,0] = 0.00
VM[12,15,0] = 0.00
VM[13,15,0] = 0.00
VM[14,15,0] = 0.00
VM[15,15,0] = 0.00
VM[0,0,1] = 0.00
VM[1,0,1] = 0.00
VM[2,0,1] = 0.00
VM[3,0,1] = 0.00
VM[4,0,1] = 0.00
VM[5,0,1] = 0.00
VM[6,0,1] = 0.00
VM[7,0,1] = 0.00
VM[8,0,1] = 0.00
VM[9,0,1] = 0.00
VM[10,0,1] = 0.00
VM[11,0,1] = 0.00
VM[12,0,1] = 0.00
VM[13,0,1] = 0.00
VM[14,0,1] = 0.00
VM[15,0,1] = 0.00
VM[0,1,1] = 0.00
VM[1,1,1] = 0.00
VM[2,1,1] = 0.00
VM[3,1,1] = 0.04
VM[4,1,1] = 0.19
VM[5,1,1] = 0.24
VM[6,1,1] = 0.19
VM[7,1,1] = 0.04
VM[8,1,1] = 0.00
VM[9,1,1] = 0.00
VM[10,1,1] = 0.00
VM[11,1,1] = 0.00
VM[12,1,1] = 0.00
VM[13,1,1] = 0.00
VM[14,1,1] = 0.00
VM[15,1,1] = 0.00
VM[0,2,1] = 0.00
VM[1,2,1] = 0.00
VM[2,2,1] = 0.14
VM[3,2,1] = 0.62
VM[4,2,1] = 0.91
VM[5,2,1] = 0.96
VM[6,2,1] = 0.91
VM[7,2,1] = 0.62
VM[8,2,1] = 0.14
VM[9,2,1] = 0.00
VM[10,2,1] = 0.00
VM[11,2,1] = 0.00
VM[12,2,1] = 0.00
VM[13,2,1] = 0.00
VM[14,2,1] = 0.00
VM[15,2,1] = 0.00
VM[0,3,1] = 0.00
VM[1,3,1] = 0.04
VM[2,3,1] = 0.62
VM[3,3,1] = 0.99
VM[4,3,1] = 1.00
VM[5,3,1] = 1.00
VM[6,3,1] = 1.00
VM[7,3,1] = 0.99
VM[8,3,1] = 0.62
VM[9,3,1] = 0.04
VM[10,3,1] = 0.00
VM[11,3,1] = 0.00
VM[12,3,1] = 0.00
VM[13,3,1] = 0.00
VM[14,3,1] = 0.00
VM[15,3,1] = 0.00
VM[0,4,1] = 0.00
VM[1,4,1] = 0.19
VM[2,4,1] = 0.91
VM[3,4,1] = 1.00
VM[4,4,1] = 1.00
VM[5,4,1] = 1.00
VM[6,4,1] = 1.00
VM[7,4,1] = 1.00
VM[8,4,1] = 0.91
VM[9,4,1] = 0.19
VM[10,4,1] = 0.00
VM[11,4,1] = 0.00
VM[12,4,1] = 0.00
VM[13,4,1] = 0.00
VM[14,4,1] = 0.00
VM[15,4,1] = 0.00
VM[0,5,1] = 0.00
VM[1,5,1] = 0.24
VM[2,5,1] = 0.96
VM[3,5,1] = 1.00
VM[4,5,1] = 1.00
VM[5,5,1] = 1.00
VM[6,5,1] = 1.00
VM[7,5,1] = 1.00
VM[8,5,1] = 0.96
VM[9,5,1] = 0.24
VM[10,5,1] = 0.00
VM[11,5,1] = 0.00
VM[12,5,1] = 0.00
VM[13,5,1] = 0.00
VM[14,5,1] = 0.00
VM[15,5,1] = 0.00
VM[0,6,1] = 0.00
VM[1,6,1] = 0.19
VM[2,6,1] = 0.91
VM[3,6,1] = 1.00
VM[4,6,1] = 1.00
VM[5,6,1] = 1.00
VM[6,6,1] = 1.00
VM[7,6,1] = 1.00
VM[8,6,1] = 0.91
VM[9,6,1] = 0.19
VM[10,6,1] = 0.00
VM[11,6,1] = 0.00
VM[12,6,1] = 0.00
VM[13,6,1] = 0.00
VM[14,6,1] = 0.00
VM[15,6,1] = 0.00
VM[0,7,1] = 0.00
VM[1,7,1] = 0.04
VM[2,7,1] = 0.62
VM[3,7,1] = 0.99
VM[4,7,1] = 1.00
VM[5,7,1] = 1.00
VM[6,7,1] = 1.00
VM[7,7,1] = 0.99
VM[8,7,1] = 0.62
VM[9,7,1] = 0.04
VM[10,7,1] = 0.00
VM[11,7,1] = 0.00
VM[12,7,1] = 0.00
VM[13,7,1] = 0.00
VM[14,7,1] = 0.00
VM[15,7,1] = 0.00
VM[0,8,1] = 0.00
VM[1,8,1] = 0.00
VM[2,8,1] = 0.14
VM[3,8,1] = 0.62
VM[4,8,1] = 0.91
VM[5,8,1] = 0.96
VM[6,8,1] = 0.91
VM[7,8,1] = 0.62
VM[8,8,1] = 0.14
VM[9,8,1] = 0.00
VM[10,8,1] = 0.00
VM[11,8,1] = 0.00
VM[12,8,1] = 0.00
VM[13,8,1] = 0.00
VM[14,8,1] = 0.00
VM[15,8,1] = 0.00
VM[0,9,1] = 0.00
VM[1,9,1] = 0.00
VM[2,9,1] = 0.00
VM[3,9,1] = 0.04
VM[4,9,1] = 0.19
VM[5,9,1] = 0.24
VM[6,9,1] = 0.19
VM[7,9,1] = 0.04
VM[8,9,1] = 0.00
VM[9,9,1] = 0.00
VM[10,9,1] = 0.00
VM[11,9,1] = 0.00
VM[12,9,1] = 0.00
VM[13,9,1] = 0.00
VM[14,9,1] = 0.00
VM[15,9,1] = 0.00
VM[0,10,1] = 0.00
VM[1,10,1] = 0.00
VM[2,10,1] = 0.00
VM[3,10,1] = 0.00
VM[4,10,1] = 0.00
VM[5,10,1] = 0.00
VM[6,10,1] = 0.00
VM[7,10,1] = 0.00
VM[8,10,1] = 0.00
VM[9,10,1] = 0.00
VM[10,10,1] = 0.00
VM[11,10,1] = 0.00
VM[12,10,1] = 0.00
VM[13,10,1] = 0.00
VM[14,10,1] = 0.00
VM[15,10,1] = 0.00
VM[0,11,1] = 0.00
VM[1,11,1] = 0.00
VM[2,11,1] = 0.00
VM[3,11,1] = 0.00
VM[4,11,1] = 0.00
VM[5,11,1] = 0.00
VM[6,11,1] = 0.00
VM[7,11,1] = 0.00
VM[8,11,1] = 0.00
VM[9,11,1] = 0.00
VM[10,11,1] = 0.00
VM[11,11,1] = 0.00
VM[12,11,1] = 0.00
VM[13,11,1] = 0.00
VM[14,11,1] = 0.00
VM[15,11,1] = 0.00
VM[0,12,1] = 0.00
VM[1,12,1] = 0.00
VM[2,12,1] = 0.00
VM[3,12,1] = 0.00
VM[4,12,1] = 0.00
VM[5,12,1] = 0.00
VM[6,12,1] = 0.00
VM[7,12,1] = 0.00
VM[8,12,1] = 0.00
VM[9,12,1] = 0.00
VM[10,12,1] = 0.00
VM[11,12,1] = 0.00
VM[12,12,1] = 0.00
VM[13,12,1] = 0.00
VM[14,12,1] = 0.00
VM[15,12,1] = 0.00
VM[0,13,1] = 0.00
VM[1,13,1] = 0.00
VM[2,13,1] = 0.00
VM[3,13,1] = 0.00
VM[4,13,1] = 0.00
VM[5,13,1] = 0.00
VM[6,13,1] = 0.00
VM[7,13,1] = 0.00
VM[8,13,1] = 0.00
VM[9,13,1] = 0.00
VM[10,13,1] = 0.00
VM[11,13,1] = 0.00
VM[12,13,1] = 0.00
VM[13,13,1] = 0.00
VM[14,13,1] = 0.00
VM[15,13,1] = 0.00
VM[0,14,1] = 0.00
VM[1,14,1] = 0.00
VM[2,14,1] = 0.00
VM[3,14,1] = 0.00
VM[4,14,1] = 0.00
VM[5,14,1] = 0.00
VM[6,14,1] = 0.00
VM[7,14,1] = 0.00
VM[8,14,1] = 0.00
VM[9,14,1] = 0.00
VM[10,14,1] = 0.00
VM[11,14,1] = 0.00
VM[12,14,1] = 0.00
VM[13,14,1] = 0.00
VM[14,14,1] = 0.00
VM[15,14,1] = 0.00
VM[0,15,1] = 0.00
VM[1,15,1] = 0.00
VM[2,15,1] = 0.00
VM[3,15,1] = 0.00
VM[4,15,1] = 0.00
VM[5,15,1] = 0.00
VM[6,15,1] = 0.00
VM[7,15,1] = 0.00
VM[8,15,1] = 0.00
VM[9,15,1] = 0.00
VM[10,15,1] = 0.00
VM[11,15,1] = 0.00
VM[12,15,1] = 0.00
VM[13,15,1] = 0.00
VM[14,15,1] = 0.00
VM[15,15,1] = 0.00
VM[0,0,2] = 0.00
VM[1,0,2] = 0.00
VM[2,0,2] = 0.00
VM[3,0,2] = 0.00
VM[4,0,2] = 0.07
VM[5,0,2] = 0.14
VM[6,0,2] = 0.07
VM[7,0,2] = 0.00
VM[8,0,2] = 0.00
VM[9,0,2] = 0.00
VM[10,0,2] = 0.00
VM[11,0,2] = 0.00
VM[12,0,2] = 0.00
VM[13,0,2] = 0.00
VM[14,0,2] = 0.00
VM[15,0,2] = 0.00
VM[0,1,2] = 0.00
VM[1,1,2] = 0.00
VM[2,1,2] = 0.14
VM[3,1,2] = 0.62
VM[4,1,2] = 0.91
VM[5,1,2] = 0.96
VM[6,1,2] = 0.91
VM[7,1,2] = 0.62
VM[8,1,2] = 0.14
VM[9,1,2] = 0.00
VM[10,1,2] = 0.00
VM[11,1,2] = 0.00
VM[12,1,2] = 0.00
VM[13,1,2] = 0.00
VM[14,1,2] = 0.00
VM[15,1,2] = 0.00
VM[0,2,2] = 0.00
VM[1,2,2] = 0.14
VM[2,2,2] = 0.84
VM[3,2,2] = 1.00
VM[4,2,2] = 1.00
VM[5,2,2] = 1.00
VM[6,2,2] = 1.00
VM[7,2,2] = 1.00
VM[8,2,2] = 0.84
VM[9,2,2] = 0.14
VM[10,2,2] = 0.00
VM[11,2,2] = 0.00
VM[12,2,2] = 0.00
VM[13,2,2] = 0.00
VM[14,2,2] = 0.00
VM[15,2,2] = 0.00
VM[0,3,2] = 0.00
VM[1,3,2] = 0.62
VM[2,3,2] = 1.00
VM[3,3,2] = 1.00
VM[4,3,2] = 1.00
VM[5,3,2] = 1.00
VM[6,3,2] = 1.00
VM[7,3,2] = 1.00
VM[8,3,2] = 1.00
VM[9,3,2] = 0.62
VM[10,3,2] = 0.00
VM[11,3,2] = 0.00
VM[12,3,2] = 0.00
VM[13,3,2] = 0.00
VM[14,3,2] = 0.00
VM[15,3,2] = 0.00
VM[0,4,2] = 0.07
VM[1,4,2] = 0.91
VM[2,4,2] = 1.00
VM[3,4,2] = 1.00
VM[4,4,2] = 1.00
VM[5,4,2] = 1.00
VM[6,4,2] = 1.00
VM[7,4,2] = 1.00
VM[8,4,2] = 1.00
VM[9,4,2] = 0.91
VM[10,4,2] = 0.07
VM[11,4,2] = 0.00
VM[12,4,2] = 0.00
VM[13,4,2] = 0.00
VM[14,4,2] = 0.00
VM[15,4,2] = 0.00
VM[0,5,2] = 0.14
VM[1,5,2] = 0.96
VM[2,5,2] = 1.00
VM[3,5,2] = 1.00
VM[4,5,2] = 1.00
VM[5,5,2] = 1.00
VM[6,5,2] = 1.00
VM[7,5,2] = 1.00
VM[8,5,2] = 1.00
VM[9,5,2] = 0.96
VM[10,5,2] = 0.14
VM[11,5,2] = 0.00
VM[12,5,2] = 0.00
VM[13,5,2] = 0.00
VM[14,5,2] = 0.00
VM[15,5,2] = 0.00
VM[0,6,2] = 0.07
VM[1,6,2] = 0.91
VM[2,6,2] = 1.00
VM[3,6,2] = 1.00
VM[4,6,2] = 1.00
VM[5,6,2] = 1.00
VM[6,6,2] = 1.00
VM[7,6,2] = 1.00
VM[8,6,2] = 1.00
VM[9,6,2] = 0.91
VM[10,6,2] = 0.07
VM[11,6,2] = 0.00
VM[12,6,2] = 0.00
VM[13,6,2] = 0.00
VM[14,6,2] = 0.00
VM[15,6,2] = 0.00
VM[0,7,2] = 0.00
VM[1,7,2] = 0.62
VM[2,7,2] = 1.00
VM[3,7,2] = 1.00
VM[4,7,2] = 1.00
VM[5,7,2] = 1.00
VM[6,7,2] = 1.00
VM[7,7,2] = 1.00
VM[8,7,2] = 1.00
VM[9,7,2] = 0.62
VM[10,7,2] = 0.00
VM[11,7,2] = 0.00
VM[12,7,2] = 0.00
VM[13,7,2] = 0.00
VM[14,7,2] = 0.00
VM[15,7,2] = 0.00
VM[0,8,2] = 0.00
VM[1,8,2] = 0.14
VM[2,8,2] = 0.84
VM[3,8,2] = 1.00
VM[4,8,2] = 1.00
VM[5,8,2] = 1.00
VM[6,8,2] = 1.00
VM[7,8,2] = 1.00
VM[8,8,2] = 0.84
VM[9,8,2] = 0.14
VM[10,8,2] = 0.00
VM[11,8,2] = 0.00
VM[12,8,2] = 0.00
VM[13,8,2] = 0.00
VM[14,8,2] = 0.00
VM[15,8,2] = 0.00
VM[0,9,2] = 0.00
VM[1,9,2] = 0.00
VM[2,9,2] = 0.14
VM[3,9,2] = 0.62
VM[4,9,2] = 0.91
VM[5,9,2] = 0.96
VM[6,9,2] = 0.91
VM[7,9,2] = 0.62
VM[8,9,2] = 0.14
VM[9,9,2] = 0.00
VM[10,9,2] = 0.00
VM[11,9,2] = 0.00
VM[12,9,2] = 0.00
VM[13,9,2] = 0.00
VM[14,9,2] = 0.00
VM[15,9,2] = 0.00
VM[0,10,2] = 0.00
VM[1,10,2] = 0.00
VM[2,10,2] = 0.00
VM[3,10,2] = 0.00
VM[4,10,2] = 0.07
VM[5,10,2] = 0.14
VM[6,10,2] = 0.07
VM[7,10,2] = 0.00
VM[8,10,2] = 0.00
VM[9,10,2] = 0.00
VM[10,10,2] = 0.00
VM[11,10,2] = 0.00
VM[12,10,2] = 0.00
VM[13,10,2] = 0.00
VM[14,10,2] = 0.00
VM[15,10,2] = 0.00
VM[0,11,2] = 0.00
VM[1,11,2] = 0.00
VM[2,11,2] = 0.00
VM[3,11,2] = 0.00
VM[4,11,2] = 0.00
VM[5,11,2] = 0.00
VM[6,11,2] = 0.00
VM[7,11,2] = 0.00
VM[8,11,2] = 0.00
VM[9,11,2] = 0.00
VM[10,11,2] = 0.00
VM[11,11,2] = 0.00
VM[12,11,2] = 0.00
VM[13,11,2] = 0.00
VM[14,11,2] = 0.00
VM[15,11,2] = 0.00
VM[0,12,2] = 0.00
VM[1,12,2] = 0.00
VM[2,12,2] = 0.00
VM[3,12,2] = 0.00
VM[4,12,2] = 0.00
VM[5,12,2] = 0.00
VM[6,12,2] = 0.00
VM[7,12,2] = 0.00
VM[8,12,2] = 0.00
VM[9,12,2] = 0.00
VM[10,12,2] = 0.00
VM[11,12,2] = 0.00
VM[12,12,2] = 0.00
VM[13,12,2] = 0.00
VM[14,12,2] = 0.00
VM[15,12,2] = 0.00
VM[0,13,2] = 0.00
VM[1,13,2] = 0.00
VM[2,13,2] = 0.00
VM[3,13,2] = 0.00
VM[4,13,2] = 0.00
VM[5,13,2] = 0.00
VM[6,13,2] = 0.00
VM[7,13,2] = 0.00
VM[8,13,2] = 0.00
VM[9,13,2] = 0.00
VM[10,13,2] = 0.00
VM[11,13,2] = 0.00
VM[12,13,2] = 0.00
VM[13,13,2] = 0.00
VM[14,13,2] = 0.00
VM[15,13,2] = 0.00
VM[0,14,2] = 0.00
VM[1,14,2] = 0.00
VM[2,14,2] = 0.00
VM[3,14,2] = 0.00
VM[4,14,2] = 0.00
VM[5,14,2] = 0.00
VM[6,14,2] = 0.00
VM[7,14,2] = 0.00
VM[8,14,2] = 0.00
VM[9,14,2] = 0.00
VM[10,14,2] = 0.00
VM[11,14,2] = 0.00
VM[12,14,2] = 0.00
VM[13,14,2] = 0.00
VM[14,14,2] = 0.00
VM[15,14,2] = 0.00
VM[0,15,2] = 0.00
VM[1,15,2] = 0.00
VM[2,15,2] = 0.00
VM[3,15,2] = 0.00
VM[4,15,2] = 0.00
VM[5,15,2] = 0.00
VM[6,15,2] = 0.00
VM[7,15,2] = 0.00
VM[8,15,2] = 0.00
VM[9,15,2] = 0.00
VM[10,15,2] = 0.00
VM[11,15,2] = 0.00
VM[12,15,2] = 0.00
VM[13,15,2] = 0.00
VM[14,15,2] = 0.00
VM[15,15,2] = 0.00
VM[0,0,3] = 0.00
VM[1,0,3] = 0.00
VM[2,0,3] = 0.00
VM[3,0,3] = 0.21
VM[4,0,3] = 0.52
VM[5,0,3] = 0.60
VM[6,0,3] = 0.52
VM[7,0,3] = 0.21
VM[8,0,3] = 0.00
VM[9,0,3] = 0.00
VM[10,0,3] = 0.00
VM[11,0,3] = 0.00
VM[12,0,3] = 0.00
VM[13,0,3] = 0.00
VM[14,0,3] = 0.00
VM[15,0,3] = 0.00
VM[0,1,3] = 0.00
VM[1,1,3] = 0.04
VM[2,1,3] = 0.62
VM[3,1,3] = 0.99
VM[4,1,3] = 1.00
VM[5,1,3] = 1.00
VM[6,1,3] = 1.00
VM[7,1,3] = 0.99
VM[8,1,3] = 0.62
VM[9,1,3] = 0.04
VM[10,1,3] = 0.00
VM[11,1,3] = 0.00
VM[12,1,3] = 0.00
VM[13,1,3] = 0.00
VM[14,1,3] = 0.00
VM[15,1,3] = 0.00
VM[0,2,3] = 0.00
VM[1,2,3] = 0.62
VM[2,2,3] = 1.00
VM[3,2,3] = 1.00
VM[4,2,3] = 1.00
VM[5,2,3] = 1.00
VM[6,2,3] = 1.00
VM[7,2,3] = 1.00
VM[8,2,3] = 1.00
VM[9,2,3] = 0.62
VM[10,2,3] = 0.00
VM[11,2,3] = 0.00
VM[12,2,3] = 0.00
VM[13,2,3] = 0.00
VM[14,2,3] = 0.00
VM[15,2,3] = 0.00
VM[0,3,3] = 0.21
VM[1,3,3] = 0.99
VM[2,3,3] = 1.00
VM[3,3,3] = 1.00
VM[4,3,3] = 1.00
VM[5,3,3] = 1.00
VM[6,3,3] = 1.00
VM[7,3,3] = 1.00
VM[8,3,3] = 1.00
VM[9,3,3] = 0.99
VM[10,3,3] = 0.21
VM[11,3,3] = 0.00
VM[12,3,3] = 0.00
VM[13,3,3] = 0.00
VM[14,3,3] = 0.00
VM[15,3,3] = 0.00
VM[0,4,3] = 0.52
VM[1,4,3] = 1.00
VM[2,4,3] = 1.00
VM[3,4,3] = 1.00
VM[4,4,3] = 1.00
VM[5,4,3] = 1.00
VM[6,4,3] = 1.00
VM[7,4,3] = 1.00
VM[8,4,3] = 1.00
VM[9,4,3] = 1.00
VM[10,4,3] = 0.52
VM[11,4,3] = 0.00
VM[12,4,3] = 0.00
VM[13,4,3] = 0.00
VM[14,4,3] = 0.00
VM[15,4,3] = 0.00
VM[0,5,3] = 0.60
VM[1,5,3] = 1.00
VM[2,5,3] = 1.00
VM[3,5,3] = 1.00
VM[4,5,3] = 1.00
VM[5,5,3] = 1.00
VM[6,5,3] = 1.00
VM[7,5,3] = 1.00
VM[8,5,3] = 1.00
VM[9,5,3] = 1.00
VM[10,5,3] = 0.60
VM[11,5,3] = 0.00
VM[12,5,3] = 0.00
VM[13,5,3] = 0.00
VM[14,5,3] = 0.00
VM[15,5,3] = 0.00
VM[0,6,3] = 0.52
VM[1,6,3] = 1.00
VM[2,6,3] = 1.00
VM[3,6,3] = 1.00
VM[4,6,3] = 1.00
VM[5,6,3] = 1.00
VM[6,6,3] = 1.00
VM[7,6,3] = 1.00
VM[8,6,3] = 1.00
VM[9,6,3] = 1.00
VM[10,6,3] = 0.52
VM[11,6,3] = 0.00
VM[12,6,3] = 0.00
VM[13,6,3] = 0.00
VM[14,6,3] = 0.00
VM[15,6,3] = 0.00
VM[0,7,3] = 0.21
VM[1,7,3] = 0.99
VM[2,7,3] = 1.00
VM[3,7,3] = 1.00
VM[4,7,3] = 1.00
VM[5,7,3] = 1.00
VM[6,7,3] = 1.00
VM[7,7,3] = 1.00
VM[8,7,3] = 1.00
VM[9,7,3] = 0.99
VM[10,7,3] = 0.21
VM[11,7,3] = 0.00
VM[12,7,3] = 0.00
VM[13,7,3] = 0.00
VM[14,7,3] = 0.00
VM[15,7,3] = 0.00
VM[0,8,3] = 0.00
VM[1,8,3] = 0.62
VM[2,8,3] = 1.00
VM[3,8,3] = 1.00
VM[4,8,3] = 1.00
VM[5,8,3] = 1.00
VM[6,8,3] = 1.00
VM[7,8,3] = 1.00
VM[8,8,3] = 1.00
VM[9,8,3] = 0.62
VM[10,8,3] = 0.00
VM[11,8,3] = 0.00
VM[12,8,3] = 0.00
VM[13,8,3] = 0.00
VM[14,8,3] = 0.00
VM[15,8,3] = 0.00
VM[0,9,3] = 0.00
VM[1,9,3] = 0.04
VM[2,9,3] = 0.62
VM[3,9,3] = 0.99
VM[4,9,3] = 1.00
VM[5,9,3] = 1.00
VM[6,9,3] = 1.00
VM[7,9,3] = 0.99
VM[8,9,3] = 0.62
VM[9,9,3] = 0.04
VM[10,9,3] = 0.00
VM[11,9,3] = 0.00
VM[12,9,3] = 0.00
VM[13,9,3] = 0.00
VM[14,9,3] = 0.00
VM[15,9,3] = 0.00
VM[0,10,3] = 0.00
VM[1,10,3] = 0.00
VM[2,10,3] = 0.00
VM[3,10,3] = 0.21
VM[4,10,3] = 0.52
VM[5,10,3] = 0.60
VM[6,10,3] = 0.52
VM[7,10,3] = 0.21
VM[8,10,3] = 0.00
VM[9,10,3] = 0.00
VM[10,10,3] = 0.00
VM[11,10,3] = 0.00
VM[12,10,3] = 0.00
VM[13,10,3] = 0.00
VM[14,10,3] = 0.00
VM[15,10,3] = 0.00
VM[0,11,3] = 0.00
VM[1,11,3] = 0.00
VM[2,11,3] = 0.00
VM[3,11,3] = 0.00
VM[4,11,3] = 0.00
VM[5,11,3] = 0.00
VM[6,11,3] = 0.00
VM[7,11,3] = 0.00
VM[8,11,3] = 0.00
VM[9,11,3] = 0.00
VM[10,11,3] = 0.00
VM[11,11,3] = 0.00
VM[12,11,3] = 0.00
VM[13,11,3] = 0.00
VM[14,11,3] = 0.00
VM[15,11,3] = 0.00
VM[0,12,3] = 0.00
VM[1,12,3] = 0.00
VM[2,12,3] = 0.00
VM[3,12,3] = 0.00
VM[4,12,3] = 0.00
VM[5,12,3] = 0.00
VM[6,12,3] = 0.00
VM[7,12,3] = 0.00
VM[8,12,3] = 0.00
VM[9,12,3] = 0.00
VM[10,12,3] = 0.00
VM[11,12,3] = 0.00
VM[12,12,3] = 0.00
VM[13,12,3] = 0.00
VM[14,12,3] = 0.00
VM[15,12,3] = 0.00
VM[0,13,3] = 0.00
VM[1,13,3] = 0.00
VM[2,13,3] = 0.00
VM[3,13,3] = 0.00
VM[4,13,3] = 0.00
VM[5,13,3] = 0.00
VM[6,13,3] = 0.00
VM[7,13,3] = 0.00
VM[8,13,3] = 0.00
VM[9,13,3] = 0.00
VM[10,13,3] = 0.00
VM[11,13,3] = 0.00
VM[12,13,3] = 0.00
VM[13,13,3] = 0.00
VM[14,13,3] = 0.00
VM[15,13,3] = 0.00
VM[0,14,3] = 0.00
VM[1,14,3] = 0.00
VM[2,14,3] = 0.00
VM[3,14,3] = 0.00
VM[4,14,3] = 0.00
VM[5,14,3] = 0.00
VM[6,14,3] = 0.00
VM[7,14,3] = 0.00
VM[8,14,3] = 0.00
VM[9,14,3] = 0.00
VM[10,14,3] = 0.00
VM[11,14,3] = 0.00
VM[12,14,3] = 0.00
VM[13,14,3] = 0.00
VM[14,14,3] = 0.00
VM[15,14,3] = 0.00
VM[0,15,3] = 0.00
VM[1,15,3] = 0.00
VM[2,15,3] = 0.00
VM[3,15,3] = 0.00
VM[4,15,3] = 0.00
VM[5,15,3] = 0.00
VM[6,15,3] = 0.00
VM[7,15,3] = 0.00
VM[8,15,3] = 0.00
VM[9,15,3] = 0.00
VM[10,15,3] = 0.00
VM[11,15,3] = 0.00
VM[12,15,3] = 0.00
VM[13,15,3] = 0.00
VM[14,15,3] = 0.00
VM[15,15,3] = 0.00
VM[0,0,4] = 0.00
VM[1,0,4] = 0.00
VM[2,0,4] = 0.07
VM[3,0,4] = 0.52
VM[4,0,4] = 0.80
VM[5,0,4] = 0.90
VM[6,0,4] = 0.80
VM[7,0,4] = 0.52
VM[8,0,4] = 0.07
VM[9,0,4] = 0.00
VM[10,0,4] = 0.00
VM[11,0,4] = 0.00
VM[12,0,4] = 0.00
VM[13,0,4] = 0.00
VM[14,0,4] = 0.00
VM[15,0,4] = 0.00
VM[0,1,4] = 0.00
VM[1,1,4] = 0.19
VM[2,1,4] = 0.91
VM[3,1,4] = 1.00
VM[4,1,4] = 1.00
VM[5,1,4] = 1.00
VM[6,1,4] = 1.00
VM[7,1,4] = 1.00
VM[8,1,4] = 0.91
VM[9,1,4] = 0.19
VM[10,1,4] = 0.00
VM[11,1,4] = 0.00
VM[12,1,4] = 0.00
VM[13,1,4] = 0.00
VM[14,1,4] = 0.00
VM[15,1,4] = 0.00
VM[0,2,4] = 0.07
VM[1,2,4] = 0.91
VM[2,2,4] = 1.00
VM[3,2,4] = 1.00
VM[4,2,4] = 1.00
VM[5,2,4] = 1.00
VM[6,2,4] = 1.00
VM[7,2,4] = 1.00
VM[8,2,4] = 1.00
VM[9,2,4] = 0.91
VM[10,2,4] = 0.07
VM[11,2,4] = 0.00
VM[12,2,4] = 0.00
VM[13,2,4] = 0.00
VM[14,2,4] = 0.00
VM[15,2,4] = 0.00
VM[0,3,4] = 0.52
VM[1,3,4] = 1.00
VM[2,3,4] = 1.00
VM[3,3,4] = 1.00
VM[4,3,4] = 1.00
VM[5,3,4] = 1.00
VM[6,3,4] = 1.00
VM[7,3,4] = 1.00
VM[8,3,4] = 1.00
VM[9,3,4] = 1.00
VM[10,3,4] = 0.52
VM[11,3,4] = 0.00
VM[12,3,4] = 0.00
VM[13,3,4] = 0.00
VM[14,3,4] = 0.00
VM[15,3,4] = 0.00
VM[0,4,4] = 0.80
VM[1,4,4] = 1.00
VM[2,4,4] = 1.00
VM[3,4,4] = 1.00
VM[4,4,4] = 1.00
VM[5,4,4] = 1.00
VM[6,4,4] = 1.00
VM[7,4,4] = 1.00
VM[8,4,4] = 1.00
VM[9,4,4] = 1.00
VM[10,4,4] = 0.80
VM[11,4,4] = 0.00
VM[12,4,4] = 0.00
VM[13,4,4] = 0.00
VM[14,4,4] = 0.00
VM[15,4,4] = 0.00
VM[0,5,4] = 0.90
VM[1,5,4] = 1.00
VM[2,5,4] = 1.00
VM[3,5,4] = 1.00
VM[4,5,4] = 1.00
VM[5,5,4] = 1.00
VM[6,5,4] = 1.00
VM[7,5,4] = 1.00
VM[8,5,4] = 1.00
VM[9,5,4] = 1.00
VM[10,5,4] = 0.90
VM[11,5,4] = 0.00
VM[12,5,4] = 0.00
VM[13,5,4] = 0.00
VM[14,5,4] = 0.00
VM[15,5,4] = 0.00
VM[0,6,4] = 0.80
VM[1,6,4] = 1.00
VM[2,6,4] = 1.00
VM[3,6,4] = 1.00
VM[4,6,4] = 1.00
VM[5,6,4] = 1.00
VM[6,6,4] = 1.00
VM[7,6,4] = 1.00
VM[8,6,4] = 1.00
VM[9,6,4] = 1.00
VM[10,6,4] = 0.80
VM[11,6,4] = 0.00
VM[12,6,4] = 0.00
VM[13,6,4] = 0.00
VM[14,6,4] = 0.00
VM[15,6,4] = 0.00
VM[0,7,4] = 0.52
VM[1,7,4] = 1.00
VM[2,7,4] = 1.00
VM[3,7,4] = 1.00
VM[4,7,4] = 1.00
VM[5,7,4] = 1.00
VM[6,7,4] = 1.00
VM[7,7,4] = 1.00
VM[8,7,4] = 1.00
VM[9,7,4] = 1.00
VM[10,7,4] = 0.52
VM[11,7,4] = 0.00
VM[12,7,4] = 0.00
VM[13,7,4] = 0.00
VM[14,7,4] = 0.00
VM[15,7,4] = 0.00
VM[0,8,4] = 0.07
VM[1,8,4] = 0.91
VM[2,8,4] = 1.00
VM[3,8,4] = 1.00
VM[4,8,4] = 1.00
VM[5,8,4] = 1.00
VM[6,8,4] = 1.00
VM[7,8,4] = 1.00
VM[8,8,4] = 1.00
VM[9,8,4] = 0.91
VM[10,8,4] = 0.07
VM[11,8,4] = 0.00
VM[12,8,4] = 0.00
VM[13,8,4] = 0.00
VM[14,8,4] = 0.00
VM[15,8,4] = 0.00
VM[0,9,4] = 0.00
VM[1,9,4] = 0.19
VM[2,9,4] = 0.91
VM[3,9,4] = 1.00
VM[4,9,4] = 1.00
VM[5,9,4] = 1.00
VM[6,9,4] = 1.00
VM[7,9,4] = 1.00
VM[8,9,4] = 0.91
VM[9,9,4] = 0.19
VM[10,9,4] = 0.00
VM[11,9,4] = 0.00
VM[12,9,4] = 0.00
VM[13,9,4] = 0.00
VM[14,9,4] = 0.00
VM[15,9,4] = 0.00
VM[0,10,4] = 0.00
VM[1,10,4] = 0.00
VM[2,10,4] = 0.07
VM[3,10,4] = 0.52
VM[4,10,4] = 0.80
VM[5,10,4] = 0.90
VM[6,10,4] = 0.80
VM[7,10,4] = 0.52
VM[8,10,4] = 0.07
VM[9,10,4] = 0.00
VM[10,10,4] = 0.00
VM[11,10,4] = 0.00
VM[12,10,4] = 0.00
VM[13,10,4] = 0.00
VM[14,10,4] = 0.00
VM[15,10,4] = 0.00
VM[0,11,4] = 0.00
VM[1,11,4] = 0.00
VM[2,11,4] = 0.00
VM[3,11,4] = 0.00
VM[4,11,4] = 0.00
VM[5,11,4] = 0.00
VM[6,11,4] = 0.00
VM[7,11,4] = 0.00
VM[8,11,4] = 0.00
VM[9,11,4] = 0.00
VM[10,11,4] = 0.00
VM[11,11,4] = 0.00
VM[12,11,4] = 0.00
VM[13,11,4] = 0.00
VM[14,11,4] = 0.00
VM[15,11,4] = 0.00
VM[0,12,4] = 0.00
VM[1,12,4] = 0.00
VM[2,12,4] = 0.00
VM[3,12,4] = 0.00
VM[4,12,4] = 0.00
VM[5,12,4] = 0.00
VM[6,12,4] = 0.00
VM[7,12,4] = 0.00
VM[8,12,4] = 0.00
VM[9,12,4] = 0.00
VM[10,12,4] = 0.00
VM[11,12,4] = 0.00
VM[12,12,4] = 0.00
VM[13,12,4] = 0.00
VM[14,12,4] = 0.00
VM[15,12,4] = 0.00
VM[0,13,4] = 0.00
VM[1,13,4] = 0.00
VM[2,13,4] = 0.00
VM[3,13,4] = 0.00
VM[4,13,4] = 0.00
VM[5,13,4] = 0.00
VM[6,13,4] = 0.00
VM[7,13,4] = 0.00
VM[8,13,4] = 0.00
VM[9,13,4] = 0.00
VM[10,13,4] = 0.00
VM[11,13,4] = 0.00
VM[12,13,4] = 0.00
VM[13,13,4] = 0.00
VM[14,13,4] = 0.00
VM[15,13,4] = 0.00
VM[0,14,4] = 0.00
VM[1,14,4] = 0.00
VM[2,14,4] = 0.00
VM[3,14,4] = 0.00
VM[4,14,4] = 0.00
VM[5,14,4] = 0.00
VM[6,14,4] = 0.00
VM[7,14,4] = 0.00
VM[8,14,4] = 0.00
VM[9,14,4] = 0.00
VM[10,14,4] = 0.00
VM[11,14,4] = 0.00
VM[12,14,4] = 0.00
VM[13,14,4] = 0.00
VM[14,14,4] = 0.00
VM[15,14,4] = 0.00
VM[0,15,4] = 0.00
VM[1,15,4] = 0.00
VM[2,15,4] = 0.00
VM[3,15,4] = 0.00
VM[4,15,4] = 0.00
VM[5,15,4] = 0.00
VM[6,15,4] = 0.00
VM[7,15,4] = 0.00
VM[8,15,4] = 0.00
VM[9,15,4] = 0.00
VM[10,15,4] = 0.00
VM[11,15,4] = 0.00
VM[12,15,4] = 0.00
VM[13,15,4] = 0.00
VM[14,15,4] = 0.00
VM[15,15,4] = 0.00
VM[0,0,5] = 0.00
VM[1,0,5] = 0.00
VM[2,0,5] = 0.14
VM[3,0,5] = 0.60
VM[4,0,5] = 0.90
VM[5,0,5] = 1.00
VM[6,0,5] = 0.90
VM[7,0,5] = 0.60
VM[8,0,5] = 0.14
VM[9,0,5] = 0.00
VM[10,0,5] = 0.00
VM[11,0,5] = 0.00
VM[12,0,5] = 0.00
VM[13,0,5] = 0.00
VM[14,0,5] = 0.00
VM[15,0,5] = 0.00
VM[0,1,5] = 0.00
VM[1,1,5] = 0.24
VM[2,1,5] = 0.96
VM[3,1,5] = 1.00
VM[4,1,5] = 1.00
VM[5,1,5] = 1.00
VM[6,1,5] = 1.00
VM[7,1,5] = 1.00
VM[8,1,5] = 0.96
VM[9,1,5] = 0.24
VM[10,1,5] = 0.00
VM[11,1,5] = 0.00
VM[12,1,5] = 0.00
VM[13,1,5] = 0.00
VM[14,1,5] = 0.00
VM[15,1,5] = 0.00
VM[0,2,5] = 0.14
VM[1,2,5] = 0.96
VM[2,2,5] = 1.00
VM[3,2,5] = 1.00
VM[4,2,5] = 1.00
VM[5,2,5] = 1.00
VM[6,2,5] = 1.00
VM[7,2,5] = 1.00
VM[8,2,5] = 1.00
VM[9,2,5] = 0.96
VM[10,2,5] = 0.14
VM[11,2,5] = 0.00
VM[12,2,5] = 0.00
VM[13,2,5] = 0.00
VM[14,2,5] = 0.00
VM[15,2,5] = 0.00
VM[0,3,5] = 0.60
VM[1,3,5] = 1.00
VM[2,3,5] = 1.00
VM[3,3,5] = 1.00
VM[4,3,5] = 1.00
VM[5,3,5] = 1.00
VM[6,3,5] = 1.00
VM[7,3,5] = 1.00
VM[8,3,5] = 1.00
VM[9,3,5] = 1.00
VM[10,3,5] = 0.60
VM[11,3,5] = 0.00
VM[12,3,5] = 0.00
VM[13,3,5] = 0.00
VM[14,3,5] = 0.00
VM[15,3,5] = 0.00
VM[0,4,5] = 0.90
VM[1,4,5] = 1.00
VM[2,4,5] = 1.00
VM[3,4,5] = 1.00
VM[4,4,5] = 1.00
VM[5,4,5] = 1.00
VM[6,4,5] = 1.00
VM[7,4,5] = 1.00
VM[8,4,5] = 1.00
VM[9,4,5] = 1.00
VM[10,4,5] = 0.90
VM[11,4,5] = 0.00
VM[12,4,5] = 0.00
VM[13,4,5] = 0.00
VM[14,4,5] = 0.00
VM[15,4,5] = 0.00
VM[0,5,5] = 1.00
VM[1,5,5] = 1.00
VM[2,5,5] = 1.00
VM[3,5,5] = 1.00
VM[4,5,5] = 1.00
VM[5,5,5] = 1.00
VM[6,5,5] = 1.00
VM[7,5,5] = 1.00
VM[8,5,5] = 1.00
VM[9,5,5] = 1.00
VM[10,5,5] = 1.00
VM[11,5,5] = 0.00
VM[12,5,5] = 0.00
VM[13,5,5] = 0.00
VM[14,5,5] = 0.00
VM[15,5,5] = 0.00
VM[0,6,5] = 0.90
VM[1,6,5] = 1.00
VM[2,6,5] = 1.00
VM[3,6,5] = 1.00
VM[4,6,5] = 1.00
VM[5,6,5] = 1.00
VM[6,6,5] = 1.00
VM[7,6,5] = 1.00
VM[8,6,5] = 1.00
VM[9,6,5] = 1.00
VM[10,6,5] = 0.90
VM[11,6,5] = 0.00
VM[12,6,5] = 0.00
VM[13,6,5] = 0.00
VM[14,6,5] = 0.00
VM[15,6,5] = 0.00
VM[0,7,5] = 0.60
VM[1,7,5] = 1.00
VM[2,7,5] = 1.00
VM[3,7,5] = 1.00
VM[4,7,5] = 1.00
VM[5,7,5] = 1.00
VM[6,7,5] = 1.00
VM[7,7,5] = 1.00
VM[8,7,5] = 1.00
VM[9,7,5] = 0.93
VM[10,7,5] = 0.46
VM[11,7,5] = 0.00
VM[12,7,5] = 0.00
VM[13,7,5] = 0.00
VM[14,7,5] = 0.00
VM[15,7,5] = 0.00
VM[0,8,5] = 0.14
VM[1,8,5] = 0.96
VM[2,8,5] = 1.00
VM[3,8,5] = 1.00
VM[4,8,5] = 1.00
VM[5,8,5] = 1.00
VM[6,8,5] = 1.00
VM[7,8,5] = 1.00
VM[8,8,5] = 0.79
VM[9,8,5] = 0.44
VM[10,8,5] = 0.00
VM[11,8,5] = 0.00
VM[12,8,5] = 0.00
VM[13,8,5] = 0.00
VM[14,8,5] = 0.00
VM[15,8,5] = 0.00
VM[0,9,5] = 0.00
VM[1,9,5] = 0.24
VM[2,9,5] = 0.96
VM[3,9,5] = 1.00
VM[4,9,5] = 1.00
VM[5,9,5] = 1.00
VM[6,9,5] = 1.00
VM[7,9,5] = 0.93
VM[8,9,5] = 0.44
VM[9,9,5] = 0.00
VM[10,9,5] = 0.00
VM[11,9,5] = 0.00
VM[12,9,5] = 0.00
VM[13,9,5] = 0.00
VM[14,9,5] = 0.00
VM[15,9,5] = 0.00
VM[0,10,5] = 0.00
VM[1,10,5] = 0.00
VM[2,10,5] = 0.14
VM[3,10,5] = 0.60
VM[4,10,5] = 0.90
VM[5,10,5] = 1.00
VM[6,10,5] = 0.90
VM[7,10,5] = 0.46
VM[8,10,5] = 0.00
VM[9,10,5] = 0.00
VM[10,10,5] = 0.00
VM[11,10,5] = 0.00
VM[12,10,5] = 0.00
VM[13,10,5] = 0.00
VM[14,10,5] = 0.00
VM[15,10,5] = 0.00
VM[0,11,5] = 0.00
VM[1,11,5] = 0.00
VM[2,11,5] = 0.00
VM[3,11,5] = 0.00
VM[4,11,5] = 0.00
VM[5,11,5] = 0.00
VM[6,11,5] = 0.00
VM[7,11,5] = 0.00
VM[8,11,5] = 0.00
VM[9,11,5] = 0.00
VM[10,11,5] = 0.00
VM[11,11,5] = 0.00
VM[12,11,5] = 0.00
VM[13,11,5] = 0.00
VM[14,11,5] = 0.00
VM[15,11,5] = 0.00
VM[0,12,5] = 0.00
VM[1,12,5] = 0.00
VM[2,12,5] = 0.00
VM[3,12,5] = 0.00
VM[4,12,5] = 0.00
VM[5,12,5] = 0.00
VM[6,12,5] = 0.00
VM[7,12,5] = 0.00
VM[8,12,5] = 0.00
VM[9,12,5] = 0.00
VM[10,12,5] = 0.00
VM[11,12,5] = 0.00
VM[12,12,5] = 0.00
VM[13,12,5] = 0.00
VM[14,12,5] = 0.00
VM[15,12,5] = 0.00
VM[0,13,5] = 0.00
VM[1,13,5] = 0.00
VM[2,13,5] = 0.00
VM[3,13,5] = 0.00
VM[4,13,5] = 0.00
VM[5,13,5] = 0.00
VM[6,13,5] = 0.00
VM[7,13,5] = 0.00
VM[8,13,5] = 0.00
VM[9,13,5] = 0.00
VM[10,13,5] = 0.00
VM[11,13,5] = 0.00
VM[12,13,5] = 0.00
VM[13,13,5] = 0.00
VM[14,13,5] = 0.00
VM[15,13,5] = 0.00
VM[0,14,5] = 0.00
VM[1,14,5] = 0.00
VM[2,14,5] = 0.00
VM[3,14,5] = 0.00
VM[4,14,5] = 0.00
VM[5,14,5] = 0.00
VM[6,14,5] = 0.00
VM[7,14,5] = 0.00
VM[8,14,5] = 0.00
VM[9,14,5] = 0.00
VM[10,14,5] = 0.00
VM[11,14,5] = 0.00
VM[12,14,5] = 0.00
VM[13,14,5] = 0.00
VM[14,14,5] = 0.00
VM[15,14,5] = 0.00
VM[0,15,5] = 0.00
VM[1,15,5] = 0.00
VM[2,15,5] = 0.00
VM[3,15,5] = 0.00
VM[4,15,5] = 0.00
VM[5,15,5] = 0.00
VM[6,15,5] = 0.00
VM[7,15,5] = 0.00
VM[8,15,5] = 0.00
VM[9,15,5] = 0.00
VM[10,15,5] = 0.00
VM[11,15,5] = 0.00
VM[12,15,5] = 0.00
VM[13,15,5] = 0.00
VM[14,15,5] = 0.00
VM[15,15,5] = 0.00
VM[0,0,6] = 0.00
VM[1,0,6] = 0.00
VM[2,0,6] = 0.07
VM[3,0,6] = 0.52
VM[4,0,6] = 0.80
VM[5,0,6] = 0.90
VM[6,0,6] = 0.80
VM[7,0,6] = 0.52
VM[8,0,6] = 0.07
VM[9,0,6] = 0.00
VM[10,0,6] = 0.00
VM[11,0,6] = 0.00
VM[12,0,6] = 0.00
VM[13,0,6] = 0.00
VM[14,0,6] = 0.00
VM[15,0,6] = 0.00
VM[0,1,6] = 0.00
VM[1,1,6] = 0.19
VM[2,1,6] = 0.91
VM[3,1,6] = 1.00
VM[4,1,6] = 1.00
VM[5,1,6] = 1.00
VM[6,1,6] = 1.00
VM[7,1,6] = 1.00
VM[8,1,6] = 0.91
VM[9,1,6] = 0.19
VM[10,1,6] = 0.00
VM[11,1,6] = 0.00
VM[12,1,6] = 0.00
VM[13,1,6] = 0.00
VM[14,1,6] = 0.00
VM[15,1,6] = 0.00
VM[0,2,6] = 0.07
VM[1,2,6] = 0.91
VM[2,2,6] = 1.00
VM[3,2,6] = 1.00
VM[4,2,6] = 1.00
VM[5,2,6] = 1.00
VM[6,2,6] = 1.00
VM[7,2,6] = 1.00
VM[8,2,6] = 1.00
VM[9,2,6] = 0.91
VM[10,2,6] = 0.07
VM[11,2,6] = 0.00
VM[12,2,6] = 0.00
VM[13,2,6] = 0.00
VM[14,2,6] = 0.00
VM[15,2,6] = 0.00
VM[0,3,6] = 0.52
VM[1,3,6] = 1.00
VM[2,3,6] = 1.00
VM[3,3,6] = 1.00
VM[4,3,6] = 1.00
VM[5,3,6] = 1.00
VM[6,3,6] = 1.00
VM[7,3,6] = 1.00
VM[8,3,6] = 1.00
VM[9,3,6] = 1.00
VM[10,3,6] = 0.52
VM[11,3,6] = 0.00
VM[12,3,6] = 0.00
VM[13,3,6] = 0.00
VM[14,3,6] = 0.00
VM[15,3,6] = 0.00
VM[0,4,6] = 0.80
VM[1,4,6] = 1.00
VM[2,4,6] = 1.00
VM[3,4,6] = 1.00
VM[4,4,6] = 1.00
VM[5,4,6] = 1.00
VM[6,4,6] = 1.00
VM[7,4,6] = 1.00
VM[8,4,6] = 1.00
VM[9,4,6] = 1.00
VM[10,4,6] = 0.80
VM[11,4,6] = 0.00
VM[12,4,6] = 0.00
VM[13,4,6] = 0.00
VM[14,4,6] = 0.00
VM[15,4,6] = 0.00
VM[0,5,6] = 0.90
VM[1,5,6] = 1.00
VM[2,5,6] = 1.00
VM[3,5,6] = 1.00
VM[4,5,6] = 1.00
VM[5,5,6] = 1.00
VM[6,5,6] = 1.00
VM[7,5,6] = 1.00
VM[8,5,6] = 1.00
VM[9,5,6] = 1.00
VM[10,5,6] = 0.90
VM[11,5,6] = 0.00
VM[12,5,6] = 0.00
VM[13,5,6] = 0.00
VM[14,5,6] = 0.00
VM[15,5,6] = 0.00
VM[0,6,6] = 0.80
VM[1,6,6] = 1.00
VM[2,6,6] = 1.00
VM[3,6,6] = 1.00
VM[4,6,6] = 1.00
VM[5,6,6] = 1.00
VM[6,6,6] = 1.00
VM[7,6,6] = 1.00
VM[8,6,6] = 0.96
VM[9,6,6] = 0.81
VM[10,6,6] = 0.56
VM[11,6,6] = 0.00
VM[12,6,6] = 0.00
VM[13,6,6] = 0.00
VM[14,6,6] = 0.00
VM[15,6,6] = 0.00
VM[0,7,6] = 0.52
VM[1,7,6] = 1.00
VM[2,7,6] = 1.00
VM[3,7,6] = 1.00
VM[4,7,6] = 1.00
VM[5,7,6] = 1.00
VM[6,7,6] = 1.00
VM[7,7,6] = 0.86
VM[8,7,6] = 0.38
VM[9,7,6] = 0.09
VM[10,7,6] = 0.00
VM[11,7,6] = 0.00
VM[12,7,6] = 0.00
VM[13,7,6] = 0.00
VM[14,7,6] = 0.00
VM[15,7,6] = 0.00
VM[0,8,6] = 0.07
VM[1,8,6] = 0.91
VM[2,8,6] = 1.00
VM[3,8,6] = 1.00
VM[4,8,6] = 1.00
VM[5,8,6] = 1.00
VM[6,8,6] = 0.96
VM[7,8,6] = 0.38
VM[8,8,6] = 0.01
VM[9,8,6] = 0.00
VM[10,8,6] = 0.00
VM[11,8,6] = 0.00
VM[12,8,6] = 0.00
VM[13,8,6] = 0.00
VM[14,8,6] = 0.00
VM[15,8,6] = 0.00
VM[0,9,6] = 0.00
VM[1,9,6] = 0.19
VM[2,9,6] = 0.91
VM[3,9,6] = 1.00
VM[4,9,6] = 1.00
VM[5,9,6] = 1.00
VM[6,9,6] = 0.81
VM[7,9,6] = 0.09
VM[8,9,6] = 0.00
VM[9,9,6] = 0.00
VM[10,9,6] = 0.00
VM[11,9,6] = 0.00
VM[12,9,6] = 0.00
VM[13,9,6] = 0.00
VM[14,9,6] = 0.00
VM[15,9,6] = 0.00
VM[0,10,6] = 0.00
VM[1,10,6] = 0.00
VM[2,10,6] = 0.07
VM[3,10,6] = 0.52
VM[4,10,6] = 0.80
VM[5,10,6] = 0.90
VM[6,10,6] = 0.56
VM[7,10,6] = 0.00
VM[8,10,6] = 0.00
VM[9,10,6] = 0.00
VM[10,10,6] = 0.00
VM[11,10,6] = 0.00
VM[12,10,6] = 0.00
VM[13,10,6] = 0.00
VM[14,10,6] = 0.00
VM[15,10,6] = 0.00
VM[0,11,6] = 0.00
VM[1,11,6] = 0.00
VM[2,11,6] = 0.00
VM[3,11,6] = 0.00
VM[4,11,6] = 0.00
VM[5,11,6] = 0.00
VM[6,11,6] = 0.00
VM[7,11,6] = 0.00
VM[8,11,6] = 0.00
VM[9,11,6] = 0.00
VM[10,11,6] = 0.00
VM[11,11,6] = 0.00
VM[12,11,6] = 0.00
VM[13,11,6] = 0.00
VM[14,11,6] = 0.00
VM[15,11,6] = 0.00
VM[0,12,6] = 0.00
VM[1,12,6] = 0.00
VM[2,12,6] = 0.00
VM[3,12,6] = 0.00
VM[4,12,6] = 0.00
VM[5,12,6] = 0.00
VM[6,12,6] = 0.00
VM[7,12,6] = 0.00
VM[8,12,6] = 0.00
VM[9,12,6] = 0.00
VM[10,12,6] = 0.00
VM[11,12,6] = 0.00
VM[12,12,6] = 0.00
VM[13,12,6] = 0.00
VM[14,12,6] = 0.00
VM[15,12,6] = 0.00
VM[0,13,6] = 0.00
VM[1,13,6] = 0.00
VM[2,13,6] = 0.00
VM[3,13,6] = 0.00
VM[4,13,6] = 0.00
VM[5,13,6] = 0.00
VM[6,13,6] = 0.00
VM[7,13,6] = 0.00
VM[8,13,6] = 0.00
VM[9,13,6] = 0.00
VM[10,13,6] = 0.00
VM[11,13,6] = 0.00
VM[12,13,6] = 0.00
VM[13,13,6] = 0.00
VM[14,13,6] = 0.00
VM[15,13,6] = 0.00
VM[0,14,6] = 0.00
VM[1,14,6] = 0.00
VM[2,14,6] = 0.00
VM[3,14,6] = 0.00
VM[4,14,6] = 0.00
VM[5,14,6] = 0.00
VM[6,14,6] = 0.00
VM[7,14,6] = 0.00
VM[8,14,6] = 0.00
VM[9,14,6] = 0.00
VM[10,14,6] = 0.00
VM[11,14,6] = 0.00
VM[12,14,6] = 0.00
VM[13,14,6] = 0.00
VM[14,14,6] = 0.00
VM[15,14,6] = 0.00
VM[0,15,6] = 0.00
VM[1,15,6] = 0.00
VM[2,15,6] = 0.00
VM[3,15,6] = 0.00
VM[4,15,6] = 0.00
VM[5,15,6] = 0.00
VM[6,15,6] = 0.00
VM[7,15,6] = 0.00
VM[8,15,6] = 0.00
VM[9,15,6] = 0.00
VM[10,15,6] = 0.00
VM[11,15,6] = 0.00
VM[12,15,6] = 0.00
VM[13,15,6] = 0.00
VM[14,15,6] = 0.00
VM[15,15,6] = 0.00
VM[0,0,7] = 0.00
VM[1,0,7] = 0.00
VM[2,0,7] = 0.00
VM[3,0,7] = 0.21
VM[4,0,7] = 0.52
VM[5,0,7] = 0.60
VM[6,0,7] = 0.52
VM[7,0,7] = 0.21
VM[8,0,7] = 0.00
VM[9,0,7] = 0.00
VM[10,0,7] = 0.00
VM[11,0,7] = 0.00
VM[12,0,7] = 0.00
VM[13,0,7] = 0.00
VM[14,0,7] = 0.00
VM[15,0,7] = 0.00
VM[0,1,7] = 0.00
VM[1,1,7] = 0.04
VM[2,1,7] = 0.62
VM[3,1,7] = 0.99
VM[4,1,7] = 1.00
VM[5,1,7] = 1.00
VM[6,1,7] = 1.00
VM[7,1,7] = 0.99
VM[8,1,7] = 0.62
VM[9,1,7] = 0.04
VM[10,1,7] = 0.00
VM[11,1,7] = 0.00
VM[12,1,7] = 0.00
VM[13,1,7] = 0.00
VM[14,1,7] = 0.00
VM[15,1,7] = 0.00
VM[0,2,7] = 0.00
VM[1,2,7] = 0.62
VM[2,2,7] = 1.00
VM[3,2,7] = 1.00
VM[4,2,7] = 1.00
VM[5,2,7] = 1.00
VM[6,2,7] = 1.00
VM[7,2,7] = 1.00
VM[8,2,7] = 1.00
VM[9,2,7] = 0.62
VM[10,2,7] = 0.00
VM[11,2,7] = 0.00
VM[12,2,7] = 0.00
VM[13,2,7] = 0.00
VM[14,2,7] = 0.00
VM[15,2,7] = 0.00
VM[0,3,7] = 0.21
VM[1,3,7] = 0.99
VM[2,3,7] = 1.00
VM[3,3,7] = 1.00
VM[4,3,7] = 1.00
VM[5,3,7] = 1.00
VM[6,3,7] = 1.00
VM[7,3,7] = 1.00
VM[8,3,7] = 1.00
VM[9,3,7] = 0.99
VM[10,3,7] = 0.21
VM[11,3,7] = 0.00
VM[12,3,7] = 0.00
VM[13,3,7] = 0.00
VM[14,3,7] = 0.00
VM[15,3,7] = 0.00
VM[0,4,7] = 0.52
VM[1,4,7] = 1.00
VM[2,4,7] = 1.00
VM[3,4,7] = 1.00
VM[4,4,7] = 1.00
VM[5,4,7] = 1.00
VM[6,4,7] = 1.00
VM[7,4,7] = 1.00
VM[8,4,7] = 1.00
VM[9,4,7] = 1.00
VM[10,4,7] = 0.52
VM[11,4,7] = 0.00
VM[12,4,7] = 0.00
VM[13,4,7] = 0.00
VM[14,4,7] = 0.00
VM[15,4,7] = 0.00
VM[0,5,7] = 0.60
VM[1,5,7] = 1.00
VM[2,5,7] = 1.00
VM[3,5,7] = 1.00
VM[4,5,7] = 1.00
VM[5,5,7] = 1.00
VM[6,5,7] = 1.00
VM[7,5,7] = 1.00
VM[8,5,7] = 1.00
VM[9,5,7] = 0.93
VM[10,5,7] = 0.46
VM[11,5,7] = 0.00
VM[12,5,7] = 0.00
VM[13,5,7] = 0.00
VM[14,5,7] = 0.00
VM[15,5,7] = 0.00
VM[0,6,7] = 0.52
VM[1,6,7] = 1.00
VM[2,6,7] = 1.00
VM[3,6,7] = 1.00
VM[4,6,7] = 1.00
VM[5,6,7] = 1.00
VM[6,6,7] = 1.00
VM[7,6,7] = 0.86
VM[8,6,7] = 0.38
VM[9,6,7] = 0.09
VM[10,6,7] = 0.00
VM[11,6,7] = 0.00
VM[12,6,7] = 0.00
VM[13,6,7] = 0.00
VM[14,6,7] = 0.00
VM[15,6,7] = 0.00
VM[0,7,7] = 0.21
VM[1,7,7] = 0.99
VM[2,7,7] = 1.00
VM[3,7,7] = 1.00
VM[4,7,7] = 1.00
VM[5,7,7] = 1.00
VM[6,7,7] = 0.86
VM[7,7,7] = 0.16
VM[8,7,7] = 0.00
VM[9,7,7] = 0.00
VM[10,7,7] = 0.00
VM[11,7,7] = 0.00
VM[12,7,7] = 0.00
VM[13,7,7] = 0.00
VM[14,7,7] = 0.00
VM[15,7,7] = 0.00
VM[0,8,7] = 0.00
VM[1,8,7] = 0.62
VM[2,8,7] = 1.00
VM[3,8,7] = 1.00
VM[4,8,7] = 1.00
VM[5,8,7] = 1.00
VM[6,8,7] = 0.38
VM[7,8,7] = 0.00
VM[8,8,7] = 0.00
VM[9,8,7] = 0.00
VM[10,8,7] = 0.00
VM[11,8,7] = 0.00
VM[12,8,7] = 0.00
VM[13,8,7] = 0.00
VM[14,8,7] = 0.00
VM[15,8,7] = 0.00
VM[0,9,7] = 0.00
VM[1,9,7] = 0.04
VM[2,9,7] = 0.62
VM[3,9,7] = 0.99
VM[4,9,7] = 1.00
VM[5,9,7] = 0.93
VM[6,9,7] = 0.09
VM[7,9,7] = 0.00
VM[8,9,7] = 0.00
VM[9,9,7] = 0.00
VM[10,9,7] = 0.00
VM[11,9,7] = 0.00
VM[12,9,7] = 0.00
VM[13,9,7] = 0.00
VM[14,9,7] = 0.00
VM[15,9,7] = 0.00
VM[0,10,7] = 0.00
VM[1,10,7] = 0.00
VM[2,10,7] = 0.00
VM[3,10,7] = 0.21
VM[4,10,7] = 0.52
VM[5,10,7] = 0.46
VM[6,10,7] = 0.00
VM[7,10,7] = 0.00
VM[8,10,7] = 0.00
VM[9,10,7] = 0.00
VM[10,10,7] = 0.00
VM[11,10,7] = 0.00
VM[12,10,7] = 0.00
VM[13,10,7] = 0.00
VM[14,10,7] = 0.00
VM[15,10,7] = 0.00
VM[0,11,7] = 0.00
VM[1,11,7] = 0.00
VM[2,11,7] = 0.00
VM[3,11,7] = 0.00
VM[4,11,7] = 0.00
VM[5,11,7] = 0.00
VM[6,11,7] = 0.00
VM[7,11,7] = 0.00
VM[8,11,7] = 0.00
VM[9,11,7] = 0.00
VM[10,11,7] = 0.00
VM[11,11,7] = 0.00
VM[12,11,7] = 0.00
VM[13,11,7] = 0.00
VM[14,11,7] = 0.00
VM[15,11,7] = 0.00
VM[0,12,7] = 0.00
VM[1,12,7] = 0.00
VM[2,12,7] = 0.00
VM[3,12,7] = 0.00
VM[4,12,7] = 0.00
VM[5,12,7] = 0.00
VM[6,12,7] = 0.00
VM[7,12,7] = 0.00
VM[8,12,7] = 0.00
VM[9,12,7] = 0.00
VM[10,12,7] = 0.00
VM[11,12,7] = 0.00
VM[12,12,7] = 0.00
VM[13,12,7] = 0.00
VM[14,12,7] = 0.00
VM[15,12,7] = 0.00
VM[0,13,7] = 0.00
VM[1,13,7] = 0.00
VM[2,13,7] = 0.00
VM[3,13,7] = 0.00
VM[4,13,7] = 0.00
VM[5,13,7] = 0.00
VM[6,13,7] = 0.00
VM[7,13,7] = 0.00
VM[8,13,7] = 0.00
VM[9,13,7] = 0.00
VM[10,13,7] = 0.00
VM[11,13,7] = 0.00
VM[12,13,7] = 0.00
VM[13,13,7] = 0.00
VM[14,13,7] = 0.00
VM[15,13,7] = 0.00
VM[0,14,7] = 0.00
VM[1,14,7] = 0.00
VM[2,14,7] = 0.00
VM[3,14,7] = 0.00
VM[4,14,7] = 0.00
VM[5,14,7] = 0.00
VM[6,14,7] = 0.00
VM[7,14,7] = 0.00
VM[8,14,7] = 0.00
VM[9,14,7] = 0.00
VM[10,14,7] = 0.00
VM[11,14,7] = 0.00
VM[12,14,7] = 0.00
VM[13,14,7] = 0.00
VM[14,14,7] = 0.00
VM[15,14,7] = 0.00
VM[0,15,7] = 0.00
VM[1,15,7] = 0.00
VM[2,15,7] = 0.00
VM[3,15,7] = 0.00
VM[4,15,7] = 0.00
VM[5,15,7] = 0.00
VM[6,15,7] = 0.00
VM[7,15,7] = 0.00
VM[8,15,7] = 0.00
VM[9,15,7] = 0.00
VM[10,15,7] = 0.00
VM[11,15,7] = 0.00
VM[12,15,7] = 0.00
VM[13,15,7] = 0.00
VM[14,15,7] = 0.00
VM[15,15,7] = 0.00
VM[0,0,8] = 0.00
VM[1,0,8] = 0.00
VM[2,0,8] = 0.00
VM[3,0,8] = 0.00
VM[4,0,8] = 0.07
VM[5,0,8] = 0.14
VM[6,0,8] = 0.07
VM[7,0,8] = 0.00
VM[8,0,8] = 0.00
VM[9,0,8] = 0.00
VM[10,0,8] = 0.00
VM[11,0,8] = 0.00
VM[12,0,8] = 0.00
VM[13,0,8] = 0.00
VM[14,0,8] = 0.00
VM[15,0,8] = 0.00
VM[0,1,8] = 0.00
VM[1,1,8] = 0.00
VM[2,1,8] = 0.14
VM[3,1,8] = 0.62
VM[4,1,8] = 0.91
VM[5,1,8] = 0.96
VM[6,1,8] = 0.91
VM[7,1,8] = 0.62
VM[8,1,8] = 0.14
VM[9,1,8] = 0.00
VM[10,1,8] = 0.00
VM[11,1,8] = 0.00
VM[12,1,8] = 0.00
VM[13,1,8] = 0.00
VM[14,1,8] = 0.00
VM[15,1,8] = 0.00
VM[0,2,8] = 0.00
VM[1,2,8] = 0.14
VM[2,2,8] = 0.84
VM[3,2,8] = 1.00
VM[4,2,8] = 1.00
VM[5,2,8] = 1.00
VM[6,2,8] = 1.00
VM[7,2,8] = 1.00
VM[8,2,8] = 0.84
VM[9,2,8] = 0.14
VM[10,2,8] = 0.00
VM[11,2,8] = 0.00
VM[12,2,8] = 0.00
VM[13,2,8] = 0.00
VM[14,2,8] = 0.00
VM[15,2,8] = 0.00
VM[0,3,8] = 0.00
VM[1,3,8] = 0.62
VM[2,3,8] = 1.00
VM[3,3,8] = 1.00
VM[4,3,8] = 1.00
VM[5,3,8] = 1.00
VM[6,3,8] = 1.00
VM[7,3,8] = 1.00
VM[8,3,8] = 1.00
VM[9,3,8] = 0.62
VM[10,3,8] = 0.00
VM[11,3,8] = 0.00
VM[12,3,8] = 0.00
VM[13,3,8] = 0.00
VM[14,3,8] = 0.00
VM[15,3,8] = 0.00
VM[0,4,8] = 0.07
VM[1,4,8] = 0.91
VM[2,4,8] = 1.00
VM[3,4,8] = 1.00
VM[4,4,8] = 1.00
VM[5,4,8] = 1.00
VM[6,4,8] = 1.00
VM[7,4,8] = 1.00
VM[8,4,8] = 1.00
VM[9,4,8] = 0.91
VM[10,4,8] = 0.07
VM[11,4,8] = 0.00
VM[12,4,8] = 0.00
VM[13,4,8] = 0.00
VM[14,4,8] = 0.00
VM[15,4,8] = 0.00
VM[0,5,8] = 0.14
VM[1,5,8] = 0.96
VM[2,5,8] = 1.00
VM[3,5,8] = 1.00
VM[4,5,8] = 1.00
VM[5,5,8] = 1.00
VM[6,5,8] = 1.00
VM[7,5,8] = 1.00
VM[8,5,8] = 0.79
VM[9,5,8] = 0.44
VM[10,5,8] = 0.00
VM[11,5,8] = 0.00
VM[12,5,8] = 0.00
VM[13,5,8] = 0.00
VM[14,5,8] = 0.00
VM[15,5,8] = 0.00
VM[0,6,8] = 0.07
VM[1,6,8] = 0.91
VM[2,6,8] = 1.00
VM[3,6,8] = 1.00
VM[4,6,8] = 1.00
VM[5,6,8] = 1.00
VM[6,6,8] = 0.96
VM[7,6,8] = 0.38
VM[8,6,8] = 0.01
VM[9,6,8] = 0.00
VM[10,6,8] = 0.00
VM[11,6,8] = 0.00
VM[12,6,8] = 0.00
VM[13,6,8] = 0.00
VM[14,6,8] = 0.00
VM[15,6,8] = 0.00
VM[0,7,8] = 0.00
VM[1,7,8] = 0.62
VM[2,7,8] = 1.00
VM[3,7,8] = 1.00
VM[4,7,8] = 1.00
VM[5,7,8] = 1.00
VM[6,7,8] = 0.38
VM[7,7,8] = 0.00
VM[8,7,8] = 0.00
VM[9,7,8] = 0.00
VM[10,7,8] = 0.00
VM[11,7,8] = 0.00
VM[12,7,8] = 0.00
VM[13,7,8] = 0.00
VM[14,7,8] = 0.00
VM[15,7,8] = 0.00
VM[0,8,8] = 0.00
VM[1,8,8] = 0.14
VM[2,8,8] = 0.84
VM[3,8,8] = 1.00
VM[4,8,8] = 1.00
VM[5,8,8] = 0.79
VM[6,8,8] = 0.01
VM[7,8,8] = 0.00
VM[8,8,8] = 0.00
VM[9,8,8] = 0.00
VM[10,8,8] = 0.00
VM[11,8,8] = 0.00
VM[12,8,8] = 0.00
VM[13,8,8] = 0.00
VM[14,8,8] = 0.00
VM[15,8,8] = 0.00
VM[0,9,8] = 0.00
VM[1,9,8] = 0.00
VM[2,9,8] = 0.14
VM[3,9,8] = 0.62
VM[4,9,8] = 0.91
VM[5,9,8] = 0.44
VM[6,9,8] = 0.00
VM[7,9,8] = 0.00
VM[8,9,8] = 0.00
VM[9,9,8] = 0.00
VM[10,9,8] = 0.00
VM[11,9,8] = 0.00
VM[12,9,8] = 0.00
VM[13,9,8] = 0.00
VM[14,9,8] = 0.00
VM[15,9,8] = 0.00
VM[0,10,8] = 0.00
VM[1,10,8] = 0.00
VM[2,10,8] = 0.00
VM[3,10,8] = 0.00
VM[4,10,8] = 0.07
VM[5,10,8] = 0.00
VM[6,10,8] = 0.00
VM[7,10,8] = 0.00
VM[8,10,8] = 0.00
VM[9,10,8] = 0.00
VM[10,10,8] = 0.00
VM[11,10,8] = 0.00
VM[12,10,8] = 0.00
VM[13,10,8] = 0.00
VM[14,10,8] = 0.00
VM[15,10,8] = 0.00
VM[0,11,8] = 0.00
VM[1,11,8] = 0.00
VM[2,11,8] = 0.00
VM[3,11,8] = 0.00
VM[4,11,8] = 0.00
VM[5,11,8] = 0.00
VM[6,11,8] = 0.00
VM[7,11,8] = 0.00
VM[8,11,8] = 0.00
VM[9,11,8] = 0.00
VM[10,11,8] = 0.00
VM[11,11,8] = 0.00
VM[12,11,8] = 0.00
VM[13,11,8] = 0.00
VM[14,11,8] = 0.00
VM[15,11,8] = 0.00
VM[0,12,8] = 0.00
VM[1,12,8] = 0.00
VM[2,12,8] = 0.00
VM[3,12,8] = 0.00
VM[4,12,8] = 0.00
VM[5,12,8] = 0.00
VM[6,12,8] = 0.00
VM[7,12,8] = 0.00
VM[8,12,8] = 0.00
VM[9,12,8] = 0.00
VM[10,12,8] = 0.00
VM[11,12,8] = 0.00
VM[12,12,8] = 0.00
VM[13,12,8] = 0.00
VM[14,12,8] = 0.00
VM[15,12,8] = 0.00
VM[0,13,8] = 0.00
VM[1,13,8] = 0.00
VM[2,13,8] = 0.00
VM[3,13,8] = 0.00
VM[4,13,8] = 0.00
VM[5,13,8] = 0.00
VM[6,13,8] = 0.00
VM[7,13,8] = 0.00
VM[8,13,8] = 0.00
VM[9,13,8] = 0.00
VM[10,13,8] = 0.00
VM[11,13,8] = 0.00
VM[12,13,8] = 0.00
VM[13,13,8] = 0.00
VM[14,13,8] = 0.00
VM[15,13,8] = 0.00
VM[0,14,8] = 0.00
VM[1,14,8] = 0.00
VM[2,14,8] = 0.00
VM[3,14,8] = 0.00
VM[4,14,8] = 0.00
VM[5,14,8] = 0.00
VM[6,14,8] = 0.00
VM[7,14,8] = 0.00
VM[8,14,8] = 0.00
VM[9,14,8] = 0.00
VM[10,14,8] = 0.00
VM[11,14,8] = 0.00
VM[12,14,8] = 0.00
VM[13,14,8] = 0.00
VM[14,14,8] = 0.00
VM[15,14,8] = 0.00
VM[0,15,8] = 0.00
VM[1,15,8] = 0.00
VM[2,15,8] = 0.00
VM[3,15,8] = 0.00
VM[4,15,8] = 0.00
VM[5,15,8] = 0.00
VM[6,15,8] = 0.00
VM[7,15,8] = 0.00
VM[8,15,8] = 0.00
VM[9,15,8] = 0.00
VM[10,15,8] = 0.00
VM[11,15,8] = 0.00
VM[12,15,8] = 0.00
VM[13,15,8] = 0.00
VM[14,15,8] = 0.00
VM[15,15,8] = 0.00
VM[0,0,9] = 0.00
VM[1,0,9] = 0.00
VM[2,0,9] = 0.00
VM[3,0,9] = 0.00
VM[4,0,9] = 0.00
VM[5,0,9] = 0.00
VM[6,0,9] = 0.00
VM[7,0,9] = 0.00
VM[8,0,9] = 0.00
VM[9,0,9] = 0.00
VM[10,0,9] = 0.00
VM[11,0,9] = 0.00
VM[12,0,9] = 0.00
VM[13,0,9] = 0.00
VM[14,0,9] = 0.00
VM[15,0,9] = 0.00
VM[0,1,9] = 0.00
VM[1,1,9] = 0.00
VM[2,1,9] = 0.00
VM[3,1,9] = 0.04
VM[4,1,9] = 0.19
VM[5,1,9] = 0.24
VM[6,1,9] = 0.19
VM[7,1,9] = 0.04
VM[8,1,9] = 0.00
VM[9,1,9] = 0.00
VM[10,1,9] = 0.00
VM[11,1,9] = 0.00
VM[12,1,9] = 0.00
VM[13,1,9] = 0.00
VM[14,1,9] = 0.00
VM[15,1,9] = 0.00
VM[0,2,9] = 0.00
VM[1,2,9] = 0.00
VM[2,2,9] = 0.14
VM[3,2,9] = 0.62
VM[4,2,9] = 0.91
VM[5,2,9] = 0.96
VM[6,2,9] = 0.91
VM[7,2,9] = 0.62
VM[8,2,9] = 0.14
VM[9,2,9] = 0.00
VM[10,2,9] = 0.00
VM[11,2,9] = 0.00
VM[12,2,9] = 0.00
VM[13,2,9] = 0.00
VM[14,2,9] = 0.00
VM[15,2,9] = 0.00
VM[0,3,9] = 0.00
VM[1,3,9] = 0.04
VM[2,3,9] = 0.62
VM[3,3,9] = 0.99
VM[4,3,9] = 1.00
VM[5,3,9] = 1.00
VM[6,3,9] = 1.00
VM[7,3,9] = 0.99
VM[8,3,9] = 0.62
VM[9,3,9] = 0.04
VM[10,3,9] = 0.00
VM[11,3,9] = 0.00
VM[12,3,9] = 0.00
VM[13,3,9] = 0.00
VM[14,3,9] = 0.00
VM[15,3,9] = 0.00
VM[0,4,9] = 0.00
VM[1,4,9] = 0.19
VM[2,4,9] = 0.91
VM[3,4,9] = 1.00
VM[4,4,9] = 1.00
VM[5,4,9] = 1.00
VM[6,4,9] = 1.00
VM[7,4,9] = 1.00
VM[8,4,9] = 0.91
VM[9,4,9] = 0.19
VM[10,4,9] = 0.00
VM[11,4,9] = 0.00
VM[12,4,9] = 0.00
VM[13,4,9] = 0.00
VM[14,4,9] = 0.00
VM[15,4,9] = 0.00
VM[0,5,9] = 0.00
VM[1,5,9] = 0.24
VM[2,5,9] = 0.96
VM[3,5,9] = 1.00
VM[4,5,9] = 1.00
VM[5,5,9] = 1.00
VM[6,5,9] = 1.00
VM[7,5,9] = 0.93
VM[8,5,9] = 0.44
VM[9,5,9] = 0.00
VM[10,5,9] = 0.00
VM[11,5,9] = 0.00
VM[12,5,9] = 0.00
VM[13,5,9] = 0.00
VM[14,5,9] = 0.00
VM[15,5,9] = 0.00
VM[0,6,9] = 0.00
VM[1,6,9] = 0.19
VM[2,6,9] = 0.91
VM[3,6,9] = 1.00
VM[4,6,9] = 1.00
VM[5,6,9] = 1.00
VM[6,6,9] = 0.81
VM[7,6,9] = 0.09
VM[8,6,9] = 0.00
VM[9,6,9] = 0.00
VM[10,6,9] = 0.00
VM[11,6,9] = 0.00
VM[12,6,9] = 0.00
VM[13,6,9] = 0.00
VM[14,6,9] = 0.00
VM[15,6,9] = 0.00
VM[0,7,9] = 0.00
VM[1,7,9] = 0.04
VM[2,7,9] = 0.62
VM[3,7,9] = 0.99
VM[4,7,9] = 1.00
VM[5,7,9] = 0.93
VM[6,7,9] = 0.09
VM[7,7,9] = 0.00
VM[8,7,9] = 0.00
VM[9,7,9] = 0.00
VM[10,7,9] = 0.00
VM[11,7,9] = 0.00
VM[12,7,9] = 0.00
VM[13,7,9] = 0.00
VM[14,7,9] = 0.00
VM[15,7,9] = 0.00
VM[0,8,9] = 0.00
VM[1,8,9] = 0.00
VM[2,8,9] = 0.14
VM[3,8,9] = 0.62
VM[4,8,9] = 0.91
VM[5,8,9] = 0.44
VM[6,8,9] = 0.00
VM[7,8,9] = 0.00
VM[8,8,9] = 0.00
VM[9,8,9] = 0.00
VM[10,8,9] = 0.00
VM[11,8,9] = 0.00
VM[12,8,9] = 0.00
VM[13,8,9] = 0.00
VM[14,8,9] = 0.00
VM[15,8,9] = 0.00
VM[0,9,9] = 0.00
VM[1,9,9] = 0.00
VM[2,9,9] = 0.00
VM[3,9,9] = 0.04
VM[4,9,9] = 0.19
VM[5,9,9] = 0.00
VM[6,9,9] = 0.00
VM[7,9,9] = 0.00
VM[8,9,9] = 0.00
VM[9,9,9] = 0.00
VM[10,9,9] = 0.00
VM[11,9,9] = 0.00
VM[12,9,9] = 0.00
VM[13,9,9] = 0.00
VM[14,9,9] = 0.00
VM[15,9,9] = 0.00
VM[0,10,9] = 0.00
VM[1,10,9] = 0.00
VM[2,10,9] = 0.00
VM[3,10,9] = 0.00
VM[4,10,9] = 0.00
VM[5,10,9] = 0.00
VM[6,10,9] = 0.00
VM[7,10,9] = 0.00
VM[8,10,9] = 0.00
VM[9,10,9] = 0.00
VM[10,10,9] = 0.00
VM[11,10,9] = 0.00
VM[12,10,9] = 0.00
VM[13,10,9] = 0.00
VM[14,10,9] = 0.00
VM[15,10,9] = 0.00
VM[0,11,9] = 0.00
VM[1,11,9] = 0.00
VM[2,11,9] = 0.00
VM[3,11,9] = 0.00
VM[4,11,9] = 0.00
VM[5,11,9] = 0.00
VM[6,11,9] = 0.00
VM[7,11,9] = 0.00
VM[8,11,9] = 0.00
VM[9,11,9] = 0.00
VM[10,11,9] = 0.00
VM[11,11,9] = 0.00
VM[12,11,9] = 0.00
VM[13,11,9] = 0.00
VM[14,11,9] = 0.00
VM[15,11,9] = 0.00
VM[0,12,9] = 0.00
VM[1,12,9] = 0.00
VM[2,12,9] = 0.00
VM[3,12,9] = 0.00
VM[4,12,9] = 0.00
VM[5,12,9] = 0.00
VM[6,12,9] = 0.00
VM[7,12,9] = 0.00
VM[8,12,9] = 0.00
VM[9,12,9] = 0.00
VM[10,12,9] = 0.00
VM[11,12,9] = 0.00
VM[12,12,9] = 0.00
VM[13,12,9] = 0.00
VM[14,12,9] = 0.00
VM[15,12,9] = 0.00
VM[0,13,9] = 0.00
VM[1,13,9] = 0.00
VM[2,13,9] = 0.00
VM[3,13,9] = 0.00
VM[4,13,9] = 0.00
VM[5,13,9] = 0.00
VM[6,13,9] = 0.00
VM[7,13,9] = 0.00
VM[8,13,9] = 0.00
VM[9,13,9] = 0.00
VM[10,13,9] = 0.00
VM[11,13,9] = 0.00
VM[12,13,9] = 0.00
VM[13,13,9] = 0.00
VM[14,13,9] = 0.00
VM[15,13,9] = 0.00
VM[0,14,9] = 0.00
VM[1,14,9] = 0.00
VM[2,14,9] = 0.00
VM[3,14,9] = 0.00
VM[4,14,9] = 0.00
VM[5,14,9] = 0.00
VM[6,14,9] = 0.00
VM[7,14,9] = 0.00
VM[8,14,9] = 0.00
VM[9,14,9] = 0.00
VM[10,14,9] = 0.00
VM[11,14,9] = 0.00
VM[12,14,9] = 0.00
VM[13,14,9] = 0.00
VM[14,14,9] = 0.00
VM[15,14,9] = 0.00
VM[0,15,9] = 0.00
VM[1,15,9] = 0.00
VM[2,15,9] = 0.00
VM[3,15,9] = 0.00
VM[4,15,9] = 0.00
VM[5,15,9] = 0.00
VM[6,15,9] = 0.00
VM[7,15,9] = 0.00
VM[8,15,9] = 0.00
VM[9,15,9] = 0.00
VM[10,15,9] = 0.00
VM[11,15,9] = 0.00
VM[12,15,9] = 0.00
VM[13,15,9] = 0.00
VM[14,15,9] = 0.00
VM[15,15,9] = 0.00
VM[0,0,10] = 0.00
VM[1,0,10] = 0.00
VM[2,0,10] = 0.00
VM[3,0,10] = 0.00
VM[4,0,10] = 0.00
VM[5,0,10] = 0.00
VM[6,0,10] = 0.00
VM[7,0,10] = 0.00
VM[8,0,10] = 0.00
VM[9,0,10] = 0.00
VM[10,0,10] = 0.00
VM[11,0,10] = 0.00
VM[12,0,10] = 0.00
VM[13,0,10] = 0.00
VM[14,0,10] = 0.00
VM[15,0,10] = 0.00
VM[0,1,10] = 0.00
VM[1,1,10] = 0.00
VM[2,1,10] = 0.00
VM[3,1,10] = 0.00
VM[4,1,10] = 0.00
VM[5,1,10] = 0.00
VM[6,1,10] = 0.00
VM[7,1,10] = 0.00
VM[8,1,10] = 0.00
VM[9,1,10] = 0.00
VM[10,1,10] = 0.00
VM[11,1,10] = 0.00
VM[12,1,10] = 0.00
VM[13,1,10] = 0.00
VM[14,1,10] = 0.00
VM[15,1,10] = 0.00
VM[0,2,10] = 0.00
VM[1,2,10] = 0.00
VM[2,2,10] = 0.00
VM[3,2,10] = 0.00
VM[4,2,10] = 0.07
VM[5,2,10] = 0.14
VM[6,2,10] = 0.07
VM[7,2,10] = 0.00
VM[8,2,10] = 0.00
VM[9,2,10] = 0.00
VM[10,2,10] = 0.00
VM[11,2,10] = 0.00
VM[12,2,10] = 0.00
VM[13,2,10] = 0.00
VM[14,2,10] = 0.00
VM[15,2,10] = 0.00
VM[0,3,10] = 0.00
VM[1,3,10] = 0.00
VM[2,3,10] = 0.00
VM[3,3,10] = 0.21
VM[4,3,10] = 0.52
VM[5,3,10] = 0.60
VM[6,3,10] = 0.52
VM[7,3,10] = 0.21
VM[8,3,10] = 0.00
VM[9,3,10] = 0.00
VM[10,3,10] = 0.00
VM[11,3,10] = 0.00
VM[12,3,10] = 0.00
VM[13,3,10] = 0.00
VM[14,3,10] = 0.00
VM[15,3,10] = 0.00
VM[0,4,10] = 0.00
VM[1,4,10] = 0.00
VM[2,4,10] = 0.07
VM[3,4,10] = 0.52
VM[4,4,10] = 0.80
VM[5,4,10] = 0.90
VM[6,4,10] = 0.80
VM[7,4,10] = 0.52
VM[8,4,10] = 0.07
VM[9,4,10] = 0.00
VM[10,4,10] = 0.00
VM[11,4,10] = 0.00
VM[12,4,10] = 0.00
VM[13,4,10] = 0.00
VM[14,4,10] = 0.00
VM[15,4,10] = 0.00
VM[0,5,10] = 0.00
VM[1,5,10] = 0.00
VM[2,5,10] = 0.14
VM[3,5,10] = 0.60
VM[4,5,10] = 0.90
VM[5,5,10] = 1.00
VM[6,5,10] = 0.90
VM[7,5,10] = 0.46
VM[8,5,10] = 0.00
VM[9,5,10] = 0.00
VM[10,5,10] = 0.00
VM[11,5,10] = 0.00
VM[12,5,10] = 0.00
VM[13,5,10] = 0.00
VM[14,5,10] = 0.00
VM[15,5,10] = 0.00
VM[0,6,10] = 0.00
VM[1,6,10] = 0.00
VM[2,6,10] = 0.07
VM[3,6,10] = 0.52
VM[4,6,10] = 0.80
VM[5,6,10] = 0.90
VM[6,6,10] = 0.56
VM[7,6,10] = 0.00
VM[8,6,10] = 0.00
VM[9,6,10] = 0.00
VM[10,6,10] = 0.00
VM[11,6,10] = 0.00
VM[12,6,10] = 0.00
VM[13,6,10] = 0.00
VM[14,6,10] = 0.00
VM[15,6,10] = 0.00
VM[0,7,10] = 0.00
VM[1,7,10] = 0.00
VM[2,7,10] = 0.00
VM[3,7,10] = 0.21
VM[4,7,10] = 0.52
VM[5,7,10] = 0.46
VM[6,7,10] = 0.00
VM[7,7,10] = 0.00
VM[8,7,10] = 0.00
VM[9,7,10] = 0.00
VM[10,7,10] = 0.00
VM[11,7,10] = 0.00
VM[12,7,10] = 0.00
VM[13,7,10] = 0.00
VM[14,7,10] = 0.00
VM[15,7,10] = 0.00
VM[0,8,10] = 0.00
VM[1,8,10] = 0.00
VM[2,8,10] = 0.00
VM[3,8,10] = 0.00
VM[4,8,10] = 0.07
VM[5,8,10] = 0.00
VM[6,8,10] = 0.00
VM[7,8,10] = 0.00
VM[8,8,10] = 0.00
VM[9,8,10] = 0.00
VM[10,8,10] = 0.00
VM[11,8,10] = 0.00
VM[12,8,10] = 0.00
VM[13,8,10] = 0.00
VM[14,8,10] = 0.00
VM[15,8,10] = 0.00
VM[0,9,10] = 0.00
VM[1,9,10] = 0.00
VM[2,9,10] = 0.00
VM[3,9,10] = 0.00
VM[4,9,10] = 0.00
VM[5,9,10] = 0.00
VM[6,9,10] = 0.00
VM[7,9,10] = 0.00
VM[8,9,10] = 0.00
VM[9,9,10] = 0.00
VM[10,9,10] = 0.00
VM[11,9,10] = 0.00
VM[12,9,10] = 0.00
VM[13,9,10] = 0.00
VM[14,9,10] = 0.00
VM[15,9,10] = 0.00
VM[0,10,10] = 0.00
VM[1,10,10] = 0.00
VM[2,10,10] = 0.00
VM[3,10,10] = 0.00
VM[4,10,10] = 0.00
VM[5,10,10] = 0.00
VM[6,10,10] = 0.00
VM[7,10,10] = 0.00
VM[8,10,10] = 0.00
VM[9,10,10] = 0.00
VM[10,10,10] = 0.00
VM[11,10,10] = 0.00
VM[12,10,10] = 0.00
VM[13,10,10] = 0.00
VM[14,10,10] = 0.00
VM[15,10,10] = 0.00
VM[0,11,10] = 0.00
VM[1,11,10] = 0.00
VM[2,11,10] = 0.00
VM[3,11,10] = 0.00
VM[4,11,10] = 0.00
VM[5,11,10] = 0.00
VM[6,11,10] = 0.00
VM[7,11,10] = 0.00
VM[8,11,10] = 0.00
VM[9,11,10] = 0.00
VM[10,11,10] = 0.00
VM[11,11,10] = 0.00
VM[12,11,10] = 0.00
VM[13,11,10] = 0.00
VM[14,11,10] = 0.00
VM[15,11,10] = 0.00
VM[0,12,10] = 0.00
VM[1,12,10] = 0.00
VM[2,12,10] = 0.00
VM[3,12,10] = 0.00
VM[4,12,10] = 0.00
VM[5,12,10] = 0.00
VM[6,12,10] = 0.00
VM[7,12,10] = 0.00
VM[8,12,10] = 0.00
VM[9,12,10] = 0.00
VM[10,12,10] = 0.00
VM[11,12,10] = 0.00
VM[12,12,10] = 0.00
VM[13,12,10] = 0.00
VM[14,12,10] = 0.00
VM[15,12,10] = 0.00
VM[0,13,10] = 0.00
VM[1,13,10] = 0.00
VM[2,13,10] = 0.00
VM[3,13,10] = 0.00
VM[4,13,10] = 0.00
VM[5,13,10] = 0.00
VM[6,13,10] = 0.00
VM[7,13,10] = 0.00
VM[8,13,10] = 0.00
VM[9,13,10] = 0.00
VM[10,13,10] = 0.00
VM[11,13,10] = 0.00
VM[12,13,10] = 0.00
VM[13,13,10] = 0.00
VM[14,13,10] = 0.00
VM[15,13,10] = 0.00
VM[0,14,10] = 0.00
VM[1,14,10] = 0.00
VM[2,14,10] = 0.00
VM[3,14,10] = 0.00
VM[4,14,10] = 0.00
VM[5,14,10] = 0.00
VM[6,14,10] = 0.00
VM[7,14,10] = 0.00
VM[8,14,10] = 0.00
VM[9,14,10] = 0.00
VM[10,14,10] = 0.00
VM[11,14,10] = 0.00
VM[12,14,10] = 0.00
VM[13,14,10] = 0.00
VM[14,14,10] = 0.00
VM[15,14,10] = 0.00
VM[0,15,10] = 0.00
VM[1,15,10] = 0.00
VM[2,15,10] = 0.00
VM[3,15,10] = 0.00
VM[4,15,10] = 0.00
VM[5,15,10] = 0.00
VM[6,15,10] = 0.00
VM[7,15,10] = 0.00
VM[8,15,10] = 0.00
VM[9,15,10] = 0.00
VM[10,15,10] = 0.00
VM[11,15,10] = 0.00
VM[12,15,10] = 0.00
VM[13,15,10] = 0.00
VM[14,15,10] = 0.00
VM[15,15,10] = 0.00
VM[0,0,11] = 0.00
VM[1,0,11] = 0.00
VM[2,0,11] = 0.00
VM[3,0,11] = 0.00
VM[4,0,11] = 0.00
VM[5,0,11] = 0.00
VM[6,0,11] = 0.00
VM[7,0,11] = 0.00
VM[8,0,11] = 0.00
VM[9,0,11] = 0.00
VM[10,0,11] = 0.00
VM[11,0,11] = 0.00
VM[12,0,11] = 0.00
VM[13,0,11] = 0.00
VM[14,0,11] = 0.00
VM[15,0,11] = 0.00
VM[0,1,11] = 0.00
VM[1,1,11] = 0.00
VM[2,1,11] = 0.00
VM[3,1,11] = 0.00
VM[4,1,11] = 0.00
VM[5,1,11] = 0.00
VM[6,1,11] = 0.00
VM[7,1,11] = 0.00
VM[8,1,11] = 0.00
VM[9,1,11] = 0.00
VM[10,1,11] = 0.00
VM[11,1,11] = 0.00
VM[12,1,11] = 0.00
VM[13,1,11] = 0.00
VM[14,1,11] = 0.00
VM[15,1,11] = 0.00
VM[0,2,11] = 0.00
VM[1,2,11] = 0.00
VM[2,2,11] = 0.00
VM[3,2,11] = 0.00
VM[4,2,11] = 0.00
VM[5,2,11] = 0.00
VM[6,2,11] = 0.00
VM[7,2,11] = 0.00
VM[8,2,11] = 0.00
VM[9,2,11] = 0.00
VM[10,2,11] = 0.00
VM[11,2,11] = 0.00
VM[12,2,11] = 0.00
VM[13,2,11] = 0.00
VM[14,2,11] = 0.00
VM[15,2,11] = 0.00
VM[0,3,11] = 0.00
VM[1,3,11] = 0.00
VM[2,3,11] = 0.00
VM[3,3,11] = 0.00
VM[4,3,11] = 0.00
VM[5,3,11] = 0.00
VM[6,3,11] = 0.00
VM[7,3,11] = 0.00
VM[8,3,11] = 0.00
VM[9,3,11] = 0.00
VM[10,3,11] = 0.00
VM[11,3,11] = 0.00
VM[12,3,11] = 0.00
VM[13,3,11] = 0.00
VM[14,3,11] = 0.00
VM[15,3,11] = 0.00
VM[0,4,11] = 0.00
VM[1,4,11] = 0.00
VM[2,4,11] = 0.00
VM[3,4,11] = 0.00
VM[4,4,11] = 0.00
VM[5,4,11] = 0.00
VM[6,4,11] = 0.00
VM[7,4,11] = 0.00
VM[8,4,11] = 0.00
VM[9,4,11] = 0.00
VM[10,4,11] = 0.00
VM[11,4,11] = 0.00
VM[12,4,11] = 0.00
VM[13,4,11] = 0.00
VM[14,4,11] = 0.00
VM[15,4,11] = 0.00
VM[0,5,11] = 0.00
VM[1,5,11] = 0.00
VM[2,5,11] = 0.00
VM[3,5,11] = 0.00
VM[4,5,11] = 0.00
VM[5,5,11] = 0.00
VM[6,5,11] = 0.00
VM[7,5,11] = 0.00
VM[8,5,11] = 0.00
VM[9,5,11] = 0.00
VM[10,5,11] = 0.00
VM[11,5,11] = 0.00
VM[12,5,11] = 0.00
VM[13,5,11] = 0.00
VM[14,5,11] = 0.00
VM[15,5,11] = 0.00
VM[0,6,11] = 0.00
VM[1,6,11] = 0.00
VM[2,6,11] = 0.00
VM[3,6,11] = 0.00
VM[4,6,11] = 0.00
VM[5,6,11] = 0.00
VM[6,6,11] = 0.00
VM[7,6,11] = 0.00
VM[8,6,11] = 0.00
VM[9,6,11] = 0.00
VM[10,6,11] = 0.00
VM[11,6,11] = 0.00
VM[12,6,11] = 0.00
VM[13,6,11] = 0.00
VM[14,6,11] = 0.00
VM[15,6,11] = 0.00
VM[0,7,11] = 0.00
VM[1,7,11] = 0.00
VM[2,7,11] = 0.00
VM[3,7,11] = 0.00
VM[4,7,11] = 0.00
VM[5,7,11] = 0.00
VM[6,7,11] = 0.00
VM[7,7,11] = 0.00
VM[8,7,11] = 0.00
VM[9,7,11] = 0.00
VM[10,7,11] = 0.00
VM[11,7,11] = 0.00
VM[12,7,11] = 0.00
VM[13,7,11] = 0.00
VM[14,7,11] = 0.00
VM[15,7,11] = 0.00
VM[0,8,11] = 0.00
VM[1,8,11] = 0.00
VM[2,8,11] = 0.00
VM[3,8,11] = 0.00
VM[4,8,11] = 0.00
VM[5,8,11] = 0.00
VM[6,8,11] = 0.00
VM[7,8,11] = 0.00
VM[8,8,11] = 0.00
VM[9,8,11] = 0.00
VM[10,8,11] = 0.00
VM[11,8,11] = 0.00
VM[12,8,11] = 0.00
VM[13,8,11] = 0.00
VM[14,8,11] = 0.00
VM[15,8,11] = 0.00
VM[0,9,11] = 0.00
VM[1,9,11] = 0.00
VM[2,9,11] = 0.00
VM[3,9,11] = 0.00
VM[4,9,11] = 0.00
VM[5,9,11] = 0.00
VM[6,9,11] = 0.00
VM[7,9,11] = 0.00
VM[8,9,11] = 0.00
VM[9,9,11] = 0.00
VM[10,9,11] = 0.00
VM[11,9,11] = 0.00
VM[12,9,11] = 0.00
VM[13,9,11] = 0.00
VM[14,9,11] = 0.00
VM[15,9,11] = 0.00
VM[0,10,11] = 0.00
VM[1,10,11] = 0.00
VM[2,10,11] = 0.00
VM[3,10,11] = 0.00
VM[4,10,11] = 0.00
VM[5,10,11] = 0.00
VM[6,10,11] = 0.00
VM[7,10,11] = 0.00
VM[8,10,11] = 0.00
VM[9,10,11] = 0.00
VM[10,10,11] = 0.00
VM[11,10,11] = 0.00
VM[12,10,11] = 0.00
VM[13,10,11] = 0.00
VM[14,10,11] = 0.00
VM[15,10,11] = 0.00
VM[0,11,11] = 0.00
VM[1,11,11] = 0.00
VM[2,11,11] = 0.00
VM[3,11,11] = 0.00
VM[4,11,11] = 0.00
VM[5,11,11] = 0.00
VM[6,11,11] = 0.00
VM[7,11,11] = 0.00
VM[8,11,11] = 0.00
VM[9,11,11] = 0.00
VM[10,11,11] = 0.00
VM[11,11,11] = 0.00
VM[12,11,11] = 0.00
VM[13,11,11] = 0.00
VM[14,11,11] = 0.00
VM[15,11,11] = 0.00
VM[0,12,11] = 0.00
VM[1,12,11] = 0.00
VM[2,12,11] = 0.00
VM[3,12,11] = 0.00
VM[4,12,11] = 0.00
VM[5,12,11] = 0.00
VM[6,12,11] = 0.00
VM[7,12,11] = 0.00
VM[8,12,11] = 0.00
VM[9,12,11] = 0.00
VM[10,12,11] = 0.00
VM[11,12,11] = 0.00
VM[12,12,11] = 0.00
VM[13,12,11] = 0.00
VM[14,12,11] = 0.00
VM[15,12,11] = 0.00
VM[0,13,11] = 0.00
VM[1,13,11] = 0.00
VM[2,13,11] = 0.00
VM[3,13,11] = 0.00
VM[4,13,11] = 0.00
VM[5,13,11] = 0.00
VM[6,13,11] = 0.00
VM[7,13,11] = 0.00
VM[8,13,11] = 0.00
VM[9,13,11] = 0.00
VM[10,13,11] = 0.00
VM[11,13,11] = 0.00
VM[12,13,11] = 0.00
VM[13,13,11] = 0.00
VM[14,13,11] = 0.00
VM[15,13,11] = 0.00
VM[0,14,11] = 0.00
VM[1,14,11] = 0.00
VM[2,14,11] = 0.00
VM[3,14,11] = 0.00
VM[4,14,11] = 0.00
VM[5,14,11] = 0.00
VM[6,14,11] = 0.00
VM[7,14,11] = 0.00
VM[8,14,11] = 0.00
VM[9,14,11] = 0.00
VM[10,14,11] = 0.00
VM[11,14,11] = 0.00
VM[12,14,11] = 0.00
VM[13,14,11] = 0.00
VM[14,14,11] = 0.00
VM[15,14,11] = 0.00
VM[0,15,11] = 0.00
VM[1,15,11] = 0.00
VM[2,15,11] = 0.00
VM[3,15,11] = 0.00
VM[4,15,11] = 0.00
VM[5,15,11] = 0.00
VM[6,15,11] = 0.00
VM[7,15,11] = 0.00
VM[8,15,11] = 0.00
VM[9,15,11] = 0.00
VM[10,15,11] = 0.00
VM[11,15,11] = 0.00
VM[12,15,11] = 0.00
VM[13,15,11] = 0.00
VM[14,15,11] = 0.00
VM[15,15,11] = 0.00
VM[0,0,12] = 0.00
VM[1,0,12] = 0.00
VM[2,0,12] = 0.00
VM[3,0,12] = 0.00
VM[4,0,12] = 0.00
VM[5,0,12] = 0.00
VM[6,0,12] = 0.00
VM[7,0,12] = 0.00
VM[8,0,12] = 0.00
VM[9,0,12] = 0.00
VM[10,0,12] = 0.00
VM[11,0,12] = 0.00
VM[12,0,12] = 0.00
VM[13,0,12] = 0.00
VM[14,0,12] = 0.00
VM[15,0,12] = 0.00
VM[0,1,12] = 0.00
VM[1,1,12] = 0.00
VM[2,1,12] = 0.00
VM[3,1,12] = 0.00
VM[4,1,12] = 0.00
VM[5,1,12] = 0.00
VM[6,1,12] = 0.00
VM[7,1,12] = 0.00
VM[8,1,12] = 0.00
VM[9,1,12] = 0.00
VM[10,1,12] = 0.00
VM[11,1,12] = 0.00
VM[12,1,12] = 0.00
VM[13,1,12] = 0.00
VM[14,1,12] = 0.00
VM[15,1,12] = 0.00
VM[0,2,12] = 0.00
VM[1,2,12] = 0.00
VM[2,2,12] = 0.00
VM[3,2,12] = 0.00
VM[4,2,12] = 0.00
VM[5,2,12] = 0.00
VM[6,2,12] = 0.00
VM[7,2,12] = 0.00
VM[8,2,12] = 0.00
VM[9,2,12] = 0.00
VM[10,2,12] = 0.00
VM[11,2,12] = 0.00
VM[12,2,12] = 0.00
VM[13,2,12] = 0.00
VM[14,2,12] = 0.00
VM[15,2,12] = 0.00
VM[0,3,12] = 0.00
VM[1,3,12] = 0.00
VM[2,3,12] = 0.00
VM[3,3,12] = 0.00
VM[4,3,12] = 0.00
VM[5,3,12] = 0.00
VM[6,3,12] = 0.00
VM[7,3,12] = 0.00
VM[8,3,12] = 0.00
VM[9,3,12] = 0.00
VM[10,3,12] = 0.00
VM[11,3,12] = 0.00
VM[12,3,12] = 0.00
VM[13,3,12] = 0.00
VM[14,3,12] = 0.00
VM[15,3,12] = 0.00
VM[0,4,12] = 0.00
VM[1,4,12] = 0.00
VM[2,4,12] = 0.00
VM[3,4,12] = 0.00
VM[4,4,12] = 0.00
VM[5,4,12] = 0.00
VM[6,4,12] = 0.00
VM[7,4,12] = 0.00
VM[8,4,12] = 0.00
VM[9,4,12] = 0.00
VM[10,4,12] = 0.00
VM[11,4,12] = 0.00
VM[12,4,12] = 0.00
VM[13,4,12] = 0.00
VM[14,4,12] = 0.00
VM[15,4,12] = 0.00
VM[0,5,12] = 0.00
VM[1,5,12] = 0.00
VM[2,5,12] = 0.00
VM[3,5,12] = 0.00
VM[4,5,12] = 0.00
VM[5,5,12] = 0.00
VM[6,5,12] = 0.00
VM[7,5,12] = 0.00
VM[8,5,12] = 0.00
VM[9,5,12] = 0.00
VM[10,5,12] = 0.00
VM[11,5,12] = 0.00
VM[12,5,12] = 0.00
VM[13,5,12] = 0.00
VM[14,5,12] = 0.00
VM[15,5,12] = 0.00
VM[0,6,12] = 0.00
VM[1,6,12] = 0.00
VM[2,6,12] = 0.00
VM[3,6,12] = 0.00
VM[4,6,12] = 0.00
VM[5,6,12] = 0.00
VM[6,6,12] = 0.00
VM[7,6,12] = 0.00
VM[8,6,12] = 0.00
VM[9,6,12] = 0.00
VM[10,6,12] = 0.00
VM[11,6,12] = 0.00
VM[12,6,12] = 0.00
VM[13,6,12] = 0.00
VM[14,6,12] = 0.00
VM[15,6,12] = 0.00
VM[0,7,12] = 0.00
VM[1,7,12] = 0.00
VM[2,7,12] = 0.00
VM[3,7,12] = 0.00
VM[4,7,12] = 0.00
VM[5,7,12] = 0.00
VM[6,7,12] = 0.00
VM[7,7,12] = 0.00
VM[8,7,12] = 0.00
VM[9,7,12] = 0.00
VM[10,7,12] = 0.00
VM[11,7,12] = 0.00
VM[12,7,12] = 0.00
VM[13,7,12] = 0.00
VM[14,7,12] = 0.00
VM[15,7,12] = 0.00
VM[0,8,12] = 0.00
VM[1,8,12] = 0.00
VM[2,8,12] = 0.00
VM[3,8,12] = 0.00
VM[4,8,12] = 0.00
VM[5,8,12] = 0.00
VM[6,8,12] = 0.00
VM[7,8,12] = 0.00
VM[8,8,12] = 0.00
VM[9,8,12] = 0.00
VM[10,8,12] = 0.00
VM[11,8,12] = 0.00
VM[12,8,12] = 0.00
VM[13,8,12] = 0.00
VM[14,8,12] = 0.00
VM[15,8,12] = 0.00
VM[0,9,12] = 0.00
VM[1,9,12] = 0.00
VM[2,9,12] = 0.00
VM[3,9,12] = 0.00
VM[4,9,12] = 0.00
VM[5,9,12] = 0.00
VM[6,9,12] = 0.00
VM[7,9,12] = 0.00
VM[8,9,12] = 0.00
VM[9,9,12] = 0.00
VM[10,9,12] = 0.00
VM[11,9,12] = 0.00
VM[12,9,12] = 0.00
VM[13,9,12] = 0.00
VM[14,9,12] = 0.00
VM[15,9,12] = 0.00
VM[0,10,12] = 0.00
VM[1,10,12] = 0.00
VM[2,10,12] = 0.00
VM[3,10,12] = 0.00
VM[4,10,12] = 0.00
VM[5,10,12] = 0.00
VM[6,10,12] = 0.00
VM[7,10,12] = 0.00
VM[8,10,12] = 0.00
VM[9,10,12] = 0.00
VM[10,10,12] = 0.00
VM[11,10,12] = 0.00
VM[12,10,12] = 0.00
VM[13,10,12] = 0.00
VM[14,10,12] = 0.00
VM[15,10,12] = 0.00
VM[0,11,12] = 0.00
VM[1,11,12] = 0.00
VM[2,11,12] = 0.00
VM[3,11,12] = 0.00
VM[4,11,12] = 0.00
VM[5,11,12] = 0.00
VM[6,11,12] = 0.00
VM[7,11,12] = 0.00
VM[8,11,12] = 0.00
VM[9,11,12] = 0.00
VM[10,11,12] = 0.00
VM[11,11,12] = 0.00
VM[12,11,12] = 0.00
VM[13,11,12] = 0.00
VM[14,11,12] = 0.00
VM[15,11,12] = 0.00
VM[0,12,12] = 0.00
VM[1,12,12] = 0.00
VM[2,12,12] = 0.00
VM[3,12,12] = 0.00
VM[4,12,12] = 0.00
VM[5,12,12] = 0.00
VM[6,12,12] = 0.00
VM[7,12,12] = 0.00
VM[8,12,12] = 0.00
VM[9,12,12] = 0.00
VM[10,12,12] = 0.00
VM[11,12,12] = 0.00
VM[12,12,12] = 0.00
VM[13,12,12] = 0.00
VM[14,12,12] = 0.00
VM[15,12,12] = 0.00
VM[0,13,12] = 0.00
VM[1,13,12] = 0.00
VM[2,13,12] = 0.00
VM[3,13,12] = 0.00
VM[4,13,12] = 0.00
VM[5,13,12] = 0.00
VM[6,13,12] = 0.00
VM[7,13,12] = 0.00
VM[8,13,12] = 0.00
VM[9,13,12] = 0.00
VM[10,13,12] = 0.00
VM[11,13,12] = 0.00
VM[12,13,12] = 0.00
VM[13,13,12] = 0.00
VM[14,13,12] = 0.00
VM[15,13,12] = 0.00
VM[0,14,12] = 0.00
VM[1,14,12] = 0.00
VM[2,14,12] = 0.00
VM[3,14,12] = 0.00
VM[4,14,12] = 0.00
VM[5,14,12] = 0.00
VM[6,14,12] = 0.00
VM[7,14,12] = 0.00
VM[8,14,12] = 0.00
VM[9,14,12] = 0.00
VM[10,14,12] = 0.00
VM[11,14,12] = 0.00
VM[12,14,12] = 0.00
VM[13,14,12] = 0.00
VM[14,14,12] = 0.00
VM[15,14,12] = 0.00
VM[0,15,12] = 0.00
VM[1,15,12] = 0.00
VM[2,15,12] = 0.00
VM[3,15,12] = 0.00
VM[4,15,12] = 0.00
VM[5,15,12] = 0.00
VM[6,15,12] = 0.00
VM[7,15,12] = 0.00
VM[8,15,12] = 0.00
VM[9,15,12] = 0.00
VM[10,15,12] = 0.00
VM[11,15,12] = 0.00
VM[12,15,12] = 0.00
VM[13,15,12] = 0.00
VM[14,15,12] = 0.00
VM[15,15,12] = 0.00
VM[0,0,13] = 0.00
VM[1,0,13] = 0.00
VM[2,0,13] = 0.00
VM[3,0,13] = 0.00
VM[4,0,13] = 0.00
VM[5,0,13] = 0.00
VM[6,0,13] = 0.00
VM[7,0,13] = 0.00
VM[8,0,13] = 0.00
VM[9,0,13] = 0.00
VM[10,0,13] = 0.00
VM[11,0,13] = 0.00
VM[12,0,13] = 0.00
VM[13,0,13] = 0.00
VM[14,0,13] = 0.00
VM[15,0,13] = 0.00
VM[0,1,13] = 0.00
VM[1,1,13] = 0.00
VM[2,1,13] = 0.00
VM[3,1,13] = 0.00
VM[4,1,13] = 0.00
VM[5,1,13] = 0.00
VM[6,1,13] = 0.00
VM[7,1,13] = 0.00
VM[8,1,13] = 0.00
VM[9,1,13] = 0.00
VM[10,1,13] = 0.00
VM[11,1,13] = 0.00
VM[12,1,13] = 0.00
VM[13,1,13] = 0.00
VM[14,1,13] = 0.00
VM[15,1,13] = 0.00
VM[0,2,13] = 0.00
VM[1,2,13] = 0.00
VM[2,2,13] = 0.00
VM[3,2,13] = 0.00
VM[4,2,13] = 0.00
VM[5,2,13] = 0.00
VM[6,2,13] = 0.00
VM[7,2,13] = 0.00
VM[8,2,13] = 0.00
VM[9,2,13] = 0.00
VM[10,2,13] = 0.00
VM[11,2,13] = 0.00
VM[12,2,13] = 0.00
VM[13,2,13] = 0.00
VM[14,2,13] = 0.00
VM[15,2,13] = 0.00
VM[0,3,13] = 0.00
VM[1,3,13] = 0.00
VM[2,3,13] = 0.00
VM[3,3,13] = 0.00
VM[4,3,13] = 0.00
VM[5,3,13] = 0.00
VM[6,3,13] = 0.00
VM[7,3,13] = 0.00
VM[8,3,13] = 0.00
VM[9,3,13] = 0.00
VM[10,3,13] = 0.00
VM[11,3,13] = 0.00
VM[12,3,13] = 0.00
VM[13,3,13] = 0.00
VM[14,3,13] = 0.00
VM[15,3,13] = 0.00
VM[0,4,13] = 0.00
VM[1,4,13] = 0.00
VM[2,4,13] = 0.00
VM[3,4,13] = 0.00
VM[4,4,13] = 0.00
VM[5,4,13] = 0.00
VM[6,4,13] = 0.00
VM[7,4,13] = 0.00
VM[8,4,13] = 0.00
VM[9,4,13] = 0.00
VM[10,4,13] = 0.00
VM[11,4,13] = 0.00
VM[12,4,13] = 0.00
VM[13,4,13] = 0.00
VM[14,4,13] = 0.00
VM[15,4,13] = 0.00
VM[0,5,13] = 0.00
VM[1,5,13] = 0.00
VM[2,5,13] = 0.00
VM[3,5,13] = 0.00
VM[4,5,13] = 0.00
VM[5,5,13] = 0.00
VM[6,5,13] = 0.00
VM[7,5,13] = 0.00
VM[8,5,13] = 0.00
VM[9,5,13] = 0.00
VM[10,5,13] = 0.00
VM[11,5,13] = 0.00
VM[12,5,13] = 0.00
VM[13,5,13] = 0.00
VM[14,5,13] = 0.00
VM[15,5,13] = 0.00
VM[0,6,13] = 0.00
VM[1,6,13] = 0.00
VM[2,6,13] = 0.00
VM[3,6,13] = 0.00
VM[4,6,13] = 0.00
VM[5,6,13] = 0.00
VM[6,6,13] = 0.00
VM[7,6,13] = 0.00
VM[8,6,13] = 0.00
VM[9,6,13] = 0.00
VM[10,6,13] = 0.00
VM[11,6,13] = 0.00
VM[12,6,13] = 0.00
VM[13,6,13] = 0.00
VM[14,6,13] = 0.00
VM[15,6,13] = 0.00
VM[0,7,13] = 0.00
VM[1,7,13] = 0.00
VM[2,7,13] = 0.00
VM[3,7,13] = 0.00
VM[4,7,13] = 0.00
VM[5,7,13] = 0.00
VM[6,7,13] = 0.00
VM[7,7,13] = 0.00
VM[8,7,13] = 0.00
VM[9,7,13] = 0.00
VM[10,7,13] = 0.00
VM[11,7,13] = 0.00
VM[12,7,13] = 0.00
VM[13,7,13] = 0.00
VM[14,7,13] = 0.00
VM[15,7,13] = 0.00
VM[0,8,13] = 0.00
VM[1,8,13] = 0.00
VM[2,8,13] = 0.00
VM[3,8,13] = 0.00
VM[4,8,13] = 0.00
VM[5,8,13] = 0.00
VM[6,8,13] = 0.00
VM[7,8,13] = 0.00
VM[8,8,13] = 0.00
VM[9,8,13] = 0.00
VM[10,8,13] = 0.00
VM[11,8,13] = 0.00
VM[12,8,13] = 0.00
VM[13,8,13] = 0.00
VM[14,8,13] = 0.00
VM[15,8,13] = 0.00
VM[0,9,13] = 0.00
VM[1,9,13] = 0.00
VM[2,9,13] = 0.00
VM[3,9,13] = 0.00
VM[4,9,13] = 0.00
VM[5,9,13] = 0.00
VM[6,9,13] = 0.00
VM[7,9,13] = 0.00
VM[8,9,13] = 0.00
VM[9,9,13] = 0.00
VM[10,9,13] = 0.00
VM[11,9,13] = 0.00
VM[12,9,13] = 0.00
VM[13,9,13] = 0.00
VM[14,9,13] = 0.00
VM[15,9,13] = 0.00
VM[0,10,13] = 0.00
VM[1,10,13] = 0.00
VM[2,10,13] = 0.00
VM[3,10,13] = 0.00
VM[4,10,13] = 0.00
VM[5,10,13] = 0.00
VM[6,10,13] = 0.00
VM[7,10,13] = 0.00
VM[8,10,13] = 0.00
VM[9,10,13] = 0.00
VM[10,10,13] = 0.00
VM[11,10,13] = 0.00
VM[12,10,13] = 0.00
VM[13,10,13] = 0.00
VM[14,10,13] = 0.00
VM[15,10,13] = 0.00
VM[0,11,13] = 0.00
VM[1,11,13] = 0.00
VM[2,11,13] = 0.00
VM[3,11,13] = 0.00
VM[4,11,13] = 0.00
VM[5,11,13] = 0.00
VM[6,11,13] = 0.00
VM[7,11,13] = 0.00
VM[8,11,13] = 0.00
VM[9,11,13] = 0.00
VM[10,11,13] = 0.00
VM[11,11,13] = 0.00
VM[12,11,13] = 0.00
VM[13,11,13] = 0.00
VM[14,11,13] = 0.00
VM[15,11,13] = 0.00
VM[0,12,13] = 0.00
VM[1,12,13] = 0.00
VM[2,12,13] = 0.00
VM[3,12,13] = 0.00
VM[4,12,13] = 0.00
VM[5,12,13] = 0.00
VM[6,12,13] = 0.00
VM[7,12,13] = 0.00
VM[8,12,13] = 0.00
VM[9,12,13] = 0.00
VM[10,12,13] = 0.00
VM[11,12,13] = 0.00
VM[12,12,13] = 0.00
VM[13,12,13] = 0.00
VM[14,12,13] = 0.00
VM[15,12,13] = 0.00
VM[0,13,13] = 0.00
VM[1,13,13] = 0.00
VM[2,13,13] = 0.00
VM[3,13,13] = 0.00
VM[4,13,13] = 0.00
VM[5,13,13] = 0.00
VM[6,13,13] = 0.00
VM[7,13,13] = 0.00
VM[8,13,13] = 0.00
VM[9,13,13] = 0.00
VM[10,13,13] = 0.00
VM[11,13,13] = 0.00
VM[12,13,13] = 0.00
VM[13,13,13] = 0.00
VM[14,13,13] = 0.00
VM[15,13,13] = 0.00
VM[0,14,13] = 0.00
VM[1,14,13] = 0.00
VM[2,14,13] = 0.00
VM[3,14,13] = 0.00
VM[4,14,13] = 0.00
VM[5,14,13] = 0.00
VM[6,14,13] = 0.00
VM[7,14,13] = 0.00
VM[8,14,13] = 0.00
VM[9,14,13] = 0.00
VM[10,14,13] = 0.00
VM[11,14,13] = 0.00
VM[12,14,13] = 0.00
VM[13,14,13] = 0.00
VM[14,14,13] = 0.00
VM[15,14,13] = 0.00
VM[0,15,13] = 0.00
VM[1,15,13] = 0.00
VM[2,15,13] = 0.00
VM[3,15,13] = 0.00
VM[4,15,13] = 0.00
VM[5,15,13] = 0.00
VM[6,15,13] = 0.00
VM[7,15,13] = 0.00
VM[8,15,13] = 0.00
VM[9,15,13] = 0.00
VM[10,15,13] = 0.00
VM[11,15,13] = 0.00
VM[12,15,13] = 0.00
VM[13,15,13] = 0.00
VM[14,15,13] = 0.00
VM[15,15,13] = 0.00
VM[0,0,14] = 0.00
VM[1,0,14] = 0.00
VM[2,0,14] = 0.00
VM[3,0,14] = 0.00
VM[4,0,14] = 0.00
VM[5,0,14] = 0.00
VM[6,0,14] = 0.00
VM[7,0,14] = 0.00
VM[8,0,14] = 0.00
VM[9,0,14] = 0.00
VM[10,0,14] = 0.00
VM[11,0,14] = 0.00
VM[12,0,14] = 0.00
VM[13,0,14] = 0.00
VM[14,0,14] = 0.00
VM[15,0,14] = 0.00
VM[0,1,14] = 0.00
VM[1,1,14] = 0.00
VM[2,1,14] = 0.00
VM[3,1,14] = 0.00
VM[4,1,14] = 0.00
VM[5,1,14] = 0.00
VM[6,1,14] = 0.00
VM[7,1,14] = 0.00
VM[8,1,14] = 0.00
VM[9,1,14] = 0.00
VM[10,1,14] = 0.00
VM[11,1,14] = 0.00
VM[12,1,14] = 0.00
VM[13,1,14] = 0.00
VM[14,1,14] = 0.00
VM[15,1,14] = 0.00
VM[0,2,14] = 0.00
VM[1,2,14] = 0.00
VM[2,2,14] = 0.00
VM[3,2,14] = 0.00
VM[4,2,14] = 0.00
VM[5,2,14] = 0.00
VM[6,2,14] = 0.00
VM[7,2,14] = 0.00
VM[8,2,14] = 0.00
VM[9,2,14] = 0.00
VM[10,2,14] = 0.00
VM[11,2,14] = 0.00
VM[12,2,14] = 0.00
VM[13,2,14] = 0.00
VM[14,2,14] = 0.00
VM[15,2,14] = 0.00
VM[0,3,14] = 0.00
VM[1,3,14] = 0.00
VM[2,3,14] = 0.00
VM[3,3,14] = 0.00
VM[4,3,14] = 0.00
VM[5,3,14] = 0.00
VM[6,3,14] = 0.00
VM[7,3,14] = 0.00
VM[8,3,14] = 0.00
VM[9,3,14] = 0.00
VM[10,3,14] = 0.00
VM[11,3,14] = 0.00
VM[12,3,14] = 0.00
VM[13,3,14] = 0.00
VM[14,3,14] = 0.00
VM[15,3,14] = 0.00
VM[0,4,14] = 0.00
VM[1,4,14] = 0.00
VM[2,4,14] = 0.00
VM[3,4,14] = 0.00
VM[4,4,14] = 0.00
VM[5,4,14] = 0.00
VM[6,4,14] = 0.00
VM[7,4,14] = 0.00
VM[8,4,14] = 0.00
VM[9,4,14] = 0.00
VM[10,4,14] = 0.00
VM[11,4,14] = 0.00
VM[12,4,14] = 0.00
VM[13,4,14] = 0.00
VM[14,4,14] = 0.00
VM[15,4,14] = 0.00
VM[0,5,14] = 0.00
VM[1,5,14] = 0.00
VM[2,5,14] = 0.00
VM[3,5,14] = 0.00
VM[4,5,14] = 0.00
VM[5,5,14] = 0.00
VM[6,5,14] = 0.00
VM[7,5,14] = 0.00
VM[8,5,14] = 0.00
VM[9,5,14] = 0.00
VM[10,5,14] = 0.00
VM[11,5,14] = 0.00
VM[12,5,14] = 0.00
VM[13,5,14] = 0.00
VM[14,5,14] = 0.00
VM[15,5,14] = 0.00
VM[0,6,14] = 0.00
VM[1,6,14] = 0.00
VM[2,6,14] = 0.00
VM[3,6,14] = 0.00
VM[4,6,14] = 0.00
VM[5,6,14] = 0.00
VM[6,6,14] = 0.00
VM[7,6,14] = 0.00
VM[8,6,14] = 0.00
VM[9,6,14] = 0.00
VM[10,6,14] = 0.00
VM[11,6,14] = 0.00
VM[12,6,14] = 0.00
VM[13,6,14] = 0.00
VM[14,6,14] = 0.00
VM[15,6,14] = 0.00
VM[0,7,14] = 0.00
VM[1,7,14] = 0.00
VM[2,7,14] = 0.00
VM[3,7,14] = 0.00
VM[4,7,14] = 0.00
VM[5,7,14] = 0.00
VM[6,7,14] = 0.00
VM[7,7,14] = 0.00
VM[8,7,14] = 0.00
VM[9,7,14] = 0.00
VM[10,7,14] = 0.00
VM[11,7,14] = 0.00
VM[12,7,14] = 0.00
VM[13,7,14] = 0.00
VM[14,7,14] = 0.00
VM[15,7,14] = 0.00
VM[0,8,14] = 0.00
VM[1,8,14] = 0.00
VM[2,8,14] = 0.00
VM[3,8,14] = 0.00
VM[4,8,14] = 0.00
VM[5,8,14] = 0.00
VM[6,8,14] = 0.00
VM[7,8,14] = 0.00
VM[8,8,14] = 0.00
VM[9,8,14] = 0.00
VM[10,8,14] = 0.00
VM[11,8,14] = 0.00
VM[12,8,14] = 0.00
VM[13,8,14] = 0.00
VM[14,8,14] = 0.00
VM[15,8,14] = 0.00
VM[0,9,14] = 0.00
VM[1,9,14] = 0.00
VM[2,9,14] = 0.00
VM[3,9,14] = 0.00
VM[4,9,14] = 0.00
VM[5,9,14] = 0.00
VM[6,9,14] = 0.00
VM[7,9,14] = 0.00
VM[8,9,14] = 0.00
VM[9,9,14] = 0.00
VM[10,9,14] = 0.00
VM[11,9,14] = 0.00
VM[12,9,14] = 0.00
VM[13,9,14] = 0.00
VM[14,9,14] = 0.00
VM[15,9,14] = 0.00
VM[0,10,14] = 0.00
VM[1,10,14] = 0.00
VM[2,10,14] = 0.00
VM[3,10,14] = 0.00
VM[4,10,14] = 0.00
VM[5,10,14] = 0.00
VM[6,10,14] = 0.00
VM[7,10,14] = 0.00
VM[8,10,14] = 0.00
VM[9,10,14] = 0.00
VM[10,10,14] = 0.00
VM[11,10,14] = 0.00
VM[12,10,14] = 0.00
VM[13,10,14] = 0.00
VM[14,10,14] = 0.00
VM[15,10,14] = 0.00
VM[0,11,14] = 0.00
VM[1,11,14] = 0.00
VM[2,11,14] = 0.00
VM[3,11,14] = 0.00
VM[4,11,14] = 0.00
VM[5,11,14] = 0.00
VM[6,11,14] = 0.00
VM[7,11,14] = 0.00
VM[8,11,14] = 0.00
VM[9,11,14] = 0.00
VM[10,11,14] = 0.00
VM[11,11,14] = 0.00
VM[12,11,14] = 0.00
VM[13,11,14] = 0.00
VM[14,11,14] = 0.00
VM[15,11,14] = 0.00
VM[0,12,14] = 0.00
VM[1,12,14] = 0.00
VM[2,12,14] = 0.00
VM[3,12,14] = 0.00
VM[4,12,14] = 0.00
VM[5,12,14] = 0.00
VM[6,12,14] = 0.00
VM[7,12,14] = 0.00
VM[8,12,14] = 0.00
VM[9,12,14] = 0.00
VM[10,12,14] = 0.00
VM[11,12,14] = 0.00
VM[12,12,14] = 0.00
VM[13,12,14] = 0.00
VM[14,12,14] = 0.00
VM[15,12,14] = 0.00
VM[0,13,14] = 0.00
VM[1,13,14] = 0.00
VM[2,13,14] = 0.00
VM[3,13,14] = 0.00
VM[4,13,14] = 0.00
VM[5,13,14] = 0.00
VM[6,13,14] = 0.00
VM[7,13,14] = 0.00
VM[8,13,14] = 0.00
VM[9,13,14] = 0.00
VM[10,13,14] = 0.00
VM[11,13,14] = 0.00
VM[12,13,14] = 0.00
VM[13,13,14] = 0.00
VM[14,13,14] = 0.00
VM[15,13,14] = 0.00
VM[0,14,14] = 0.00
VM[1,14,14] = 0.00
VM[2,14,14] = 0.00
VM[3,14,14] = 0.00
VM[4,14,14] = 0.00
VM[5,14,14] = 0.00
VM[6,14,14] = 0.00
VM[7,14,14] = 0.00
VM[8,14,14] = 0.00
VM[9,14,14] = 0.00
VM[10,14,14] = 0.00
VM[11,14,14] = 0.00
VM[12,14,14] = 0.00
VM[13,14,14] = 0.00
VM[14,14,14] = 0.00
VM[15,14,14] = 0.00
VM[0,15,14] = 0.00
VM[1,15,14] = 0.00
VM[2,15,14] = 0.00
VM[3,15,14] = 0.00
VM[4,15,14] = 0.00
VM[5,15,14] = 0.00
VM[6,15,14] = 0.00
VM[7,15,14] = 0.00
VM[8,15,14] = 0.00
VM[9,15,14] = 0.00
VM[10,15,14] = 0.00
VM[11,15,14] = 0.00
VM[12,15,14] = 0.00
VM[13,15,14] = 0.00
VM[14,15,14] = 0.00
VM[15,15,14] = 0.00
VM[0,0,15] = 0.00
VM[1,0,15] = 0.00
VM[2,0,15] = 0.00
VM[3,0,15] = 0.00
VM[4,0,15] = 0.00
VM[5,0,15] = 0.00
VM[6,0,15] = 0.00
VM[7,0,15] = 0.00
VM[8,0,15] = 0.00
VM[9,0,15] = 0.00
VM[10,0,15] = 0.00
VM[11,0,15] = 0.00
VM[12,0,15] = 0.00
VM[13,0,15] = 0.00
VM[14,0,15] = 0.00
VM[15,0,15] = 0.00
VM[0,1,15] = 0.00
VM[1,1,15] = 0.00
VM[2,1,15] = 0.00
VM[3,1,15] = 0.00
VM[4,1,15] = 0.00
VM[5,1,15] = 0.00
VM[6,1,15] = 0.00
VM[7,1,15] = 0.00
VM[8,1,15] = 0.00
VM[9,1,15] = 0.00
VM[10,1,15] = 0.00
VM[11,1,15] = 0.00
VM[12,1,15] = 0.00
VM[13,1,15] = 0.00
VM[14,1,15] = 0.00
VM[15,1,15] = 0.00
VM[0,2,15] = 0.00
VM[1,2,15] = 0.00
VM[2,2,15] = 0.00
VM[3,2,15] = 0.00
VM[4,2,15] = 0.00
VM[5,2,15] = 0.00
VM[6,2,15] = 0.00
VM[7,2,15] = 0.00
VM[8,2,15] = 0.00
VM[9,2,15] = 0.00
VM[10,2,15] = 0.00
VM[11,2,15] = 0.00
VM[12,2,15] = 0.00
VM[13,2,15] = 0.00
VM[14,2,15] = 0.00
VM[15,2,15] = 0.00
VM[0,3,15] = 0.00
VM[1,3,15] = 0.00
VM[2,3,15] = 0.00
VM[3,3,15] = 0.00
VM[4,3,15] = 0.00
VM[5,3,15] = 0.00
VM[6,3,15] = 0.00
VM[7,3,15] = 0.00
VM[8,3,15] = 0.00
VM[9,3,15] = 0.00
VM[10,3,15] = 0.00
VM[11,3,15] = 0.00
VM[12,3,15] = 0.00
VM[13,3,15] = 0.00
VM[14,3,15] = 0.00
VM[15,3,15] = 0.00
VM[0,4,15] = 0.00
VM[1,4,15] = 0.00
VM[2,4,15] = 0.00
VM[3,4,15] = 0.00
VM[4,4,15] = 0.00
VM[5,4,15] = 0.00
VM[6,4,15] = 0.00
VM[7,4,15] = 0.00
VM[8,4,15] = 0.00
VM[9,4,15] = 0.00
VM[10,4,15] = 0.00
VM[11,4,15] = 0.00
VM[12,4,15] = 0.00
VM[13,4,15] = 0.00
VM[14,4,15] = 0.00
VM[15,4,15] = 0.00
VM[0,5,15] = 0.00
VM[1,5,15] = 0.00
VM[2,5,15] = 0.00
VM[3,5,15] = 0.00
VM[4,5,15] = 0.00
VM[5,5,15] = 0.00
VM[6,5,15] = 0.00
VM[7,5,15] = 0.00
VM[8,5,15] = 0.00
VM[9,5,15] = 0.00
VM[10,5,15] = 0.00
VM[11,5,15] = 0.00
VM[12,5,15] = 0.00
VM[13,5,15] = 0.00
VM[14,5,15] = 0.00
VM[15,5,15] = 0.00
VM[0,6,15] = 0.00
VM[1,6,15] = 0.00
VM[2,6,15] = 0.00
VM[3,6,15] = 0.00
VM[4,6,15] = 0.00
VM[5,6,15] = 0.00
VM[6,6,15] = 0.00
VM[7,6,15] = 0.00
VM[8,6,15] = 0.00
VM[9,6,15] = 0.00
VM[10,6,15] = 0.00
VM[11,6,15] = 0.00
VM[12,6,15] = 0.00
VM[13,6,15] = 0.00
VM[14,6,15] = 0.00
VM[15,6,15] = 0.00
VM[0,7,15] = 0.00
VM[1,7,15] = 0.00
VM[2,7,15] = 0.00
VM[3,7,15] = 0.00
VM[4,7,15] = 0.00
VM[5,7,15] = 0.00
VM[6,7,15] = 0.00
VM[7,7,15] = 0.00
VM[8,7,15] = 0.00
VM[9,7,15] = 0.00
VM[10,7,15] = 0.00
VM[11,7,15] = 0.00
VM[12,7,15] = 0.00
VM[13,7,15] = 0.00
VM[14,7,15] = 0.00
VM[15,7,15] = 0.00
VM[0,8,15] = 0.00
VM[1,8,15] = 0.00
VM[2,8,15] = 0.00
VM[3,8,15] = 0.00
VM[4,8,15] = 0.00
VM[5,8,15] = 0.00
VM[6,8,15] = 0.00
VM[7,8,15] = 0.00
VM[8,8,15] = 0.00
VM[9,8,15] = 0.00
VM[10,8,15] = 0.00
VM[11,8,15] = 0.00
VM[12,8,15] = 0.00
VM[13,8,15] = 0.00
VM[14,8,15] = 0.00
VM[15,8,15] = 0.00
VM[0,9,15] = 0.00
VM[1,9,15] = 0.00
VM[2,9,15] = 0.00
VM[3,9,15] = 0.00
VM[4,9,15] = 0.00
VM[5,9,15] = 0.00
VM[6,9,15] = 0.00
VM[7,9,15] = 0.00
VM[8,9,15] = 0.00
VM[9,9,15] = 0.00
VM[10,9,15] = 0.00
VM[11,9,15] = 0.00
VM[12,9,15] = 0.00
VM[13,9,15] = 0.00
VM[14,9,15] = 0.00
VM[15,9,15] = 0.00
VM[0,10,15] = 0.00
VM[1,10,15] = 0.00
VM[2,10,15] = 0.00
VM[3,10,15] = 0.00
VM[4,10,15] = 0.00
VM[5,10,15] = 0.00
VM[6,10,15] = 0.00
VM[7,10,15] = 0.00
VM[8,10,15] = 0.00
VM[9,10,15] = 0.00
VM[10,10,15] = 0.00
VM[11,10,15] = 0.00
VM[12,10,15] = 0.00
VM[13,10,15] = 0.00
VM[14,10,15] = 0.00
VM[15,10,15] = 0.00
VM[0,11,15] = 0.00
VM[1,11,15] = 0.00
VM[2,11,15] = 0.00
VM[3,11,15] = 0.00
VM[4,11,15] = 0.00
VM[5,11,15] = 0.00
VM[6,11,15] = 0.00
VM[7,11,15] = 0.00
VM[8,11,15] = 0.00
VM[9,11,15] = 0.00
VM[10,11,15] = 0.00
VM[11,11,15] = 0.00
VM[12,11,15] = 0.00
VM[13,11,15] = 0.00
VM[14,11,15] = 0.00
VM[15,11,15] = 0.00
VM[0,12,15] = 0.00
VM[1,12,15] = 0.00
VM[2,12,15] = 0.00
VM[3,12,15] = 0.00
VM[4,12,15] = 0.00
VM[5,12,15] = 0.00
VM[6,12,15] = 0.00
VM[7,12,15] = 0.00
VM[8,12,15] = 0.00
VM[9,12,15] = 0.00
VM[10,12,15] = 0.00
VM[11,12,15] = 0.00
VM[12,12,15] = 0.00
VM[13,12,15] = 0.00
VM[14,12,15] = 0.00
VM[15,12,15] = 0.00
VM[0,13,15] = 0.00
VM[1,13,15] = 0.00
VM[2,13,15] = 0.00
VM[3,13,15] = 0.00
VM[4,13,15] = 0.00
VM[5,13,15] = 0.00
VM[6,13,15] = 0.00
VM[7,13,15] = 0.00
VM[8,13,15] = 0.00
VM[9,13,15] = 0.00
VM[10,13,15] = 0.00
VM[11,13,15] = 0.00
VM[12,13,15] = 0.00
VM[13,13,15] = 0.00
VM[14,13,15] = 0.00
VM[15,13,15] = 0.00
VM[0,14,15] = 0.00
VM[1,14,15] = 0.00
VM[2,14,15] = 0.00
VM[3,14,15] = 0.00
VM[4,14,15] = 0.00
VM[5,14,15] = 0.00
VM[6,14,15] = 0.00
VM[7,14,15] = 0.00
VM[8,14,15] = 0.00
VM[9,14,15] = 0.00
VM[10,14,15] = 0.00
VM[11,14,15] = 0.00
VM[12,14,15] = 0.00
VM[13,14,15] = 0.00
VM[14,14,15] = 0.00
VM[15,14,15] = 0.00
VM[0,15,15] = 0.00
VM[1,15,15] = 0.00
VM[2,15,15] = 0.00
VM[3,15,15] = 0.00
VM[4,15,15] = 0.00
VM[5,15,15] = 0.00
VM[6,15,15] = 0.00
VM[7,15,15] = 0.00
VM[8,15,15] = 0.00
VM[9,15,15] = 0.00
VM[10,15,15] = 0.00
VM[11,15,15] = 0.00
VM[12,15,15] = 0.00
VM[13,15,15] = 0.00
VM[14,15,15] = 0.00
VM[15,15,15] = 0.00
N = 16
M = 16
K = 16
g = draw_voxel_model(VM, N, M, K)

max_stars_repo_name: oreuta/fuzzy-voxels

max_issues_repo_name: oreuta/fuzzy-voxels

max_forks_repo_name: oreuta/fuzzy-voxels


text:
import numpy as np
from sklearn.model_selection import train_test_split

def from_pandas_dataframe(d_frame=None, target=None,
                          test_size=None, shuffle=False, random_seed=1992):
    r"""
    NAME: from_pandas_dataframe

    This is a utility provided in mlots package. The function helps representing a pandas dataframe into numpy arrays.

    Parameters
    ----------
    d_frame                             :       pandas.DataFrame (default None)
                                                The pandas DataFrame that needs to be transformed.
    target                              :       str or int (default None)
                                                The column name of the target (y) variable.
    test_size                           :       float or int (default None)
                                                The value is the percentage of the data to be split for the test set.
    shuffle                             :       bool (default False)
                                                If True, the data is shuffled randomly.
    random_seed                         :       int (default 1992)
                                                The initial seed to be used by random function.

    Returns
    -------
    X_train, X_test, y_train, y_test    :       ndarray, ndarray, ndarray, ndarray
                                                If target is None, only X is returned.
                                                If test_size is None, no train and test split is performed.
    Examples
    --------
    >>> from mlots.utilities import from_pandas_dataframe
    >>> X_train, X_test, y_train, y_test = from_pandas_dataframe(d_frame, target="labels", test_size=0.33, shuffle=True)
    """
    if target is not None:
        y = d_frame[target].values
        d_frame.drop([target], axis=1, inplace=True)
    else:
        y = None

    X = d_frame.to_numpy(dtype=np.float32)

    if test_size is None:
        if y is None:
            return X
        return X, y

    if isinstance(test_size, int):
        test_size = test_size / 100
    if y is None:
        return train_test_split(X, test_size=test_size, random_state=random_seed, shuffle=shuffle)
    return train_test_split(X, y, test_size=test_size, random_state=random_seed, shuffle=shuffle)

# def dataframe_repr(X=None):
#     r"""
#     NAME: dataframe_repr
#
#     This is a utility provided in mlots package. The function helps representing a ndarray into a pandas dataframe.
#
#     Parameters
#     ----------
#     X               :       ndarray
#                             The ndarray data to be represented as pandas.DataFrame.
#     Returns
#     -------
#     X_df            :       X represented as a 1-col pandas.DataFrame
#     """
#     X_df = pd.DataFrame(columns=["dim0"])
#     for i, row in enumerate(X):
#         X_df.at[i, "dim0"] = np.asarray(row, dtype=np.float32)
#     return X_df

ext: py

max_stars_repo_name: I-Form/mlots

max_issues_repo_name: vivekmahato/mlots

max_forks_repo_name: vivekmahato/mlots

reason: import numpy


text:
# Originally from https://github.com/deepmind/dm-haiku/blob/main/examples/mnist.py
"""MNIST classifier example"""

from typing import Generator, Mapping, Tuple

from absl import app
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
import tensorflow as tf
import optax
import tensorflow_datasets as tfds
import os

# For me, tfds and jax struggles to get GPU memory. Below will prohibit using GPU memory by TF.
tf.config.experimental.set_visible_devices([], "GPU")
Batch = Mapping[str, np.ndarray]    # Mapping is dict-like which can use __getitem__

def net_fn(batch: Batch) -> jnp.ndarray:
    """Standard LeNet-300-100 MLP network."""
    x = batch["image"].astype(jnp.float32) / 255.
    mlp = hk.Sequential([
        hk.Flatten(),
        hk.Linear(300), jax.nn.relu,
        hk.Linear(100), jax.nn.relu,
        hk.Linear(10),
    ])
    return mlp(x)

def load_dataset(
        split: str,
        *,
        is_training: bool,
        batch_size: int,
) -> Generator[Batch, None, None]:
    """Loads the dataset as a generator of batches."""
    ds = tfds.load("mnist:3.*.*", split=split).cache().repeat()
    if is_training:
        ds = ds.shuffle(10 * batch_size, seed=0)
    ds = ds.batch(batch_size)
    return iter(tfds.as_numpy(ds))

def main(_):
    # Make the network and optimizer.
    net = hk.without_apply_rng(hk.transform(net_fn))
    opt = optax.adam(1e-3)

    # Training loss (cross-entropy).
    def loss(params: hk.Params, batch: Batch) -> jnp.ndarray:
        """Compute the loss of the network, including L2."""
        logits = net.apply(params, batch)
        labels = jax.nn.one_hot(batch["label"], 10)

        l2_loss = 0.5 * sum(jnp.sum(jnp.square(p)) for p in jax.tree_leaves(params))    # l2_norm of parameters
        softmax_xent = -jnp.sum(labels * jax.nn.log_softmax(logits))
        softmax_xent /= labels.shape[0]

        return softmax_xent + 1e-4 * l2_loss

    # Evaluation metric (classification accuracy).
    @jax.jit
    def accuracy(params: hk.Params, batch: Batch) -> jnp.ndarray:
        predictions = net.apply(params, batch)
        return jnp.mean(jnp.argmax(predictions, axis=-1) == batch["label"])

    @jax.jit
    def update(
            params: hk.Params,
            opt_state: optax.OptState,
            batch: Batch,
    ) -> Tuple[hk.Params, optax.OptState]:
        """Learning rule (stochastic gradient descent)."""
        grads = jax.grad(loss)(params, batch)
        updates, opt_state = opt.update(grads, opt_state)
        new_params = optax.apply_updates(params, updates)
        return new_params, opt_state

    # DeepMind's comment
    # We maintain avg_params, the exponential moving average of the "live" params.
    # avg_params is used only for evaluation (cf. https://doi.org/10.1137/0330046)
    @jax.jit
    def ema_update(params, avg_params):
        return optax.incremental_update(params, avg_params, step_size=0.001)

    # Make datasets.
    train = load_dataset("train", is_training=True, batch_size=1000)
    train_eval = load_dataset("train", is_training=False, batch_size=10000)
    test_eval = load_dataset("test", is_training=False, batch_size=10000)

    # Initialize network and optimizer; note we draw an input to get shapes.
    params = avg_params = net.init(jax.random.PRNGKey(42), next(train))
    opt_state = opt.init(params)

    # Train/eval loop.
    for step in range(10001):
        if step % 1000 == 0:
            # Periodically evaluate classification accuracy on train & test sets.
            train_accuracy = accuracy(avg_params, next(train_eval))
            test_accuracy = accuracy(avg_params, next(test_eval))
            train_loss = loss(params, next(train_eval))
            train_loss = jax.device_get(train_loss)
            train_accuracy, test_accuracy = jax.device_get(
                (train_accuracy, test_accuracy))
            print(f"Step {step}] Train / Test accuracy / Train loss: "
                  f"{train_accuracy:.3f} / {test_accuracy:.3f} / {train_loss:.3f}.")

        # Do SGD on a batch of training examples.
        params, opt_state = update(params, opt_state, next(train))
        avg_params = ema_update(params, avg_params)

if __name__ == '__main__':
    app.run(main)

reason: import numpy,import jax


text:
import numpy

hiddenwords_orig = numpy.genfromtxt('julekalender2020/luke3/matrix.txt',dtype=str)

hiddenwords_transposed = []
for i in range(len(hiddenwords_orig[0])):
    new_string = ''.join([line[i] for line in hiddenwords_orig])
    hiddenwords_transposed.append(new_string)

hiddenwords_chararray = numpy.chararray((1000,1000), unicode=True)
for i in range(len(hiddenwords_orig[0])):
    hiddenwords_chararray[i] = numpy.char.array(hiddenwords_orig[i], itemsize=1)

wordlist = numpy.genfromtxt('julekalender2020/luke3/wordlist.txt', dtype=str)
wordsfound = []

for word in wordlist:

    for row in hiddenwords_orig:
        if word in row:
            wordsfound.append(word)
            print("Found word, correct way: ", word)

        if str(word)[::-1] in row:
            wordsfound.append(word)
            print("Found word, reversed: ", word)

    for row in hiddenwords_transposed:
        if word in row:
            wordsfound.append(word)
            print("Found word, transposed, correct way: ", word)

        if str(word)[::-1] in row:
            wordsfound.append(word)
            print("Found word, transposed, reversed: ", word)

print("Size hiddenwords: ", len(hiddenwords_orig))
print("Size wordlist: ", len(wordlist))
print("Size wordsfound: ", len(wordsfound))
print(wordsfound)

print("Shape of orig", numpy.shape(hiddenwords_orig))
print("Shape of transposed: ", numpy.shape(hiddenwords_transposed))
print("Shape of chararray: ", numpy.shape(hiddenwords_chararray))
print(hiddenwords_chararray[0,1000])


text:
from audioop import reverse
import numpy as np
import pandas as pd
import csv
import time
import argparse
from collections import defaultdict
from sklearn import metrics
import random
random.seed(2)
parser = argparse.ArgumentParser(description="Sample extraction")

parser.add_argument("--dir_name_in", type=str, help="dir", default='./batched')
parser.add_argument("--dir_name_out", type=str, help="dir", default='./batched')

parser.add_argument("--dir_name_ref_in", type=str, help="dir", default='./batched')
parser.add_argument("--dir_name_ref_out", type=str, help="dir", default='./batched')

parser.add_argument("--dir_name_train_in", type=str, help="dir")
parser.add_argument("--dir_name_train_out", type=str, help="dir")
parser.add_argument("--dir_name_population_in", type=str, help="dir")
parser.add_argument("--dir_name_population_out", type=str, help="dir")

parser.add_argument("--root_path", type=str, help="dir", default='./loss_values/')

args = parser.parse_args()

SUBSAMPLE = True

def load_loss_vals(file):
    loss_vals = []
    with open(file+'/loss.txt','r') as loss_file:
        for line in loss_file:
            loss_vals.append(float(line[:-1]))

    return loss_vals

def get_loss_diff_auc(main_in_list, main_out_list, ref_in_list,ref_out_list):

    diff_in = [(s_m-s_ref) for s_m,s_ref in zip(main_in_list,ref_in_list)]
    diff_out = [(s_m-s_ref) for s_m,s_ref in zip(main_out_list,ref_out_list)]

    y_test_in = [1]*len(diff_in)
    y_test_out = [0]*len(diff_out)
    y_test = y_test_in + y_test_out

    y_test_np = np.array(y_test)

    diff_list = diff_in + diff_out
    diff_np = np.array(diff_list)

    max_diff = np.max(diff_np)
    min_diff = np.min(diff_np)

    diff_normalized = (max_diff -diff_np)/(max_diff-min_diff)

    auc = metrics.roc_auc_score(y_test_np, diff_normalized)

    return auc

def get_loss_avg_auc(main_in_list, main_out_list):

    y_test_in = [1]*len(main_in_list)
    y_test_out = [0]*len(main_out_list)
    y_test = y_test_in + y_test_out

    y_test_np = np.array(y_test)

    diff_list = main_in_list + main_out_list
    diff_np = np.array(diff_list)

    max_diff = np.max(diff_np)
    min_diff = np.min(diff_np)

    diff_normalized = (max_diff -diff_np)/(max_diff-min_diff)

    auc = metrics.roc_auc_score(y_test_np, diff_normalized)
    #print(auc, '    ',len(main_in_list),'   ',len(main_out_list))

    return auc, len(main_in_list),len(main_out_list)

def get_threshold_precision_diff_tail_acc(main_in_list, main_out_list, ref_in_list,ref_out_list,population_out_list=None, population_in_list=None):

    diff_in = [(s_m-s_ref) for s_m,s_ref in zip(main_in_list,ref_in_list)]
    diff_out = [(s_m-s_ref) for s_m,s_ref in zip(main_out_list,ref_out_list)]

    if population_in_list is not None and population_out_list is not None:
        diff_population = [(s_m-s_ref) for s_m,s_ref in zip(population_in_list,population_out_list)]
        #diff_population.sort(reverse=True)
        diff_pop_sorted = sorted(diff_population,reverse=True)
        threshold = diff_pop_sorted[int(len(diff_pop_sorted)*0.9)+1]#-150
        population_in_list.sort(reverse=True)
        threshold_pop = population_in_list[int(len(population_in_list)*0.9)+1]#-150
    else:
        diff_out_sorted = sorted(diff_out, reverse=True)
        threshold = diff_out_sorted[int(len(diff_out_sorted)*0.9)+1]#-150
        main_out_list_sorted = sorted(main_out_list, reverse=True)
        threshold_pop=  main_out_list_sorted[int(len(main_out_list_sorted)*0.9)+1]#-150

    corr_in = lambda a,th : sum([1 for sample in a if sample<th ])

    cnt_corr_in = corr_in(diff_in,threshold)
    cnt_corr_in_pop = corr_in(main_in_list,threshold_pop)
    cnt_wrong_out = corr_in(diff_out, threshold)
    cnt_wrong_out_pop=corr_in(main_out_list,threshold_pop)

    #print(cnt_corr_in,cnt_wrong_out)
    #precision, accuracy, threshold,

    return cnt_corr_in/(cnt_corr_in+cnt_wrong_out), (cnt_corr_in+len(diff_out)-cnt_wrong_out)/(len(diff_out)+len(diff_in)),cnt_corr_in/(len((main_in_list))) ,     cnt_corr_in_pop/(cnt_corr_in_pop+cnt_wrong_out_pop), (cnt_corr_in_pop+len(main_out_list)-cnt_wrong_out_pop)/(len(main_out_list)+len(main_in_list)),cnt_corr_in_pop/(len((main_in_list))) ,threshold, threshold_pop,cnt_corr_in, cnt_wrong_out

def get_threshold_precision_avg_acc(main_in_list, main_out_list, training_in_list=None, training_out_list=None):

    if training_in_list is not None and training_out_list is not None:
        threshold = sum(training_in_list)/len(training_in_list)
    else:
        threshold = sum(main_in_list)/len(main_in_list)
    corr_in = lambda a,th : sum([1 for sample in a if sample<th ])

    cnt_corr_in = corr_in(main_in_list,threshold)
    cnt_wrong_out = corr_in(main_out_list, threshold)

    #print(cnt_corr_in,cnt_wrong_out)
    #precision, accuracy, threshold,

    return cnt_corr_in/(cnt_corr_in+cnt_wrong_out), (cnt_corr_in+len(main_out_list)-cnt_wrong_out)/(len(main_out_list)+len(main_in_list)), cnt_corr_in/(len(main_in_list)),threshold, cnt_corr_in, cnt_wrong_out

root_path = args.root_path
loss_root= root_path

path_main_in=args.dir_name_in
path_main_out = args.dir_name_out

path_ref_in = args.dir_name_ref_in
path_ref_out = args.dir_name_ref_out

if args.dir_name_train_in is not None and args.dir_name_train_out is not None:

    loss_train_in = load_loss_vals(loss_root+'/'+args.dir_name_train_in)
    loss_train_out = load_loss_vals(loss_root+'/'+args.dir_name_train_out)
else:
    loss_train_in = None
    loss_train_out = None

if args.dir_name_population_in is not None and args.dir_name_population_out is not None:

    loss_population_in = load_loss_vals(loss_root+'/'+args.dir_name_population_in)
    loss_population_out = load_loss_vals(loss_root+'/'+args.dir_name_population_out)

else:

    loss_population_in = None
    loss_population_out = None

loss_main_in_s = load_loss_vals(loss_root+'/'+path_main_in)
loss_main_out =  load_loss_vals(loss_root+'/'+path_main_out)

loss_ref_in_s = load_loss_vals(loss_root+'/'+path_ref_in)
loss_ref_out =  load_loss_vals(loss_root+'/'+path_ref_out)

if SUBSAMPLE and (len(loss_main_in_s) >  len(loss_main_out)):
    in_len = len(loss_main_in_s)
    out_len = len(loss_main_out)

    if in_len > out_len :
        avg_auc , len_in, len_out , recall, recall_avg= 0,0,0,0,0
        diff_auc = 0
        precision,acc,recall, threshold,cnt_corr_in,cnt_wrong_out = 0,0,0,0,0,0
        precision_pop,acc_pop,recall_pop,threshold_pop,cnt_corr_in,cnt_wrong_out = 0,0,0,0,0,0
        precision_avg,acc_avg,threshold_avg,cnt_corr_in_avg,cnt_wrong_out_avg  = 0,0,0,0,0
        for i in range(10):

            loss_main_in = random.sample(loss_main_in_s,out_len)
            loss_ref_in = random.sample(loss_ref_in_s, out_len)

            avg_auc_ , len_in_, len_out_= get_loss_avg_auc(loss_main_in, loss_main_out)
            avg_auc += avg_auc_
            len_in += len_in_
            len_out += len_out_

            diff_auc += get_loss_diff_auc(loss_main_in, loss_main_out, loss_ref_in,loss_ref_out)

            precision_,acc_,recall_,precision_pop_,acc_pop_,recall_pop_,threshold_,threshold_pop_,cnt_corr_in_,cnt_wrong_out_ = get_threshold_precision_diff_tail_acc(loss_main_in, loss_main_out, loss_ref_in,loss_ref_out, population_in_list= loss_population_in, population_out_list=loss_population_out)
            precision_avg_,acc_avg_,recall_avg_,threshold_avg_,cnt_corr_in_avg_,cnt_wrong_out_avg_= get_threshold_precision_avg_acc(loss_main_in, loss_main_out, training_in_list=loss_train_in, training_out_list=loss_train_out)

            precision += precision_
            precision_pop += precision_pop_
            acc += acc_
            acc_pop += acc_pop_
            recall+= recall_
            recall_pop += recall_pop_
            threshold += threshold_
            threshold_pop += threshold_pop_
            cnt_corr_in += cnt_corr_in_
            cnt_wrong_out += cnt_wrong_out_

            precision_avg += precision_avg_
            acc_avg += acc_avg_
            recall_avg += recall_avg_
            threshold_avg += threshold_avg_
            cnt_corr_in_avg += cnt_corr_in_avg_
            cnt_wrong_out_avg += cnt_wrong_out_avg_

    print(f'{avg_auc/10},{diff_auc/10},{precision_avg/10},{precision_pop/10},{precision/10},{recall_avg/10},{recall_pop/10},{recall/10},{acc_avg/10},{acc_pop/10},{acc/10},{threshold_avg/10},{threshold_pop/10},{threshold/10},{len_in/10},{len_out/10}')

else:
    loss_main_in = load_loss_vals(loss_root+'/'+path_main_in)
    loss_main_out =  load_loss_vals(loss_root+'/'+path_main_out)

    loss_ref_in = load_loss_vals(loss_root+'/'+path_ref_in)
    loss_ref_out =  load_loss_vals(loss_root+'/'+path_ref_out)

    avg_auc , len_in, len_out= get_loss_avg_auc(loss_main_in, loss_main_out)
    diff_auc = get_loss_diff_auc(loss_main_in, loss_main_out, loss_ref_in,loss_ref_out)

    precision,acc,recall,precision_pop,acc_pop,recall_pop,threshold,threshold_pop,cnt_corr_in,cnt_wrong_out = get_threshold_precision_diff_tail_acc(loss_main_in, loss_main_out, loss_ref_in,loss_ref_out, population_in_list= loss_population_in, population_out_list=loss_population_out)
    precision_avg,acc_avg,recall_avg,threshold_avg,cnt_corr_in_avg,cnt_wrong_out_avg = get_threshold_precision_avg_acc(loss_main_in, loss_main_out, training_in_list=loss_train_in, training_out_list=loss_train_out)

    print(f'{avg_auc},{diff_auc},{precision_avg},{precision_pop},{precision},{recall_avg},{recall_pop},{recall},{acc_avg},{acc},{len_in},{len_out},{threshold_avg},{threshold_pop},{threshold},{cnt_corr_in_avg},{cnt_wrong_out_avg},{cnt_corr_in},{cnt_wrong_out}')

max_stars_repo_name: Susanou/MasterProject

max_issues_repo_name: Susanou/MasterProject

max_forks_repo_name: Susanou/MasterProject