from LoadFiles import load, dist

data = load("flaredata2.txt")

# 10 dimensional matrix, to be filled with the count of each potential data element

gridcounts = [[[[[[[[[[0 for u in range(2)] for y in range(2)] for t in range(2)] for r in range(2)] for e in range(3)] for w in range(3)] for q in range(2)] for i in range(4)] for j in range(6)] for x in range(7)]

# populate the data

for e in data:

gridcounts[e[0]][e[1]][e[2]][e[3]-1][e[4]-1][e[5]-1][e[6]-1][e[7]-1][e[8]-1][e[9]-1] = gridcounts[e[0]][e[1]][e[2]][e[3]-1][e[4]-1][e[5]-1][e[6]-1][e[7]-1][e[8]-1][e[9]-1] + 1

# collapse data into a useable list

gridlist = []

for a in range(len(gridcounts)):

for b in range(len(gridcounts[a])):

for c in range(len(gridcounts[a][b])):

for d in range(len(gridcounts[a][b][c])):

for e in range(len(gridcounts[a][b][c][d])):

for f in range(len(gridcounts[a][b][c][d])):

for g in range(len(gridcounts[a][b][c][d][e][f])):

for h in range(len(gridcounts[a][b][c][d][e][f][g])):

for j in range(len(gridcounts[a][b][c][d][e][f][g][h])):

for k in range(len(gridcounts[a][b][c][d][e][f][g][h][j])):

gridlist.append([gridcounts[a][b][c][d][e][f][g][h][j][k], a/6, b/5, c/3, d, e/2, f/2, g, h, j, k])

# sort by count

gridlist.sort(key = lambda el: el[0], reverse = True)

#for x in range(len(gridlist)):

# print(gridlist[x])

total = len(data)

count = 0

previousCount = 0

cutoff = int(0.75*total)

noiseCutOff = gridlist[0][0]

previousCutOff = noiseCutOff

i = 0

while(count < cutoff):

previousCutOff = noiseCutOff

noiseCutOff = gridlist[i][0]

previousCount = count

while(noiseCutOff == gridlist[i][0]):

count = count + gridlist[i][0]

i = i + 1

#print(previousCutOff)

#print(count)

#print(previousCount)

#print(cutoff)

#print(i/len(gridlist))

def NonNoise(data, cutoff):

break

def addToCluster(nx, clusters, max_dist):

return False

clusters = []

for nc in NonNoise(gridlist, noiseCutOff):

if(addToCluster(nc, clusters, 1)):

clusters.append([nc])

#print(clusters)

def dataEqauls(e, o):

return e[0]/6 == o[1] and e[1]/5 == o[2] and e[2]/3 == o[3] and e[3]-1 == o[4] and (e[4]-1)/2 == o[5] and (e[5]-1)/2 == o[6] and e[6]-1 == o[7] and e[7]-1 == o[8] and e[8]-1 == o[9] and e[9]-1 == o[10]

Csets = [dict() for x in range(len(clusters))]

for e in data:

for i in range(len(clusters)):

for o in clusters[i]:

if dataEqauls(e, o):

if (e[10], e[11], e[12]) in Csets[i]:

Csets[i][(e[10], e[11], e[12])] = Csets[i][(e[10], e[11], e[12])] + 1

else:

Csets[i][(e[10], e[11], e[12])] = 1

print("number of clusters: ", end = "")

print(len(clusters))

for i in range(len(clusters)):

print("CLUSTER:")

for grid in clusters[i]:

print("\t", end = "")

print('{:3d}, {:.2f}, {:.2f}, {:.2f}, {:.2f}, {:.2f}, {:.2f}, {:.2f}, {:.2f}, {:.2f}, {:.2f}'.format(grid[0], grid[1],grid[2], grid[3],grid[4], grid[5],grid[6], grid[7],grid[8], grid[9],grid[10]))

print("Classifiers:")

for d in iter(Csets[i]):

print("\t", end = "")

print(d, end = ": ")

print(Csets[i][d])