labels = list(labels)
labels.sort()
dimension = len(labels)
distance_matrix = np.zeros( (dimension, dimension), 'f')
for pair in pairs:
i = labels.index(pair[0])
j = labels.index(pair[1])
distance_matrix[i][j] = -1*float(pair[2]) ## negating for affinity
return distance_matrix, labels
fname = sys.argv[1]
pairs = []
categories, topLevelCategories = dynamic.loadCategories("categories-500.txt") ## Load categories from cat file
resfile = csv.reader(open(fname, 'r'), delimiter=" ")
for row in resfile:
pairs.append(row)
distance_matrix, orig_labels = matrix_from_pairs(pairs)
hlabels = orig_labels[:]
hlabels.insert(0,0)
writer = csv.writer(open("matrix.csv",'w'),delimiter = ',')
writer.writerow(hlabels)
temp = []
counter = 0
for row in distance_matrix:
if counter <= max:
temp = list(row)
temp.insert(0,orig_labels[counter])
# return threshDict
################################################################
## MAIN STARTS HERE ##
#########################
fname = sys.argv[1] ## the file name
output = "output/" ## the output folder
#thres = sys.argv[2]
threshDict = {}
infile = open(output+fname, 'r') ## open input file in read mode
rows = infile.readlines() ## read lines from file
infile.close()
#print "Threshold is: ",thres
#threshold = float(thres)
categories, topLevelCategories = dynamic.loadCategories("categories-500.txt")
loadModel("mymodel.txt",categories) ## load the threshold file and build a dictionary
#print "Threshdict:",threshDict
bios = []
links = []
sig = []
for r in rows:
row = r.split()
# print row
# print "Row[2]=",row[2]
part1, part2, value = r.split() # Split the three parts of the line
part1Class, part1Details = part1.split("-") # split the first and second headers at the "-" to extract class
part2Class, part2Details = part2.split("-")
category1 = dynamic.categorisePayload(part1Class, categories) # Classify the header -> return 14 for 14.1, 14.1.1, 14.1.2
category2 = dynamic.categorisePayload(part2Class, categories)
