def k_nearest_neighbor(distance_matrix,labels,exemplars,k,sourcedir,destdir):
counter = 0
tp = fp = 0
knn_index = []
selection = set()
for row in distance_matrix:
idx = argsort(row)
# print "idx = ",idx
# print "k = ",k
knn_index = idx[:int(k)]
w_cat = 0 ## wrong category
r_cat = 0 ## right category
tp_once = 0
for x in knn_index:
# print("datapoint = %s, index = %s , exemplar/neighbor = %s") % (labels[counter] , idx , exemplars[idx])
part1Class, part1Details = labels[counter].split("-")
category1 = dynamic.categorisePayload(part1Class, categories) # Classify the header -> return 14 for 14.1, 14.1.1, 14.1.2
part2Class, part2Details = exemplars[x].split("-")
category2 = dynamic.categorisePayload(part2Class, categories) # Classify the header -> return 14 for 14.1, 14.1.1, 14.1.2
if category1 != category2:
w_cat = w_cat + 1
print("datapoint = %s, index = %s , exemplar/neighbor = %s , FP") % (labels[counter] , idx[:int(k)] , exemplars[x])
else:
r_cat = r_cat + 1
print("datapoint = %s, index = %s , exemplar/neighbor = %s , TP") % (labels[counter] , idx[:int(k)] , exemplars[x])
if r_cat > w_cat:
tp = tp + 1
print "Majority vote True: ", r_cat
print "Adding to set: ", labels[counter]
selection.add(labels[counter])
else:
fp = fp + 1
print "Majority vote False: ", w_cat
counter = counter + 1
dimension_c = len(exemplars)
dimension_r = len(labels)
print "Exemplars: ", dimension_c
print "Datapoints: ", dimension_r
print "Total TP = ",tp
print "Total FP = ",fp
selection = list(selection)
selection.sort()
s = open("knn-select.txt",'a')
for item in selection:
s.write("\n" + str(item))
os.system("cp " + sourcedir + "/" + str(item) + " " + destdir)
s.close()
print "Selection set size: ", len(selection)
f = open("knn.txt",'a')
# f.write("\n####### NEW RECORD ########")
# f.write( "\nExemplars: " + str(dimension_c) )
# f.write( "\nDatapoints: " + str(dimension_r) )
f.write( "\n" + k + "," + str(dimension_c) + "," + str(dimension_r) + "," + str(tp) + "," + str(fp) )
# f.write( "\nTotal FP = " + str(fp) )
f.close()
def sum_labels(labels, distance_matrix):
val = {}
match_labels = {}
possfpval = {}
possfp_labels = {}
res = {}
ressum = 0
global categories
for label in labels:
part1Class, part1Details = label.split("-")
category1 = dynamic.categorisePayload(
part1Class, categories
) # Classify the header -> return 14 for 14.1, 14.1.1, 14.1.2
for i in range(0, len(labels)):
part2Class, part2Details = labels[i].split("-")
category2 = dynamic.categorisePayload(part2Class, categories)
if category1 == category2: ## Match main category or sub-cat
if label in val:
val[label].append(distance_matrix[labels.index(label)][i]) ## Append values from same category
match_labels[label].append(
labels[i]
) ## Append corresponding label for the values from same category
if label not in val:
val[label] = [] ## setup dictionary key for val
match_labels[label] = []
res[label] = [] ## setup dicitonary key for res
val[label].append(
distance_matrix[labels.index(label)][i]
) ## Append corresponding values from same category
match_labels[label].append(
labels[i]
) ## Append corresponding label for the values from same category
else: ## the categories dont match list of possible fp
if label in possfpval:
possfpval[label].append(
distance_matrix[labels.index(label)][i]
) ## Append values from diff category
possfp_labels[label].append(
labels[i]
) ## Append corresponding label for the values from diff category
if label not in possfpval:
possfpval[label] = [] ## setup dictionary key for fpval
possfp_labels[label] = []
# res[label] = [] ## setup dicitonary key for res
possfpval[label].append(
distance_matrix[labels.index(label)][i]
) ## Append corresponding values from same category
possfp_labels[label].append(
labels[i]
) ## Append corresponding label for the values from same category
ressum = 0
for item in val[label]: ## for every item in the dictionary key
ressum = ressum + item ## Add each item with its predecessor
res[label].append(ressum) ## Append the sum to the result corresponding the label
return possfpval, possfp_labels, res, val, match_labels ## return result and val dict
def k_nearest_neighbor(distance_matrix,labels,exemplars,k):
counter = 0
tp = fp = 0
knn_index = []
for row in distance_matrix:
idx = argsort(row)
# print "idx = ",idx
# print "k = ",k
knn_index = idx[:int(k)]
w_cat = 0 ## wrong category
r_cat = 0 ## right category
tp_once = 0
for x in knn_index:
# print("datapoint = %s, index = %s , exemplar/neighbor = %s") % (labels[counter] , idx , exemplars[idx])
part1Class, part1Details = labels[counter].split("-")
category1 = dynamic.categorisePayload(part1Class, categories) # Classify the header -> return 14 for 14.1, 14.1.1, 14.1.2
part2Class, part2Details = exemplars[x].split("-")
category2 = dynamic.categorisePayload(part2Class, categories) # Classify the header -> return 14 for 14.1, 14.1.1, 14.1.2
if category1 != category2:
w_cat = w_cat + 1
print("datapoint = %s, index = %s , exemplar/neighbor = %s , FP") % (labels[counter] , idx[:int(k)] , exemplars[x])
else:
r_cat = r_cat + 1
print("datapoint = %s, index = %s , exemplar/neighbor = %s , TP") % (labels[counter] , idx[:int(k)] , exemplars[x])
counter = counter + 1
if r_cat > w_cat:
tp = tp + 1
print "Majority vote True: ", r_cat
else:
fp = fp + 1
print "Majority vote False: ", w_cat
dimension_c = len(exemplars)
dimension_r = len(labels)
print "Exemplars: ", dimension_c
print "Datapoints: ", dimension_r
print "Total TP = ",tp
print "Total FP = ",fp
f = open("knn.txt",'a')
# f.write("\n####### NEW RECORD ########")
# f.write( "\nExemplars: " + str(dimension_c) )
# f.write( "\nDatapoints: " + str(dimension_r) )
f.write( "\n" + k + "," + str(dimension_c) + "," + str(dimension_r) + "," + str(tp) + "," + str(fp) )
# f.write( "\nTotal FP = " + str(fp) )
f.close()
def loadModel(filename,categories):
global threshDict;
f = open(filename,'r')
for line in f:
if line == "": continue ## ignore empty lines
line = line.strip()
ex_label, ex_thresh = line.split(',')
part1Class, part1Details = ex_label.split("-")
categ = dynamic.categorisePayload(part1Class, categories) # Classify the header -> return 14 for 14.1, 14.1.1, 14.1.2
# print "categ: ",categ
if categ not in threshDict:
threshDict[categ] = []
threshDict[categ].append([ex_label,ex_thresh]) ## Append the model to the dictionary for the category
else:
threshDict[categ].append([ex_label,ex_thresh])
temp = []
counter = 0
for row in distance_matrix:
if counter <= max:
temp = list(row)
temp.insert(0,labels[counter])
writer.writerow(temp)
counter = counter + 1
counter = 0
tp = fp = 0
for row in distance_matrix:
idx = nonzero(row==min(row))[0][0]
# print("datapoint = %s, index = %s , exemplar/neighbor = %s") % (labels[counter] , idx , exemplars[idx])
part1Class, part1Details = labels[counter].split("-")
category1 = dynamic.categorisePayload(part1Class, categories) # Classify the header -> return 14 for 14.1, 14.1.1, 14.1.2
part2Class, part2Details = exemplars[idx].split("-")
category2 = dynamic.categorisePayload(part2Class, categories) # Classify the header -> return 14 for 14.1, 14.1.1, 14.1.2
if category1 == category2:
print("datapoint = %s, index = %s , exemplar/neighbor = %s , TP") % (labels[counter] , idx , exemplars[idx])
tp = tp + 1
if category1 != category2:
print("datapoint = %s, index = %s , exemplar/neighbor = %s , FP") % (labels[counter] , idx , exemplars[idx])
fp = fp + 1
counter = counter + 1
print "Exemplars: ", dimension_c
print "Datapoints: ", dimension_r
print "Total TP = ",tp
print "Total FP = ",fp
