def six_3():
# Process input and returns graph dictionary
G = ip.process_input_6_3()
# Changing attributes of the graph to maintain different terms used;
# Pass boolean value to tell the method if a 'min' value is wanted in the graph
newG = ip.changeGraphAttributes(G, False, 'ToCapFlow')
ts1 = datetime.datetime.now()
resG = FF1.fordfulkersan(newG)
ts2 = datetime.datetime.now()
ft.write('\n 6.3 FF :'+ str((ts2 - ts1).microseconds)+ 'ms')
# Modify result graph to 'max'
newResG = ip.changeGraphAttributes(resG, False, 'ToMaxMin')
A, B = mc.create_min_cut_sets(newResG, [], 'S', [])
profit, output = mc.computeProfit_Output(A, B, G)
file_result = open('Result_File_6.3.txt', 'ab')
file_result.write('\nFF Tasks Chosen and Profit: \n')
file_result.write('6.3: Tasks Chosen: '+ str(output)+'\n')
file_result.write('6.3: Total Profit: ' + str(profit)+ '\n')
def assign_task_workers(inputgraph):
# Modify the attributes of the graph from min-max to cap-flow
ipt = pipt.changeGraphAttributes(inputgraph, True, 'ToCapFlow')
#Call to FF algorithm
resultgraph = fordfulkersan(ipt)
file_result = open('Result_File_6.1.txt', 'ab')
file_result.write('\nFF Assignments: \n')
# Print the assignments
for x in sorted(resultgraph['S']):
if(x!='tt'):
for y in resultgraph[x]:
if(y!='tt'):
if resultgraph[x][y]['flow']!=0:
file_result.write('Task '+x+' --> '+y+'\n')
file_result.write('FF Assignments\n')
return resultgraph
