def graphMaker():
#Initializing lists
nonHeap_x = []
nonHeap_y = []
heap_x = []
heap_y = []
#For 8 files 0 through 8
for i in range(9):
#Initialize
weights_list = []
values_list = []
heap_weight = []
heap_values = []
used_vals = []
used_Heapvals = []
#Reopening and initailizing files
c_file = "./KnapsackTestData/p0" + str(i) + "_c.txt"
w_file = "./KnapsackTestData/p0" + str(i) + "_w.txt"
v_file = "./KnapsackTestData/p0" + str(i) + "_v.txt"
#Capacity
file = open(c_file)
maxCap = int(file.readline())
file.close()
#Weights
file = open(w_file)
for line in file:
weights_list.append(int(line))
heap_weight.append(int(line))
file.close()
#Values
file = open(v_file)
for line in file:
values_list.append(int(line))
heap_values.append(int(line))
file.close()
#Greedy non heap
greedyClass = Greedy()
greedyClass.quickSort(0,
len(values_list) - 1, values_list, weights_list)
greedyClass.greedyApproach(values_list, maxCap, weights_list,
used_vals)
greedyOps = greedyClass.greedyOperations
nonHeap_x.append(len(values_list))
nonHeap_y.append(greedyOps)
#Greedy heap
heapClass = Heap()
heapClass.heapSort(heap_values, heap_weight, len(heap_values))
heapClass.heapApproach(heap_values, heap_weight, maxCap, used_Heapvals)
heapGredOps = heapClass.heapOperations
heap_x.append(len(values_list))
heap_y.append(heapGredOps)
#Printing the two graphs one by one.
print("Printing Non-Heap Greedy")
plt.title("Greedy Non-Heap")
plt.xlabel("n")
plt.ylabel("Time (B-ops)")
plt.plot(nonHeap_x, nonHeap_y, 'bo')
plt.show()
print("Printing Heap Greedy")
plt.title("Heap Greedy")
plt.xlabel("n")
plt.ylabel("Time (B-ops)")
plt.plot(heap_x, heap_y, 'go')
plt.show()
def main():
#For argument passing
fileargs = argparse.ArgumentParser()
fileargs.add_argument('file', help="file number to run", type=int)
args = fileargs.parse_args()
#Initialize Knapsack Objects for tasks
weights = []
values = []
valueIndices = [] #Copy of the original order of values
heapWeights = []
heapValues = []
#Concate checks and balances
if args.file < 10:
c_file = "./KnapsackTestData/p0" + str(args.file) + "_c.txt"
w_file = "./KnapsackTestData/p0" + str(args.file) + "_w.txt"
v_file = "./KnapsackTestData/p0" + str(args.file) + "_v.txt"
else:
c_file = "./KnapsackTestData/p" + str(args.file) + "_c.txt"
w_file = "./KnapsackTestData/p" + str(args.file) + "_w.txt"
v_file = "./KnapsackTestData/p" + str(args.file) + "_v.txt"
print("File containing the capacity, weights, and values are:",
c_file[19:] + ", " + w_file[19:] + ", " + v_file[19:])
print()
#Read in from files
file = open(c_file)
#Max weight
maxCapacity = int(file.readline())
file.close()
#Weights
file = open(w_file)
for line in file:
weights.append(int(line))
heapWeights.append(int(line))
file.close()
#Values
file = open(v_file)
for line in file:
valueIndices.append(int(line))
values.append(int(line))
heapValues.append(int(line))
file.close()
totalWeights = len(weights)
totalValues = len(values)
print("Knapsack capacity =", maxCapacity, ". Total number of items =",
totalWeights)
print()
#Dynamic Programming
dynamicClass = Dynamic()
dpTable = [[0 for x in range(maxCapacity + 1)]
for x in range(totalWeights + 1)]
begin = time.time()
dpResult = dynamicClass.dynamicApproach(weights, values, maxCapacity,
totalWeights, dpTable)
dpOptimal = dynamicClass.dynamicOptimal(weights, maxCapacity, totalWeights,
dpTable)
dpTime = time.time() - begin
print("Traditional Dynamic Programming Optimal value:", dpResult)
print("Traditional Dynamic Programming Optimal subset:", dpOptimal)
print("Traditional Dynamic Programming Time Taken:", dpTime)
print()
#Greedy approach using in-built sorting
greedyOptSubset = []
greedyOptValues = []
greedyClass = Greedy()
greedyClass.quickSort(0, totalValues - 1, values, weights)
greedyResult = greedyClass.greedyApproach(values, maxCapacity, weights,
greedyOptValues)
greedyOptSubset = optimalSubset(valueIndices, greedyOptValues)
print("Greedy Approach Optimal value:", greedyResult)
print("Greedy Approach Optimal subset:", greedyOptSubset)
print("Greedy Approach Number of Operations:",
greedyClass.greedyOperations)
print()
#Greedy approach using a max-heap
heapOptSubset = []
heapOptValues = []
totalHeapValues = len(heapValues)
heapClass = Heap()
heapClass.heapSort(heapValues, heapWeights, totalHeapValues)
heapOptValue = heapClass.heapApproach(heapValues, heapWeights, maxCapacity,
heapOptValues)
heapOptSubset = optimalSubset(valueIndices, heapOptValues)
print("Heap-based Greedy Approach Optimal value:", heapOptValue)
print("Heap-based Greedy Approach Optimal subset:", heapOptSubset)
print("Heap-based Approach Number of Operations:",
heapClass.heapOperations)
print()
graphMaker()
