def compute_defense(att_stg,
prod_dist,
num_of_hp=args.fix_honeypots,
rationality=args.fix_rationality):
# production ports and attacker"s strategy
df = DataFrame('P')
ports = getRelPorts(att_stg, prod_dist, num=25)
df.setColumn('P', list(ports))
#ports = getAllPorts(att_stg, prod_dist)
#print(('Considered ports are: ', ports))
att = [att_stg.get(x, 0) for x in ports]
prod = [prod_dist.get(x, 0) for x in ports]
#print(('Attack ports: ', att, len(att)))
#print(('Dist ports: ', prod, len(prod)))
df.addColumn('s', prod)
df.addColumn('p', att)
ampl = AMPL(Environment(args.ampl))
ampl.setOption('solver', args.solver)
# ampl.setOption('verbosity', 'terse')
# Read the model file
ampl.read(args.model)
# Assign data to s
ampl.setData(df, 'P')
ampl.eval('let L := {}; let rat := {};'.format(num_of_hp, rationality))
#print(df)
# Solve the model
with suppress_stdout():
ampl.solve()
reward = ampl.getObjective("reward").value()
hp_stg = ampl.getData("{j in P} h[j]")
output = dict()
stg_json = list()
for k, v in hp_stg.toDict().items():
stg_json.append({"port": int(k), "prob": v})
output.update({"stg": stg_json})
output.update({"reward": reward})
output.update({"rationality": rationality})
output.update({"num_of_hp": num_of_hp})
output.update({"used_hps": ampl.getData("tot").toDict().popitem()[1]})
ampl.close()
return output
def testDataFrame(self):
ampl = self.ampl
# Create first dataframe (for data indexed over NUTR)
# Add data row by row
df1 = DataFrame('NUTR', ('n_min', 'n_max'))
df1.addRow(('A', 700, 20000))
df1.addRow(('B1', 700, 20000))
df1.addRow(('B2', 700, 20000))
df1.addRow(('C', 700, 20000))
df1.addRow(('CAL', 16000, 24000))
df1.addRow(('NA', 0.0, 50000))
# Create second dataframe (for data indexed over FOOD)
# Add column by column
df2 = DataFrame('FOOD')
foods = ['BEEF', 'CHK', 'FISH', 'HAM', 'MCH', 'MTL', 'SPG', 'TUR']
df2.setColumn('FOOD', foods)
contents = [2] * 8
df2.addColumn('f_min', contents)
contents = [10] * 8
df2.addColumn('f_max', contents)
costs = [3.19, 2.59, 2.29, 2.89, 1.89, 1.99, 1.99, 2.49]
df2.addColumn('cost', costs)
print(df2.getColumn('FOOD'))
for index in df2.getColumn('FOOD'):
print(df2.getRow(index))
# Create third dataframe, to assign data to the AMPL entity
# param amt{NUTR, FOOD};
df3 = DataFrame(('NUTR', 'FOOD'))
# Populate the set columns
nutrWithMultiplicity = [''] * 48
foodWithMultiplicity = [''] * 48
i = 0
for n in range(6):
for f in range(8):
print(df1.getRowByIndex(n)[0])
nutrWithMultiplicity[i] = df1.getRowByIndex(n)[0]
foodWithMultiplicity[i] = foods[f]
i += 1
df3.setColumn('NUTR', nutrWithMultiplicity)
df3.setColumn('FOOD', foodWithMultiplicity)
# Populate with all these values
values = [
60, 8, 8, 40, 15, 70, 25, 60, 10, 20, 15, 35, 15, 15, 25, 15, 15,
20, 10, 10, 15, 15, 15, 10, 20, 0, 10, 40, 35, 30, 50, 20, 295,
770, 440, 430, 315, 400, 370, 450, 968, 2180, 945, 278, 1182, 896,
1329, 1397
]
df3.addColumn('amt', values)
def main(argc, argv):
from amplpy import AMPL, DataFrame
os.chdir(os.path.dirname(__file__) or os.curdir)
try:
ampl = AMPL()
if argc > 1:
ampl.setOption('solver', argv[1])
# Read the model file
modelDirectory = argv[2] if argc == 3 else os.path.join('..', 'models')
ampl.read(os.path.join(modelDirectory, 'diet/diet.mod'))
foods = ['BEEF', 'CHK', 'FISH', 'HAM', 'MCH', 'MTL', 'SPG', 'TUR']
costs = [3.59, 2.59, 2.29, 2.89, 1.89, 1.99, 1.99, 2.49]
fmin = [2, 2, 2, 2, 2, 2, 2, 2]
fmax = [10, 10, 10, 10, 10, 10, 10, 10]
df = DataFrame('FOOD')
df.setColumn('FOOD', foods)
df.addColumn('cost', costs)
df.addColumn('f_min', fmin)
df.addColumn('f_max', fmax)
ampl.setData(df, 'FOOD')
nutrients = ['A', 'C', 'B1', 'B2', 'NA', 'CAL']
nmin = [700, 700, 700, 700, 0, 16000]
nmax = [20000, 20000, 20000, 20000, 50000, 24000]
df = DataFrame('NUTR')
df.setColumn('NUTR', nutrients)
df.addColumn('n_min', nmin)
df.addColumn('n_max', nmax)
ampl.setData(df, 'NUTR')
amounts = [[60, 8, 8, 40, 15, 70, 25, 60],
[20, 0, 10, 40, 35, 30, 50, 20],
[10, 20, 15, 35, 15, 15, 25, 15],
[15, 20, 10, 10, 15, 15, 15, 10],
[928, 2180, 945, 278, 1182, 896, 1329, 1397],
[295, 770, 440, 430, 315, 400, 379, 450]]
df = DataFrame(('NUTR', 'FOOD'), 'amt')
df.setValues({(nutrient, food): amounts[i][j]
for i, nutrient in enumerate(nutrients)
for j, food in enumerate(foods)})
ampl.setData(df)
ampl.solve()
print('Objective: {}'.format(ampl.getObjective('total_cost').value()))
except Exception as e:
print(e)
raise
def testDataFrame(self):
ampl = self.ampl
# Create first dataframe (for data indexed over NUTR)
# Add data row by row
df1 = DataFrame("NUTR", ("n_min", "n_max"))
df1.addRow(("A", 700, 20000))
df1.addRow(("B1", 700, 20000))
df1.addRow(("B2", 700, 20000))
df1.addRow(("C", 700, 20000))
df1.addRow(("CAL", 16000, 24000))
df1.addRow(("NA", 0.0, 50000))
# Create second dataframe (for data indexed over FOOD)
# Add column by column
df2 = DataFrame("FOOD")
foods = ["BEEF", "CHK", "FISH", "HAM", "MCH", "MTL", "SPG", "TUR"]
df2.setColumn("FOOD", foods)
self.assertEqual(list(df2.getColumn("FOOD")), foods)
contents = [2] * 8
df2.addColumn("f_min", contents)
self.assertEqual(list(df2.getColumn("f_min")), contents)
contents = [10] * 8
df2.addColumn("f_max", contents)
self.assertEqual(list(df2.getColumn("f_max")), contents)
costs = [3.19, 2.59, 2.29, 2.89, 1.89, 1.99, 1.99, 2.49]
df2.addColumn("cost", costs)
self.assertEqual(list(df2.getColumn("cost")), costs)
labels = [random.choice(string.ascii_letters)] * 8
df2.addColumn("labels", labels)
self.assertEqual(list(df2.getColumn("labels")), labels)
df2.addColumn("empty", [])
self.assertEqual(list(df2.getColumn("empty")), [None] * 8)
print(df2.getColumn("FOOD"))
for index in df2.getColumn("FOOD"):
print(df2.getRow(index))
# Create third dataframe, to assign data to the AMPL entity
# param amt{NUTR, FOOD};
df3 = DataFrame(("NUTR", "FOOD"))
# Populate the set columns
nutrWithMultiplicity = [""] * 48
foodWithMultiplicity = [""] * 48
i = 0
for n in range(6):
for f in range(8):
print(df1.getRowByIndex(n)[0])
nutrWithMultiplicity[i] = df1.getRowByIndex(n)[0]
foodWithMultiplicity[i] = foods[f]
i += 1
df3.setColumn("NUTR", nutrWithMultiplicity)
df3.setColumn("FOOD", foodWithMultiplicity)
# Populate with all these values
values = [
60,
8,
8,
40,
15,
70,
25,
60,
10,
20,
15,
35,
15,
15,
25,
15,
15,
20,
10,
10,
15,
15,
15,
10,
20,
0,
10,
40,
35,
30,
50,
20,
295,
770,
440,
430,
315,
400,
370,
450,
968,
2180,
945,
278,
1182,
896,
1329,
1397,
]
df3.addColumn("amt", values)
def main(argc, argv):
from amplpy import AMPL, DataFrame
os.chdir(os.path.dirname(__file__) or os.curdir)
try:
# Create first dataframe (for data indexed over NUTR)
# Add data row by row
df1 = DataFrame('NUTR', ('n_min', 'n_max'))
df1.addRow('A', 700, 20000)
df1.addRow('B1', 700, 20000)
df1.addRow('B2', 700, 20000)
df1.addRow('C', 700, 20000)
df1.addRow('CAL', 16000, 24000)
df1.addRow('NA', 0.0, 50000)
# Create second dataframe (for data indexed over FOOD)
# Add column by column
df2 = DataFrame('FOOD')
foods = ['BEEF', 'CHK', 'FISH', 'HAM', 'MCH', 'MTL', 'SPG', 'TUR']
df2.setColumn('FOOD', foods)
contents = [2] * 8
df2.addColumn('f_min', contents)
contents = [10] * 8
df2.addColumn('f_max', contents)
costs = [3.19, 2.59, 2.29, 2.89, 1.89, 1.99, 1.99, 2.49]
df2.addColumn('cost', costs)
# Create third dataframe, to assign data to the AMPL entity
# param amt{NUTR, FOOD};
df3 = DataFrame(index=('NUTR', 'FOOD'))
# Populate the set columns
nutrWithMultiplicity = [''] * 48
foodWithMultiplicity = [''] * 48
i = 0
for n in range(6):
for f in range(8):
print(df1.getRowByIndex(n)[0])
nutrWithMultiplicity[i] = df1.getRowByIndex(n)[0]
foodWithMultiplicity[i] = foods[f]
i += 1
df3.setColumn('NUTR', nutrWithMultiplicity)
df3.setColumn('FOOD', foodWithMultiplicity)
# Populate with all these values
values = [
60, 8, 8, 40, 15, 70, 25, 60, 10, 20, 15, 35, 15, 15, 25, 15, 15,
20, 10, 10, 15, 15, 15, 10, 20, 0, 10, 40, 35, 30, 50, 20, 295,
770, 440, 430, 315, 400, 370, 450, 968, 2180, 945, 278, 1182, 896,
1329, 1397
]
df3.addColumn('amt', values)
# Create AMPL object
ampl = AMPL()
if argc > 1:
ampl.setOption('solver', argv[1])
# Read the model file
modelDirectory = argv[2] if argc == 3 else os.path.join('..', 'models')
ampl.read(os.path.join(modelDirectory, 'diet/diet.mod'))
# Assign data to NUTR, n_min and n_max
ampl.setData(df1, 'NUTR')
# Assign data to FOOD, f_min, f_max and cost
ampl.setData(df2, 'FOOD')
# Assign data to amt
ampl.setData(df3)
# Solve the model
ampl.solve()
# Print out the result
print("Objective function value: {}".format(
ampl.getObjective('total_cost').value()))
# Get the values of the variable Buy in a dataframe
results = ampl.getVariable('Buy').getValues()
# Print
print(results)
except Exception as e:
print(e)
raise
