def test_eight_items(self):
self.assertEqual(
solve_knapsack(104, [{
"weight": 25,
"value": 350
}, {
"weight": 35,
"value": 400
}, {
"weight": 45,
"value": 450
}, {
"weight": 5,
"value": 20
}, {
"weight": 25,
"value": 70
}, {
"weight": 3,
"value": 8
}, {
"weight": 2,
"value": 5
}, {
"weight": 2,
"value": 5
}]), 900)
def test_knapsack(self):
w = [1, 2, 3, 5, 6]
v = [2, 3, 4, 8, 9]
b = 5
ans1 = 8
ans2 = [4]
self.assertEqual((ans1, ans2), knapsack.solve_knapsack(w, v, b))
pass
def _exact_solver_driver(self, data_file, max_weight, expected_energy,
expected_item_indices):
"""Utility routine to perform a test on the exact solver and compare to expected solution"""
sampler = dimod.ExactSolver()
df = pd.read_csv(data_file, names=['cost', 'weight'])
selected_item_indices, energy = knapsack.solve_knapsack(
df['cost'], df['weight'], max_weight, sampler=sampler)
self.assertEqual(energy, expected_energy)
self.assertEqual(selected_item_indices, expected_item_indices)
def test_example_knapsack(self):
self.assertEqual(
solve_knapsack(10, [{
"weight": 5,
"value": 10
}, {
"weight": 4,
"value": 40
}, {
"weight": 6,
"value": 30
}, {
"weight": 4,
"value": 50
}]), 90)
def test_fifteen_items(self):
self.assertEqual(
solve_knapsack(750, [{
"weight": 70,
"value": 135
}, {
"weight": 73,
"value": 139
}, {
"weight": 77,
"value": 149
}, {
"weight": 80,
"value": 150
}, {
"weight": 82,
"value": 156
}, {
"weight": 87,
"value": 163
}, {
"weight": 90,
"value": 173
}, {
"weight": 94,
"value": 184
}, {
"weight": 98,
"value": 192
}, {
"weight": 106,
"value": 201
}, {
"weight": 110,
"value": 210
}, {
"weight": 113,
"value": 214
}, {
"weight": 115,
"value": 221
}, {
"weight": 118,
"value": 229
}, {
"weight": 120,
"value": 240
}]), 1458)
def test_cannot_be_greedy_by_value(self):
self.assertEqual(
solve_knapsack(10, [{
"weight": 2,
"value": 20
}, {
"weight": 2,
"value": 20
}, {
"weight": 2,
"value": 20
}, {
"weight": 2,
"value": 20
}, {
"weight": 10,
"value": 50
}]), 80)
def test_cannot_be_greedy_by_weight(self):
self.assertEqual(
solve_knapsack(10, [{
"weight": 2,
"value": 5
}, {
"weight": 2,
"value": 5
}, {
"weight": 2,
"value": 5
}, {
"weight": 2,
"value": 5
}, {
"weight": 10,
"value": 21
}]), 21)
def test_no_items(self):
self.assertEqual(solve_knapsack(100, []), 0)
def test_one_item_too_heavy(self):
self.assertEqual(solve_knapsack(10, [{"weight": 100, "value": 1}]), 0)
To find such d and w, the biggest value must be equal to out.
'''
import numpy as np
from numpy.random import randint
import knapsack
#d: list of w/v. d is sorted from larger one.
d = np.sort([randint(100) for i in range(5)])[::-1]
check = 1
while (check == 1):
#w: weight of a bag
w = [randint(1, 100) for i in range(5)]
#v: value of a bag
v = [d[i] * w[i] for i in range(5)]
#l: index of the biggest value
l = v.index(max(v))
if (l > 1):
check = 0
max_weight = w[0] + w[1] + w[2] - 1
out = max(v[0] + v[1], v[l])
opt, used = knapsack.solve_knapsack(w, v, max_weight)
print(d)
print(w)
print(v, l)
print("b=%d opt=%d out=%d rate=%f " %
(max_weight, opt, out, float(opt) / float(out)))
print(used)