def read_file(self, filepath, verbose_mode):
A = []
c = []
b = []
signals = []
non_negativity = []
try:
file_pointer = open(filepath, 'r')
except IOError:
print("Error: File Doesn't Exists!")
return -1
num_variables = int(file_pointer.readline())
num_constraints = int(file_pointer.readline())
non_negativity = file_pointer.readline().replace("\r\n", "").split(" ")
c = file_pointer.readline().replace("\r\n", "").split(" ")
i = 0
line = file_pointer.readline()
while (line):
line = line.split(" ")
b.append(float(line[len(line) - 1]))
signals.append(line[len(line) - 2])
A.append([line[k] for k in (range(len(line) - 2))])
line = file_pointer.readline()
i += 1
file_pointer.close()
return linear_programming.LinearProgramming(num_variables,
num_constraints, A, b, c,
signals, non_negativity,
verbose_mode)
def setUp(self):
self.A1 = matrix.Matrix([
[3, 2, 1],
[2, 5, 3]
])
self.b1 = [10, 15]
self.c1 = [2, 3, 4]
self.lp1 = linear_programming.LinearProgramming(self.c1, self.A1, self.b1)
def test_unfeasible(self):
A = matrix.Matrix([
[1, 1, 1],
[-1, -1, 1]
])
b = [-5, -5]
c = [-1, 2, -2]
lp = linear_programming.LinearProgramming(c, A, b)
result = lp.run()
self.assertEqual(result.status, "UNFEASIBLE")
def test_unbounded(self):
A = matrix.Matrix([
[-1, 1],
[1, -2]
])
b = [1, 2]
c = [2, 1]
lp = linear_programming.LinearProgramming(c, A, b)
result = lp.run()
# self.assertAlmostEqual(result.value, 4)
# self.assertAlmostEqual(result.x_values[0]*c[0]+result.x_values[1]*c[1], 4)
self.assertEqual(result.status, "UNBOUNDED")
def test_success(self):
A = matrix.Matrix([
[2, -1],
[1, 2],
[-1, 2]
])
b = [4, 9, 3]
c = [2, 5]
lp = linear_programming.LinearProgramming(c, A, b)
result = lp.run()
self.assertAlmostEqual(result.value, 21)
self.assertAlmostEqual(result.x_values[0]*c[0]+result.x_values[1]*c[1], 21)
self.assertEqual(result.status, "SUCCESSFUL")
for i in range(len(b)):
s = 0
for j in range(len(result.x_values)):
s += result.x_values[j]*A.elements[i][j]
self.assertLessEqual(round(s,7), b[i])
def test_degenerate(self):
A = matrix.Matrix([
[3, 1],
[1, -1],
[0, 1]
])
b = [6, 2, 3]
c = [2, 1]
lp = linear_programming.LinearProgramming(c, A, b)
result = lp.run()
self.assertAlmostEqual(result.value, 5)
self.assertAlmostEqual(result.x_values[0]*c[0]+result.x_values[1]*c[1], 5)
self.assertEqual(result.status, "SUCCESSFUL")
for i in range(len(b)):
s = 0
for j in range(len(result.x_values)):
s += result.x_values[j]*A.elements[i][j]
self.assertLessEqual(s, b[i])
def test_repr(self):
primal = """max (-1*x0)+(2*x1)+(-2*x2)
(1*x0)+(1*x1)+(-1*x2) <= 5
(-1*x0)+(-1*x1)+(1*x2) <= -5
x0, x1, x2 >= 0"""
dual = """min (5*y0)+(-5*y1)
(1*y0)+(-1*y1) >= -1
(1*y0)+(-1*y1) >= 2
(-1*y0)+(1*y1) >= -2
y0, y1 >= 0"""
A = matrix.Matrix([
[1, 1, -1],
[-1, -1, 1]
])
b = [5, -5]
c = [-1, 2, -2]
lp = linear_programming.LinearProgramming(c, A, b)
self.assertEqual(str(lp), primal)
self.assertEqual(lp.dual(), dual)
def test_negative_b(self):
A = matrix.Matrix([
[-1,0,-1,0,0],
[-1,0,0,-1,0],
[-1,-1,0,0,-1],
[0,-1,0,0,0],
[0,-1,0,0,0]
]).transposed()
b = [-1,-1,-1,-1,-1]
c = [-1,-1,-1,-1,-1]
lp = linear_programming.LinearProgramming(c, A, b)
result = lp.run()
self.assertAlmostEqual(result.value, -3)
s = 0
for j in range(len(result.x_values)):
s += result.x_values[j]*c[0]
self.assertAlmostEqual(s, -3)
self.assertEqual(result.status, "SUCCESSFUL")
for i in range(len(b)):
s = 0
for j in range(len(result.x_values)):
s += result.x_values[j]*A.elements[i][j]
self.assertLessEqual(s, b[i])