def test_solver_data_class():
horizon = 3
n, b_0, M, searchers, g = parameters_7v_random_motion2()
# solve
# create model
md = Model("my_model")
start, vertices_t, times_v = cm.get_vertices_and_steps(
g, horizon, searchers)
start1, vertices_t1, times_v1 = cm.get_vertices_and_steps(
g, horizon, searchers)
my_vars = mf.add_variables(md, g, horizon, start, vertices_t)
mf.add_constraints(md, g, my_vars, searchers, vertices_t, horizon, b_0, M)
mf.set_solver_parameters(md, 0.99, horizon, my_vars)
md.update()
# Optimize model
md.optimize()
x_s, b_target = mf.query_variables(md)
obj_fun = md.objVal
gap = md.MIPGap
time_sol = round(md.Runtime, 4)
threads = md.Params.Threads
deadline = 6
theta = 3
my_data = MySolverData(horizon, deadline, theta, g, 'central')
t = 0
my_data.store_new_data(obj_fun, time_sol, gap, threads, x_s, b_target, t,
horizon)
assert all(start1) == all(start)
assert all(vertices_t) == all(vertices_t1)
assert all(times_v) == all(times_v1)
assert my_data.obj_value[0] == obj_fun
assert my_data.solve_time[0] == time_sol
assert my_data.threads[0] == md.Params.Threads
assert my_data.gap[0] == gap
assert my_data.x_s[0] == x_s
assert my_data.belief[0] == b_target
assert my_data.solver_type == 'central'
assert my_data.threads[0] == threads
def central_wrapper(g, horizon, searchers, b0, M_target, gamma, timeout):
"""Add variables, constraints, objective function and solve the model
compute all paths"""
solver_type = 'central'
# V^{s, t}
start, vertices_t, times_v = cm.get_vertices_and_steps(
g, horizon, searchers)
# create model
md = mf.create_model()
# add variables
my_vars = mf.add_variables(md, g, horizon, start, vertices_t, searchers)
# add constraints (central algorithm)
mf.add_constraints(md, g, my_vars, searchers, vertices_t, horizon, b0,
M_target)
# objective function
mf.set_solver_parameters(md, gamma, horizon, my_vars, timeout)
# solve and save results
obj_fun, time_sol, gap, x_searchers, b_target, threads = mf.solve_model(md)
# clean things
md.reset()
md.terminate()
del md
#
# clean things
return obj_fun, time_sol, gap, x_searchers, b_target, threads
def test_add_searcher_variables_y():
"""Test for expected Y in simple graph"""
# load graph
graph_file = 'G7V_test.p'
g = ext.get_graph(graph_file)
v0_searchers = [3, 1]
deadline = 3
# searchers
searchers = cp.create_dict_searchers(g, v0_searchers)
start, vertices_t, times_v = cm.get_vertices_and_steps(
g, deadline, searchers)
md = Model("my_model")
var_for_test = mf.add_searcher_variables(md, g, start, vertices_t,
deadline)[1]
assert var_for_test.get('y')[0] == 'y[1,3,1,0]'
assert var_for_test.get('y')[1] == 'y[1,3,5,0]'
assert var_for_test.get('y')[2] == 'y[1,3,3,0]'
assert var_for_test.get('y')[3] == 'y[1,1,2,1]'
assert var_for_test.get('y')[4] == 'y[1,1,3,1]'
assert var_for_test.get('y')[5] == 'y[1,1,1,1]'
assert var_for_test.get('y')[6] == 'y[1,3,1,1]'
assert var_for_test.get('y')[7] == 'y[1,3,5,1]'
assert var_for_test.get('y')[8] == 'y[1,3,3,1]'
def test_get_var():
"""Test for expected B in simple graph"""
# load graph
graph_file = 'G7V_test.p'
g = ext.get_graph(graph_file)
v0_searchers = [3, 1]
deadline = 3
# searchers
searchers = cp.create_dict_searchers(g, v0_searchers)
start, vertices_t, times_v = cm.get_vertices_and_steps(
g, deadline, searchers)
md = Model("my_model")
# time indexes
Tau_ = ext.get_idx_time(deadline)
searchers_vars = mf.add_searcher_variables(md, g, start, vertices_t,
deadline)[0]
# variables related to target position belief and capture
target_vars = mf.add_target_variables(md, g, deadline)[0]
# get my variables together in one dictionary
my_vars = {}
my_vars.update(searchers_vars)
my_vars.update(target_vars)
my_chosen_var = mf.get_var(my_vars, 'x')
my_empty_var = mf.get_var(my_vars, 'f')
assert my_chosen_var == searchers_vars.get('x')
assert my_empty_var is None
def test_run_solver_get_model_data():
horizon, theta, deadline, solver_type = get_solver_param()
g, v0_target, v0_searchers, target_motion, belief_distribution = parameters_sim()
gamma = 0.99
timeout = 60
# initialize parameters according to inputs
b_0 = cp.set_initial_belief(g, v0_target, belief_distribution)
M = cp.set_motion_matrix(g, target_motion)
searchers = cp.create_dict_searchers(g, v0_searchers)
# solve: 1 [low level]
start, vertices_t, times_v = cm.get_vertices_and_steps(g, horizon, searchers)
# create model
md = mf.create_model()
# add variables
my_vars = mf.add_variables(md, g, horizon, start, vertices_t, searchers)
# add constraints (central algorithm)
mf.add_constraints(md, g, my_vars, searchers, vertices_t, horizon, b_0, M)
# objective function
mf.set_solver_parameters(md, gamma, horizon, my_vars, timeout)
# update
md.update()
# Optimize model
md.optimize()
x_s1, b_target1 = mf.query_variables(md)
obj_fun1, time_sol1, gap1, threads1 = mf.get_model_data(md)
pi_dict1 = core.extract_info.xs_to_path_dict(x_s1)
path1 = core.extract_info.path_as_list(pi_dict1)
# solve: 2
obj_fun2, time_sol2, gap2, x_s2, b_target2, threads2 = pln.run_solver(g, horizon, searchers, b_0, M)
pi_dict2 = core.extract_info.xs_to_path_dict(x_s2)
path2 = core.extract_info.path_as_list(pi_dict2)
# solve: 3
specs = my_specs()
# initialize instances of classes
path3 = pln.run_planner(specs)
assert obj_fun1 == md.objVal
assert round(time_sol1, 2) == round(md.Runtime, 2)
assert gap1 == md.MIPGap
# 1 x 2
assert x_s1 == x_s2
assert b_target1 == b_target2
assert obj_fun2 == obj_fun1
assert round(time_sol2, 2) == round(time_sol1, 2)
assert gap2 == gap1
assert threads2 == threads1
# paths
assert pi_dict1 == pi_dict2
assert path1 == path2
# 1 x 3
assert path1 == path3
def test_get_vertices_and_steps_start():
# load graph
graph_file = 'G7V_test.p'
g = ext.get_graph(graph_file)
v0_searchers = [3, 1]
deadline = 3
# searchers
searchers = cp.create_dict_searchers(g, v0_searchers)
start, vertices_t, times_v = cm.get_vertices_and_steps(
g, deadline, searchers)
assert start[0] == v0_searchers[0]
assert start[1] == v0_searchers[1]
def test_get_vertices_and_steps_vertices2():
# load graph
graph_file = 'G7V_test.p'
g = ext.get_graph(graph_file)
v0 = [3, 1]
deadline = 3
# searchers
searchers = cp.create_dict_searchers(g, v0)
start, vertices_t, times_v = cm.get_vertices_and_steps(
g, deadline, searchers)
assert vertices_t.get((1, 0)) == [3]
assert vertices_t.get((1, 1)) == [1, 3, 5]
assert vertices_t.get((1, 2)) == [1, 2, 3, 5, 6]
assert vertices_t.get((1, 3)) == [1, 2, 3, 4, 5, 6, 7]
def test_get_vertices_and_steps_times():
# load graph
graph_file = 'G7V_test.p'
g = ext.get_graph(graph_file)
v0 = [3, 1]
deadline = 3
# searchers
searchers = cp.create_dict_searchers(g, v0)
start, vertices_t, times_v = cm.get_vertices_and_steps(
g, deadline, searchers)
assert times_v.get((1, 1)) == [1, 2, 3]
assert times_v.get((1, 2)) == [2, 3]
assert times_v.get((1, 3)) == [0, 1, 2, 3]
assert times_v.get((1, 4)) == [3]
assert times_v.get((1, 5)) == [1, 2, 3]
assert times_v.get((1, 6)) == [2, 3]
assert times_v.get((1, 7)) == [3]
def test_neighbors():
# load graph
graph_file = 'G7V_test.p'
g = ext.get_graph(graph_file)
v0 = [3, 1]
deadline = 3
# searchers
searchers = cp.create_dict_searchers(g, v0)
start, vertices_t, times_v = cm.get_vertices_and_steps(
g, deadline, searchers)
s = 1
v = 3
t = 2
tau_ext = ext.get_set_time_u_0(deadline)
v_possible = cm.get_next_vertices(g, s, v, t, vertices_t, tau_ext)
assert v_possible == [1, 5, 3]
def test_position_searchers():
# load graph
graph_file = 'G7V_test.p'
g = ext.get_graph(graph_file)
# input for target initial vertices (belief)
v_target = [7]
# initial searcher vertices
v0_searchers = [1, 2]
horizon = 3
# type of motion
target_motion = 'random'
belief_distribution = 'uniform'
b0, M = cp.my_target_motion(g, v_target, belief_distribution,
target_motion)
# searchers
searchers = cp.create_dict_searchers(g, v0_searchers)
# solve
# create model
md = Model("my_model")
start, vertices_t, times_v = cm.get_vertices_and_steps(
g, horizon, searchers)
# add variables
my_vars = mf.add_variables(
md,
g,
horizon,
start,
vertices_t,
)
# add constraints (central algorithm)
mf.add_constraints(md, g, my_vars, searchers, vertices_t, horizon, b0, M)
mf.set_solver_parameters(md, 0.99, horizon, my_vars)
md.update()
# Optimize model
md.optimize()
x_s, b_target = mf.query_variables(md)
# check searcher position (1)
assert x_s.get((1, 1, 0)) == 1
assert x_s.get((1, 3, 1)) == 1
assert x_s.get((1, 5, 2)) == 1
assert x_s.get((1, 6, 3)) == 1
# check searcher position (2)
assert x_s.get((2, 2, 0)) == 1
assert x_s.get((2, 5, 1)) == 1
assert x_s.get((2, 6, 2)) == 1
assert x_s.get((2, 7, 3)) == 1
# check target belief t = 0
assert b_target.get((0, 0)) == 0
assert b_target.get((1, 0)) == 0
assert b_target.get((2, 0)) == 0
assert b_target.get((3, 0)) == 0
assert b_target.get((4, 0)) == 0
assert b_target.get((5, 0)) == 0
assert b_target.get((6, 0)) == 0
assert b_target.get((7, 0)) == 1
# check target belief t = 1
assert b_target.get((0, 1)) == 0
assert b_target.get((1, 1)) == 0
assert b_target.get((2, 1)) == 0
assert b_target.get((3, 1)) == 0
assert b_target.get((4, 1)) == 0
assert b_target.get((5, 1)) == 0
assert b_target.get((6, 1)) == 0.5
assert b_target.get((7, 1)) == 0.5
# check target belief t = 2
assert round(b_target.get((0, 2)), 3) == 0.583
assert b_target.get((1, 2)) == 0
assert b_target.get((2, 2)) == 0
assert b_target.get((3, 2)) == 0
assert b_target.get((4, 2)) == 0
assert b_target.get((5, 2)) == 0
assert b_target.get((6, 2)) == 0
assert round(b_target.get((7, 2)), 3) == 0.417
# check target belief t = 3
assert b_target.get((0, 3)) == 1
assert b_target.get((1, 3)) == 0
assert b_target.get((2, 3)) == 0
assert b_target.get((3, 3)) == 0
assert b_target.get((4, 3)) == 0
assert b_target.get((5, 3)) == 0
assert b_target.get((6, 3)) == 0
assert b_target.get((7, 3)) == 0