def solveforlocation():
sensorloc_array = create_sensorlocarray(ndim, no_sensors)
target_loc = create_target(ndim)
curr_arena = ar.arena(no_sensors, ndim, sensorloc_array,target_loc)
# curr_arena = localize(no_sensors,np.load('psosolsens.npy'));
# curr_arena = localize(no_sensors,np.load('gdssolsens.npy'));
# curr_arena = localize(no_sensors,np.load('fr.npy'));
# indx = 0;
# lst_score = float("inf");
# pos_tracker = np.empty(np.shape(pso.current_pos));
# spread = 0;
# centroid = np.empty(pso.no_dim);
ExampleSolSpacePoint = np.random.random((1, ndim))
iterindx = 1
gdsolver = gds.GDS(curr_arena.get_score, curr_arena.gradient_score, ExampleSolSpacePoint)
psosolver = psos.PSO(curr_arena.get_score, 10, ndim, 1, 1.5, 1, ExampleSolSpacePoint,
[-10 * np.ones(ndim), 10 * np.ones(ndim)])
psodata = stlog.statelogger('localize_psodata', 'localize_psolog',
np.vstack((psosolver.current_pos, curr_arena.sensor_loc, curr_arena.target_loc)),
psosolver.centroid, psosolver.spread,
psosolver.globalmin) # Plotter is just expecting these.
# gddata = stlog.statelogger('gddata2', 'gdslog',np.vstack((gdsolver.new_point, curr_arena.sensor_loc, curr_arena.target_loc)),
# gdsolver.new_point, 0,gdsolver.curr_arena.get_score(gdsolver.new_point)) #Combined position data of moving point sensors and sensor target, i.e the object to be localized, in the first vstack array.
# Dummy gdsolver.new_point to replace centroid that the plotter is expecting in the second term.
indx = 0
# while np.abs(np.amin(pso.curr_score)-lst_score)>.001:
point = ExampleSolSpacePoint
while indx < 300:
psosolver.update_pos()
psosolver.update_currscores()
psosolver.update_selfmin()
psosolver.update_globalmin()
psosolver.update_velocities()
(centroid, spread) = psosolver.calc_swarm_props()
print('\n \n \n \n \n')
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \n')
print("The running index is " + str(indx) + "\n")
print(str(psosolver.globalminlocation))
indx += 1
if psosolver.no_particles < 20:
psosolver.report()
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \n')
curr_score = curr_arena.get_score(point)
point = gdsolver.get_descented_point(point)
descented_score = curr_arena.get_score(point)
deltascore = descented_score - curr_score
curr_score = descented_score
gdsolver.report()
psodata.add_state(np.vstack((psosolver.current_pos, curr_arena.sensor_loc, curr_arena.target_loc)),
psosolver.centroid, psosolver.spread, psosolver.globalmin)
distancevector = np.array(buff)
max_distance = np.max(distancevector)
roadlength = 21.4;
# targetloc = create_target()
sensorloc = create_sensorlocarray(2,3)
targetloc = create_target()
curr_arena = arena.arena(3, 2, sensorloc, targetloc);
def scorefunc_spline(dist):
point =getpoint_spline(dist,tck)
score = curr_arena.get_score(point)
return score
def convertpsotopos(psoobject):
l = []
for pos in psoobject.current_pos:
l.append(np.array(getpoint_spline(pos, tck) ))
print (np.shape(np.array(l)))
return np.vstack(l)
def solveforlocation():
sensorloc_array = create_sensorlocarray(ndim, no_sensors)
target_loc = create_target(ndim)
curr_arena = ar.arena(no_sensors, ndim, sensorloc_array, target_loc)
# curr_arena = localize(no_sensors,np.load('psosolsens.npy'));
# curr_arena = localize(no_sensors,np.load('gdssolsens.npy'));
# curr_arena = localize(no_sensors,np.load('fr.npy'));
# indx = 0;
# lst_score = float("inf");
# pos_tracker = np.empty(np.shape(pso.current_pos));
# spread = 0;
# centroid = np.empty(pso.no_dim);
ExampleSolSpacePoint = np.random.random((1, ndim))
iterindx = 1
gdsolver = gds.GDS(curr_arena.get_score, curr_arena.gradient_score,
ExampleSolSpacePoint)
psosolver = psos.PSO(curr_arena.get_score, 10, ndim, 1, 1.5, 1,
ExampleSolSpacePoint,
[-10 * np.ones(ndim), 10 * np.ones(ndim)])
psodata = stlog.statelogger(
'localize_psodata', 'localize_psolog',
np.vstack(
(psosolver.current_pos, curr_arena.sensor_loc,
curr_arena.target_loc)), psosolver.centroid, psosolver.spread,
psosolver.globalmin) # Plotter is just expecting these.
# gddata = stlog.statelogger('gddata2', 'gdslog',np.vstack((gdsolver.new_point, curr_arena.sensor_loc, curr_arena.target_loc)),
# gdsolver.new_point, 0,gdsolver.curr_arena.get_score(gdsolver.new_point)) #Combined position data of moving point sensors and sensor target, i.e the object to be localized, in the first vstack array.
# Dummy gdsolver.new_point to replace centroid that the plotter is expecting in the second term.
indx = 0
# while np.abs(np.amin(pso.curr_score)-lst_score)>.001:
point = ExampleSolSpacePoint
while indx < 300:
psosolver.update_pos()
psosolver.update_currscores()
psosolver.update_selfmin()
psosolver.update_globalmin()
psosolver.update_velocities()
(centroid, spread) = psosolver.calc_swarm_props()
print('\n \n \n \n \n')
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \n')
print("The running index is " + str(indx) + "\n")
print(str(psosolver.globalminlocation))
indx += 1
if psosolver.no_particles < 20:
psosolver.report()
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \n')
curr_score = curr_arena.get_score(point)
point = gdsolver.get_descented_point(point)
descented_score = curr_arena.get_score(point)
deltascore = descented_score - curr_score
curr_score = descented_score
gdsolver.report()
psodata.add_state(
np.vstack((psosolver.current_pos, curr_arena.sensor_loc,
curr_arena.target_loc)), psosolver.centroid,
psosolver.spread, psosolver.globalmin)
(terrain_x, terrain_y,
terrain_z) = np.load('simulationdata/visualize/subterrain.npy')
(road_xcords, road_ycords) = np.load('simulationdata/visualize/road_cords.npy')
(road_x, road_y) = (terrain_x[road_ycords,
road_xcords], terrain_y[road_ycords,
road_xcords])
r = terrain.road((road_xcords, road_ycords), (terrain_x, terrain_y, terrain_z))
print(r.maxdistance)
# r.plotroad()
sensor_loc = [[39, 82], [148, 158], [214, 57]]
sensor_real_loc = np.array(
[[r.x[ele[0], ele[1]], r.y[ele[0], ele[1]], r.z[ele[0], ele[1]]]
for ele in sensor_loc])
target_dw = [600, 3]
target_real_loc = r.dwtoxyz(target_dw[0], target_dw[1])
ar = arena.arena(3, 3, sensor_real_loc, target_real_loc)
ssb = np.array([[10, -3], [r.maxdistance - 10, 3]])
pslr = psos.PSO(scorefunc, 2, searchspaceboundaries=ssb)
pslr.solve_iters(10, verbose=True, out=True)
sol = []
for i in range(10):
sol.append(pslr.solve_convergence(0.001, checkiters=20, out=True))
sorted_sols = sorted(sol, key=lambda solution: solution['extremum value'])
def create_target():
targetdist = 5
return getpoint_spline(targetdist,tck)
roadlength = 21.4;
# targetloc = create_target()
sensorloc = create_sensorlocarray(2, 3)
targetloc = create_target()
curr_arena = arena.arena(3, 2, sensorloc, targetloc);
road_xpoints = np.linspace(0,10,1000)
road_ypoints = interpolate.splev(road_xpoints,tck,der=0)
plt.plot(road_xpoints,road_ypoints)
plt.ion()
plt.show()
def scorefunc_spline(dist):
point =getpoint_spline(dist,tck)
score = curr_arena.get_score(point)
return score
# def gradscorefunc(dist):
# point = getpoint(dist, distancevector, road_points)
# dist_left = (np.roll(road_points==point,1))
# pdb.set_trace()
(terrain_x,terrain_y,terrain_z) = np.load('simulationdata/visualize/subterrain.npy')
(road_xcords,road_ycords) = np.load('simulationdata/visualize/road_cords.npy')
(road_x,road_y) = (terrain_x[road_ycords,road_xcords],terrain_y[road_ycords,road_xcords])
r = terrain.road((road_xcords,road_ycords),(terrain_x,terrain_y,terrain_z))
print (r.maxdistance)
# r.plotroad()
sensor_loc = [[39,82],[148,158],[214,57]]
sensor_real_loc = np.array([[r.x[ele[0],ele[1]],r.y[ele[0],ele[1]],r.z[ele[0],ele[1]]] for ele in sensor_loc])
target_dw = [600, 3]
target_real_loc = r.dwtoxyz(target_dw[0],target_dw[1])
ar = arena.arena(3,3,sensor_real_loc,target_real_loc)
ssb = np.array([[10,-3],[r.maxdistance-10,3]])
pslr = psos.PSO(scorefunc,2,searchspaceboundaries=ssb)
pslr.solve_iters(10, verbose=True, out=True)
sol = []
for i in range(10):
sol.append(pslr.solve_convergence(0.001,checkiters=20,out=True))
sorted_sols = sorted(sol, key=lambda solution: solution['extremum value'])
#
g = grid_matrix.flatten()
g[[np.random.choice(np.arange(100), 83, replace=False)]] = 0
grid_matrix = g.reshape((10, 10))
grid_matrix[3, 4] = 0
grid_matrix[5, 5] = 0
grid_matrix[6, 7] = 0
grid_matrix[7, 7] = 0
np.save('grid.npy', grid_matrix)
grid_matrix = np.load('grid.npy')
grid_matrix /= 2.0
g2 = copy.deepcopy(grid_matrix)
are = arena(grid_matrix, False)
# are = arena(grid_matrix,True)
a1 = Agent(0.42, .46, .25, 0, np.array([3, 4]), are)
a1.load = False
# a2 = Agent(0.2,3,4,3,np.array([5,5]),2,are)
# a2.load = True
a3 = Agent(.49, .2, .2, 2, np.array([6, 7]), are)
a3.load = False
a2 = Agent(.3, .25, .3, 0, np.array([7, 7]), are)
a2.load = False
# are.add_agents([a4,a2,a3,a1])
