def solve_iters(self, no_iters, verbose=True, out=True, randomname=False):
# pdb.set_trace()
# self.psodatalogger = stlog.statelogger(self.datafilename,self.datalogfilename,self.current_pos,self.centroid,self.spread,self.globalmin)
if randomname:
randomname = str(randint(100000, 999999))
self.psodatalogger = stlog.statelogger(randomname,
self.datalogfilename,
self.current_pos,
self.centroid, self.spread,
self.globalmin)
else:
self.psodatalogger = stlog.statelogger(self.datafilename,
self.datalogfilename,
self.current_pos,
self.centroid, self.spread,
self.globalmin)
self.initialize_swarm()
for i in range(no_iters):
self.update_pos()
self.update_currscores()
self.update_selfmin()
self.update_globalmin()
self.update_velocities()
if verbose:
(centroid, spread) = self.calc_swarm_props()
print('\n \n \n \n \n')
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \n')
print("The running index is " + str(i) + "\n")
# print(str(psoslr.globalminlocation))
self.report()
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \n')
# curr_score = curr_arena.get_score(point)
self.psodatalogger.add_state(self.current_pos, self.centroid,
self.spread, self.globalmin)
# point = gdsolver.get_descented_point(point)
# descented_score = curr_arena.get_score(point)
# deltascore = descented_score - curr_score
# curr_score = descented_score
# gdsolver.report()
# pdb.set_trace()
self.psodatalogger.close_logger()
if out:
if randomname:
output = dict([('extremum value', self.globalmin),
('extremum location', self.globalminlocation),
('filename', randomname)])
else:
output = dict([('extremum value', self.globalmin),
('extremum location', self.globalminlocation)])
return output
else:
return
def solve_iters(self, no_iters, verbose=True, out=True, randomname=False):
# pdb.set_trace()
# self.psodatalogger = stlog.statelogger(self.datafilename,self.datalogfilename,self.current_pos,self.centroid,self.spread,self.globalmin)
if randomname:
randomname = str(randint(100000, 999999))
self.psodatalogger = stlog.statelogger(randomname, self.datalogfilename, self.current_pos, self.centroid,
self.spread, self.globalmin)
else:
self.psodatalogger = stlog.statelogger(self.datafilename, self.datalogfilename, self.current_pos,
self.centroid, self.spread, self.globalmin)
self.initialize_swarm()
for i in range(no_iters):
self.update_pos()
self.update_currscores()
self.update_selfmin()
self.update_globalmin()
self.update_velocities()
if verbose:
(centroid, spread) = self.calc_swarm_props()
print('\n \n \n \n \n')
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \n')
print("The running index is " + str(i) + "\n")
# print(str(psoslr.globalminlocation))
self.report()
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \n')
# curr_score = curr_arena.get_score(point)
self.psodatalogger.add_state(self.current_pos,self.centroid,self.spread,self.globalmin)
# point = gdsolver.get_descented_point(point)
# descented_score = curr_arena.get_score(point)
# deltascore = descented_score - curr_score
# curr_score = descented_score
# gdsolver.report()
# pdb.set_trace()
self.psodatalogger.close_logger()
if out:
if randomname:
output = dict([('extremum value', self.globalmin), ('extremum location', self.globalminlocation),
('filename', randomname)])
else:
output = dict([('extremum value', self.globalmin), ('extremum location', self.globalminlocation)])
return output
else:
return
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)
def solve_convergence(self,
convergencelimit,
iterbound=1000,
verbose=False,
out=True,
checkiters=10,
randomname=True):
if randomname:
randomname = str(randint(100000, 999999))
self.psodatalogger = stlog.statelogger(randomname,
self.datalogfilename,
self.current_pos,
self.centroid, self.spread,
self.globalmin)
else:
self.psodatalogger = stlog.statelogger(self.datafilename,
self.datalogfilename,
self.current_pos,
self.centroid, self.spread,
self.globalmin)
self.initialize_swarm()
iter = 0
cur_score = self.globalmin
# diff = np.inf
errorlist = [np.inf for i in range(checkiters)]
# pdb.set_trace()
while ((np.any(np.abs(errorlist[-checkiters::]) > convergencelimit)) &
(iter < iterbound)):
# iter = 0
# cur_score = self.globalmin
iter += 1
self.update_pos()
self.update_currscores()
self.update_selfmin()
self.update_globalmin()
self.update_velocities()
if verbose:
(centroid, spread) = self.calc_swarm_props()
print('\n \n \n \n \n')
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \n')
print("The running index is " + str(iter) + "\n")
# print(str(psoslr.globalminlocation))
self.report()
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \n')
# curr_score = curr_arena.get_score(point)
self.psodatalogger.add_state(self.current_pos, self.centroid,
self.spread, self.globalmin)
diff = cur_score - self.globalmin
errorlist.append(diff)
cur_score = self.globalmin
# point = gdsolver.get_descented_point(point)
# descented_score = curr_arena.get_score(point)
# deltascore = descented_score - curr_score
# curr_score = descented_score
# gdsolver.report()
self.psodatalogger.close_logger()
if iter == iterbound:
print('Iterations bound exceeded')
if out:
if randomname:
output = dict([('extremum value', self.globalmin),
('extremum location', self.globalminlocation),
('Number of iterations', iter),
('filename', randomname)])
else:
output = dict([('extremum value', self.globalmin),
('extremum location', self.globalminlocation),
('Number of iterations', iter)])
print errorlist
return output
else:
return
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)
def solve_convergence(self, convergencelimit, iterbound=1000, verbose=False, out=True, checkiters=10,
randomname=True):
if randomname:
randomname = str(randint(100000, 999999))
self.psodatalogger = stlog.statelogger(randomname, self.datalogfilename, self.current_pos, self.centroid,
self.spread, self.globalmin)
else:
self.psodatalogger = stlog.statelogger(self.datafilename, self.datalogfilename, self.current_pos,
self.centroid, self.spread, self.globalmin)
self.initialize_swarm()
iter = 0
cur_score = self.globalmin
# diff = np.inf
errorlist = [np.inf for i in range(checkiters)]
# pdb.set_trace()
while ((np.any(np.abs(errorlist[-checkiters::]) > convergencelimit)) & (iter<iterbound) ):
# iter = 0
# cur_score = self.globalmin
iter += 1
self.update_pos()
self.update_currscores()
self.update_selfmin()
self.update_globalmin()
self.update_velocities()
if verbose:
(centroid, spread) = self.calc_swarm_props()
print('\n \n \n \n \n')
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \n')
print("The running index is " + str(iter) + "\n")
# print(str(psoslr.globalminlocation))
self.report()
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \n')
# curr_score = curr_arena.get_score(point)
self.psodatalogger.add_state(self.current_pos,self.centroid,self.spread,self.globalmin)
diff = cur_score - self.globalmin
errorlist.append(diff)
cur_score = self.globalmin
# point = gdsolver.get_descented_point(point)
# descented_score = curr_arena.get_score(point)
# deltascore = descented_score - curr_score
# curr_score = descented_score
# gdsolver.report()
self.psodatalogger.close_logger()
if iter == iterbound:
print ('Iterations bound exceeded')
if out:
if randomname:
output = dict([('extremum value', self.globalmin), ('extremum location', self.globalminlocation),
('Number of iterations', iter), ('filename', randomname)])
else:
output = dict([('extremum value', self.globalmin), ('extremum location', self.globalminlocation),
('Number of iterations', iter)])
print errorlist
return output
else:
return
i += 1
return 0-gradient/i
# def score_glb(sensorloc):
# return currscript_score(sensorloc,avgspace,no_sensors,dim,alpha,sigma)
#
# def gradient_glb(sensorloc):
# return
#
psol = pssol.PSO(currscript_score, 20, no_sensors*dim,.5,.5,1,-50+np.random.random(no_sensors*dim)*100,[-10*np.ones(no_sensors*dim),10*np.ones(no_sensors*dim)])
point = np.random.random(no_sensors*dim)
gdssol = gdslr.GDS(currscript_score,currscript_gradient,point)
rec_obj_pso = stlg.statelogger('optms_pso','optmspsolog',psol.curr_score,psol.globalmin,psol.current_pos)
rec_obj_gds = stlg.statelogger('optms_gds','optmsgdslog',gdssol.score_func(point))
for i in range(100):
psol.update_pos()
psol.update_currscores()
psol.update_selfmin()
psol.update_globalmin()
psol.update_velocities()
(centroid, spread) = psol.calc_swarm_props()
print('\n\n\n')
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \n')
print('PARTICLE SWARM OPTIMIZER REPORT')
