def optimize(): state = [0, 3, 2, 1, 0, 3, 2, 3, 1, 2, 2, 2, 3, 2, 1, 3, 0, 0, 0, 1, 3, 2, 0, 2, 0, 1, 0, 2, 2, 1, 2, 2, 0, 3] annealer = Annealer(energy, move) schedule = annealer.auto(state, minutes=0.1) state, e = annealer.anneal(state, schedule['tmax'], schedule['tmin'], schedule['steps'], updates=6) print(state) # the "final" solution
def optimize():
state = [
0, 3, 2, 1, 0, 3, 2, 3, 1, 2, 2, 2, 3, 2, 1, 3, 0, 0, 0, 1, 3, 2, 0, 2,
0, 1, 0, 2, 2, 1, 2, 2, 0, 3
]
annealer = Annealer(energy, move)
schedule = annealer.auto(state, minutes=0.1)
state, e = annealer.anneal(state,
schedule['tmax'],
schedule['tmin'],
schedule['steps'],
updates=6)
print(state) # the "final" solution
system[1] = cvalues[index]
elif component == 2:
index = random.randrange(0, len(rvalues))
system[2] = rvalues[index]
elif component == 3:
index = random.randrange(0, len(rvalues))
system[3] = rvalues[index]
if __name__ == '__main__':
# set up simulated annealing algorithm
units=('F', 'F', u'Ω', u'Ω') # units of the values in the system
initial_system = [cvalues[0], cvalues[0], rvalues[0], rvalues[0]]
annealer = Annealer(energy, move)
schedule = annealer.auto(initial_system, minutes=0.1)
# run simulated annealing algorithm and compute properties of the final system
final_system, error = annealer.anneal(initial_system, schedule['tmax'], schedule['tmin'], schedule['steps'], updates=100)
final_frf = frf(final_system)
final_f0 = f0(final_system)
final_q = q(final_system)
final_vals = [metric_prefix(*s) for s in zip(final_system, units)]
print 'Soln: (%s), Remaining Energy: %s' % (', '.join(final_vals), error)
# calculate data for graphs
freqs = range(1000000) # response from 0 Hz to 1 MHz
response = [dB(final_frf(f)) for f in freqs]
natural = final_f0, dB(final_frf(final_f0))
target = target_freq, dB(final_frf(target_freq))
def run(schedule=None):
state = []
def reserve_move(state):
"""
Select random watershed
then
Add watershed (if not already in state) OR remove it.
* This is the Marxan technique as well
"""
huc = hucs[int(random.random() * num_hucs)]
#huc = hucs[random.randint(0,num_hucs-1)]
if huc in state:
state.remove(huc)
else:
state.append(huc)
def reserve_energy(state):
"""
The "Objective Function"...
Calculates the 'energy' of the reserve.
Should incorporate costs of reserve and penalties
for not meeting species targets.
Note: This example is extremely simplistic compared to
the Marxan objective function (see Appendix B in Marxan manual)
but at least we have access to it!
"""
# Initialize variables
energy = 0
totals = {}
for fish in species:
totals[fish] = 0
# Get total cost and habitat in current state
for huc in state:
watershed = watersheds[huc]
# Sum up total habitat for each fish
for fish in species:
if energy == 0:
# reset for new calcs ie first watershed
totals[fish] = watershed[fish]
else:
# add for additional watersheds
totals[fish] += watershed[fish]
# Incorporate Cost of including watershed
energy += watershed['watershed_cost']
# incorporate penalties for missing species targets
for fish in species:
pct = totals[fish] / targets[fish]
if pct < 1.0: # if missed target, ie total < target
if pct < 0.1:
# Avoid zerodivision errors
# Limits the final to 10x specified penalty
pct = 0.1
penalty = int(penalties[fish] / pct)
energy += penalty
return energy
annealer = Annealer(reserve_energy, reserve_move)
if schedule is None:
print('----\nAutomatically determining optimal temperature schedule')
schedule = annealer.auto(state, minutes=6)
try:
schedule['steps'] = NUMITER
except:
pass # just keep the auto one
print( '---\nAnnealing from %.2f to %.2f over %i steps:' % (schedule['tmax'],
schedule['tmin'], schedule['steps']))
state, e = annealer.anneal(state, schedule['tmax'], schedule['tmin'],
schedule['steps'], updates=6)
print("Reserve cost = %r" % reserve_energy(state))
state.sort()
for watershed in state:
print("\t", watershed, watersheds[watershed])
return state, reserve_energy(state), schedule
def run(schedule=None):
state = []
def reserve_move(state):
"""
Select random watershed
then
Add watershed (if not already in state) OR remove it.
* This is the Marxan technique as well
"""
huc = random.choice(hucs)
if huc in state:
state.remove(huc)
else:
state.append(huc)
def reserve_energy(state):
"""
The "Objective Function"...
Calculates the 'energy' of the reserve.
Should incorporate costs of reserve and penalties
for not meeting species targets.
Note: This example is extremely simplistic compared to
the Marxan objective function (see Appendix B in Marxan manual)
but at least we have access to it!
"""
# Initialize variables
energy = 0
totals = {}
for fish in species:
totals[fish] = 0
# Get total cost and habitat in current state
for huc in state:
watershed = watersheds[huc]
# Sum up total habitat for each fish
for fish in species:
if energy == 0:
# reset for new calcs ie first watershed
totals[fish] = float(watershed[fish])
else:
# add for additional watersheds
totals[fish] += float(watershed[fish])
# Incorporate Cost of including watershed
energy += watershed["total_cost"]
# incorporate penalties for missing species targets
for fish in species:
if run_target[fish] > 0:
pct = totals[fish] / run_target[fish]
else:
pct = 1
if pct < 1.0: # if missed target, ie total < target
if pct < 0.1:
# Avoid zerodivision errors
# Limits the final to 10x specified penalty
pct = 0.1
penalty = int(penalties[fish] / pct)
energy += penalty
return energy
annealer = Annealer(reserve_energy, reserve_move)
if schedule is None:
print "----\nAutomatically determining optimal temperature schedule"
schedule = annealer.auto(state, minutes=6)
try:
schedule["steps"] = NUMITER
except:
pass # just keep the auto one
print "---\nAnnealing from %.2f to %.2f over %i steps:" % (schedule["tmax"], schedule["tmin"], schedule["steps"])
state, e = annealer.anneal(state, schedule["tmax"], schedule["tmin"], schedule["steps"], updates=6)
print "Reserve cost = %r" % reserve_energy(state)
state.sort()
for watershed in state:
print "\t", watershed, watersheds[watershed]
return state, reserve_energy(state), schedule
system[1] = cvalues[index]
elif component == 2:
index = random.randrange(0, len(rvalues))
system[2] = rvalues[index]
elif component == 3:
index = random.randrange(0, len(rvalues))
system[3] = rvalues[index]
if __name__ == '__main__':
# set up simulated annealing algorithm
units = ('F', 'F', u'Ω', u'Ω') # units of the values in the system
initial_system = [cvalues[0], cvalues[0], rvalues[0], rvalues[0]]
annealer = Annealer(energy, move)
schedule = annealer.auto(initial_system, minutes=0.1)
# run simulated annealing algorithm and compute properties of the final system
final_system, error = annealer.anneal(initial_system,
schedule['tmax'],
schedule['tmin'],
schedule['steps'],
updates=100)
final_frf = frf(final_system)
final_f0 = f0(final_system)
final_q = q(final_system)
final_vals = [metric_prefix(*s) for s in zip(final_system, units)]
print 'Soln: (%s), Remaining Energy: %s' % (', '.join(final_vals), error)
# calculate data for graphs
# Valime ühe klaasi suurima võimaliku pikkuse (700 mm) ja muudame käesolevat väärtust vastavalt sellele, n-ö kontrollitult juhuslikult
def move(state):
i = random.randint(0, 5)
if state[i] < 700 and state[i] > 0:
state[i] = random.randint(state[i] - 1, state[i] + 1)
else:
state[i] = random.randint(0, 700)
import time
start_time = time.time()
annealer = Annealer(energy, move)
schedule = annealer.auto(state, minutes=5)
state, e = annealer.anneal(state,
schedule['tmax'],
schedule['tmin'],
schedule['steps'],
updates=1000)
state, e = annealer.anneal(state, 10000, 0.1, 100000, updates=30)
# Parim klaaside kombinatsioon
for i in range(len(state)):
print(klaasid[i])
print(state[i])
# Minimeeritava funktsiooni väärtus, mis vastab parimale klaasi kombinatsioonile
print('Minimeeritava funktsiooni väärtus:')
print(energy(state))
def run(schedule=None):
state = []
def reserve_move(state):
"""
1st Random choice: Select watershed
then
Add watershed (if not already in state) OR remove it.
This is the Marxan method
"""
huc = hucs[random.randint(num_hucs)]
if huc in state:
state.remove(huc)
return ('r', huc)
else:
state.append(huc)
return ('a', huc)
def reserve_energy(state):
"""
The "Objective Function"...
Calculates the 'energy' of the reserve.
Should incorporate costs of reserve and penalties
for not meeting species targets.
Note: This example is extremely simplistic compared to
the Marxan objective function (see Appendix B in Marxan manual)
but at least we have access to it!
"""
energy = 0
totals = {}
for huc in state:
watershed = watersheds[huc]
# Sum up total habitat for each fish
for fish in species:
print fish
if energy == 0:
# reset for new calcs
totals[fish] = watershed[fish]
print fish, totals
else:
totals[fish] += watershed[fish]
print fish, totals
# Incorporate Cost of including watershed
energy += watershed['total_cost']
# incorporate penalties for missing species targets
for fish in species:
print totals
print targets
pct = totals[fish] / targets[fish]
if pct < 1.0: # if missed target, ie total < target
if pct < 0.1:
# Avoid zerodivision errors
# Limits the final to 10x specified penalty
pct = 0.1
penalty = int(penalties[fish] / pct)
energy += penalty
return energy
annealer = Annealer(reserve_energy, reserve_move)
if schedule is None:
# Automatically chosen temperature schedule
schedule = annealer.auto(state, minutes=0.3)
try:
schedule['steps'] = NUMITER
except:
pass # just keep the auto one
print '---\nAnnealing from %.2f to %.2f over %i steps:' % (schedule['tmax'],
schedule['tmin'], schedule['steps'])
state, e = annealer.anneal(state, schedule['tmax'], schedule['tmin'],
schedule['steps'], updates=6)
print "Reserve cost = %r" % reserve_energy(state)
state.sort()
for watershed in state:
print "\t", watershed, watersheds[watershed]
return state, reserve_energy(state), schedule
