def fmin_particle_swarm(f, x0, err_crit, iter_max, popsize=100, c1=2, c2=2):
"""
A simple implementation of the Particle Swarm Optimization Algorithm.
Pradeep Gowda 2009-03-16
Parameters
----------
f : function
The function to minimize.
x0 : numpy array
The starting point (argument to fn).
err_crit : float
Critical error (i.e. tolerance). Stops when error < err_crit.
iter_max : int
Maximum iterations.
popsize : int, optional
Population size. Larger populations are better at finding the global
optimum but make the algorithm take longer to run.
c1 : float, optional
Coefficient describing a particle's affinity for it's (local) maximum.
c2 : float, optional
Coefficient describing a particle's affinity for the best maximum any
particle has seen (the current global max).
Returns
-------
scipy.optimize.Result object
Includes members 'x', 'fun', 'success', and 'message'.
"""
dimensions = len(x0)
LARGE = 1e10
class Particle:
pass
#initialize the particles
particles = []
for i in range(popsize):
p = Particle()
p.params = x0 + 2 * (_np.random.random(dimensions) - 0.5)
p.best = p.params[:]
p.fitness = LARGE # large == bad fitness
p.v = _np.zeros(dimensions)
particles.append(p)
# let the first particle be the global best
gbest = particles[0]
ibest = 0
# bDoLocalFitnessOpt = False
#DEBUG
#if False:
# import pickle as _pickle
# bestGaugeMx = _pickle.load(open("bestGaugeMx.debug"))
# lbfgsbGaugeMx = _pickle.load(open("lbfgsbGaugeMx.debug"))
# cgGaugeMx = _pickle.load(open("cgGaugeMx.debug"))
# initialGaugeMx = x0.reshape( (4,4) )
#
# #DEBUG: dump line cut to plot
# nPts = 100
# print "DEBUG: best offsets = \n", bestGaugeMx - initialGaugeMx
# print "DEBUG: lbfgs offsets = \n", lbfgsbGaugeMx - initialGaugeMx
# print "DEBUG: cg offsets = \n", cgGaugeMx - initialGaugeMx
#
# print "# DEBUG plot"
# #fDebug = open("x0ToBest.dat","w")
# #fDebug = open("x0ToLBFGS.dat","w")
# fDebug = open("x0ToCG.dat","w")
# #fDebug = open("LBFGSToBest.dat","w")
# #fDebug = open("CGToBest.dat","w")
# #fDebug = open("CGToLBFGS.dat","w")
#
# for i in range(nPts+1):
# alpha = float(i) / nPts
# #matM = (1.0-alpha) * initialGaugeMx + alpha*bestGaugeMx
# #matM = (1.0-alpha) * initialGaugeMx + alpha*lbfgsbGaugeMx
# matM = (1.0-alpha) * initialGaugeMx + alpha*cgGaugeMx
# #matM = (1.0-alpha) * lbfgsbGaugeMx + alpha*bestGaugeMx
# #matM = (1.0-alpha) * cgGaugeMx + alpha*bestGaugeMx
# #matM = (1.0-alpha) * cgGaugeMx + alpha*lbfgsbGaugeMx
# print >> fDebug, "%g %g" % (alpha, f(matM.flatten()))
# exit()
#
#
# fDebug = open("lineDataFromX0.dat","w")
# min_offset = -1; max_offset = 1
# for i in range(nPts+1):
# offset = min_offset + float(i)/nPts * (max_offset-min_offset)
# print >> fDebug, "%g" % offset,
#
# for k in range(len(x0)):
# x = x0.copy(); x[k] += offset
# try:
# print >> fDebug, " %g" % f(x),
# except:
# print >> fDebug, " nan",
# print >> fDebug, ""
#
# print >> fDebug, "#END DEBUG plot"
# exit()
#END DEBUG
#err = 1e10
for iter_num in range(iter_max):
w = 1.0 #- i/iter_max
#bDoLocalFitnessOpt = bool(iter_num > 20 and abs(lastBest-gbest.fitness) < 0.001 and iter_num % 10 == 0)
# lastBest = gbest.fitness
# minDistToBest = 1e10; minV = 1e10; maxV = 0 #DEBUG
for (ip, p) in enumerate(particles):
fitness = f(p.params)
#if bDoLocalFitnessOpt:
# opts = {'maxiter': 100, 'maxfev': 100, 'disp': False }
# local_soln = _spo.minimize(f,p.params,options=opts, method='L-BFGS-B',callback=None, tol=1e-2)
# p.params = local_soln.x
# fitness = local_soln.fun
if fitness < p.fitness: #low 'fitness' is good b/c we're minimizing
p.fitness = fitness
p.best = p.params
if fitness < gbest.fitness:
gbest = p
ibest = ip
v = w*p.v + c1 * _np.random.random() * (p.best - p.params) \
+ c2 * _np.random.random() * (gbest.params - p.params)
p.params = p.params + v
for (i, pv) in enumerate(p.params):
p.params[i] = (
(pv + 1) % 2) - 1 #periodic b/c on box between -1 and 1
#from .. import tools as tools_
#matM = p.params.reshape( (4,4) ) #DEBUG
#minDistToBest = min(minDistToBest, _tools.frobeniusdist(
# bestGaugeMx,matM)) #DEBUG
#minV = min( _np.linalg.norm(v), minV)
#maxV = max( _np.linalg.norm(v), maxV)
#print "DB: min diff from best = ", minDistToBest #DEBUG
#print "DB: min,max v = ", (minV,maxV)
#if False: #bDoLocalFitnessOpt:
# opts = {'maxiter': 100, 'maxfev': 100, 'disp': False }
# print "initial fun = ",gbest.fitness,
# local_soln = _spo.minimize(f,gbest.params,options=opts, method='L-BFGS-B',callback=None, tol=1e-5)
# gbest.params = local_soln.x
# gbest.fitness = local_soln.fun
# print " final fun = ",gbest.fitness
print("Iter %d: global best = %g (index %d)" %
(iter_num, gbest.fitness, ibest))
#if err < err_crit: break #TODO: stopping condition
## Uncomment to print particles
#for p in particles:
# print 'params: %s, fitness: %s, best: %s' % (p.params, p.fitness, p.best)
solution = _optResult()
solution.x = gbest.params
solution.fun = gbest.fitness
solution.success = True
# if iter_num < maxiter:
# solution.success = True
# else:
# solution.success = False
# solution.message = "Maximum iterations exceeded"
return solution
def fmin_evolutionary(f, x0, num_generations, num_individuals):
"""
Minimize a function using an evolutionary algorithm.
Uses python's deap package to perform an evolutionary
algorithm to find a function's global minimum.
Parameters
----------
fn : function
The function to minimize.
x0 : numpy array
The starting point (argument to fn).
num_generations : int
The number of generations to carry out. (similar to the number
of iterations)
num_individuals : int
The number of individuals in each generation. More individuals
make finding the global optimum more likely, but take longer
to run.
Returns
-------
scipy.optimize.Result object
Includes members 'x', 'fun', 'success', and 'message'.
"""
import deap as _deap
import deap.creator as _creator
import deap.base as _base
import deap.tools as _tools
numParams = len(x0)
# Create the individual class
_creator.create("FitnessMin", _base.Fitness, weights=(-1.0, ))
_creator.create("Individual", list, fitness=_creator.FitnessMin)
# Create initialization functions
toolbox = _base.Toolbox()
toolbox.register("random", _np.random.random)
toolbox.register(
"individual",
_tools.initRepeat,
_creator.Individual,
toolbox.random,
n=numParams
) # fn to init an individual from a list of numParams random numbers
toolbox.register("population", _tools.initRepeat, list, toolbox.individual
) # fn to create a population (still need to specify n)
# Create operation functions
def evaluate(individual):
return f(_np.array(individual)), #note: must return a tuple
toolbox.register("mate", _tools.cxTwoPoint)
toolbox.register("mutate", _tools.mutGaussian, mu=0, sigma=0.5, indpb=0.1)
toolbox.register("select", _tools.selTournament, tournsize=3)
toolbox.register("evaluate", evaluate)
# Create the population
pop = toolbox.population(n=num_individuals)
# Evaluate the entire population
fitnesses = list(map(toolbox.evaluate, pop))
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
PROB_TO_CROSS = 0.5
PROB_TO_MUTATE = 0.2
# Initialize statistics
stats = _tools.Statistics(key=lambda ind: ind.fitness.values)
stats.register("avg", _np.mean)
stats.register("std", _np.std)
stats.register("min", _np.min)
stats.register("max", _np.max)
logbook = _tools.Logbook()
#Run algorithm
for g in range(num_generations):
record = stats.compile(pop)
logbook.record(gen=g, **record)
print("Gen %d: %s" % (g, record))
# Select the next generation individuals
offspring = toolbox.select(pop, len(pop))
# Clone the selected individuals
offspring = list(map(toolbox.clone, offspring))
# Apply crossover on the offspring
for child1, child2 in zip(offspring[::2], offspring[1::2]):
if _np.random.random() < PROB_TO_CROSS:
toolbox.mate(child1, child2)
del child1.fitness.values
del child2.fitness.values
# Apply mutation on the offspring
for mutant in offspring:
if _np.random.random() < PROB_TO_MUTATE:
toolbox.mutate(mutant)
del mutant.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# The population is entirely replaced by the offspring
pop[:] = offspring
#get best individual and return params
indx_min_fitness = _np.argmin([ind.fitness.values[0] for ind in pop])
best_params = _np.array(pop[indx_min_fitness])
solution = _optResult()
solution.x = best_params
solution.fun = pop[indx_min_fitness].fitness.values[0]
solution.success = True
return solution
def fmin_supersimplex(fn, x0, outer_tol, inner_tol, max_outer_iter,
min_inner_maxiter, max_inner_maxiter):
"""
Minimize a function using repeated applications of the simplex algorithm.
By varying the maximum number of iterations and repeatedly calling scipy's
Nelder-Mead simplex optimization, this function performs as a robust (but
slow) minimization.
Parameters
----------
fn : function
The function to minimize.
x0 : numpy array
The starting point (argument to fn).
outer_tol : float
Tolerance of outer loop
inner_tol : float
Tolerance fo inner loop
max_outer_iter : int
Maximum number of outer-loop iterations
min_inner_maxiter : int
Minimum number of inner-loop iterations
max_inner_maxiter : int
Maxium number of outer-loop iterations
Returns
-------
scipy.optimize.Result object
Includes members 'x', 'fun', 'success', and 'message'.
"""
f_init = fn(x0)
f_final = f_init - 10 * outer_tol #prime the loop
x_start = x0
i = 1
cnt_at_same_maxiter = 1
inner_maxiter = min_inner_maxiter
while (f_init - f_final > outer_tol
or inner_maxiter < max_inner_maxiter) and i < max_outer_iter:
if f_init - f_final <= outer_tol and inner_maxiter < max_inner_maxiter:
inner_maxiter *= 10
cnt_at_same_maxiter = 1
if cnt_at_same_maxiter > 10 and inner_maxiter > min_inner_maxiter:
inner_maxiter /= 10
cnt_at_same_maxiter = 1
f_init = f_final
print(
">>> fmin_supersimplex: outer iteration %d (inner_maxiter = %d)" %
(i, inner_maxiter))
i += 1
cnt_at_same_maxiter += 1
opts = {
'maxiter': inner_maxiter,
'maxfev': inner_maxiter,
'disp': False
}
inner_solution = _spo.minimize(fn,
x_start,
options=opts,
method='Nelder-Mead',
callback=None,
tol=inner_tol)
if not inner_solution.success:
print("WARNING: fmin_supersimplex inner loop failed (tol=%g, maxiter=%d): %s" \
% (inner_tol,inner_maxiter,inner_solution.message))
f_final = inner_solution.fun
x_start = inner_solution.x
print(">>> fmin_supersimplex: outer iteration %d gives min = %f" %
(i, f_final))
solution = _optResult()
solution.x = inner_solution.x
solution.fun = inner_solution.fun
if i < max_outer_iter:
solution.success = True
else:
solution.success = False
solution.message = "Maximum iterations exceeded"
return solution
def fmin_simplex(fn, x0, slide=1.0, tol=1e-8, maxiter=1000):
"""
Minimizes a function using a custom simplex implmentation.
This was used primarily to check scipy's Nelder-Mead method
and runs much slower, so there's not much reason for using
this method.
Parameters
----------
fn : function
The function to minimize.
x0 : numpy array
The starting point (argument to fn).
slide : float, optional
Affects initial simplex point locations
tol : float, optional
Relative tolerance as a convergence criterion.
maxiter : int, optional
Maximum iterations.
Returns
-------
scipy.optimize.Result object
Includes members 'x', 'fun', 'success', and 'message'.
"""
# Setup intial values
n = len(x0)
f = _np.zeros(n + 1)
x = _np.zeros((n + 1, n))
x[0] = x0
# Setup intial X range
for i in range(1, n + 1):
x[i] = x0
x[i, i - 1] = x0[i - 1] + slide
# Setup intial functions based on x's just defined
for i in range(n + 1):
f[i] = fn(x[i])
# Main Loop operation, loops infinitly until break condition
counter = 0
while True:
low = _np.argmin(f)
high = _np.argmax(f)
counter += 1
# Compute Migration
d = (-(n + 1) * x[high] + sum(x)) / n
# Break if value is close
if _np.sqrt(_np.dot(d, d) / n) < tol or counter == maxiter:
solution = _optResult()
solution.x = x[low]
solution.fun = f[low]
if counter < maxiter:
solution.success = True
else:
solution.success = False
solution.message = "Maximum iterations exceeded"
return solution
newX = x[high] + 2.0 * d
newF = fn(newX)
if newF <= f[low]:
# Bad news, new value is lower than any other point => replace high point with new values
x[high] = newX
f[high] = newF
newX = x[high] + d
newF = fn(newX)
# Check if need to expand
if newF <= f[low]:
x[high] = newX
f[high] = newF
else:
# Good news, new value is higher than lowest point
# Check if need to contract
if newF <= f[high]:
x[high] = newX
f[high] = newF
else:
# Contraction
newX = x[high] + 0.5 * d
newF = fn(newX)
if newF <= f[high]:
x[high] = newX
f[high] = newF
else:
for i in range(len(x)):
if i != low:
x[i] = (x[i] - x[low])
f[i] = fn(x[i])
def _fmin_supersimplex(fn, x0, abs_outer_tol, rel_outer_tol, inner_tol, max_outer_iter,
min_inner_maxiter, max_inner_maxiter, callback, printer):
"""
Minimize a function using repeated applications of the simplex algorithm.
By varying the maximum number of iterations and repeatedly calling scipy's
Nelder-Mead simplex optimization, this function performs as a robust (but
slow) minimization.
Parameters
----------
fn : function
The function to minimize.
x0 : numpy array
The starting point (argument to fn).
abs_outer_tol : float
Absolute tolerance of outer loop
rel_outer_tol : float
Relative tolerance of outer loop
inner_tol : float
Tolerance fo inner loop
max_outer_iter : int
Maximum number of outer-loop iterations
min_inner_maxiter : int
Minimum number of inner-loop iterations
max_inner_maxiter : int
Maxium number of outer-loop iterations
printer : VerbosityPrinter
Printer for displaying output status messages.
Returns
-------
scipy.optimize.Result object
Includes members 'x', 'fun', 'success', and 'message'.
"""
f_init = fn(x0)
f_final = f_init - 10 * abs_outer_tol # prime the loop
x_start = x0
i = 1
cnt_at_same_maxiter = 1
inner_maxiter = min_inner_maxiter
def check_convergence(fi, ff): # absolute and relative tolerance exit condition
return ((fi - ff) < abs_outer_tol) or abs(fi - ff) / max(abs(ff), abs_outer_tol) < rel_outer_tol
def check_convergence_str(fi, ff): # for printing status messages
return "abs=%g, rel=%g" % (fi - ff, abs(fi - ff) / max(abs(ff), abs_outer_tol))
for i in range(max_outer_iter):
# increase inner_maxiter if seems to be converging
if check_convergence(f_init, f_final) and inner_maxiter < max_inner_maxiter:
inner_maxiter *= 10; cnt_at_same_maxiter = 1
# reduce inner_maxiter if things aren't progressing (hail mary)
if cnt_at_same_maxiter > 10 and inner_maxiter > min_inner_maxiter:
inner_maxiter /= 10; cnt_at_same_maxiter = 1
printer.log("Supersimplex: outer iteration %d (inner_maxiter = %d)" % (i, inner_maxiter))
f_init = f_final
cnt_at_same_maxiter += 1
opts = {'maxiter': inner_maxiter, 'maxfev': inner_maxiter, 'disp': False}
inner_solution = _spo.minimize(fn, x_start, options=opts, method='Nelder-Mead',
callback=callback, tol=inner_tol)
if not inner_solution.success:
printer.log("WARNING: Supersimplex inner loop failed (tol=%g, maxiter=%d): %s"
% (inner_tol, inner_maxiter, inner_solution.message), 2)
f_final = inner_solution.fun
x_start = inner_solution.x
printer.log("Supersimplex: outer iteration %d gives min = %f (%s)" % (i, f_final,
check_convergence_str(f_init, f_final)))
if inner_maxiter >= max_inner_maxiter: # to exit, we must be have been using our *maximum* inner_maxiter
if check_convergence(f_init, f_final): break # converged!
solution = _optResult()
solution.x = inner_solution.x
solution.fun = inner_solution.fun
if i < max_outer_iter:
solution.success = True
else:
solution.success = False
solution.message = "Maximum iterations exceeded"
return solution
def fmax_cg(f, x0, maxiters=100, tol=1e-8, dfdx_and_bdflag=None, xopt=None):
"""
Custom conjugate-gradient (CG) routine for maximizing a function.
This function runs slower than scipy.optimize's 'CG' method, but doesn't
give up or get stuck as easily, and so sometimes can be a better option.
Parameters
----------
fn : function
The function to minimize.
x0 : numpy array
The starting point (argument to fn).
maxiters : int, optional
Maximum iterations.
tol : float, optional
Tolerace for convergence (compared to absolute difference in f)
dfdx_and_bdflag : function, optional
Function to compute jacobian of f as well as a boundary-flag.
xopt : numpy array, optional
Used for debugging, output can be printed relating current optimum
relative xopt, assumed to be a known good optimum.
Returns
-------
scipy.optimize.Result object
Includes members 'x', 'fun', 'success', and 'message'. **Note:** returns
the negated maximum in 'fun' in order to conform to the return value of
other minimization routines.
"""
MIN_STEPSIZE = 1e-8
FINITE_DIFF_STEP = 1e-4
RESET = 5
stepsize = 1e-6
#if no dfdx specifed, use finite differences
if dfdx_and_bdflag is None:
def dfdx_and_bdflag(x):
return _finite_diff_dfdx_and_bdflag(f, x, FINITE_DIFF_STEP)
step = 0
x = x0
last_fx = f(x0)
last_x = x0
lastchange = 0
lastgradnorm = 0.0 # Safer than relying on uninitialized variables
lastgrad = 0.0
if last_fx is None: raise ValueError("fmax_cg was started out of bounds!")
while (step < maxiters and ((stepsize > MIN_STEPSIZE) or
(step % RESET != 1 and RESET > 1))):
grad, boundaryFlag = dfdx_and_bdflag(x)
gradnorm = _np.dot(grad, grad)
if step % RESET == 0: # reset change == gradient
change = grad[:]
else: # add gradient to change (conjugate gradient)
#beta = gradnorm / lastgradnorm # Fletcher-Reeves
beta = (gradnorm -
_np.dot(grad, lastgrad)) / lastgradnorm # Polak-Ribiere
#beta = (gradnorm - _np.dot(grad,lastgrad))/(_np.dot(lastchange,grad-lastgrad)) #Hestenes-Stiefel
change = grad + beta * lastchange
for i in range(len(change)):
if boundaryFlag[i] * change[i] > 0:
change[i] = 0
print("DEBUG: fmax Preventing motion along dim %s" % i)
if max(abs(change)) == 0:
print("Warning: Completely Boxed in!")
fx = last_fx
x = last_x
assert (abs(last_fx - f(last_x)) < 1e-6)
break
#i = list(abs(grad)).index(min(abs(grad)))
#change[i] = -boundaryFlag[i] * 1.0 # could pick a random direction to move in?
#gradnorm = 1.0 # punt...
lastgrad = grad
lastgradnorm = gradnorm
multiplier = 1.0 / max(abs(change))
change *= multiplier
# Now "change" has largest element 1. Time to do a linear search to find optimal stepsize.
# If the last step had crazy short length, reset stepsize
if stepsize < MIN_STEPSIZE: stepsize = MIN_STEPSIZE
def g(s):
return f(
x + s * change
) # f along a line given by changedir. Argument to function is stepsize.
stepsize = _maximize1D(
g, 0, abs(stepsize),
last_fx) # find optimal stepsize along change direction
predicted_difference = stepsize * _np.dot(grad, change)
if xopt is not None: xopt_dot = _np.dot(change, xopt - x) / \
(_np.linalg.norm(change) * _np.linalg.norm(xopt - x))
x += stepsize * change
fx = f(x)
difference = fx - last_fx
print(
"DEBUG: Max iter ", step, ": f=", fx, ", dexpct=",
predicted_difference - difference, ", step=", stepsize,
", xopt_dot=", xopt_dot if xopt is not None else "--",
", chg_dot=",
_np.dot(change, lastchange) /
(_np.linalg.norm(change) * _np.linalg.norm(lastchange) + 1e-6))
if abs(difference) < tol: break # Convergence condition
lastchange = change
last_fx = fx
last_x = x.copy()
step += 1
print("Finished Custom Contrained Newton CG Method")
print(" iterations = %d" % step)
print(" maximum f = %g" % fx)
solution = _optResult()
# negate maximum to conform to other minimization routines
solution.x = x
solution.fun = -fx if fx is not None else None
if step < maxiters:
solution.success = True
else:
solution.success = False
solution.message = "Maximum iterations exceeded"
return solution
def fmin_evolutionary(f, x0, num_generations, num_individuals):
"""
Minimize a function using an evolutionary algorithm.
Uses python's deap package to perform an evolutionary
algorithm to find a function's global minimum.
Parameters
----------
fn : function
The function to minimize.
x0 : numpy array
The starting point (argument to fn).
num_generations : int
The number of generations to carry out. (similar to the number
of iterations)
num_individuals : int
The number of individuals in each generation. More individuals
make finding the global optimum more likely, but take longer
to run.
Returns
-------
scipy.optimize.Result object
Includes members 'x', 'fun', 'success', and 'message'.
"""
import deap as _deap
import deap.creator as _creator
import deap.base as _base
import deap.tools as _tools
numParams = len(x0)
# Create the individual class
_creator.create("FitnessMin", _base.Fitness, weights=(-1.0,))
_creator.create("Individual", list, fitness=_creator.FitnessMin)
# Create initialization functions
toolbox = _base.Toolbox()
toolbox.register("random", _np.random.random)
toolbox.register("individual", _tools.initRepeat, _creator.Individual,
toolbox.random, n=numParams) # fn to init an individual from a list of numParams random numbers
toolbox.register("population", _tools.initRepeat, list, toolbox.individual) # fn to create a population (still need to specify n)
# Create operation functions
def evaluate(individual):
return f( _np.array(individual) ), #note: must return a tuple
toolbox.register("mate", _tools.cxTwoPoint)
toolbox.register("mutate", _tools.mutGaussian, mu=0, sigma=0.5, indpb=0.1)
toolbox.register("select", _tools.selTournament, tournsize=3)
toolbox.register("evaluate", evaluate)
# Create the population
pop = toolbox.population(n=num_individuals)
# Evaluate the entire population
fitnesses = map(toolbox.evaluate, pop)
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
PROB_TO_CROSS = 0.5
PROB_TO_MUTATE = 0.2
# Initialize statistics
stats = _tools.Statistics(key=lambda ind: ind.fitness.values)
stats.register("avg", _np.mean)
stats.register("std", _np.std)
stats.register("min", _np.min)
stats.register("max", _np.max)
logbook = _tools.Logbook()
#Run algorithm
for g in range(num_generations):
record = stats.compile(pop)
logbook.record(gen=g, **record)
print "Gen %d: %s" % (g,record)
# Select the next generation individuals
offspring = toolbox.select(pop, len(pop))
# Clone the selected individuals
offspring = map(toolbox.clone, offspring)
# Apply crossover on the offspring
for child1, child2 in zip(offspring[::2], offspring[1::2]):
if _np.random.random() < PROB_TO_CROSS:
toolbox.mate(child1, child2)
del child1.fitness.values
del child2.fitness.values
# Apply mutation on the offspring
for mutant in offspring:
if _np.random.random() < PROB_TO_MUTATE:
toolbox.mutate(mutant)
del mutant.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# The population is entirely replaced by the offspring
pop[:] = offspring
#get best individual and return params
indx_min_fitness = _np.argmin( [ ind.fitness.values[0] for ind in pop ] )
best_params = _np.array(pop[indx_min_fitness])
solution = _optResult()
solution.x = best_params; solution.fun = pop[indx_min_fitness].fitness.values[0]
solution.success = True
return solution
def fmin_particle_swarm(f, x0, err_crit, iter_max, popsize=100, c1=2, c2=2):
"""
A simple implementation of the Particle Swarm Optimization Algorithm.
Pradeep Gowda 2009-03-16
Parameters
----------
f : function
The function to minimize.
x0 : numpy array
The starting point (argument to fn).
err_crit : float
Critical error (i.e. tolerance). Stops when error < err_crit.
iter_max : int
Maximum iterations.
popsize : int, optional
Population size. Larger populations are better at finding the global
optimum but make the algorithm take longer to run.
c1 : float, optional
Coefficient describing a particle's affinity for it's (local) maximum.
c2 : float, optional
Coefficient describing a particle's affinity for the best maximum any
particle has seen (the current global max).
Returns
-------
scipy.optimize.Result object
Includes members 'x', 'fun', 'success', and 'message'.
"""
dimensions = len(x0)
LARGE = 1e10
class Particle:
pass
#initialize the particles
particles = []
for i in range(popsize):
p = Particle()
p.params = x0 + 2 * (_np.random.random(dimensions) - 0.5)
p.best = p.params[:]
p.fitness = LARGE # large == bad fitness
p.v = _np.zeros(dimensions)
particles.append(p)
# let the first particle be the global best
gbest = particles[0]; ibest = 0
bDoLocalFitnessOpt = False
#DEBUG
#if False:
# import pickle as _pickle
# bestGaugeMx = _pickle.load(open("bestGaugeMx.debug"))
# lbfgsbGaugeMx = _pickle.load(open("lbfgsbGaugeMx.debug"))
# cgGaugeMx = _pickle.load(open("cgGaugeMx.debug"))
# initialGaugeMx = x0.reshape( (4,4) )
#
# #DEBUG: dump line cut to plot
# nPts = 100
# print "DEBUG: best offsets = \n", bestGaugeMx - initialGaugeMx
# print "DEBUG: lbfgs offsets = \n", lbfgsbGaugeMx - initialGaugeMx
# print "DEBUG: cg offsets = \n", cgGaugeMx - initialGaugeMx
#
# print "# DEBUG plot"
# #fDebug = open("x0ToBest.dat","w")
# #fDebug = open("x0ToLBFGS.dat","w")
# fDebug = open("x0ToCG.dat","w")
# #fDebug = open("LBFGSToBest.dat","w")
# #fDebug = open("CGToBest.dat","w")
# #fDebug = open("CGToLBFGS.dat","w")
#
# for i in range(nPts+1):
# alpha = float(i) / nPts
# #matM = (1.0-alpha) * initialGaugeMx + alpha*bestGaugeMx
# #matM = (1.0-alpha) * initialGaugeMx + alpha*lbfgsbGaugeMx
# matM = (1.0-alpha) * initialGaugeMx + alpha*cgGaugeMx
# #matM = (1.0-alpha) * lbfgsbGaugeMx + alpha*bestGaugeMx
# #matM = (1.0-alpha) * cgGaugeMx + alpha*bestGaugeMx
# #matM = (1.0-alpha) * cgGaugeMx + alpha*lbfgsbGaugeMx
# print >> fDebug, "%g %g" % (alpha, f(matM.flatten()))
# exit()
#
#
# fDebug = open("lineDataFromX0.dat","w")
# min_offset = -1; max_offset = 1
# for i in range(nPts+1):
# offset = min_offset + float(i)/nPts * (max_offset-min_offset)
# print >> fDebug, "%g" % offset,
#
# for k in range(len(x0)):
# x = x0.copy(); x[k] += offset
# try:
# print >> fDebug, " %g" % f(x),
# except:
# print >> fDebug, " nan",
# print >> fDebug, ""
#
# print >> fDebug, "#END DEBUG plot"
# exit()
#END DEBUG
#err = 1e10
for iter_num in range(iter_max):
w = 1.0 #- i/iter_max
#bDoLocalFitnessOpt = bool(iter_num > 20 and abs(lastBest-gbest.fitness) < 0.001 and iter_num % 10 == 0)
lastBest = gbest.fitness
minDistToBest = 1e10; minV = 1e10; maxV = 0 #DEBUG
for (ip,p) in enumerate(particles):
fitness = f(p.params)
#if bDoLocalFitnessOpt:
# opts = {'maxiter': 100, 'maxfev': 100, 'disp': False }
# local_soln = _spo.minimize(f,p.params,options=opts, method='L-BFGS-B',callback=None, tol=1e-2)
# p.params = local_soln.x
# fitness = local_soln.fun
if fitness < p.fitness: #low 'fitness' is good b/c we're minimizing
p.fitness = fitness
p.best = p.params
if fitness < gbest.fitness:
gbest = p; ibest = ip
v = w*p.v + c1 * _np.random.random() * (p.best - p.params) \
+ c2 * _np.random.random() * (gbest.params - p.params)
p.params = p.params + v
for (i,pv) in enumerate(p.params):
p.params[i] = ((pv+1) % 2) - 1 #periodic b/c on box between -1 and 1
#from .. import tools as tools_
#matM = p.params.reshape( (4,4) ) #DEBUG
#minDistToBest = min(minDistToBest, _tools.frobeniusdist(
# bestGaugeMx,matM)) #DEBUG
#minV = min( _np.linalg.norm(v), minV)
#maxV = max( _np.linalg.norm(v), maxV)
#print "DB: min diff from best = ", minDistToBest #DEBUG
#print "DB: min,max v = ", (minV,maxV)
#if False: #bDoLocalFitnessOpt:
# opts = {'maxiter': 100, 'maxfev': 100, 'disp': False }
# print "initial fun = ",gbest.fitness,
# local_soln = _spo.minimize(f,gbest.params,options=opts, method='L-BFGS-B',callback=None, tol=1e-5)
# gbest.params = local_soln.x
# gbest.fitness = local_soln.fun
# print " final fun = ",gbest.fitness
print "Iter %d: global best = %g (index %d)" % (iter_num, gbest.fitness, ibest)
#if err < err_crit: break #TODO: stopping condition
## Uncomment to print particles
#for p in particles:
# print 'params: %s, fitness: %s, best: %s' % (p.params, p.fitness, p.best)
solution = _optResult()
solution.x = gbest.params; solution.fun = gbest.fitness
solution.success = True
# if iter_num < maxiter:
# solution.success = True
# else:
# solution.success = False
# solution.message = "Maximum iterations exceeded"
return solution
def fmin_simplex(fn, x0, slide=1.0, tol=1e-8, maxiter=1000):
"""
Minimizes a function using a custom simplex implmentation.
This was used primarily to check scipy's Nelder-Mead method
and runs much slower, so there's not much reason for using
this method.
Parameters
----------
fn : function
The function to minimize.
x0 : numpy array
The starting point (argument to fn).
slide : float, optional
Affects initial simplex point locations
tol : float, optional
Relative tolerance as a convergence criterion.
maxiter : int, optional
Maximum iterations.
Returns
-------
scipy.optimize.Result object
Includes members 'x', 'fun', 'success', and 'message'.
"""
# Setup intial values
n = len(x0)
f = _np.zeros(n+1)
x = _np.zeros((n+1,n))
x[0] = x0
# Setup intial X range
for i in range(1,n+1):
x[i] = x0
x[i,i-1] = x0[i-1] + slide
# Setup intial functions based on x's just defined
for i in range(n+1):
f[i] = fn(x[i])
# Main Loop operation, loops infinitly until break condition
counter = 0
while True:
low = _np.argmin(f)
high = _np.argmax(f)
counter += 1
# Compute Migration
d = (-(n+1)*x[high]+sum(x))/n
# Break if value is close
if _np.sqrt(_np.dot(d,d)/n)<tol or counter == maxiter:
solution = _optResult()
solution.x = x[low]; solution.fun = f[low]
if counter < maxiter:
solution.success = True
else:
solution.success = False
solution.message = "Maximum iterations exceeded"
return solution
newX = x[high] + 2.0*d
newF = fn(newX)
if newF <= f[low]:
# Bad news, new value is lower than any other point => replace high point with new values
x[high] = newX
f[high] = newF
newX = x[high] + d
newF = fn(newX)
# Check if need to expand
if newF <= f[low]:
x[high] = newX
f[high] = newF
else:
# Good news, new value is higher than lowest point
# Check if need to contract
if newF <= f[high]:
x[high] = newX
f[high] = newF
else:
# Contraction
newX = x[high] + 0.5*d
newF = fn(newX)
if newF <= f[high]:
x[high] = newX
f[high] = newF
else:
for i in range(len(x)):
if i!=low:
x[i] = (x[i]-x[low])
f[i] = fn(x[i])
def fmin_supersimplex(fn, x0, outer_tol, inner_tol, max_outer_iter, min_inner_maxiter, max_inner_maxiter):
"""
Minimize a function using repeated applications of the simplex algorithm.
By varying the maximum number of iterations and repeatedly calling scipy's
Nelder-Mead simplex optimization, this function performs as a robust (but
slow) minimization.
Parameters
----------
fn : function
The function to minimize.
x0 : numpy array
The starting point (argument to fn).
outer_tol : float
Tolerance of outer loop
inner_tol : float
Tolerance fo inner loop
max_outer_iter : int
Maximum number of outer-loop iterations
min_inner_maxiter : int
Minimum number of inner-loop iterations
max_inner_maxiter : int
Maxium number of outer-loop iterations
Returns
-------
scipy.optimize.Result object
Includes members 'x', 'fun', 'success', and 'message'.
"""
f_init = fn(x0)
f_final = f_init - 10*outer_tol #prime the loop
x_start = x0
i = 1
cnt_at_same_maxiter = 1
inner_maxiter = min_inner_maxiter
while ( f_init-f_final > outer_tol or inner_maxiter < max_inner_maxiter) and i < max_outer_iter:
if f_init-f_final <= outer_tol and inner_maxiter < max_inner_maxiter:
inner_maxiter *= 10; cnt_at_same_maxiter = 1
if cnt_at_same_maxiter > 10 and inner_maxiter > min_inner_maxiter:
inner_maxiter /= 10; cnt_at_same_maxiter = 1
f_init = f_final
print ">>> fmin_supersimplex: outer iteration %d (inner_maxiter = %d)" % (i,inner_maxiter)
i += 1; cnt_at_same_maxiter += 1
opts = {'maxiter': inner_maxiter, 'maxfev': inner_maxiter, 'disp': False }
inner_solution = _spo.minimize(fn,x_start,options=opts, method='Nelder-Mead',callback=None, tol=inner_tol)
if not inner_solution.success:
print "WARNING: fmin_supersimplex inner loop failed (tol=%g, maxiter=%d): %s" \
% (inner_tol,inner_maxiter,inner_solution.message)
f_final = inner_solution.fun
x_start = inner_solution.x
print ">>> fmin_supersimplex: outer iteration %d gives min = %f" % (i,f_final)
solution = _optResult()
solution.x = inner_solution.x
solution.fun = inner_solution.fun
if i < max_outer_iter:
solution.success = True
else:
solution.success = False
solution.message = "Maximum iterations exceeded"
return solution
def fmax_cg(f, x0, maxiters=100, tol=1e-8, dfdx_and_bdflag = None, xopt=None):
"""
Custom conjugate-gradient (CG) routine for maximizing a function.
This function runs slower than scipy.optimize's 'CG' method, but doesn't
give up or get stuck as easily, and so sometimes can be a better option.
Parameters
----------
fn : function
The function to minimize.
x0 : numpy array
The starting point (argument to fn).
maxiters : int, optional
Maximum iterations.
tol : float, optional
Tolerace for convergence (compared to absolute difference in f)
dfdx_and_bdflag : function, optional
Function to compute jacobian of f as well as a boundary-flag.
xopt : numpy array, optional
Used for debugging, output can be printed relating current optimum
relative xopt, assumed to be a known good optimum.
Returns
-------
scipy.optimize.Result object
Includes members 'x', 'fun', 'success', and 'message'. **Note:** returns
the negated maximum in 'fun' in order to conform to the return value of
other minimization routines.
"""
MIN_STEPSIZE = 1e-8
FINITE_DIFF_STEP = 1e-4
RESET = 5
stepsize = 1e-6
#if no dfdx specifed, use finite differences
if dfdx_and_bdflag is None:
dfdx_and_bdflag = lambda x: _finite_diff_dfdx_and_bdflag(f,x,FINITE_DIFF_STEP)
step = 0
x = x0; last_fx = f(x0); last_x = x0
lastchange = 0
if last_fx is None: raise ValueError("fmax_cg was started out of bounds!")
while( step < maxiters and ((stepsize>MIN_STEPSIZE) or (step%RESET !=1 and RESET>1))):
grad,boundaryFlag = dfdx_and_bdflag(x)
gradnorm = _np.dot(grad,grad)
if step%RESET == 0: #reset change == gradient
change = grad[:]
else: # add gradient to change (conjugate gradient)
#beta = gradnorm / lastgradnorm # Fletcher-Reeves
beta = (gradnorm - _np.dot(grad,lastgrad))/lastgradnorm # Polak-Ribiere
#beta = (gradnorm - _np.dot(grad,lastgrad))/(_np.dot(lastchange,grad-lastgrad)) #Hestenes-Stiefel
change = grad + beta * lastchange
for i in range(len(change)):
if boundaryFlag[i]*change[i] > 0:
change[i] = 0
print "DEBUG: fmax Preventing motion along dim ",i
if max(abs(change)) == 0:
print "Warning: Completely Boxed in!"
fx = last_fx; x = last_x
assert( abs(last_fx - f(last_x)) < 1e-6)
break
#i = list(abs(grad)).index(min(abs(grad)))
#change[i] = -boundaryFlag[i] * 1.0 # could pick a random direction to move in?
#gradnorm = 1.0 # punt...
lastgrad = grad
lastgradnorm = gradnorm
multiplier = 1.0/max(abs(change))
change *= multiplier
# Now "change" has largest element 1. Time to do a linear search to find optimal stepsize.
# If the last step had crazy short length, reset stepsize
if stepsize < MIN_STEPSIZE: stepsize = MIN_STEPSIZE;
g = lambda s: f(x+s*change) # f along a line given by changedir. Argument to function is stepsize.
stepsize = _maximize1D(g,0,abs(stepsize),last_fx) #find optimal stepsize along change direction
predicted_difference = stepsize * _np.dot(grad,change)
if xopt is not None: xopt_dot = _np.dot(change, xopt-x) / (_np.linalg.norm(change) * _np.linalg.norm(xopt-x))
x += stepsize * change; fx = f(x)
difference = fx - last_fx
print "DEBUG: Max iter ", step, ": f=",fx,", dexpct=",predicted_difference-difference, \
", step=",stepsize,", xopt_dot=", xopt_dot if xopt is not None else "--", \
", chg_dot=",_np.dot(change,lastchange)/(_np.linalg.norm(change)*_np.linalg.norm(lastchange)+1e-6)
if abs(difference) < tol: break #Convergence condition
lastchange = change
last_fx = fx
last_x = x.copy()
step += 1
print "Finished Custom Contrained Newton CG Method"
print " iterations = %d" % step
print " maximum f = %g" % fx
solution = _optResult()
solution.x = x; solution.fun = -fx if fx is not None else None # negate maximum to conform to other minimization routines
if step < maxiters:
solution.success = True
else:
solution.success = False
solution.message = "Maximum iterations exceeded"
return solution
