def __init__(self, evaluator, evaluable, **parameters):
BlackBoxOptimizer.__init__(self, evaluator, evaluable, **parameters)
self.alphas = ones(self.numberOfCenters) / self.numberOfCenters
self.mus = []
self.sigmas = []
self.tau = 1.
if self.rangemins == None:
self.rangemins = -ones(self.xdim)
if self.rangemaxs == None:
self.rangemaxs = ones(self.xdim)
if self.initCovariances == None:
self.initCovariances = eye(self.xdim)
if self.elitist and self.numberOfCenters == 1 and not self.noisyEvaluator:
# in the elitist case seperate evaluations are not necessary.
# CHECKME: maybe in the noisy case?
self.evalMus = False
assert not (self.useCauchy and self.numberOfCenters > 1)
for dummy in range(self.numberOfCenters):
self.mus.append(
rand(self.xdim) * (self.rangemaxs - self.rangemins) +
self.rangemins)
self.sigmas.append(dot(eye(self.xdim), self.initCovariances))
self.reset()
def __init__(self, evaluator, evaluable, **parameters):
BlackBoxOptimizer.__init__(self, evaluator, evaluable, **parameters)
self.alphas = ones(self.numberOfCenters)/self.numberOfCenters
self.mus = []
self.sigmas = []
self.tau = 1.
if self.rangemins == None:
self.rangemins = -ones(self.xdim)
if self.rangemaxs == None:
self.rangemaxs = ones(self.xdim)
if self.initCovariances == None:
self.initCovariances = eye(self.xdim)
if self.elitist and self.numberOfCenters == 1 and not self.noisyEvaluator:
# in the elitist case seperate evaluations are not necessary.
# CHECKME: maybe in the noisy case?
self.evalMus = False
assert not(self.useCauchy and self.numberOfCenters > 1)
for dummy in range(self.numberOfCenters):
self.mus.append(rand(self.xdim) * (self.rangemaxs-self.rangemins) + self.rangemins)
self.sigmas.append(dot(eye(self.xdim), self.initCovariances))
self.reset()
def __init__(self, evaluator, evaluable, **parameters):
BlackBoxOptimizer.__init__(self, evaluator, evaluable, **parameters)
n = self.xdim
# internal executution variables
self.generation = 0 # current generation
self.fevals = 0 # nb of evaluations
# determine batch size
minlambd = 1 + self.mu * (1 + n + n * n)
if not self.lambd:
self.lambd = minlambd
if self.verbose and self.lambd < minlambd:
print "Warning! Underconstrained linear regression"
if not self.lrSigma:
self.lrSigma = self.lr
self.genfitnesses = []
self.alpha = ones(
self.mu
) / self.mu # relative probabilities of drawing from each of the mu centers
self.basealpha = ones(self.mu) / self.mu
# list of the mu current centers
if self.x0 == None:
self.x = [self.initialsearchrange * mat(randn(n, 1))]
else:
self.x0 = reshape(mat(self.x0), (n, 1))
self.x = [self.x0]
for i in range(self.mu - 1):
# CHECKME: should the centers not be different initially?
self.x.append(self.x[0].copy())
self.fx = zeros(self.mu) # function values for x
self.factorSigma = [self.genInitSigmaFactor()
] # the cholesky(??) factor version of sigma
for dummy in range(self.mu - 1):
self.factorSigma.append(self.factorSigma[-1] * 1.1)
self.sigma = [] # the list of the mu current covariance matrices
for dummy in range(self.mu):
self.sigma.append(self.factorSigma[dummy].T *
self.factorSigma[dummy])
self.zs = [None
] * self.lambd # most recent batch of lambd samples drawn
self.chosenCenter = [None
] * self.lambd # the center chosen for drawing z
self.R = mat(zeros((self.lambd, 1))) # function values for zs
self.w = None # the vector with the computed updates for all parameters
self.rellhood = zeros(
self.lambd
) # vector containing rel. likelihood under current policy
# a special matrix, containing the log-derivatives
self.phi = mat(
ones((self.lambd, 1 + schwupps + self.mu * (n * n + n + 1))))
# iRPROP+ learning rate multiplier gamma for every generation
self.delta = []
self.delta.append(self.rpropInitDelta * ones(1 + (self.mu *
(n * n + n + 1))))
# store old parameters
self.wStored = []
self.rpropPerformance = []
self.oldParams = []
# used for the one-fith-rule
self.lrMultiplier = 1.
self.blackmagic = 1.
if self.advancedfifth and not self.dsigma:
self.dsigma = float(n + 8) / 4 # CMA heuristic
if self.returnall:
self.allsigmas = []
self.allxs = []
for i in range(self.mu):
self.allsigmas.append(self.sigma[i].copy())
self.allxs.append(self.x[i].copy())
def __init__(self, evaluator, evaluable, **parameters):
BlackBoxOptimizer.__init__(self, evaluator, evaluable, **parameters)
n = self.xdim
# internal executution variables
self.generation = 0 # current generation
self.fevals = 0 # nb of evaluations
# determine batch size
minlambd = 1 + self.mu*(1+n+n*n)
if not self.lambd:
self.lambd = minlambd
if self.verbose and self.lambd < minlambd:
print "Warning! Underconstrained linear regression"
if not self.lrSigma:
self.lrSigma = self.lr
self.genfitnesses = []
self.alpha = ones(self.mu)/self.mu # relative probabilities of drawing from each of the mu centers
self.basealpha = ones(self.mu)/self.mu
# list of the mu current centers
if self.x0 == None:
self.x = [self.initialsearchrange * mat(randn(n,1))]
else:
self.x0 = reshape(mat(self.x0), (n,1))
self.x = [self.x0]
for i in range(self.mu - 1):
# CHECKME: should the centers not be different initially?
self.x.append(self.x[0].copy())
self.fx = zeros(self.mu) # function values for x
self.factorSigma = [self.genInitSigmaFactor()] # the cholesky(??) factor version of sigma
for dummy in range(self.mu - 1):
self.factorSigma.append(self.factorSigma[-1]*1.1)
self.sigma = [] # the list of the mu current covariance matrices
for dummy in range(self.mu):
self.sigma.append(self.factorSigma[dummy].T*self.factorSigma[dummy])
self.zs = [None]*self.lambd # most recent batch of lambd samples drawn
self.chosenCenter = [None]*self.lambd # the center chosen for drawing z
self.R = mat(zeros((self.lambd, 1))) # function values for zs
self.w = None # the vector with the computed updates for all parameters
self.rellhood = zeros(self.lambd) # vector containing rel. likelihood under current policy
# a special matrix, containing the log-derivatives
self.phi = mat(ones((self.lambd, 1 + schwupps + self.mu*(n*n+n+1))))
# iRPROP+ learning rate multiplier gamma for every generation
self.delta = []
self.delta.append(self.rpropInitDelta * ones(1 + (self.mu*(n*n+n+1))))
# store old parameters
self.wStored = []
self.rpropPerformance = []
self.oldParams = []
# used for the one-fith-rule
self.lrMultiplier = 1.
self.blackmagic = 1.
if self.advancedfifth and not self.dsigma:
self.dsigma = float(n+8)/4 # CMA heuristic
if self.returnall:
self.allsigmas = []
self.allxs = []
for i in range(self.mu):
self.allsigmas.append(self.sigma[i].copy())
self.allxs.append(self.x[i].copy())
