def oneSample(self, k):
""" produce one new sample and update phi correspondingly """
thesum = 0.0
for i in range(self.mu):
thesum += exp(self.basealpha[i])
for i in range(self.mu):
self.alpha[i] = exp(self.basealpha[i]) / thesum
choosem = drawIndex(self.alpha, tolerant=True)
self.chosenCenter[k] = choosem
z = mat(
multivariate_normal(
array(self.x[choosem]).flatten(), self.sigma[choosem])).T
self.zs[k] = z
self.R[k] = self.evaluateAt(z)
# TODO make for all mu
if self.importanceSampling:
self.rellhood[k] = multivariateNormalPdf(z, self.x[0],
self.sigma[0])
logderivbasealpha = zeros((self.mu, 1))
logderivx = zeros((self.mu, self.xdim))
logderivfactorsigma = zeros((self.mu, self.xdim, self.xdim))
for m in range(self.mu):
self.sigma[m] = dot(self.factorSigma[m].T, self.factorSigma[m])
if self.mu > 1:
relresponsibility = (self.alpha[m] * multivariateNormalPdf(
ravel(z), ravel(self.x[m]), self.sigma[m]) / sum(
map(
lambda mm: self.alpha[mm] * multivariateNormalPdf(
ravel(z), ravel(self.x[mm]), self.sigma[mm]),
range(self.mu))))
else:
relresponsibility = 1.0
if self.mu > 1:
logderivbasealpha[m] = relresponsibility * (1.0 -
self.alpha[m])
else:
logderivbasealpha[m] = 0.0
logderivx[m] = relresponsibility * (self.sigma[m].I *
(z - self.x[m])).flatten()
A = 0.5 * self.sigma[m].I * (z - self.x[m]) * (
z - self.x[m]).T * self.sigma[m].I - 0.5 * self.sigma[m].I
logderivsigma_m = self.blackmagic * relresponsibility * A #0.5 * (A + diag(diag(A))) #* 2.0
logderivfactorsigma[m] = self.factorSigma[m] * (logderivsigma_m +
logderivsigma_m.T)
#print 'logalpha', logderivbasealpha.flatten(), self.alpha, sum(logderivbasealpha)
tmp = self.combineParams(logderivbasealpha, logderivx,
logderivfactorsigma)
self.phi[k] = tmp
def _updateShaping(self):
""" Daan: "This won't work. I like it!" """
assert self.numberOfCenters == 1
possible = self.shapingFunction.getPossibleParameters(self.windowSize)
matchValues = []
pdfs = [
multivariateNormalPdf(s, self.mus[0], self.sigmas[0])
for s in self.samples
]
for p in possible:
self.shapingFunction.setParameter(p)
transformedFitnesses = self.shapingFunction(self.fitnesses)
#transformedFitnesses /= sum(transformedFitnesses)
sumValue = sum([
x * log(y) for x, y in zip(pdfs, transformedFitnesses) if y > 0
])
normalization = sum(
[x * y for x, y in zip(pdfs, transformedFitnesses) if y > 0])
matchValues.append(sumValue / normalization)
self.shapingFunction.setParameter(possible[argmax(matchValues)])
if len(self.allsamples) % 100 == 0:
print(possible[argmax(matchValues)])
print(fListToString(matchValues, 3))
def oneSample(self, k):
""" produce one new sample and update phi correspondingly """
thesum = 0.0
for i in range(self.mu):
thesum += exp(self.basealpha[i])
for i in range(self.mu):
self.alpha[i] = exp(self.basealpha[i])/thesum
choosem = drawIndex(self.alpha, tolerant = True)
self.chosenCenter[k] = choosem
z = mat(multivariate_normal(array(self.x[choosem]).flatten(), self.sigma[choosem])).T
self.zs[k] = z
self.R[k] = self.evaluateAt(z)
# TODO make for all mu
if self.importanceSampling:
self.rellhood[k] = multivariateNormalPdf(z, self.x[0], self.sigma[0])
logderivbasealpha = zeros((self.mu, 1))
logderivx = zeros((self.mu, self.xdim))
logderivfactorsigma = zeros((self.mu, self.xdim, self.xdim))
for m in range(self.mu):
self.sigma[m] = dot(self.factorSigma[m].T,self.factorSigma[m])
if self.mu > 1:
relresponsibility = (self.alpha[m] * multivariateNormalPdf(ravel(z), ravel(self.x[m]), self.sigma[m]) /
sum(map(lambda mm: self.alpha[mm]*multivariateNormalPdf(ravel(z), ravel(self.x[mm]), self.sigma[mm]), range(self.mu))))
else:
relresponsibility = 1.0
if self.mu > 1:
logderivbasealpha[m] = relresponsibility * (1.0 - self.alpha[m])
else:
logderivbasealpha[m] = 0.0
logderivx[m] = relresponsibility * (self.sigma[m].I * (z - self.x[m])).flatten()
A = 0.5 * self.sigma[m].I * (z - self.x[m]) * (z - self.x[m]).T * self.sigma[m].I - 0.5 * self.sigma[m].I
logderivsigma_m = self.blackmagic * relresponsibility * A#0.5 * (A + diag(diag(A))) #* 2.0
logderivfactorsigma[m] = self.factorSigma[m]*(logderivsigma_m + logderivsigma_m.T)
#print 'logalpha', logderivbasealpha.flatten(), self.alpha, sum(logderivbasealpha)
tmp = self.combineParams(logderivbasealpha, logderivx, logderivfactorsigma)
self.phi[k] = tmp
def _produceNewSample(self):
""" returns a new sample, its fitness and its densities """
sample = self._generateSample()
fit = self.evaluator(sample)
if fit >= self.bestEvaluation:
self.bestEvaluation = fit
self.bestEvaluable = sample.copy()
self.allsamples.append(sample)
# compute densities, and normalize
densities = zeros(self.numberOfCenters)
if self.numberOfCenters > 1:
for c in range(self.numberOfCenters):
densities[c] = self.alphas[c] * multivariateNormalPdf(sample, self.mus[c], self.sigmas[c])
densities /= sum(densities)
return sample, fit, densities
def _produceNewSample(self):
""" returns a new sample, its fitness and its densities """
sample = self._generateSample()
fit = self.evaluator(sample)
if fit >= self.bestEvaluation:
self.bestEvaluation = fit
self.bestEvaluable = sample.copy()
self.allsamples.append(sample)
# compute densities, and normalize
densities = zeros(self.numberOfCenters)
if self.numberOfCenters > 1:
for c in range(self.numberOfCenters):
densities[c] = self.alphas[c] * multivariateNormalPdf(
sample, self.mus[c], self.sigmas[c])
densities /= sum(densities)
return sample, fit, densities
def _computeDensities(self, sample):
""" compute densities, and normalize """
densities = zeros(self.numberOfCenters)
for c in range(self.numberOfCenters):
if self.diagonalOnly:
pdf = product([norm.pdf(x, self.mus[c][i], self.sigmas[c][i]) for i, x in enumerate(sample)])
else:
pdf = multivariateNormalPdf(sample, self.mus[c], self.sigmas[c])
if pdf > 1e40:
pdf = 1e40
elif pdf < 1e-40:
pdf = 1e-40
if isnan(pdf):
print('NaN!')
pdf = 0.
densities[c] = self.alphas[c] * pdf
densities /= sum(densities)
return densities
def _computeDensities(self, sample):
""" compute densities, and normalize """
densities = zeros(self.numberOfCenters)
for c in range(self.numberOfCenters):
if self.diagonalOnly:
pdf = product([norm.pdf(x, self.mus[c][i], self.sigmas[c][i]) for i, x in enumerate(sample)])
else:
pdf = multivariateNormalPdf(sample, self.mus[c], self.sigmas[c])
if pdf > 1e40:
pdf = 1e40
elif pdf < 1e-40:
pdf = 1e-40
if isnan(pdf):
print('NaN!')
pdf = 0.
densities[c] = self.alphas[c] * pdf
densities /= sum(densities)
return densities
def _updateShaping(self):
""" Daan: "This won't work. I like it!" """
assert self.numberOfCenters == 1
possible = self.shapingFunction.getPossibleParameters(self.windowSize)
matchValues = []
pdfs = [multivariateNormalPdf(s, self.mus[0], self.sigmas[0])
for s in self.samples]
for p in possible:
self.shapingFunction.setParameter(p)
transformedFitnesses = self.shapingFunction(self.fitnesses)
#transformedFitnesses /= sum(transformedFitnesses)
sumValue = sum([x * log(y) for x, y in zip(pdfs, transformedFitnesses) if y > 0])
normalization = sum([x * y for x, y in zip(pdfs, transformedFitnesses) if y > 0])
matchValues.append(sumValue / normalization)
self.shapingFunction.setParameter(possible[argmax(matchValues)])
if len(self.allsamples) % 100 == 0:
print((possible[argmax(matchValues)]))
print((fListToString(matchValues, 3)))
