diff options
Diffstat (limited to 'Wrappers/Python')
| -rw-r--r-- | Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py | 48 | ||||
| -rw-r--r-- | Wrappers/Python/ccpi/optimisation/functions/KullbackLeibler.py | 60 |
2 files changed, 71 insertions, 37 deletions
diff --git a/Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py b/Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py index d1b5351..a165e55 100644 --- a/Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py +++ b/Wrappers/Python/ccpi/optimisation/algorithms/PDHG.py @@ -152,28 +152,28 @@ def PDHG_old(f, g, operator, tau = None, sigma = None, opt = None, **kwargs): if not memopt: - y_old += sigma * operator.direct(xbar) - y = f.proximal_conjugate(y_old, sigma) - - x_old -= tau*operator.adjoint(y) - x = g.proximal(x_old, tau) - + y_tmp = y_old + sigma * operator.direct(xbar) + y = f.proximal_conjugate(y_tmp, sigma) + + x_tmp = x_old - tau*operator.adjoint(y) + x = g.proximal(x_tmp, tau) + x.subtract(x_old, out=xbar) xbar *= theta xbar += x - + x_old.fill(x) - y_old.fill(y) + y_old.fill(y) -# if i%100==0: -# -# p1 = f(operator.direct(x)) + g(x) -# d1 = - ( f.convex_conjugate(y) + g(-1*operator.adjoint(y)) ) -# primal.append(p1) -# dual.append(d1) -# pdgap.append(p1-d1) -# print(p1, d1, p1-d1) + if i%100==0: + + p1 = f(operator.direct(x)) + g(x) + d1 = - ( f.convex_conjugate(y) + g(-1*operator.adjoint(y)) ) + primal.append(p1) + dual.append(d1) + pdgap.append(p1-d1) + print(p1, d1, p1-d1) else: @@ -196,14 +196,14 @@ def PDHG_old(f, g, operator, tau = None, sigma = None, opt = None, **kwargs): x_old.fill(x) y_old.fill(y) -# if i%100==0: -# -# p1 = f(operator.direct(x)) + g(x) -# d1 = - ( f.convex_conjugate(y) + g(-1*operator.adjoint(y)) ) -# primal.append(p1) -# dual.append(d1) -# pdgap.append(p1-d1) -# print(p1, d1, p1-d1) + if i%100==0: + + p1 = f(operator.direct(x)) + g(x) + d1 = - ( f.convex_conjugate(y) + g(-1*operator.adjoint(y)) ) + primal.append(p1) + dual.append(d1) + pdgap.append(p1-d1) + print(p1, d1, p1-d1) t_end = time.time() diff --git a/Wrappers/Python/ccpi/optimisation/functions/KullbackLeibler.py b/Wrappers/Python/ccpi/optimisation/functions/KullbackLeibler.py index 18af154..ae25bdb 100644 --- a/Wrappers/Python/ccpi/optimisation/functions/KullbackLeibler.py +++ b/Wrappers/Python/ccpi/optimisation/functions/KullbackLeibler.py @@ -19,23 +19,29 @@ import numpy from ccpi.optimisation.functions import Function -from ccpi.optimisation.functions.ScaledFunction import ScaledFunction -from ccpi.framework import DataContainer, ImageData, ImageGeometry +from ccpi.optimisation.functions.ScaledFunction import ScaledFunction class KullbackLeibler(Function): - def __init__(self,data,**kwargs): + ''' Assume that data > 0 + + ''' + + def __init__(self,data, **kwargs): super(KullbackLeibler, self).__init__() self.b = data self.bnoise = kwargs.get('bnoise', 0) + + def __call__(self, x): + + # TODO check + self.sum_value = self.b + self.bnoise if (self.sum_value.as_array()<0).any(): self.sum_value = numpy.inf - - def __call__(self, x): if self.sum_value==numpy.inf: return numpy.inf @@ -43,22 +49,50 @@ class KullbackLeibler(Function): return numpy.sum( x.as_array() - self.b.as_array() * numpy.log(self.sum_value.as_array())) - def gradient(self, x): + def gradient(self, x, out=None): #TODO Division check - return 1 - self.b/(x + self.bnoise) + if out is None: + return 1 - self.b/(x + self.bnoise) + else: + self.b.divide(x+self.bnoise, out=out) + out.subtract(1, out=out) - def convex_conjugate(self, x, out=None): - pass + def convex_conjugate(self, x): + + return self.b * ( numpy.log(self.b/(1-x)) - 1 ) - self.bnoise * (x - 1) def proximal(self, x, tau, out=None): - z = x + tau * self.bnoise - return (z + 1) - ((z-1)**2 + 4 * tau * self.b).sqrt() - + if out is None: + return 0.5 *( (x - self.bnoise - tau) + ( (x + self.bnoise - tau)**2 + 4*tau*self.b ) .sqrt() ) + else: + tmp = 0.5 *( (x - self.bnoise - tau) + ( (x + self.bnoise - tau)**2 + 4*tau*self.b ) .sqrt() ) + out.fill(tmp) + def proximal_conjugate(self, x, tau, out=None): - pass + + + if out is None: + z = x + tau * self.bnoise + return 0.5*((z + 1) - ((z-1)**2 + 4 * tau * self.b).sqrt()) + else: + z = x + tau * self.bnoise + res = 0.5*((z + 1) - ((z-1)**2 + 4 * tau * self.b).sqrt()) + out.fill(res) + + + + def __rmul__(self, scalar): + + ''' Multiplication of L2NormSquared with a scalar + + Returns: ScaledFunction + + ''' + + return ScaledFunction(self, scalar) |