Commit fc68b05e by Martin Reinecke

### curvature -> metric

parent a890fda5
 ... @@ -108,7 +108,7 @@ For a quick start, you can browse through the [informal ... @@ -108,7 +108,7 @@ For a quick start, you can browse through the [informal introduction](http://ift.pages.mpcdf.de/NIFTy/code.html) or introduction](http://ift.pages.mpcdf.de/NIFTy/code.html) or dive into NIFTy by running one of the demonstrations, e.g.: dive into NIFTy by running one of the demonstrations, e.g.: python demos/wiener_filter_via_curvature.py python demos/getting_started_1.py ### Acknowledgement ### Acknowledgement ... ...
 ... @@ -74,7 +74,7 @@ if __name__ == '__main__': ... @@ -74,7 +74,7 @@ if __name__ == '__main__': N_samples = 20 N_samples = 20 for i in range(5): for i in range(5): H = H.at(position) H = H.at(position) samples = [H.curvature.draw_sample(from_inverse=True) samples = [H.metric.draw_sample(from_inverse=True) for _ in range(N_samples)] for _ in range(N_samples)] KL = ift.SampledKullbachLeiblerDivergence(H, samples) KL = ift.SampledKullbachLeiblerDivergence(H, samples) ... ...
 ... @@ -306,16 +306,16 @@ Energy functionals ... @@ -306,16 +306,16 @@ Energy functionals In NIFTy5 such functions are represented by objects of type :class:`Energy`. In NIFTy5 such functions are represented by objects of type :class:`Energy`. These hold the prescription how to calculate the function's These hold the prescription how to calculate the function's :attr:`~Energy.value`, :attr:`~Energy.gradient` and :attr:`~Energy.value`, :attr:`~Energy.gradient` and (optionally) :attr:`~Energy.curvature` at any given :attr:`~Energy.position` (optionally) :attr:`~Energy.metric` at any given :attr:`~Energy.position` in parameter space. in parameter space. Function values are floating-point scalars, gradients have the form of fields Function values are floating-point scalars, gradients have the form of fields living on the energy's position domain, and curvatures are represented by living on the energy's position domain, and metrics are represented by linear operator objects. linear operator objects. Energies are classes that typically have to be provided by the user when Energies are classes that typically have to be provided by the user when tackling new IFT problems. tackling new IFT problems. Some examples of concrete energy classes delivered with NIFTy5 are Some examples of concrete energy classes delivered with NIFTy5 are :class:`QuadraticEnergy` (with position-independent curvature, mainly used with :class:`QuadraticEnergy` (with position-independent metric, mainly used with conjugate gradient minimization) and :class:`~nifty5.library.WienerFilterEnergy`. conjugate gradient minimization) and :class:`~nifty5.library.WienerFilterEnergy`. ... @@ -367,7 +367,7 @@ This family of algorithms is encapsulated in NIFTy's :class:`DescentMinimizer` ... @@ -367,7 +367,7 @@ This family of algorithms is encapsulated in NIFTy's :class:`DescentMinimizer` class, which currently has three concrete implementations: class, which currently has three concrete implementations: :class:`SteepestDescent`, :class:`VL_BFGS`, and :class:`RelaxedNewton`. :class:`SteepestDescent`, :class:`VL_BFGS`, and :class:`RelaxedNewton`. Of these algorithms, only :class:`RelaxedNewton` requires the energy object to Of these algorithms, only :class:`RelaxedNewton` requires the energy object to provide a :attr:`~Energy.curvature` property, the others only need energy provide a :attr:`~Energy.metric` property, the others only need energy values and gradients. values and gradients. The flexibility of NIFTy's design allows using externally provided The flexibility of NIFTy's design allows using externally provided ... ...
 ... @@ -49,13 +49,13 @@ class Hamiltonian(Energy): ... @@ -49,13 +49,13 @@ class Hamiltonian(Energy): @property @property @memo @memo def curvature(self): def metric(self): prior_curv = self._prior.curvature prior_mtr = self._prior.metric if self._ic_samp is None: if self._ic_samp is None: return self._lh.curvature + prior_curv return self._lh.metric + prior_mtr else: else: return SamplingEnabler(self._lh.curvature, prior_curv.inverse, return SamplingEnabler(self._lh.metric, prior_mtr.inverse, self._ic_samp, prior_curv.inverse) self._ic_samp, prior_mtr.inverse) def __str__(self): def __str__(self): res = 'Likelihood:\t{:.2E}\n'.format(self._lh.value) res = 'Likelihood:\t{:.2E}\n'.format(self._lh.value) ... ...
 ... @@ -33,6 +33,6 @@ class SampledKullbachLeiblerDivergence(Energy): ... @@ -33,6 +33,6 @@ class SampledKullbachLeiblerDivergence(Energy): @property @property @memo @memo def curvature(self): def metric(self): return (my_sum(map(lambda v: v.curvature, self._energy_list)) * return (my_sum(map(lambda v: v.metric, self._energy_list)) * (1./len(self._energy_list))) (1./len(self._energy_list)))
 ... @@ -62,7 +62,7 @@ class GaussianEnergy(Energy): ... @@ -62,7 +62,7 @@ class GaussianEnergy(Energy): @property @property @memo @memo def curvature(self): def metric(self): if self._cov is None: if self._cov is None: return SandwichOperator.make(self._inp.jacobian, None) return SandwichOperator.make(self._inp.jacobian, None) return SandwichOperator.make(self._inp.jacobian, self._cov.inverse) return SandwichOperator.make(self._inp.jacobian, self._cov.inverse)
 ... @@ -44,7 +44,7 @@ class PoissonianEnergy(Energy): ... @@ -44,7 +44,7 @@ class PoissonianEnergy(Energy): # metric = makeOp(d/lamb_val/lamb_val) # metric = makeOp(d/lamb_val/lamb_val) metric = makeOp(1./lamb_val) metric = makeOp(1./lamb_val) self._curvature = SandwichOperator.make(self._lamb.jacobian, metric) self._metric = SandwichOperator.make(self._lamb.jacobian, metric) def at(self, position): def at(self, position): return self.__class__(self._lamb.at(position), self._d) return self.__class__(self._lamb.at(position), self._d) ... @@ -58,5 +58,5 @@ class PoissonianEnergy(Energy): ... @@ -58,5 +58,5 @@ class PoissonianEnergy(Energy): return self._gradient return self._gradient @property @property def curvature(self): def metric(self): return self._curvature return self._metric
 ... @@ -47,7 +47,7 @@ class ConjugateGradient(Minimizer): ... @@ -47,7 +47,7 @@ class ConjugateGradient(Minimizer): Parameters Parameters ---------- ---------- energy : Energy object at the starting point of the iteration. energy : Energy object at the starting point of the iteration. Its curvature operator must be independent of position, otherwise Its metric operator must be independent of position, otherwise linear conjugate gradient minimization will fail. linear conjugate gradient minimization will fail. preconditioner : Operator *optional* preconditioner : Operator *optional* This operator can be provided which transforms the variables of the This operator can be provided which transforms the variables of the ... @@ -73,7 +73,7 @@ class ConjugateGradient(Minimizer): ... @@ -73,7 +73,7 @@ class ConjugateGradient(Minimizer): return energy, controller.CONVERGED return energy, controller.CONVERGED while True: while True: q = energy.curvature(d) q = energy.metric(d) ddotq = d.vdot(q).real ddotq = d.vdot(q).real if ddotq == 0.: if ddotq == 0.: logger.error("Error: ConjugateGradient: ddotq==0.") logger.error("Error: ConjugateGradient: ddotq==0.") ... ...
 ... @@ -51,7 +51,7 @@ class DescentMinimizer(Minimizer): ... @@ -51,7 +51,7 @@ class DescentMinimizer(Minimizer): Parameters Parameters ---------- ---------- energy : Energy energy : Energy Energy object which provides value, gradient and curvature at a Energy object which provides value, gradient and metric at a specific position in parameter space. specific position in parameter space. Returns Returns ... ...
 ... @@ -67,6 +67,6 @@ class EnergySum(Energy): ... @@ -67,6 +67,6 @@ class EnergySum(Energy): @property @property @memo @memo def curvature(self): def metric(self): return my_lincomb(map(lambda v: v.curvature, self._energies), return my_lincomb(map(lambda v: v.metric, self._energies), self._factors) self._factors)
 ... @@ -21,7 +21,7 @@ from .energy import Energy ... @@ -21,7 +21,7 @@ from .energy import Energy class QuadraticEnergy(Energy): class QuadraticEnergy(Energy): """The Energy for a quadratic form. """The Energy for a quadratic form. The most important aspect of this energy is that its curvature must be The most important aspect of this energy is that its metric must be position-independent. position-independent. """ """ ... @@ -74,5 +74,5 @@ class QuadraticEnergy(Energy): ... @@ -74,5 +74,5 @@ class QuadraticEnergy(Energy): return self._grad return self._grad @property @property def curvature(self): def metric(self): return self._A return self._A
 ... @@ -24,7 +24,7 @@ class RelaxedNewton(DescentMinimizer): ... @@ -24,7 +24,7 @@ class RelaxedNewton(DescentMinimizer): """ Calculates the descent direction according to a Newton scheme. """ Calculates the descent direction according to a Newton scheme. The descent direction is determined by weighting the gradient at the The descent direction is determined by weighting the gradient at the current parameter position with the inverse local curvature. current parameter position with the inverse local metric. """ """ def __init__(self, controller, line_searcher=None): def __init__(self, controller, line_searcher=None): if line_searcher is None: if line_searcher is None: ... @@ -34,4 +34,4 @@ class RelaxedNewton(DescentMinimizer): ... @@ -34,4 +34,4 @@ class RelaxedNewton(DescentMinimizer): line_searcher=line_searcher) line_searcher=line_searcher) def get_descent_direction(self, energy): def get_descent_direction(self, energy): return -energy.curvature.inverse_times(energy.gradient) return -energy.metric.inverse_times(energy.gradient)
 ... @@ -56,7 +56,7 @@ class _MinHelper(object): ... @@ -56,7 +56,7 @@ class _MinHelper(object): def hessp(self, x, p): def hessp(self, x, p): self._update(x) self._update(x) res = self._energy.curvature(_toField(p, self._domain)) res = self._energy.metric(_toField(p, self._domain)) return _toFlatNdarray(res) return _toFlatNdarray(res) ... ...
 ... @@ -32,9 +32,9 @@ class SamplingEnabler(EndomorphicOperator): ... @@ -32,9 +32,9 @@ class SamplingEnabler(EndomorphicOperator): Parameters Parameters ---------- ---------- likelihood : :class:`EndomorphicOperator` likelihood : :class:`EndomorphicOperator` Curvature of the likelihood Metric of the likelihood prior : :class:`EndomorphicOperator` prior : :class:`EndomorphicOperator` Inverse curvature of the prior Inverse metric of the prior iteration_controller : :class:`IterationController` iteration_controller : :class:`IterationController` The iteration controller to use for the iterative numerical inversion The iteration controller to use for the iterative numerical inversion done by a :class:`ConjugateGradient` object. done by a :class:`ConjugateGradient` object. ... ...
 ... @@ -59,7 +59,7 @@ class Energy_Tests(unittest.TestCase): ... @@ -59,7 +59,7 @@ class Energy_Tests(unittest.TestCase): S = ift.create_power_operator(hspace, power_spectrum=_flat_PS) S = ift.create_power_operator(hspace, power_spectrum=_flat_PS) energy = ift.WienerFilterEnergy( energy = ift.WienerFilterEnergy( position=s0, d=d, R=R, N=N, S=S, iteration_controller=IC) position=s0, d=d, R=R, N=N, S=S, iteration_controller=IC) ift.extra.check_value_gradient_curvature_consistency( ift.extra.check_value_gradient_metric_consistency( energy, ntries=10) energy, ntries=10) @expand(product([ift.GLSpace(15), @expand(product([ift.GLSpace(15), ... @@ -94,7 +94,7 @@ class Energy_Tests(unittest.TestCase): ... @@ -94,7 +94,7 @@ class Energy_Tests(unittest.TestCase): energy = ift.GaussianEnergy(d_model, d, N) energy = ift.GaussianEnergy(d_model, d, N) if isinstance(nonlinearity(), ift.Linear): if isinstance(nonlinearity(), ift.Linear): ift.extra.check_value_gradient_curvature_consistency( ift.extra.check_value_gradient_metric_consistency( energy, ntries=10) energy, ntries=10) else: else: ift.extra.check_value_gradient_consistency( ift.extra.check_value_gradient_consistency( ... ...
 ... @@ -94,7 +94,7 @@ class Test_Minimizers(unittest.TestCase): ... @@ -94,7 +94,7 @@ class Test_Minimizers(unittest.TestCase): return out return out @property @property def curvature(self): def metric(self): class RBCurv(ift.EndomorphicOperator): class RBCurv(ift.EndomorphicOperator): def __init__(self, loc): def __init__(self, loc): self._loc = loc.to_global_data().copy() self._loc = loc.to_global_data().copy() ... @@ -150,7 +150,7 @@ class Test_Minimizers(unittest.TestCase): ... @@ -150,7 +150,7 @@ class Test_Minimizers(unittest.TestCase): return ift.Field(self.position.domain, val=2*x*np.exp(-(x**2))) return ift.Field(self.position.domain, val=2*x*np.exp(-(x**2))) @property @property def curvature(self): def metric(self): x = self.position.to_global_data()[0] x = self.position.to_global_data()[0] v = (2 - 4*x*x)*np.exp(-x**2) v = (2 - 4*x*x)*np.exp(-x**2) return ift.DiagonalOperator( return ift.DiagonalOperator( ... @@ -188,7 +188,7 @@ class Test_Minimizers(unittest.TestCase): ... @@ -188,7 +188,7 @@ class Test_Minimizers(unittest.TestCase): return ift.Field(self.position.domain, val=np.sinh(x)) return ift.Field(self.position.domain, val=np.sinh(x)) @property @property def curvature(self): def metric(self): x = self.position.to_global_data()[0] x = self.position.to_global_data()[0] v = np.cosh(x) v = np.cosh(x) return ift.DiagonalOperator( return ift.DiagonalOperator( ... ...
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!