NIFTy merge requestshttps://gitlab.mpcdf.mpg.de/ift/nifty/-/merge_requests2020-07-17T13:10:31Zhttps://gitlab.mpcdf.mpg.de/ift/nifty/-/merge_requests/536WIP: Catch FloatingPointError exeception in `energy.at` to store crashing pos...2020-07-17T13:10:31ZLukas PlatzWIP: Catch FloatingPointError exeception in `energy.at` to store crashing positionMost numerical errors (overflows, divisions by zero, …) with NIFTy occur during minimization runs. Tracking down what caused the fault condition is hard, as the NIFTy minimizers only return positions on non-faulty exits and the exact loc...Most numerical errors (overflows, divisions by zero, …) with NIFTy occur during minimization runs. Tracking down what caused the fault condition is hard, as the NIFTy minimizers only return positions on non-faulty exits and the exact location of the numerical error has to be reconstructed manually by observing the tracelog.
Typically, the numerical errors occur in the energy functionals to be minimized. Because of this, I propose to augment those to pickle their position on internal crashes.
This will help users in reconstructing the state at crash and in finding the crash reasons.https://gitlab.mpcdf.mpg.de/ift/nifty/-/merge_requests/537WIP: Test Fisher matrix using definition2020-06-19T08:53:13ZReimar H LeikeWIP: Test Fisher matrix using definitionIt was long requested to have a way to test Fisher matrices for their correctness. In this branch, we test whether the sample expectation value
```math
\left<\frac{\partial H(d|x)}{\partial x}\frac{\partial H(d|x)}{\partial x^\dagger}\ri...It was long requested to have a way to test Fisher matrices for their correctness. In this branch, we test whether the sample expectation value
```math
\left<\frac{\partial H(d|x)}{\partial x}\frac{\partial H(d|x)}{\partial x^\dagger}\right>_{P(d|x)}
```
agrees with the actual fisher metric. We do so by comparing the fisher application at one random vector with the expectation value defined above. The test is however only statistically true for the limit of many data realizations $d$, and thus the error margins have to be taken large in order to avoid false postives.Philipp Arrasparras@mpa-garching.mpg.dePhilipp Arrasparras@mpa-garching.mpg.dehttps://gitlab.mpcdf.mpg.de/ift/nifty/-/merge_requests/538WIP: Pointwise Safe Exponential Function2020-07-16T08:17:03ZLukas PlatzWIP: Pointwise Safe Exponential FunctionExponential overflows introduce an often times unnessecary source of run abortions into reconstructions. Especially in early reconstructions, when the position is still far from converged, stray samples tend to cause exponential overflow...Exponential overflows introduce an often times unnessecary source of run abortions into reconstructions. Especially in early reconstructions, when the position is still far from converged, stray samples tend to cause exponential overflows during the minimizations, if not mitigated.
I propose to add a 'safe' exponential function to the pointwise functions which surpesses exponential overflows. It does this by clipping input value to safe limits prior to applying the exponential function.https://gitlab.mpcdf.mpg.de/ift/nifty/-/merge_requests/541WIP: More constant support (new version)2020-06-18T17:26:40ZPhilipp Arrasparras@mpa-garching.mpg.deWIP: More constant support (new version)https://gitlab.mpcdf.mpg.de/ift/nifty/-/merge_requests/546WIP: Allow for opting out of asperity and flexibility2020-06-30T21:11:27ZGordian EdenhoferWIP: Allow for opting out of asperity and flexibilityhttps://gitlab.mpcdf.mpg.de/ift/nifty/-/merge_requests/560Leaky clip2020-07-17T13:09:27ZLukas PlatzLeaky clipProposed by @lerou in !538, this MR adds the pointwise function `leaky_clip` to NIFTy.
Not sure if this should go into NIFTy 6 or 7, but it would be useful in both.Proposed by @lerou in !538, this MR adds the pointwise function `leaky_clip` to NIFTy.
Not sure if this should go into NIFTy 6 or 7, but it would be useful in both.Martin ReineckeMartin Reineckehttps://gitlab.mpcdf.mpg.de/ift/nifty/-/merge_requests/571Instance helpers2020-10-20T10:44:25ZPhilipp Arrasparras@mpa-garching.mpg.deInstance helpershttps://gitlab.mpcdf.mpg.de/ift/nifty/-/merge_requests/590open scr/ instead of symlink nifty7/2021-03-24T08:20:04ZJakob Rothopen scr/ instead of symlink nifty7/The current NIFT_7 installer is incompatible with windows since it uses a symlink to /src. Since some NIFTy users use Windows it would be better to directly open /src in the nifty installer.The current NIFT_7 installer is incompatible with windows since it uses a symlink to /src. Since some NIFTy users use Windows it would be better to directly open /src in the nifty installer.https://gitlab.mpcdf.mpg.de/ift/nifty/-/merge_requests/594Draft: Resolve "MaternKernel implementation"2021-01-27T10:42:01ZGordian EdenhoferDraft: Resolve "MaternKernel implementation"Closes #317Closes #317https://gitlab.mpcdf.mpg.de/ift/nifty/-/merge_requests/603Draft: Parametric MGVI2021-03-24T07:52:14ZPhilipp Arrasparras@mpa-garching.mpg.deDraft: Parametric MGVIhttps://gitlab.mpcdf.mpg.de/ift/nifty/-/merge_requests/615extra.check_linear_operator: add option to test on positive input values only2021-05-03T16:23:43ZPhilipp Arrasparras@mpa-garching.mpg.deextra.check_linear_operator: add option to test on positive input values onlyhttps://gitlab.mpcdf.mpg.de/ift/nifty/-/merge_requests/622Geometric kl2021-05-31T13:17:38ZPhilipp FrankGeometric klhttps://gitlab.mpcdf.mpg.de/ift/nifty/-/merge_requests/624Draft: Likelihoods2021-05-27T15:44:05ZPhilipp FrankDraft: Likelihoodshttps://gitlab.mpcdf.mpg.de/ift/nifty/-/merge_requests/629Work on geokl2021-05-31T13:45:52ZPhilipp FrankWork on geoklhttps://gitlab.mpcdf.mpg.de/ift/nifty/-/merge_requests/630Draft: Fix floating point error domain inequality bug2021-06-06T11:32:59ZLukas PlatzDraft: Fix floating point error domain inequality bugAs it turns out, in Python `1. / i * i` is not exactly one for many integers, for example 49. In the range of 1-10000, this is true for 1178 ints. When creating an RGSpace `d` with the shape `(i,)`, `d == d.get_default_codomain().get_def...As it turns out, in Python `1. / i * i` is not exactly one for many integers, for example 49. In the range of 1-10000, this is true for 1178 ints. When creating an RGSpace `d` with the shape `(i,)`, `d == d.get_default_codomain().get_default_codomain()` is not necessarily true, because of potentially unequal `distances` values.
This is problematic since harmonic transform operators constructed with `domain=d.get_default_domain()` without giving an explicit target will for many values of `i` have a target unequal from `d`.
To alleviate this, this patch modiefies the `domain.__eq__()` function to compare `__hash__()` return values instead of manually comparing domain values and modiefies the `domain.__hash__()` function to round the `distances` entries to 15 decimals prior to hashing.
If using the hash in the comparison is unfavorable because of its performance impact, `__eq__()` could be restored to is previous state and the distance rounding be implemented in it, too.
@mtr: what do you think about this patch? Is there an obviously better solution?Martin ReineckeMartin Reineckehttps://gitlab.mpcdf.mpg.de/ift/nifty/-/merge_requests/669Fix CorrelatedFieldMaker fluctuation normalization for more than one amplitude2021-08-03T08:30:39ZLukas PlatzFix CorrelatedFieldMaker fluctuation normalization for more than one amplitudehttps://gitlab.mpcdf.mpg.de/ift/nifty/-/merge_requests/670Fix CorrelatedFieldMaker fluctuation normalization for more than one amplitud...2021-08-03T08:30:55ZLukas PlatzFix CorrelatedFieldMaker fluctuation normalization for more than one amplitude (NIFTy_8)https://gitlab.mpcdf.mpg.de/ift/nifty/-/merge_requests/672Draft: Test if energy is NaN in `IterationController.check()`2021-08-31T09:28:18ZLukas PlatzDraft: Test if energy is NaN in `IterationController.check()`Until now, neither minimizers nor iteration controllers check whether the `energy.value` has become NaN.
The proposed changes are just a quick draft - please change them if needed.Until now, neither minimizers nor iteration controllers check whether the `energy.value` has become NaN.
The proposed changes are just a quick draft - please change them if needed.https://gitlab.mpcdf.mpg.de/ift/nifty/-/merge_requests/676JaxLinearOperator: Fix error in adjoint_times when supplying manually impleme...2021-08-24T10:36:23ZLukas PlatzJaxLinearOperator: Fix error in adjoint_times when supplying manually implemented transpose functionThe apply-method of `JaxLinearOperator` expects `func_T` to return a tuple of values because `jax.linear_transform` does so. If the user supplies a manually implemented transpose function implementation which returns just a plain value a...The apply-method of `JaxLinearOperator` expects `func_T` to return a tuple of values because `jax.linear_transform` does so. If the user supplies a manually implemented transpose function implementation which returns just a plain value array, this assumption is broken, causing a processing error in the `apply` method.
To fix this, this patch wraps manually supplied transpose function implementations in a lambda function that encapsulates their result in a tuple.Philipp Arrasparras@mpa-garching.mpg.dePhilipp Arrasparras@mpa-garching.mpg.dehttps://gitlab.mpcdf.mpg.de/ift/nifty/-/merge_requests/701update mean2021-11-09T12:17:40ZJakob Knollmuellerupdate mean@parras@parras