- added more debug checks

backport 997b7b18

SVM sigmoid kernel fix (issue #13621) (#13718)

* Added test for sigmoid case for retrieving support vectors

* undo unhelpful test

* add test for sigmoid SVM with data that is easily separable into two concentric circles

* Update sigmoid kernel to use tanh(gamma * <x, y> + coef0) instead of -tanh(gamma * <x, y> + coef0)

* remove unnecessary constraint on coef0

* cleanup

* fixing inappropriate use of doubles

* Add f to float literal

* replace CV_Assert with ASSERT_EQ where appropriate

It is designed for 1D vectors only

Found via `codespell -q 3 -I ../opencv-whitelist.txt --skip="./3rdparty"`

In case of regression trees, node risk is computed as sum of squared
error. To get a meaningfull value to compare with it needs to be
normalized to the number of samples in the node (or more generally to
the sum of sample weights in this node). Otherwise the sum of squared
error is highly dependend on the number of samples in the node and
comparision with `regressionAccuracy` parameter is not very meaningful.

After normalization `node_risk` means in fact sample variance for all
samples in the node, which makes much more sence and seams to be what
was originaly intended by the code given that node risk is later used as
a split termination criteria by
```
sqrt(node.node_risk) < params.getRegressionAccuracy()
```

Found via `codespell`

* Calcerror uses now sample weights

* catree comment in #10319

* Simulated Annealing for ANN_MLP training method

* EXPECT_LT

* just to test new data

* manage RNG

* Try again

* Just run buildbot with new data

* try to understand

* Test layer

* New data- new test

* Force RNG in backprop

* Use Impl to avoid virtual method

* reset all weights

* try to solve ABI

* retry

* ABI solved?

* till problem with dynamic_cast

* Something is wrong

* Solved?

* disable backprop test

* remove ANN_MLP_ANNEALImpl

* Disable weight in varmap

* Add example for SimulatedAnnealing

This is at least useful when using an SVM one-class linear classifier, so there are valid use cases.

* export SVM::trainAuto to python #7224

* workaround for ABI compatibility of SVM::trainAuto

* add parameter comments to new SVM::trainAuto function

* Export ParamGrid member variables

Finished with several samples support, need regression testing

Gave a more relevant name to function (getVotes)

Finished implicit implementation

Removed printf, finished regresion testing

Fixed conversion warning

Finished test for Rtrees

Fixed documentation

Initialized variable

Added doxygen documentation

Added parameter name