Fix KD Tree kNN Implementation
* Make KDTree mode in kNN functional
remove docs and revert change
Make KDTree mode in kNN functional
spacing
Make KDTree mode in kNN functional
fix window compilations warnings
Make KDTree mode in kNN functional
fix window compilations warnings
Make KDTree mode in kNN functional
casting
Make KDTree mode in kNN functional
formatting
Make KDTree mode in kNN functional
* test coding style
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
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()
```
* 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
