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()
```

Added a writeFormat() method to Algorithm which must be called by the
write() method of derived classes.

- added test for loading legacy files
- added version to new written models
- fixed loading of several fields in some models
- added generation of new fields from old data

initial commit; ml has been refactored; it compiles and the tests run well; some other modules, apps and samples do not compile; to be fixed

removed invalid condition, it is always false

Conflicts:
	modules/core/include/opencv2/core/cuda.hpp
	modules/cudacodec/src/thread.cpp
	modules/cudacodec/src/thread.hpp
	modules/superres/perf/perf_superres.cpp
	modules/superres/src/btv_l1_cuda.cpp
	modules/superres/src/optical_flow.cpp
	modules/videostab/src/global_motion.cpp
	modules/videostab/src/inpainting.cpp
	samples/cpp/stitching_detailed.cpp
	samples/cpp/videostab.cpp
	samples/gpu/stereo_multi.cpp

Made all STL usages explicit to be able automatically find all usages of
particular class or function.

Also corrected some indexes to be size_t.

replaced alloca() (a.k.a. cvStackAlloc) with AutoBuffer or vector() everywhere. cvStackAlloc() is still defined, but we do not need alloca() anymore to compile and run OpenCV (fixes #889 and may be some others)

fixed the ordering of parameters in PyArg_ParseTupleAndKeywords, added correct Ptr<CvDTreeSplit>::delete_obj() (ticket #406)