Increased limit of predictors in GLM2 to 10k from 5k
Merge branch 'master' into tomas_glm
GLM2 update: fix of validation with lambda_search and strong_rules on. GLM2 now passes in set of active columns (to avoid running xval models on full set or again the whole regularization path) to the n-fold models. Active columns should be largely compatible.
Merge branch 'tomas_glm' of github.com:0xdata/h2o into tomas_glm
GLM2 update: lambda_search only keeps (and cross-validates) "significant lambdas". Lambda is (for now) considered significant if it improves deviance explained (on the training data) by at least 1%.
GLM2 update: AUC thresholds can now be changed (preparation for having proper AUC computed by collecting meaningful thresholds in special task executed after glm (sub)model is done.
Merge branch 'master' into tomas_glm

Conflicts:
	src/main/java/hex/glm/GLMModel.java
GLM2 update: Fixed precision by testing for poorly selected rho. If default rho does not work, try to find better one by doing a grid search (expensive - so need to remember the multiplication factor to not to do it every time).

Also updated x-validation to better distribute tasks around the cluster.
fixed shrinkage.
GLM2 update
Updated line-search invoked when reaching NaNs/Infs in gram/xy to work with lambda-search + strong rules as well.
Added option to autoselect list of lambdas so that next lambda is computes as max lambda for which the number of variables added to the model is below 5% of total number of predictors. Tends to do lot less lambdas and be lot faster (4-5x)!

Still possible to explicitly set lambda_min and lambda to invoke glmnet-like mode.
Added glm2 stopping criterium to stop when reached more than requested number of non-zero predictors.
GLM2 update: updated predictors stopping criterium and cross-validation (auc was divided by 10 twice).
Merge branch 'master' into tomas_glm

Conflicts:
	src/main/java/hex/glm/GLMModel.java
	src/main/java/water/api/AUC.java
renamed model.lambda to be model.lambda_value not to conflict with lambda keyword in python.
updated h2o_glm.py to work with slightly updated json interface.
Fixed my previous update of h2o_glm.py (missing colon)
Added check for constant response int GLM2.
Fixed GLM2 single lambda path.
Fixed glm runit. When computing penalty term for h2o glm coefficients we included intercept in the beta coefficient, but intercept is not penalized.