Use bst_float consistently throughout (#1824)

* Fix various typos

* Add override to functions that are overridden

gcc gives warnings about functions that are being overridden by not
being marked as oveirridden. This fixes it.

* Use bst_float consistently

Use bst_float for all the variables that involve weight,
leaf value, gradient, hessian, gain, loss_chg, predictions,
base_margin, feature values.

In some cases, when due to additions and so on the value can
take a larger value, double is used.

This ensures that type conversions are minimal and reduces loss of
precision.

demo/kaggle-higgs/higgs-numpy.py

@@ -44,7 +44,7 @@ param['nthread'] = 16
 plst = list(param.items())+[('eval_metric', 'ams@0.15')]
 
 watchlist = [ (xgmat,'train') ]
-# boost 120 tres
+# boost 120 trees
 num_round = 120
 print ('loading data end, start to boost trees')
 bst = xgb.train( plst, xgmat, num_round, watchlist );

demo/kaggle-higgs/speedtest.py

@@ -42,7 +42,7 @@ param['nthread'] = 4
 plst = param.items()+[('eval_metric', 'ams@0.15')]
 
 watchlist = [ (xgmat,'train') ]
-# boost 10 tres
+# boost 10 trees
 num_round = 10
 print ('loading data end, start to boost trees')
 print ("training GBM from sklearn")