--bs arg and --bs_percentile for bootstrapping vw

Here is an implementation of boostrapping vw predictions by online importance sampling from Poisson distribution suggested by Nikos.

bootstrapping vw to get confidence interval on predictions. Implemented as a reduction to the current highest-level reduction.
--bs arg to specify arg numbers of bootstrapping rounds.
--bs_type {mean, vote} to specify how to get final prediction based on the n bootstrapped predictions. By default it is mean, but vote (a majority voting strategy is implemented. It outputs the majority label if it exists. If it does not exist, it outputs a label arbitrarily depending on the value order) is suggested for classification problems.

Example:
in training: 
./vowpalwabbit/vw -d multiclass -k -c --passes 2000 --holdout_off -f model --bs 100
for testing:
./vowpalwabbit/vw -d multiclass -k -c -p result.txt -i model -t (--bs will be read from the model file)

Then in result.txt we get:
```
1.645815 1.543533 1.725334
2.532156 2.468851 2.575895
3.527597 3.478951 3.574625
4.556210 4.514622 4.596277
5.475436 5.451206 5.500712
6.129002 6.075596 6.164859
6.529974 6.453068 6.593857
6.790787 6.671144 6.866988
6.961917 6.856057 7.052547
7.075695 6.946770 7.191328
```
The first number is final_prediction (mean value of bootstrapped predictions), followed by the minimum and maximum from all the bootstrapped predictors.