[MRG] Locality Sensitive Hashing for approximate nearest neighbor search(GSoC)

Please increase this list to add more points trees such as `[1, 5, 10, 20, 50]`. The goal here is to highlight the fact that adding more trees has diminishing returns and quickly reaches an accuracy plateau around 10 or 20 estimators. If this cannot be highlighted in the example we would need to:

- investigate where the plateau is,
- check whether this contradicts the message of the LSH Forest paper,
- adjust the message in the narrative doc.

Please also refactor this example to compute the std deviations of the accuracies for all those plots and display them as error bars. This will require doing several iterations with different random_state values for the LSHForest index:

```python
# LSH Forest is a stochastic index, perform several iteration to estimate expected accuracy
# and standard deviation displayed as error bars in the plots
for seed in range(10):
    for j, value in enumerate(n_estimators_for_candidate_value):
        for i, n_candidates in enumerate(n_candidates_values):
            lshf = LSHForest(n_estimators=value, n_candidates=n_candidates, n_neighbors=1,
                random_state=seed)
            ...
```