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

Yes,

On 28 September 2014 23:25, Maheshakya Wijewardena <notifications@github.com
> wrote:

>  In sklearn/neighbors/approximate.py:
>
> > +        self.radius_cutoff_ratio = radius_cutoff_ratio
> > +
> > +    def _create_tree(self):
> > +        """Builds a single tree.
> > +
> > +        Here, it creates a sorted array of binary hashes.
> > +        Hashing is done on an array of data points.
> > +        `GaussianRandomProjection` is used for hashing.
> > +        `n_components=hash_size and n_features=n_dim.
> > +
> > +        This creates binary hashes by getting the dot product of
> > +        input points and hash_function then transforming the projection
> > +        into a binary string array based on the sign (positive/negative)
> > +        of the projection.
> > +        """
> > +        random_state = check_random_state(self.random_state)
>
> So this issue can be fixed by having rng in the fit method where
> create_tree is called and passing a seed generated from that to
> create_tree, so that it uses that seed in GaussianRandomProjection, right?
>
> —
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> <https://github.com/scikit-learn/scikit-learn/pull/3304/files#r18129646>.
>