class_docstr: Use GaussianRandomProjection to produce a cosine LSH fingerprint
class_docstr_tok: ["Use","GaussianRandomProjection","to","produce","a","cosine","LSH","fingerprint"]
code: def __init__(self, n_components=8, random_state=None):
      super(GaussianRandomProjectionHash, self).__init__(
          n_components=n_components, random_state=random_state
      )
  
imports: import numpy
  import warnings
  from scipy import sparse
  from .base import KNeighborsMixin, RadiusNeighborsMixin
  from ..base import BaseEstimator
  from ..utils.validation import check_array
  from ..utils import check_random_state
  from ..metrics.pairwise import pairwise_distances
  from ..random_projection import GaussianRandomProjection
label_desc: Use image node_id 1 to create a new GaussianRandomProjectionHash object from inherited base classes: ProjectionToHashMixin, GaussianRandomProjection with example: obj = GaussianRandomProjectionHash(n_components, random_state)