class_docstr: A container class of sentence vectors for easy query similar sentences etc.
class_docstr_tok: ["A","container","class","of","sentence","vectors","for","easy","query","similar","sentences","etc","."]
docstr: Find similar sentences by vector distances
  metric = dot(vectors_of_vectors, vectors_of_target_vector)
  Uses a precomputed vectors of the vectors
  Parameters
  
  Arguments:
      query (ndarray): query sentence vector
      n (int): top n number of neighbors
  
  Returns:
      position in self.vocab_w2id
      cosine similarity
docstr_tok: ["Find","similar","sentences","by","vector","distances","metric","=","dot","(","vectors_of_vectors",",","vectors_of_target_vector",")","Uses","a","precomputed","vectors","of","the","vectors","Parameters","Arguments",":","query","(","ndarray",")",":","query","sentence","vector","n","(","int",")",":","top","n","number","of","neighbors","Returns",":","position","in","self.vocab_w2id","cosine","similarity"]
code: def find_similar_idx(self, query, n=10):
      """
      Find similar sentences by vector distances
      metric = dot(vectors_of_vectors, vectors_of_target_vector)
      Uses a precomputed vectors of the vectors
      Parameters
  
      Arguments:
          query (ndarray): query sentence vector
          n (int): top n number of neighbors
  
      Returns:
          position in self.vocab_w2id
          cosine similarity
      """
      query = query / np.linalg.norm(query)
      metrics = np.dot(self.vectors, query.T)
  
      best = np.argsort(metrics.ravel())[::-1][:n]
      best_metrics = metrics[best]
  
      return best, best_metrics
  
imports: import os
  import numpy
  from collections import defaultdict, OrderedDict
  from neon.layers import LookupTable, RecurrentSum, RecurrentLast, Linear, Bias, GeneralizedCost
  from neon.initializers import Gaussian, Constant
  from neon.data import ArrayIterator
  from neon.data.text_preprocessing import clean_string
  from neon.optimizers import GradientDescentMomentum
  from neon.transforms import SumSquared
  from neon.callbacks.callbacks import Callbacks
  from neon.models import Model
  from neon.util.persist import load_obj, save_obj
  from neon import logger
label_desc: Use image node_id 2 for calling the SentenceVector obj's underlying member method code with example usage: obj.find_similar_idx(query, n) and returns: best,  best_metrics