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: Initialize a SentenceVectors class object
  
  Arguments:
      vectors (ndarray, (#sentences, vector dimension)): sentence vectors
      text (list, #sentences): sentence texts
docstr_tok: ["Initialize","a","SentenceVectors","class","object","Arguments",":","vectors","(","ndarray",",","(","#","sentences",",","vector","dimension",")",")",":","sentence","vectors","text","(","list",",","#","sentences",")",":","sentence","texts"]
code: def __init__(self, vectors, text):
      """
      Initialize a SentenceVectors class object
  
      Arguments:
          vectors (ndarray, (#sentences, vector dimension)): sentence vectors
          text (list, #sentences): sentence texts
      """
      self.vectors = vectors
      self.text = text
  
      if isinstance(self.text, list):
          assert self.vectors.shape[0] == len(self.text)
      elif isinstance(self.text, np.ndarray):
          assert self.vectors.shape[0] == self.text.shape[0]
  
      norms = np.linalg.norm(self.vectors, axis=1)
      self.vectors = self.vectors / norms.reshape(-1, 1)
  
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 1 to create a new SentenceVector object from inherited base classes: object with example: obj = SentenceVector(vectors, text)