distance_calc.py

import numpy as np

def distances(vector, vectors_train):
    armin = np.argmin( list(map(np.linalg.norm, np.array(list(vectors_train.values())) - vector)) )
    return list(vectors_train.keys())[armin]
    # distances = dict( zip(  vectors_train , map(np.linalg.norm, vectors_train.values()) ))
    # return min(distances, key=distances.get)

def distances_old(vector, vectors_train):
    distances = { filename: np.linalg.norm(vector-vec, ord=2) for filename,
                                                             vec in vectors_train.items() }
    return min(distances, key=distances.get)

#
# if __name__ == "__main__":
#     import json
#     import numpy as np
#     from doc2vec import  LoadedModel
#     from ocrfunction import  *
#     TRAINED_EMBEDDINGS = 'vectors_train_500_4.json'
#     TRAINED_MODEL = 'doc2vec_model_500_4'
#
#     with open(TRAINED_EMBEDDINGS) as json_file:
#         vectors_train = json.load(json_file)
#
#     for i,j in enumerate( vectors_train.values()):
#         if i == 6:
#             vec = np.array(j)
#
#     print(vec.shape)
#     print('=============================================')
#     print( np.array( list(vectors_train.values())).shape )
#     # Method 1
#     print(distances(vec, vectors_train))
#     # This was used
#     # ag= np.argmin( list( map(np.linalg.norm, np.array( list(vectors_train.values()))-vec )))
#     #
#     # for i in map(np.linalg.norm, np.array( list(vectors_train.values()))-vec ):
#     #     print(i)
#     # print('####################### The minimum distance index ')
#     # print(ag)
#
#     # print( list(vectors_train.keys())[ag])
#
#
#