'''
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______         _                          ___  ______ _____     
|  ___|       | |                        / _ \ | ___ \_   _|  _ 
| |_ ___  __ _| |_ _   _ _ __ ___  ___  / /_\ \| |_/ / | |   (_)
|  _/ _ \/ _` | __| | | | '__/ _ \/ __| |  _  ||  __/  | |      
| ||  __/ (_| | |_| |_| | | |  __/\__ \ | | | || |    _| |_   _ 
\_| \___|\__,_|\__|\__,_|_|  \___||___/ \_| |_/\_|    \___/  (_)
                                                                
                                                                
 _____         _   
|_   _|       | |  
  | | _____  _| |_ 
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  | |  __/>  <| |_ 
  \_/\___/_/\_\\__|
                   
		   
Featurize folders of text files if default_text_features = ['glove_features']

This uses a GloVE embedding with 100 dimensions:
https://machinelearningmastery.com/develop-word-embeddings-python-gensim/
'''

import numpy as np

def glove_featurize(transcript,model):

    # set to 100 size 
    sentences2=transcript.split()
    size=100
    w2v_embed=list()
    for i in range(len(sentences2)):
        try:
            w2v_embed.append(model[sentences2[i]])
        except:
            #pass if there is an error to not distort averages... :)
            pass

    out_embed=np.zeros(size)
    for j in range(len(w2v_embed)):
        out_embed=out_embed+np.array(w2v_embed[j])

    out_embed=(1/len(w2v_embed))*out_embed
    features=out_embed
    labels=list()
    for i in range(len(features)):
        labels.append('glove_feature_%s'%(str(i+1)))

    return features, labels 
