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Line: 4698
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Function: require_once

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From Mammoth Ibis, 5 Years ago, written in Python.

URL http://geopaste.scratchbook.ch/view/4e11f4c6

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def CountVectorizer(texts, vocabulary):

"""

Transform a collection of texts to a matrix of token counts

In order to be memory-efficient, the matrix of token counts

has a sparse representation of the counts using scipy.sparse.csr_matrix

"""

j_indices = [] #indices is array of column indices

indptr = [] # indptr points to row starts in indices and data

values = [] #values is array of corresponding nonzero values

indptr.append(0)

for text in texts:

token_counter = {}

for token in text:

if token in vocabulary:

if vocabulary[token] in token_counter:

token_counter[vocabulary[token]] += 1

else:

token_counter[vocabulary[token]] = 1

j_indices.extend(token_counter.keys())

values.extend(token_counter.values())

indptr.append(len(j_indices))

X = scipy.sparse.csr_matrix((values, j_indices, indptr),

shape=(len(indptr) - 1, len(vocabulary)),

)

return X

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def CountVectorizer(texts, vocabulary):
        &quot;&quot;&quot;
            Transform a collection of texts to a matrix of token counts
            In order to be memory-efficient, the matrix of token counts 
            has a sparse representation of the counts using scipy.sparse.csr_matrix
        &quot;&quot;&quot;
        
        j_indices = [] #indices is array of column indices
        indptr = [] # indptr points to row starts in indices and data
        values = [] #values is array of corresponding nonzero values
        indptr.append(0)
        for text in texts:
            token_counter = {}
            for token in text:
                if token in vocabulary:
                    if vocabulary[token] in token_counter:
                        token_counter[vocabulary[token]] += 1
                    else:
                        token_counter[vocabulary[token]] = 1
                    
            j_indices.extend(token_counter.keys())
            
            values.extend(token_counter.values())
            indptr.append(len(j_indices))
        
        X = scipy.sparse.csr_matrix((values, j_indices, indptr),
                          shape=(len(indptr) - 1, len(vocabulary)),
                          )
    
        return X

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