gistfile1.py

import numpy
import six
from modelfile import model

# Inputs
positive = []
negative = []

# add weights for each word, if not already present;
# default to 1.0 for positive and -1.0 for negative words
# Produces list of word tuples, with weights
positive = [(word, 1.0) if isinstance(word, six.string_types + (numpy.ndarray,)) else word for word in positive]
negative = [(word, -1.0) if isinstance(word, six.string_types + (numpy.ndarray,)) else word for word in negative]

# compute the weighted average of all words
all_words = set()
mean = []

# Loop through combined lists
for word, weight in positive + negative:
    if isinstance(word, numpy.ndarray):
        mean.append(weight * word)
    elif word in model.vocab:
        word_index = model.vocab[word].index
        mean.append(weight * model.syn0norm[word_index])
        all_words.add(word_index)
    else:
        raise KeyError("word '%s' not in vocabulary" % word)

if not mean:
    raise ValueError("cannot compute similarity with no input")

mean = matutils.unitvec(numpy.array(mean).mean(axis=0)).astype(numpy.float32)

# NOTE: SLOW 1.5s
dists = numpy.dot(model.syn0norm, mean)

# NOTE: SLOW 0.5s
best = numpy.argsort(dists)[::-1][:topn + len(all_words)]

# ignore (don't return) words from the input
result = [(model.index2word[sim], float(dists[sim])) for sim in best if sim not in all_words]