Mercurial > pylearn

annotate embeddings/read-original.py @ 470:bd937e845bbb

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new stuff: algorithms/logistic_regression, datasets/MNIST
author James Bergstra <bergstrj@iro.umontreal.ca>
date Wed, 22 Oct 2008 15:56:53 -0400
parents a07948f780b9
children
rev   line source
456
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Joseph Turian <turian@iro.umontreal.ca>
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1 #!/usr/bin/python
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2
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3 import string
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4 #import psyco
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5
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6 weightsfile = "lm-weights.txt"
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7 vocabfile = "words.asc"
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8 size = 30000
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9 dimensions = 50
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10
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11 words = [string.strip(w) for w in open(vocabfile).readlines()]
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12 assert len(words) == 30000
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13
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14 import numpy, math
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15 import sys
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16 from common.str import percent
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17
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18 word_to_vector = {}
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19
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20 f = open(weightsfile)
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21 f.readline()
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22 vals = [float(v) for v in string.split(f.readline())]
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23 assert len(vals) == size * dimensions
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24 vals.reverse()
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25 for i in range(size):
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26 l = vals[dimensions*i:dimensions*(i+1)]
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27 w = words[i]
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28 word_to_vector[w] = l
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29
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30 # l2 = numpy.asarray(l)
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31 # print math.fabs(50 - numpy.sum(l2*l2)), w
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32
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33 cnt = 0
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34 for i1 in range(len(words)):
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35 for i2 in range(len(words)):
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36 w1 = words[i1]
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37 w2 = words[i2]
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38 cnt += 1
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39 if i1 <= i2: continue
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40 l1 = numpy.asarray(word_to_vector[w1])
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41 l2 = numpy.asarray(word_to_vector[w2])
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42 d = l2 - l1
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43 dist = numpy.sum(d * d)
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44 if dist < 50:
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45 print numpy.sum(d * d), w1, w2, i1, i2
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46 if cnt % 1000 == 0:
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47 sys.stderr.write("%s done...\n" % percent(cnt, len(word_to_vector) * len(word_to_vector)))