init

.gitignore

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+*.py[cod]
+
+# C extensions
+*.so
+
+# Packages
+*.egg
+*.egg-info
+dist
+build
+eggs
+parts
+bin
+var
+sdist
+develop-eggs
+.installed.cfg
+lib
+lib64
+
+# Installer logs
+pip-log.txt
+
+# Unit test / coverage reports
+.coverage
+.tox
+nosetests.xml
+
+#Translations
+*.mo
+
+#Mr Developer
+.mr.developer.cfg
+
+.DS_Store
+*.dat

README.md

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+Online Latent Dirichlet Allocation
+===
+Online inference for the Latent Dirichlet Allocation probabilistic topic model by Matt Hoffman <mdhoffma@cs.princeton.edu>

disptopics.py

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+import sys, os, re, random, math, urllib2, time, cPickle
+import numpy
+from termcolor import colored
+import onlineldavb
+
+def blah(testlambdaLine):
+  lambdak = list(testlambdaLine)
+  lambdak = lambdak / sum(lambdak)
+  temp = zip(lambdak, range(0, len(lambdak)))
+  return sorted(temp, key = lambda x: x[0], reverse=True)
+
+def main():
+    """
+    Displays topics fit by onlineldavb.py. The first column gives the
+    (expected) most prominent words in the topics, the second column
+    gives their (expected) relative prominence.
+    """
+    print colored('Loading', 'yellow')
+    vocab = str.split(file(sys.argv[1]).read())
+    testlambda1 = numpy.loadtxt(sys.argv[2])
+    testlambda2 = numpy.loadtxt(sys.argv[3])
+
+    for k in range(0, len(testlambda1)):
+        temp1 = blah(testlambda1[k, :])
+        temp2 = blah(testlambda2[k, :])
+        # print 'topic %d:' % (k)
+        # feel free to change the "53" here to whatever fits your screen nicely.
+        print colored("Topic %s" % k, 'green')
+        print ' '.join([vocab[temp1[i][1]] for i in range(0, 10)])
+        print colored(' '.join([vocab[temp2[i][1]] for i in range(0, 10)]), 'yellow')
+
+if __name__ == '__main__':
+    main()