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#!/usr/bin/env python
""" Example use of the maximum entropy module:
Machine translation example -- English to French -- from the paper 'A
maximum entropy approach to natural language processing' by Berger et
al., 1996.
Consider the translation of the English word 'in' into French. We
notice in a corpus of parallel texts the following facts:
(1) p(dans) + p(en) + p(a) + p(au cours de) + p(pendant) = 1
(2) p(dans) + p(en) = 3/10
(3) p(dans) + p(a) = 1/2
This code finds the probability distribution with maximal entropy
subject to these constraints.
"""
__author__ = 'Ed Schofield'
__version__= '2.1'
from scipy import maxentropy
a_grave = u'\u00e0'
samplespace = ['dans', 'en', a_grave, 'au cours de', 'pendant']
def f0(x):
return x in samplespace
def f1(x):
return x=='dans' or x=='en'
def f2(x):
return x=='dans' or x==a_grave
f = [f0, f1, f2]
model = maxentropy.model(f, samplespace)
# Now set the desired feature expectations
K = [1.0, 0.3, 0.5]
model.verbose = True
# Fit the model
model.fit(K)
# Output the distribution
print "\nFitted model parameters are:\n" + str(model.params)
print "\nFitted distribution is:"
p = model.probdist()
for j in range(len(model.samplespace)):
x = model.samplespace[j]
print ("\tx = %-15s" %(x + ":",) + " p(x) = "+str(p[j])).encode('utf-8')
# Now show how well the constraints are satisfied:
print
print "Desired constraints:"
print "\tp['dans'] + p['en'] = 0.3"
print ("\tp['dans'] + p['" + a_grave + "'] = 0.5").encode('utf-8')
print
print "Actual expectations under the fitted model:"
print "\tp['dans'] + p['en'] =", p[0] + p[1]
print ("\tp['dans'] + p['" + a_grave + "'] = " + str(p[0]+p[2])).encode('utf-8')
# (Or substitute "x.encode('latin-1')" if you have a primitive terminal.)
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