# determine how predictive variables are of whether an animal is fast. Linfoot
# if basically the information-theoretic counterpart to correlation.
linfoot_lean = engine.mutual_information('fast', 'lean', linfoot=False)
linfoot_stripes = engine.mutual_information('fast', 'stripes', linfoot=False)
print('Linfoot(fast, lean) = %f' % (linfoot_lean,))
print('Linfoot(fast, stripes) = %f' % (linfoot_stripes,))
# We can also figure out which animals are more similar. Is a wolf more
# similar to a dalmatian or a rat.
sim_wolves = engine.row_similarity('chihuahua', 'wolf')
sim_rats = engine.row_similarity('chihuahua', 'rat')
print('Similarity between Chihuahuas and wolves is %f' % (sim_wolves,))
print('Similarity between Chihuahuas and rats is %f' % (sim_rats,))
# Which animals are outliers with respect to their being fast. We can find out
# by calculating the surprisal (self infotmation).
s = engine.surprisal('fast')
s.sort(['surprisal'], ascending=False, inplace=True)
print(s.head(10))
# Lets say we're out in the woods and we see a lean, spotted animal with a
# tail. What is the probability that it is fierce and fast?
# Note that for continuous variables, Engine.probability returns the log PDF
# of an event given observations.
logp = engine.probability([1, 1], ['fierce', 'fast'],
given=[('lean', 1,), ('spots', 1,), ('tail', 1,)])
print('p(fierce, fast | lean, spots, tail) = %s' % (exp(logp),))
linfoot_stripes = engine.mutual_information('fast', 'stripes', linfoot=False)
print('Linfoot(fast, lean) = %f' % (linfoot_lean, ))
print('Linfoot(fast, stripes) = %f' % (linfoot_stripes, ))
# We can also figure out which animals are more similar. Is a wolf more
# similar to a dalmatian or a rat.
sim_wolves = engine.row_similarity('chihuahua', 'wolf')
sim_rats = engine.row_similarity('chihuahua', 'rat')
print('Similarity between Chihuahuas and wolves is %f' % (sim_wolves, ))
print('Similarity between Chihuahuas and rats is %f' % (sim_rats, ))
# Which animals are outliers with respect to their being fast. We can find out
# by calculating the surprisal (self infotmation).
s = engine.surprisal('fast')
s.sort(['surprisal'], ascending=False, inplace=True)
print(s.head(10))
# Lets say we're out in the woods and we see a lean, spotted animal with a
# tail. What is the probability that it is fierce and fast?
# Note that for continuous variables, Engine.probability returns the log PDF
# of an event given observations.
logp = engine.probability([1, 1], ['fierce', 'fast'],
given=[(
'lean',
1,
), (
'spots',
1,
), (
