def __init__(self, training, ngram_degree, alphabet):
'''
training
[(Iterable, str), (Iterable, str)]
ngram_degree
'''
self.ngrams = {}
self.classes = {}
self.degree = ngram_degree
# get frequency count
for tuple in training:
examples = tuple[0]
name = tuple[1]
self.classes[name] = {}
for example in examples:
freq = __ling__.ngram_frequency(example, ngram_degree)
for ngram in freq:
if ngram not in self.classes[name]:
self.classes[name][ngram] = 1
else:
self.classes[name][ngram] += 1
if ngram not in self.ngrams:
self.ngrams[ngram] = 1
else:
self.ngrams[ngram] += 1
# laplace smooting
for ngram in alphabet:
# for the ngrams
if ngram not in self.ngrams:
self.ngrams[ngram] = 1
else:
self.ngrams[ngram] += 1
# for each class
for clazz in self.classes:
if ngram not in self.classes[clazz]:
self.classes[clazz][ngram] = 1
else:
self.classes[clazz][ngram] += 1
# turn everything into probabilities
self.ngrams = __normalise__(self.ngrams)
for clazz in self.classes:
self.classes[clazz] = __normalise__(self.classes[clazz])
def classify(self, string):
# these strings cannot be classified!
if (len(string) < self.degree):
return None
# get frequency count for the ngrams in the string
freq = __ling__.ngram_frequency(string, self.degree)
clazz_probs = { cl : [] for cl in self.classes.keys() }
# create a vector of probabilites for each ngram for each class
for ngram in freq:
for clazz in self.classes:
prob_c = self.prob_class(clazz)
prob_xc = self.prob_ngram_class(ngram, clazz)
p_i = prob_c * prob_xc
for i in range(freq[ngram]):
clazz_probs[clazz].append(p_i)
# debugging purposes
# outputs the vector of probabilities for each class
#for clazz in clazz_probs:
# print(clazz_probs[clazz])
# reduce the probability vectors into a probability for that one class
# map each class's probability vector to its aggregated probability
for clazz in clazz_probs:
pvector = clazz_probs[clazz]
if len(pvector) == 0:
pvector = [0]
p = reduce(lambda x,y : x*y, pvector)
clazz_probs[clazz] = p
# return the class with the highest probability
best_category = None
best_prob = None
for clazz in clazz_probs:
prob = clazz_probs[clazz]
if best_category == None or prob > best_prob:
best_prob = prob
best_category = clazz
return best_category
