def __init__(self,
output_path,
docs,
vocab,
topic_count=20,
alpha=0.1,
beta=0.1,
total_iteration=1000):
self.output_path = output_path
self.vocab = vocab
self.k, self.alpha, self.beta = topic_count, alpha, beta
self.model = FixedTopicModel(topic_count, len(vocab), alpha, beta)
for doc in docs:
self.model.add_new_document(doc)
self.sampler = BaseSampler(self.model, total_iteration)
class LDARunner:
def __init__(self, output_path, docs,
vocab, topic_count = 20, alpha = 0.1, beta = 0.1,
total_iteration=1000):
self.output_path = output_path
self.vocab = vocab
self.k, self.alpha, self.beta = topic_count, alpha, beta
self.model = FixedTopicModel(topic_count, len(vocab), alpha, beta)
for doc in docs:
self.model.add_new_document(doc)
self.sampler = BaseSampler(self.model, total_iteration)
def run(self):
self.sampler.inference()
save_topic_matrix(self.model, self.vocab, self.output_path)
def __init__(self, output_path, docs,
vocab, topic_count = 20, alpha = 0.1, beta = 0.1,
total_iteration=1000):
self.output_path = output_path
self.vocab = vocab
self.k, self.alpha, self.beta = topic_count, alpha, beta
self.model = FixedTopicModel(topic_count, len(vocab), alpha, beta)
for doc in docs:
self.model.add_new_document(doc)
self.sampler = BaseSampler(self.model, total_iteration)
class LDARunner:
def __init__(self,
output_path,
docs,
vocab,
topic_count=20,
alpha=0.1,
beta=0.1,
total_iteration=1000):
self.output_path = output_path
self.vocab = vocab
self.k, self.alpha, self.beta = topic_count, alpha, beta
self.model = FixedTopicModel(topic_count, len(vocab), alpha, beta)
for doc in docs:
self.model.add_new_document(doc)
self.sampler = BaseSampler(self.model, total_iteration)
def run(self):
self.sampler.inference()
save_topic_matrix(self.model, self.vocab, self.output_path)
