def __init__(self, docs, K, alpha, eta):
LDA.__init__(self, docs, K, alpha, eta)
### Gibbs sampler related data structures ###
# C_VK[w,k] := number of times word w is assigned to topic k
self.C_VK = np.zeros((self.V, self.K), dtype=int)
# C_DK[d,k] := number of times topic k is present in document d
self.C_DK = np.zeros((self.D, self.K), dtype=int)
# Cache these values as we go (equivalent to performing column sums for above matrices)
# For each document, total number of topics assigned
self.total_topics_per_doc = np.zeros(self.D)
# For each topic, total number of words assigned to it
self.total_words_per_topic = np.zeros(self.K)
# Save results here
self.log_prob = []
self.samples = []
def __init__(self, n_topics, alpha=0.1, beta=0.01, random_state=0):
LDA.__init__(self, n_topics, alpha=0.1, beta=0.01, random_state=0)
def __init__(self, n_topics, alpha=0.1, beta=0.01, random_state=0):
LDA.__init__(self, n_topics, alpha=0.1, beta=0.01, random_state=0)
