def _initialize(self, corpus, vocab, prior_tree, number_of_topics, alpha_alpha):
Inferencer._initialize(self, vocab, prior_tree, number_of_topics, alpha_alpha);
# initialize the documents, key by the document path, value by a list of non-stop and tokenized words, with duplication.
self._corpus = corpus;
self._parsed_corpus = self.parse_data();
# initialize the size of the collection, i.e., total number of documents.
self._number_of_documents = len(self._parsed_corpus);
'''
# initialize a D-by-K matrix gamma, valued at N_d/K
#self._gamma = numpy.zeros((self._number_of_documents, self._number_of_topics)) + self._alpha_alpha[numpy.newaxis, :] + 1.0 * self._number_of_types / self._number_of_topics;
# initialize a V-by-K matrix beta, valued at 1/V, subject to the sum over every row is 1
#self._eta = numpy.random.gamma(100., 1. / 100., (self._number_of_topics, self._number_of_types));
#self._E_log_eta = compute_dirichlet_expectation(self._eta);
'''
# initialize the size of the vocabulary, i.e. total number of distinct tokens.
# self._number_of_terms = len(self._type_to_index)
# initialize a D-by-K matrix gamma, valued at N_d/K
# self._gamma = numpy.zeros((self._number_of_documents, self._number_of_topics)) + self._alpha_alpha + 1.0 * self._number_of_paths / self._number_of_topics;
# self._gamma = numpy.tile(self._alpha_alpha + 1.0 * self._number_of_terms / self._number_of_topics, (self._number_of_documents, 1));
self._gamma = self._alpha_alpha + 2.0 * self._number_of_paths / self._number_of_topics * numpy.random.random((self._number_of_documents, self._number_of_topics));
# initialize a _E_log_beta variable, indexed by node, valued by a K-by-C matrix, where C stands for the number of children of that node
self._var_beta = numpy.random.gamma(100., 1. / 100., (self._number_of_topics, self._number_of_edges));
# for edge_index in self._index_to_edge:
# self._var_beta[:, [edge_index]] += numpy.sum(phi_sufficient_statistics[:, self._paths_through_edge[edge_index]], axis=1)[:, numpy.newaxis];
'''
def _initialize(self,
corpus,
vocab,
number_of_topics,
alpha_alpha,
alpha_beta,
alpha_eta=0,
alpha_sigma_square=1.0
):
Inferencer._initialize(self, vocab, number_of_topics, alpha_alpha, alpha_beta);
self._parsed_corpus, self._responses = self.parse_data(corpus);
# define the total number of document
self._number_of_documents = len(self._parsed_corpus);
# initialize a D-by-K matrix gamma, valued at N_d/K
self._gamma = numpy.zeros((self._number_of_documents, self._number_of_topics)) + self._alpha_alpha[numpy.newaxis, :] + 1.0 * self._number_of_types / self._number_of_topics;
# self._gamma = numpy.random.gamma(100., 1./100, (self._number_of_documents, self._number_of_topics))
# initialize a V-by-K matrix _eta, valued at 1/V, subject to the sum over every row is 1
self._beta = numpy.random.gamma(100., 1. / 100., (self._number_of_topics, self._number_of_types));
# self._beta /= numpy.sum(self._beta, 1)[:, numpy.newaxis]
# self._E_log_eta = compute_dirichlet_expectation(self._beta);
self._eta = numpy.zeros((1, self._number_of_topics)) + alpha_eta
self._sigma_square = alpha_sigma_square
def _initialize(self, corpus, vocab, number_of_topics, alpha_alpha, alpha_beta):
Inferencer._initialize(self, vocab, number_of_topics, alpha_alpha, alpha_beta);
#self._corpus = corpus;
self._parsed_corpus = self.parse_data(corpus);
# define the total number of document
self._number_of_documents = len(self._parsed_corpus[0]);
# initialize a D-by-K matrix gamma, valued at N_d/K
self._gamma = numpy.zeros((self._number_of_documents, self._number_of_topics)) + self._alpha_alpha[numpy.newaxis, :] + 1.0 * self._number_of_types / self._number_of_topics;
# initialize a V-by-K matrix beta, valued at 1/V, subject to the sum over every row is 1
self._eta = numpy.random.gamma(100., 1. / 100., (self._number_of_topics, self._number_of_types));
def _initialize(self, corpus, vocab, number_of_topics, alpha_alpha, alpha_beta):
Inferencer._initialize(self, vocab, number_of_topics, alpha_alpha, alpha_beta);
self._corpus = corpus;
self._parsed_corpus = self.parse_data();
# define the total number of document
self._number_of_documents = len(self._parsed_corpus[0]);
# initialize a D-by-K matrix gamma, valued at N_d/K
self._gamma = numpy.zeros((self._number_of_documents, self._number_of_topics)) + self._alpha_alpha[numpy.newaxis, :] + 1.0 * self._number_of_types / self._number_of_topics;
# initialize a V-by-K matrix beta, valued at 1/V, subject to the sum over every row is 1
self._eta = numpy.random.gamma(100., 1. / 100., (self._number_of_topics, self._number_of_types));
def _initialize(self, corpus, vocab, number_of_topics, alpha_alpha, alpha_beta):
Inferencer._initialize(self, vocab, number_of_topics, alpha_alpha, alpha_beta);
self._parsed_corpus = self.parse_data(corpus);
# define the total number of document
self._number_of_documents = len(self._parsed_corpus);
# define the counts over different topics for all documents, first indexed by doc_id id, the indexed by topic id
self._n_dk = numpy.zeros((self._number_of_documents, self._number_of_topics));
# define the counts over words for all topics, first indexed by topic id, then indexed by token id
self._n_kv = numpy.zeros((self._number_of_topics, self._number_of_types));
self._n_k = numpy.zeros(self._number_of_topics);
# define the topic assignment for every word in every document, first indexed by doc_id id, then indexed by word word_pos
self._k_dn = {};
self.random_initialize();
def _initialize(self, corpus_en, voc_en, corpus_cn, voc_cn,
number_of_topics_ge, alpha_alpha, alpha_beta, lam):
Inferencer._initialize(self,
voc_en,
voc_cn,
number_of_topics_ge,
alpha_alpha,
alpha_beta,
lam=0.5)
self._corpus_en = corpus_en
self._corpus_cn = corpus_cn
self._trans_en_cn = np.zeros(
(self._number_of_types_cn, self._number_of_types_en))
self._trans_cn_en = np.zeros(
(self._number_of_types_en, self._number_of_types_cn))
self.parse_data()
# define the total number of document
self._number_of_documents_en = len(self._word_idss_en)
self._number_of_documents_cn = len(self._word_idss_cn)
self._number_of_documents = self._number_of_documents_en + self._number_of_documents_cn
self._n_dk_en = np.zeros(
(self._number_of_documents_en, self._number_of_topics))
self._n_dk_cn = np.zeros(
(self._number_of_documents_cn, self._number_of_topics))
self._n_kv_en = np.zeros(
(self._number_of_topics, self._number_of_types_en))
self._n_kv_cn = np.zeros(
(self._number_of_topics, self._number_of_types_cn))
# define the topic assignment for every word in every document, first indexed by doc_id id, then indexed by word word_pos
self._n_k_en = np.zeros(self._number_of_topics)
self._n_k_cn = np.zeros(self._number_of_topics)
self._k_dn_en = {}
self._k_dn_cn = {}
self.psi_en = np.zeros(
(self._number_of_topics, self._number_of_types_en))
self.psi_cn = np.zeros(
(self._number_of_topics, self._number_of_types_cn))
self.phi_en = np.zeros(
(self._number_of_topics, self._number_of_types_en))
self.phi_cn = np.zeros(
(self._number_of_topics, self._number_of_types_cn))
self.random_initialize()
def _initialize(self, corpus, vocab, number_of_topics, alpha_alpha, alpha_beta):
Inferencer._initialize(self, vocab, number_of_topics, alpha_alpha, alpha_beta);
self._corpus = corpus;
self.parse_data();
# define the total number of document
self._number_of_documents = len(self._word_idss);
# define the counts over different topics for all documents, first indexed by doc_id id, the indexed by topic id
self._n_dk = numpy.zeros((self._number_of_documents, self._number_of_topics));
# define the counts over words for all topics, first indexed by topic id, then indexed by token id
self._n_kv = numpy.zeros((self._number_of_topics, self._number_of_types));
self._n_k = numpy.zeros(self._number_of_topics);
# define the topic assignment for every word in every document, first indexed by doc_id id, then indexed by word word_pos
self._k_dn = {};
self.random_initialize();
def _initialize(self, corpus, vocab, labels, alpha_alpha, alpha_beta):
Inferencer._initialize(self, vocab, labels, alpha_alpha, alpha_beta)
self._number_of_topics = len(self._label_to_index)
self._parsed_corpus, self._parsed_labels = self.parse_data(corpus)
'''
# define the total number of document
self._number_of_documents = len(self._parsed_corpus);
# define the counts over different topics for all documents, first indexed by doc_id id, the indexed by topic id
self._n_dk = numpy.zeros((self._number_of_documents, self._number_of_topics), dtype=int)
# define the counts over words for all topics, first indexed by topic id, then indexed by token id
self._n_kv = numpy.zeros((self._number_of_topics, self._number_of_types), dtype=int)
self._n_k = numpy.zeros(self._number_of_topics, dtype=int)
# define the topic assignment for every word in every document, first indexed by doc_id id, then indexed by word word_pos
self._k_dn = {};
'''
# self._number_of_documents, self._k_dn, self._n_dk, self._n_k, self._n_kv = self.random_initialize();
self._k_dn, self._n_dk, self._n_k, self._n_kv = self.random_initialize(
)