def fit(self, X, y):
"""
Estimate the topic distributions per document (theta), term
distributions per topic (phi), and regression coefficients (eta).
Parameters
----------
X : array-like, shape = (n_docs, n_terms)
The document-term matrix.
y : array-like, shape = (n_edges, 3)
Each entry of y is an ordered triple (d_1, d_2, y_(d_1, d_2)),
where d_1 and d_2 are documents and y_(d_1, d_2) is an indicator of
a directed edge from d_1 to d_2.
"""
self.doc_term_matrix = X
self.n_docs, self.n_terms = X.shape
self.n_tokens = X.sum()
self.n_edges = y.shape[0]
doc_lookup, term_lookup = self._create_lookups(X)
# edge info
y = np.ascontiguousarray(np.column_stack((range(self.n_edges), y)))
# we use a view here so that we can sort in-place using named columns
y_rec = y.view(dtype=list(zip(('index', 'tail', 'head', 'data'),
4 * [y.dtype])))
edge_tail = np.ascontiguousarray(y_rec['tail'].flatten(),
dtype=np.intc)
edge_head = np.ascontiguousarray(y_rec['head'].flatten(),
dtype=np.intc)
edge_data = np.ascontiguousarray(y_rec['data'].flatten(),
dtype=np.float64)
out_docs, out_edges = self._create_edges(y_rec, order='tail')
in_docs, in_edges = self._create_edges(y_rec, order='head')
# iterate
self.theta, self.phi, self.H, self.loglikelihoods = gibbs_sampler_grtm(
self.n_iter, self.n_report_iter, self.n_topics, self.n_docs,
self.n_terms, self.n_tokens, self.n_edges, self.alpha, self.beta,
self.mu, self.nu2, self.b, doc_lookup, term_lookup, out_docs,
out_edges, in_docs, in_edges, edge_tail, edge_head, edge_data,
self.seed)
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