def __init__(self, data, T, alpha_beta):
assert isinstance(data, scipy.sparse.csr.csr_matrix)
self.D, self.V = data.shape
self.T = T
self.alpha_beta = alpha_beta
self.data = data
self.pyrngs = initialize_pyrngs()
self.initialize_beta()
self.initialize_theta()
self.z = np.zeros((data.data.shape[0], T), dtype="uint32")
self.resample_z()
# precompute
self._training_gammalns = gammaln(data.sum(1) + 1).sum() - gammaln(data.data + 1).sum()
def __init__(self, data, T, alpha_beta):
assert isinstance(data, scipy.sparse.csr.csr_matrix)
self.D, self.V = data.shape
self.T = T
self.alpha_beta = alpha_beta
self.data = data
self.pyrngs = initialize_pyrngs()
self.initialize_beta()
self.initialize_theta()
self.z = np.zeros((data.data.shape[0], T), dtype='uint32')
self.resample_z()
# precompute
self._training_gammalns = \
gammaln(data.sum(1)+1).sum() - gammaln(data.data+1).sum()
def __init__(self, data, timestamps, K, alpha_theta):
assert isinstance(data, scipy.sparse.csr.csr_matrix)
self.alpha_theta = alpha_theta
self.D, self.V = data.shape
self.K = K
self.data = data
self.timestamps = timestamps
self.timeidx = self._get_timeidx(timestamps, data)
self.T = self.timeidx.max() - self.timeidx.min() + 1
self.ppgs = initialize_polya_gamma_samplers()
self.pyrngs = initialize_pyrngs()
self.initialize_parameters()
self._training_gammalns = gammaln(data.sum(1) + 1).sum() - gammaln(data.data + 1).sum()
def __init__(self, data, timestamps, K, alpha_theta):
assert isinstance(data, scipy.sparse.csr.csr_matrix)
self.alpha_theta = alpha_theta
self.D, self.V = data.shape
self.K = K
self.data = data
self.timestamps = timestamps
self.timeidx = self._get_timeidx(timestamps, data)
self.T = self.timeidx.max() - self.timeidx.min() + 1
self.ppgs = initialize_polya_gamma_samplers()
self.pyrngs = initialize_pyrngs()
self.initialize_parameters()
self._training_gammalns = \
gammaln(data.sum(1)+1).sum() - gammaln(data.data+1).sum()
