def initialize(self, datas, inputs=None, masks=None, tags=None):
datas = [
interpolate_data(data, mask) for data, mask in zip(datas, masks)
]
yhats = [self.link(np.clip(d, .1, .9)) for d in datas]
self._initialize_with_pca(yhats, inputs=inputs, masks=masks, tags=tags)
def initialize_variational_params(self, data, input, mask, tag):
data = interpolate_data(data, mask)
mu = np.concatenate((np.zeros((1, self.N)), self.As[0] * data[:-1]))
residual = data - mu
return self._initialize_variational_params(residual, input, mask, tag)
def initialize(self, datas, inputs=None, masks=None, tags=None):
datas = [interpolate_data(data, mask) for data, mask in zip(datas, masks)]
pca = self._initialize_with_pca(datas, inputs=inputs, masks=masks, tags=tags)
self.inv_etas[:,...] = np.log(pca.noise_variance_)
def initialize(self, datas, inputs=None, masks=None, tags=None):
datas = [
interpolate_data(data, mask) for data, mask in zip(datas, masks)
]
logrates = [self.link(np.clip(d, .25, np.inf)) for d in datas]
self._initialize_with_pca(datas, masks)
