def _Gauss_block_sparse_pre(self, x:np.array, y:np.array, K_ij:LazyTensor,
sigma:float = 1., eps:float = 0.05):
'''
Helper function to preprocess data for block-sparse reduction
of the Gaussian kernel
Args:
x[np.array], y[np.array] = arrays giving rise to Gaussian kernel K(x,y)
K_ij[LazyTensor_n] = symbolic representation of K(x,y)
eps[float] = size for square bins
Returns:
K_ij[LazyTensor_n] = symbolic representation of K(x,y) with
set sparse ranges
'''
# class labels
x_labels = grid_cluster(x, eps)
y_labels = grid_cluster(y, eps)
# compute one range and centroid per class
x_ranges, x_centroids, _ = cluster_ranges_centroids(x, x_labels)
y_ranges, y_centroids, _ = cluster_ranges_centroids(y, y_labels)
# sort points
x, x_labels = sort_clusters(x, x_labels)
y, y_labels = sort_clusters(y, y_labels)
# Compute a coarse Boolean mask:
D = np.sum((x_centroids[:, None, :] - y_centroids[None, :, :]) ** 2, 2)
keep = D < (4 * sigma) ** 2 # self.sigma
# mask -> set of integer tensors
ranges_ij = from_matrix(x_ranges, y_ranges, keep)
K_ij.ranges = ranges_ij # block-sparsity pattern
return K_ij
def _Gauss_block_sparse_pre(self, x: np.array, y: np.array,
K_ij: LazyTensor):
'''
Helper function to preprocess data for block-sparse reduction
of the Gaussian kernel
Args:
x[np.array], y[np.array] = arrays giving rise to Gaussian kernel K(x,y)
K_ij[LazyTensor_n] = symbolic representation of K(x,y)
eps[float] = size for square bins
Returns:
K_ij[LazyTensor_n] = symbolic representation of K(x,y) with
set sparse ranges
'''
# labels for low dimensions
if x.shape[1] < 4 or y.shape[1] < 4:
x_labels = grid_cluster(x, self.eps)
y_labels = grid_cluster(y, self.eps)
# range and centroid per class
x_ranges, x_centroids, _ = cluster_ranges_centroids(x, x_labels)
y_ranges, y_centroids, _ = cluster_ranges_centroids(y, y_labels)
else:
# labels for higher dimensions
x_labels, x_centroids = self._KMeans(x)
y_labels, y_centroids = self._KMeans(y)
# compute ranges
x_ranges = cluster_ranges(x_labels)
y_ranges = cluster_ranges(y_labels)
# sort points
x, x_labels = sort_clusters(x, x_labels)
y, y_labels = sort_clusters(y, y_labels)
# Compute a coarse Boolean mask:
if self.kernel == 'rbf':
D = np.sum((x_centroids[:, None, :] - y_centroids[None, :, :])**2,
2)
elif self.kernel == 'exp':
D = np.sqrt(
np.sum((x_centroids[:, None, :] - y_centroids[None, :, :])**2,
2))
keep = D < (self.mask_radius)**2
# mask -> set of integer tensors
ranges_ij = from_matrix(x_ranges, y_ranges, keep)
K_ij.ranges = ranges_ij # block-sparsity pattern
return K_ij
