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 kneighbors(self,y,sparse=True):
'''
Obtain the k nearest neighbors of the query dataset y
'''
if self.__x is None:
raise ValueError('Input dataset not fitted yet! Call .fit() first!')
if type(y)!=np.ndarray:
raise ValueError("Query dataset must be a numpy ndarray")
if len(y.shape)!=2:
raise ValueError('Query dataset must be a 2D array')
if self.__x.shape[-1]!=y.shape[-1]:
raise ValueError('Query and dataset must have same dimensions')
y_labels=self.__assign(y,self.__c)
y_ranges,_,_ = cluster_ranges_centroids(y, y_labels)
y, y_labels = self.__sort_clusters(y, y_labels,store_x=False)
x_LT=LazyTensor(np.expand_dims(self.__x,0))
y_LT=LazyTensor(np.expand_dims(y,1))
D_ij=((y_LT-x_LT)**2).sum(-1)
ranges_ij = from_matrix(y_ranges,self.__x_ranges,self.__keep)
D_ij.ranges=ranges_ij
if self.__use_gpu:
D_ij.backend='GPU'
else:
D_ij.backend='CPU'
nn=D_ij.argKmin(K=self.__k,axis=1)
return self.__unsort(nn)
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
sigma = .05 # Characteristic length of interaction
start = time.time()
# Compute a coarse Boolean mask:
D = np.sum((x_centroids[:, None, :] - y_centroids[None, :, :])**2, 2)
keep = D < (4 * sigma)**2
##############################################################################
# To turn this mask into a set of integer Tensors which
# is more palatable to KeOps's low-level CUDA API,
# we then use the :func:`from_matrix <pykeops.numpy.cluster.from_matrix>`
# routine...
from pykeops.numpy.cluster import from_matrix
ranges_ij = from_matrix(x_ranges, y_ranges, keep)
end = time.time()
print("Process the ranges : {:.4f}s".format(end - start))
End = time.time()
t_cluster = End - Start
print("Total time (synchronized): {:.4f}s".format(End - Start))
print("")
###############################################################################
# And we're done: here is the **ranges** argument that can
# be fed to the KeOps reduction routines!
# For large point clouds, we can expect a speed-up that is directly
# proportional to the ratio of mass between our **fine binary mask**
# (encoded in **ranges_ij**) and the full, N-by-M kernel matrix:
