Table Of Contents
Table Of Contents

Source code for gluonts.kernels._kernel

# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
#     http://www.apache.org/licenses/LICENSE-2.0
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# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
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# First-party imports
from gluonts.model.common import Tensor


[docs]class Kernel: # noinspection PyMethodOverriding,PyPep8Naming
[docs] def kernel_matrix(self, x1: Tensor, x2: Tensor): # raise error in the base Kernel class, implement in the concrete subclasses raise NotImplementedError()
# noinspection PyMethodOverriding,PyPep8Naming def _compute_square_dist(self, F, x1: Tensor, x2: Tensor) -> None: r""" Parameters -------------------- F : ModuleType A module that can either refer to the Symbol API or the NDArray API in MXNet. x1 : Tensor Feature data of shape (batch_size, history_length, num_features). x2 : Tensor Feature data of shape (batch_size, history_length, num_features). Returns -------------------- Tensor square distance matrix of shape (batch_size, history_length, history_length) :math: `\|\mathbf{x_1}-\mathbf{x_2}\|_2^2 = (\mathbf{x_1}-\mathbf{x_2})^T(\mathbf{x_1}-\mathbf{x_2}) = \|\mathbf{x_1}\|_2^2 - 2\mathbf{x_1}^T\mathbf{x_2} + \|\mathbf{x_2}\|_2^2`. """ feature_axis = 2 # Column vector: Add to math:`x_i^Tx_i` to every column in row i x1_norm_square = ( F.norm(x1, ord=2, axis=feature_axis) ** 2 ).expand_dims(2) # Row vector: Add to math:`x_i^Tx_i` to every row in column i x2_norm_square = ( F.norm(x2, ord=2, axis=feature_axis) ** 2 ).expand_dims(1) x1x2_trans = F.linalg.gemm2(x1, x2, transpose_b=True) self.square_dist = F.broadcast_add( F.broadcast_sub(x1_norm_square, 2 * x1x2_trans), x2_norm_square )