@thi.ng/tensors/apply-kernel.d.ts

0D/1D/2D/3D/4D tensors with extensible polymorphic operations and customizable storage

import type { KernelSpec, ITensor } from "./api.js";
/**
 * Generalized convolution using a set of kenrel functions instead of static
 * kernel tensor (as with {@link convolve}). The kernel function is applied as a
 * windowed reducer. For each window (size defined by the kernel), the
 * {@link KernelSpec.init} function initializes an accumulator value. Then
 * {@link KernelSpec.reduce} is called with the current accumulator, a domain
 * value from tensor `a` and the current kernel coordinates. The final window
 * result is produced by the kernel's {@link KernelSpec.complete} function.
 *
 * @remarks
 * The output tensor has the same shape as domain `a`. If `pad = true`
 * (default), edge values in `a` will be repeated, otherwise padded with given
 * value. If `out` is null, a new tensor will be created using `a`'s storage
 * backend.
 *
 * References:
 *
 * - https://en.wikipedia.org/wiki/Convolution
 * - https://en.wikipedia.org/wiki/Kernel_(image_processing)#Convolution
 *
 * @param out - output tensor
 * @param a - input tensor
 * @param kernel - kernel spec
 * @param pad - padding
 */
export declare const applyKernel: import("./api.js").MultiTensorOpImpl<(<T extends ITensor>(out: T | null, a: T, kernel: KernelSpec, pad?: true | number) => T)>;
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