| 1 | /**
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| 2 | * @license
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| 3 | * Copyright 2018 Google LLC. All Rights Reserved.
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| 4 | * Licensed under the Apache License, Version 2.0 (the "License");
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| 5 | * you may not use this file except in compliance with the License.
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| 6 | * You may obtain a copy of the License at
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| 7 | *
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| 8 | * http://www.apache.org/licenses/LICENSE-2.0
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| 9 | *
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| 10 | * Unless required by applicable law or agreed to in writing, software
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| 11 | * distributed under the License is distributed on an "AS IS" BASIS,
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| 12 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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| 13 | * See the License for the specific language governing permissions and
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| 14 | * limitations under the License.
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| 15 | * =============================================================================
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| 16 | */
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| 17 | /// <amd-module name="@tensorflow/tfjs-core/dist/ops/norm" />
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| 18 | import { Tensor } from '../tensor';
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| 19 | import { TensorLike } from '../types';
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| 20 | /**
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| 21 | * Computes the norm of scalar, vectors, and matrices.
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| 22 | * This function can compute several different vector norms (the 1-norm, the
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| 23 | * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)
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| 24 | * and matrix norms (Frobenius, 1-norm, and inf-norm).
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| 25 | *
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| 26 | * ```js
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| 27 | * const x = tf.tensor1d([1, 2, 3, 4]);
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| 28 | *
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| 29 | * x.norm().print(); // or tf.norm(x)
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| 30 | * ```
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| 31 | *
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| 32 | * @param x The input array.
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| 33 | * @param ord Optional. Order of the norm. Supported norm types are
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| 34 | * following:
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| 35 | *
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| 36 | * | ord | norm for matrices | norm for vectors
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| 37 | * |------------|---------------------------|---------------------
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| 38 | * |'euclidean' |Frobenius norm |2-norm
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| 39 | * |'fro' |Frobenius norm |
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| 40 | * |Infinity |max(sum(abs(x), axis=1)) |max(abs(x))
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| 41 | * |-Infinity |min(sum(abs(x), axis=1)) |min(abs(x))
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| 42 | * |1 |max(sum(abs(x), axis=0)) |sum(abs(x))
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| 43 | * |2 | |sum(abs(x)^2)^1/2*
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| 44 | *
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| 45 | * @param axis Optional. If axis is null (the default), the input is
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| 46 | * considered a vector and a single vector norm is computed over the entire
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| 47 | * set of values in the Tensor, i.e. norm(x, ord) is equivalent
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| 48 | * to norm(x.reshape([-1]), ord). If axis is a integer, the input
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| 49 | * is considered a batch of vectors, and axis determines the axis in x
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| 50 | * over which to compute vector norms. If axis is a 2-tuple of integer it is
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| 51 | * considered a batch of matrices and axis determines the axes in NDArray
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| 52 | * over which to compute a matrix norm.
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| 53 | * @param keepDims Optional. If true, the norm have the same dimensionality
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| 54 | * as the input.
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| 55 | *
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| 56 | * @doc {heading: 'Operations', subheading: 'Matrices'}
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| 57 | */
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| 58 | declare function norm_(x: Tensor | TensorLike, ord?: number | 'euclidean' | 'fro', axis?: number | number[], keepDims?: boolean): Tensor;
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| 59 | export declare const norm: typeof norm_;
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| 60 | export {};
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