@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @license
* Copyright 2020 Google LLC. 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.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License 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 permissions and
* limitations under the License.
* =============================================================================
*/
import { ENGINE } from '../engine';
import { DenseBincount } from '../kernel_names';
import { convertToTensor } from '../tensor_util_env';
import * as util from '../util';
import { op } from './operation';
/**
* Outputs a vector with length `size` and the same dtype as `weights`.
*
* If `weights` are empty, then index `i` stores the number of times the value
* `i` is counted in `x`. If `weights` are non-empty, then index `i` stores the
* sum of the value in `weights` at each index where the corresponding value in
* `x` is `i`.
*
* Values in `x` outside of the range [0, size) are ignored.
*
* @param x The input int tensor, rank 1 or rank 2.
* @param weights The weights tensor, must have the same shape as x, or a
* length-0 Tensor, in which case it acts as all weights equal to 1.
* @param size Non-negative integer.
* @param binaryOutput Optional. Whether the kernel should count the appearance
* or number of occurrences. Defaults to False.
*
* @doc {heading: 'Operations', subheading: 'Reduction'}
*/
function denseBincount_(x, weights, size, binaryOutput = false) {
const $x = convertToTensor(x, 'x', 'denseBincount');
const $weights = convertToTensor(weights, 'weights', 'denseBincount');
util.assert($x.dtype === 'int32', () => `Error in denseBincount: input ` +
`dtype must be int32, but got ${$x.dtype}`);
util.assert($x.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got ` +
`rank ${$x.rank}.`);
util.assert(size >= 0, () => `size must be non-negative, but got ${size}.`);
util.assert($weights.size === $x.size || $weights.size === 0, () => `Error in denseBincount: weights must have the same shape as x or ` +
`0-length, but got x shape: ${$x.shape}, weights shape: ` +
`${$weights.shape}.`);
const inputs = { x: $x, weights: $weights };
const attrs = { size, binaryOutput };
return ENGINE.runKernel(DenseBincount, inputs, attrs);
}
export const denseBincount = op({ denseBincount_ });
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