/** * @license * Copyright 2018 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 { Cumsum } from '../kernel_names'; import { convertToTensor } from '../tensor_util_env'; import { op } from './operation'; /** * Computes the cumulative sum of a `tf.Tensor` along `axis`. * * ```js * const x = tf.tensor([1, 2, 3, 4]); * x.cumsum().print(); * ``` * ```js * const x = tf.tensor([[1, 2], [3, 4]]); * x.cumsum().print(); * ``` * * @param x The input tensor to be summed. * @param axis The axis along which to sum. Optional. Defaults to 0. * @param exclusive Whether to perform exclusive cumulative sum. Optional. * Defaults to false. If set to true then the sum of each tensor entry * does not include its own value, but only the values previous to it * along the specified axis. * @param reverse Whether to sum in the opposite direction. Optional. * Defaults to false. * * @doc {heading: 'Operations', subheading: 'Scan'} */ function cumsum_(x, axis = 0, exclusive = false, reverse = false) { const $x = convertToTensor(x, 'x', 'cumsum'); const inputs = { x: $x }; const attrs = { axis, exclusive, reverse }; return ENGINE.runKernel(Cumsum, inputs, attrs); } export const cumsum = /* @__PURE__ */ op({ cumsum_ }); //# sourceMappingURL=data:application/json;base64,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