gx

chenyc

2025-02-12 ea42ff3ebee1eeb3fb29423aa848a249441db81c

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/**
 * @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 { buffer } from './buffer';
import { expandDims } from './expand_dims';
import { op } from './operation';
import { reshape } from './reshape';
import { tile } from './tile';
/**
 * Create an identity matrix.
 *
 * @param numRows Number of rows.
 * @param numColumns Number of columns. Defaults to `numRows`.
 * @param batchShape If provided, will add the batch shape to the beginning
 *   of the shape of the returned `tf.Tensor` by repeating the identity
 *   matrix.
 * @param dtype Data type.
 * @returns Identity matrix of the specified size and data type, possibly
 *   with batch repetition if `batchShape` is specified.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */
function eye_(numRows, numColumns, batchShape, dtype = 'float32') {
    if (numColumns == null) {
        numColumns = numRows;
    }
    const buff = buffer([numRows, numColumns], dtype);
    const n = numRows <= numColumns ? numRows : numColumns;
    for (let i = 0; i < n; ++i) {
        buff.set(1, i, i);
    }
    const out = reshape(buff.toTensor(), [numRows, numColumns]);
    if (batchShape == null) {
        return out;
    }
    else {
        if (batchShape.length === 1) {
            return tile(expandDims(out, 0), [batchShape[0], 1, 1]);
        }
        else if (batchShape.length === 2) {
            // tslint:disable-next-line:no-unnecessary-type-assertion
            return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);
        }
        else if (batchShape.length === 3) {
            // tslint:disable-next-line:no-unnecessary-type-assertion
            return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [
                batchShape[0], batchShape[1], batchShape[2], 1, 1
            ]);
        }
        else {
            throw new Error(`eye() currently supports only 1D and 2D ` +
                // tslint:disable-next-line:no-any
                `batchShapes, but received ${batchShape.length}D.`);
        }
    }
}
export const eye = /* @__PURE__ */ op({ eye_ });
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