gx

chenyc

2025-02-12 ea42ff3ebee1eeb3fb29423aa848a249441db81c

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/**
 * @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 { complex } from '../complex';
import { concat } from '../concat';
import { imag } from '../imag';
import { mul } from '../mul';
import { op } from '../operation';
import { real } from '../real';
import { reshape } from '../reshape';
import { reverse } from '../reverse';
import { scalar } from '../scalar';
import { slice } from '../slice';
import { ifft } from './ifft';
/**
 * Inversed real value input fast Fourier transform.
 *
 * Computes the 1-dimensional inversed discrete Fourier transform over the
 * inner-most dimension of the real input.
 *
 * ```js
 * const real = tf.tensor1d([1, 2, 3]);
 * const imag = tf.tensor1d([0, 0, 0]);
 * const x = tf.complex(real, imag);
 *
 * x.irfft().print();
 * ```
 * @param input The real value input to compute an irfft over.
 *
 * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}
 */
function irfft_(input) {
    const innerDimensionSize = input.shape[input.shape.length - 1];
    const batch = input.size / innerDimensionSize;
    let ret;
    if (innerDimensionSize <= 2) {
        const complexInput = reshape(input, [batch, innerDimensionSize]);
        ret = ifft(complexInput);
    }
    else {
        // The length of unique components of the DFT of a real-valued signal
        // is 2 * (input_len - 1)
        const outputShape = [batch, 2 * (innerDimensionSize - 1)];
        const realInput = reshape(real(input), [batch, innerDimensionSize]);
        const imagInput = reshape(imag(input), [batch, innerDimensionSize]);
        const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);
        const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));
        const r = concat([realInput, realConjugate], 1);
        const i = concat([imagInput, imagConjugate], 1);
        const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);
        ret = ifft(complexInput);
    }
    ret = real(ret);
    // reshape the result if the input is 3D tensor.
    if (input.rank === 3 && input.shape[0] !== 0) {
        const temp = ret;
        const batch = input.shape[0];
        ret = reshape(ret, [batch, ret.shape[0] / batch, ret.shape[1]]);
        temp.dispose();
    }
    return ret;
}
export const irfft = /* @__PURE__ */ op({ irfft_ });
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