/**
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* Copyright 2022 Xiaomi Corporation (authors: Fangjun Kuang)
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*
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* See LICENSE for clarification regarding multiple authors
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "gtest/gtest.h"
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#include "kaldi-native-fbank/csrc/rfft.h"
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namespace knf {
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#if 0
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>>> import torch
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>>> a = torch.tensor([1., -1, 3, 8, 20, 6, 0, 2])
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>>> torch.fft.rfft(a)
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tensor([ 39.0000+0.0000j, -28.1924-2.2929j, 18.0000+5.0000j, -9.8076+3.7071j,
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9.0000+0.0000j])
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#endif
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TEST(Rfft, TestRfft) {
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knf::Rfft fft(8);
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for (int32_t i = 0; i != 10; ++i) {
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std::vector<float> d = {1, -1, 3, 8, 20, 6, 0, 2};
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fft.Compute(d.data());
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EXPECT_EQ(d[0], 39);
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EXPECT_EQ(d[1], 9);
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EXPECT_NEAR(d[2], -28.1924, 1e-3);
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EXPECT_NEAR(-d[3], -2.2929, 1e-3);
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EXPECT_NEAR(d[4], 18, 1e-3);
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EXPECT_NEAR(-d[5], 5, 1e-3);
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EXPECT_NEAR(d[6], -9.8076, 1e-3);
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EXPECT_NEAR(-d[7], 3.7071, 1e-3);
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}
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}
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} // namespace knf
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