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112 lines
3.5 KiB
Plaintext
112 lines
3.5 KiB
Plaintext
/*
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* Copyright 2023 NVIDIA Corporation
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*
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* Licensed under the Apache License, Version 2.0 with the LLVM exception
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* (the "License"); you may not use this file except in compliance with
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* the License.
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*
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* You may obtain a copy of the License at
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*
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* http://llvm.org/foundation/relicensing/LICENSE.txt
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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 <nvbench/detail/statistics.cuh>
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#include <nvbench/types.cuh>
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#include "test_asserts.cuh"
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#include <vector>
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namespace statistics = nvbench::detail::statistics;
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void test_std()
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{
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std::vector<nvbench::float64_t> data{1.0, 2.0, 3.0, 4.0, 5.0};
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const nvbench::float64_t mean = 3.0;
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const nvbench::float64_t actual = statistics::standard_deviation(std::begin(data), std::end(data), mean);
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const nvbench::float64_t expected = 1.581;
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ASSERT(std::abs(actual - expected) < 0.001);
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}
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void test_lin_regression()
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{
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{
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std::vector<nvbench::float64_t> ys{1.0, 2.0, 3.0, 4.0, 5.0};
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auto [slope, intercept] = statistics::compute_linear_regression(std::begin(ys), std::end(ys));
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ASSERT(slope == 1.0);
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ASSERT(intercept == 1.0);
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}
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{
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std::vector<nvbench::float64_t> ys{42.0, 42.0, 42.0};
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auto [slope, intercept] = statistics::compute_linear_regression(std::begin(ys), std::end(ys));
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ASSERT(slope == 0.0);
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ASSERT(intercept == 42.0);
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}
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{
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std::vector<nvbench::float64_t> ys{8.0, 4.0, 0.0};
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auto [slope, intercept] = statistics::compute_linear_regression(std::begin(ys), std::end(ys));
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ASSERT(slope == -4.0);
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ASSERT(intercept == 8.0);
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}
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}
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void test_r2()
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{
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{
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std::vector<nvbench::float64_t> ys{1.0, 2.0, 3.0, 4.0, 5.0};
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auto [slope, intercept] = statistics::compute_linear_regression(std::begin(ys), std::end(ys));
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const nvbench::float64_t actual = statistics::compute_r2(std::begin(ys), std::end(ys), slope, intercept);
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const nvbench::float64_t expected = 1.0;
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ASSERT(std::abs(actual - expected) < 0.001);
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}
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{
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std::vector<nvbench::float64_t> signal{1.0, 2.0, 3.0, 4.0, 5.0};
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std::vector<nvbench::float64_t> noise{-1.0, 1.0, -1.0, 1.0, -1.0};
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std::vector<nvbench::float64_t> ys(signal.size());
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std::transform(std::begin(signal),
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std::end(signal),
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std::begin(noise),
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std::begin(ys),
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std::plus<nvbench::float64_t>());
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auto [slope, intercept] = statistics::compute_linear_regression(std::begin(ys), std::end(ys));
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const nvbench::float64_t expected = 0.675;
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const nvbench::float64_t actual = statistics::compute_r2(std::begin(ys), std::end(ys), slope, intercept);
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ASSERT(std::abs(actual - expected) < 0.001);
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}
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}
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void test_slope_conversion()
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{
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{
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const nvbench::float64_t actual = statistics::slope2deg(0.0);
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const nvbench::float64_t expected = 0.0;
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ASSERT(std::abs(actual - expected) < 0.001);
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}
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{
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const nvbench::float64_t actual = statistics::slope2deg(1.0);
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const nvbench::float64_t expected = 45.0;
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ASSERT(std::abs(actual - expected) < 0.001);
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}
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{
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const nvbench::float64_t actual = statistics::slope2deg(5.0);
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const nvbench::float64_t expected = 78.69;
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ASSERT(std::abs(actual - expected) < 0.001);
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}
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}
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int main()
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{
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test_std();
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test_lin_regression();
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test_r2();
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test_slope_conversion();
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}
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