1297 lines
42 KiB
C++
1297 lines
42 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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* Copyright (c) 2019 Konduit K.K.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://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, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author raver119@gmail.com
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//
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#include "testlayers.h"
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#include <chrono>
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#include <array/NDArray.h>
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#include <helpers/RandomLauncher.h>
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#include <ops/declarable/LegacyRandomOp.h>
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#include <ops/declarable/CustomOperations.h>
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using namespace sd;
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class RNGTests : public testing::Test {
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private:
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public:
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long _seed = 119L;
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sd::graph::RandomGenerator _rngA;
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sd::graph::RandomGenerator _rngB;
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NDArray* nexp0 = NDArrayFactory::create_<float>('c', {10, 10});
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NDArray* nexp1 = NDArrayFactory::create_<float>('c', {10, 10});
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NDArray* nexp2 = NDArrayFactory::create_<float>('c', {10, 10});
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RNGTests() {
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_rngA.setStates(_seed * 0xDEADBEEF * 13, _seed * 0xDEADBEEF * 7);
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_rngB.setStates(_seed * 0xDEADBEEF * 13, _seed * 0xDEADBEEF * 7);
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nexp0->assign(-1.0f);
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nexp1->assign(-2.0f);
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nexp2->assign(-3.0f);
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}
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~RNGTests() {
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delete nexp0;
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delete nexp1;
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delete nexp2;
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}
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};
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TEST_F(RNGTests, TestSeeds_1) {
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RandomGenerator generator(123L, 456L);
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ASSERT_EQ(123, generator.rootState());
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ASSERT_EQ(456, generator.nodeState());
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Nd4jPointer ptr = malloc(sizeof(RandomGenerator));
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memcpy(ptr, &generator, sizeof(RandomGenerator));
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auto cast = reinterpret_cast<RandomGenerator*>(ptr);
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ASSERT_EQ(123, cast->rootState());
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ASSERT_EQ(456, cast->nodeState());
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free(ptr);
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}
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TEST_F(RNGTests, TestSeeds_2) {
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RandomGenerator generator(12, 13);
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generator.setStates(123L, 456L);
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ASSERT_EQ(123, generator.rootState());
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ASSERT_EQ(456, generator.nodeState());
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}
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TEST_F(RNGTests, TestGenerator_SGA_1) {
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RandomGenerator generator(12, 13);
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auto array= NDArrayFactory::create<float>('c',{10000000});
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generator.setStates(123L, 456L);
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for (auto idx = 0; idx < array.lengthOf(); idx++) {
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float x = generator.relativeT(idx, -sd::DataTypeUtils::template max<float>() / 10,
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sd::DataTypeUtils::template max<float>() / 10);
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array.r<float>(idx) = x;
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}
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auto minimum = array.reduceNumber(reduce::AMin);
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ASSERT_EQ(123, generator.rootState());
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ASSERT_EQ(456, generator.nodeState());
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}
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TEST_F(RNGTests, Test_Dropout_1) {
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auto x0 = NDArrayFactory::create<float>('c', {10, 10});
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auto x1 = NDArrayFactory::create<float>('c', {10, 10});
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x0.linspace(1);
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x1.linspace(1);
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float prob[] = {0.5f};
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RandomLauncher::applyDropOut(LaunchContext::defaultContext(), _rngA, &x0, 0.5);
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RandomLauncher::applyDropOut(LaunchContext::defaultContext(), _rngB, &x1, 0.5);
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ASSERT_TRUE(x0.equalsTo(&x1));
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ASSERT_FALSE(x0.equalsTo(nexp0));
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ASSERT_FALSE(x0.equalsTo(nexp1));
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ASSERT_FALSE(x0.equalsTo(nexp2));
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}
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TEST_F(RNGTests, Test_DropoutInverted_1) {
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auto x0 = NDArrayFactory::create<float>('c', {10, 10});
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auto x1 = NDArrayFactory::create<float>('c', {10, 10});
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x0.linspace(1);
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x1.linspace(1);
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float prob[] = {0.5f};
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RandomLauncher::applyInvertedDropOut(LaunchContext::defaultContext(), _rngA, &x0, 0.5);
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RandomLauncher::applyInvertedDropOut(LaunchContext::defaultContext(), _rngB, &x1, 0.5);
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ASSERT_TRUE(x0.equalsTo(&x1));
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ASSERT_FALSE(x0.equalsTo(nexp0));
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ASSERT_FALSE(x0.equalsTo(nexp1));
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ASSERT_FALSE(x0.equalsTo(nexp2));
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}
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TEST_F(RNGTests, Test_Launcher_1) {
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auto x0 = NDArrayFactory::create<float>('c', {10, 10});
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auto x1 = NDArrayFactory::create<float>('c', {10, 10});
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RandomLauncher::applyDropOut(LaunchContext::defaultContext(), _rngA, &x0, 0.5f);
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RandomLauncher::applyDropOut(LaunchContext::defaultContext(), _rngB, &x1, 0.5f);
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ASSERT_TRUE(x0.equalsTo(&x1));
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ASSERT_FALSE(x0.equalsTo(nexp0));
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ASSERT_FALSE(x0.equalsTo(nexp1));
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ASSERT_FALSE(x0.equalsTo(nexp2));
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}
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TEST_F(RNGTests, Test_Launcher_2) {
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auto x0 = NDArrayFactory::create<float>('c', {10, 10});
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auto x1 = NDArrayFactory::create<float>('c', {10, 10});
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RandomLauncher::applyInvertedDropOut(LaunchContext::defaultContext(), _rngA, &x0, 0.5f);
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RandomLauncher::applyInvertedDropOut(LaunchContext::defaultContext(), _rngB, &x1, 0.5f);
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ASSERT_TRUE(x0.equalsTo(&x1));
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ASSERT_FALSE(x0.equalsTo(nexp0));
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ASSERT_FALSE(x0.equalsTo(nexp1));
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ASSERT_FALSE(x0.equalsTo(nexp2));
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}
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TEST_F(RNGTests, Test_Launcher_3) {
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auto x0 = NDArrayFactory::create<float>('c', {10, 10});
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auto x1 = NDArrayFactory::create<float>('c', {10, 10});
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RandomLauncher::applyAlphaDropOut(LaunchContext::defaultContext(), _rngA, &x0, 0.5f, 0.2f, 0.1f, 0.3f);
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RandomLauncher::applyAlphaDropOut(LaunchContext::defaultContext(), _rngB, &x1, 0.5f, 0.2f, 0.1f, 0.3f);
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ASSERT_TRUE(x0.equalsTo(&x1));
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ASSERT_FALSE(x0.equalsTo(nexp0));
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ASSERT_FALSE(x0.equalsTo(nexp1));
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ASSERT_FALSE(x0.equalsTo(nexp2));
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}
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TEST_F(RNGTests, Test_Uniform_1) {
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auto x0 = NDArrayFactory::create<float>('c', {10, 10});
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auto x1 = NDArrayFactory::create<float>('c', {10, 10});
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RandomLauncher::fillUniform(LaunchContext::defaultContext(), _rngA, &x0, 1.0f, 2.0f);
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RandomLauncher::fillUniform(LaunchContext::defaultContext(), _rngB, &x1, 1.0f, 2.0f);
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ASSERT_TRUE(x0.equalsTo(&x1));
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ASSERT_FALSE(x0.equalsTo(nexp0));
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ASSERT_FALSE(x0.equalsTo(nexp1));
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ASSERT_FALSE(x0.equalsTo(nexp2));
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for (int e = 0; e < x0.lengthOf(); e++) {
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float v = x0.e<float>(e);
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ASSERT_TRUE(v >= 1.0f && v <= 2.0f);
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}
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}
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TEST_F(RNGTests, Test_Uniform_10) {
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auto x = NDArrayFactory::create<float>('c', {10000, 10000});
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auto z = NDArrayFactory::create<float>(0.0f);
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RandomLauncher::fillUniform(LaunchContext::defaultContext(), _rngA, &x, 0.0f, 1.0f);
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sd::ops::reduce_max op;
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auto status = op.execute({&x}, {&z});
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ASSERT_EQ(Status::OK(), status);
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ASSERT_LT(z.t<float>(0), 1.0f);
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}
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TEST_F(RNGTests, Test_Uniform_10_double) {
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auto x = NDArrayFactory::create<double>('c', {10000, 10000});
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auto z = NDArrayFactory::create<double>(0.0f);
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RandomLauncher::fillUniform(LaunchContext::defaultContext(), _rngA, &x, 0.0f, 1.0f);
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sd::ops::reduce_max op;
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auto status = op.execute({&x}, {&z});
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ASSERT_EQ(Status::OK(), status);
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ASSERT_LT(z.t<double>(0), 1.0);
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}
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TEST_F(RNGTests, Test_Uniform_11) {
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uint32_t max = 0;
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for (int e = 0; e < 100000000; e++) {
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auto v = _rngA.xoroshiro32(e) >> 8;
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if (v > max)
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max = v;
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}
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}
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TEST_F(RNGTests, Test_Uniform_12) {
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float max = -std::numeric_limits<float>::infinity();
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float min = std::numeric_limits<float>::infinity();
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for (int e = 0; e < 100000000; e++) {
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auto v = _rngA.relativeT<float>(e);
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if (v > max)
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max = v;
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if (v < min)
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min = v;
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}
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ASSERT_LT(max, 1.0f);
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ASSERT_GE(min, 0.0);
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}
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TEST_F(RNGTests, Test_Uniform_13) {
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double max = -std::numeric_limits<double>::infinity();
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double min = std::numeric_limits<double>::infinity();
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for (int e = 0; e < 100000000; e++) {
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auto v = _rngA.relativeT<double>(e);
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if (v > max)
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max = v;
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if (v < min)
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min = v;
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}
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ASSERT_LT(max, 1.0);
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ASSERT_GE(min, 0.0);
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}
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TEST_F(RNGTests, Test_Uniform_3) {
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auto x0 = NDArrayFactory::create<double>('c', {1000000});
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RandomLauncher::fillUniform(LaunchContext::defaultContext(), _rngA, &x0, 1.0f, 2.0f);
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for (int e = 0; e < x0.lengthOf(); e++) {
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auto v = x0.t<double>(e);
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ASSERT_TRUE(v >= 1.0 && v <= 2.0);
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}
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}
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TEST_F(RNGTests, Test_Bernoulli_1) {
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auto x0 = NDArrayFactory::create<float>('c', {10, 10});
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auto x1 = NDArrayFactory::create<float>('c', {10, 10});
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RandomLauncher::fillBernoulli(LaunchContext::defaultContext(), _rngA, &x0, 1.0f);
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RandomLauncher::fillBernoulli(LaunchContext::defaultContext(), _rngB, &x1, 1.0f);
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ASSERT_TRUE(x0.equalsTo(&x1));
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ASSERT_FALSE(x0.equalsTo(nexp0));
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ASSERT_FALSE(x0.equalsTo(nexp1));
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ASSERT_FALSE(x0.equalsTo(nexp2));
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}
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TEST_F(RNGTests, Test_Gaussian_1) {
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auto x0 = NDArrayFactory::create<float>('c', {10, 10});
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auto x1 = NDArrayFactory::create<float>('c', {10, 10});
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RandomLauncher::fillGaussian(LaunchContext::defaultContext(), _rngA, &x0, 1.0f, 2.0f);
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RandomLauncher::fillGaussian(LaunchContext::defaultContext(), _rngB, &x1, 1.0f, 2.0f);
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ASSERT_TRUE(x0.equalsTo(&x1));
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ASSERT_FALSE(x0.equalsTo(nexp0));
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ASSERT_FALSE(x0.equalsTo(nexp1));
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ASSERT_FALSE(x0.equalsTo(nexp2));
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}
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TEST_F(RNGTests, Test_Gaussian_21) {
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auto x0 = NDArrayFactory::create<float>('c', {1000, 1000});
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auto x1 = NDArrayFactory::create<float>('c', {1000, 1000});
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RandomLauncher::fillGaussian(LaunchContext::defaultContext(), _rngA, &x0, 0.0f, 1.0f);
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RandomLauncher::fillGaussian(LaunchContext::defaultContext(), _rngB, &x1, 0.0f, 1.0f);
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ASSERT_TRUE(x0.equalsTo(&x1));
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ASSERT_FALSE(x0.equalsTo(nexp0));
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ASSERT_FALSE(x0.equalsTo(nexp1));
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ASSERT_FALSE(x0.equalsTo(nexp2));
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sd::ops::moments op;
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auto result = op.evaluate({&x0}, {}, {});
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ASSERT_TRUE(result.status() == Status::OK());
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auto mean = result.at(0);
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auto variance = result.at(1);
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ASSERT_NEAR(sd::math::nd4j_abs(mean->e<float>(0)), 0.f, 0.2f);
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ASSERT_NEAR(variance->e<float>(0), 1.0f, 0.2f);
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}
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#ifdef DEBUG_BUILD
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TEST_F(RNGTests, Test_Gaussian_22) {
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auto x0 = NDArrayFactory::create<float>('c', {1000, 800});
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auto x1 = NDArrayFactory::create<float>('c', {1000, 800});
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RandomLauncher::fillGaussian(sd::LaunchContext::defaultContext(), _rngA, &x0, 0.0f, 1.0f);
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RandomLauncher::fillGaussian(LaunchContext::defaultContext(), _rngB, &x1, 0.0f, 1.0f);
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ASSERT_TRUE(x0.equalsTo(&x1));
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ASSERT_FALSE(x0.equalsTo(nexp0));
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ASSERT_FALSE(x0.equalsTo(nexp1));
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ASSERT_FALSE(x0.equalsTo(nexp2));
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sd::ops::moments op;
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auto result = op.evaluate({&x0}, {}, {});
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ASSERT_TRUE(result.status() == Status::OK());
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auto mean0 = result.at(0);
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auto variance0 = result.at(1);
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ASSERT_NEAR(sd::math::nd4j_abs(mean0->e<float>(0)), 0.f, 1.0e-3f);
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ASSERT_NEAR(variance0->e<float>(0), 1.0f, 1.e-3f);
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}
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TEST_F(RNGTests, Test_Gaussian_3) {
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auto x0 = NDArrayFactory::create<double>('c', {800000});
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RandomLauncher::fillGaussian(LaunchContext::defaultContext(), _rngA, &x0, 0.0, 1.0);
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auto mean = x0.meanNumber();
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auto stdev = x0.varianceNumber(sd::variance::SummaryStatsStandardDeviation, false);
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auto meanExp = NDArrayFactory::create<double>(0.);
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auto devExp = NDArrayFactory::create<double>(1.);
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ASSERT_TRUE(meanExp.equalsTo(mean, 1.e-3));
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ASSERT_TRUE(devExp.equalsTo(stdev, 1.e-3));
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}
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TEST_F(RNGTests, Test_LogNormal_1) {
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auto x0 = NDArrayFactory::create<float>('c', {10, 10});
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auto x1 = NDArrayFactory::create<float>('c', {10, 10});
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RandomLauncher::fillLogNormal(LaunchContext::defaultContext(), _rngA, &x0, 1.0f, 2.0f);
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RandomLauncher::fillLogNormal(LaunchContext::defaultContext(), _rngB, &x1, 1.0f, 2.0f);
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ASSERT_TRUE(x0.equalsTo(&x1));
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ASSERT_FALSE(x0.equalsTo(nexp0));
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ASSERT_FALSE(x0.equalsTo(nexp1));
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ASSERT_FALSE(x0.equalsTo(nexp2));
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}
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TEST_F(RNGTests, Test_Truncated_1) {
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auto x0 = NDArrayFactory::create<float>('c', {10, 10});
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auto x1 = NDArrayFactory::create<float>('c', {10, 10});
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RandomLauncher::fillTruncatedNormal(LaunchContext::defaultContext(), _rngA, &x0, 1.0f, 2.0f);
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RandomLauncher::fillTruncatedNormal(LaunchContext::defaultContext(), _rngB, &x1, 1.0f, 2.0f);
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ASSERT_TRUE(x0.equalsTo(&x1));
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ASSERT_FALSE(x0.equalsTo(nexp0));
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ASSERT_FALSE(x0.equalsTo(nexp1));
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ASSERT_FALSE(x0.equalsTo(nexp2));
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/* Check up distribution */
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auto mean = x1.reduceNumber(reduce::Mean);
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auto sumA = x1 - mean; //.reduceNumber(reduce::Sum);
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auto deviation = x1.varianceNumber(variance::SummaryStatsStandardDeviation, false);
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}
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TEST_F(RNGTests, Test_Truncated_2) {
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auto x0 = NDArrayFactory::create<float>('c', {1000, 1000});
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auto x1 = NDArrayFactory::create<float>('c', {1000, 1000});
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RandomLauncher::fillTruncatedNormal(LaunchContext::defaultContext(), _rngA, &x0, 1.0f, 2.0f);
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RandomLauncher::fillTruncatedNormal(LaunchContext::defaultContext(), _rngB, &x1, 1.0f, 2.0f);
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ASSERT_TRUE(x0.equalsTo(&x1));
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/* Check up distribution */
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auto mean = x1.reduceNumber(reduce::Mean);
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auto deviation = x1.varianceNumber(variance::SummaryStatsStandardDeviation, false);
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ASSERT_NEAR(mean.e<float>(0), 1.f, 0.5);
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ASSERT_NEAR(deviation.e<float>(0), 2.f, 0.5);
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}
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TEST_F(RNGTests, Test_Truncated_21) {
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auto x0 = NDArrayFactory::create<float>('c', {100, 100});
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auto x1 = NDArrayFactory::create<float>('c', {100, 100});
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RandomLauncher::fillTruncatedNormal(LaunchContext::defaultContext(), _rngA, &x0, 1.0f, 2.0f);
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RandomLauncher::fillTruncatedNormal(LaunchContext::defaultContext(), _rngB, &x1, 1.0f, 2.0f);
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ASSERT_TRUE(x0.equalsTo(&x1));
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auto mean0 = x0.reduceNumber(reduce::Mean);
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auto deviation0 = x0.varianceNumber(variance::SummaryStatsStandardDeviation, false);
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/* Check up distribution */
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auto mean = x1.reduceNumber(reduce::Mean);
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auto deviation = x1.varianceNumber(variance::SummaryStatsStandardDeviation, false);
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ASSERT_NEAR(mean.e<float>(0), 1.f, 0.002);
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ASSERT_NEAR(deviation.e<float>(0), 2.f, 0.5);
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sd::ops::moments op;
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auto result = op.evaluate({&x0}, {}, {}, {}, {}, false);
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sd::ops::reduce_min minOp;
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sd::ops::reduce_max maxOp;
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auto minRes = minOp.evaluate({&x1}, {}, {}, {});
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auto maxRes = maxOp.evaluate({&x0}, {}, {}, {});
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}
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TEST_F(RNGTests, Test_Truncated_22) {
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auto x0 = NDArrayFactory::create<float>('c', {100, 100});
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auto x1 = NDArrayFactory::create<float>('c', {100, 100});
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RandomLauncher::fillTruncatedNormal(LaunchContext::defaultContext(), _rngA, &x0, 2.0f, 4.0f);
|
|
RandomLauncher::fillTruncatedNormal(LaunchContext::defaultContext(), _rngB, &x1, 2.0f, 4.0f);
|
|
|
|
ASSERT_TRUE(x0.equalsTo(&x1));
|
|
|
|
auto mean0 = x0.reduceNumber(reduce::Mean);
|
|
|
|
auto deviation0 = x0.varianceNumber(variance::SummaryStatsStandardDeviation, false);
|
|
|
|
/* Check up distribution */
|
|
auto mean = x1.reduceNumber(reduce::Mean);
|
|
|
|
auto deviation = x1.varianceNumber(variance::SummaryStatsStandardDeviation, false);
|
|
ASSERT_NEAR(mean.e<float>(0), 2.f, 0.01);
|
|
ASSERT_NEAR(deviation.e<float>(0), 4.f, 0.52);
|
|
sd::ops::moments op;
|
|
auto result = op.evaluate({&x0}, {}, {}, {}, {}, false);
|
|
|
|
sd::ops::reduce_min minOp;
|
|
sd::ops::reduce_max maxOp;
|
|
|
|
auto minRes = minOp.evaluate({&x1}, {}, {}, {});
|
|
auto maxRes = maxOp.evaluate({&x0}, {}, {}, {});
|
|
}
|
|
|
|
TEST_F(RNGTests, Test_Truncated_23) {
|
|
auto x0 = NDArrayFactory::create<float>('c', {1000, 1000});
|
|
auto x1 = NDArrayFactory::create<float>('c', {1000, 1000});
|
|
|
|
RandomLauncher::fillTruncatedNormal(LaunchContext::defaultContext(), _rngA, &x0, 0.0f, 1.0f);
|
|
RandomLauncher::fillTruncatedNormal(LaunchContext::defaultContext(), _rngB, &x1, 0.0f, 1.0f);
|
|
|
|
ASSERT_TRUE(x0.equalsTo(&x1));
|
|
|
|
auto mean0 = x0.reduceNumber(reduce::Mean);
|
|
|
|
auto deviation0 = x0.varianceNumber(variance::SummaryStatsStandardDeviation, false);
|
|
|
|
/* Check up distribution */
|
|
auto mean = x1.reduceNumber(reduce::Mean);
|
|
|
|
auto deviation = x1.varianceNumber(variance::SummaryStatsStandardDeviation, false);
|
|
ASSERT_NEAR(mean.e<float>(0), 0.f, 0.01);
|
|
ASSERT_NEAR(deviation.e<float>(0), 1.f, 0.5);
|
|
sd::ops::moments op;
|
|
auto result = op.evaluate({&x0});
|
|
sd::ops::reduce_min minOp;
|
|
sd::ops::reduce_max maxOp;
|
|
|
|
auto minRes = minOp.evaluate({&x1}, {}, {}, {});
|
|
auto maxRes = maxOp.evaluate({&x0}, {}, {}, {});
|
|
|
|
}
|
|
|
|
TEST_F(RNGTests, Test_Truncated_3) {
|
|
auto x0 = NDArrayFactory::create<float>('c', {2000, 2000});
|
|
auto x1 = NDArrayFactory::create<float>('c', {2000, 2000});
|
|
|
|
RandomLauncher::fillTruncatedNormal(LaunchContext::defaultContext(), _rngA, &x0, 1.0f, 2.0f);
|
|
RandomLauncher::fillTruncatedNormal(LaunchContext::defaultContext(), _rngB, &x1, 1.0f, 2.0f);
|
|
|
|
ASSERT_TRUE(x0.equalsTo(&x1));
|
|
|
|
// Check up distribution
|
|
auto mean = x1.reduceNumber(reduce::Mean);
|
|
|
|
auto deviation = x1.varianceNumber(variance::SummaryStatsStandardDeviation, false);
|
|
ASSERT_NEAR(mean.e<float>(0), 1.f, 0.001);
|
|
ASSERT_NEAR(deviation.e<float>(0), 2.f, 0.3);
|
|
}
|
|
#endif
|
|
|
|
TEST_F(RNGTests, Test_Binomial_1) {
|
|
auto x0 = NDArrayFactory::create<float>('c', {10, 10});
|
|
auto x1 = NDArrayFactory::create<float>('c', {10, 10});
|
|
|
|
RandomLauncher::fillBinomial(LaunchContext::defaultContext(), _rngA, &x0, 3, 2.0f);
|
|
RandomLauncher::fillBinomial(LaunchContext::defaultContext(), _rngB, &x1, 3, 2.0f);
|
|
|
|
ASSERT_TRUE(x0.equalsTo(&x1));
|
|
|
|
ASSERT_FALSE(x0.equalsTo(nexp0));
|
|
ASSERT_FALSE(x0.equalsTo(nexp1));
|
|
ASSERT_FALSE(x0.equalsTo(nexp2));
|
|
}
|
|
|
|
|
|
TEST_F(RNGTests, Test_Uniform_2) {
|
|
auto input = NDArrayFactory::create<Nd4jLong>('c', {1, 2}, {10, 10});
|
|
auto x1 = NDArrayFactory::create<float>('c', {10, 10});
|
|
|
|
RandomLauncher::fillUniform(LaunchContext::defaultContext(), _rngB, &x1, 1.0f, 2.0f);
|
|
|
|
auto op = new sd::ops::LegacyRandomOp(0);
|
|
auto result = op->execute(_rngA, {&input}, {1.0f, 2.0f}, {});
|
|
|
|
ASSERT_EQ(Status::OK(), result.status());
|
|
|
|
auto z = result.at(0);
|
|
|
|
ASSERT_TRUE(x1.isSameShape(z));
|
|
ASSERT_TRUE(x1.equalsTo(z));
|
|
|
|
delete op;
|
|
|
|
}
|
|
|
|
TEST_F(RNGTests, Test_Uniform_SGA_3) {
|
|
//auto input = NDArrayFactory::create<Nd4jLong>('c', {1, 2}, {10, 10});
|
|
auto x1 = NDArrayFactory::create<float>('c', {10, 10});
|
|
|
|
RandomLauncher::fillUniform(LaunchContext::defaultContext(), _rngB, &x1, -sd::DataTypeUtils::template max<float>(), sd::DataTypeUtils::template max<float>());
|
|
auto minimumU = x1.reduceNumber(reduce::AMin);
|
|
}
|
|
|
|
TEST_F(RNGTests, Test_Gaussian_2) {
|
|
auto input = NDArrayFactory::create<Nd4jLong>('c', {1, 2}, {10, 10});
|
|
auto x1 = NDArrayFactory::create<float>('c', {10, 10});
|
|
|
|
RandomLauncher::fillGaussian(LaunchContext::defaultContext(), _rngB, &x1, 1.0f, 2.0f);
|
|
|
|
auto op = new sd::ops::LegacyRandomOp(random::GaussianDistribution);
|
|
auto result = op->execute(_rngA, {&input}, {1.0f, 2.0f}, {});
|
|
|
|
ASSERT_EQ(Status::OK(), result.status());
|
|
|
|
auto z = result.at(0);
|
|
|
|
ASSERT_TRUE(x1.isSameShape(z));
|
|
ASSERT_TRUE(x1.equalsTo(z));
|
|
|
|
delete op;
|
|
|
|
}
|
|
|
|
TEST_F(RNGTests, Test_LogNorm_2) {
|
|
auto input = NDArrayFactory::create<Nd4jLong>('c', {1, 2}, {10, 10});
|
|
auto x1 = NDArrayFactory::create<float>('c', {10, 10});
|
|
|
|
RandomLauncher::fillLogNormal(LaunchContext::defaultContext(), _rngB, &x1, 1.0f, 2.0f);
|
|
|
|
auto op = new sd::ops::LegacyRandomOp(random::LogNormalDistribution);
|
|
auto result = op->execute(_rngA, {&input}, {1.0f, 2.0f}, {});
|
|
|
|
ASSERT_EQ(Status::OK(), result.status());
|
|
|
|
auto z = result.at(0);
|
|
|
|
ASSERT_TRUE(x1.isSameShape(z));
|
|
ASSERT_TRUE(x1.equalsTo(z));
|
|
|
|
delete op;
|
|
|
|
}
|
|
|
|
TEST_F(RNGTests, Test_TruncatedNorm_2) {
|
|
auto input = NDArrayFactory::create<Nd4jLong>('c', {1, 2}, {10, 10});
|
|
auto x1 = NDArrayFactory::create<float>('c', {10, 10});
|
|
|
|
RandomLauncher::fillTruncatedNormal(LaunchContext::defaultContext(), _rngB, &x1, 1.0f, 2.0f);
|
|
|
|
auto op = new sd::ops::LegacyRandomOp(random::TruncatedNormalDistribution);
|
|
auto result = op->execute(_rngA, {&input}, {1.0f, 2.0f}, {});
|
|
|
|
ASSERT_EQ(Status::OK(), result.status());
|
|
|
|
auto z = result.at(0);
|
|
|
|
ASSERT_TRUE(x1.isSameShape(z));
|
|
ASSERT_TRUE(x1.equalsTo(z));
|
|
delete op;
|
|
|
|
}
|
|
|
|
|
|
TEST_F(RNGTests, Test_Binomial_2) {
|
|
auto input = NDArrayFactory::create<Nd4jLong>('c', {1, 2}, {10, 10});
|
|
auto x1 = NDArrayFactory::create<float>('c', {10, 10});
|
|
|
|
RandomLauncher::fillBinomial(LaunchContext::defaultContext(), _rngB, &x1, 3, 0.5f);
|
|
|
|
auto op = new sd::ops::LegacyRandomOp(random::BinomialDistributionEx);
|
|
auto result = op->execute(_rngA, {&input}, {0.5f}, {3});
|
|
|
|
ASSERT_EQ(Status::OK(), result.status());
|
|
|
|
auto z = result.at(0);
|
|
|
|
ASSERT_TRUE(x1.isSameShape(z));
|
|
ASSERT_TRUE(x1.equalsTo(z));
|
|
|
|
delete op;
|
|
|
|
}
|
|
|
|
|
|
TEST_F(RNGTests, Test_Bernoulli_2) {
|
|
auto input = NDArrayFactory::create<Nd4jLong>('c', {1, 2}, {10, 10});
|
|
auto x1 = NDArrayFactory::create<float>('c', {10, 10});
|
|
|
|
RandomLauncher::fillBernoulli(LaunchContext::defaultContext(), _rngB, &x1, 0.5f);
|
|
|
|
auto op = new sd::ops::LegacyRandomOp(random::BernoulliDistribution);
|
|
auto result = op->execute(_rngA, {&input}, {0.5f}, {});
|
|
|
|
ASSERT_EQ(Status::OK(), result.status());
|
|
|
|
auto z = result.at(0);
|
|
|
|
ASSERT_TRUE(x1.isSameShape(z));
|
|
ASSERT_TRUE(x1.equalsTo(z));
|
|
|
|
delete op;
|
|
|
|
}
|
|
|
|
TEST_F(RNGTests, Test_GaussianDistribution_1) {
|
|
auto x = NDArrayFactory::create<Nd4jLong>('c', {2}, {10, 10});
|
|
auto exp0 = NDArrayFactory::create<float>('c', {10, 10});
|
|
|
|
|
|
sd::ops::random_normal op;
|
|
auto result = op.evaluate({&x}, {0.0, 1.0f}, {});
|
|
ASSERT_EQ(Status::OK(), result.status());
|
|
|
|
auto z = result.at(0);
|
|
ASSERT_TRUE(exp0.isSameShape(z));
|
|
ASSERT_FALSE(exp0.equalsTo(z));
|
|
|
|
|
|
ASSERT_FALSE(nexp0->equalsTo(z));
|
|
ASSERT_FALSE(nexp1->equalsTo(z));
|
|
ASSERT_FALSE(nexp2->equalsTo(z));
|
|
|
|
|
|
}
|
|
|
|
TEST_F(RNGTests, Test_BernoulliDistribution_1) {
|
|
auto x = NDArrayFactory::create<Nd4jLong>('c', {2}, {10, 10});
|
|
auto exp0 = NDArrayFactory::create<float>('c', {10, 10});
|
|
|
|
|
|
sd::ops::random_bernoulli op;
|
|
auto result = op.evaluate({&x}, {0.5f}, {});
|
|
ASSERT_EQ(Status::OK(), result.status());
|
|
|
|
auto z = result.at(0);
|
|
|
|
ASSERT_FALSE(exp0.equalsTo(z));
|
|
|
|
ASSERT_FALSE(nexp0->equalsTo(z));
|
|
ASSERT_FALSE(nexp1->equalsTo(z));
|
|
ASSERT_FALSE(nexp2->equalsTo(z));
|
|
|
|
|
|
}
|
|
|
|
|
|
TEST_F(RNGTests, Test_ExponentialDistribution_1) {
|
|
auto x = NDArrayFactory::create<Nd4jLong>('c', {2}, {10, 10});
|
|
auto exp0 = NDArrayFactory::create<float>('c', {10, 10});
|
|
|
|
|
|
sd::ops::random_exponential op;
|
|
auto result = op.evaluate({&x}, {0.25f}, {0});
|
|
ASSERT_EQ(Status::OK(), result.status());
|
|
|
|
auto z = result.at(0);
|
|
ASSERT_TRUE(exp0.isSameShape(z));
|
|
ASSERT_FALSE(exp0.equalsTo(z));
|
|
|
|
auto mean = z->reduceNumber(reduce::Mean);
|
|
auto variance = z->varianceNumber(variance::SummaryStatsVariance, false);
|
|
|
|
ASSERT_FALSE(nexp0->equalsTo(z));
|
|
ASSERT_FALSE(nexp1->equalsTo(z));
|
|
ASSERT_FALSE(nexp2->equalsTo(z));
|
|
|
|
}
|
|
|
|
TEST_F(RNGTests, Test_ExponentialDistribution_1_SGA) {
|
|
auto x = NDArrayFactory::create<Nd4jLong>('c', {2}, {10, 10});
|
|
auto exp0 = NDArrayFactory::create<float>('c', {10, 10});
|
|
|
|
|
|
sd::ops::random_exponential op;
|
|
auto result = op.evaluate({&x}, {1.f}, {0});
|
|
ASSERT_EQ(Status::OK(), result.status());
|
|
|
|
auto z = result.at(0);
|
|
ASSERT_TRUE(exp0.isSameShape(z));
|
|
ASSERT_FALSE(exp0.equalsTo(z));
|
|
|
|
auto mean = z->reduceNumber(reduce::Mean);
|
|
auto variance = z->varianceNumber(variance::SummaryStatsVariance, false);
|
|
|
|
ASSERT_FALSE(nexp0->equalsTo(z));
|
|
ASSERT_FALSE(nexp1->equalsTo(z));
|
|
ASSERT_FALSE(nexp2->equalsTo(z));
|
|
|
|
|
|
}
|
|
|
|
TEST_F(RNGTests, Test_ExponentialDistribution_2_SGA) {
|
|
auto x = NDArrayFactory::create<Nd4jLong>('c', {2}, {10, 10});
|
|
auto exp0 = NDArrayFactory::create<float>('c', {10, 10});
|
|
RandomGenerator oc(2716049175077475646L, -6182841917129177862L);
|
|
|
|
sd::ops::random_exponential op;
|
|
RandomLauncher::fillExponential(x.getContext(), oc, &exp0, 2.f);
|
|
auto result = op.evaluate({&x}, {1.f}, {0});
|
|
ASSERT_EQ(Status::OK(), result.status());
|
|
|
|
auto z = result.at(0);
|
|
ASSERT_TRUE(exp0.isSameShape(z));
|
|
ASSERT_FALSE(exp0.equalsTo(z));
|
|
|
|
auto mean = z->reduceNumber(reduce::Mean);
|
|
auto variance = z->varianceNumber(variance::SummaryStatsVariance, false);
|
|
|
|
ASSERT_FALSE(nexp0->equalsTo(z));
|
|
ASSERT_FALSE(nexp1->equalsTo(z));
|
|
ASSERT_FALSE(nexp2->equalsTo(z));
|
|
mean = exp0.reduceNumber(reduce::Mean);
|
|
variance = exp0.varianceNumber(variance::SummaryStatsVariance, false);
|
|
|
|
}
|
|
|
|
TEST_F(RNGTests, Test_ExponentialDistribution_2) {
|
|
auto x = NDArrayFactory::create<Nd4jLong>('c', {2}, {10, 10});
|
|
auto y = NDArrayFactory::create<float>('c', {10, 10});
|
|
auto exp0 = NDArrayFactory::create<float>('c', {10, 10});
|
|
|
|
y.assign(1.0);
|
|
|
|
|
|
sd::ops::random_exponential op;
|
|
auto result = op.evaluate({&x, &y}, {0.25f}, {0});
|
|
ASSERT_EQ(Status::OK(), result.status());
|
|
|
|
auto z = result.at(0);
|
|
ASSERT_TRUE(exp0.isSameShape(z));
|
|
ASSERT_FALSE(exp0.equalsTo(z));
|
|
|
|
|
|
ASSERT_FALSE(nexp0->equalsTo(z));
|
|
ASSERT_FALSE(nexp1->equalsTo(z));
|
|
ASSERT_FALSE(nexp2->equalsTo(z));
|
|
|
|
|
|
}
|
|
|
|
TEST_F(RNGTests, Test_PoissonDistribution_1) {
|
|
auto x = NDArrayFactory::create<Nd4jLong>('c', {1}, {10});
|
|
auto la = NDArrayFactory::create<float>('c', {2, 3});
|
|
auto exp0 = NDArrayFactory::create<float>('c', {10, 2, 3});
|
|
|
|
la.linspace(1.0);
|
|
|
|
|
|
sd::ops::random_poisson op;
|
|
auto result = op.evaluate({&x, &la}, {}, {});
|
|
ASSERT_EQ(Status::OK(), result.status());
|
|
|
|
auto z = result.at(0);
|
|
ASSERT_TRUE(exp0.isSameShape(z));
|
|
ASSERT_FALSE(exp0.equalsTo(z));
|
|
}
|
|
|
|
TEST_F(RNGTests, Test_GammaDistribution_1) {
|
|
auto x = NDArrayFactory::create<Nd4jLong>('c', {1}, {10});
|
|
auto al = NDArrayFactory::create<float>('c', {2, 3});
|
|
auto exp0 = NDArrayFactory::create<float>('c', {10, 2, 3});
|
|
|
|
al.linspace(1.0);
|
|
|
|
|
|
sd::ops::random_gamma op;
|
|
auto result = op.evaluate({&x, &al}, {}, {});
|
|
ASSERT_EQ(Status::OK(), result.status());
|
|
|
|
auto z = result.at(0);
|
|
|
|
ASSERT_TRUE(exp0.isSameShape(z));
|
|
ASSERT_FALSE(exp0.equalsTo(z));
|
|
}
|
|
|
|
TEST_F(RNGTests, Test_GammaDistribution_2) {
|
|
auto x = NDArrayFactory::create<Nd4jLong>('c', {1}, {10});
|
|
auto al = NDArrayFactory::create<float>('c', {2, 3});
|
|
auto be = NDArrayFactory::create<float>('c', {2, 3});
|
|
auto exp0 = NDArrayFactory::create<float>('c', {10, 2, 3});
|
|
|
|
al.linspace(1.0);
|
|
be.assign(1.0);
|
|
|
|
sd::ops::random_gamma op;
|
|
auto result = op.evaluate({&x, &al, &be}, {}, {});
|
|
ASSERT_EQ(Status::OK(), result.status());
|
|
|
|
auto z = result.at(0);
|
|
|
|
ASSERT_TRUE(exp0.isSameShape(z));
|
|
ASSERT_FALSE(exp0.equalsTo(z));
|
|
}
|
|
|
|
TEST_F(RNGTests, Test_GammaDistribution_3) {
|
|
auto x = NDArrayFactory::create<Nd4jLong>('c', {1}, {10});
|
|
auto al = NDArrayFactory::create<float>('c', {3, 1});
|
|
auto be = NDArrayFactory::create<float>('c', {1, 2});
|
|
auto exp0 = NDArrayFactory::create<float>('c', {10, 3, 2});
|
|
|
|
al.linspace(1.0);
|
|
be.assign(2.0);
|
|
|
|
sd::ops::random_gamma op;
|
|
auto result = op.evaluate({&x, &al, &be}, {}, {});
|
|
ASSERT_EQ(Status::OK(), result.status());
|
|
|
|
auto z = result.at(0);
|
|
ASSERT_TRUE(exp0.isSameShape(z));
|
|
ASSERT_FALSE(exp0.equalsTo(z));
|
|
|
|
|
|
}
|
|
|
|
TEST_F(RNGTests, Test_GammaDistribution_4) {
|
|
auto x = NDArrayFactory::create<Nd4jLong>('c', {2}, {1000, 1000});
|
|
auto al = NDArrayFactory::create<float>(2.f);
|
|
auto be = NDArrayFactory::create<float>(2.f);
|
|
auto exp0 = NDArrayFactory::create<float>('c', {1000, 1000});
|
|
|
|
// al.linspace(1.0);
|
|
// be.assign(2.0);
|
|
|
|
sd::ops::random_gamma op;
|
|
auto result = op.evaluate({&x, &al, &be}, {}, {});
|
|
ASSERT_EQ(Status::OK(), result.status());
|
|
|
|
auto z = result.at(0);
|
|
// z->printIndexedBuffer("Gamma distribution");
|
|
ASSERT_TRUE(exp0.isSameShape(z));
|
|
ASSERT_FALSE(exp0.equalsTo(z));
|
|
sd::ops::reduce_mean testOps1;
|
|
sd::ops::reduce_variance testOps2;
|
|
auto testRes1 = testOps1.evaluate({z});
|
|
auto testRes2 = testOps2.evaluate({z});
|
|
// testRes1[0]->printBuffer("Mean (expected 1.0)");
|
|
// testRes2[0]->printBuffer("Variance (expected 0.5)");
|
|
ASSERT_NEAR(testRes1[0]->t<float>(0), 1.0f, 0.01);
|
|
ASSERT_NEAR(testRes2[0]->t<float>(0), 0.5f, 0.02);
|
|
}
|
|
|
|
TEST_F(RNGTests, Test_GammaDistribution_5) {
|
|
auto x = NDArrayFactory::create<Nd4jLong>('c', {2}, {100, 100});
|
|
auto al = NDArrayFactory::create<float>(0.2f);
|
|
auto be = NDArrayFactory::create<float>(2.f);
|
|
auto exp0 = NDArrayFactory::create<float>('c', {100, 100});
|
|
|
|
// al.linspace(1.0);
|
|
// be.assign(2.0);
|
|
|
|
sd::ops::random_gamma op;
|
|
auto result = op.evaluate({&x, &al, &be}, {}, {});
|
|
ASSERT_EQ(Status::OK(), result.status());
|
|
|
|
auto z = result.at(0);
|
|
// z->printIndexedBuffer("Gamma distribution");
|
|
ASSERT_TRUE(exp0.isSameShape(z));
|
|
ASSERT_FALSE(exp0.equalsTo(z));
|
|
// z->printIndexedBuffer("Gamma distributed");
|
|
sd::ops::reduce_mean testOps1;
|
|
sd::ops::reduce_variance testOps2;
|
|
auto testRes1 = testOps1.evaluate({z});
|
|
auto testRes2 = testOps2.evaluate({z});
|
|
// testRes1[0]->printBuffer("Mean (expected 0.1)");
|
|
// testRes2[0]->printBuffer("Variance (expected 0.05)");
|
|
ASSERT_NEAR(testRes1[0]->t<float>(0), 0.1f, 0.02);
|
|
ASSERT_NEAR(testRes2[0]->t<float>(0), 0.05f, 0.02);
|
|
}
|
|
|
|
TEST_F(RNGTests, Test_UniformDistribution_04) {
|
|
auto x = NDArrayFactory::create<Nd4jLong>('c', {1}, {10});
|
|
auto al = NDArrayFactory::create<int>(1);
|
|
auto be = NDArrayFactory::create<int>(20);
|
|
auto exp0 = NDArrayFactory::create<float>('c', {10});
|
|
|
|
|
|
sd::ops::randomuniform op;
|
|
auto result = op.evaluate({&x, &al, &be}, {}, {DataType::INT32});
|
|
ASSERT_EQ(Status::OK(), result.status());
|
|
|
|
auto z = result.at(0);
|
|
ASSERT_TRUE(exp0.isSameShape(z));
|
|
ASSERT_FALSE(exp0.equalsTo(z));
|
|
|
|
}
|
|
|
|
TEST_F(RNGTests, Test_UniformDistribution_05) {
|
|
auto x = NDArrayFactory::create<Nd4jLong>('c', {2}, {10000, 10000});
|
|
auto al = NDArrayFactory::create<float>(0.f);
|
|
auto be = NDArrayFactory::create<float>(1.f);
|
|
auto exp0 = NDArrayFactory::create<float>('c', {10000, 10000});
|
|
|
|
|
|
sd::ops::randomuniform op;
|
|
auto result = op.evaluate({&x, &al, &be}, {}, {},{}, {DataType::FLOAT32});
|
|
ASSERT_EQ(Status::OK(), result.status());
|
|
|
|
auto z = result.at(0);
|
|
ASSERT_TRUE(exp0.isSameShape(z));
|
|
ASSERT_FALSE(exp0.equalsTo(z));
|
|
|
|
sd::ops::reduce_max checkOp;
|
|
auto checkResult = checkOp.evaluate({z});
|
|
}
|
|
|
|
namespace sd {
|
|
namespace tests {
|
|
static void fillList(Nd4jLong seed, int numberOfArrays, std::vector<Nd4jLong> &shape, std::vector<NDArray*> &list, sd::graph::RandomGenerator *rng) {
|
|
rng->setSeed((int) seed);
|
|
|
|
for (int i = 0; i < numberOfArrays; i++) {
|
|
auto arrayI = NDArrayFactory::create<Nd4jLong>(shape);
|
|
auto arrayR = NDArrayFactory::create_<double>('c', shape);
|
|
auto min = NDArrayFactory::create(0.0);
|
|
auto max = NDArrayFactory::create(1.0);
|
|
sd::ops::randomuniform op;
|
|
op.execute(*rng, {&arrayI, &min, &max}, {arrayR}, {}, {DataType::DOUBLE}, {}, {}, false);
|
|
|
|
list.emplace_back(arrayR);
|
|
}
|
|
};
|
|
}
|
|
}
|
|
|
|
TEST_F(RNGTests, Test_Reproducibility_1) {
|
|
Nd4jLong seed = 123;
|
|
|
|
std::vector<Nd4jLong> shape = {32, 3, 28, 28};
|
|
sd::graph::RandomGenerator rng;
|
|
|
|
std::vector<NDArray*> expList;
|
|
sd::tests::fillList(seed, 10, shape, expList, &rng);
|
|
|
|
for (int e = 0; e < 2; e++) {
|
|
std::vector<NDArray *> trialList;
|
|
sd::tests::fillList(seed, 10, shape, trialList, &rng);
|
|
|
|
for (int a = 0; a < expList.size(); a++) {
|
|
auto arrayE = expList[a];
|
|
auto arrayT = trialList[a];
|
|
|
|
bool t = arrayE->equalsTo(arrayT);
|
|
if (!t) {
|
|
ASSERT_TRUE(false);
|
|
}
|
|
|
|
delete arrayT;
|
|
}
|
|
}
|
|
|
|
for (auto v: expList)
|
|
delete v;
|
|
}
|
|
|
|
#ifndef DEBUG_BUILD
|
|
TEST_F(RNGTests, Test_Reproducibility_2) {
|
|
Nd4jLong seed = 123;
|
|
|
|
std::vector<Nd4jLong> shape = {32, 3, 64, 64};
|
|
sd::graph::RandomGenerator rng;
|
|
|
|
std::vector<NDArray*> expList;
|
|
sd::tests::fillList(seed, 10, shape, expList, &rng);
|
|
|
|
for (int e = 0; e < 2; e++) {
|
|
std::vector<NDArray*> trialList;
|
|
sd::tests::fillList(seed, 10, shape, trialList, &rng);
|
|
|
|
for (int a = 0; a < expList.size(); a++) {
|
|
auto arrayE = expList[a];
|
|
auto arrayT = trialList[a];
|
|
|
|
bool t = arrayE->equalsTo(arrayT);
|
|
if (!t) {
|
|
|
|
for (Nd4jLong f = 0; f < arrayE->lengthOf(); f++) {
|
|
double x = arrayE->e<double>(f);
|
|
double y = arrayT->e<double>(f);
|
|
|
|
if (sd::math::nd4j_re(x, y) > 0.1) {
|
|
throw std::runtime_error("boom");
|
|
}
|
|
}
|
|
ASSERT_TRUE(false);
|
|
}
|
|
|
|
delete arrayT;
|
|
}
|
|
}
|
|
|
|
for (auto v: expList)
|
|
delete v;
|
|
}
|
|
|
|
TEST_F(RNGTests, Test_Uniform_4) {
|
|
auto x1 = NDArrayFactory::create<double>('c', {1000000});
|
|
|
|
RandomLauncher::fillUniform(LaunchContext::defaultContext(), _rngB, &x1, 1.0, 2.0);
|
|
|
|
/* Check up distribution */
|
|
auto mean = x1.reduceNumber(reduce::Mean);
|
|
// mean.printIndexedBuffer("Mean should be 1.5");
|
|
auto sumA = x1 - mean; //.reduceNumber(reduce::Sum);
|
|
|
|
auto deviation = x1.varianceNumber(variance::SummaryStatsVariance, false);
|
|
|
|
ASSERT_NEAR(mean.e<double>(0), 1.5, 1e-3);
|
|
ASSERT_NEAR(1/12., deviation.e<double>(0), 1e-3);
|
|
}
|
|
#endif
|
|
|
|
TEST_F(RNGTests, test_choice_1) {
|
|
const auto x = NDArrayFactory::linspace<double>(0, 10, 11);
|
|
const auto prob = NDArrayFactory::valueOf<double>({11}, 1.0/11, 'c');
|
|
auto z = NDArrayFactory::create<double>('c', {1000});
|
|
|
|
RandomGenerator rng(119, 256);
|
|
NativeOpExecutioner::execRandom(sd::LaunchContext ::defaultContext(), random::Choice, &rng, x->buffer(), x->shapeInfo(), x->specialBuffer(), x->specialShapeInfo(), prob->buffer(), prob->shapeInfo(), prob->specialBuffer(), prob->specialShapeInfo(), z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr);
|
|
|
|
delete x;
|
|
delete prob;
|
|
}
|
|
|
|
TEST_F(RNGTests, test_uniform_119) {
|
|
auto x = NDArrayFactory::create<int>('c', {2}, {1, 5});
|
|
auto z = NDArrayFactory::create<float>('c', {1, 5});
|
|
|
|
|
|
sd::ops::randomuniform op;
|
|
auto status = op.execute({&x}, {&z}, {1.0, 2.0}, {}, {});
|
|
ASSERT_EQ(Status::OK(), status);
|
|
}
|
|
|
|
TEST_F(RNGTests, test_multinomial_1) {
|
|
|
|
NDArray probs('f', { 3, 3 }, { 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3 }, sd::DataType::FLOAT32);
|
|
NDArray expected('f', { 3, 3 }, { 0., 1, 2, 2, 0, 0, 1, 2, 1 }, sd::DataType::INT64);
|
|
NDArray output('f', { 3, 3 }, sd::DataType::INT64);
|
|
NDArray samples('f', { 1 }, std::vector<double>({3}), sd::DataType::INT32);
|
|
|
|
sd::ops::random_multinomial op;
|
|
RandomGenerator rng(1234, 1234);
|
|
ASSERT_EQ(Status::OK(), op.execute(rng, { &probs, &samples }, { &output }, {}, { 0, INT64}, {}, {}, false) );
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
NDArray probsZ('c', { 1, 3 }, { 0.3, 0.3, 0.3 }, sd::DataType::FLOAT32);
|
|
NDArray expectedZ('c', { 3, 3 }, { 0., 0, 0, 0, 0, 0, 0, 0, 0 }, sd::DataType::INT64);
|
|
|
|
auto result = op.evaluate({ &probsZ, &samples }, { }, { 1, INT64 });
|
|
auto outputZ = result.at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), result.status());
|
|
ASSERT_TRUE(expectedZ.isSameShape(outputZ));
|
|
ASSERT_TRUE(expectedZ.equalsTo(outputZ));
|
|
}
|
|
|
|
TEST_F(RNGTests, test_multinomial_2) {
|
|
|
|
NDArray samples('c', { 1 }, std::vector<double>{ 20 }, sd::DataType::INT32);
|
|
NDArray probs('c', { 3, 5 }, { 0.2, 0.3, 0.5, 0.3, 0.5, 0.2, 0.5, 0.2, 0.3, 0.35, 0.25, 0.3, 0.25, 0.25, 0.5 }, sd::DataType::FLOAT32);
|
|
NDArray expected('c', { 3, 20 }, { 0, 2, 0, 2, 0, 4, 2, 0, 1, 2, 0, 2, 3, 0, 0, 2, 4, 4, 1, 0, 2, 3, 2, 3, 0, 1, 3, 1, 1, 1, 2, 4, 3, 3, 1, 4, 4, 2, 0, 0, 3, 3, 3, 0, 0, 2, 2, 3, 3, 0, 0, 2, 3, 4, 2, 2, 3, 2, 1, 2 }, sd::DataType::INT64);
|
|
NDArray output('c', { 3, 20 }, sd::DataType::INT64);
|
|
|
|
sd::ops::random_multinomial op;
|
|
RandomGenerator rng(1234, 1234);
|
|
ASSERT_EQ(Status::OK(), op.execute(rng, { &probs, &samples }, { &output }, {}, { 0, INT64 }, {}, {}, false));
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
NDArray probs2('c', { 5, 3 }, { 0.2, 0.3, 0.5, 0.3, 0.5, 0.2, 0.5, 0.2, 0.3, 0.35, 0.25, 0.3, 0.25, 0.25, 0.5 }, sd::DataType::FLOAT32);
|
|
NDArray expected2('c', { 20, 3 }, { 0, 2, 3, 2, 3, 3, 0, 2, 3, 2, 3, 0, 0, 0, 0, 4, 1, 2, 2, 3, 2, 3, 1, 3, 1, 1, 3, 2, 1, 0, 0, 2, 0, 2, 4, 2, 3, 3, 3, 0, 3, 4, 0, 1, 2, 2, 0, 2, 4, 4, 0, 4, 2, 2, 1, 0, 1, 0, 0, 2 }, sd::DataType::INT64);
|
|
NDArray output2('c', { 20, 3 }, sd::DataType::INT64);
|
|
|
|
rng.setStates(1234, 1234);
|
|
ASSERT_EQ(Status::OK(), op.execute(rng, { &probs2, &samples }, { &output2 }, {}, { 1, INT64 }, {}, {}, false));
|
|
ASSERT_TRUE(expected2.isSameShape(output2));
|
|
ASSERT_TRUE(expected2.equalsTo(output2));
|
|
}
|
|
|
|
TEST_F(RNGTests, test_multinomial_3) {
|
|
|
|
NDArray probs('c', { 4, 3 }, { 0.3, 0.3, 0.4, 0.3, 0.4, 0.3, 0.3, 0.3, 0.4, 0.4, 0.3, 0.3 }, sd::DataType::FLOAT32);
|
|
NDArray expected('c', { 4, 5 }, sd::DataType::INT64);
|
|
NDArray output('c', { 4, 5 }, sd::DataType::INT64);
|
|
NDArray samples('c', { 1 }, std::vector<double>{ 5 }, sd::DataType::INT32);
|
|
RandomGenerator rng(1234, 1234);
|
|
|
|
sd::ops::random_multinomial op;
|
|
|
|
ASSERT_EQ(Status::OK(), op.execute(rng, { &probs, &samples }, { &expected }, {}, { 0, INT64 }, {}, {}, false));
|
|
|
|
rng.setStates(1234, 1234);
|
|
ASSERT_EQ(Status::OK(), op.execute(rng, { &probs, &samples }, { &output }, {}, { 0, INT64 }, {}, {}, false));
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
}
|
|
|
|
TEST_F(RNGTests, test_multinomial_4) {
|
|
|
|
NDArray probs('c', { 3, 4 }, { 0.3, 0.3, 0.4, 0.3, 0.4, 0.3, 0.3, 0.3, 0.4, 0.4, 0.3, 0.3 }, sd::DataType::FLOAT32);
|
|
NDArray expected('c', { 5, 4 }, sd::DataType::INT64);
|
|
NDArray output('c', { 5, 4 }, sd::DataType::INT64);
|
|
NDArray samples('c', { 1 }, std::vector<double>{ 5 }, sd::DataType::INT32);
|
|
|
|
RandomGenerator rng(1234, 1234);
|
|
sd::ops::random_multinomial op;
|
|
ASSERT_EQ(Status::OK(), op.execute(rng, { &probs, &samples }, { &expected }, {}, { 1, INT64 }, {}, {}, false));
|
|
|
|
rng.setStates(1234, 1234);
|
|
ASSERT_EQ(Status::OK(), op.execute(rng, { &probs, &samples }, { &output }, {}, { 1, INT64 }, {}, {}, false));
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
}
|
|
|
|
TEST_F(RNGTests, test_multinomial_5) {
|
|
// multinomial as binomial if 2 classes used
|
|
int batchValue = 1;
|
|
int ClassValue = 2;
|
|
int Samples = 100000;
|
|
|
|
NDArray samples('c', { 1 }, std::vector<double>{ 1.*Samples }, sd::DataType::INT32);
|
|
|
|
NDArray probs('c', { ClassValue, batchValue }, { 1.0, 1.0 }, sd::DataType::FLOAT32);
|
|
|
|
sd::ops::random_multinomial op;
|
|
|
|
NDArray output('c', { Samples, batchValue }, sd::DataType::INT64);
|
|
RandomGenerator rng(1234, 1234);
|
|
|
|
ASSERT_EQ(Status::OK(), op.execute(rng, { &probs, &samples }, { &output }, {}, { 1 }, {}, {}, false));
|
|
auto deviation = output.varianceNumber(variance::SummaryStatsStandardDeviation, false);
|
|
auto mean = output.meanNumber();
|
|
|
|
// theoretical values for binomial
|
|
ASSERT_NEAR(0.5, deviation.e<double>(0), 4e-3); // 1000000 3e-3);
|
|
ASSERT_NEAR(0.5, mean.e<double>(0), 4e-3); // 1000000 3e-3);
|
|
|
|
for (int i = 0; i < output.lengthOf(); i++) {
|
|
auto value = output.e<Nd4jLong>(i);
|
|
ASSERT_TRUE(value >= 0 && value < ClassValue);
|
|
}
|
|
|
|
auto resultR = op.evaluate({ &probs, &samples }, { }, { 1 });
|
|
auto outputR = resultR.at(0);
|
|
ASSERT_EQ(Status::OK(), resultR.status());
|
|
|
|
deviation = outputR->varianceNumber(variance::SummaryStatsStandardDeviation, false);
|
|
mean = outputR->meanNumber();
|
|
|
|
ASSERT_NEAR(0.5, deviation.e<double>(0), 45e-3); // 1000000 35e-3);
|
|
ASSERT_NEAR(0.5, mean.e<double>(0), 45e-3); // 1000000 35e-3);
|
|
|
|
for (int i = 0; i < outputR->lengthOf(); i++) {
|
|
auto value = outputR->e<Nd4jLong>(i);
|
|
ASSERT_TRUE(value >= 0 && value < ClassValue);
|
|
}
|
|
|
|
}
|
|
|
|
|
|
TEST_F(RNGTests, test_multinomial_6) {
|
|
|
|
int batchValue = 1;
|
|
int ClassValue = 5;
|
|
int Samples = 100000;
|
|
|
|
NDArray samples('c', { 1 }, std::vector<double>{ 1. * Samples }, sd::DataType::INT32);
|
|
|
|
sd::ops::random_multinomial op;
|
|
NDArray probExpect('c', { ClassValue }, { 0.058, 0.096, 0.1576, 0.2598, 0.4287 }, sd::DataType::DOUBLE);
|
|
|
|
// without seed
|
|
NDArray probsR('c', { batchValue, ClassValue }, { 1., 1.5, 2., 2.5, 3. }, sd::DataType::FLOAT32);
|
|
|
|
auto resultR = op.evaluate({ &probsR, &samples }, { }, { 0 });
|
|
auto outputR = resultR.at(0);
|
|
ASSERT_EQ(Status::OK(), resultR.status());
|
|
|
|
NDArray countsR('c', { ClassValue }, { 0., 0, 0, 0, 0 }, sd::DataType::DOUBLE);
|
|
|
|
for (int i = 0; i < outputR->lengthOf(); i++) {
|
|
auto value = outputR->e<Nd4jLong>(i);
|
|
ASSERT_TRUE(value >= 0 && value < ClassValue);
|
|
double* z = countsR.bufferAsT<double>();
|
|
z[value] += 1;
|
|
}
|
|
|
|
for (int i = 0; i < countsR.lengthOf(); i++) {
|
|
auto c = countsR.e<double>(i);
|
|
auto p = probExpect.e<double>(i);
|
|
|
|
ASSERT_NEAR((c / Samples), p, 45e-3); // 1000000 35e-3);
|
|
}
|
|
|
|
auto deviation = outputR->varianceNumber(variance::SummaryStatsStandardDeviation, false);
|
|
auto mean = outputR->meanNumber();
|
|
|
|
ASSERT_NEAR(1.2175, deviation.e<double>(0), 45e-3); // 1000000 35e-3);
|
|
ASSERT_NEAR(2.906, mean.e<double>(0), 45e-3); // 1000000 35e-3);
|
|
|
|
|
|
RandomGenerator rng(1234, 1234);
|
|
NDArray probs('c', { batchValue, ClassValue }, { 1., 1.5, 2., 2.5, 3. }, sd::DataType::FLOAT32);
|
|
NDArray output('c', { batchValue, Samples }, sd::DataType::INT64);
|
|
|
|
ASSERT_EQ(Status::OK(), op.execute(rng, { &probs, &samples }, { &output }, {}, { 0, INT64 }, {}, {}, false));
|
|
|
|
NDArray counts('c', { ClassValue }, { 0., 0, 0, 0, 0 }, sd::DataType::DOUBLE);
|
|
|
|
for (int i = 0; i < output.lengthOf(); i++) {
|
|
auto value = output.e<Nd4jLong>(i);
|
|
ASSERT_TRUE(value >= 0 && value < ClassValue);
|
|
double* z = counts.bufferAsT<double>();
|
|
z[value] += 1;
|
|
}
|
|
|
|
for (int i = 0; i < counts.lengthOf(); i++) {
|
|
auto c = counts.e<double>(i);
|
|
auto p = probExpect.e<double>(i);
|
|
|
|
ASSERT_NEAR((c / Samples), p, 4e-3); // 1000000 3e-3);
|
|
}
|
|
|
|
deviation = output.varianceNumber(variance::SummaryStatsStandardDeviation, false);
|
|
mean = output.meanNumber();
|
|
ASSERT_NEAR(1.2175, deviation.e<double>(0), 5e-3); // 1000000 3e-3);
|
|
ASSERT_NEAR(2.906, mean.e<double>(0), 5e-3); // 1000000 3e-3);
|
|
}
|