| /* |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you 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. |
| */ |
| |
| /*! |
| * Copyright (c) 2016 by Contributors |
| * \file sample_op.cc |
| * \brief CPU Implementation of sample op |
| */ |
| |
| #include "./sample_op.h" |
| #include "../tensor/init_op.h" |
| |
| namespace mxnet { |
| namespace op { |
| |
| DMLC_REGISTER_PARAMETER(SampleUniformParam); |
| DMLC_REGISTER_PARAMETER(SampleNormalParam); |
| DMLC_REGISTER_PARAMETER(SampleGammaParam); |
| DMLC_REGISTER_PARAMETER(SampleExponentialParam); |
| DMLC_REGISTER_PARAMETER(SamplePoissonParam); |
| DMLC_REGISTER_PARAMETER(SampleNegBinomialParam); |
| DMLC_REGISTER_PARAMETER(SampleGenNegBinomialParam); |
| |
| #define MXNET_OPERATOR_REGISTER_SAMPLE(name, ParamType) \ |
| NNVM_REGISTER_OP(name) \ |
| .set_num_inputs(0) \ |
| .set_num_outputs(1) \ |
| .set_attr_parser(ParamParser<ParamType>) \ |
| .set_attr<nnvm::FInferShape>("FInferShape", InitShape<ParamType>) \ |
| .set_attr<nnvm::FInferType>("FInferType", SampleOpType<ParamType>) \ |
| .set_attr<FResourceRequest>("FResourceRequest", SampleResource) \ |
| .add_arguments(ParamType::__FIELDS__()) |
| |
| // Add "uniform" alias for backward compatibility |
| MXNET_OPERATOR_REGISTER_SAMPLE(_random_uniform, SampleUniformParam) |
| .add_alias("uniform") |
| .add_alias("random_uniform") |
| .describe(R"code(Draw random samples from a uniform distribution. |
| |
| .. note:: The existing alias ``uniform`` is deprecated. |
| |
| Samples are uniformly distributed over the half-open interval *[low, high)* |
| (includes *low*, but excludes *high*). |
| |
| Example:: |
| |
| uniform(low=0, high=1, shape=(2,2)) = [[ 0.60276335, 0.85794562], |
| [ 0.54488319, 0.84725171]] |
| |
| )code" ADD_FILELINE) |
| .set_attr<FInferStorageType>("FInferStorageType", InitStorageType<SampleUniformParam, true, false>) |
| .set_attr<FCompute>("FCompute<cpu>", Sample_<cpu, UniformSampler<cpu>>) |
| .set_attr<FComputeEx>("FComputeEx<cpu>", SampleEx_<cpu, UniformSampler<cpu>>); |
| |
| // Add "normal" alias for backward compatibility |
| MXNET_OPERATOR_REGISTER_SAMPLE(_random_normal, SampleNormalParam) |
| .add_alias("normal") |
| .add_alias("random_normal") |
| .describe(R"code(Draw random samples from a normal (Gaussian) distribution. |
| |
| .. note:: The existing alias ``normal`` is deprecated. |
| |
| Samples are distributed according to a normal distribution parametrized by *loc* (mean) and *scale* (standard deviation). |
| |
| Example:: |
| |
| normal(loc=0, scale=1, shape=(2,2)) = [[ 1.89171135, -1.16881478], |
| [-1.23474145, 1.55807114]] |
| )code" ADD_FILELINE) |
| .set_attr<FInferStorageType>("FInferStorageType", InitStorageType<SampleNormalParam, true, false>) |
| .set_attr<FCompute>("FCompute<cpu>", Sample_<cpu, NormalSampler<cpu>>) |
| .set_attr<FComputeEx>("FComputeEx<cpu>", SampleEx_<cpu, NormalSampler<cpu>>); |
| |
| MXNET_OPERATOR_REGISTER_SAMPLE(_random_gamma, SampleGammaParam) |
| .add_alias("random_gamma") |
| .describe(R"code(Draw random samples from a gamma distribution. |
| |
| Samples are distributed according to a gamma distribution parametrized by *alpha* (shape) and *beta* (scale). |
| |
| Example:: |
| |
| gamma(alpha=9, beta=0.5, shape=(2,2)) = [[ 7.10486984, 3.37695289], |
| [ 3.91697288, 3.65933681]] |
| )code" ADD_FILELINE) |
| .set_attr<FInferStorageType>("FInferStorageType", InitStorageType<SampleGammaParam, true, false>) |
| .set_attr<FCompute>("FCompute<cpu>", Sample_<cpu, GammaSampler<cpu>>) |
| .set_attr<FComputeEx>("FComputeEx<cpu>", SampleEx_<cpu, GammaSampler<cpu>>); |
| |
| MXNET_OPERATOR_REGISTER_SAMPLE(_random_exponential, SampleExponentialParam) |
| .add_alias("random_exponential") |
| .describe(R"code(Draw random samples from an exponential distribution. |
| |
| Samples are distributed according to an exponential distribution parametrized by *lambda* (rate). |
| |
| Example:: |
| |
| exponential(lam=4, shape=(2,2)) = [[ 0.0097189 , 0.08999364], |
| [ 0.04146638, 0.31715935]] |
| )code" ADD_FILELINE) |
| .set_attr<FInferStorageType>("FInferStorageType", |
| InitStorageType<SampleExponentialParam, true, false>) |
| .set_attr<FCompute>("FCompute<cpu>", Sample_<cpu, ExponentialSampler<cpu>>) |
| .set_attr<FComputeEx>("FComputeEx<cpu>", SampleEx_<cpu, ExponentialSampler<cpu>>); |
| |
| MXNET_OPERATOR_REGISTER_SAMPLE(_random_poisson, SamplePoissonParam) |
| .add_alias("random_poisson") |
| .describe(R"code(Draw random samples from a Poisson distribution. |
| |
| Samples are distributed according to a Poisson distribution parametrized by *lambda* (rate). |
| Samples will always be returned as a floating point data type. |
| |
| Example:: |
| |
| poisson(lam=4, shape=(2,2)) = [[ 5., 2.], |
| [ 4., 6.]] |
| )code" ADD_FILELINE) |
| .set_attr<FInferStorageType>("FInferStorageType", InitStorageType<SamplePoissonParam, true, false>) |
| .set_attr<FCompute>("FCompute<cpu>", Sample_<cpu, PoissonSampler<cpu>>) |
| .set_attr<FComputeEx>("FComputeEx<cpu>", SampleEx_<cpu, PoissonSampler<cpu>>); |
| |
| MXNET_OPERATOR_REGISTER_SAMPLE(_random_negative_binomial, SampleNegBinomialParam) |
| .add_alias("random_negative_binomial") |
| .describe(R"code(Draw random samples from a negative binomial distribution. |
| |
| Samples are distributed according to a negative binomial distribution parametrized by |
| *k* (limit of unsuccessful experiments) and *p* (failure probability in each experiment). |
| Samples will always be returned as a floating point data type. |
| |
| Example:: |
| |
| negative_binomial(k=3, p=0.4, shape=(2,2)) = [[ 4., 7.], |
| [ 2., 5.]] |
| )code" ADD_FILELINE) |
| .set_attr<FInferStorageType>("FInferStorageType", |
| InitStorageType<SampleNegBinomialParam, true, false>) |
| .set_attr<FCompute>("FCompute<cpu>", Sample_<cpu, NegativeBinomialSampler<cpu>>) |
| .set_attr<FComputeEx>("FComputeEx<cpu>", SampleEx_<cpu, NegativeBinomialSampler<cpu>>); |
| |
| MXNET_OPERATOR_REGISTER_SAMPLE(_random_generalized_negative_binomial, SampleGenNegBinomialParam) |
| .add_alias("random_generalized_negative_binomial") |
| .describe(R"code(Draw random samples from a generalized negative binomial distribution. |
| |
| Samples are distributed according to a generalized negative binomial distribution parametrized by |
| *mu* (mean) and *alpha* (dispersion). *alpha* is defined as *1/k* where *k* is the failure limit of the |
| number of unsuccessful experiments (generalized to real numbers). |
| Samples will always be returned as a floating point data type. |
| |
| Example:: |
| |
| generalized_negative_binomial(mu=2.0, alpha=0.3, shape=(2,2)) = [[ 2., 1.], |
| [ 6., 4.]] |
| )code" ADD_FILELINE) |
| .set_attr<FInferStorageType>("FInferStorageType", |
| InitStorageType<SampleGenNegBinomialParam, true, false>) |
| .set_attr<FCompute>("FCompute<cpu>", Sample_<cpu, GeneralizedNegativeBinomialSampler<cpu>>) |
| .set_attr<FComputeEx>("FComputeEx<cpu>", SampleEx_<cpu, GeneralizedNegativeBinomialSampler<cpu>>); |
| |
| } // namespace op |
| } // namespace mxnet |
