243 lines
7.7 KiB
C
243 lines
7.7 KiB
C
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/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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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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#ifndef LIBND4J_RANDOM_OPS_H
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#define LIBND4J_RANDOM_OPS_H
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#ifdef __CUDACC__
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#define random_def __device__ __host__ inline static
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#else
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#define random_def inline static
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#endif
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// since we can't inherit/overwrite static methods - we just define default impls
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#define method_idx random_def T op(Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator* rng, T *extraParams) { return -1.0f; }
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#define method_X random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator* rng, T *extraParams) { return -2.0f; }
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#define method_XY random_def T op(T valueX, T valueY, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator* rng, T *extraParams) { return -3.0f; }
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#define no_exec_special static const bool requiresSpecial = false; static inline void specialOp(Nd4jPointer state, T *x, Nd4jLong *xShapeBuffer, T *y, Nd4jLong *yShapeBuffer, T *z, Nd4jLong *zShapeBuffer, T *extraArguments) { }
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#ifdef __CUDACC__
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#define no_exec_special_cuda __device__ static inline void specialOpCuda(Nd4jPointer state, T *x, Nd4jLong *xShapeBuffer, T *y, Nd4jLong *yShapeBuffer, T *z, Nd4jLong *zShapeBuffer, T *extraArguments) { }
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#else
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#define no_exec_special_cuda
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#endif
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#include <helpers/helper_generator.h>
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#include <graph/RandomGenerator.h>
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#include <array/DataTypeUtils.h>
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namespace randomOps {
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/**
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* This Op merges two arrays per-element, if probability meets threshold
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*/
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template<typename T>
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class ProbablisticMerge {
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public:
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no_exec_special
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no_exec_special_cuda
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method_idx
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method_X
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random_def T op(T valueX, T valueY, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
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T threshold = extraParams[0];
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T randVal = helper->relativeT<T>(idx);
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return randVal <= threshold ? valueY : valueX;
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}
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};
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/**
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* This Op produces random values within specified boundaries. Disribution is uniform
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*/
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template<typename T>
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class UniformDistribution {
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public:
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no_exec_special
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no_exec_special_cuda
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method_XY
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method_X
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random_def T op(Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
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return helper->relativeT<T>(idx, extraParams[0], extraParams[1]);
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}
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};
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/**
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* This op produces single bernoulli trial
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*/
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template <typename T>
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class BernoulliDistribution {
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public:
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no_exec_special
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no_exec_special_cuda
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method_XY
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random_def T op(Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
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return extraParams[0] >= helper->relativeT<T>(idx) ? (T) 1.0f : (T) 0.0f;
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}
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random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
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return valueX >= helper->relativeT<T>(idx) ? (T) 1.0f : (T) 0.0f;
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}
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};
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/**
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* This op produces single bernoulli trial
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*/
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template <typename T>
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class ExponentialDistribution {
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public:
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no_exec_special
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no_exec_special_cuda
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method_XY
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random_def T op(Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
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T lambda = extraParams[0];
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T x = helper->relativeT(idx, -nd4j::DataTypeUtils::template max<T>() / 10 , nd4j::DataTypeUtils::template max<T>() / 10);
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return x <= (T)0.f ? (T)0.f : (T)1.f - nd4j::math::nd4j_pow<T, T, T>((T) M_E, -(lambda * x));
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}
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random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
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T lambda = extraParams[0];
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return valueX <= (T)0.f ? (T)0.f : (T)1.f - nd4j::math::nd4j_pow<T, T, T>((T) M_E, -(lambda * valueX));
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}
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};
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/**
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* Basic DropOut/DropConnect Op
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*/
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template<typename T>
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class DropOut {
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public:
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no_exec_special
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no_exec_special_cuda
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method_idx
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method_XY
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// please note: prob is chance to retain original value
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random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
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T randVal = helper->relativeT<T>(idx);
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return randVal >= extraParams[0] ? (T) 0.0f : valueX;
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}
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};
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template<typename T>
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class AlphaDropOut {
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public:
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no_exec_special
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no_exec_special_cuda
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method_idx
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method_XY
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// please note: prob is chance to retain original value
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random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
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T randVal = helper->relativeT<T>(idx);
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// extraParams[0] == p
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// [1] = a
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// [2] = b
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// [3] = alphaPrime
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return randVal >= extraParams[0] ? (T) extraParams[1] * extraParams[3] + extraParams[2] : extraParams[1] * valueX + extraParams[2];
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}
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};
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/**
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* Inverted DropOut implementation, used in DL4j
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*/
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template<typename T>
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class DropOutInverted {
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public:
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no_exec_special
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no_exec_special_cuda
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method_idx
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method_XY
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// please note: prob is chance to retain original value
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random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
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T prob = extraParams[0];
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T randVal = helper->relativeT<T>(idx);
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return randVal >= prob ? (T) 0.0f : valueX / prob;
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}
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};
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template<typename T>
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class Linspace {
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public:
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no_exec_special
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no_exec_special_cuda
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method_X
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method_XY
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random_def T op(Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
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T from = extraParams[0];
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T to = extraParams[1];
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T step = extraParams[2];
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if (step == static_cast<T>(0.0f)) {
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step = (T) idx / ((T)length - (T) 1.0f);
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return from * ((T) 1.0f - step) + step * to;
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}
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return from + (idx * step);
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}
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};
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template <typename T>
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class ExponentialDistributionInv { // inverse exponential distribution
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public:
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no_exec_special
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no_exec_special_cuda
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method_XY
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random_def T op(Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
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T lambda = extraParams[0];
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T x = helper->relativeT(idx, nd4j::DataTypeUtils::template min<T>(), (T)1.f);
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return -nd4j::math::nd4j_log<T, T>((T)1.f - x) / lambda;
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}
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random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
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T lambda = extraParams[0];
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return -nd4j::math::nd4j_log<T, T>((T)1.f - valueX) / lambda; // valueX must be within (0, 1]
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}
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};
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}
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#endif //LIBND4J_RANDOM_OPS_H
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