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cavis/libnd4j/include/ops/random_ops.h

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://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.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author raver119@gmail.com
//
#ifndef LIBND4J_RANDOM_OPS_H
#define LIBND4J_RANDOM_OPS_H
#ifdef __CUDACC__
#define random_def __device__ __host__ inline static
#else
#define random_def inline static
#endif
// since we can't inherit/overwrite static methods - we just define default impls
#define method_idx random_def T op(Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator* rng, T *extraParams) { return -1.0f; }
#define method_X random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator* rng, T *extraParams) { return -2.0f; }
#define method_XY random_def T op(T valueX, T valueY, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator* rng, T *extraParams) { return -3.0f; }
#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) { }
#ifdef __CUDACC__
#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) { }
#else
#define no_exec_special_cuda
#endif
#include <helpers/helper_generator.h>
#include <graph/RandomGenerator.h>
#include <array/DataTypeUtils.h>
namespace randomOps {
/**
* This Op merges two arrays per-element, if probability meets threshold
*/
template<typename T>
class ProbablisticMerge {
public:
no_exec_special
no_exec_special_cuda
method_idx
method_X
random_def T op(T valueX, T valueY, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
T threshold = extraParams[0];
T randVal = helper->relativeT<T>(idx);
return randVal <= threshold ? valueY : valueX;
}
};
/**
* This Op produces random values within specified boundaries. Disribution is uniform
*/
template<typename T>
class UniformDistribution {
public:
no_exec_special
no_exec_special_cuda
method_XY
method_X
random_def T op(Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
return helper->relativeT<T>(idx, extraParams[0], extraParams[1]);
}
};
/**
* This op produces single bernoulli trial
*/
template <typename T>
class BernoulliDistribution {
public:
no_exec_special
no_exec_special_cuda
method_XY
random_def T op(Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
return extraParams[0] >= helper->relativeT<T>(idx) ? (T) 1.0f : (T) 0.0f;
}
random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
return valueX >= helper->relativeT<T>(idx) ? (T) 1.0f : (T) 0.0f;
}
};
/**
* This op produces single bernoulli trial
*/
template <typename T>
class ExponentialDistribution {
public:
no_exec_special
no_exec_special_cuda
method_XY
random_def T op(Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
T lambda = extraParams[0];
T x = helper->relativeT(idx, -nd4j::DataTypeUtils::template max<T>() / 10 , nd4j::DataTypeUtils::template max<T>() / 10);
return x <= (T)0.f ? (T)0.f : (T)1.f - nd4j::math::nd4j_pow<T, T, T>((T) M_E, -(lambda * x));
}
random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
T lambda = extraParams[0];
return valueX <= (T)0.f ? (T)0.f : (T)1.f - nd4j::math::nd4j_pow<T, T, T>((T) M_E, -(lambda * valueX));
}
};
template <typename T>
class PoissonDistribution {
public:
no_exec_special
no_exec_special_cuda
method_XY
random_def T op(Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
T lambda = extraParams[0];
T x = helper->relativeT(idx, -nd4j::DataTypeUtils::template max<T>() / 10 , nd4j::DataTypeUtils::template max<T>() / 10);
return x <= (T)0.f ? (T)0.f : nd4j::math::nd4j_igammac<T,T,T>(nd4j::math::nd4j_floor<T,T>(x), lambda);
}
random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
T lambda = extraParams[0];
return valueX <= (T)0.f ? (T)0.f : (T)nd4j::math::nd4j_igammac<T,T,T>(nd4j::math::nd4j_floor<T,T>(valueX), lambda);
}
};
template <typename T>
class GammaDistribution {
public:
no_exec_special
no_exec_special_cuda
method_XY
random_def T op(Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
T alpha = extraParams[0];
T beta = extraParams[1];
T x = helper->relativeT(idx, -nd4j::DataTypeUtils::template max<T>() / 10 , nd4j::DataTypeUtils::template max<T>() / 10);
return x <= (T)0.f ? (T)0.f : nd4j::math::nd4j_igamma<T,T,T>(alpha, x * beta);
}
random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
T alpha = extraParams[0];
T beta = extraParams[1];
return valueX <= (T)0.f ? (T)0.f : nd4j::math::nd4j_igamma<T,T,T>(alpha, beta * valueX);
}
};
/**
* Basic DropOut/DropConnect Op
*/
template<typename T>
class DropOut {
public:
no_exec_special
no_exec_special_cuda
method_idx
method_XY
// please note: prob is chance to retain original value
random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
T randVal = helper->relativeT<T>(idx);
return randVal >= extraParams[0] ? (T) 0.0f : valueX;
}
};
template<typename T>
class AlphaDropOut {
public:
no_exec_special
no_exec_special_cuda
method_idx
method_XY
// please note: prob is chance to retain original value
random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
T randVal = helper->relativeT<T>(idx);
// extraParams[0] == p
// [1] = a
// [2] = b
// [3] = alphaPrime
return randVal >= extraParams[0] ? (T) extraParams[1] * extraParams[3] + extraParams[2] : extraParams[1] * valueX + extraParams[2];
}
};
/**
* Inverted DropOut implementation, used in DL4j
*/
template<typename T>
class DropOutInverted {
public:
no_exec_special
no_exec_special_cuda
method_idx
method_XY
// please note: prob is chance to retain original value
random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
T prob = extraParams[0];
T randVal = helper->relativeT<T>(idx);
return randVal >= prob ? (T) 0.0f : valueX / prob;
}
};
template<typename T>
class Linspace {
public:
no_exec_special
no_exec_special_cuda
method_X
method_XY
random_def T op(Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
T from = extraParams[0];
T to = extraParams[1];
T step = extraParams[2];
if (step == static_cast<T>(0.0f)) {
step = (T) idx / ((T)length - (T) 1.0f);
return from * ((T) 1.0f - step) + step * to;
}
return from + (idx * step);
}
};
template <typename T>
class ExponentialDistributionInv { // inverse exponential distribution
public:
no_exec_special
no_exec_special_cuda
method_XY
random_def T op(Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
T lambda = extraParams[0];
T x = helper->relativeT(idx, nd4j::DataTypeUtils::template min<T>(), (T)1.f);
return -nd4j::math::nd4j_log<T, T>((T)1.f - x) / lambda;
}
random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) {
T lambda = extraParams[0];
return -nd4j::math::nd4j_log<T, T>((T)1.f - valueX) / lambda; // valueX must be within (0, 1]
}
};
}
#endif //LIBND4J_RANDOM_OPS_H
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