shugeo
GitHub
commit
3662657d5c
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@ -78,7 +78,9 @@
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(28, LogicalXor) ,\
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(29, LogicalNot) ,\
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(30, LogicalAnd), \
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(31, DivideNoNan)
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(31, DivideNoNan), \
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(32, IGamma), \
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(33, IGammac)
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// these ops return same data type as input
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#define TRANSFORM_SAME_OPS \
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@ -245,7 +247,9 @@
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(43, TruncateMod) ,\
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(44, SquaredReverseSubtract) ,\
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(45, ReversePow), \
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(46, DivideNoNan)
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(46, DivideNoNan), \
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(47, IGamma), \
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(48, IGammac)
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@ -380,7 +384,9 @@
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(35, AMinPairwise) ,\
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(36, TruncateMod), \
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(37, ReplaceNans), \
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(38, DivideNoNan)
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(38, DivideNoNan), \
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(39, IGamma), \
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(40, IGammac)
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@ -49,6 +49,8 @@ namespace nd4j {
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static BroadcastOpsTuple DivideNoNan();
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static BroadcastOpsTuple Multiply();
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static BroadcastOpsTuple Subtract();
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static BroadcastOpsTuple IGamma();
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static BroadcastOpsTuple IGammac();
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};
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}
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@ -0,0 +1,59 @@
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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 sgazeos@gmail.com
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//
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_igamma)
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#include <ops/declarable/generic/helpers/BroadcastHelper.h>
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#include <ops/declarable/CustomOperations.h>
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namespace nd4j {
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namespace ops {
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BROADCASTABLE_OP_IMPL(igamma, 0, 0) {
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auto x = INPUT_VARIABLE(0);
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auto y = INPUT_VARIABLE(1);
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auto z = OUTPUT_VARIABLE(0);
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BROADCAST_CHECK_EMPTY(x,y,z);
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//REQUIRE_TRUE(!y->isB(), 0, "Pairwise OP: you can't divide by bool array!");
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// auto tZ = BroadcastHelper::broadcastApply({scalar::IGamma, pairwise::IGamma, broadcast::IGamma}, x, y, z);
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auto tZ = BroadcastHelper::broadcastApply(BroadcastOpsTuple::IGamma(), x, y, z);
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if (tZ == nullptr)
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return ND4J_STATUS_KERNEL_FAILURE;
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else if (tZ != z) {
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OVERWRITE_RESULT(tZ);
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}
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return Status::OK();
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}
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DECLARE_TYPES(igamma) {
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getOpDescriptor()
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->setAllowedInputTypes(0, {ALL_FLOATS})
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->setAllowedInputTypes(1, {ALL_FLOATS})
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->setAllowedOutputTypes(0, {ALL_FLOATS});
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}
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}
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}
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#endif
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@ -0,0 +1,58 @@
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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 sgazeos@gmail.com
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//
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_igammac)
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#include <ops/declarable/generic/helpers/BroadcastHelper.h>
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#include <ops/declarable/CustomOperations.h>
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namespace nd4j {
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namespace ops {
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BROADCASTABLE_OP_IMPL(igammac, 0, 0) {
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auto x = INPUT_VARIABLE(0);
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auto y = INPUT_VARIABLE(1);
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auto z = OUTPUT_VARIABLE(0);
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BROADCAST_CHECK_EMPTY(x,y,z);
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//REQUIRE_TRUE(!y->isB(), 0, "Pairwise OP: you can't divide by bool array!");
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// auto tZ = BroadcastHelper::broadcastApply({scalar::IGammac, pairwise::IGammac, broadcast::IGammac}, x, y, z);
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auto tZ = BroadcastHelper::broadcastApply(BroadcastOpsTuple::IGammac(), x, y, z);
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if (tZ == nullptr)
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return ND4J_STATUS_KERNEL_FAILURE;
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else if (tZ != z) {
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OVERWRITE_RESULT(tZ);
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}
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return Status::OK();
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}
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DECLARE_TYPES(igammac) {
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getOpDescriptor()
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->setAllowedInputTypes(0, {ALL_FLOATS})
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->setAllowedInputTypes(1, {ALL_FLOATS})
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->setAllowedOutputTypes(0, {ALL_FLOATS});
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}
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}
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}
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#endif
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@ -357,6 +357,28 @@ namespace nd4j {
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#if NOT_EXCLUDED(OP_Pow)
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DECLARE_BROADCASTABLE_OP(Pow, 0, 0);
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#endif
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/**
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* Broadcastable igamma implementation
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*
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* igamma(a, x) = gamma(а, x) / Gamma(a) - Gamma distribution function P(a,x)
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* Gamma(a) = int from 0 to infinity { t ^ {a - 1} e^{-t}dt }
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* gamma(a, x) = int from 0 to x { t ^ {a - 1} e^{-t}dt }
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* @tparam T
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*/
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#if NOT_EXCLUDED(OP_igamma)
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DECLARE_BROADCASTABLE_OP(igamma, 0, 0);
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#endif
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/**
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* Broadcastable igammac implementation
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* igammac(a, x) = Gamma(a,x)/Gamma(а) - Gamma distribution function Q(a,x)
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* Gamma(a) = int from 0 to infinity { t ^ {a - 1} e^{-t}dt }
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* Gamma(a, x) = int from x to infinity { t ^ {a - 1} e^{-t}dt }
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* @tparam T
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*/
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#if NOT_EXCLUDED(OP_igammac)
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DECLARE_BROADCASTABLE_OP(igammac, 0, 0);
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#endif
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}
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}
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@ -48,4 +48,11 @@ namespace nd4j {
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BroadcastOpsTuple BroadcastOpsTuple::Subtract() {
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return custom(nd4j::scalar::Subtract, nd4j::pairwise::Subtract, nd4j::broadcast::Subtract);
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}
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BroadcastOpsTuple BroadcastOpsTuple::IGamma() {
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return custom(nd4j::scalar::IGamma, nd4j::pairwise::IGamma, nd4j::broadcast::IGamma);
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}
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BroadcastOpsTuple BroadcastOpsTuple::IGammac() {
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return custom(nd4j::scalar::IGammac, nd4j::pairwise::IGammac, nd4j::broadcast::IGammac);
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}
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}
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@ -1482,6 +1482,52 @@ namespace simdOps {
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};
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template <typename X, typename Y, typename Z>
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class IGamma {
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public:
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no_op_exec_special
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no_op_exec_special_cuda
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op_def static Z op(X d1, Z *params) {
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return nd4j::math::nd4j_igamma<X, X, Z>(d1, params[0]);
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}
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op_def static Z op(X d1, Y d2) {
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return nd4j::math::nd4j_igamma<X, Y, Z>(d1, d2);
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}
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op_def static Z op(X d1, Y d2, Z *params) {
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return nd4j::math::nd4j_igamma<X, Y, Z>(d1, d2);
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}
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op_def static Z op(X d1) {
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return d1;
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}
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};
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template <typename X, typename Y, typename Z>
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class IGammac {
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public:
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no_op_exec_special
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no_op_exec_special_cuda
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op_def static Z op(X d1, Z *params) {
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return nd4j::math::nd4j_igammac<X, X, Z>(d1, params[0]);
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}
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op_def static Z op(X d1, Y d2) {
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return nd4j::math::nd4j_igammac<X, Y, Z>(d1, d2);
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}
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op_def static Z op(X d1, Y d2, Z *params) {
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return nd4j::math::nd4j_igammac<X, Y, Z>(d1, d2);
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}
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op_def static Z op(X d1) {
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return d1;
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}
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};
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template <typename X>
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class Round {
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public:
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@ -223,6 +223,12 @@ namespace nd4j {
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return nd4j_sgn<T, Z>(val);
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}
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template<typename X, typename Z>
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math_def inline Z nd4j_gamma(X a);
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template<typename X, typename Z>
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math_def inline Z nd4j_lgamma(X x);
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//#ifndef __CUDACC__
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/*
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template<>
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@ -656,9 +662,56 @@ namespace nd4j {
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return p_pow<Z>(static_cast<Z>(val), static_cast<Z>(val2));
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}
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/**
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* LogGamma(a) - float point extension of ln(n!)
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**/
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template <typename X, typename Z>
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math_def inline Z nd4j_lgamma(X x) {
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// if (x <= X(0.0))
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// {
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// std::stringstream os;
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// os << "Logarithm of Gamma has sence only for positive values, but " << x << " was given.";
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// throw std::invalid_argument( os.str() );
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// }
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if (x < X(12.0)) {
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return nd4j_log<Z,Z>(nd4j_gamma<X,Z>(x));
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}
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// Abramowitz and Stegun 6.1.41
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// Asymptotic series should be good to at least 11 or 12 figures
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// For error analysis, see Whittiker and Watson
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// A Course in Modern Analysis (1927), page 252
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static const double c[8] = {
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1.0/12.0,
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-1.0/360.0,
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1.0/1260.0,
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-1.0/1680.0,
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1.0/1188.0,
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-691.0/360360.0,
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1.0/156.0,
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-3617.0/122400.0
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};
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double z = Z(1.0 / Z(x * x));
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double sum = c[7];
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for (int i = 6; i >= 0; i--) {
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sum *= z;
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sum += c[i];
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}
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double series = sum / Z(x);
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static const double halfLogTwoPi = 0.91893853320467274178032973640562;
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return Z((double(x) - 0.5) * nd4j_log<X,double>(x) - double(x) + halfLogTwoPi + series);
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}
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template<typename T>
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template<typename T>
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math_def inline T nd4j_re(T val1, T val2) {
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if (val1 == (T) 0.0f && val2 == (T) 0.0f)
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return (T) 0.0f;
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@ -731,7 +784,127 @@ namespace nd4j {
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template<typename T>
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math_def inline void nd4j_swap(T &val1, T &val2) {
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T temp = val1; val1=val2; val2=temp;
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};
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};
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template <typename X, typename Z>
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math_def inline Z nd4j_gamma(X a) {
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// nd4j_lgamma<X,Z>(a);
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// return (Z)std::tgamma(a);
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// Split the function domain into three intervals:
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// (0, 0.001), [0.001, 12), and (12, infinity)
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///////////////////////////////////////////////////////////////////////////
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// First interval: (0, 0.001)
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//
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// For small a, 1/Gamma(a) has power series a + gamma a^2 - ...
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// So in this range, 1/Gamma(a) = a + gamma a^2 with error on the order of a^3.
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// The relative error over this interval is less than 6e-7.
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const double eulerGamma = 0.577215664901532860606512090; // Euler's gamma constant
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if (a < X(0.001))
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return Z(1.0 / ((double)a * (1.0 + eulerGamma * (double)a)));
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///////////////////////////////////////////////////////////////////////////
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// Second interval: [0.001, 12)
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if (a < X(12.0)) {
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// The algorithm directly approximates gamma over (1,2) and uses
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// reduction identities to reduce other arguments to this interval.
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double y = (double)a;
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int n = 0;
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bool argWasLessThanOne = y < 1.0;
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// Add or subtract integers as necessary to bring y into (1,2)
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// Will correct for this below
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if (argWasLessThanOne) {
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y += 1.0;
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}
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else {
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n = static_cast<int>(floor(y)) - 1; // will use n later
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y -= n;
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}
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// numerator coefficients for approximation over the interval (1,2)
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static const double p[] = {
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-1.71618513886549492533811E+0,
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2.47656508055759199108314E+1,
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-3.79804256470945635097577E+2,
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6.29331155312818442661052E+2,
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8.66966202790413211295064E+2,
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-3.14512729688483675254357E+4,
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-3.61444134186911729807069E+4,
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6.64561438202405440627855E+4
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};
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// denominator coefficients for approximation over the interval (1,2)
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static const double q[] = {
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-3.08402300119738975254353E+1,
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3.15350626979604161529144E+2,
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-1.01515636749021914166146E+3,
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-3.10777167157231109440444E+3,
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2.25381184209801510330112E+4,
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4.75584627752788110767815E+3,
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-1.34659959864969306392456E+5,
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-1.15132259675553483497211E+5
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};
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double num = 0.0;
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double den = 1.0;
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double z = y - 1;
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for (auto i = 0; i < 8; i++) {
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num = (num + p[i]) * z;
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den = den * z + q[i];
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}
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double result = num / den + 1.0;
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// Apply correction if argument was not initially in (1,2)
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if (argWasLessThanOne) {
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// Use identity gamma(z) = gamma(z+1)/z
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// The variable "result" now holds gamma of the original y + 1
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// Thus we use y-1 to get back the orginal y.
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result /= (y - 1.0);
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}
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else {
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// Use the identity gamma(z+n) = z*(z+1)* ... *(z+n-1)*gamma(z)
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for (auto i = 0; i < n; i++)
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result *= y++;
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}
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return Z(result);
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}
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///////////////////////////////////////////////////////////////////////////
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// Third interval: [12, infinity)
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if (a > 171.624) {
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// Correct answer too large to display. Force +infinity.
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return Z(DOUBLE_MAX_VALUE);
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//DataTypeUtils::infOrMax<Z>();
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}
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return nd4j::math::nd4j_exp<Z,Z>(nd4j::math::nd4j_lgamma<X,Z>(a));
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}
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|
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template <typename X, typename Y, typename Z>
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math_def inline Z nd4j_igamma(X a, Y x) {
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Z aim = nd4j_pow<X, X, Z>(x, a) / (nd4j_exp<X, Z>(x) * nd4j_gamma<Y, Z>(a));
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auto sum = Z(0.);
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auto denom = Z(1.);
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for (int i = 0; Z(1./denom) > Z(1.0e-12); i++) {
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denom *= (a + i);
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sum += nd4j_pow<X, int, Z>(x, i) / denom;
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}
|
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return aim * sum;
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}
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|
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template <typename X, typename Y, typename Z>
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math_def inline Z nd4j_igammac(X a, Y x) {
|
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return Z(1.) - nd4j_igamma<X, Y, Z>(a, x);
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}
|
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|
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#ifdef __CUDACC__
|
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namespace atomics {
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|
|
@ -1473,4 +1646,4 @@ inline __device__ bfloat16 nd4j_atomicDiv<bfloat16>(bfloat16* address, bfloat16
|
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|
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}
|
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|
||||
#endif /* TEMPLATEMATH_H_ */
|
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#endif /* TEMPLATEMATH_H_ */
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@ -537,6 +537,50 @@ TEST_F(DeclarableOpsTests10, atan2_test6) {
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delete result;
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}
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//////////////////////////////////////////////////////////////////////////////
|
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TEST_F(DeclarableOpsTests10, IGamma_Test1) {
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|
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auto y = NDArrayFactory::create<double>('c', {1, 3, 4}, {1.1 , 2.1 , 3.1 ,4.1 , 5.1 , 6.1 ,7.1 ,8.1 ,9.1 ,10.1,11.1 ,12.1});
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auto x = NDArrayFactory::create<double>('c', { 4}, {1.2, 2.2, 3.2, 4.2});
|
||||
|
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auto exp = NDArrayFactory::create<double>('c', {1,3,4}, {
|
||||
0.659917, 0.61757898, 0.59726304, 0.58478117,
|
||||
0.0066205109, 0.022211598, 0.040677428, 0.059117373,
|
||||
0.0000039433403, 0.000086064574, 0.000436067, 0.0012273735});
|
||||
|
||||
nd4j::ops::igamma op;
|
||||
auto result = op.execute({&y, &x}, {}, {}, {});
|
||||
ASSERT_EQ(ND4J_STATUS_OK, result->status());
|
||||
auto z = result->at(0);
|
||||
// z->printBuffer("OUtput");
|
||||
// exp.printBuffer("EXpect");
|
||||
ASSERT_TRUE(exp.isSameShape(z));
|
||||
ASSERT_TRUE(exp.equalsTo(z));
|
||||
|
||||
delete result;
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////////
|
||||
TEST_F(DeclarableOpsTests10, IGamma_Test2) {
|
||||
|
||||
auto y = NDArrayFactory::create<double>('c', {1, 3, 4}, {1.1 , 2.1 , 3.1 ,4.1 , 5.1 , 6.1 ,
|
||||
7.1 ,8.1 ,9.1 ,10.1,11.1 ,12.1});
|
||||
auto x = NDArrayFactory::create<double>('c', { 4}, {1.2, 2.2, 3.2, 4.2});
|
||||
auto exp = NDArrayFactory::create<double>('c', {1,3,4}, {0.340083, 0.382421, 0.402737, 0.415221,
|
||||
0.993379, 0.977788, 0.959323, 0.940883,
|
||||
0.999996, 0.999914, 0.999564, 0.998773});
|
||||
|
||||
nd4j::ops::igammac op;
|
||||
auto result = op.execute({&y, &x}, {}, {}, {});
|
||||
ASSERT_EQ(ND4J_STATUS_OK, result->status());
|
||||
auto z = result->at(0);
|
||||
// z->printBuffer("OUtput");
|
||||
// exp.printBuffer("EXpect");
|
||||
ASSERT_TRUE(exp.isSameShape(z));
|
||||
ASSERT_TRUE(exp.equalsTo(z));
|
||||
|
||||
delete result;
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////////
|
||||
TEST_F(DeclarableOpsTests10, range_test10) {
|
||||
|
|
|
|||
Loading…
Reference in New Issue