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// $Id$
//
// Copyright (C) 2007-2008 Greg Landrum
//
// @@ All Rights Reserved @@
// This file is part of the RDKit.
// The contents are covered by the terms of the BSD license
// which is included in the file license.txt, found at the root
// of the RDKit source tree.
//
#include <RDBoost/python.h>
#include <RDGeneral/BoostStartInclude.h>
#include <boost/cstdint.hpp>
#include <RDGeneral/BoostEndInclude.h>
#include <RDGeneral/types.h>
#include <RDGeneral/Invariant.h>
#include <RDBoost/PySequenceHolder.h>
#include <DataStructs/SparseIntVect.h>
using namespace RDKit;
namespace {
template <typename IndexType>
python::object SIVToBinaryText(const SparseIntVect<IndexType> &siv) {
std::string res = siv.toString();
python::object retval = python::object(
python::handle<>(PyBytes_FromStringAndSize(res.c_str(), res.length())));
return retval;
}
}
template <typename IndexType>
struct siv_pickle_suite : python::pickle_suite {
static python::tuple getinitargs(const SparseIntVect<IndexType> &self) {
return python::make_tuple(SIVToBinaryText(self));
};
};
namespace {
template <typename IndexType>
void pyUpdateFromSequence(SparseIntVect<IndexType> &vect, python::object &seq) {
PySequenceHolder<IndexType> seqL(seq);
for (unsigned int i = 0; i < seqL.size(); ++i) {
IndexType idx = seqL[i];
vect.setVal(idx, vect[idx] + 1);
}
}
template <typename IndexType>
python::dict pyGetNonzeroElements(SparseIntVect<IndexType> &vect) {
python::dict res;
auto iter = vect.getNonzeroElements().begin();
while (iter != vect.getNonzeroElements().end()) {
res[iter->first] = iter->second;
++iter;
}
return res;
}
template <typename T>
python::list BulkDice(const T &siv1, python::list sivs, bool returnDistance) {
python::list res;
unsigned int nsivs = python::extract<unsigned int>(sivs.attr("__len__")());
for (unsigned int i = 0; i < nsivs; ++i) {
double simVal;
const T &siv2 = python::extract<T>(sivs[i])();
simVal = DiceSimilarity(siv1, siv2, returnDistance);
res.append(simVal);
}
return res;
}
template <typename T>
python::list BulkTanimoto(const T &siv1, python::list sivs,
bool returnDistance) {
python::list res;
unsigned int nsivs = python::extract<unsigned int>(sivs.attr("__len__")());
for (unsigned int i = 0; i < nsivs; ++i) {
double simVal;
const T &siv2 = python::extract<T>(sivs[i])();
simVal = TanimotoSimilarity(siv1, siv2, returnDistance);
res.append(simVal);
}
return res;
}
template <typename T>
python::list BulkTversky(const T &siv1, python::list sivs, double a, double b,
bool returnDistance) {
python::list res;
unsigned int nsivs = python::extract<unsigned int>(sivs.attr("__len__")());
for (unsigned int i = 0; i < nsivs; ++i) {
double simVal;
const T &siv2 = python::extract<T>(sivs[i])();
simVal = TverskySimilarity(siv1, siv2, a, b, returnDistance);
res.append(simVal);
}
return res;
}
}
std::string sparseIntVectDoc =
"A container class for storing integer\n\
values within a particular range.\n\
\n\
The length of the vector is set at construction time.\n\
\n\
As you would expect, _SparseIntVects_ support a set of binary operations\n\
so you can do things like:\n\
Arithmetic:\n\
siv1 += siv2\n\
siv3 = siv1 + siv2\n\
siv1 -= siv3\n\
siv3 = siv1 - siv2\n\
\"Fuzzy\" binary operations:\n\
siv3 = siv1 & siv2 the result contains the smallest value in each entry\n\
siv3 = siv1 | siv2 the result contains the largest value in each entry\n\
\n\
Elements can be set and read using indexing (i.e. siv[i] = 4 or val=siv[i])\n\
\n";
struct sparseIntVec_wrapper {
template <typename IndexType>
static void wrapOne(const char *className) {
python::class_<SparseIntVect<IndexType>,
boost::shared_ptr<SparseIntVect<IndexType> > >(
className, sparseIntVectDoc.c_str(),
python::init<IndexType>("Constructor"))
.def(python::init<std::string>())
// Note: we cannot support __len__ because, at least at the moment
// (BPL v1.34.1), it must return an int.
.def("__setitem__", &SparseIntVect<IndexType>::setVal,
"Set the value at a specified location")
.def("__getitem__", &SparseIntVect<IndexType>::getVal,
"Get the value at a specified location")
.def(python::self & python::self)
.def(python::self | python::self)
.def(python::self - python::self)
.def(python::self -= python::self)
.def(python::self + python::self)
.def(python::self += python::self)
.def(python::self == python::self)
.def(python::self != python::self)
//.def(python::self - int())
.def(python::self -= int())
//.def(python::self + int())
.def(python::self += int())
//.def(python::self / int())
.def(python::self /= int())
//.def(python::self * int())
.def(python::self *= int())
.def("GetTotalVal", &SparseIntVect<IndexType>::getTotalVal,
(python::args("useAbs") = false),
"Get the sum of the values in the vector, basically L1 norm")
.def("GetLength", &SparseIntVect<IndexType>::getLength,
"Returns the length of the vector")
.def("ToBinary", &SIVToBinaryText<IndexType>,
"returns a binary (pickle) representation of the vector")
.def("UpdateFromSequence", &pyUpdateFromSequence<IndexType>,
"update the vector based on the values in the list or tuple")
.def("GetNonzeroElements", &pyGetNonzeroElements<IndexType>,
"returns a dictionary of the nonzero elements")
.def_pickle(siv_pickle_suite<IndexType>());
python::def(
"DiceSimilarity", &DiceSimilarity<IndexType>,
(python::args("siv1"), python::args("siv2"),
python::args("returnDistance") = false, python::args("bounds") = 0.0),
"return the Dice similarity between two vectors");
python::def("BulkDiceSimilarity", &BulkDice<SparseIntVect<IndexType> >,
(python::args("v1"), python::args("v2"),
python::args("returnDistance") = false),
"return the Dice similarities between one vector and a "
"sequence of others");
python::def(
"TanimotoSimilarity", &TanimotoSimilarity<IndexType>,
(python::args("siv1"), python::args("siv2"),
python::args("returnDistance") = false, python::args("bounds") = 0.0),
"return the Tanimoto similarity between two vectors");
python::def("BulkTanimotoSimilarity",
&BulkTanimoto<SparseIntVect<IndexType> >,
(python::args("v1"), python::args("v2"),
python::args("returnDistance") = false),
"return the Tanimoto similarities between one vector and a "
"sequence of others");
python::def("TverskySimilarity", &TverskySimilarity<IndexType>,
(python::args("siv1"), python::args("siv2"), python::args("a"),
python::args("b"), python::args("returnDistance") = false,
python::args("bounds") = 0.0),
"return the Tversky similarity between two vectors");
python::def("BulkTverskySimilarity",
&BulkTversky<SparseIntVect<IndexType> >,
(python::args("v1"), python::args("v2"), python::args("a"),
python::args("b"), python::args("returnDistance") = false),
"return the Tversky similarities between one vector and a "
"sequence of others");
}
static void wrap() {
wrapOne<boost::int32_t>("IntSparseIntVect");
wrapOne<boost::int64_t>("LongSparseIntVect");
wrapOne<boost::uint32_t>("UIntSparseIntVect");
wrapOne<boost::uint64_t>("ULongSparseIntVect");
}
};
void wrap_sparseIntVect() { sparseIntVec_wrapper::wrap(); }
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