kmeans: add naive kmeans impl

This commit is contained in:
Danny Robson 2018-04-18 21:48:24 +10:00
parent dcd789a075
commit f31a344912
3 changed files with 105 additions and 0 deletions

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@ -303,6 +303,7 @@ list (
json2/personality/rfc7519.hpp json2/personality/rfc7519.hpp
json2/tree.cpp json2/tree.cpp
json2/tree.hpp json2/tree.hpp
kmeans.hpp
library.hpp library.hpp
log.cpp log.cpp
log.hpp log.hpp
@ -509,6 +510,7 @@ if (TESTS)
job/queue job/queue
json_types json_types
json2/event json2/event
kmeans
maths maths
maths/fast maths/fast
matrix matrix

68
kmeans.hpp Normal file
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@ -0,0 +1,68 @@
/*
* Licensed 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 2018 Danny Robson <danny@nerdcruft.net>
*/
#pragma once
#include "debug.hpp"
#include "iterator.hpp"
#include "point.hpp"
#include <iterator>
namespace util {
// a simplistic implementation of Lloyd's algorithm
//
// returns index of the closest output for each input
template <typename OutputT, typename InputT>
std::vector<size_t>
kmeans (util::view<InputT> src, util::view<OutputT> dst)
{
CHECK_GE (src.size (), dst.size ());
using coord_t = typename std::iterator_traits<InputT>::value_type;
const int iterations = 100;
std::vector<coord_t> means (src.begin (), src.begin () + dst.size ());
std::vector<coord_t> accum (dst.size ());
std::vector<size_t> count (dst.size ());
std::vector<size_t> closest (src.size ());
for (auto i = 0; i < iterations; ++i) {
std::fill (std::begin (accum), std::end (accum), 0);
std::fill (std::begin (count), std::end (count), 0);
for (auto const& [j,p]: util::izip (src)) {
size_t bucket = 0;
for (size_t k = 1; k < dst.size (); ++k) {
if (norm2 (p - means[k]) < norm2 (p - means[bucket]))
bucket = k;
}
accum[bucket] += p;
count[bucket] += 1;
closest[j] = bucket;
}
for (size_t j = 0; j < dst.size (); ++j)
means[j] = accum[j] / count[j];
}
std::copy (std::begin (means), std::end (means), std::begin (dst));
return closest;
}
}

35
test/kmeans.cpp Normal file
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#include "tap.hpp"
#include "kmeans.hpp"
#include <cruft/util/point.hpp>
///////////////////////////////////////////////////////////////////////////////
int
main ()
{
util::TAP::logger tap;
// create one point and check it 'converges' to this one point
{
const std::array<util::point3f,1> p { {{1,2,3}} };
std::array<util::point3f,1> q;
util::kmeans (util::view{p}, util::view{q});
tap.expect_eq (p, q, "single point, single k");
}
// create two vectors, check if the mean converges to their average
{
const std::array<util::vector3f,2> p {{
{1}, {2}
}};
std::array<util::vector3f,1> q;
util::kmeans (util::view{p}, util::view{q});
tap.expect_eq (q[0], (p[0]+p[1])/2, "two point, single k");
}
return tap.status ();
}