SparseVector (#140)

* Add SparseVector

temp

* Add gtest

* Some reworking of the sparse concept

* Change type of M from int to size_t

* Add const modifier

* Add needed declaration for accessing elements of _indices

* Add norm_squared, norm, longerThan

* Add test for all sparse vector functions

* Add missing const to slice's norm_squared, norm and longerThan

* Construction from Vector<M> and carray[N]

* try to fix ci

Co-authored-by: Julian Kent <julian@auterion.com>

.travis.yml

@@ -25,7 +25,7 @@ addons:
     packages:
     - clang
     - cmake
-    - g++
+    - g++-8
     - gcc
     - lcov
 

CMakeLists.txt

@@ -6,7 +6,7 @@ set(VERSION_PATCH "2")
 
 project(matrix CXX)
 
-set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_CXX_STANDARD 14)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 
 if (NOT CMAKE_BUILD_TYPE)

matrix/Slice.hpp

@@ -240,9 +240,9 @@ public:
         return res;
     }
 
-    Type norm_squared()
+    Type norm_squared() const
     {
-        Slice<Type, P, Q, M, N>& self = *this;
+        const Slice<Type, P, Q, M, N>& self = *this;
         Type accum(0);
         for (size_t i = 0; i < P; i++) {
             for (size_t j = 0; j < Q; j++) {
@@ -252,12 +252,12 @@ public:
         return accum;
     }
 
-    Type norm()
+    Type norm() const
     {
         return matrix::sqrt(norm_squared());
     }
 
-    bool longerThan(Type testVal)
+    bool longerThan(Type testVal) const
     {
         return norm_squared() > testVal*testVal;
     }

matrix/SparseVector.hpp

@@ -0,0 +1,160 @@
+/**
+ * @file SparseVector.hpp
+ *
+ * SparseVector class.
+ *
+ * @author Kamil Ritz <kritz@ethz.ch>
+ * @author Julian Kent <julian@auterion.com>
+ *
+ */
+
+#pragma once
+
+#include "math.hpp"
+
+namespace matrix {
+template<int N> struct force_constexpr_eval {
+    static const int value = N;
+};
+
+// Vector that only store nonzero elements,
+// which indices are specified as parameter pack
+template<typename Type, size_t M, int... Idxs>
+class SparseVector {
+private:
+    static constexpr size_t N = sizeof...(Idxs);
+    static constexpr int _indices[N] {Idxs...};
+
+    static constexpr bool duplicateIndices() {
+        for (int i = 0; i < N; i++)
+            for (int j = 0; j < i; j++)
+                if (_indices[i] == _indices[j])
+                    return true;
+        return false;
+    }
+    static constexpr int findMaxIndex() {
+        int maxIndex = -1;
+        for (int i = 0; i < N; i++) {
+            if (maxIndex < _indices[i]) {
+                maxIndex = _indices[i];
+            }
+        }
+        return maxIndex;
+    }
+    static_assert(duplicateIndices() == false, "Duplicate indices");
+    static_assert(N < M, "More entries than elements, use a dense vector");
+    static_assert(findMaxIndex() < M, "Largest entry doesn't fit in sparse vector");
+
+    Type _data[N] {};
+
+    static constexpr int findCompressedIndex(int index) {
+        int compressedIndex = -1;
+        for (int i = 0; i < N; i++) {
+            if (index == _indices[i]) {
+                compressedIndex = i;
+            }
+        }
+        return compressedIndex;
+    }
+
+public:
+    static constexpr int non_zeros() {
+        return N;
+    }
+
+    constexpr int index(int i) const {
+        return SparseVector::_indices[i];
+    }
+
+    SparseVector() = default;
+
+    SparseVector(const matrix::Vector<Type, M>& data) {
+        for (int i = 0; i < N; i++) {
+            _data[i] = data(_indices[i]);
+        }
+    }
+
+    explicit SparseVector(const Type data[N]) {
+        memcpy(_data, data, sizeof(_data));
+    }
+
+    template <int i>
+    inline Type at() const {
+        static constexpr int compressed_index = force_constexpr_eval<findCompressedIndex(i)>::value;
+        static_assert(compressed_index >= 0, "cannot access unpopulated indices");
+        return _data[compressed_index];
+    }
+
+    template <int i>
+    inline Type& at() {
+        static constexpr int compressed_index = force_constexpr_eval<findCompressedIndex(i)>::value;
+        static_assert(compressed_index >= 0, "cannot access unpopulated indices");
+        return _data[compressed_index];
+    }
+
+    void setZero() {
+        for (size_t i = 0; i < N; i++) {
+            _data[i] = Type(0);
+        }
+    }
+
+    Type dot(const matrix::Vector<Type, M>& other) const {
+        Type accum (0);
+        for (size_t i = 0; i < N; i++) {
+            accum += _data[i] * other(_indices[i]);
+        }
+        return accum;
+    }
+
+    matrix::Vector<Type, M> operator+(const matrix::Vector<Type, M>& other) const {
+        matrix::Vector<Type, M> vec = other;
+        for (size_t i = 0; i < N; i++) {
+            vec(_indices[i]) +=  _data[i];
+        }
+        return vec;
+    }
+
+    SparseVector& operator+=(Type t) {
+        for (size_t i = 0; i < N; i++) {
+            _data[i] += t;
+        }
+        return *this;
+    }
+
+    Type norm_squared() const
+    {
+        Type accum(0);
+        for (size_t i = 0; i < N; i++) {
+            accum += _data[i] * _data[i];
+        }
+        return accum;
+    }
+
+    Type norm() const
+    {
+        return matrix::sqrt(norm_squared());
+    }
+
+    bool longerThan(Type testVal) const
+    {
+        return norm_squared() > testVal*testVal;
+    }
+};
+
+template<typename Type, size_t Q, size_t M, int ... Idxs>
+matrix::Vector<Type, Q> operator*(const matrix::Matrix<Type, Q, M>& mat, const matrix::SparseVector<Type, M, Idxs...>& vec) {
+    matrix::Vector<Type, Q> res;
+    for (size_t i = 0; i < Q; i++) {
+        const Vector<Type, M> row = mat.row(i);
+        res(i) = vec.dot(row);
+    }
+    return res;
+}
+
+template<typename Type,size_t M, int... Idxs>
+constexpr int SparseVector<Type, M, Idxs...>::_indices[SparseVector<Type, M, Idxs...>::N];
+
+template<size_t M, int ... Idxs>
+using SparseVectorf = SparseVector<float, M, Idxs...>;
+
+}

matrix/math.hpp

@@ -20,3 +20,4 @@
 #include "LeastSquaresSolver.hpp"
 #include "Dual.hpp"
 #include "PseudoInverse.hpp"
+#include "SparseVector.hpp"

test/CMakeLists.txt

@@ -1,3 +1,5 @@
+include(gtest.cmake)
+
 set(tests
     setIdentity
     inverse
@@ -13,6 +15,7 @@ set(tests
     attitude
     filter
     integration
+    sparseVector
     squareMatrix
     helper
     hatvee
@@ -30,6 +33,7 @@ foreach(test_name ${tests})
     add_test(test_${test_name} ${test_name})
     add_dependencies(test_build ${test_name})
 endforeach()
+target_link_libraries(sparseVector gtest_main)
 
 if (${CMAKE_BUILD_TYPE} STREQUAL "Coverage")
 

test/CMakeLists.txt.in

@@ -0,0 +1,18 @@
+cmake_minimum_required(VERSION 2.8.4)
+
+project(googletest-download NONE)
+
+include(ExternalProject)
+ExternalProject_Add(googletest
+	URL https://github.com/google/googletest/archive/8b6d3f9c4a774bef3081195d422993323b6bb2e0.zip
+	SOURCE_DIR "${CMAKE_CURRENT_BINARY_DIR}/googletest-src"
+	BINARY_DIR "${CMAKE_CURRENT_BINARY_DIR}/googletest-build"
+	CONFIGURE_COMMAND ""
+	BUILD_COMMAND ""
+	INSTALL_COMMAND ""
+	TEST_COMMAND ""
+	# Wrap download, configure and build steps in a script to log output
+    LOG_DOWNLOAD ON
+    LOG_CONFIGURE ON
+    LOG_BUILD ON
+)

test/gtest.cmake

@@ -0,0 +1,12 @@
+set_directory_properties(PROPERTIES COMPILE_OPTIONS "")
+
+# Download and unpack googletest at configure time
+configure_file(${CMAKE_CURRENT_LIST_DIR}/CMakeLists.txt.in googletest-download/CMakeLists.txt)
+execute_process(COMMAND ${CMAKE_COMMAND} -G "${CMAKE_GENERATOR}" . RESULT_VARIABLE result1 WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/googletest-download)
+execute_process(COMMAND ${CMAKE_COMMAND} --build . RESULT_VARIABLE result2 WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/googletest-download)
+if(result1 OR result2)
+	message(FATAL_ERROR "Preparing googletest failed: ${result1} ${result2}")
+endif()
+
+# Add googletest, defines gtest and gtest_main targets
+add_subdirectory(${CMAKE_CURRENT_BINARY_DIR}/googletest-src ${CMAKE_CURRENT_BINARY_DIR}/googletest-build EXCLUDE_FROM_ALL)

test/sparseVector.cpp

@@ -0,0 +1,98 @@
+#include <matrix/math.hpp>
+#include <gtest/gtest.h>
+
+using namespace matrix;
+
+TEST(sparseVectorTest, defaultConstruction) {
+    SparseVectorf<24, 4, 6> a;
+    EXPECT_EQ(a.non_zeros(), 2);
+    EXPECT_EQ(a.index(0), 4);
+    EXPECT_EQ(a.index(1), 6);
+    a.at<4>() = 1.f;
+    a.at<6>() = 2.f;
+}
+
+TEST(sparseVectorTest, initializationWithData) {
+    const float data[3] = {1.f, 2.f, 3.f};
+    SparseVectorf<24, 4, 6, 22> a(data);
+    EXPECT_EQ(a.non_zeros(), 3);
+    EXPECT_EQ(a.index(0), 4);
+    EXPECT_EQ(a.index(1), 6);
+    EXPECT_EQ(a.index(2), 22);
+    EXPECT_FLOAT_EQ(a.at<4>(), data[0]);
+    EXPECT_FLOAT_EQ(a.at<6>(), data[1]);
+    EXPECT_FLOAT_EQ(a.at<22>(), data[2]);
+}
+
+TEST(sparseVectorTest, initialisationFromVector) {
+    const Vector3f vec(1.f, 2.f, 3.f);
+    const SparseVectorf<3, 0, 2> a(vec);
+    EXPECT_FLOAT_EQ(a.at<0>(), vec(0));
+    EXPECT_FLOAT_EQ(a.at<2>(), vec(2));
+}
+
+TEST(sparseVectorTest, setZero) {
+    const float data[3] = {1.f, 2.f, 3.f};
+    SparseVectorf<24, 4, 6, 22> a(data);
+    a.setZero();
+    EXPECT_FLOAT_EQ(a.at<4>(), 0.f);
+    EXPECT_FLOAT_EQ(a.at<6>(), 0.f);
+    EXPECT_FLOAT_EQ(a.at<22>(), 0.f);
+}
+
+TEST(sparseVectorTest, additionWithDenseVector) {
+    Vector<float, 4> dense_vec;
+    dense_vec.setAll(1.f);
+    const float data[3] = {1.f, 2.f, 3.f};
+    const SparseVectorf<4, 1, 2, 3> sparse_vec(data);
+    const Vector<float, 4> res = sparse_vec + dense_vec;
+    EXPECT_FLOAT_EQ(res(0), 1.f);
+    EXPECT_FLOAT_EQ(res(1), 2.f);
+    EXPECT_FLOAT_EQ(res(2), 3.f);
+    EXPECT_FLOAT_EQ(res(3), 4.f);
+}
+
+TEST(sparseVectorTest, addScalar) {
+    const float data[3] = {1.f, 2.f, 3.f};
+    SparseVectorf<4, 1, 2, 3> sparse_vec(data);
+    sparse_vec += 2.f;
+    EXPECT_FLOAT_EQ(sparse_vec.at<1>(), 3.f);
+    EXPECT_FLOAT_EQ(sparse_vec.at<2>(), 4.f);
+    EXPECT_FLOAT_EQ(sparse_vec.at<3>(), 5.f);
+}
+
+TEST(sparseVectorTest, dotProductWithDenseVector) {
+    Vector<float, 4> dense_vec;
+    dense_vec.setAll(3.f);
+    const float data[3] = {1.f, 2.f, 3.f};
+    const SparseVectorf<4, 1, 2, 3> sparse_vec(data);
+    float res = sparse_vec.dot(dense_vec);
+    EXPECT_FLOAT_EQ(res, 18.f);
+}
+
+TEST(sparseVectorTest, multiplicationWithDenseMatrix) {
+    Matrix<float, 2, 3> dense_matrix;
+    dense_matrix.setAll(2.f);
+    dense_matrix(1, 1) = 3.f;
+    const Vector3f dense_vec(0.f, 1.f, 5.f);
+    const SparseVectorf<3, 1, 2> sparse_vec(dense_vec);
+    const Vector<float, 2> res_sparse = dense_matrix * sparse_vec;
+    const Vector<float, 2> res_dense = dense_matrix * dense_vec;
+    EXPECT_TRUE(isEqual(res_dense, res_sparse));
+}
+
+TEST(sparseVectorTest, norms) {
+    const float data[2] = {3.f, 4.f};
+    const SparseVectorf<4, 1, 3> sparse_vec(data);
+    EXPECT_FLOAT_EQ(sparse_vec.norm_squared(), 25.f);
+    EXPECT_FLOAT_EQ(sparse_vec.norm(), 5.f);
+    EXPECT_TRUE(sparse_vec.longerThan(4.5f));
+    EXPECT_FALSE(sparse_vec.longerThan(5.5f));
+}
+
+
+int main(int argc, char **argv) {
+    testing::InitGoogleTest(&argc, argv);
+    std::cout << "Run SparseVector tests" << std::endl;
+    return RUN_ALL_TESTS();
+}