C++ Archives - Robotics with ROS

Developing Redis applications with C++

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I have found the following libraries to develop redis applications with c++ : hiredis (only available for c) redis-plus-plus (based on hiredis, available for C++) cpp-redis Asynchronous Multi-Platform, no dependency Installation of redis-plus-plus First lets install hiredis

git clone https://github.com/redis/hiredis.git
cd hiredis
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX:PATH=~/usr .. && make -j8 all install

git clone https://github.com/redis/hiredis.git

cd hiredis

mkdir build

cd build

cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX:PATH=~/usr .. && make -j8 all install

Then we install redis-plus-plus

git clone https://github.com/sewenew/redis-plus-plus.git
cd redis-plus-plus
mkdir build
cd build

cmake -DCMAKE_BUILD_TYPE=Release -DREDIS_PLUS_PLUS_BUILD_TEST=ON -DREDIS_PLUS_PLUS_CXX_STANDARD=17 -DCMAKE_INSTALL_PREFIX:PATH=~/usr .. && make -j8 all install

git clone https://github.com/sewenew/redis-plus-plus.git

cd redis-plus-plus

mkdir build

cd build

cmake -DCMAKE_BUILD_TYPE=Release -DREDIS_PLUS_PLUS_BUILD_TEST=ON -DREDIS_PLUS_PLUS_CXX_STANDARD=17 -DCMAKE_INSTALL_PREFIX:PATH=~/usr .. && make -j8 all install

Using redis-plus-plus in your application I have created the following CMake to […]

Developing Redis applications with C++ Read More »

Example of Test Driven Development with C++ and Google Test

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To develop a software based on “Test Driven Development” one should follow the following steps:

1)Write enough failing Test Code (include compile time and run time failures)
2)Write Production code to pass the failing tests.
3)Refactor the production code and verify the same with existing test.
4)Go to step 1

1)Write enough failing Test Code (include compile time and run time failures)

2)Write Production code to pass the failing tests.

3)Refactor the production code and verify the same with existing test.

4)Go to step 1

so let say we want to develop a binary search tree, here I’m using Google Test. Step 1, Write enough failing Test Code so the first step would be something like this:

class testingTree: public ::testing::Test
{
protected:
    testingTree(){}
    ~testingTree(){}
    void SetUp(){}
    void TearDown(){}
};

TEST_F(testingTree,makeTree)
{
    std::shared_ptr<Tree> tree=makeTree();
    EXPECT_NE(tree.get(),nullptr);
}


int main(int argc, char ** argv)
{
    testing::InitGoogleTest(&argc, argv);
    return RUN_ALL_TESTS();
}

class testingTree: public ::testing::Test

{

protected:

testingTree(){}

~testingTree(){}

void SetUp(){}

void TearDown(){}

};

TEST_F(testingTree,makeTree)

{

std::shared_ptr<Tree> tree=makeTree();

EXPECT_NE(tree.get(),nullptr);

}

int main(int argc, char ** argv)

{

testing::InitGoogleTest(&argc, argv);

return RUN_ALL_TESTS();

}

Step 2, Write Production code to

Example of Test Driven Development with C++ and Google Test Read More »

Dijkstra’s algorithm with C++

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Dijkstra’s algorithm is an algorithm for finding the shortest path in a graph. First drawing the graph:

import networkx as nx
import matplotlib.pyplot as plt
from networkx.drawing.nx_agraph import write_dot, graphviz_layout


def coloring_tree(G):

    visited_list=[]
    for i in G.nodes():
        visited_list.append( g.nodes[i]['visited'] )
    
    graph_color_map=[]
    for i  in visited_list:
        graph_color_map.append('red')
        
    pos =graphviz_layout(G, prog='dot')
    labels = nx.get_edge_attributes(G,'weight')
    nx.draw(G,pos,with_labels=True, arrows=True, node_color =  graph_color_map)
    nx.draw_networkx_edge_labels(G, pos,edge_labels=labels)
    plt.show()
    
    return

g=nx.DiGraph()
g.add_node('a',visited=False)
g.add_node('b',visited=False)
g.add_node('c',visited=False)
g.add_node('d',visited=False)
g.add_node('e',visited=False)
g.add_node('f',visited=False)
g.add_node('g',visited=False)
g.add_node('h',visited=False)
g.add_node('i',visited=False)


g.add_edge('a','b',weight=4)
g.add_edge('a','h',weight=8)


g.add_edge('b','a',weight=4)
g.add_edge('b','c',weight=8)
g.add_edge('b','h',weight=11)


g.add_edge('c','b',weight=8)
g.add_edge('c','d',weight=7)
g.add_edge('c','f',weight=4)
g.add_edge('c','i',weight=2)

g.add_edge('d','d',weight=7)
g.add_edge('d','e',weight=9)
g.add_edge('d','f',weight=14)

g.add_edge('e','d',weight=9)
g.add_edge('e','f',weight=10)

g.add_edge('f','c',weight=4)
g.add_edge('f','d',weight=14)
g.add_edge('f','e',weight=10)
g.add_edge('f','g',weight=2)

g.add_edge('g','f',weight=2)
g.add_edge('g','h',weight=1)
g.add_edge('g','i',weight=6)


g.add_edge('h','a',weight=8)
g.add_edge('g','b',weight=11)
g.add_edge('h','g',weight=1)
g.add_edge('h','i',weight=7)

g.add_edge('i','c',weight=2)
g.add_edge('i','g',weight=6)
g.add_edge('i','h',weight=7)

coloring_tree(g)

import networkx as nx

import matplotlib.pyplot as plt

from networkx.drawing.nx_agraph import write_dot, graphviz_layout

def coloring_tree(G):

visited_list=[]

for i in G.nodes():

visited_list.append( g.nodes[i]['visited'] )

graph_color_map=[]

for i in visited_list:

graph_color_map.append('red')

pos =graphviz_layout(G, prog='dot')

labels = nx.get_edge_attributes(G,'weight')

nx.draw(G,pos,with_labels=True, arrows=True, node_color = graph_color_map)

nx.draw_networkx_edge_labels(G, pos,edge_labels=labels)

plt.show()

return

g=nx.DiGraph()

g.add_node('a',visited=False)

g.add_node('b',visited=False)

g.add_node('c',visited=False)

g.add_node('d',visited=False)

g.add_node('e',visited=False)

g.add_node('f',visited=False)

g.add_node('g',visited=False)

g.add_node('h',visited=False)

g.add_node('i',visited=False)

g.add_edge('a','b',weight=4)

g.add_edge('a','h',weight=8)

g.add_edge('b','a',weight=4)

g.add_edge('b','c',weight=8)

g.add_edge('b','h',weight=11)

g.add_edge('c','b',weight=8)

g.add_edge('c','d',weight=7)

g.add_edge('c','f',weight=4)

g.add_edge('c','i',weight=2)

g.add_edge('d','d',weight=7)

g.add_edge('d','e',weight=9)

g.add_edge('d','f',weight=14)

g.add_edge('e','d',weight=9)

g.add_edge('e','f',weight=10)

g.add_edge('f','c',weight=4)

g.add_edge('f','d',weight=14)

g.add_edge('f','e',weight=10)

g.add_edge('f','g',weight=2)

g.add_edge('g','f',weight=2)

g.add_edge('g','h',weight=1)

g.add_edge('g','i',weight=6)

g.add_edge('h','a',weight=8)

g.add_edge('g','b',weight=11)

g.add_edge('h','g',weight=1)

g.add_edge('h','i',weight=7)

g.add_edge('i','c',weight=2)

g.add_edge('i','g',weight=6)

g.add_edge('i','h',weight=7)

coloring_tree(g)

#include <iostream>
#include <vector>
#include <string>
#include <list>
 
#include <limits> // for numeric_limits
 
#include <set>
#include <utility> // for pair
#include <algorithm>
#include <iterator>
 
 
typedef int vertex_t;
typedef double weight_t;
 
const weight_t max_weight = std::numeric_limits<double>::infinity();
 
struct neighbor {
    vertex_t target;
    weight_t weight;
    neighbor(vertex_t arg_target, weight_t arg_weight)
        : target(arg_target), weight(arg_weight) { }
};
 
typedef std::vector<std::vector<neighbor> > adjacency_list_t;
 
 
void DijkstraComputePaths(vertex_t source,
                          const adjacency_list_t &adjacency_list,
                          std::vector<weight_t> &min_distance,
                          std::vector<vertex_t> &previous)
{
    int n = adjacency_list.size();
    min_distance.clear();
    min_distance.resize(n, max_weight);
    min_distance[source] = 0;
    previous.clear();
    previous.resize(n, -1);
    std::set<std::pair<weight_t, vertex_t> > vertex_queue;
    vertex_queue.insert(std::make_pair(min_distance[source], source));
 
    while (!vertex_queue.empty()) 
    {
        weight_t dist = vertex_queue.begin()->first;
        vertex_t u = vertex_queue.begin()->second;
        vertex_queue.erase(vertex_queue.begin());
 
        // Visit each edge exiting u
	const std::vector<neighbor> &neighbors = adjacency_list[u];
        for (std::vector<neighbor>::const_iterator neighbor_iter = neighbors.begin();
             neighbor_iter != neighbors.end();
             neighbor_iter++)
        {
            vertex_t v = neighbor_iter->target;
            weight_t weight = neighbor_iter->weight;
            weight_t distance_through_u = dist + weight;
	    if (distance_through_u < min_distance[v]) {
	        vertex_queue.erase(std::make_pair(min_distance[v], v));
 
	        min_distance[v] = distance_through_u;
	        previous[v] = u;
	        vertex_queue.insert(std::make_pair(min_distance[v], v));
 
	    }
 
        }
    }
}
 
 
std::list<vertex_t> DijkstraGetShortestPathTo(
    vertex_t vertex, const std::vector<vertex_t> &previous)
{
    std::list<vertex_t> path;
    for ( ; vertex != -1; vertex = previous[vertex])
        path.push_front(vertex);
    return path;
}
 
 
int main()
{
    adjacency_list_t adjacency_list(9);
    adjacency_list[0].push_back(neighbor(1, 4));
    adjacency_list[0].push_back(neighbor(7, 8));

    adjacency_list[1].push_back(neighbor(0, 4));
    adjacency_list[1].push_back(neighbor(2, 8));
    adjacency_list[1].push_back(neighbor(7, 11));

    adjacency_list[2].push_back(neighbor(1, 8));
    adjacency_list[2].push_back(neighbor(3, 7));
    adjacency_list[2].push_back(neighbor(5, 4));
    adjacency_list[2].push_back(neighbor(8, 2));

    adjacency_list[3].push_back(neighbor(3, 7));
    adjacency_list[3].push_back(neighbor(4, 9));
    adjacency_list[3].push_back(neighbor(5, 14));


    adjacency_list[4].push_back(neighbor(3, 9));
    adjacency_list[4].push_back(neighbor(5, 10));
    
    adjacency_list[5].push_back(neighbor(2, 4));
    adjacency_list[5].push_back(neighbor(3, 14));
    adjacency_list[5].push_back(neighbor(4, 10));
    adjacency_list[5].push_back(neighbor(6, 2));

    adjacency_list[6].push_back(neighbor(5, 2));
    adjacency_list[6].push_back(neighbor(7, 1)); 
    adjacency_list[6].push_back(neighbor(8, 6)); 

    
    adjacency_list[7].push_back(neighbor(0, 9));
    adjacency_list[7].push_back(neighbor(1, 9));    
    adjacency_list[7].push_back(neighbor(6, 9));    
    adjacency_list[7].push_back(neighbor(8, 9));    
    

    adjacency_list[8].push_back(neighbor(2, 2));
    adjacency_list[8].push_back(neighbor(6, 6));
    adjacency_list[8].push_back(neighbor(7, 7));

    std::vector<weight_t> min_distance;
    std::vector<vertex_t> previous;
    DijkstraComputePaths(0, adjacency_list, min_distance, previous);
    std::cout << "Distance from 0 to 5: " << min_distance[5] << std::endl;
    std::list<vertex_t> path = DijkstraGetShortestPathTo(5, previous);
    std::cout << "Path : ";
    std::copy(path.begin(), path.end(), std::ostream_iterator<vertex_t>(std::cout, " "));
    std::cout << std::endl;
    return 0;
}

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#include <iostream>

#include <vector>

#include <string>

#include <list>

#include <limits> // for numeric_limits

#include <set>

#include <utility> // for pair

#include <algorithm>

#include <iterator>

typedef int vertex_t;

typedef double weight_t;

const weight_t max_weight = std::numeric_limits<double>::infinity();

struct neighbor {

vertex_t target;

weight_t weight;

neighbor(vertex_t arg_target, weight_t arg_weight)

: target(arg_target), weight(arg_weight) { }

};

typedef std::vector<std::vector<neighbor> > adjacency_list_t;

void DijkstraComputePaths(vertex_t source,

const adjacency_list_t &adjacency_list,

std::vector<weight_t> &min_distance,

std::vector<vertex_t> &previous)

{

int n = adjacency_list.size();

min_distance.clear();

min_distance.resize(n, max_weight);

min_distance[source] = 0;

previous.clear();

previous.resize(n, -1);

std::set<std::pair<weight_t, vertex_t> > vertex_queue;

vertex_queue.insert(std::make_pair(min_distance[source], source));

while (!vertex_queue.empty())

{

weight_t dist = vertex_queue.begin()->first;

vertex_t u = vertex_queue.begin()->second;

vertex_queue.erase(vertex_queue.begin());

// Visit each edge exiting u

const std::vector<neighbor> &neighbors = adjacency_list[u];

for (std::vector<neighbor>::const_iterator neighbor_iter = neighbors.begin();

neighbor_iter != neighbors.end();

neighbor_iter++)

{

vertex_t v = neighbor_iter->target;

weight_t weight = neighbor_iter->weight;

weight_t distance_through_u = dist + weight;

if (distance_through_u < min_distance[v]) {

vertex_queue.erase(std::make_pair(min_distance[v], v));

min_distance[v] = distance_through_u;

previous[v] = u;

vertex_queue.insert(std::make_pair(min_distance[v], v));

}

std::list<vertex_t> DijkstraGetShortestPathTo(

vertex_t vertex, const std::vector<vertex_t> &previous)

{

std::list<vertex_t> path;

for ( ; vertex != -1; vertex = previous[vertex])

path.push_front(vertex);

return path;

}

int main()

{

adjacency_list_t adjacency_list(9);

adjacency_list[0].push_back(neighbor(1, 4));

adjacency_list[0].push_back(neighbor(7, 8));

adjacency_list[1].push_back(neighbor(0, 4));

adjacency_list[1].push_back(neighbor(2, 8));

adjacency_list[1].push_back(neighbor(7, 11));

adjacency_list[2].push_back(neighbor(1, 8));

adjacency_list[2].push_back(neighbor(3, 7));

adjacency_list[2].push_back(neighbor(5, 4));

adjacency_list[2].push_back(neighbor(8, 2));

adjacency_list[3].push_back(neighbor(3, 7));

adjacency_list[3].push_back(neighbor(4, 9));

adjacency_list[3].push_back(neighbor(5, 14));

adjacency_list[4].push_back(neighbor(3, 9));

adjacency_list[4].push_back(neighbor(5, 10));

adjacency_list[5].push_back(neighbor(2, 4));

adjacency_list[5].push_back(neighbor(3, 14));

adjacency_list[5].push_back(neighbor(4, 10));

adjacency_list[5].push_back(neighbor(6, 2));

adjacency_list[6].push_back(neighbor(5, 2));

adjacency_list[6].push_back(neighbor(7, 1));

adjacency_list[6].push_back(neighbor(8, 6));

adjacency_list[7].push_back(neighbor(0, 9));

adjacency_list[7].push_back(neighbor(1, 9));

adjacency_list[7].push_back(neighbor(6, 9));

adjacency_list[7].push_back(neighbor(8, 9));

adjacency_list[8].push_back(neighbor(2, 2));

adjacency_list[8].push_back(neighbor(6, 6));

adjacency_list[8].push_back(neighbor(7, 7));

std::vector<weight_t> min_distance;

std::vector<vertex_t> previous;

DijkstraComputePaths(0, adjacency_list, min_distance, previous);

std::cout << "Distance from 0 to 5: " << min_distance[5] << std::endl;

std::list<vertex_t> path = DijkstraGetShortestPathTo(5, previous);

std::cout << "Path : ";

std::copy(path.begin(), path.end(), std::ostream_iterator<vertex_t>(std::cout, " "));

std::cout << std::endl;

return 0;

}

Distance from 0 to 5: 16
Path : 0 1 2 5

1 2	Distance from 0 to 5: 16 Path : 0 1 2 5

ref [1]

Dijkstra’s algorithm with C++ Read More »

Standard Exception Handler in C++ and Custom Exception Handler with Examples

Leave a Comment / C++, Tutorials / admin

Exception handling

try
{
    some code
}
catch (Exception e)
{
    throw e;
}
catch (...)//... will catch any exception
{
    throw;
}

try

{

some code

}

catch (Exception e)

{

throw e;

}

catch (...)//... will catch any exception

{

throw;

}

C++ Standard Exceptions

1)std::bad_alloc
2)std::bad_cast
3)std::bad_exception
4)std::bad_typeid
5)std::logic_error
	5-1)std::domain_error: exception thrown when a mathematically invalid domain is used.
	5-2)std::invalid_argument
	5-3)std::length_error
	5-4)std::out_of_range
6)std::runtime_error: An exception that theoretically cannot be detected by reading the code.
	6-1)std::overflow_error :
    The only standard library components that
    throw std::overflow_error are std::bitset::to_ulong and std::bitset::to_ullong.
	6-2)std::underflow_error
	6-3)std::range_error

1)std::bad_alloc

2)std::bad_cast

3)std::bad_exception

4)std::bad_typeid

5)std::logic_error

5-1)std::domain_error: exception thrown when a mathematically invalid domain is used.

5-2)std::invalid_argument

5-3)std::length_error

5-4)std::out_of_range

6)std::runtime_error: An exception that theoretically cannot be detected by reading the code.

6-1)std::overflow_error :

The only standard library components that

throw std::overflow_error are std::bitset::to_ulong and std::bitset::to_ullong.

6-2)std::underflow_error

6-3)std::range_error

List of Example

struct Foo { virtual ~Foo() {} };
struct Bar { virtual ~Bar() {} };


void bad_allocExample()
{
    try
    {
        while (true)
        {
            new int[100000000ul];
        }
    } catch (const std::bad_alloc& e)
    {
        std::cout << "Allocation failed: " << e.what() << '\n';
    }
}


void bad_castExample()
{
    Bar b;
    try
    {
        Foo& f = dynamic_cast<Foo&>(b);
    } catch(const std::bad_cast& e)
    {
        std::cout << e.what() << '\n';
    }
}


void bad_typeidExample()
{
    Foo* p = nullptr;
    try
    {
        std::cout << typeid(*p).name() << '\n';
    } catch(const std::bad_typeid& e) {
        std::cout << e.what() << '\n';
    }
}


void logical_ErrorExample()
{
    int amount, available;
    amount=10;
    available=9;
    if(amount>available)
    {
        throw std::logic_error("Error");
    }
    catch ( std::exception &e )
    {
      std::cerr << "Caught: " << e.what( ) << std::endl;
      std::cerr << "Type: " << typeid( e ).name( ) << std::endl;
    };
}

void domain_errorExample()
{
    try
    {
        const double x = std::acos(2.0);
        std::cout << x << '\n';
    }
    catch (std::domain_error& e)
    {
        std::cout << e.what() << '\n';
    }
    catch (...)
    {
        std::cout << "Something unexpected happened" << '\n';
    }

}

void invalid_argumentExample()
{
    try
    {
    // binary wrongly represented by char X
        std::bitset<32> bitset(std::string("0101001X01010110000"));
    }
    catch (std::exception &err)
    {
        std::cerr<<"Caught "<<err.what()<<std::endl;
        std::cerr<<"Type "<<typeid(err).name()<<std::endl;
    }
}

void length_errorExample()
{
    try
    {
        // vector throws a length_error if resized above max_size
        std::vector<int> myvector;
        myvector.resize(myvector.max_size()+1);
    }
    catch (const std::length_error& le)
    {
        std::cerr << "Length error: " << le.what() << '\n';
    }
}

void out_of_rangeExample()
{
    std::vector<int> myvector(10);
    try
    {
        myvector.at(20)=100;      // vector::at throws an out-of-range
    }
    catch (const std::out_of_range& oor)
    {
        std::cerr << "Out of Range error: " << oor.what() << '\n';
    }
}

void overflow_errorExample()
{
    try
    {
        std::bitset<100> bitset;
        bitset[99] = 1;
        bitset[0] = 1;
        // to_ulong(), converts a bitset object to the integer that would generate the sequence of bits
        unsigned long Test = bitset.to_ulong();
    }
    catch(std::exception &err)
    {
        std::cerr<<"Caught "<<err.what()<<std::endl;
        std::cerr<<"Type "<<typeid(err).name()<<std::endl;
    }
}


void range_errorExample()
{
    try
    {
       throw std::range_error( "The range is in error!" );
    }
    catch (std::range_error &e)
    {
       std::cerr << "Caught: " << e.what( ) << std::endl;
       std::cerr << "Type: " << typeid( e ).name( ) << std::endl;
    }
}


//Defining your exceptions

struct CustomException : public std::exception
{
   const char * what () const throw ()
   {
      return "CustomException happened";
   }
};

void customExceptionExample()
{
    try
    {
        throw CustomException();
    } catch(CustomException& e)
    {
        std::cout << "CustomException caught" << std::endl;
        std::cout << e.what() << std::endl;
    } catch(std::exception& e)
    {
    //Other errors
    }
}

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struct Foo { virtual ~Foo() {} };

struct Bar { virtual ~Bar() {} };

void bad_allocExample()

{

try

{

while (true)

{

new int[100000000ul];

}

} catch (const std::bad_alloc& e)

{

std::cout << "Allocation failed: " << e.what() << '\n';

}

void bad_castExample()

{

Bar b;

try

{

Foo& f = dynamic_cast<Foo&>(b);

} catch(const std::bad_cast& e)

{

std::cout << e.what() << '\n';

}

void bad_typeidExample()

{

Foo* p = nullptr;

try

{

std::cout << typeid(*p).name() << '\n';

} catch(const std::bad_typeid& e) {

std::cout << e.what() << '\n';

}

void logical_ErrorExample()

{

int amount, available;

amount=10;

available=9;

if(amount>available)

{

throw std::logic_error("Error");

}

catch ( std::exception &e )

{

std::cerr << "Caught: " << e.what( ) << std::endl;

std::cerr << "Type: " << typeid( e ).name( ) << std::endl;

};

}

void domain_errorExample()

{

try

{

const double x = std::acos(2.0);

std::cout << x << '\n';

}

catch (std::domain_error& e)

{

std::cout << e.what() << '\n';

}

catch (...)

{

std::cout << "Something unexpected happened" << '\n';

}

void invalid_argumentExample()

{

try

{

// binary wrongly represented by char X

std::bitset<32> bitset(std::string("0101001X01010110000"));

}

catch (std::exception &err)

{

std::cerr<<"Caught "<<err.what()<<std::endl;

std::cerr<<"Type "<<typeid(err).name()<<std::endl;

}

void length_errorExample()

{

try

{

// vector throws a length_error if resized above max_size

std::vector<int> myvector;

myvector.resize(myvector.max_size()+1);

}

catch (const std::length_error& le)

{

std::cerr << "Length error: " << le.what() << '\n';

}

void out_of_rangeExample()

{

std::vector<int> myvector(10);

try

{

myvector.at(20)=100; // vector::at throws an out-of-range

}

catch (const std::out_of_range& oor)

{

std::cerr << "Out of Range error: " << oor.what() << '\n';

}

void overflow_errorExample()

{

try

{

std::bitset<100> bitset;

bitset[99] = 1;

bitset[0] = 1;

// to_ulong(), converts a bitset object to the integer that would generate the sequence of bits

unsigned long Test = bitset.to_ulong();

}

catch(std::exception &err)

{

std::cerr<<"Caught "<<err.what()<<std::endl;

std::cerr<<"Type "<<typeid(err).name()<<std::endl;

}

void range_errorExample()

{

try

{

throw std::range_error( "The range is in error!" );

}

catch (std::range_error &e)

{

std::cerr << "Caught: " << e.what( ) << std::endl;

std::cerr << "Type: " << typeid( e ).name( ) << std::endl;

}

//Defining your exceptions

struct CustomException : public std::exception

{

const char * what () const throw ()

{

return "CustomException happened";

}

};

void customExceptionExample()

{

try

{

throw CustomException();

} catch(CustomException& e)

{

std::cout << "CustomException caught" << std::endl;

std::cout << e.what() << std::endl;

} catch(std::exception& e)

{

//Other errors

}

Standard Exception Handler in C++ and Custom Exception Handler with Examples Read More »

Finding Memory leaking, Stack and Heap overflow

Leave a Comment / C++, Tutorials / admin

When you access an array index, C and C++ don’t do bound checking. Segmentation faults only happen when you try to read or write to a page that was not allocated (or try to do something on a page which isn’t permitted, e.g. trying to write to a read-only page), but since pages are usually

Finding Memory leaking, Stack and Heap overflow Read More »

Kalman Filter Explained With Python Code From Scratch

23 Comments / C++, Machine Learning, Python, Robotic, Tutorials / admin

This snippet shows tracking mouse cursor with Python code from scratch and comparing the result with OpenCV. The CSV file that has been used are being created with below c++ code. A sample could be downloaded from here 1, 2, 3. Python Kalman Filter

import numpy as np
np.set_printoptions(threshold=3)
np.set_printoptions(suppress=True)
from numpy import genfromtxt


#Notation used coming from: https://www.bzarg.com/p/how-a-kalman-filter-works-in-pictures/


def prediction(X_hat_t_1,P_t_1,F_t,B_t,U_t,Q_t):
    X_hat_t=F_t.dot(X_hat_t_1)+(B_t.dot(U_t).reshape(B_t.shape[0],-1) )
    P_t=np.diag(np.diag(F_t.dot(P_t_1).dot(F_t.transpose())))+Q_t
    return X_hat_t,P_t
    

def update(X_hat_t,P_t,Z_t,R_t,H_t):
    
    K_prime=P_t.dot(H_t.transpose()).dot( np.linalg.inv ( H_t.dot(P_t).dot(H_t.transpose()) +R_t ) )  
    print("K:\n",K_prime)
    
    X_t=X_hat_t+K_prime.dot(Z_t-H_t.dot(X_hat_t))
    P_t=P_t-K_prime.dot(H_t).dot(P_t)
    
    return X_t,P_t


acceleration=0
delta_t=1/20#milisecond

groundTruth = genfromtxt('data/groundTruth.csv', delimiter=',',skip_header=1)

#Observations: position_X, position_Y
measurmens = genfromtxt('data/measurmens.csv', delimiter=',',skip_header=1)


#Checking our result with OpenCV
opencvKalmanOutput = genfromtxt('data/kalmanv.csv', delimiter=',',skip_header=1)

#Transition matrix
F_t=np.array([ [1 ,0,delta_t,0] , [0,1,0,delta_t] , [0,0,1,0] , [0,0,0,1] ])

#Initial State cov
P_t= np.identity(4)*0.2

#Process cov
Q_t= np.identity(4)

#Control matrix
B_t=np.array( [ [0] , [0], [0] , [0] ])

#Control vector
U_t=acceleration

#Measurment Matrix
H_t = np.array([ [1, 0, 0, 0], [ 0, 1, 0, 0]])

#Measurment cov
R_t= np.identity(2)*5

# Initial State
X_hat_t = np.array( [[0],[0],[0],[0]] )
print("X_hat_t",X_hat_t.shape)
print("P_t",P_t.shape)
print("F_t",F_t.shape)
print("B_t",B_t.shape)
print("Q_t",Q_t.shape)
print("R_t",R_t.shape)
print("H_t",H_t.shape)

for i in range(measurmens.shape[0]):
    X_hat_t,P_hat_t = prediction(X_hat_t,P_t,F_t,B_t,U_t,Q_t)
    print("Prediction:")
    print("X_hat_t:\n",X_hat_t,"\nP_t:\n",P_t)
    
    Z_t=measurmens[i].transpose()
    Z_t=Z_t.reshape(Z_t.shape[0],-1)
    
    print(Z_t.shape)
    
    X_t,P_t=update(X_hat_t,P_hat_t,Z_t,R_t,H_t)
    print("Update:")
    print("X_t:\n",X_t,"\nP_t:\n",P_t)
    X_hat_t=X_t
    P_hat_t=P_t
    
    print("=========================================")
    print("Opencv Kalman Output:")
    print("X_t:\n",opencvKalmanOutput[i])

import numpy as np

np.set_printoptions(threshold=3)

np.set_printoptions(suppress=True)

from numpy import genfromtxt

#Notation used coming from: https://www.bzarg.com/p/how-a-kalman-filter-works-in-pictures/

def prediction(X_hat_t_1,P_t_1,F_t,B_t,U_t,Q_t):

X_hat_t=F_t.dot(X_hat_t_1)+(B_t.dot(U_t).reshape(B_t.shape[0],-1) )

P_t=np.diag(np.diag(F_t.dot(P_t_1).dot(F_t.transpose())))+Q_t

return X_hat_t,P_t

def update(X_hat_t,P_t,Z_t,R_t,H_t):

K_prime=P_t.dot(H_t.transpose()).dot( np.linalg.inv ( H_t.dot(P_t).dot(H_t.transpose()) +R_t ) )

print("K:\n",K_prime)

X_t=X_hat_t+K_prime.dot(Z_t-H_t.dot(X_hat_t))

P_t=P_t-K_prime.dot(H_t).dot(P_t)

return X_t,P_t

acceleration=0

delta_t=1/20#milisecond

groundTruth = genfromtxt('data/groundTruth.csv', delimiter=',',skip_header=1)

#Observations: position_X, position_Y

measurmens = genfromtxt('data/measurmens.csv', delimiter=',',skip_header=1)

#Checking our result with OpenCV

opencvKalmanOutput = genfromtxt('data/kalmanv.csv', delimiter=',',skip_header=1)

#Transition matrix

F_t=np.array([ [1 ,0,delta_t,0] , [0,1,0,delta_t] , [0,0,1,0] , [0,0,0,1] ])

#Initial State cov

P_t= np.identity(4)*0.2

#Process cov

Q_t= np.identity(4)

#Control matrix

B_t=np.array( [ [0] , [0], [0] , [0] ])

#Control vector

U_t=acceleration

#Measurment Matrix

H_t = np.array([ [1, 0, 0, 0], [ 0, 1, 0, 0]])

#Measurment cov

R_t= np.identity(2)*5

# Initial State

X_hat_t = np.array( [[0],[0],[0],[0]] )

print("X_hat_t",X_hat_t.shape)

print("P_t",P_t.shape)

print("F_t",F_t.shape)

print("B_t",B_t.shape)

print("Q_t",Q_t.shape)

print("R_t",R_t.shape)

print("H_t",H_t.shape)

for i in range(measurmens.shape[0]):

X_hat_t,P_hat_t = prediction(X_hat_t,P_t,F_t,B_t,U_t,Q_t)

print("Prediction:")

print("X_hat_t:\n",X_hat_t,"\nP_t:\n",P_t)

Z_t=measurmens[i].transpose()

Z_t=Z_t.reshape(Z_t.shape[0],-1)

print(Z_t.shape)

X_t,P_t=update(X_hat_t,P_hat_t,Z_t,R_t,H_t)

print("Update:")

print("X_t:\n",X_t,"\nP_t:\n",P_t)

X_hat_t=X_t

P_hat_t=P_t

print("=========================================")

print("Opencv Kalman Output:")

print("X_t:\n",opencvKalmanOutput[i])

C++ and OpenCV Kalman Filter Rapidcsv has been downloaded from

Kalman Filter Explained With Python Code From Scratch Read More »

Eigen unaryExpr (Function Pointer, Lambda Expression) Example

Leave a Comment / C++, Linear Algebra, Tutorials / admin

double ramp(double x)
{
    if (x > 0)
        return x;
    else
        return 0;
}

void unaryExprExample()
{
    //unaryExpr is a const function so it will not give you the possibility to modify values in place
    Eigen::ArrayXd x = Eigen::ArrayXd::Random(5);
    std::cout<<x <<std::endl;
    x = x.unaryExpr([](double elem) // changed type of parameter
    {
        return elem < 0.0 ? 0.0 : 1.0; // return instead of assignment
    });
    std::cout<<x <<std::endl;
    std::cout << x.unaryExpr(std::ptr_fun(ramp)) << std::endl;

}

double ramp(double x)

{

if (x > 0)

return x;

else

return 0;

}

void unaryExprExample()

{

//unaryExpr is a const function so it will not give you the possibility to modify values in place

Eigen::ArrayXd x = Eigen::ArrayXd::Random(5);

std::cout<<x <<std::endl;

x = x.unaryExpr([](double elem) // changed type of parameter

{

return elem < 0.0 ? 0.0 : 1.0; // return instead of assignment

});

std::cout<<x <<std::endl;

std::cout << x.unaryExpr(std::ptr_fun(ramp)) << std::endl;

}

Eigen unaryExpr (Function Pointer, Lambda Expression) Example Read More »

Matrix Decomposition with Eigen: QR, Cholesky Decomposition LU, UL

Leave a Comment / C++, Linear Algebra, Tutorials / admin

void qRDecomposition(Eigen::MatrixXd &A,Eigen::MatrixXd &Q, Eigen::MatrixXd &R)
{
    /*
        A=QR
        Q: is orthogonal matrix-> columns of Q are orthonormal
        R: is upper triangulate matrix

        this is possible when columns of A are linearly indipendent

    */

    Eigen::MatrixXd thinQ(A.rows(),A.cols() ), q(A.rows(),A.rows());

    Eigen::HouseholderQR<Eigen::MatrixXd> householderQR(A);
    q = householderQR.householderQ();
    thinQ.setIdentity();
    Q = householderQR.householderQ() * thinQ;
    R=Q.transpose()*A;
}

void qRExample()
{

    Eigen::MatrixXd A;
    A.setRandom(3,4);

    std::cout<<"A" <<std::endl;
    std::cout<<A <<std::endl;
    Eigen::MatrixXd Q(A.rows(),A.rows());
    Eigen::MatrixXd R(A.rows(),A.cols());

    /////////////////////////////////HouseholderQR////////////////////////
    Eigen::MatrixXd thinQ(A.rows(),A.cols() ), q(A.rows(),A.rows());

    Eigen::HouseholderQR<Eigen::MatrixXd> householderQR(A);
    q = householderQR.householderQ();
    thinQ.setIdentity();
    Q = householderQR.householderQ() * thinQ;

    std::cout << "HouseholderQR" <<std::endl;

    std::cout << "Q" <<std::endl;
    std::cout << Q <<std::endl;

    R = householderQR.matrixQR().template  triangularView<Eigen::Upper>();
    std::cout << R<<std::endl;
    std::cout << R.rows()<<std::endl;
    std::cout << R.cols()<<std::endl;


    R=Q.transpose()*A;
// 	std::cout << "R" <<std::endl;
// 	std::cout << R<<std::endl;

    std::cout << "A-Q*R" <<std::endl;
    std::cout << A-Q*R <<std::endl;

    /////////////////////////////////ColPivHouseholderQR////////////////////////
    Eigen::ColPivHouseholderQR<Eigen::MatrixXd> colPivHouseholderQR(A.rows(), A.cols());
    colPivHouseholderQR.compute(A);
    //R = colPivHouseholderQR.matrixR().template triangularView<Upper>();
    R = colPivHouseholderQR.matrixR();
    Q = colPivHouseholderQR.matrixQ();

    std::cout << "ColPivHouseholderQR" <<std::endl;

    std::cout << "Q" <<std::endl;
    std::cout << Q <<std::endl;

    std::cout << "R" <<std::endl;
    std::cout << R <<std::endl;

    std::cout << "A-Q*R" <<std::endl;
    std::cout << A-Q*R <<std::endl;

    /////////////////////////////////FullPivHouseholderQR////////////////////////
    std::cout << "FullPivHouseholderQR" <<std::endl;

    Eigen::FullPivHouseholderQR<Eigen::MatrixXd> fullPivHouseholderQR(A.rows(), A.cols());
    fullPivHouseholderQR.compute(A);
    Q=fullPivHouseholderQR.matrixQ();
    R=fullPivHouseholderQR.matrixQR().template  triangularView<Eigen::Upper>();

    std::cout << "Q" <<std::endl;
    std::cout << Q <<std::endl;

    std::cout << "R" <<std::endl;
    std::cout << R <<std::endl;

    std::cout << "A-Q*R" <<std::endl;
    std::cout << A-Q*R <<std::endl;

}

void lDUDecomposition()
{
/*

    L: lower triangular matrix L
    U: upper triangular matrix U
    D: is a diagonal matrix
    A=LDU


*/
}

void lUDecomposition()
{
/*
    L: lower triangular matrix L
    U: upper triangular matrix U
    A=LU
*/
}

double exp(double x) // the functor we want to apply
{
    std::setprecision(5);
        return std::trunc(x);
}

void gramSchmidtOrthogonalization(Eigen::MatrixXd &matrix,Eigen::MatrixXd &orthonormalMatrix)
{
/*
    In this method you make every column perpendicular to it's previous columns,
    here if a and b are representation vector of two columns, c=b-((b.a)/|a|).a
        ^
       /
    b /
     /
    /
    ---------->
        a

        ^
       /|
    b / |
     /  | c
    /   |
    ---------->
        a

    you just have to normilze every vector after make it perpendicular to previous columns
    so:
    q1=a.normalized();
    q2=b-(b.q1).q1
    q2=q2.normalized();
    q3=c-(c.q1).q1 - (c.q2).q2
    q3=q3.normalized();


    Now we have Q, but we want A=QR so we just multiply both side by Q.transpose(), since Q is orthonormal, Q*Q.transpose() is I
    A=QR;
    Q.transpose()*A=R;
*/
    Eigen::VectorXd col;
    for(int i=0;i<matrix.cols();i++)
    {
        col=matrix.col(i);
        col=col.normalized();
        for(int j=0;j<i-1;j++)
        {
            //orthonormalMatrix.col(i)
        }

        orthonormalMatrix.col(i)=col;
    }
    Eigen::MatrixXd A(4,3);

    A<<1,2,3,-1,1,1,1,1,1,1,1,1;
    Eigen::Vector4d a=A.col(0);
    Eigen::Vector4d b=A.col(1);
    Eigen::Vector4d c=A.col(2);

    Eigen::Vector4d q1=  a.normalized();
    Eigen::Vector4d q2=b-(b.dot(q1))*q1;
    q2=q2.normalized();

    Eigen::Vector4d q3=c-(c.dot(q1))*q1 - (c.dot(q2))*q2;
    q3=q3.normalized();

    std::cout<< "q1:"<<std::endl;
    std::cout<< q1<<std::endl;
    std::cout<< "q2"<<std::endl;
    std::cout<< q2<<std::endl;
    std::cout<< "q3:"<<std::endl;
    std::cout<< q3<<std::endl;

    Eigen::MatrixXd Q(4,3);
    Q.col(0)=q1;
    Q.col(1)=q2;
    Q.col(2)=q3;

    Eigen::MatrixXd R(3,3);
    R=Q.transpose()*(A);


    std::cout<<"Q"<<std::endl;
    std::cout<< Q<<std::endl;


    std::cout<<"R"<<std::endl;
    std::cout<< R.unaryExpr(std::ptr_fun(exp))<<std::endl;



    //MatrixXd A(4,3), thinQ(4,3), Q(4,4);

    Eigen::MatrixXd thinQ(4,3), q(4,4);

    //A.setRandom();
    Eigen::HouseholderQR<Eigen::MatrixXd> qr(A);
    q = qr.householderQ();
    thinQ.setIdentity();
    thinQ = qr.householderQ() * thinQ;
    std::cout << "Q computed by Eigen" << "\n\n" << thinQ << "\n\n";
    std::cout << q << "\n\n" << thinQ << "\n\n";


}

void gramSchmidtOrthogonalizationExample()
{
    Eigen::MatrixXd matrix(3,4),orthonormalMatrix(3,4) ;
    matrix=Eigen::MatrixXd::Random(3,4);////A.setRandom();


    gramSchmidtOrthogonalization(matrix,orthonormalMatrix);
}

void choleskyDecompositionExample()
{
/*
Positive-definite matrix:
    Matrix Mnxn is said to be positive definite if the scalar zTMz is strictly positive for every non-zero
    column vector z n real numbers. zTMz>0


Hermitian matrix:
    Matrix Mnxn is said to be positive definite if the scalar z*Mz is strictly positive for every non-zero
    column vector z n real numbers. z*Mz>0
    z* is the conjugate transpose of z.

Positive semi-definite same as above except zTMz>=0 or  z*Mz>=0


    Example:
            ┌2  -1  0┐
        M=	|-1  2 -1|
            |0 - 1  2|
            └        ┘
          ┌ a ┐
        z=| b |
          | c |
          └	  ┘
        zTMz=a^2 +c^2+ (a-b)^2+ (b-c)^2

Cholesky decomposition:
Cholesky decomposition of a Hermitian positive-definite matrix A is:
    A=LL*

    L is a lower triangular matrix with real and positive diagonal entries
    L* is the conjugate transpose of L
*/

    Eigen::MatrixXd A(3,3);
    A << 6, 0, 0, 0, 4, 0, 0, 0, 7;
    Eigen::MatrixXd L( A.llt().matrixL() );
    Eigen::MatrixXd L_T=L.adjoint();//conjugate transpose

    std::cout << "L" << std::endl;
    std::cout << L << std::endl;
    std::cout << "L_T" << std::endl;
    std::cout << L_T << std::endl;
    std::cout << "A" << std::endl;
    std::cout << A << std::endl;
    std::cout << "L*L_T" << std::endl;
    std::cout << L*L_T << std::endl;

}

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void qRDecomposition(Eigen::MatrixXd &A,Eigen::MatrixXd &Q, Eigen::MatrixXd &R)

{

A=QR

Q: is orthogonal matrix-> columns of Q are orthonormal

R: is upper triangulate matrix

this is possible when columns of A are linearly indipendent

Eigen::MatrixXd thinQ(A.rows(),A.cols() ), q(A.rows(),A.rows());

Eigen::HouseholderQR<Eigen::MatrixXd> householderQR(A);

q = householderQR.householderQ();

thinQ.setIdentity();

Q = householderQR.householderQ() * thinQ;

R=Q.transpose()*A;

}

void qRExample()

{

Eigen::MatrixXd A;

A.setRandom(3,4);

std::cout<<"A" <<std::endl;

std::cout<<A <<std::endl;

Eigen::MatrixXd Q(A.rows(),A.rows());

Eigen::MatrixXd R(A.rows(),A.cols());

/////////////////////////////////HouseholderQR////////////////////////

Eigen::MatrixXd thinQ(A.rows(),A.cols() ), q(A.rows(),A.rows());

Eigen::HouseholderQR<Eigen::MatrixXd> householderQR(A);

q = householderQR.householderQ();

thinQ.setIdentity();

Q = householderQR.householderQ() * thinQ;

std::cout << "HouseholderQR" <<std::endl;

std::cout << "Q" <<std::endl;

std::cout << Q <<std::endl;

R = householderQR.matrixQR().template triangularView<Eigen::Upper>();

std::cout << R<<std::endl;

std::cout << R.rows()<<std::endl;

std::cout << R.cols()<<std::endl;

R=Q.transpose()*A;

// std::cout << "R" <<std::endl;

// std::cout << R<<std::endl;

std::cout << "A-Q*R" <<std::endl;

std::cout << A-Q*R <<std::endl;

/////////////////////////////////ColPivHouseholderQR////////////////////////

Eigen::ColPivHouseholderQR<Eigen::MatrixXd> colPivHouseholderQR(A.rows(), A.cols());

colPivHouseholderQR.compute(A);

//R = colPivHouseholderQR.matrixR().template triangularView<Upper>();

R = colPivHouseholderQR.matrixR();

Q = colPivHouseholderQR.matrixQ();

std::cout << "ColPivHouseholderQR" <<std::endl;

std::cout << "Q" <<std::endl;

std::cout << Q <<std::endl;

std::cout << "R" <<std::endl;

std::cout << R <<std::endl;

std::cout << "A-Q*R" <<std::endl;

std::cout << A-Q*R <<std::endl;

/////////////////////////////////FullPivHouseholderQR////////////////////////

std::cout << "FullPivHouseholderQR" <<std::endl;

Eigen::FullPivHouseholderQR<Eigen::MatrixXd> fullPivHouseholderQR(A.rows(), A.cols());

fullPivHouseholderQR.compute(A);

Q=fullPivHouseholderQR.matrixQ();

R=fullPivHouseholderQR.matrixQR().template triangularView<Eigen::Upper>();

std::cout << "Q" <<std::endl;

std::cout << Q <<std::endl;

std::cout << "R" <<std::endl;

std::cout << R <<std::endl;

std::cout << "A-Q*R" <<std::endl;

std::cout << A-Q*R <<std::endl;

}

void lDUDecomposition()

{

L: lower triangular matrix L

U: upper triangular matrix U

D: is a diagonal matrix

A=LDU

}

void lUDecomposition()

{

L: lower triangular matrix L

U: upper triangular matrix U

A=LU

}

double exp(double x) // the functor we want to apply

{

std::setprecision(5);

return std::trunc(x);

}

void gramSchmidtOrthogonalization(Eigen::MatrixXd &matrix,Eigen::MatrixXd &orthonormalMatrix)

{

In this method you make every column perpendicular to it's previous columns,

here if a and b are representation vector of two columns, c=b-((b.a)/|a|).a

b /

---------->

b / |

/ | c

/ |

---------->

you just have to normilze every vector after make it perpendicular to previous columns

so:

q1=a.normalized();

q2=b-(b.q1).q1

q2=q2.normalized();

q3=c-(c.q1).q1 - (c.q2).q2

q3=q3.normalized();

Now we have Q, but we want A=QR so we just multiply both side by Q.transpose(), since Q is orthonormal, Q*Q.transpose() is I

A=QR;

Q.transpose()*A=R;

Eigen::VectorXd col;

for(int i=0;i<matrix.cols();i++)

{

col=matrix.col(i);

col=col.normalized();

for(int j=0;j<i-1;j++)

{

//orthonormalMatrix.col(i)

}

orthonormalMatrix.col(i)=col;

}

Eigen::MatrixXd A(4,3);

A<<1,2,3,-1,1,1,1,1,1,1,1,1;

Eigen::Vector4d a=A.col(0);

Eigen::Vector4d b=A.col(1);

Eigen::Vector4d c=A.col(2);

Eigen::Vector4d q1= a.normalized();

Eigen::Vector4d q2=b-(b.dot(q1))*q1;

q2=q2.normalized();

Eigen::Vector4d q3=c-(c.dot(q1))*q1 - (c.dot(q2))*q2;

q3=q3.normalized();

std::cout<< "q1:"<<std::endl;

std::cout<< q1<<std::endl;

std::cout<< "q2"<<std::endl;

std::cout<< q2<<std::endl;

std::cout<< "q3:"<<std::endl;

std::cout<< q3<<std::endl;

Eigen::MatrixXd Q(4,3);

Q.col(0)=q1;

Q.col(1)=q2;

Q.col(2)=q3;

Eigen::MatrixXd R(3,3);

R=Q.transpose()*(A);

std::cout<<"Q"<<std::endl;

std::cout<< Q<<std::endl;

std::cout<<"R"<<std::endl;

std::cout<< R.unaryExpr(std::ptr_fun(exp))<<std::endl;

//MatrixXd A(4,3), thinQ(4,3), Q(4,4);

Eigen::MatrixXd thinQ(4,3), q(4,4);

//A.setRandom();

Eigen::HouseholderQR<Eigen::MatrixXd> qr(A);

q = qr.householderQ();

thinQ.setIdentity();

thinQ = qr.householderQ() * thinQ;

std::cout << "Q computed by Eigen" << "\n\n" << thinQ << "\n\n";

std::cout << q << "\n\n" << thinQ << "\n\n";

}

void gramSchmidtOrthogonalizationExample()

{

Eigen::MatrixXd matrix(3,4),orthonormalMatrix(3,4) ;

matrix=Eigen::MatrixXd::Random(3,4);////A.setRandom();

gramSchmidtOrthogonalization(matrix,orthonormalMatrix);

}

void choleskyDecompositionExample()

{

Positive-definite matrix:

Matrix Mnxn is said to be positive definite if the scalar zTMz is strictly positive for every non-zero

column vector z n real numbers. zTMz>0

Hermitian matrix:

Matrix Mnxn is said to be positive definite if the scalar z*Mz is strictly positive for every non-zero

column vector z n real numbers. z*Mz>0

z* is the conjugate transpose of z.

Positive semi-definite same as above except zTMz>=0 or z*Mz>=0

Example:

┌2 -1 0┐

M= |-1 2 -1|

|0 - 1 2|

└ ┘

┌ a ┐

z=| b |

| c |

└ ┘

zTMz=a^2 +c^2+ (a-b)^2+ (b-c)^2

Cholesky decomposition:

Cholesky decomposition of a Hermitian positive-definite matrix A is:

A=LL*

L is a lower triangular matrix with real and positive diagonal entries

L* is the conjugate transpose of L

Eigen::MatrixXd A(3,3);

A << 6, 0, 0, 0, 4, 0, 0, 0, 7;

Eigen::MatrixXd L( A.llt().matrixL() );

Eigen::MatrixXd L_T=L.adjoint();//conjugate transpose

std::cout << "L" << std::endl;

std::cout << L << std::endl;

std::cout << "L_T" << std::endl;

std::cout << L_T << std::endl;

std::cout << "A" << std::endl;

std::cout << A << std::endl;

std::cout << "L*L_T" << std::endl;

std::cout << L*L_T << std::endl;

}

Matrix Decomposition with Eigen: QR, Cholesky Decomposition LU, UL Read More »

Eigen Memory Mapping

Leave a Comment / C++, Tutorials / admin

struct point {
    double a;
    double b;
};

void EigenMapExample()
{
    ////////////////////////////////////////First Example/////////////////////////////////////////
    Eigen::VectorXd solutionVec(12,1);
    solutionVec<<1,2,3,4,5,6,7,8,9,10,11,12;
    Eigen::Map<Eigen::MatrixXd> solutionColMajor(solutionVec.data(),4,3);

    Eigen::Map<Eigen::Matrix<double, 3, 4, Eigen::RowMajor> >solutionRowMajor (solutionVec.data());


    std::cout << "solutionColMajor: "<< std::endl;
    std::cout << solutionColMajor<< std::endl;

    std::cout << "solutionRowMajor"<< std::endl;
    std::cout << solutionRowMajor<< std::endl;

    ////////////////////////////////////////Second Example/////////////////////////////////////////

    // https://stackoverflow.com/questions/49813340/stdvectoreigenvector3d-to-eigenmatrixxd-eigen

    int array[9];
    for (int i = 0; i < 9; ++i) {
        array[i] = i;
    }

    Eigen::MatrixXi a(9, 1);
    a = Eigen::Map<Eigen::Matrix3i>(array);
    std::cout << a << std::endl;

    std::vector<point> pointsVec;
    point point1, point2, point3;

    point1.a = 1.0;
    point1.b = 1.5;

    point2.a = 2.4;
    point2.b = 3.5;

    point3.a = -1.3;
    point3.b = 2.4;

    pointsVec.push_back(point1);
    pointsVec.push_back(point2);
    pointsVec.push_back(point3);

    Eigen::Matrix2Xd pointsMatrix2d = Eigen::Map<Eigen::Matrix2Xd>(
        reinterpret_cast<double*>(pointsVec.data()), 2,  long(pointsVec.size()));

    Eigen::MatrixXd pointsMatrixXd = Eigen::Map<Eigen::MatrixXd>(
        reinterpret_cast<double*>(pointsVec.data()), 2, long(pointsVec.size()));

    std::cout << pointsMatrix2d << std::endl;
    std::cout << "==============================" << std::endl;
    std::cout << pointsMatrixXd << std::endl;
    std::cout << "==============================" << std::endl;

    std::vector<Eigen::Vector3d> eigenPointsVec;
    eigenPointsVec.push_back(Eigen::Vector3d(2, 4, 1));
    eigenPointsVec.push_back(Eigen::Vector3d(7, 3, 9));
    eigenPointsVec.push_back(Eigen::Vector3d(6, 1, -1));
    eigenPointsVec.push_back(Eigen::Vector3d(-6, 9, 8));

    Eigen::MatrixXd pointsMatrix = Eigen::Map<Eigen::MatrixXd>(eigenPointsVec[0].data(), 3, long(eigenPointsVec.size()));

    std::cout << pointsMatrix << std::endl;
    std::cout << "==============================" << std::endl;

    pointsMatrix = Eigen::Map<Eigen::MatrixXd>(reinterpret_cast<double*>(eigenPointsVec.data()), 3, long(eigenPointsVec.size()));

    std::cout << pointsMatrix << std::endl;

    std::vector<double> aa = { 1, 2, 3, 4 };
    Eigen::VectorXd b = Eigen::Map<Eigen::VectorXd, Eigen::Unaligned>(aa.data(), long(aa.size()));
}

struct point {

double a;

double b;

};

void EigenMapExample()

{

////////////////////////////////////////First Example/////////////////////////////////////////

Eigen::VectorXd solutionVec(12,1);

solutionVec<<1,2,3,4,5,6,7,8,9,10,11,12;

Eigen::Map<Eigen::MatrixXd> solutionColMajor(solutionVec.data(),4,3);

Eigen::Map<Eigen::Matrix<double, 3, 4, Eigen::RowMajor> >solutionRowMajor (solutionVec.data());

std::cout << "solutionColMajor: "<< std::endl;

std::cout << solutionColMajor<< std::endl;

std::cout << "solutionRowMajor"<< std::endl;

std::cout << solutionRowMajor<< std::endl;

////////////////////////////////////////Second Example/////////////////////////////////////////

// https://stackoverflow.com/questions/49813340/stdvectoreigenvector3d-to-eigenmatrixxd-eigen

int array[9];

for (int i = 0; i < 9; ++i) {

array[i] = i;

}

Eigen::MatrixXi a(9, 1);

a = Eigen::Map<Eigen::Matrix3i>(array);

std::cout << a << std::endl;

std::vector<point> pointsVec;

point point1, point2, point3;

point1.a = 1.0;

point1.b = 1.5;

point2.a = 2.4;

point2.b = 3.5;

point3.a = -1.3;

point3.b = 2.4;

pointsVec.push_back(point1);

pointsVec.push_back(point2);

pointsVec.push_back(point3);

Eigen::Matrix2Xd pointsMatrix2d = Eigen::Map<Eigen::Matrix2Xd>(

reinterpret_cast<double*>(pointsVec.data()), 2, long(pointsVec.size()));

Eigen::MatrixXd pointsMatrixXd = Eigen::Map<Eigen::MatrixXd>(

reinterpret_cast<double*>(pointsVec.data()), 2, long(pointsVec.size()));

std::cout << pointsMatrix2d << std::endl;

std::cout << "==============================" << std::endl;

std::cout << pointsMatrixXd << std::endl;

std::cout << "==============================" << std::endl;

std::vector<Eigen::Vector3d> eigenPointsVec;

eigenPointsVec.push_back(Eigen::Vector3d(2, 4, 1));

eigenPointsVec.push_back(Eigen::Vector3d(7, 3, 9));

eigenPointsVec.push_back(Eigen::Vector3d(6, 1, -1));

eigenPointsVec.push_back(Eigen::Vector3d(-6, 9, 8));

Eigen::MatrixXd pointsMatrix = Eigen::Map<Eigen::MatrixXd>(eigenPointsVec[0].data(), 3, long(eigenPointsVec.size()));

std::cout << pointsMatrix << std::endl;

std::cout << "==============================" << std::endl;

pointsMatrix = Eigen::Map<Eigen::MatrixXd>(reinterpret_cast<double*>(eigenPointsVec.data()), 3, long(eigenPointsVec.size()));

std::cout << pointsMatrix << std::endl;

std::vector<double> aa = { 1, 2, 3, 4 };

Eigen::VectorXd b = Eigen::Map<Eigen::VectorXd, Eigen::Unaligned>(aa.data(), long(aa.size()));

}

Eigen Memory Mapping Read More »

Eigen Arrays, Matrices and Vectors: Definition, Initialization Resizing, Populating and Coefficient Wise Operations

Leave a Comment / C++, Linear Algebra, Tutorials / admin

    std::cout <<"/////////////////Definition////////////////"<<std::endl;

/*
    Definition of vectors and matrices in Eigen comes in the following form:

    Eigen::MatrixSizeType
    Eigen::VectorSizeType
    Eigen::ArraySizeType

    Size can be 2,3,4 for fixed size square matrices or X for dynamic size
    Type can be:
    i for integer,
    f for float,
    d for double,
    c for complex,
    cf for complex float,
    cd for complex double.
*/

    // Vector3f is a fixed column vector of 3 floats:
    Eigen::Vector3f objVector3f;

    // RowVector2i is a fixed row vector of 3 integer:
    Eigen::RowVector2i objRowVector2i;

    // VectorXf is a column vector of size 10 floats:
    Eigen::VectorXf objv(10);


    Eigen::Matrix4d m; // 4x4 double

    Eigen::Matrix4cd objMatrix4cd; // 4x4 double complex


    //a is a 3x3 matrix, with a static float[9] array of uninitialized coefficients,
    Eigen::Matrix3f a;

    //b is a dynamic-size matrix whose size is currently 0x0, and whose array of coefficients hasn't yet been allocated at all.
    Eigen::MatrixXf b;

    //A is a 10x15 dynamic-size matrix, with allocated but currently uninitialized coefficients.
    Eigen::MatrixXf A(10, 15);

    //V is a dynamic-size vector of size 30, with allocated but currently uninitialized coefficients.
    Eigen::VectorXf V(30);


    //Template style definition
    // Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic>  &matrix

    Eigen::Matrix<double, 2, 3> my_matrix;
    my_matrix << 1, 2, 3, 4, 5, 6;

    // ArrayXf
    Eigen::Array<float, Eigen::Dynamic, 1> a1;
    // Array3f
    Eigen::Array<float, 3, 1> a2;
    // ArrayXXd
    Eigen::Array<double, Eigen::Dynamic, Eigen::Dynamic> a3;
    // Array33d
    Eigen::Array<double, 3, 3> a4;
    Eigen::Matrix3d matrix_from_array = a4.matrix();


    std::cout <<"///////////////////Initialization//////////////////"<< std::endl;

    Eigen::Matrix2d a_2d;
    a_2d.setRandom();

    a_2d.setConstant(4.3);

    Eigen::MatrixXd identity=Eigen::MatrixXd::Identity(6,6);

    Eigen::MatrixXd zeros=Eigen::MatrixXd::Zero(3, 3);

    Eigen::ArrayXXf table(10, 4);
    table.col(0) = Eigen::ArrayXf::LinSpaced(10, 0, 90);


    std::cout <<"/////////////Matrix Coefficient Wise Operations///////////////////"<< std::endl;
    int i,j;
    std::cout << my_matrix << std::endl;
    std::cout << my_matrix.transpose()<< std::endl;

    std::cout<<my_matrix.minCoeff(&i, &j)<<std::endl;
    std::cout<<my_matrix.maxCoeff(&i, &j)<<std::endl;
    std::cout<<my_matrix.prod()<<std::endl;
    std::cout<<my_matrix.sum()<<std::endl;
    std::cout<<my_matrix.mean()<<std::endl;
    std::cout<<my_matrix.trace()<<std::endl;
    std::cout<<my_matrix.colwise().mean()<<std::endl;
    std::cout<<my_matrix.rowwise().maxCoeff()<<std::endl;
    std::cout<<my_matrix.lpNorm<2>()<<std::endl;
    std::cout<<my_matrix.lpNorm<Eigen::Infinity>()<<std::endl;
    std::cout<<(my_matrix.array()>0).all()<<std::endl;// if all elemnts are positive
    std::cout<<(my_matrix.array()>2).any()<<std::endl;//if any element is greater than 2
    std::cout<<(my_matrix.array()>1).count()<<std::endl;// count the number of elements greater than 1
    std::cout << my_matrix.array() - 2 << std::endl;
    std::cout << my_matrix.array().abs() << std::endl;
    std::cout << my_matrix.array().square() << std::endl;
    std::cout << my_matrix.array() * my_matrix.array() << std::endl;
    std::cout << my_matrix.array().exp() << std::endl;
    std::cout << my_matrix.array().log() << std::endl;
    std::cout << my_matrix.array().sqrt() << std::endl;

    std::cout <<"//////////////////Block Elements Access////////////////////"<< std::endl;
    //Block of size (p,q), starting at (i,j)	matrix.block(i,j,p,q)
    Eigen::MatrixXf mat(4, 4);
    mat << 1, 2, 3, 4,
        5, 6, 7, 8,
        9, 10, 11, 12,
        13, 14, 15, 16;
    std::cout << "Block in the middle" << std::endl;
    std::cout << mat.block<2, 2>(1, 1) << std::endl;

    for (int i = 1; i <= 3; ++i)
    {
        std::cout << "Block of size " << i << "x" << i << std::endl;
        std::cout << mat.block(0, 0, i, i) << std::endl;
    }


    /////////////build matrix from vector, resizing matrix, dynamic size ////////////////
    Eigen::MatrixXd dynamicMatrix;
    int rows, cols;
    rows=3;
    cols=2;
    dynamicMatrix.resize(rows,cols);
    dynamicMatrix<<-1,7,3,4,5,1;

    //If you want a conservative variant of resize() which does not change the coefficients, use conservativeResize()
    dynamicMatrix.conservativeResize(dynamicMatrix.rows(), dynamicMatrix.cols()+1);
    dynamicMatrix.col(dynamicMatrix.cols()-1) = Eigen::Vector3d(1, 4, 0);

    dynamicMatrix.conservativeResize(dynamicMatrix.rows(), dynamicMatrix.cols()+1);
    dynamicMatrix.col(dynamicMatrix.cols()-1) = Eigen::Vector3d(5, -8, 6);
/*
    you should expect this:
     1  7  1  5
     3  4  4 -8
     5  1  0  6

*/
    std::cout<< dynamicMatrix<<std::endl;

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std::cout <<"/////////////////Definition////////////////"<<std::endl;

Definition of vectors and matrices in Eigen comes in the following form:

Eigen::MatrixSizeType

Eigen::VectorSizeType

Eigen::ArraySizeType

Size can be 2,3,4 for fixed size square matrices or X for dynamic size

Type can be:

i for integer,

f for float,

d for double,

c for complex,

cf for complex float,

cd for complex double.

// Vector3f is a fixed column vector of 3 floats:

Eigen::Vector3f objVector3f;

// RowVector2i is a fixed row vector of 3 integer:

Eigen::RowVector2i objRowVector2i;

// VectorXf is a column vector of size 10 floats:

Eigen::VectorXf objv(10);

Eigen::Matrix4d m; // 4x4 double

Eigen::Matrix4cd objMatrix4cd; // 4x4 double complex

//a is a 3x3 matrix, with a static float[9] array of uninitialized coefficients,

Eigen::Matrix3f a;

//b is a dynamic-size matrix whose size is currently 0x0, and whose array of coefficients hasn't yet been allocated at all.

Eigen::MatrixXf b;

//A is a 10x15 dynamic-size matrix, with allocated but currently uninitialized coefficients.

Eigen::MatrixXf A(10, 15);

//V is a dynamic-size vector of size 30, with allocated but currently uninitialized coefficients.

Eigen::VectorXf V(30);

//Template style definition

// Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic> &matrix

Eigen::Matrix<double, 2, 3> my_matrix;

my_matrix << 1, 2, 3, 4, 5, 6;

// ArrayXf

Eigen::Array<float, Eigen::Dynamic, 1> a1;

// Array3f

Eigen::Array<float, 3, 1> a2;

// ArrayXXd

Eigen::Array<double, Eigen::Dynamic, Eigen::Dynamic> a3;

// Array33d

Eigen::Array<double, 3, 3> a4;

Eigen::Matrix3d matrix_from_array = a4.matrix();

std::cout <<"///////////////////Initialization//////////////////"<< std::endl;

Eigen::Matrix2d a_2d;

a_2d.setRandom();

a_2d.setConstant(4.3);

Eigen::MatrixXd identity=Eigen::MatrixXd::Identity(6,6);

Eigen::MatrixXd zeros=Eigen::MatrixXd::Zero(3, 3);

Eigen::ArrayXXf table(10, 4);

table.col(0) = Eigen::ArrayXf::LinSpaced(10, 0, 90);

std::cout <<"/////////////Matrix Coefficient Wise Operations///////////////////"<< std::endl;

int i,j;

std::cout << my_matrix << std::endl;

std::cout << my_matrix.transpose()<< std::endl;

std::cout<<my_matrix.minCoeff(&i, &j)<<std::endl;

std::cout<<my_matrix.maxCoeff(&i, &j)<<std::endl;

std::cout<<my_matrix.prod()<<std::endl;

std::cout<<my_matrix.sum()<<std::endl;

std::cout<<my_matrix.mean()<<std::endl;

std::cout<<my_matrix.trace()<<std::endl;

std::cout<<my_matrix.colwise().mean()<<std::endl;

std::cout<<my_matrix.rowwise().maxCoeff()<<std::endl;

std::cout<<my_matrix.lpNorm<2>()<<std::endl;

std::cout<<my_matrix.lpNorm<Eigen::Infinity>()<<std::endl;

std::cout<<(my_matrix.array()>0).all()<<std::endl;// if all elemnts are positive

std::cout<<(my_matrix.array()>2).any()<<std::endl;//if any element is greater than 2

std::cout<<(my_matrix.array()>1).count()<<std::endl;// count the number of elements greater than 1

std::cout << my_matrix.array() - 2 << std::endl;

std::cout << my_matrix.array().abs() << std::endl;

std::cout << my_matrix.array().square() << std::endl;

std::cout << my_matrix.array() * my_matrix.array() << std::endl;

std::cout << my_matrix.array().exp() << std::endl;

std::cout << my_matrix.array().log() << std::endl;

std::cout << my_matrix.array().sqrt() << std::endl;

std::cout <<"//////////////////Block Elements Access////////////////////"<< std::endl;

//Block of size (p,q), starting at (i,j) matrix.block(i,j,p,q)

Eigen::MatrixXf mat(4, 4);

mat << 1, 2, 3, 4,

5, 6, 7, 8,

9, 10, 11, 12,

13, 14, 15, 16;

std::cout << "Block in the middle" << std::endl;

std::cout << mat.block<2, 2>(1, 1) << std::endl;

for (int i = 1; i <= 3; ++i)

{

std::cout << "Block of size " << i << "x" << i << std::endl;

std::cout << mat.block(0, 0, i, i) << std::endl;

}

/////////////build matrix from vector, resizing matrix, dynamic size ////////////////

Eigen::MatrixXd dynamicMatrix;

int rows, cols;

rows=3;

cols=2;

dynamicMatrix.resize(rows,cols);

dynamicMatrix<<-1,7,3,4,5,1;

//If you want a conservative variant of resize() which does not change the coefficients, use conservativeResize()

dynamicMatrix.conservativeResize(dynamicMatrix.rows(), dynamicMatrix.cols()+1);

dynamicMatrix.col(dynamicMatrix.cols()-1) = Eigen::Vector3d(1, 4, 0);

dynamicMatrix.conservativeResize(dynamicMatrix.rows(), dynamicMatrix.cols()+1);

dynamicMatrix.col(dynamicMatrix.cols()-1) = Eigen::Vector3d(5, -8, 6);

you should expect this:

1 7 1 5

3 4 4 -8

5 1 0 6

std::cout<< dynamicMatrix<<std::endl;

Eigen Arrays, Matrices and Vectors: Definition, Initialization Resizing, Populating and Coefficient Wise Operations Read More »

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