Dijkstra’s algorithm with C++

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]

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

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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

}

Finding Memory leaking, Stack and Heap overflow

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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 pretty big (multiples of a few kilobytes), it often leaves you with lots of room to overflow.

If your array is on the stack, it can be even worse as the stack is usually pretty large (up to several megabytes). This is also the cause of security concerns: writing past the bounds of an array on the stack may overwrite the return address of the function
and lead to arbitrary code execution (the famous “buffer overflow” security breaches).

By setting the following flags you can find the issue:

set(CMAKE_CXX_FLAGS "-fsanitize=address ${CMAKE_CXX_FLAGS}")
set(CMAKE_CXX_FLAGS "-fno-omit-frame-pointer ${CMAKE_CXX_FLAGS}")

1 2	set(CMAKE_CXX_FLAGS "-fsanitize=address ${CMAKE_CXX_FLAGS}") set(CMAKE_CXX_FLAGS "-fno-omit-frame-pointer ${CMAKE_CXX_FLAGS}")

Example:

void heapBufferOverflow()
{
    int *x=new int[3];
    for(int i=0;i<100000000;i++)
    {
        std::cout<<"i is: "<<i <<std::endl;
        std::cout<< x[i] <<std::endl;
    }
}

void stackBufferOverflow()
{
    char x[2];
    for(int i=0;i<100000000;i++)
    {
        std::cout<<"i is: "<<i <<std::endl;
        std::cout<< x[i] <<std::endl;
    }
}

void heapBufferOverflow()

{

int *x=new int[3];

for(int i=0;i<100000000;i++)

{

std::cout<<"i is: "<<i <<std::endl;

std::cout<< x[i] <<std::endl;

}

void stackBufferOverflow()

{

char x[2];

for(int i=0;i<100000000;i++)

{

std::cout<<"i is: "<<i <<std::endl;

std::cout<< x[i] <<std::endl;

}

References: [1]

Extended Kalman Filter Explained with Python Code

1 Reply

In the following code, I have implemented an Extended Kalman Filter for modeling the movement of a car with constant turn rate and velocity. The code is mainly based on this work (I did some bug fixing and some adaptation such that the code runs similar to the Kalman filter that I have earlier implemented).

Trajectory of the car, click on the image for large scale

import matplotlib.dates as mdates
import numpy as np
np.set_printoptions(threshold=3)
np.set_printoptions(suppress=True)

from numpy import genfromtxt
import matplotlib.pyplot as plt
from scipy.stats import norm
from sympy import Symbol, symbols, Matrix, sin, cos
from sympy import init_printing
from sympy.utilities.codegen import codegen
init_printing(use_latex=True)

x0 = []
x1 = []
x2 = []

def prediction(X_hat_t_1,P_t_1,Q_t,drivingStraight):
    X_hat_t=X_hat_t_1

    if drivingStraight: # Driving straight
        X_hat_t[0] = X_hat_t_1[0] + X_hat_t_1[3]*dt * np.cos(X_hat_t_1[2])
        X_hat_t[1] = X_hat_t_1[1] + X_hat_t_1[3]*dt * np.sin(X_hat_t_1[2])
        X_hat_t[2] = X_hat_t_1[2]
        X_hat_t[3] = X_hat_t_1[3] + X_hat_t_1[5]*dt
        X_hat_t[4] = 0.0000001 # avoid numerical issues in Jacobians
        X_hat_t[5] = X_hat_t_1[5]
       
    else: # otherwise
        X_hat_t[0] = X_hat_t_1[0] + (X_hat_t_1[3]/X_hat_t_1[4]) * (np.sin(X_hat_t_1[4]*dt+X_hat_t_1[2]) - np.sin(X_hat_t_1[2]))
        X_hat_t[1] = X_hat_t_1[1] + (X_hat_t_1[3]/X_hat_t_1[4]) * (-np.cos(X_hat_t_1[4]*dt+X_hat_t_1[2])+ np.cos(X_hat_t_1[2]))
        X_hat_t[2] = (X_hat_t_1[2] + X_hat_t_1[4]*dt + np.pi) % (2.0*np.pi) - np.pi
        X_hat_t[3] = X_hat_t_1[3] + X_hat_t_1[5]*dt
        X_hat_t[4] = X_hat_t_1[4] # Constant Turn Rate
        X_hat_t[5] = X_hat_t_1[5] # Constant Acceleration
        
    
    # Calculate the Jacobian of the Dynamic Matrix A
    # see "Calculate the Jacobian of the Dynamic Matrix with respect to the state vector"
    a13 = float((X_hat_t[3]/X_hat_t[4]) * (np.cos(X_hat_t[4]*dt+X_hat_t[2]) - np.cos(X_hat_t[2])))
    a14 = float((1.0/X_hat_t[4]) * (np.sin(X_hat_t[4]*dt+X_hat_t[2]) - np.sin(X_hat_t[2])))
    a15 = float((dt*X_hat_t[3]/X_hat_t[4])*np.cos(X_hat_t[4]*dt+X_hat_t[2]) - (X_hat_t[3]/X_hat_t[4]**2)*(np.sin(X_hat_t[4]*dt+X_hat_t[2]) - np.sin(X_hat_t[2])))
    a23 = float((X_hat_t[3]/X_hat_t[4]) * (np.sin(X_hat_t[4]*dt+X_hat_t[2]) - np.sin(X_hat_t[2])))
    a24 = float((1.0/X_hat_t[4]) * (-np.cos(X_hat_t[4]*dt+X_hat_t[2]) + np.cos(X_hat_t[2])))
    a25 = float((dt*X_hat_t[3]/X_hat_t[4])*np.sin(X_hat_t[4]*dt+X_hat_t[2]) - (X_hat_t[3]/X_hat_t[4]**2)*(-np.cos(X_hat_t[4]*dt+X_hat_t[2]) + np.cos(X_hat_t[2])))
    JA = np.matrix([[1.0, 0.0, a13, a14, a15, 0.0],
                    [0.0, 1.0, a23, a24, a25, 0.0],
                    [0.0, 0.0, 1.0, 0.0, dt, 0.0],
                    [0.0, 0.0, 0.0, 1.0, 0.0, dt],
                    [0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
                    [0.0, 0.0, 0.0, 0.0, 0.0, 1.0]])
    
    
    # Project the error covariance ahead
    P_t_1 = JA*P_t_1*JA.T + Q_t
    return X_hat_t,P_t_1

def update(X_hat_t,P_t,Z_t,R_t,GPSAvailable):
    hx = np.matrix([[float(X_hat_t[0])],
                    [float(X_hat_t[1])],
                    [float(X_hat_t[3])],
                    [float(X_hat_t[4])],
                    [float(X_hat_t[5])]])

    if GPSAvailable: # with 10Hz, every 5th step
        JH = np.matrix([[1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                        [0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
                        [0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
                        [0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
                        [0.0, 0.0, 0.0, 0.0, 0.0, 1.0]])
    else: # every other step
        JH = np.matrix([[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                        [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                        [0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
                        [0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
                        [0.0, 0.0, 0.0, 0.0, 0.0, 1.0]])        
    
    S = JH*P_t*JH.T + R_t
    K = (P_t*JH.T) * np.linalg.inv(S)
    #print("K:\n",K)
    # Update the estimate via
    Z = Z_t.reshape(JH.shape[0],1)
    #print("Z:\n",Z)
    y = Z - (hx)                         # Innovation or Residual
    X_t = X_hat_t + (K*y)

    # Update the error covariance
    
    
    I = np.eye(X_hat_t.shape[0])
    P_t = (I - (K*JH))*P_t


    x0.append(float(X_t[0]))
    x1.append(float(X_t[1]))
    x2.append(float(X_t[2]))
    return X_t,P_t

numstates=6 # States

dt = 1.0/50.0 # Sample Rate of the Measurements is 50Hz
dtGPS=1.0/10.0 # Sample Rate of GPS is 10Hz

vs, psis, dpsis, dts, xs, ys, lats, lons, axs = symbols('v \psi \dot\psi T x y lat lon a')

gs = Matrix([[xs+(vs/dpsis)*(sin(psis+dpsis*dts)-sin(psis))],
             [ys+(vs/dpsis)*(-cos(psis+dpsis*dts)+cos(psis))],
             [psis+dpsis*dts],
             [axs*dts + vs],
             [dpsis],
             [axs]])
state = Matrix([xs,ys,psis,vs,dpsis,axs])


#Initial State cov
P_t = np.diag([1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0])

#Measurment cov

varGPS = 5.0 # Standard Deviation of GPS Measurement
varspeed = 3.0 # Variance of the speed measurement
varyaw = 0.1 # Variance of the yawrate measurement
varacc = 1.0 # Variance of the longitudinal Acceleration
R_t = np.diag([varGPS**2, varGPS**2, varspeed**2, varyaw**2, varacc**2])


#Measurment Matrix
hs = Matrix([[xs],
             [ys],
             [vs],
             [dpsis],
             [axs]])

#Process cov
sGPS     = 0.5*8.8*dt**2  # assume 8.8m/s2 as maximum acceleration, forcing the vehicle
sCourse  = 0.1*dt # assume 0.1rad/s as maximum turn rate for the vehicle
sVelocity= 8.8*dt # assume 8.8m/s2 as maximum acceleration, forcing the vehicle
sYaw     = 1.0*dt # assume 1.0rad/s2 as the maximum turn rate acceleration for the vehicle
sAccel   = 0.5

Q_t = np.diag([sGPS**2, sGPS**2, sCourse**2, sVelocity**2, sYaw**2, sAccel**2])

path="/home/behnam/Kalman/"
datafile = '2014-03-26-000-Data.csv'

fullPath=path+datafile

def bytespdate2num(fmt, encoding='utf-8'):
    strconverter = mdates.strpdate2num(fmt)
    def bytesconverter(b):
        s = b.decode(encoding)
        return strconverter(s)
    return bytesconverter


date, time, millis, ax, ay, az, rollrate, pitchrate, yawrate, roll, pitch, yaw, speed, course, latitude, longitude, altitude, pdop, hdop, vdop, epe, fix, satellites_view, satellites_used, temp = np.loadtxt(fullPath, delimiter=',', unpack=True, 
                  converters={1:bytespdate2num('%H%M%S%f'),
                              0: bytespdate2num('%y%m%d')},
                  skiprows=1)


# A course of 0 means the Car is traveling north bound
# and 90 means it is traveling east bound.
# In the Calculation following, East is Zero and North is 90
# We need an offset.
course =(-course+90.0)

# ## Approx. Lat/Lon to Meters to check Location

# In[17]:

RadiusEarth = 6378388.0 # m
arc= 2.0*np.pi*(RadiusEarth+altitude)/360.0 # m/

dx = arc * np.cos(latitude*np.pi/180.0) * np.hstack((0.0, np.diff(longitude))) # in m
dy = arc * np.hstack((0.0, np.diff(latitude))) # in m

mx = np.cumsum(dx)
my = np.cumsum(dy)

ds = np.sqrt(dx**2+dy**2)

GPS=(ds!=0.0).astype('bool') # GPS Trigger for Kalman Filter

# ## Initial State
X_hat_t = np.matrix([[mx[0], my[0], course[0]/180.0*np.pi, speed[0]/3.6+0.001, yawrate[0]/180.0*np.pi, ax[0]]]).T

measurements = np.vstack((mx, my, speed/3.6, yawrate/180.0*np.pi, ax))
# Lenth of the measurement
m = measurements.shape[1]

for i in range(measurements.shape[1]):
#for i in range(3):
    
    if  np.abs(yawrate[i])<0.0001:
        drivingStraight=True
    else:
        drivingStraight=False
    
    
    X_hat_t,P_hat_t = prediction(X_hat_t,P_t,Q_t,drivingStraight)
    #print("Prediction:")
    #print("X_hat_t:\n",X_hat_t,"\nP_t:\n",P_hat_t)
   
    Z_t=measurements[:,i]
    if GPS[i]:
        GPSAvailable=True
    else:
        GPSAvailable=False
    
    X_t,P_t=update(X_hat_t,P_hat_t,Z_t,R_t,GPSAvailable)
    #print("Update:")
    #print("X_t:\n",X_t,"\nP_t:\n",P_t)
    X_hat_t=X_t
    P_hat_t=P_t
    
fig = plt.figure(figsize=(16,9))

# EKF State
plt.quiver(x0,x1,np.cos(x2), np.sin(x2), color='#94C600', units='xy', width=0.05, scale=0.5)
plt.plot(x0,x1, label='EKF Position', c='k', lw=5)

# Measurements
plt.scatter(mx[::5],my[::5], s=50, label='GPS Measurements', marker='+')

# Start/Goal
plt.scatter(x0[0],x1[0], s=60, label='Start', c='g')
plt.scatter(x0[-1],x1[-1], s=60, label='Goal', c='r')

plt.xlabel('X [m]')
plt.ylabel('Y [m]')
plt.title('Position')
plt.legend(loc='best')
plt.axis('equal')
plt.show()

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import matplotlib.dates as mdates

import numpy as np

np.set_printoptions(threshold=3)

np.set_printoptions(suppress=True)

from numpy import genfromtxt

import matplotlib.pyplot as plt

from scipy.stats import norm

from sympy import Symbol, symbols, Matrix, sin, cos

from sympy import init_printing

from sympy.utilities.codegen import codegen

init_printing(use_latex=True)

x0 = []

x1 = []

x2 = []

def prediction(X_hat_t_1,P_t_1,Q_t,drivingStraight):

X_hat_t=X_hat_t_1

if drivingStraight: # Driving straight

X_hat_t[0] = X_hat_t_1[0] + X_hat_t_1[3]*dt * np.cos(X_hat_t_1[2])

X_hat_t[1] = X_hat_t_1[1] + X_hat_t_1[3]*dt * np.sin(X_hat_t_1[2])

X_hat_t[2] = X_hat_t_1[2]

X_hat_t[3] = X_hat_t_1[3] + X_hat_t_1[5]*dt

X_hat_t[4] = 0.0000001 # avoid numerical issues in Jacobians

X_hat_t[5] = X_hat_t_1[5]

else: # otherwise

X_hat_t[0] = X_hat_t_1[0] + (X_hat_t_1[3]/X_hat_t_1[4]) * (np.sin(X_hat_t_1[4]*dt+X_hat_t_1[2]) - np.sin(X_hat_t_1[2]))

X_hat_t[1] = X_hat_t_1[1] + (X_hat_t_1[3]/X_hat_t_1[4]) * (-np.cos(X_hat_t_1[4]*dt+X_hat_t_1[2])+ np.cos(X_hat_t_1[2]))

X_hat_t[2] = (X_hat_t_1[2] + X_hat_t_1[4]*dt + np.pi) % (2.0*np.pi) - np.pi

X_hat_t[3] = X_hat_t_1[3] + X_hat_t_1[5]*dt

X_hat_t[4] = X_hat_t_1[4] # Constant Turn Rate

X_hat_t[5] = X_hat_t_1[5] # Constant Acceleration

# Calculate the Jacobian of the Dynamic Matrix A

# see "Calculate the Jacobian of the Dynamic Matrix with respect to the state vector"

a13 = float((X_hat_t[3]/X_hat_t[4]) * (np.cos(X_hat_t[4]*dt+X_hat_t[2]) - np.cos(X_hat_t[2])))

a14 = float((1.0/X_hat_t[4]) * (np.sin(X_hat_t[4]*dt+X_hat_t[2]) - np.sin(X_hat_t[2])))

a15 = float((dt*X_hat_t[3]/X_hat_t[4])*np.cos(X_hat_t[4]*dt+X_hat_t[2]) - (X_hat_t[3]/X_hat_t[4]**2)*(np.sin(X_hat_t[4]*dt+X_hat_t[2]) - np.sin(X_hat_t[2])))

a23 = float((X_hat_t[3]/X_hat_t[4]) * (np.sin(X_hat_t[4]*dt+X_hat_t[2]) - np.sin(X_hat_t[2])))

a24 = float((1.0/X_hat_t[4]) * (-np.cos(X_hat_t[4]*dt+X_hat_t[2]) + np.cos(X_hat_t[2])))

a25 = float((dt*X_hat_t[3]/X_hat_t[4])*np.sin(X_hat_t[4]*dt+X_hat_t[2]) - (X_hat_t[3]/X_hat_t[4]**2)*(-np.cos(X_hat_t[4]*dt+X_hat_t[2]) + np.cos(X_hat_t[2])))

JA = np.matrix([[1.0, 0.0, a13, a14, a15, 0.0],

[0.0, 1.0, a23, a24, a25, 0.0],

[0.0, 0.0, 1.0, 0.0, dt, 0.0],

[0.0, 0.0, 0.0, 1.0, 0.0, dt],

[0.0, 0.0, 0.0, 0.0, 1.0, 0.0],

[0.0, 0.0, 0.0, 0.0, 0.0, 1.0]])

# Project the error covariance ahead

P_t_1 = JA*P_t_1*JA.T + Q_t

return X_hat_t,P_t_1

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

hx = np.matrix([[float(X_hat_t[0])],

[float(X_hat_t[1])],

[float(X_hat_t[3])],

[float(X_hat_t[4])],

[float(X_hat_t[5])]])

if GPSAvailable: # with 10Hz, every 5th step

JH = np.matrix([[1.0, 0.0, 0.0, 0.0, 0.0, 0.0],

[0.0, 1.0, 0.0, 0.0, 0.0, 0.0],

[0.0, 0.0, 0.0, 1.0, 0.0, 0.0],

[0.0, 0.0, 0.0, 0.0, 1.0, 0.0],

[0.0, 0.0, 0.0, 0.0, 0.0, 1.0]])

else: # every other step

JH = np.matrix([[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],

[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],

[0.0, 0.0, 0.0, 1.0, 0.0, 0.0],

[0.0, 0.0, 0.0, 0.0, 1.0, 0.0],

[0.0, 0.0, 0.0, 0.0, 0.0, 1.0]])

S = JH*P_t*JH.T + R_t

K = (P_t*JH.T) * np.linalg.inv(S)

#print("K:\n",K)

# Update the estimate via

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

#print("Z:\n",Z)

y = Z - (hx) # Innovation or Residual

X_t = X_hat_t + (K*y)

# Update the error covariance

I = np.eye(X_hat_t.shape[0])

P_t = (I - (K*JH))*P_t

x0.append(float(X_t[0]))

x1.append(float(X_t[1]))

x2.append(float(X_t[2]))

return X_t,P_t

numstates=6 # States

dt = 1.0/50.0 # Sample Rate of the Measurements is 50Hz

dtGPS=1.0/10.0 # Sample Rate of GPS is 10Hz

vs, psis, dpsis, dts, xs, ys, lats, lons, axs = symbols('v \psi \dot\psi T x y lat lon a')

gs = Matrix([[xs+(vs/dpsis)*(sin(psis+dpsis*dts)-sin(psis))],

[ys+(vs/dpsis)*(-cos(psis+dpsis*dts)+cos(psis))],

[psis+dpsis*dts],

[axs*dts + vs],

[dpsis],

[axs]])

state = Matrix([xs,ys,psis,vs,dpsis,axs])

#Initial State cov

P_t = np.diag([1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0])

#Measurment cov

varGPS = 5.0 # Standard Deviation of GPS Measurement

varspeed = 3.0 # Variance of the speed measurement

varyaw = 0.1 # Variance of the yawrate measurement

varacc = 1.0 # Variance of the longitudinal Acceleration

R_t = np.diag([varGPS**2, varGPS**2, varspeed**2, varyaw**2, varacc**2])

#Measurment Matrix

hs = Matrix([[xs],

[ys],

[vs],

[dpsis],

[axs]])

#Process cov

sGPS = 0.5*8.8*dt**2 # assume 8.8m/s2 as maximum acceleration, forcing the vehicle

sCourse = 0.1*dt # assume 0.1rad/s as maximum turn rate for the vehicle

sVelocity= 8.8*dt # assume 8.8m/s2 as maximum acceleration, forcing the vehicle

sYaw = 1.0*dt # assume 1.0rad/s2 as the maximum turn rate acceleration for the vehicle

sAccel = 0.5

Q_t = np.diag([sGPS**2, sGPS**2, sCourse**2, sVelocity**2, sYaw**2, sAccel**2])

path="/home/behnam/Kalman/"

datafile = '2014-03-26-000-Data.csv'

fullPath=path+datafile

def bytespdate2num(fmt, encoding='utf-8'):

strconverter = mdates.strpdate2num(fmt)

def bytesconverter(b):

s = b.decode(encoding)

return strconverter(s)

return bytesconverter

date, time, millis, ax, ay, az, rollrate, pitchrate, yawrate, roll, pitch, yaw, speed, course, latitude, longitude, altitude, pdop, hdop, vdop, epe, fix, satellites_view, satellites_used, temp = np.loadtxt(fullPath, delimiter=',', unpack=True,

converters={1:bytespdate2num('%H%M%S%f'),

0: bytespdate2num('%y%m%d')},

skiprows=1)

# A course of 0 means the Car is traveling north bound

# and 90 means it is traveling east bound.

# In the Calculation following, East is Zero and North is 90

# We need an offset.

course =(-course+90.0)

# ## Approx. Lat/Lon to Meters to check Location

# In[17]:

RadiusEarth = 6378388.0 # m

arc= 2.0*np.pi*(RadiusEarth+altitude)/360.0 # m/

dx = arc * np.cos(latitude*np.pi/180.0) * np.hstack((0.0, np.diff(longitude))) # in m

dy = arc * np.hstack((0.0, np.diff(latitude))) # in m

mx = np.cumsum(dx)

my = np.cumsum(dy)

ds = np.sqrt(dx**2+dy**2)

GPS=(ds!=0.0).astype('bool') # GPS Trigger for Kalman Filter

# ## Initial State

X_hat_t = np.matrix([[mx[0], my[0], course[0]/180.0*np.pi, speed[0]/3.6+0.001, yawrate[0]/180.0*np.pi, ax[0]]]).T

measurements = np.vstack((mx, my, speed/3.6, yawrate/180.0*np.pi, ax))

# Lenth of the measurement

m = measurements.shape[1]

for i in range(measurements.shape[1]):

#for i in range(3):

if np.abs(yawrate[i])<0.0001:

drivingStraight=True

else:

drivingStraight=False

X_hat_t,P_hat_t = prediction(X_hat_t,P_t,Q_t,drivingStraight)

#print("Prediction:")

#print("X_hat_t:\n",X_hat_t,"\nP_t:\n",P_hat_t)

Z_t=measurements[:,i]

if GPS[i]:

GPSAvailable=True

else:

GPSAvailable=False

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

#print("Update:")

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

X_hat_t=X_t

P_hat_t=P_t

fig = plt.figure(figsize=(16,9))

# EKF State

plt.quiver(x0,x1,np.cos(x2), np.sin(x2), color='#94C600', units='xy', width=0.05, scale=0.5)

plt.plot(x0,x1, label='EKF Position', c='k', lw=5)

# Measurements

plt.scatter(mx[::5],my[::5], s=50, label='GPS Measurements', marker='+')

# Start/Goal

plt.scatter(x0[0],x1[0], s=60, label='Start', c='g')

plt.scatter(x0[-1],x1[-1], s=60, label='Goal', c='r')

plt.xlabel('X [m]')

plt.ylabel('Y [m]')

plt.title('Position')

plt.legend(loc='best')

plt.axis('equal')

plt.show()

References: [1] [2] [3] [4] [5]

Parcticle Filter Explained With Python Code From Scratch

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In the following code I have implemented a localization algorithm based on particle filter.

I have used conda to run my code, you can run the following for installation of dependencies:

conda create -n Filters python=3
conda activate Filters
conda install -c menpo opencv3
conda install numpy scipy matplotlib sympy

conda create -n Filters python=3

conda activate Filters

conda install -c menpo opencv3

conda install numpy scipy matplotlib sympy

and the code:

import numpy as np
import scipy as scipy
from numpy.random import uniform
import scipy.stats


np.set_printoptions(threshold=3)
np.set_printoptions(suppress=True)
import cv2


def drawLines(img, points, r, g, b):
    cv2.polylines(img, [np.int32(points)], isClosed=False, color=(r, g, b))

def drawCross(img, center, r, g, b):
    d = 5
    t = 2
    LINE_AA = cv2.LINE_AA if cv2.__version__[0] == '3' else cv2.CV_AA
    color = (r, g, b)
    ctrx = center[0,0]
    ctry = center[0,1]
    cv2.line(img, (ctrx - d, ctry - d), (ctrx + d, ctry + d), color, t, LINE_AA)
    cv2.line(img, (ctrx + d, ctry - d), (ctrx - d, ctry + d), color, t, LINE_AA)
    

def mouseCallback(event, x, y, flags,null):
    global center
    global trajectory
    global previous_x
    global previous_y
    global zs
    
    center=np.array([[x,y]])
    trajectory=np.vstack((trajectory,np.array([x,y])))
    #noise=sensorSigma * np.random.randn(1,2) + sensorMu
    
    if previous_x >0:
        heading=np.arctan2(np.array([y-previous_y]), np.array([previous_x-x ]))

        if heading>0:
            heading=-(heading-np.pi)
        else:
            heading=-(np.pi+heading)
            
        distance=np.linalg.norm(np.array([[previous_x,previous_y]])-np.array([[x,y]]) ,axis=1)
        
        std=np.array([2,4])
        u=np.array([heading,distance])
        predict(particles, u, std, dt=1.)
        zs = (np.linalg.norm(landmarks - center, axis=1) + (np.random.randn(NL) * sensor_std_err))
        update(particles, weights, z=zs, R=50, landmarks=landmarks)
        
        indexes = systematic_resample(weights)
        resample_from_index(particles, weights, indexes)

    previous_x=x
    previous_y=y
    


WIDTH=800
HEIGHT=600
WINDOW_NAME="Particle Filter"

#sensorMu=0
#sensorSigma=3

sensor_std_err=5


def create_uniform_particles(x_range, y_range, N):
    particles = np.empty((N, 2))
    particles[:, 0] = uniform(x_range[0], x_range[1], size=N)
    particles[:, 1] = uniform(y_range[0], y_range[1], size=N)
    return particles



def predict(particles, u, std, dt=1.):
    N = len(particles)
    dist = (u[1] * dt) + (np.random.randn(N) * std[1])
    particles[:, 0] += np.cos(u[0]) * dist
    particles[:, 1] += np.sin(u[0]) * dist
   
def update(particles, weights, z, R, landmarks):
    weights.fill(1.)
    for i, landmark in enumerate(landmarks):
       
        distance=np.power((particles[:,0] - landmark[0])**2 +(particles[:,1] - landmark[1])**2,0.5)
        weights *= scipy.stats.norm(distance, R).pdf(z[i])

 
    weights += 1.e-300 # avoid round-off to zero
    weights /= sum(weights)
    
def neff(weights):
    return 1. / np.sum(np.square(weights))

def systematic_resample(weights):
    N = len(weights)
    positions = (np.arange(N) + np.random.random()) / N

    indexes = np.zeros(N, 'i')
    cumulative_sum = np.cumsum(weights)
    i, j = 0, 0
    while i < N and j<N:
        if positions[i] < cumulative_sum[j]:
            indexes[i] = j
            i += 1
        else:
            j += 1
    return indexes
    
def estimate(particles, weights):
    pos = particles[:, 0:1]
    mean = np.average(pos, weights=weights, axis=0)
    var = np.average((pos - mean)**2, weights=weights, axis=0)
    return mean, var

def resample_from_index(particles, weights, indexes):
    particles[:] = particles[indexes]
    weights[:] = weights[indexes]
    weights /= np.sum(weights)

    
x_range=np.array([0,800])
y_range=np.array([0,600])

#Number of partciles
N=400

landmarks=np.array([ [144,73], [410,13], [336,175], [718,159], [178,484], [665,464]  ])
NL = len(landmarks)
particles=create_uniform_particles(x_range, y_range, N)


weights = np.array([1.0]*N)


# Create a black image, a window and bind the function to window
img = np.zeros((HEIGHT,WIDTH,3), np.uint8)
cv2.namedWindow(WINDOW_NAME)
cv2.setMouseCallback(WINDOW_NAME,mouseCallback)

center=np.array([[-10,-10]])

trajectory=np.zeros(shape=(0,2))
robot_pos=np.zeros(shape=(0,2))
previous_x=-1
previous_y=-1
DELAY_MSEC=50

while(1):

    cv2.imshow(WINDOW_NAME,img)
    img = np.zeros((HEIGHT,WIDTH,3), np.uint8)
    drawLines(img, trajectory,   0,   255, 0)
    drawCross(img, center, r=255, g=0, b=0)
    
    #landmarks
    for landmark in landmarks:
        cv2.circle(img,tuple(landmark),10,(255,0,0),-1)
    
    #draw_particles:
    for particle in particles:
        cv2.circle(img,tuple((int(particle[0]),int(particle[1]))),1,(255,255,255),-1)

    if cv2.waitKey(DELAY_MSEC) & 0xFF == 27:
        break
    
    cv2.circle(img,(10,10),10,(255,0,0),-1)
    cv2.circle(img,(10,30),3,(255,255,255),-1)
    cv2.putText(img,"Landmarks",(30,20),1,1.0,(255,0,0))
    cv2.putText(img,"Particles",(30,40),1,1.0,(255,255,255))
    cv2.putText(img,"Robot Trajectory(Ground truth)",(30,60),1,1.0,(0,255,0))

    drawLines(img, np.array([[10,55],[25,55]]), 0, 255, 0)
    


cv2.destroyAllWindows()

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import numpy as np

import scipy as scipy

from numpy.random import uniform

import scipy.stats

np.set_printoptions(threshold=3)

np.set_printoptions(suppress=True)

import cv2

def drawLines(img, points, r, g, b):

cv2.polylines(img, [np.int32(points)], isClosed=False, color=(r, g, b))

def drawCross(img, center, r, g, b):

d = 5

t = 2

LINE_AA = cv2.LINE_AA if cv2.__version__[0] == '3' else cv2.CV_AA

color = (r, g, b)

ctrx = center[0,0]

ctry = center[0,1]

cv2.line(img, (ctrx - d, ctry - d), (ctrx + d, ctry + d), color, t, LINE_AA)

cv2.line(img, (ctrx + d, ctry - d), (ctrx - d, ctry + d), color, t, LINE_AA)

def mouseCallback(event, x, y, flags,null):

global center

global trajectory

global previous_x

global previous_y

global zs

center=np.array([[x,y]])

trajectory=np.vstack((trajectory,np.array([x,y])))

#noise=sensorSigma * np.random.randn(1,2) + sensorMu

if previous_x >0:

heading=np.arctan2(np.array([y-previous_y]), np.array([previous_x-x ]))

if heading>0:

heading=-(heading-np.pi)

else:

heading=-(np.pi+heading)

distance=np.linalg.norm(np.array([[previous_x,previous_y]])-np.array([[x,y]]) ,axis=1)

std=np.array([2,4])

u=np.array([heading,distance])

predict(particles, u, std, dt=1.)

zs = (np.linalg.norm(landmarks - center, axis=1) + (np.random.randn(NL) * sensor_std_err))

update(particles, weights, z=zs, R=50, landmarks=landmarks)

indexes = systematic_resample(weights)

resample_from_index(particles, weights, indexes)

previous_x=x

previous_y=y

WIDTH=800

HEIGHT=600

WINDOW_NAME="Particle Filter"

#sensorMu=0

#sensorSigma=3

sensor_std_err=5

def create_uniform_particles(x_range, y_range, N):

particles = np.empty((N, 2))

particles[:, 0] = uniform(x_range[0], x_range[1], size=N)

particles[:, 1] = uniform(y_range[0], y_range[1], size=N)

return particles

def predict(particles, u, std, dt=1.):

N = len(particles)

dist = (u[1] * dt) + (np.random.randn(N) * std[1])

particles[:, 0] += np.cos(u[0]) * dist

particles[:, 1] += np.sin(u[0]) * dist

def update(particles, weights, z, R, landmarks):

weights.fill(1.)

for i, landmark in enumerate(landmarks):

distance=np.power((particles[:,0] - landmark[0])**2 +(particles[:,1] - landmark[1])**2,0.5)

weights *= scipy.stats.norm(distance, R).pdf(z[i])

weights += 1.e-300 # avoid round-off to zero

weights /= sum(weights)

def neff(weights):

return 1. / np.sum(np.square(weights))

def systematic_resample(weights):

N = len(weights)

positions = (np.arange(N) + np.random.random()) / N

indexes = np.zeros(N, 'i')

cumulative_sum = np.cumsum(weights)

i, j = 0, 0

while i < N and j<N:

if positions[i] < cumulative_sum[j]:

indexes[i] = j

i += 1

else:

j += 1

return indexes

def estimate(particles, weights):

pos = particles[:, 0:1]

mean = np.average(pos, weights=weights, axis=0)

var = np.average((pos - mean)**2, weights=weights, axis=0)

return mean, var

def resample_from_index(particles, weights, indexes):

particles[:] = particles[indexes]

weights[:] = weights[indexes]

weights /= np.sum(weights)

x_range=np.array([0,800])

y_range=np.array([0,600])

#Number of partciles

N=400

landmarks=np.array([ [144,73], [410,13], [336,175], [718,159], [178,484], [665,464] ])

NL = len(landmarks)

particles=create_uniform_particles(x_range, y_range, N)

weights = np.array([1.0]*N)

# Create a black image, a window and bind the function to window

img = np.zeros((HEIGHT,WIDTH,3), np.uint8)

cv2.namedWindow(WINDOW_NAME)

cv2.setMouseCallback(WINDOW_NAME,mouseCallback)

center=np.array([[-10,-10]])

trajectory=np.zeros(shape=(0,2))

robot_pos=np.zeros(shape=(0,2))

previous_x=-1

previous_y=-1

DELAY_MSEC=50

while(1):

cv2.imshow(WINDOW_NAME,img)

img = np.zeros((HEIGHT,WIDTH,3), np.uint8)

drawLines(img, trajectory, 0, 255, 0)

drawCross(img, center, r=255, g=0, b=0)

#landmarks

for landmark in landmarks:

cv2.circle(img,tuple(landmark),10,(255,0,0),-1)

#draw_particles:

for particle in particles:

cv2.circle(img,tuple((int(particle[0]),int(particle[1]))),1,(255,255,255),-1)

if cv2.waitKey(DELAY_MSEC) & 0xFF == 27:

break

cv2.circle(img,(10,10),10,(255,0,0),-1)

cv2.circle(img,(10,30),3,(255,255,255),-1)

cv2.putText(img,"Landmarks",(30,20),1,1.0,(255,0,0))

cv2.putText(img,"Particles",(30,40),1,1.0,(255,255,255))

cv2.putText(img,"Robot Trajectory(Ground truth)",(30,60),1,1.0,(0,255,0))

drawLines(img, np.array([[10,55],[25,55]]), 0, 255, 0)

cv2.destroyAllWindows()

Kalman Filter Explained With Python Code From Scratch

2 Replies

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 here

//http://www.morethantechnical.com/2011/06/17/simple-kalman-filter-for-tracking-using-opencv-2-2-w-code/
#include <iostream>
#include <vector>
#include <random>
#include <ctime>
#include <chrono>
#include <iomanip>

#include <Eigen/Dense>
#include <boost/random/mersenne_twister.hpp>
#include <boost/random/normal_distribution.hpp>

#include <opencv2/highgui/highgui.hpp>
#include <opencv2/video/tracking.hpp>
#include "rapidcsv.h"

namespace Eigen
{
    namespace internal
    {
        template<typename Scalar>
        struct scalar_normal_dist_op
        {
            static boost::mt19937 rng;    // The uniform pseudo-random algorithm
            mutable boost::normal_distribution<Scalar> norm;  // The gaussian combinator

            EIGEN_EMPTY_STRUCT_CTOR(scalar_normal_dist_op)

            template<typename Index>
            inline const Scalar operator() (Index, Index = 0) const { return norm(rng); }
        };

        template<typename Scalar> boost::mt19937 scalar_normal_dist_op<Scalar>::rng;

        template<typename Scalar>
        struct functor_traits<scalar_normal_dist_op<Scalar> >
        { enum
            { Cost = 50 * NumTraits<Scalar>::MulCost, PacketAccess = false, IsRepeatable = false };
        };
    }
}

template<typename Clock, typename Duration>
std::ostream &operator<<(std::ostream &stream,  const std::chrono::time_point<Clock, Duration> &time_point)
{
    const time_t time = Clock::to_time_t(time_point);
    struct tm tm;
    localtime_r(&time, &tm);
    return stream << std::put_time(&tm, "%c"); // Print standard date&time

}

auto start = std::chrono::high_resolution_clock::now();
int k=5;

Eigen::MatrixXd multivariateNormalDistribution()
{
    int size = 2; // Dimensionality (rows)
    int nn=1;     // How many samples (columns) to draw
    Eigen::internal::scalar_normal_dist_op<double> randN; // Gaussian functor
    //Eigen::internal::scalar_normal_dist_op<double>::rng.seed(1);
    auto elapsed = std::chrono::high_resolution_clock::now() - start;
    auto microseconds = std::chrono::duration_cast<std::chrono::microseconds>(elapsed).count();
    //std::cout<<"microseconds:" <<microseconds <<std::endl;
    Eigen::internal::scalar_normal_dist_op<double>::rng.seed(microseconds);
    Eigen::VectorXd mean(size);
    Eigen::MatrixXd covar(size,size);

    mean  <<  0,  0;
    covar <<  k*1e-0, 0,
       0,  k*1e-0;

    Eigen::MatrixXd normTransform(size,size);
    Eigen::LLT<Eigen::MatrixXd> cholSolver(covar);

    // We can only use the cholesky decomposition if
    // the covariance matrix is symmetric, pos-definite.
    // But a covariance matrix might be pos-semi-definite.
    // In that case, we'll go to an EigenSolver
    if (cholSolver.info()==Eigen::Success)
    {
    // Use cholesky solver
        normTransform = cholSolver.matrixL();
    }
    else
    {
        // Use eigen solver
        Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> eigenSolver(covar);
        normTransform = eigenSolver.eigenvectors() * eigenSolver.eigenvalues().cwiseSqrt().asDiagonal();
    }

    Eigen::MatrixXd samples = (normTransform * Eigen::MatrixXd::NullaryExpr(size,nn,randN)).colwise() + mean;
//    std::cout << "Mean\n" << mean << std::endl;
//    std::cout << "Covar\n" << covar << std::endl;
//    std::cout << "Samples\n" << samples << std::endl;
    return samples;
}

struct mouse_info_struct { int x,y; };
struct mouse_info_struct mouse_info = {-1,-1}, last_mouse;

std::vector<cv::Point> groundTruth,kalmanv,measurmens;

void on_mouse(int event, int x, int y, int flags, void* param) {
	{
		last_mouse = mouse_info;
		mouse_info.x = x;
		mouse_info.y = y;
	}
}

// plot points
#define drawCross( center, color, d )                                 \
cv::line( img, cv::Point( center.x - d, center.y - d ),                \
cv::Point( center.x + d, center.y + d ), color, 2, CV_AA, 0); \
cv::line( img, cv::Point( center.x + d, center.y - d ),                \
cv::Point( center.x - d, center.y + d ), color, 2, CV_AA, 0 )


int main (int argc, char * const argv[]) {
    cv::Mat img(500, 500, CV_8UC3);
    cv::KalmanFilter KF(4, 2, 0);
    cv::Mat_<float> state(4, 1); /* (x, y, Vx, Vy) */
    cv::Mat processNoise(4, 1, CV_32F);
    cv::Mat_<float> measurement(2,1); measurement.setTo(cv::Scalar(0));
    char code = char(-1);
	
    cv::namedWindow("Mouse Tracking with Kalman Filter");
    cv::setMouseCallback("Mouse Tracking with Kalman Filter", on_mouse, nullptr);
    double delta_t=1/20.0;
	
    for(;;)
    {
		if (mouse_info.x < 0 || mouse_info.y < 0) {
            imshow("Mouse Tracking with Kalman Filter", img);
            cv::waitKey(30);
			continue;
		}
        cv::Mat transitionMatrix=(cv::Mat_<float>(4, 4) << 1,0,delta_t,0,   0,1,0,delta_t,  0,0,1,0,  0,0,0,1);
        KF.transitionMatrix = transitionMatrix;
		
        setIdentity(KF.measurementMatrix);
        cv::setIdentity(KF.processNoiseCov, cv::Scalar::all(1e-0));
        cv::setIdentity(KF.measurementNoiseCov, cv::Scalar::all(k*1e-0));
        cv::setIdentity(KF.errorCovPost, cv::Scalar::all(.2));
        cv::setIdentity(KF.errorCovPre,cv::Scalar::all(.1));

        measurmens.clear();
        groundTruth.clear();
		kalmanv.clear();
        std::cout<< "measurementMatrix"<<std::endl;
        std::cout<<KF.measurementMatrix<<std::endl;
		
        for(;;)
        {
            std::cout<< "processNoiseCov"<<std::endl;
            std::cout<< KF.processNoiseCov<<std::endl;

            std::cout<< "measurementNoiseCov"<<std::endl;
            std::cout<< KF.measurementNoiseCov<<std::endl;

            std::cout<<"State Prior (before calling predict function):" <<std::endl;
            std::cout<<KF.statePre <<std::endl;

            std::cout<<"Cov Prior (before calling predict function):" <<std::endl;
            std::cout<<KF.errorCovPre <<std::endl;

            std::cout<<"Cov Posterior (before calling predict function):" <<std::endl;
            std::cout<<KF.errorCovPost <<std::endl;

            //KF.controlMatrix
            std::cout<<"My State Prediction:" <<std::endl;
            std::cout<<KF.transitionMatrix*KF.statePost<<std::endl;
            std::cout<<"My Cov Prediction:" <<std::endl;
            std::cout<<KF.transitionMatrix*KF.errorCovPost*KF.transitionMatrix.t()+KF.processNoiseCov<<std::endl;

            cv::Mat prediction = KF.predict();
            cv::Point predictPt(prediction.at<float>(0),prediction.at<float>(1));

            std::cout<<"OpenCV Prediction:" <<std::endl;

            std::cout<<"State Prior:" <<std::endl;
            std::cout<<KF.statePre <<std::endl;

            std::cout<<"OpenCV Cov Prior:" <<std::endl;
            std::cout<<KF.errorCovPre <<std::endl;


            measurement(0) = mouse_info.x+multivariateNormalDistribution()(0,0);
            measurement(1) = mouse_info.y+multivariateNormalDistribution()(1,0);

            cv::Point groundtruth(mouse_info.x,mouse_info.y);
            groundTruth.push_back(groundtruth);


            std::cout<<"Ground Truth:" <<std::endl;
            std::cout<< mouse_info.x<<" , "<<mouse_info.y <<std::endl;

            cv::Point measPt(measurement(0),measurement(1));
            measurmens.push_back(measPt);


            cv::Mat estimated = KF.correct(measurement);
            cv::Point statePt(estimated.at<float>(0),estimated.at<float>(1));
			kalmanv.push_back(statePt);

            std::cout<<"My State Posterior:" <<std::endl;
            std::cout<<KF.statePre+KF.gain*(measurement-KF.measurementMatrix*KF.statePre) <<std::endl;

            std::cout<<"My Cov Posterior:" <<std::endl;
            std::cout<<(cv::Mat::eye(4,4, CV_32F) - KF.gain*KF.measurementMatrix)*KF.errorCovPre <<std::endl;

            std::cout<<"Opencv State Posterior:" <<std::endl;
            std::cout<<KF.statePost <<std::endl;

            std::cout<<"Opencv Cov Posterior:" <<std::endl;
            std::cout<<KF.errorCovPost <<std::endl;

            std::cout<<"-----------------------------------------------" <<std::endl;
            multivariateNormalDistribution();

            img = cv::Scalar::all(0);
            drawCross( statePt, cv::Scalar(255,255,255), 5 );
            drawCross( measPt, cv::Scalar(0,0,255), 5 );
            for (std::size_t i = 0; i < groundTruth.size()-1; i++)
            {
                line(img, groundTruth[i], groundTruth[i+1], cv::Scalar(0,255,0), 1);
			}
            for (std::size_t i = 0; i < kalmanv.size()-1; i++)
            {
                line(img, kalmanv[i], kalmanv[i+1], cv::Scalar(255,0,0), 1);
			}
            for (std::size_t i = 0; i < measurmens.size()-1; i++)
            {
                line(img, measurmens[i], measurmens[i+1], cv::Scalar(0,255,255), 1);
            }
            cv::putText(img,"Noisy Measurements",cv::Point(10,10),cv::FONT_HERSHEY_PLAIN,1,cv::Scalar(0,255,255) 	);
            cv::putText(img,"Real Mouse Position(ground truth)",cv::Point(10,25),cv::FONT_HERSHEY_PLAIN,1,cv::Scalar(0,255,0));
            cv::putText(img,"Kalman Sate",cv::Point(10,35),cv::FONT_HERSHEY_PLAIN,1,cv::Scalar(255,0,0));


            imshow( "Mouse Tracking with Kalman Filter", img );
            code = char(cv::waitKey(1000.0*delta_t));
			
            if( code > 0 )
                break;
        }
        if( code == 27 || code == 'q' || code == 'Q' )
            break;
    }


    std::ofstream groundTruthfile("groundTruth.csv",std::ofstream::ate );
    groundTruthfile<<"x,y"<<std::endl;
    for (std::size_t i = 0; i < groundTruth.size(); i++)
    {
        groundTruthfile<<groundTruth[i].x << ","<<groundTruth[i].y <<std::endl;
    }
    groundTruthfile.close();




    std::ofstream kalmanvfile("kalmanv.csv",std::ofstream::ate );
    kalmanvfile<<"x,y"<<std::endl;
    for (std::size_t i = 0; i < kalmanv.size()-1; i++)
    {
        kalmanvfile<<kalmanv[i].x << ","<<kalmanv[i].y <<std::endl;
    }
    kalmanvfile.close();

    std::ofstream measurmensfile("measurmens.csv",std::ofstream::ate );
    measurmensfile<<"x,y"<<std::endl;
    for (std::size_t i = 0; i < kalmanv.size()-1; i++)
    {
        measurmensfile<<measurmens[i].x << ","<<measurmens[i].y <<std::endl;
    }
    measurmensfile.close();

    std::cout<<"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~" <<std::endl;

    return 0;
}

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//http://www.morethantechnical.com/2011/06/17/simple-kalman-filter-for-tracking-using-opencv-2-2-w-code/

#include <iostream>

#include <vector>

#include <random>

#include <ctime>

#include <chrono>

#include <iomanip>

#include <Eigen/Dense>

#include <boost/random/mersenne_twister.hpp>

#include <boost/random/normal_distribution.hpp>

#include <opencv2/highgui/highgui.hpp>

#include <opencv2/video/tracking.hpp>

#include "rapidcsv.h"

namespace Eigen

{

namespace internal

{

template<typename Scalar>

struct scalar_normal_dist_op

{

static boost::mt19937 rng; // The uniform pseudo-random algorithm

mutable boost::normal_distribution<Scalar> norm; // The gaussian combinator

EIGEN_EMPTY_STRUCT_CTOR(scalar_normal_dist_op)

template<typename Index>

inline const Scalar operator() (Index, Index = 0) const { return norm(rng); }

};

template<typename Scalar> boost::mt19937 scalar_normal_dist_op<Scalar>::rng;

template<typename Scalar>

struct functor_traits<scalar_normal_dist_op<Scalar> >

{ enum

{ Cost = 50 * NumTraits<Scalar>::MulCost, PacketAccess = false, IsRepeatable = false };

};

}

template<typename Clock, typename Duration>

std::ostream &operator<<(std::ostream &stream, const std::chrono::time_point<Clock, Duration> &time_point)

{

const time_t time = Clock::to_time_t(time_point);

struct tm tm;

localtime_r(&time, &tm);

return stream << std::put_time(&tm, "%c"); // Print standard date&time

}

auto start = std::chrono::high_resolution_clock::now();

int k=5;

Eigen::MatrixXd multivariateNormalDistribution()

{

int size = 2; // Dimensionality (rows)

int nn=1; // How many samples (columns) to draw

Eigen::internal::scalar_normal_dist_op<double> randN; // Gaussian functor

//Eigen::internal::scalar_normal_dist_op<double>::rng.seed(1);

auto elapsed = std::chrono::high_resolution_clock::now() - start;

auto microseconds = std::chrono::duration_cast<std::chrono::microseconds>(elapsed).count();

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

Eigen::internal::scalar_normal_dist_op<double>::rng.seed(microseconds);

Eigen::VectorXd mean(size);

Eigen::MatrixXd covar(size,size);

mean << 0, 0;

covar << k*1e-0, 0,

0, k*1e-0;

Eigen::MatrixXd normTransform(size,size);

Eigen::LLT<Eigen::MatrixXd> cholSolver(covar);

// We can only use the cholesky decomposition if

// the covariance matrix is symmetric, pos-definite.

// But a covariance matrix might be pos-semi-definite.

// In that case, we'll go to an EigenSolver

if (cholSolver.info()==Eigen::Success)

{

// Use cholesky solver

normTransform = cholSolver.matrixL();

}

else

{

// Use eigen solver

Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> eigenSolver(covar);

normTransform = eigenSolver.eigenvectors() * eigenSolver.eigenvalues().cwiseSqrt().asDiagonal();

}

Eigen::MatrixXd samples = (normTransform * Eigen::MatrixXd::NullaryExpr(size,nn,randN)).colwise() + mean;

// std::cout << "Mean\n" << mean << std::endl;

// std::cout << "Covar\n" << covar << std::endl;

// std::cout << "Samples\n" << samples << std::endl;

return samples;

}

struct mouse_info_struct { int x,y; };

struct mouse_info_struct mouse_info = {-1,-1}, last_mouse;

std::vector<cv::Point> groundTruth,kalmanv,measurmens;

void on_mouse(int event, int x, int y, int flags, void* param) {

{

last_mouse = mouse_info;

mouse_info.x = x;

mouse_info.y = y;

}

// plot points

#define drawCross( center, color, d ) \

cv::line( img, cv::Point( center.x - d, center.y - d ), \

cv::Point( center.x + d, center.y + d ), color, 2, CV_AA, 0); \

cv::line( img, cv::Point( center.x + d, center.y - d ), \

cv::Point( center.x - d, center.y + d ), color, 2, CV_AA, 0 )

int main (int argc, char * const argv[]) {

cv::Mat img(500, 500, CV_8UC3);

cv::KalmanFilter KF(4, 2, 0);

cv::Mat_<float> state(4, 1); /* (x, y, Vx, Vy) */

cv::Mat processNoise(4, 1, CV_32F);

cv::Mat_<float> measurement(2,1); measurement.setTo(cv::Scalar(0));

char code = char(-1);

cv::namedWindow("Mouse Tracking with Kalman Filter");

cv::setMouseCallback("Mouse Tracking with Kalman Filter", on_mouse, nullptr);

double delta_t=1/20.0;

for(;;)

{

if (mouse_info.x < 0 || mouse_info.y < 0) {

imshow("Mouse Tracking with Kalman Filter", img);

cv::waitKey(30);

continue;

}

cv::Mat transitionMatrix=(cv::Mat_<float>(4, 4) << 1,0,delta_t,0, 0,1,0,delta_t, 0,0,1,0, 0,0,0,1);

KF.transitionMatrix = transitionMatrix;

setIdentity(KF.measurementMatrix);

cv::setIdentity(KF.processNoiseCov, cv::Scalar::all(1e-0));

cv::setIdentity(KF.measurementNoiseCov, cv::Scalar::all(k*1e-0));

cv::setIdentity(KF.errorCovPost, cv::Scalar::all(.2));

cv::setIdentity(KF.errorCovPre,cv::Scalar::all(.1));

measurmens.clear();

groundTruth.clear();

kalmanv.clear();

std::cout<< "measurementMatrix"<<std::endl;

std::cout<<KF.measurementMatrix<<std::endl;

for(;;)

{

std::cout<< "processNoiseCov"<<std::endl;

std::cout<< KF.processNoiseCov<<std::endl;

std::cout<< "measurementNoiseCov"<<std::endl;

std::cout<< KF.measurementNoiseCov<<std::endl;

std::cout<<"State Prior (before calling predict function):" <<std::endl;

std::cout<<KF.statePre <<std::endl;

std::cout<<"Cov Prior (before calling predict function):" <<std::endl;

std::cout<<KF.errorCovPre <<std::endl;

std::cout<<"Cov Posterior (before calling predict function):" <<std::endl;

std::cout<<KF.errorCovPost <<std::endl;

//KF.controlMatrix

std::cout<<"My State Prediction:" <<std::endl;

std::cout<<KF.transitionMatrix*KF.statePost<<std::endl;

std::cout<<"My Cov Prediction:" <<std::endl;

std::cout<<KF.transitionMatrix*KF.errorCovPost*KF.transitionMatrix.t()+KF.processNoiseCov<<std::endl;

cv::Mat prediction = KF.predict();

cv::Point predictPt(prediction.at<float>(0),prediction.at<float>(1));

std::cout<<"OpenCV Prediction:" <<std::endl;

std::cout<<"State Prior:" <<std::endl;

std::cout<<KF.statePre <<std::endl;

std::cout<<"OpenCV Cov Prior:" <<std::endl;

std::cout<<KF.errorCovPre <<std::endl;

measurement(0) = mouse_info.x+multivariateNormalDistribution()(0,0);

measurement(1) = mouse_info.y+multivariateNormalDistribution()(1,0);

cv::Point groundtruth(mouse_info.x,mouse_info.y);

groundTruth.push_back(groundtruth);

std::cout<<"Ground Truth:" <<std::endl;

std::cout<< mouse_info.x<<" , "<<mouse_info.y <<std::endl;

cv::Point measPt(measurement(0),measurement(1));

measurmens.push_back(measPt);

cv::Mat estimated = KF.correct(measurement);

cv::Point statePt(estimated.at<float>(0),estimated.at<float>(1));

kalmanv.push_back(statePt);

std::cout<<"My State Posterior:" <<std::endl;

std::cout<<KF.statePre+KF.gain*(measurement-KF.measurementMatrix*KF.statePre) <<std::endl;

std::cout<<"My Cov Posterior:" <<std::endl;

std::cout<<(cv::Mat::eye(4,4, CV_32F) - KF.gain*KF.measurementMatrix)*KF.errorCovPre <<std::endl;

std::cout<<"Opencv State Posterior:" <<std::endl;

std::cout<<KF.statePost <<std::endl;

std::cout<<"Opencv Cov Posterior:" <<std::endl;

std::cout<<KF.errorCovPost <<std::endl;

std::cout<<"-----------------------------------------------" <<std::endl;

multivariateNormalDistribution();

img = cv::Scalar::all(0);

drawCross( statePt, cv::Scalar(255,255,255), 5 );

drawCross( measPt, cv::Scalar(0,0,255), 5 );

for (std::size_t i = 0; i < groundTruth.size()-1; i++)

{

line(img, groundTruth[i], groundTruth[i+1], cv::Scalar(0,255,0), 1);

}

for (std::size_t i = 0; i < kalmanv.size()-1; i++)

{

line(img, kalmanv[i], kalmanv[i+1], cv::Scalar(255,0,0), 1);

}

for (std::size_t i = 0; i < measurmens.size()-1; i++)

{

line(img, measurmens[i], measurmens[i+1], cv::Scalar(0,255,255), 1);

}

cv::putText(img,"Noisy Measurements",cv::Point(10,10),cv::FONT_HERSHEY_PLAIN,1,cv::Scalar(0,255,255) );

cv::putText(img,"Real Mouse Position(ground truth)",cv::Point(10,25),cv::FONT_HERSHEY_PLAIN,1,cv::Scalar(0,255,0));

cv::putText(img,"Kalman Sate",cv::Point(10,35),cv::FONT_HERSHEY_PLAIN,1,cv::Scalar(255,0,0));

imshow( "Mouse Tracking with Kalman Filter", img );

code = char(cv::waitKey(1000.0*delta_t));

if( code > 0 )

break;

}

if( code == 27 || code == 'q' || code == 'Q' )

break;

}

std::ofstream groundTruthfile("groundTruth.csv",std::ofstream::ate );

groundTruthfile<<"x,y"<<std::endl;

for (std::size_t i = 0; i < groundTruth.size(); i++)

{

groundTruthfile<<groundTruth[i].x << ","<<groundTruth[i].y <<std::endl;

}

groundTruthfile.close();

std::ofstream kalmanvfile("kalmanv.csv",std::ofstream::ate );

kalmanvfile<<"x,y"<<std::endl;

for (std::size_t i = 0; i < kalmanv.size()-1; i++)

{

kalmanvfile<<kalmanv[i].x << ","<<kalmanv[i].y <<std::endl;

}

kalmanvfile.close();

std::ofstream measurmensfile("measurmens.csv",std::ofstream::ate );

measurmensfile<<"x,y"<<std::endl;

for (std::size_t i = 0; i < kalmanv.size()-1; i++)

{

measurmensfile<<measurmens[i].x << ","<<measurmens[i].y <<std::endl;

}

measurmensfile.close();

std::cout<<"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~" <<std::endl;

return 0;

}

Eigen unaryExpr (Function Pointer, Lambda Expression) Example

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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;

}

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

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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 /
     /
    /
    ---------->
        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;

}

Eigen Memory Mapping

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

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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;

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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;