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DATE 2017-03-01

LEARN

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MESSAGE
DATE 2017-03-29
FROM Ruben Safir
SUBJECT Subject: [Learn] perseptors
From learn-bounces-at-nylxs.com Wed Mar 29 22:50:35 2017
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To: "learn-at-nylxs.com"
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base gig
--
So many immigrant groups have swept through our town
that Brooklyn, like Atlantis, reaches mythological
proportions in the mind of the world - RI Safir 1998
http://www.mrbrklyn.com

DRM is THEFT - We are the STAKEHOLDERS - RI Safir 2002
http://www.nylxs.com - Leadership Development in Free Software
http://www2.mrbrklyn.com/resources - Unpublished Archive
http://www.coinhangout.com - coins!
http://www.brooklyn-living.com

Being so tracked is for FARM ANIMALS and and extermination camps,
but incompatible with living as a free human being. -RI Safir 2013


--------------A7A24B8B06275A636C512F1E
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name="learn.cpp"
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/*
* =====================================================================================
*
* Filename: learn.cpp
*
* Description: Learn with a Neuronet and Perceptrons
*
* Version: 1.0
* Created: 03/29/2017 04:14:13 PM
* Revision: none
* Compiler: gcc
*
* Author: Ruben Safir (mn), ruben-at-mrbrklyn.com
* Company: NYLXS Inc
*
* =====================================================================================
*/
#include "perceptron.h"
const float LEARN_RATE = 0.2;
const int PRECISION = 3;
const int NUMWIDTH = PRECISION + 3;

void tt(std::vector truth_table ){
std::cout << std::endl << "__Truth Table__" << std::endl;
for(auto x : truth_table)
{
std::cout << x << "\t";
}
std::cout << std::endl;

}
int main (int argc, char ** argv )
{
std::vector * sensor_inputs[4] = {
new std::vector {0,0},
new std::vector {0,1},
new std::vector {1,0},
new std::vector{1,1}
};

std::vector weight_inputs = { 0.4, 0.4, 0.0 };
std::vector truth_table(4);
for(int i = 0; i<4; i++){
network::Perceptron p_and (*sensor_inputs[i],weight_inputs, 0.5);
std::cout << "AND sum =>" << p_and.get_sum() << std::endl << std::endl;
std::cout << "AND threshold =>" << p_and.get_threshold() << std::endl;
std::cout << "AND Functor =>" << p_and.functor() << std::endl << std::endl;
truth_table[i] = p_and.functor();
}

tt(truth_table);

weight_inputs[0] = 0.6;
weight_inputs[1] = 0.6;
weight_inputs[2] = 0.0;
for(int i = 0; i<4; i++){
network::Perceptron p_or (*sensor_inputs[i],weight_inputs, 0.5);
std::cout << "XOR sum =>" << p_or.get_sum() << std::endl;
std::cout << "XOR threshold =>" << p_or.get_threshold() << std::endl;
std::cout << "XOR Functor =>" << p_or.functor() << std::endl;
truth_table[i] = p_or.functor();
}
tt(truth_table);

weight_inputs[0] = -0.4;
weight_inputs[1] = -0.4;
weight_inputs[2] = 0.0;
for(int i = 0; i<4; i++){
network::Perceptron p_or (*sensor_inputs[i],weight_inputs, -0.5);
std::cout << "NAND sum =>" << p_or.get_sum() << std::endl;
std::cout << "NAND threshold =>" << p_or.get_threshold() << std::endl;
std::cout << "NAND Functor =>" << p_or.functor() << std::endl;
truth_table[i] = p_or.functor();
}
tt(truth_table);

return 1;
}


--------------A7A24B8B06275A636C512F1E
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/*
* =====================================================================================
*
* Filename: perceptron.h
*
* Description: Basic Unit for AI machine learning
*
* Version: 1.0
* Created Mon Mar 27 22:33:19 EDT 2017
* Revision: none
* Compiler: gcc
*
* Author: Ruben Safir (mn), ruben-at-mrbrklyn.com
* Company: NYLXS Inc - LIU Thesis
*
* =====================================================================================
*/

#ifndef PERCEPTRON_H
#define PERCEPTRON_H
#include
#include
#include
namespace network {

/*
* =====================================================================================
* Class: Perceptron
* Description: This class describes a single node and its data
* =====================================================================================
*/
template < class T, class W >
class Perceptron
{
public:
/* ==================== LIFECYCLE ======================================= */
Perceptron ( std::vector s1, std::vector w1, float thresh ): sensors{s1}, weights{w1}, threshold{thresh} {
sensors.push_back(1);
weighted_sum();
}; /* constructor */

/* ==================== ACCESSORS ======================================= */
W get_sum()
{
// std::cout << "Called get sum" << std::endl;
return sum;
};

float get_threshold()
{
return threshold;
};

/* ==================== MUTATORS ======================================= */
int functor()
{
if(sum < get_threshold())
{
return 0;
}else{
return 1;
}
}
/* ==================== OPERATORS ======================================= */
/* ==================== DATA MEMBERS ======================================= */
T data;
protected:

private:
std::vector sensors;
std::vector weights;
float threshold;
float sum = 0;
float weighted_sum()
{
if( sensors.size() != weights.size() )
{
std::cerr << __LINE__ << " You broke my perceptron by failing to give a weight for the bias";
exit(1);
}
for (int i = 0; i < static_cast ( sensors.size()) ; i++)
{
std::cout << "sensor =>" << sensors[i] << std::endl;
std::cout << "weight =>" << weights[i] << std::endl;
sum += weights[i] * sensors[i];
// std::cout << "sum =>" << sum << std::endl;
// std::cout << "get sum =>" << get_sum() << std::endl;

};
return sum;
};



}; /* ---------- end of template class Perceptron ---------- */


}//close of namespace network

#endif


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#include "perceptron.h"
#include



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_______________________________________________
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Learn-at-nylxs.com
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--------------A7A24B8B06275A636C512F1E--

  1. 2017-03-02 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] neutal networks and pacman
  2. 2017-03-02 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Ultrametric networks: a new tool for phylogenetic analysis
  3. 2017-03-05 Ruben Safir <mrbrklyn-at-panix.com> Subject: [Learn] cost in evolution
  4. 2017-03-09 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] CatScan fossil files
  5. 2017-03-10 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] neural Networks and Quantum Mechanics
  6. 2017-03-12 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] I just found a GREAT video on partial derivatives
  7. 2017-03-13 Ruben Safir <mrbrklyn-at-panix.com> Subject: [Learn] Contact DOJ and thell them to blow it out their ass
  8. 2017-03-14 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] CatScan fossil files
  9. 2017-03-14 Ramon Nagesan <ramon.nagesan-at-gmail.com> Re: [Learn] CatScan fossil files
  10. 2017-03-16 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] is this up
  11. 2017-03-16 Ruben Safir <mrbrklyn-at-panix.com> Subject: [Learn] hang out is down
  12. 2017-03-16 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] is this working
  13. 2017-03-16 Charlie Gonzalez <itcharlie-at-gmail.com> Subject: [Learn] Registration for The Perl Conference 2017 is now open!!
  14. 2017-03-16 From: "soledad.esteban" <soledad.esteban-at-icp.cat> Subject: [Learn] [dinosaur] Advanced course Geometric Morphometrics in R,
  15. 2017-03-17 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: [dinosaur] Advanced course Geometric Morphometrics in
  16. 2017-03-17 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] good news !
  17. 2017-03-18 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] circles
  18. 2017-03-20 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Alice
  19. 2017-03-20 Ruben Safir <mrbrklyn-at-panix.com> Subject: [Learn] hough transform - Lecture 9
  20. 2017-03-21 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Anyone understand this well
  21. 2017-03-21 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: [dinosaur] Digital mapping of dinosaurian tracksites
  22. 2017-03-22 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] ODBASE 2017 - The 16th International Conference on
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  24. 2017-03-24 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Genetic Modification with decent
  25. 2017-03-27 Ruben Safir <mrbrklyn-at-panix.com> Subject: [Learn] Fwd: Re: hough transform - Lecture 9
  26. 2017-03-27 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] MOOCS
  27. 2017-03-27 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Peter Novig learning and on line teaching
  28. 2017-03-28 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] computations
  29. 2017-03-29 Christopher League <league-at-contrapunctus.net> Re: [Learn] This is hard to understand what the logic is here
  30. 2017-03-29 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] This is hard to understand what the logic is here
  31. 2017-03-29 Christopher League <league-at-contrapunctus.net> Re: [Learn] This is hard to understand what the logic is here
  32. 2017-03-29 Ruben Safir <mrbrklyn-at-panix.com> Re: [Learn] This is hard to understand what the logic is here
  33. 2017-03-29 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] perseptors
  34. 2017-03-29 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] This is hard to understand what the logic is here
  35. 2017-03-29 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] This is hard to understand what the logic is here
  36. 2017-03-30 Ruben Safir <mrbrklyn-at-panix.com> Re: [Learn] c arrays
  37. 2017-03-30 Ruben Safir <mrbrklyn-at-panix.com> Subject: [Learn] c arrays
  38. 2017-03-30 Ruben Safir <mrbrklyn-at-panix.com> Subject: [Learn] random weights
  39. 2017-03-30 Ruben Safir <mrbrklyn-at-panix.com> Subject: [Learn] randomize with commentary
  40. 2017-03-31 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Computational Paleo

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