ollewelin

Ubuntu 16.04
sudo apt-get update
CMake GUI
sudo apt-get install cmake-qt-gui

GTK2.0
sudo apt-get install gtk2.0
sudo apt-get install build-essential libgtk2.0-dev

==============

ollewelin

//This is a code made to run 3-phase PWM drive stage with 6-transistors, in other words 3 half bridge transistors with an Arduino UNO 
//This setup is made with User settings of DEAD TIME. So here the the dead time can be adjusted by user. const int dead_time = 10;//10 = 0.625 us
//The PWM work here with fix frequancy of 31.25kHz (Other settings may not work proper when use all 3 TIMERS, if you want more flexibilty use Arduino MEGA 2560 or some other plattform)
//Olle Welin [email protected]

// TL Transistor Low  side of half bridge pair
// TH Transistor High side of half bridge pair

// PWM assignement at Arduino UNO is by following:
//~6  TL, PH0, TMR0, pwm_ph0 data value

ollewelin

///Add advanced game avoid rectangles but catch circles
///Fix bugg in pinball_game.hpp (was work on raspierry pi but not on PC) replay_count was not set to 0 at init state. Now replay_count=0;
/// Only dependancy is OpenCV C++ library need to be installed
/// Update with gamma parameter now
/// Example of Reinforced Machine Learning attached on a simple Pinball game
/// The enviroment (enviroment = data feedback) for the Agient (Agient = machine learning system)
/// is the raw pixels 50x50 pixels (2500 input nodes) and 200 hidden nodes on 100 frames
/// So the input to hidden weights is 50x50x100x200 x4 bytes (float) = is 200Mbytes huges but it work anyway!!
///Enhancment to do in future.
///TODO: Add some layers of Convolutions (with unsupervised Learning for learning feature patches) will probably enhance preformance.

ollewelin

//This is a code made to run 3-phase PWM drive stage with 6-transistors, in other words 3 half bridge transistors with an Arduino UNO 
//This setup is made with User settings of DEAD TIME. So here the the dead time can be adjusted by user. const int dead_time = 10;//10 = 0.625 us
//The PWM work here with fix frequancy of 31.25kHz (Other settings may not work proper when use all 3 TIMERS, if you want more flexibilty use Arduino MEGA 2560 or some other plattform)
//Olle Welin [email protected]

// TL Transistor Low  side of half bridge pair
// TH Transistor High side of half bridge pair

// PWM assignement at Arduino UNO is by following:
//~6  TL, PH0, TMR0, pwm_ph0 data value

ollewelin

//This is a code made to run 3-phase PWM drive stage with 6-transistors, in other words 3 half bridge transistors with an Arduino UNO 
//This setup is made with User settings of DEAD TIME. So here the the dead time can be adjusted by user. const int dead_time = 10;//10 = 0.625 us
//The PWM work here with fix frequancy of 31.25kHz (Other settings may not work proper when use all 3 TIMERS, if you want more flexibilty use Arduino MEGA 2560 or some other plattform)
//Olle Welin [email protected]

// TL Transistor Low  side of half bridge pair
// TH Transistor High side of half bridge pair

// PWM assignement at Arduino UNO is by following:
//~6  TL, PH0, TMR0, pwm_ph0 data value

ollewelin

/// Change some settings 2017-10-25
/// Only dependancy is OpenCV C++ library need to be installed
/// Example of Reinforced Machine Learning attached on a simple Pinball game
/// The enviroment (enviroment = data feedback) for the Agient (Agient = machine learning system)
/// is the raw pixels 50x50 pixels (2500 input nodes) and 200 hidden nodes on 100 frames
/// So the input to hidden weights is 50x50x100x200 x4 bytes (float) = is 200Mbytes huges but it work anyway!!
///Enhancment to do in future.
///TODO: Add some layers of Convolutions (with unsupervised Learning for learning feature patches) will probably enhance preformance.
///TODO: Maybe add bias weigth is a good idee to enhance preformance or stability during training.
#include <opencv2/highgui/highgui.hpp>  // OpenCV window I/O

ollewelin

/// Add gamma parameter and use dice and network probability for make action decisions UP/DOWN.
/// Example of Reinforced Machine Learning attached on a simple Pinball game
/// The enviroment (enviroment = data feedback) for the Agient (Agient = machine learning system)
/// is the raw pixels 50x50 pixels (2500 input nodes) and 200 hidden nodes on 100 frames
/// So the input to hidden weights is 50x50x100x200 x4 bytes (float) = is 200Mbytes huges but it work anyway!!
///Enhancment to do in future.
///TODO: Add some layers of Convolutions (with unsupervised Learning for learning feature patches) will probably enhance preformance.
///TODO: Maybe add bias weigth is a good idee to enhance preformance or stability during training.
///#define USE_PRINT_OUTPUT_NODE_VALUE ///Uncomment this to see print out of output node value. Only used for evaluation
#include <opencv2/highgui/highgui.hpp>  // OpenCV window I/O

ollewelin

#include <stdio.h>
#include <stdlib.h>// exit(0);

float abs_value(float signed_value)
{
	float abs_v;
	abs_v = signed_value;
	if(abs_v < 0)
	{
		abs_v = -abs_v;

ollewelin

#include <stdio.h>
#include <stdlib.h>// exit(0);

float abs_value(float signed_value)
{
	float abs_v;
	abs_v = signed_value;
	if(abs_v < 0)
	{
		abs_v = -abs_v;

ollewelin

#include <stdio.h>
#include <stdlib.h>// exit(0);

float abs_value(float signed_value)
{
	float abs_v;
	abs_v = signed_value;
	if(abs_v < 0)
	{
		abs_v = -abs_v;