Wiki

User Tools

Site Tools

Trace:
brain:brain

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Next revision		Previous revision
brain:brain [2016/12/12 11:18] – created peterbrain:brain [2020/07/15 10:30] (current) – external edit 127.0.0.1
Line 1: Line 1:
 +Brain.cpp
  
 <file cpp brain.cpp> <file cpp brain.cpp>
Line 422: Line 423:
 } }
 </file> </file>
 +
 +
 +activation.cpp
 +
 +<file cpp activation.cpp>
 +#include <cmath>
 +
 +
 +#include "activation.h"
 +
 +
 +Activation::Activation()
 +{
 +  activation_type = ACTIVATION_SIGMOID;
 +}
 +
 +
 +Activation::Activation(Activation_Types _activation_type)
 +{
 +  activation_type = _activation_type;
 +}
 +
 +
 +Activation::~Activation()
 +{
 +}
 +
 +
 +
 +double Activation::activate(const double& value, const bool derivative)
 +{
 +  switch (activation_type)
 +  {
 +  case (ACTIVATION_ABS) :
 +    return (abs(value, derivative));
 +    break;
 +
 +  case (ACTIVATION_ARCTAN) :
 +    return (arctan(value, derivative));
 +    break;
 +
 +  case (ACTIVATION_BOUNDEDRELU) :
 +    return (boundedRelu(value, derivative));
 +    break;
 +
 +  case (ACTIVATION_ELU) :
 +    return (elu(value, derivative));
 +    break;
 +
 +  case (ACTIVATION_GAUSSIAN) :
 +    return (gaussian(value, derivative));
 +    break;
 +
 +  case (ACTIVATION_LINEAR) :
 +    return (linear(value, derivative));
 +    break;
 +
 +  case (ACTIVATION_LOG) :
 +    return (log(value, derivative));
 +    break;
 +
 +  case (ACTIVATION_RELU) :
 +    return (relu(value, derivative));
 +    break;
 +
 +  case (ACTIVATION_SCALED_TANH) :
 +    return (tanh(value, derivative));
 +    break;
 +
 +  case (ACTIVATION_SIGMOID) :
 +    return (sigmoid(value, derivative));
 +    break;
 +
 +  case (ACTIVATION_SOFTRELU) :
 +    return (softRelu(value, derivative));
 +    break;
 +
 +  case (ACTIVATION_SQRT) :
 +    return (sqrt(value, derivative));
 +    break;
 +
 +  case (ACTIVATION_SQUARE) :
 +    return (square(value, derivative));
 +    break;
 +
 +  case (ACTIVATION_SQUASH) :
 +    return (squash(value, derivative));
 +    break;
 +
 +  case (ACTIVATION_STEP) :
 +    return (step(value, derivative));
 +    break;
 +
 +  case (ACTIVATION_TANH) :
 +    return (tanh(value, derivative));
 +    break;
 +
 +  default:
 +    return (sigmoid(value, derivative));
 +    break;
 +  }
 +}
 +
 +
 +// Returns a value between -1.0 and +1.0.
 +//
 +// f(x) = abs(x)
 +double Activation::abs(const double& value, const bool derivative)
 +{
 +  if (derivative)
 +    return value < 0 ? -1 : 1;
 +  else
 +    return std::abs(value);
 +}
 +
 +
 +// Returns a value between -1.0 and +1.0.
 +double Activation::arctan(const double& value, const bool derivative)
 +{
 +  if (derivative)
 +    return (std::cos(value) * std::cos(value));
 +  else
 +    return std::atan(value);
 +}
 +
 +
 +// Returns a value between -1.0 and +1.0.
 +//
 +// f(x) = min(a, max(0, x))
 +double Activation::boundedRelu(const double& value, const bool derivative)
 +{
 +  if (derivative)
 +    return 0; // TODO
 +  else
 +    return 0; // TODO
 +}
 +
 +
 +// Returns a value between -1.0 and +1.0.
 +//
 +// f(x) = 
 +double Activation::elu(const double& value, const bool derivative)
 +{
 +  if (derivative)
 +  {
 +    double output = elu(value);
 +    return output > 0 ? 1.0 : output + 1;
 +  }
 +  else
 +    return value > 0 ? value : std::exp(value) - 1;
 +}
 +
 +
 +// Returns a value between -1.0 and +1.0.
 +//
 +// f(x) = 
 +double Activation::gaussian(const double& value, const bool derivative)
 +{
 +  if (derivative)
 +    return -2 * value * std::exp(-value * -value);
 +  else
 +    return std::exp(-value * -value);
 +}
 +
 +
 +
 +// Returns a value between -1.0 and +1.0.
 +//
 +// f(x) = a*x + b
 +double Activation::linear(const double& value, const bool derivative)
 +{
 +  if (derivative)
 +    return 1; 
 +  else
 +    return value; 
 +}
 +
 +
 +// Returns a value between -1.0 and +1.0.
 +//
 +// f(x) = 1 / (1 + e^-x)
 +double Activation::log(const double& value, const bool derivative)
 +{
 +  if (derivative)
 +    return 0; // TODO
 +  else
 +    return 1.0 / (1.0 + std::exp(-value));
 +
 +  /*
 +  if (value < -45.0)
 +    return 0.0;
 +  else
 +  if (value > 45.0)
 +    return 1.0;
 +  else
 +    return 1.0 / (1.0 + std::exp(-value));
 +  */
 +}
 +
 +
 +// Returns a value between -1.0 and +1.0.
 +//
 +// f(x) = max(0, x)
 +double Activation::relu(const double& value, const bool derivative)
 +{
 +  if (derivative)
 +    return value > 0 ? 1.0 : 0.0;
 +  else
 +    return value > 0 ? value : 0;
 +}
 +
 +
 +
 +// Returns a value between -1.0 and +1.0.
 +//
 +// f(x) = 1.7159 * tanh(0.66667 * x)
 +double Activation::scaledTanh(const double& value, const bool derivative)
 +{
 +  if (derivative) // TODO...
 +  {
 +    double tanh_value = std::tanh(value);
 +    return 0.66667f * (1.7159f - 1 / 1.7159f * tanh_value * tanh_value);
 +  }
 +  else
 +    return 1.7159 * std::tanh(0.66667 * value);
 +}
 +
 +
 +// Returns a value between 0.0 and 1.0.
 +double Activation::sigmoid(const double& value, const bool derivative)
 +{
 +  if (derivative)
 +    return sigmoid(value) * (1.0 - sigmoid(value));
 +  else
 +    return 1.0 / double((1.0 + exp(-value)));
 +}
 +
 +
 +/*
 +// Returns a value between 0.0 and 1.0.
 +double Activation::sigmoid(const double& value)
 +{
 + return 1.0 / double((1.0 + exp(-value)));
 +}
 +
 +
 +double Activation::sigmoid_derivative(const double& value)
 +{
 + return sigmoid(value) * (1.0 - sigmoid(value));
 +}
 +*/
 +
 +
 +double Activation::sigmoid_limit(double value, double positive_limit, double negative_limit)
 +{
 + if (value < negative_limit)
 + return 0.0;
 + else
 + if (value > positive_limit)
 + return 1.0;
 + else
 + return 1.0 / (1.0 + exp(-value));
 +}
 +
 +
 +// Returns a value between -1.0 and +1.0.
 +//
 +// f(x) = log(1 + e^x)
 +double Activation::softRelu(const double& value, const bool derivative)
 +{
 +  if (derivative)
 +    return 0; // TODO
 +  else
 +    return 0; // TODO
 +}
 +
 +
 +// Returns a value between -1.0 and +1.0.
 +//
 +// f(x) = sqrt(x)
 +double Activation::sqrt(const double& value, const bool derivative)
 +{
 +  if (derivative)
 +    return 0; // TODO
 +  else
 +    return std::sqrt(value); // TODO
 +}
 +
 +
 +// Returns a value between -1.0 and +1.0.
 +//
 +// f(x) = x^2
 +double Activation::square(const double& value, const bool derivative)
 +{
 +  if (derivative)
 +    return 0; // TODO
 +  else
 +    return value * value; // TODO
 +}
 +
 +
 +// Returns a value between -1.0 and +1.0.
 +//
 +// f(x) = 
 +double Activation::squash(const double& value, const bool derivative)
 +{
 +  if (derivative)
 +  {
 +    if (value > 0)
 +      return (value) / (1 + value);
 +    else
 +      return (value) / (1 - value);
 +  }
 +  else
 +    return (value) / (1 + std::abs(value));
 +}
 +
 +
 +// Returns a value between -1.0 and +1.0.
 +//
 +// f(x) = 
 +double Activation::step(const double& value, const bool derivative)
 +{
 +  if (derivative)
 +  {
 +    if (value > 0)
 +      return 0;
 +    else
 +      return value;
 +  }
 +  else
 +  {
 +    if (value > 0)
 +      return 1;
 +    else
 +      return 0;
 +  }
 +}
 +
 +
 +
 +// Returns a value between -1.0 and +1.0.
 +//
 +// f(x) = a*tanh(b*x)
 +double Activation::tanh(const double& value, const bool derivative)
 +{
 +  if (derivative)
 +  {
 +    double tanh_value = std::tanh(value);
 +    return (1.0 - tanh_value * tanh_value);
 +    //return (1.0 - std::tanh(value)) * (1.0 + std::tanh(value));
 +  }
 +  else
 +    return std::tanh(value);
 +
 +  /*
 +  if (value < -45.0)
 +    return -1.0;
 +  else
 +  if (value > 45.0)
 +    return 1.0;
 +  else
 +    return std::tanh(value);
 +  */
 +
 +}
 +
 +
 +// Returns a value between -1.0 and +1.0.
 +double Activation::tanh_limit(double& value, double positive_limit, double negative_limit)
 +{
 + if (value < negative_limit)
 + return -1.0;
 + else
 + if (value > positive_limit)
 + return 1.0;
 + else
 + return tanh(value);
 +}
 +
 +
 +Activation_Types Activation::getActivationType()
 +{
 +  return activation_type;
 +}
 +
 +
 +void Activation::setActivationType(Activation_Types _activation_type)
 +{
 +  activation_type = _activation_type;
 +}
 +
 +
 +
 +
 +
 +
 +
 +/*
 +public double SoftMax(double x, string layer)
 +{
 +  // Determine max
 +  double max = double.MinValue;
 +  if (layer == "ih")
 +    max = (ihSum0 > ihSum1) ? ihSum0 : ihSum1;
 +  else 
 +  if (layer == "ho")
 +    max = (hoSum0 > hoSum1) ? hoSum0 : hoSum1;
 +
 +  // Compute scale
 +  double scale = 0.0;
 +  if (layer == "ih")
 +    scale = Math.Exp(ihSum0 - max) + Math.Exp(ihSum1 - max);
 +  else 
 +  if (layer == "ho")
 +    scale = Math.Exp(hoSum0 - max ) + Math.Exp(hoSum1 - max);
 +
 +  return Math.Exp(x - max) / scale;
 +}
 +
 +*/
 +</file>
 +
 +activation.h
 +
 +<file h activation.h>
 +#ifndef __SHAREWIZ_ACTIVATION_H__
 +#define __SHAREWIZ_ACTIVATION_H__
 +
 +#include <memory>
 +
 +
 +// Built-in activation functions.
 +
 +enum Activation_Types
 +{
 +  ACTIVATION_ABS,                      // Absolute value.
 +  ACTIVATION_ARCTAN,                   // Arctan.
 +  ACTIVATION_BOUNDEDRELU,              // Bounded rectified linear.
 +  ACTIVATION_ELU,                      // 
 +  ACTIVATION_GAUSSIAN,                 // Gaussian.
 +  ACTIVATION_LINEAR,                   // Linear.
 +  ACTIVATION_LOG,                      // Logistic.
 +  ACTIVATION_RELU,                     // Rectified linear.
 +  ACTIVATION_SCALED_TANH,              // Scaled Tanh 1.7159 * tanh(0.66667 * x ).
 +  ACTIVATION_SIGMOID,                  // Sigmoid.
 +  ACTIVATION_SOFTRELU,                 // Soft rectified linear. 
 +  ACTIVATION_SQRT,                     // Square Root.
 +  ACTIVATION_SQUARE,                   // Square.
 +  ACTIVATION_SQUASH,                   // Squash.
 +  ACTIVATION_STEP,                     // Step.
 +  ACTIVATION_TANH                      // Hyperbolic tangent.
 +};
 +
 +class Activation;
 +
 +typedef std::shared_ptr<Activation> pActivationX;
 +//typedef std::vector<pActivationX> pActivation;
 +
 +class Activation
 +{
 +private:
 + //enum {SIGMOID, TANH, RELU, LINEAR} types;
 + //types type;
 +  Activation_Types activation_type;
 +
 +public:
 + Activation();
 +  Activation(Activation_Types _activation_type);
 + ~Activation();
 +
 +  double activate(const double& value, const bool derivative=false);
 +
 +  double abs(const double& value, const bool derivative=false);
 +  double arctan(const double& value, const bool derivative=false);
 +  double boundedRelu(const double& value, const bool derivative=false);
 +  double elu(const double& value, const bool derivative = false);
 +  double gaussian(const double& value, const bool derivative = false);
 +  double linear(const double& value, const bool derivative=false);
 +  double log(const double& value, const bool derivative=false);
 +  double relu(const double& value, const bool derivative = false);
 +  double scaledTanh(const double& value, const bool derivative = false);
 +  double sigmoid(const double& value, const bool derivative=false);
 +  double sigmoid_limit(double value, double positive_limit=45.0, double negative_limit=-45.0);
 +  double softRelu(const double& value, const bool derivative=false);
 +  double sqrt(const double& value, const bool derivative = false);
 +  double square(const double& value, const bool derivative=false);
 +  double squash(const double& value, const bool derivative = false);
 +  double step(const double& value, const bool derivative = false);
 +  double tanh(const double& value, const bool derivative = false);
 +  double tanh_limit(double& value, double positive_limit=10.0, double negative_limit=-10.0);
 +
 +
 +  Activation_Types getActivationType();
 +  void setActivationType(Activation_Types _activation_type);
 +
 +
 + //double sigmoid(const double& value);
 + //double sigmoid_derivative(const double& value);
 +
 + //double tanh_derivative(const double& value);
 +};
 +
 +
 +
 +/*
 +
 +
 + // Built-in activation functions
 + export class Activations {
 + public static TANH: ActivationFunction = {
 + output: x = > (<any>Math).tanh(x),
 + der: x = > {
 + let output = Activations.TANH.output(x);
 + return 1 - output * output;
 + }
 + };
 + public static RELU: ActivationFunction = {
 + output: x = > Math.max(0, x),
 + der: x = > x <= 0 ? 0 : 1
 + };
 + public static SIGMOID: ActivationFunction = {
 + output: x = > 1 / (1 + Math.exp(-x)),
 + der: x = > {
 + let output = Activations.SIGMOID.output(x);
 + return output * (1 - output);
 + }
 + };
 + public static LINEAR: ActivationFunction = {
 + output: x = > x,
 + der: x = > 1
 + };
 + }
 +
 +
 +/* Build-in regularization functions.
 +export class RegularizationFunction {
 + public static L1: RegularizationFunction = {
 + output: w = > Math.abs(w),
 + der: w = > w < 0 ? -1 : 1
 + };
 + public static L2: RegularizationFunction = {
 + output: w = > 0.5 * w * w,
 + der: w = > w
 + };
 +}
 +*/
 +
 +
 +
 +
 +
 +#endif
 +</file>
 +
 +connection.cpp
 +
 +<file cpp connection.cpp>
 +#include <iostream>
 +#include <cassert>
 +
 +#include "neuron.h"
 +#include "connection.h"
 +
 +
 +
 +Connection::Connection()
 +{
 +#ifdef DEBUG
 + std::cout << "Connection::Connection1" << std::endl;
 +#endif
 +
 + index = -1;
 + deltaWeight = 0;
 + weight = 0;
 + momentum = 0.4;
 +
 +  Q = 0;
 +  R = -1;
 +
 + randomizeWeight();
 +}
 +
 +
 +Connection::Connection(const pNeuronX& from, const pNeuronX& to)
 +{
 +#ifdef DEBUG
 + std::cout << "Connection::Connection2" << std::endl;
 +#endif
 +
 + index = -1;
 + deltaWeight = 0;
 + weight = 0;
 + momentum = 0.4;
 +
 +  Q = 0;
 +  R = -1;
 +
 + this->from = from;
 + this->to = to;
 +
 + randomizeWeight();
 +}
 +
 +
 +double Connection::getError(void)
 +{
 +#ifdef DEBUG
 + std::cout << "Connection::getError" << std::endl;
 +#endif
 +
 + return error;
 +}
 +
 +
 +void Connection::setError(const double& e)
 +{
 +#ifdef DEBUG
 + std::cout << "Connection::setError" << std::endl;
 +#endif
 +
 + error = e;
 +}
 +
 +
 +int Connection::getIndex(void)
 +{
 +#ifdef DEBUG
 + std::cout << "Connection::getIndex" << std::endl;
 +#endif
 +
 + return index;
 +}
 +
 +
 +void Connection::setIndex(const int& index)
 +{
 +#ifdef DEBUG
 + std::cout << "Connection::setIndex" << std::endl;
 +#endif
 +
 + this->index = index;
 +}
 +
 +
 +double Connection::getWeight(void)
 +{
 +#ifdef DEBUG
 + std::cout << "Connection::getWeight" << std::endl;
 +#endif
 +
 + return weight;
 +}
 +
 +
 +void Connection::setWeight(const double& w)
 +{
 +#ifdef DEBUG
 + std::cout << "Connection::setWeight" << std::endl;
 +#endif
 +
 + //deltaWeight = weight - w;
 + weight = w;
 +}
 +
 +
 +double Connection::getDeltaWeight(void)
 +{
 +#ifdef DEBUG
 + std::cout << "Connection::getDeltaWeight" << std::endl;
 +#endif
 +
 + return deltaWeight;
 +}
 +
 +
 +void Connection::setDeltaWeight(const double& dw)
 +{
 +#ifdef DEBUG
 + std::cout << "Connection::setDeltaWeight" << std::endl;
 +#endif
 +
 + deltaWeight = dw;
 +}
 +
 +
 +// Controls how much the weights are changed during a weight update by factoring in previous weight updates.
 +// It acts as a smoothing parameter that reduces oscillation and helps attain convergence.
 +// This must be a real value between 0.0 and 1.0, a typical value for momentum is 0.9.
 +double Connection::getMomentum(void)
 +{
 +#ifdef DEBUG
 + std::cout << "Connection::getMomentum" << std::endl;
 +#endif
 +
 + return momentum;
 +}
 +
 +
 +void  Connection::setMomentum(const double& momentum)
 +{
 +#ifdef DEBUG
 + std::cout << "Connection::setMomentum" << std::endl;
 +#endif
 +
 + this->momentum = momentum;
 +}
 +
 +
 +double Connection::getQ(void)
 +{
 +  return Q;
 +}
 +
 +
 +void Connection::setQ(const double& _Q)
 +{
 +  Q = _Q;
 +}
 +
 +
 +double Connection::getR(void)
 +{
 +  return R;
 +}
 +
 +
 +void Connection::setR(const double& _R)
 +{
 +  R = _R;
 +}
 +
 +
 +pNeuronX& Connection::getFrom(void)
 +{
 +#ifdef DEBUG
 + std::cout << "Connection::getFrom" << std::endl;
 +#endif
 +
 + return from;
 +}
 +
 +
 +void Connection::setFrom(const pNeuronX& from)
 +{
 +#ifdef DEBUG
 + std::cout << "Connection::setFrom" << std::endl;
 +#endif
 +
 + this->from = from;
 +}
 +
 +
 +pNeuronX& Connection::getTo(void)
 +{
 +#ifdef DEBUG
 + std::cout << "Connection::getTo" << std::endl;
 +#endif
 +
 + return to;
 +}
 +
 +
 +void Connection::setTo(const pNeuronX& to)
 +{
 +#ifdef DEBUG
 + std::cout << "Connection::setTo" << std::endl;
 +#endif
 +
 + this->to = to;
 +}
 +
 +
 +double Connection::randomizeWeight(void)
 +{
 +#ifdef DEBUG
 + std::cout << "Connection::randomizeWeight" << std::endl;
 +#endif
 +
 + weight = rand() / double(RAND_MAX);
 + //deltaWeight = weight;
 +
 + return weight;
 +}
 +
 +
 +void Connection::printOutput(void)
 +{
 +#ifdef DEBUG
 + std::cout << "Connection::printOutput" << std::endl;
 +#endif
 +
 + if (!from)
 + return;
 + if (!to)
 + return;
 +
 + int f = from->getIndex();
 + int t = to->getIndex();
 +
 + //std::cout << "    Connection[" << index << "] w=" << weight << ", From=" << f << ", To=" << t << ", d=" << deltaWeight << std::endl;
 +  std::cout << "    Connection[" << index << "] w=" << weight << ", From=" << f << ", To=" << t << ", d=" << deltaWeight << ", Q=" << Q << ", R=" << R << std::endl;
 +}
 +
 +
 +/*
 +
 +#include <algorithm>
 +#include <iostream>
 +#include <vector>
 +
 +int random_number(int N) // random value in [0, N)
 +{
 +static std::random_device seed;
 +static std::mt19937 eng(seed());
 +std::uniform_int_distribution<> dist(0, N - 1);
 +return dist(eng);
 +}
 +
 +std::vector<int> random_sample(int first, int last, int n)
 +{
 +std::vector<int> numbers;
 +int remaining = last - first + 1;
 +int m = std::min(n, remaining);
 +while (m > 0) {
 +if (random_number(remaining) < m) {
 +numbers.push_back(first);
 +--m;
 +}
 +--remaining;
 +++first;
 +}
 +return numbers;
 +}
 +
 +int main()
 +{
 +auto numbers = random_sample(1, 100, 20);
 +for (int value : numbers) {
 +std::cout << value << " ";
 +}
 +std::cout << '\n';
 +}
 +*/
 +/*
 +very simple random is 1+((power(r,x)-1) mod p) will be from 1 to p for values of x from 1 to p and will be random where r and p are prime numbers and r <> p.
 +
 +To shuffle an array a of n elements:
 +for i from n ? 1 downto 1 do
 +j ? random integer with 0 ? j ? i
 +exchange a[j] and a[i]
 +
 +
 +for (int i = cards.Length - 1; i > 0; i--)
 +{
 +int n = rand.Next(i + 1);
 +Swap(ref cards[i], ref cards[n]);
 +}
 +*/
 +</file>
 +
 +connection.h
 +
 +<file h connection.h>
 +#ifndef __SHAREWIZ_CONNECTION_H__
 +#define __SHAREWIZ_CONNECTION_H__
 +
 +//#include <memory>
 +//#include <vector>
 +
 +// Connection class.
 +// 
 +// A Connection links two Neurons.
 +
 +
 +class Neuron;
 +
 +typedef std::shared_ptr<Neuron> pNeuronX;
 +typedef std::vector<pNeuronX> pNeuron;
 +
 +
 +class Connection
 +{
 +private:
 + int index;
 + double weight;
 + double deltaWeight;
 + double error;
 + double momentum;                      // alpha.
 +
 +  // For DQN.  
 +  // Should we re-use weight as Q and deltaWeight as R.
 +  double Q;
 +  double R;
 +
 +  double odds;                         // Odds of being chosen as next action for DQN.
 +
 +
 + double randomizeWeight(void);
 +
 + pNeuronX from;
 + pNeuronX to;
 +
 +public:
 + Connection();
 + Connection(const pNeuronX& from, const pNeuronX& to);
 +
 + double getError(void);
 + void setError(const double& e);
 +
 + int getIndex(void);
 + void setIndex(const int& index);
 +
 + double getWeight(void);
 + void setWeight(const double& w);
 +
 + double getDeltaWeight(void);
 + void setDeltaWeight(const double& w);
 +
 + double getMomentum(void);
 + void setMomentum(const double& momentum);
 +
 +  double getQ(void);
 +  void setQ(const double& _Q);
 +
 +  double getR(void);
 +  void setR(const double& _R);
 +
 +  pNeuronX& getFrom(void);
 + void setFrom(const pNeuronX& from);
 +
 + pNeuronX& getTo(void);
 + void setTo(const pNeuronX& to);
 +
 + void printOutput(void);
 +};
 +
 +
 +#endif
 +</file>
 +
 +layer.cpp
 +
 +<file cpp layer.cpp>
 +#include <iostream>
 +#include <cassert>
 +
 +#include "layer.h"
 +#include "neuron.h"
 +#include "connection.h"
 +#include "activation.h"
 +
 +
 +//Layer::Layer() :
 +Layer::Layer()
 +//  index(-1),
 +//  neurons(10)
 +//  index(0)
 +//neurons(10)
 +{
 + //idx++;
 + //index++;  
 + index = -1;
 +
 + neurons.reserve(10);
 +
 + //neurons = std::vector<Neuron>();
 + //std::cout << "neurons size: " << neurons.size() << std::endl;
 +}
 +
 +
 +Layer::Layer(unsigned int num_neurons)
 +{
 + index = -1;
 +
 + neurons.reserve(num_neurons);
 +
 + for (unsigned int i = 0; i<num_neurons; i++)
 + {
 + pNeuronX tmp(new Neuron());
 + tmp->setIndex(i);
 + neurons.push_back(tmp);
 + }
 +
 +
 + /*
 + // Add a bias neuron in each layer.
 + // Force the bias node's output to 1.0 (it was the last neuron pushed in this layer):
 + pNeuronX tmp(new Neuron());
 + tmp->setIndex(100);
 + tmp->setValue(1);
 + neurons.push_back(tmp);
 +
 + //neurons.back().back().setOutputVal(1.0);
 + //neurons.back()->setValue(1.0);
 + */
 +}
 +
 +
 +int Layer::getIndex(void)
 +{
 + return index;
 +}
 +
 +
 +void Layer::setIndex(const int& index)
 +{
 + this->index = index;
 +}
 +
 +
 +unsigned int Layer::getSize(void)
 +{
 + return neurons.size();
 +}
 +
 +
 +void Layer::addNeuron(const pNeuronX& n)
 +{
 + neurons.push_back(n);
 +}
 +
 +
 +void Layer::removeNeuron(const int& idx)
 +{
 + assert(neurons.size() >= idx);
 +
 +  for (unsigned i = neurons.size()-1; i > 0;  i--)
 +  {
 +    if (neurons[i]->getIndex() == idx)
 +    {
 +      neurons.erase(neurons.begin() + i);
 +      return;
 +    }
 +  }
 +}
 +
 +
 +pNeuronX &Layer::getNeuron(const int& idx)
 +{
 + assert(neurons.size() >= idx);
 +
 + return neurons[idx];
 +}
 +
 +
 +void Layer::feedForward(const pLayerX& prevLayer)
 +{
 + /*
 + // INPUT -> HIDDEN
 + for(y=0; y<hidden_array_size; y++) {
 + for(x=0; x<input_array_size; x++) {
 + temp += (input[pattern][x] * weight_i_h[x][y]);
 + }
 + hidden[y] = (1.0 / (1.0 + exp(-1.0 * (temp + bias[y]))));
 + temp = 0;
 + }
 +
 + // HIDDEN -> OUTPUT
 + for(y=0; y<output_array_size; y++) {
 + for(x=0; x<hidden_array_size; x++) {
 + temp += (hidden[x] * weight_h_o[x][y]);
 + }
 + output[pattern][y] = (1.0 / (1.0 + exp(-1.0 * (temp + bias[y + hidden_array_size]))));
 + temp = 0;
 + }
 + return;
 + }
 + */
 +
 + for (unsigned int i = 0; i<getSize(); i++) // How many Neurons in current layer.
 + {
 + // Weight sum of the previous layer's output values.
 + double weightedSum = 0;
 + //weightedSum += .5; // Add a 1 to act as a bias.
 + weightedSum += 1.0; // Add a 1 to act as a bias.
 +
 + pNeuronX& currentNeuron = neurons[i];
 + if (!currentNeuron)
 + continue;
 + unsigned int currentIndex = currentNeuron->getIndex();
 +
 + for (unsigned int j = 0; j<prevLayer->getSize(); j++) // How many Neurons in prev layer.
 + {
 + pNeuronX& prevNeuron = prevLayer->getNeuron(j);
 + if (!prevNeuron)
 + continue;
 + //std::cout << "J=" << j << std::endl;
 + for (unsigned int k = 0; k<prevNeuron->getSizeOut(); k++)
 + {
 + if (!prevNeuron->getConnectionOut(k))
 + continue;
 + if (!prevNeuron->getConnectionOut(k)->getTo())
 + continue;
 +
 + // We are only interested in connections going into the currentNeuron.
 + unsigned int prevIndex = prevNeuron->getConnectionOut(k)->getTo()->getIndex();
 +
 + //if (prevNeuron.getConnectionOut(k).getTo() == currentNeuron)
 + if (prevIndex == currentIndex)
 + {
 + weightedSum += prevLayer->getNeuron(j)->getValue()*prevLayer->getNeuron(j)->getConnectionOut(k)->getWeight();
 + }
 + }
 + }
 +
 + // Add in Bias?
 + //weightedSum += .5; // Add a 1 to act as a bias. 
 +
 + //std::cout << "*************" << std::endl;
 +    if (currentNeuron)
 +    {
 +      pActivationX act = currentNeuron->getActivation();
 +      if (!act)
 +        continue;
 +
 +      // Sigmoid function.  Activation function is applied to this intermediate value to yield the local value of the neuron.
 +      //currentNeuron->setValue(currentNeuron->sigmoid(weightedSum));
 +      //currentNeuron->setValue(act.activate(weightedSum));
 +      //currentNeuron->setValue(currentNeuron->getActivation()->activate(weightedSum));
 +      currentNeuron->setValue(act->activate(weightedSum));
 +
 +      //std::cout << "------------------" << std::endl;      
 +    }
 + }
 +
 + //  std::cout << "++++++++++++++++" << std::endl;  
 +}
 +
 +
 +void Layer::printOutput(void)
 +{
 + std::cout << "Layer " << index << " has " << neurons.size() << " Neurons" << std::endl;
 +
 + for (unsigned int i = 0; i<neurons.size(); i++)
 + {
 + if (!neurons[i])
 + continue;
 +
 + std::cout << "  Neuron[" << i << "] v=" << neurons[i]->getValue() << ", g=" << neurons[i]->getGradient() << std::endl;
 +
 + for (unsigned int j = 0; j<neurons[i]->getSizeOut(); j++)
 + {
 + pConnectionX& currentConnection = neurons[i]->getConnectionOut(j);
 + if (!currentConnection)
 + continue;
 + currentConnection->printOutput();
 + }
 + }
 +}
 +</file>
 +
 +layer.h
 +
 +<file h layer.h>
 +#ifndef __SHAREWIZ_LAYER_H__
 +#define __SHAREWIZ_LAYER_H__
 +
 +#include <memory>
 +#include <vector>
 +
 +//#include "neuron.h"
 +
 +// Layer class.
 +//
 +// A Neural Network can have multiple Layers.
 +
 +class Layer;
 +class Neuron;
 +
 +
 +typedef std::shared_ptr<Layer> pLayerX;
 +typedef std::vector<pLayerX> pLayer;
 +
 +typedef std::shared_ptr<Neuron> pNeuronX;
 +typedef std::vector<pNeuronX> pNeuron;
 +
 +
 +
 +class Layer
 +{
 +private:
 + int index;
 +
 + pNeuron neurons;
 +
 +public:
 + Layer();
 + Layer(unsigned int num_neurons);
 +
 + unsigned int getSize(void);  // Returns how many neurons.
 +
 + int getIndex(void);
 + void setIndex(const int& index);
 +
 + void addNeuron(const pNeuronX& n);
 + void removeNeuron(const int& idx);
 +
 + pNeuronX& getNeuron(const int& idx);
 +
 + void feedForward(const pLayerX& prevLayer);
 +
 + void printOutput(void);
 +};
 +
 +
 +#endif
 +</file>
 +
 +net.cpp
 +
 +<file cpp net.cpp>
 +#include <iostream>
 +#include <cassert>
 +
 +#include "net.h"
 +#include "layer.h"
 +#include "neuron.h"
 +#include "connection.h"
 +
 +typedef std::shared_ptr<Connection> pConnectionX;
 +typedef std::vector<pConnectionX> pConnection;
 +
 +/*
 +It has been shown that the error surface of a backpropagation network with one hidden layer and hidden units 
 +has no local minima, if the network is trained with an arbitrary set containing different inputs1 (Yu, 1992).
 +In practice, however, other features of the error surface such as "ravines" and "plateaus" (Baldi and Hornik, 1988) 
 +can present difficulty for optimisation. For example, two error functions (from (Gori, 1996)) do not have local 
 +minima. However, the function on the left is expected to be more difficult to optimise with gradient descent. 
 +For the purposes of this paper, the criterion of interest considered is "the best solution found in a given 
 +practical time limit.
 +*/
 +
 +
 +Net::Net() :
 +learning_rate(0.5),
 +max_error_tollerance(0.1),
 +goal_amount(100.0)
 +{
 + layers.reserve(10);
 +}
 +
 +
 +// Example:
 +//   std::vector<unsigned int> myTopology;
 +//   myTopology.push_back(2);
 +//   myTopology.push_back(4);
 +//   myTopology.push_back(1);
 +//   Net myNet(myTopology);
 +Net::Net(const std::vector<unsigned int>& topology) :
 +learning_rate(0.5),
 +max_error_tollerance(0.1),
 +goal_amount(100.0)
 +{
 +  assert(topology.size() > 0);
 +
 +  learning_rate = 0.5;
 +  max_error_tollerance = 0.1;
 +  goal_amount = 100.0;
 +
 +  layers.reserve(topology.size());
 +
 + // obtain a time-based seed:
 + //unsigned seed = std::chrono::system_clock::now().time_since_epoch().count();
 + // using built-in random generator:
 + //shuffle (topology.begin(), topology.end(), std::default_random_engine(seed));
 + //auto engine = std::default_random_engine{};
 + //std::shuffle(std::begin(topology), std::end(topology), engine);
 + //std::random_shuffle ( topology.begin(), topology.end());
 + //std::shuffle ( topology.begin(), topology.end() );
 +
 +
 + for (unsigned int i = 0; i<topology.size(); i++)
 + {
 + pLayerX tmp(new Layer(topology[i]));
 +    tmp->setIndex(i);
 + layers.push_back(tmp);
 + }
 +
 +
 + std::cout << "layers size: " << layers.size() << std::endl;
 +  for (unsigned int i = 0; i < layers.size(); i++)
 +  {
 +    std::cout << "layers " << i << " neurons size: " << layers[i]->getSize() << std::endl;
 +  }
 +
 +
 + //printOutput();
 +
 + // Add Bias to input and hidden layers.
 + //layers[1]->addNeuron(
 +
 +  //connectAll();
 +  //connectForward();
 +
 + //printOutput();
 +}
 +
 +
 +// Connects the "From" Neuron to the "To" Neuron.
 +void Net::connect(const std::vector< std::vector<double> > connections)
 +{
 +  //unsigned int connection_idx = 1;
 +  int connection_idx = 0;
 +
 +  for (unsigned int i = 0; i < connections.size(); i++)
 +  {
 +    //for (unsigned int j = 0; j < connections[i].size(); j++)
 +    //{
 +      int layerFrom = (int)connections[i][0];
 +      int neuronFrom = (int)connections[i][1];
 +      int layerTo = (int)connections[i][2];
 +      int neuronTo = (int)connections[i][3];
 +      double _R = connections[i][4];
 +
 +      pConnectionX tmp(new Connection(layers[layerFrom]->getNeuron(neuronFrom), layers[layerTo]->getNeuron(neuronTo)));
 +      tmp->setIndex(connection_idx++);
 +      tmp->setQ(0);
 +      tmp->setR(_R);
 +      layers[layerFrom]->getNeuron(neuronFrom)->addConnectionOut(tmp);
 +      layers[layerTo]->getNeuron(neuronTo)->addConnectionIn(tmp);
 +    //}
 +  }
 +}
 +
 +
 +// Connects the "From" Neuron to the "To" Neuron.
 +void Net::connect(int layerFrom, int neuronFrom, int layerTo, int neuronTo, double _R, int connection_idx)
 +{
 +  //unsigned int connection_idx = 1;
 +
 +  pConnectionX tmp(new Connection(layers[layerFrom]->getNeuron(neuronFrom), layers[layerTo]->getNeuron(neuronTo)));
 +  tmp->setIndex(connection_idx);
 +  tmp->setQ(0);
 +  tmp->setR(_R);
 +  layers[layerFrom]->getNeuron(neuronFrom)->addConnectionOut(tmp);
 +  layers[layerTo]->getNeuron(neuronTo)->addConnectionIn(tmp);
 +}
 +
 +
 +
 +// Connects all Neurons to each other.
 +void Net::connectAll()
 +{
 +  // assert(layer.size() > 1); // There must be more than 1 neuron to connect.
 +
 +  int connection_idx = 0;
 +
 +  for (unsigned int i = 0; i<layers.size(); i++) // For each Sending Layer.
 +  {
 +    for (unsigned int j = 0; j<layers[i]->getSize(); j++)  // For each neuron in Sending Layer.
 +    {
 +      for (unsigned int k = 0; k<layers.size(); k++)  // For each Receiving layer.
 +      {
 +        for (unsigned int l = 0; l < layers[k]->getSize(); l++)  // For each neuron in Receiving layer.
 +        {
 +          pConnectionX tmp(new Connection(layers[i]->getNeuron(j), layers[k]->getNeuron(l)));
 +          tmp->setIndex(connection_idx++);
 +          layers[i]->getNeuron(j)->addConnectionOut(tmp);
 +          layers[k]->getNeuron(l)->addConnectionIn(tmp);
 +
 +          //std::cout << "I[" << j << "] connected to H[" << k << "] with Connection IDX=" << connection_idx-1 << std::endl;
 +        }
 +      }
 +    }
 +  }
 +}
 +
 +
 +// Connects all Neurons in a layer to all Neurons in the next layer.
 +void Net::connectForward()
 +{
 +  unsigned int connection_idx = 0;
 +
 +  for (unsigned int i = 0; i<layers.size()-1; i++) // For each Sending Layer.
 +  {
 +    for (unsigned int j = 0; j<layers[i]->getSize(); j++)  // how many input neurons in input level.
 +    {
 +      for (unsigned int k = 0; k<layers[i+1]->getSize(); k++)  // how many neurons in next level.
 +      {
 +        pConnectionX tmp(new Connection(layers[i]->getNeuron(j), layers[i + 1]->getNeuron(k)));
 +        tmp->setIndex(connection_idx++);
 +        layers[i]->getNeuron(j)->addConnectionOut(tmp);
 +        layers[i + 1]->getNeuron(k)->addConnectionIn(tmp);
 +
 +        //std::cout << "I[" << j << "] connected to H[" << k << "] with Connection IDX=" << connection_idx-1 << std::endl;
 +      }
 +    }
 +  }
 +}
 +
 +
 +// Same as connectForward() but code spread out between layers.
 +// Connects all Neurons to Neurons in next layer.
 +void Net::connectForward2()
 +{
 +  unsigned int connection_idx = 0;
 +
 +  // Create the input to hidden connections.
 +  // assert(layers.size() > 1); // There must be more than 1 layers to connect.
 +
 +  for (unsigned int i = 0; i<layers.size() - 1; i++) // get how many Layers
 +  {
 +    // Create the input to hidden connections.
 +    if (i == 0)
 +    {
 +      if ((layers.size()) > 1)  // there are other layers
 +      {
 +        for (unsigned int j = 0; j<layers[0]->getSize(); j++)  // how many input neurons in input level.
 +        {
 +          for (unsigned int k = 0; k<layers[1]->getSize(); k++)  // how many neurons in next level.
 +          {
 +            pConnectionX tmp(new Connection(layers[0]->getNeuron(j), layers[1]->getNeuron(k)));
 +            tmp->setIndex(connection_idx++);
 +            layers[0]->getNeuron(j)->addConnectionOut(tmp);
 +            layers[1]->getNeuron(k)->addConnectionIn(tmp);
 +
 +            //std::cout << "I[" << j << "] connected to H[" << k << "] with Connection IDX=" << connection_idx-1 << std::endl;
 +          }
 +        }
 +      }
 +      else //  no other layers.  so no connections possible.
 +      {
 +
 +      }
 +    }
 +
 +
 +    // Create the inside hidden connections...and hidden to output connection.
 +    if ((i>0) && (i <= layers.size() - 2))
 +    {
 +      for (unsigned int j = 0; j<layers[i]->getSize(); j++)  // how many input neurons.
 +      {
 +        for (unsigned int k = 0; k<layers[i + 1]->getSize(); k++)  // how many neurons in next level.
 +        {
 +          pConnectionX tmp(new Connection(layers[i]->getNeuron(j), layers[i + 1]->getNeuron(k)));
 +          tmp->setIndex(connection_idx++);
 +          layers[i]->getNeuron(j)->addConnectionOut(tmp);
 +          layers[i + 1]->getNeuron(k)->addConnectionIn(tmp);
 +
 +          //std::cout << "HI[" << j << "] connected to O[" << k << "] with Connection IDX=" << connection_idx-1 << std::endl;
 +        }
 +      }
 +    }
 +  }
 +}
 +
 +
 +// Connects all Neurons to each other.
 +void Net::connectAllInLayer(const pLayerX& layer)
 +{
 +  // assert(layer.size() > 1); // There must be more than 1 neuron to connect.
 +
 +  unsigned int connection_idx = 0;
 +
 +  for (unsigned int i = 0; i<layer->getSize(); i++)  // For each "from" neuron in Layer.
 +  {
 +    for (unsigned int j = 0; j<layer->getSize(); j++)  // For each "to" neuron in layer.
 +    {
 +      pConnectionX tmp(new Connection(layer->getNeuron(i), layer->getNeuron(j)));
 +      tmp->setIndex(connection_idx++);
 +      layer->getNeuron(i)->addConnectionOut(tmp);
 +      layer->getNeuron(j)->addConnectionIn(tmp);
 +
 +        //std::cout << "I[" << j << "] connected to H[" << k << "] with Connection IDX=" << connection_idx-1 << std::endl;
 +    }
 +  }
 +}
 +
 +
 +
 +void Net::DQN(void)
 +{
 +  int numLayers = layers.size();
 +  int numNeurons = 0;
 +  int numConnections = 0;
 +
 +  // Determine how many layers, neurons (states) and connections (actions) we have.
 +  for (unsigned int i = 0; i < layers.size(); i++) // get how many Layers
 +  {
 +    numNeurons += layers[i]->getSize();
 +        
 +    for (unsigned int j = 0; j < layers[i]->getSize(); j++)  // For each neuron in the Sending Layer.
 +    {
 +      pNeuronX& currentNeuron = layers[i]->getNeuron(j);  // Get current neuron.
 +      if (!currentNeuron)
 +        continue;
 +
 +      numConnections += currentNeuron->getSizeOut();  // Get how many connections from the current neuron.
 +    }
 +  }
 +
 +
 +  // Select random initial neuron (state).
 +  int rnd_layer = randomBetween(0, numLayers-1);
 +  int rnd_state = randomBetween(0, numNeurons-1);
 +  pLayerX currentLayer = layers[rnd_layer];
 +  pNeuronX currentState = layers[rnd_layer]->getNeuron(rnd_state);
 +
 +  // Set initial reward.
 +  double R = -1;
 +
 +  // Loop until a reward matching the goal_amount has been found.
 +  while (R != goal_amount)
 +  {
 +    // Select one amongst all possible actions for the current state.
 +    // TODO: Simply using random treats all possible actions as equal.
 +    // TODO: Should cater for giving different actions different odds of being chosen.
 +    int rnd_action = randomBetween(0, currentState->getSizeOut()-1); 
 +    pConnectionX currentAction = currentState->getConnectionOut(rnd_action);
 +
 +    // Action outcome is set to deterministic in this example
 +    // Transition probability is 1.
 +    pNeuronX nextState = currentAction->getTo();
 +
 +    // Get reward.
 +    R = currentAction->getR();
 +
 +    // Get Q.
 +    double Q = currentAction->getQ();
 +
 +    // Determine the maximum Q.
 +    double maxQ = DBL_MIN;
 +    for (unsigned int i = 0; i<nextState->getSizeOut(); i++)
 +    {
 +      double tmpQ = nextState->getConnectionOut(i)->getQ();
 +
 +      if (maxQ < tmpQ)
 +        maxQ = tmpQ;
 +    }
 +    if (maxQ == DBL_MIN) maxQ = 0;
 +
 +    // Update the Q.
 +    //double v = Q + alpha * (R + gamma * maxQ - Q);
 +    double target = R + gamma * maxQ;
 +    //double error = R + gamma * maxQ - Q;
 +    double error = target - Q;
 +
 +
 +    // Experience Replay Memory.
 +    // To suggest an experience replay memory.
 +    // This is loosely inspired by the brain, and in particular the way it syncs memory traces in the hippocampus 
 +    // with the cortex. 
 +    // What this amounts to is that instead of performing an update and then throwing away the experience tuple, 
 +    // i.e. the original Q, we keep it around and effectively build up a training set of experiences. 
 +    // Then, we don't learn based on the new experience that comes in at time t, but instead sample random 
 +    // expriences from the replay memory and perform an update on each sample. 
 +    // This feature has the effect of removing correlations in the observed state,action,reward sequence and 
 +    // reduces gradual drift and forgetting.
 +    // If the size of the memory pool is greater than some threshold, start replacing old experiences.
 +    // or those further from the current Q, or randomly etc.
 +    int rnd_replay_memory = randomBetween(0, 100);
 +    if (rnd_replay_memory > 99) // if rnd > some value
 +    {
 +      //experience_add_every = 5; // number of time steps before we add another experience to replay memory.
 +      //experience_size = 10000;  // size of experience.
 +      
 +      // Record old Q value into array of stored memories.
 +      // Now select new Q value from randomly selecting one of the old Q memory values - perhaps by using odds.
 +      // i.e. most fresh Q value might have slightly greater chance of being selected etc.
 +    }
 +
 +    // Clamping TD Error.
 +    // Clamp the TD Error gradient at some fixed maximum value.
 +    // If the error is greater in magnitude then some threshold (tderror_clamp) then we cap it at that value.
 +    // This makes the learning more robust to outliers and has the interpretation of using Huber loss, which 
 +    // is an L2 penalty in a small region around the target value and an L1 penalty further away.
 +//    double tderror_clamp = 1.0; // for robustness
 +//    if (error > tderror_clamp)
 +//      error = tderror_clamp;
 +
 +    // Periodic Target Q Value Updates.
 +    // Periodically freeze the Q where it is.
 +    // Aims to reduce correlations between updates and the immediately undertaken behavior.
 +    // The idea is to freeze the Q network once in a while into a frozen, copied network, which is used to 
 +    // only compute the targets.
 +    // This target network is once in a while updated to the actual current.
 +    int rnd_freeze = randomBetween(0, 100);
 +    if (rnd_freeze > 99)
 +    {
 +    }
 +
 +    double v = Q + alpha * (error);
 +
 +    currentAction->setQ(v);
 +
 +    // Update the state.
 +    currentState = nextState;
 +  }
 +}
 +
 +
 +// Determine the maximum Q for the state.
 +double Net::getMaxQ(pNeuronX state)
 +{
 +  double maxQ = DBL_MIN;
 +  for (unsigned int i = 0; i<state->getSizeOut(); i++)
 +  {
 +    double tmpQ = state->getConnectionOut(i)->getQ();
 +
 +    if (maxQ < tmpQ)
 +      maxQ = tmpQ;
 +  }
 +
 +  if (maxQ == DBL_MIN) 
 +    maxQ = 0;
 +
 +  return maxQ;
 +}
 +
 +
 +// Get policy from state.
 +pNeuronX Net::getPolicy(pNeuronX currentState)
 +{
 +  double maxValue = DBL_MIN;
 +  pNeuronX policyGotoState = currentState; // Default goto self if not found.
 +
 +  for (unsigned int i = 0; i < currentState->getSizeOut(); i++)
 +  {
 +    pNeuronX nextState = currentState->getConnectionOut(i)->getTo();
 +    double value = currentState->getConnectionOut(i)->getQ();
 +
 +    if (value > maxValue)
 +    {
 +      maxValue = value;
 +      policyGotoState = nextState;
 +    }
 +  }
 +
 +  return policyGotoState;
 +}
 +
 +  
 +// Policy is maxQ(states).
 +void Net::showPolicy(void) 
 +{
 +  for (unsigned int i = 0; i < layers.size(); i++)
 +  {
 +    for (unsigned int j = 0; j < layers[i]->getSize(); j++)
 +    {
 +      pNeuronX fromState = layers[i]->getNeuron(j);
 +      int from = fromState->getIndex(); 
 +
 +      pNeuronX toState = getPolicy(fromState);
 +      int to = toState->getIndex(); 
 +
 +      std::cout << "From " << from << " goto " << to << std::endl;
 +    }
 +  }
 +}
 +
 +
 +// This method has a bit of a bias towards the low end if the range of rand() isn't divisible 
 +// by highestNumber - lowestNumber + 1.
 +int Net::randomBetween(int lowestNumber, int highestNumber)
 +{
 +  assert(highestNumber >= lowestNumber);
 +
 +  //return rand() % to + from;
 +
 +  return rand() % (highestNumber - lowestNumber + 1) + lowestNumber;
 +}
 +
 +
 +
 +// Controls how much the weights are changed during a weight update.
 +// The larger the value, the more the weights are changed.
 +// This must be a real value between 0.0 and 10.0.
 +// These values are commonly set from 0.5 to 0.7.
 +double Net::getLearningRate(void)
 +{
 + return learning_rate;
 +}
 +
 +
 +void Net::setLearningRate(const double& learning_rate)
 +{
 + this->learning_rate = learning_rate;
 +}
 +
 +
 +double Net::getMaxErrorTollerance(void)
 +{
 + return max_error_tollerance;
 +}
 +
 +
 +void Net::setMaxErrorTollerance(const double& max_error_tollerance)
 +{
 + this->max_error_tollerance = max_error_tollerance;
 +}
 +
 +
 +double Net::getAlpha(void)
 +{
 +  return alpha;
 +}
 +
 +
 +// Learning rate.
 +//
 +// The Alpha parameter has a range of 0 to 1 (0 <= Gamma > 1).
 +//
 +// Set this by trial and error.  That's Pretty much the best thing we have.
 +void Net::setAlpha(const double& _alpha_amount)
 +{
 +  alpha = _alpha_amount;
 +}
 +
 +
 +double Net::getGamma(void)
 +{
 +  return gamma;
 +}
 +
 +
 +// Discount factor.
 +//
 +// The Gamma parameter has a range of 0 to 1 (0 <= Gamma > 1).
 +//
 +// If Gamma is closer to zero, the agent will tend to consider only immediate rewards.
 +//
 +// If Gamma is closer to one, the agent will consider future rewards with greater weight, 
 +// willing to delay the reward.
 +void Net::setGamma(const double& _gamma_amount)
 +{
 +  gamma = _gamma_amount;
 +}
 +
 +
 +
 +double Net::getGoalAmount(void)
 +{
 +  return goal_amount;
 +}
 +
 +
 +void Net::setGoalAmount(const double& _goal_amount)
 +{
 +  this->goal_amount = _goal_amount;
 +}
 +
 +
 +
 +/*
 +void Net::load_data(char *arg)
 +{
 +int x, y;
 +ifstream in(arg);
 +if(!in)
 +{
 +cout << endl << "failed to load data file" << endl; file_loaded = 0;
 +return;
 +}
 +
 +in >> input_array_size;
 +in >> hidden_array_size;
 +in >> output_array_size;
 +in >> learning_rate;
 +in >> number_of_input_patterns;
 +bias_array_size = hidden_array_size + output_array_size;
 +initialize_net();
 +for (x=0; x<bias_array_size; x++)
 +in >> bias[x];
 +for(x=0; x<input_array_size; x++)
 +{
 +for(y=0; y<hidden_array_size; y++)
 +in >> weight_i_h[x][y];
 +}
 +for(x=0; x<hidden_array_size; x++)
 +{
 +for(y=0; y<output_array_size; y++) in >> weight_h_o[x][y];
 +}
 +for(x=0; x<number_of_input_patterns; x++)
 +{
 +for(y=0; y<input_array_size; y++)
 +in >> input[x][y];
 +}
 +for(x=0; x<number_of_input_patterns; x++)
 +{
 +for(y=0; y<output_array_size; y++)
 +in >> target[x][y];
 +}
 +in.close();
 +cout << endl << "data loaded" << endl;
 +return;
 +}
 +
 +
 +
 +void Net::save_data(char *argres)
 +{
 +int x, y;
 +ofstream out;
 +out.open(argres);
 +if(!out)
 +{
 +cout << endl << "failed to save file" << endl;
 +return;
 +}
 +out << input_array_size << endl;
 +out << hidden_array_size << endl;
 +out << output_array_size << endl;
 +out << learning_rate << endl;
 +out << number_of_input_patterns << endl << endl;
 +for(x=0; x<bias_array_size; x++)
 +out << bias[x] << ' ';
 +out << endl << endl;
 +for(x=0; x<input_array_size; x++)
 +{
 +for(y=0; y<hidden_array_size; y++)
 +out << weight_i_h[x][y] << ' ';
 +}
 +out << endl << endl;
 +for(x=0; x<hidden_array_size; x++)
 +{
 +for(y=0; y<output_array_size; y++) out << weight_h_o[x][y] << ' ';
 +}
 +out << endl << endl;
 +for(x=0; x<number_of_input_patterns; x++)
 +{
 +for(y=0; y<input_array_size; y++)
 +out << input[x][y] << ' ';
 +out << endl;
 +}
 +out << endl;
 +for(x=0; x<number_of_input_patterns; x++)
 +{
 +for(y=0; y<output_array_size; y++)
 +out << target[x][y] << ' ';
 +out << endl;
 +}
 +out.close();
 +cout << endl << "data saved" << endl;
 +return;
 +}
 +
 +*/
 +
 +void Net::setTest(void)
 +{
 + layers[0]->getNeuron(0)->setValue(1);
 + layers[0]->getNeuron(1)->setValue(1);
 + //layers[0]->getNeuron(2)->setValue(1);
 +
 + layers[1]->getNeuron(0)->setValue(123);
 + layers[1]->getNeuron(1)->setValue(456);
 +
 + layers[2]->getNeuron(0)->setValue(0);
 +
 + layers[0]->getNeuron(0)->getConnectionOut(0)->setWeight(0.1);
 + layers[0]->getNeuron(0)->getConnectionOut(1)->setWeight(0.2);
 + layers[0]->getNeuron(1)->getConnectionOut(0)->setWeight(0.3);
 + layers[0]->getNeuron(1)->getConnectionOut(1)->setWeight(0.4);
 + layers[1]->getNeuron(0)->getConnectionOut(0)->setWeight(0.5);
 + layers[1]->getNeuron(1)->getConnectionOut(0)->setWeight(0.6);
 +
 + // Add connection between two neurons in same level 1
 + //pConnectionX tmp(new Connection(layers[1]->getNeuron(0), layers[1]->getNeuron(1)));    
 + //tmp->setIndex(100);
 + //layers[1]->getNeuron(0)->addConnectionOut(tmp);
 +
 +
 + printOutput();
 +
 + //layers[1]->getNeuron(0)->removeConnectionOut(0)->setWeight(0.5);
 + //layers[1]->getNeuron(0)->getConnectionOut(0) = nullptr;
 +
 + //layers[1]->getNeuron(0)->getConnectionIn(0) = nullptr;
 + //layers[0]->getNeuron(0)->getConnectionOut(0) = nullptr;
 + //layers[0]->getNeuron(0)->getConnectionOut(1) = nullptr;
 + //layers[0]->getNeuron(0) = nullptr;
 +
 + printOutput();
 + std::cout << "**************************************************************" << std::endl;
 +}
 +
 +
 +// TODO: Only works if 3 or more layers.  Perhaps we should cater when zero hidden layers?
 +void Net::feedForward(const std::vector<double>& inputVals)
 +{
 + //  std::cout << "inputVals.size=" << inputVals.size() << std::endl;
 + //  std::cout << "layers[0]->getSize()=" << layers[0]->getSize() << std::endl;
 +
 +
 + assert(layers[0]->getSize() == inputVals.size());
 +
 + //std::cout << "inputVals.size=" << inputVals.size() << std::endl;
 +
 + // Setting input vals to input layer.
 + for (unsigned int i = 0; i<inputVals.size(); i++)
 + {
 + if (!layers[0]->getNeuron(i))
 + continue;
 +
 + layers[0]->getNeuron(i)->setValue(inputVals[i]); // layers[0] is the input layer.
 + }
 +
 +  
 + // Updating hidden layers.
 + for (unsigned int i = 1; i<layers.size() - 1; i++)
 + {
 + layers[i]->feedForward(layers[i - 1]); // Updating the neurons output based on the neurons of the previous layer.
 + }
 +  
 +
 + // Updating output layer.
 + for (unsigned int i = 0; i<layers.back()->getSize(); i++) // How many neurons in the output layer
 + {
 + pLayerX& prevLayer = layers[layers.size() - 2];
 + layers[layers.size() - 1]->feedForward(prevLayer); // Updating the neurons output based on the neurons of the previous layer.
 + }
 +}
 +
 +
 +/*
 +void backward_pass(int pattern)
 +{
 +register int x, y;
 +register double temp = 0;
 +
 +// COMPUTE ERRORSIGNAL FOR OUTPUT UNITS
 +for(x=0; x<output_array_size; x++) {
 +errorsignal_output[x] = (target[pattern][x] - output[pattern][x]);
 +}
 +
 +// COMPUTE ERRORSIGNAL FOR HIDDEN UNITS
 +for(x=0; x<hidden_array_size; x++) {
 +for(y=0; y<output_array_size; y++) {
 +temp += (errorsignal_output[y] * weight_h_o[x][y]);
 +}
 +errorsignal_hidden[x] = hidden[x] * (1-hidden[x]) * temp;
 +temp = 0.0;
 +}
 +
 +// ADJUST WEIGHTS OF CONNECTIONS FROM HIDDEN TO OUTPUT UNITS
 +double length = 0.0;
 +for (x=0; x<hidden_array_size; x++) {
 +length += hidden[x]*hidden[x];
 +}
 +if (length<=0.1) length = 0.1;
 +for(x=0; x<hidden_array_size; x++) {
 +for(y=0; y<output_array_size; y++) {
 +weight_h_o[x][y] += (learning_rate * errorsignal_output[y] *
 +hidden[x]/length);
 +}
 +}
 +
 +// ADJUST BIASES OF HIDDEN UNITS
 +for(x=hidden_array_size; x<bias_array_size; x++) {
 +bias[x] += (learning_rate * errorsignal_output[x] / length);
 +}
 +
 +// ADJUST WEIGHTS OF CONNECTIONS FROM INPUT TO HIDDEN UNITS
 +length = 0.0;
 +for (x=0; x<input_array_size; x++) {
 +length += input[pattern][x]*input[pattern][x];
 +}
 +if (length<=0.1) length = 0.1;
 +for(x=0; x<input_array_size; x++) {
 +for(y=0; y<hidden_array_size; y++) {
 +weight_i_h[x][y] += (learning_rate * errorsignal_hidden[y] *
 +input[pattern][x]/length);
 +}
 +}
 +
 +// ADJUST BIASES FOR OUTPUT UNITS
 +for(x=0; x<hidden_array_size; x++) {
 +bias[x] += (learning_rate * errorsignal_hidden[x] / length);
 +}
 +return;
 +}
 +*/
 +
 +void Net::backPropagate(const std::vector<double>& targetVals)
 +{
 + // COMPUTE ERRORSIGNAL FOR OUTPUT UNITS
 + pLayerX& outputLayer = layers.back();
 + assert(targetVals.size() == outputLayer->getSize());
 +
 + // Traversing output layer.
 + for (unsigned int i = 0; i<outputLayer->getSize(); i++) // For every output Neuron.
 + {
 + if (!outputLayer->getNeuron(i))
 + continue;
 +
 +    double outputValue = outputLayer->getNeuron(i)->getValue();
 +    double gradient = (targetVals[i] - outputValue) * outputValue * (1.0 - outputValue);
 + outputLayer->getNeuron(i)->setGradient(gradient);
 + }
 +
 + // COMPUTE ERRORSIGNAL FOR HIDDEN UNITS
 + for (unsigned int i = layers.size() - 2; i>0; i--) // for every hidden layer
 + {
 + pLayerX& currentLayer = layers[i];
 + pLayerX& nextLayer = layers[i + 1];
 +
 + for (unsigned int j = 0; j<currentLayer->getSize(); j++) // for every neuron
 + {
 + double temp = 0.0;
 + pNeuronX& currentNeuron = layers[i]->getNeuron(j);  // current neuron.
 + if (!currentNeuron)
 + continue;
 +
 + for (unsigned int k = 0; k<currentNeuron->getSizeOut(); k++)  // for every connection in current layer.
 + {
 + pConnectionX &currentConnection = currentNeuron->getConnectionOut(k);
 + if (!currentConnection)
 + continue;
 + if (!currentConnection->getTo())
 + continue;
 +
 + int currentIndex = currentNeuron->getConnectionOut(k)->getTo()->getIndex();
 +
 + for (unsigned int l = 0; l<nextLayer->getSize(); l++) // for every neuron in next layer
 + {
 + pNeuronX& nextNeuron = nextLayer->getNeuron(l);  // next layers neuron.
 + if (!nextNeuron)
 + continue;
 +
 + int nextIndex = nextNeuron->getIndex();
 +
 + if (currentIndex == nextIndex)
 + {
 + temp += (nextNeuron->getGradient() * currentConnection->getWeight());    // output_error + weight-h-o
 + }
 + }
 + }
 +
 + currentNeuron->setGradient(currentNeuron->getValue() * (1.0 - currentNeuron->getValue()) * temp);
 + }
 + }
 +
 +
 + // ADJUST WEIGHTS OF CONNECTIONS FROM HIDDEN TO OUTPUT UNITS
 + for (unsigned int i = 0; i<layers.size() - 1; i++)  // for every layer.
 + {
 + for (unsigned int j = 0; j<layers[i]->getSize(); j++)  // for every neuron.
 + {
 + pNeuronX& currentNeuron = layers[i]->getNeuron(j);
 + if (!currentNeuron)
 + continue;
 + double currentValue = currentNeuron->getValue();
 +
 + for (unsigned int k = 0; k<layers[i]->getNeuron(j)->getSizeOut(); k++) // for every connection.
 + {
 + pConnectionX& currentConnection = currentNeuron->getConnectionOut(k);
 + if (!currentConnection)
 + continue;
 + pNeuronX& nextNeuron = currentConnection->getTo();
 + if (!nextNeuron)
 + continue;
 +
 + double nextGradient = nextNeuron->getGradient();
 + //double delta = 0.5 * nextGradient * currentNeuron->getValue();
 +        double delta = 0.5 * nextGradient * currentValue;
 +
 + //currentConnection->setWeight(currentConnection->getWeight() + delta + (0.4 * currentConnection->getDeltaWeight()));
 + currentConnection->setWeight(currentConnection->getWeight() + delta + (currentConnection->getMomentum() * currentConnection->getDeltaWeight()));
 + currentConnection->setDeltaWeight(delta);
 + }
 + }
 + }
 +}
 +
 +
 +void Net::backPropagate2(const std::vector<double>& targetVals)
 +{
 + pLayerX& outputLayer = layers.back();
 + //assert(targetVals.size() == outputLayer.size());
 + assert(targetVals.size() == outputLayer->getSize());
 +
 + // Starting with the output layer.
 + //for (unsigned int i=0; i<outputLayer.size(); i++)
 + for (unsigned int i = 0; i<outputLayer->getSize(); i++) // How many neurons in output layer.
 + {
 + pNeuronX& currentNeuron = outputLayer->getNeuron(i);
 +
 + double output = outputLayer->getNeuron(i)->getValue();
 + // COMPUTE ERRORSIGNAL FOR OUTPUT UNITS
 + double error = output * (1 - output) * (pow(targetVals[i] - output, 2)); // std::cout << "good4" << std::endl;
 + //std::cout << "Error Output=" << error << std::endl;
 +
 + // ADJUST WEIGHTS OF CONNECTIONS FROM HIDDEN TO OUTPUT UNITS
 + for (unsigned int j = 0; j<outputLayer->getNeuron(i)->getSizeIn(); j++)
 + {
 + outputLayer->getNeuron(i)->getConnectionIn(j)->setError(error); // Set error against each connection into the output layer.
 + double newWeight = outputLayer->getNeuron(i)->getConnectionIn(j)->getWeight();
 + newWeight += (error * outputLayer->getNeuron(i)->getValue());
 + outputLayer->getNeuron(i)->getConnectionIn(j)->setWeight(newWeight); // Setting new weight of each connection into the output layer.
 + }
 + }
 +
 + for (unsigned int i = layers.size() - 2; i>0; i--) // Traversing hidden layers all the way to input layer.
 + {
 + pLayerX& currentLayer = layers[i];
 + pLayerX& nextLayer = layers[i + 1];
 +
 + // Traversing current layer
 + //for (unsigned int j=0; j<currentLayer.size(); j++)
 + for (unsigned int j = 0; j<currentLayer->getSize(); j++)  // for every neuron in current layer.
 + {
 + const double& output = currentLayer->getNeuron(j)->getValue(); // get its value.
 + double subSum = 0.0; // Initializing subsum.
 +
 + // Traversing next layer.
 + for (unsigned int k = 0; k<nextLayer->getSize(); k++) // for every neuron in next layer.
 + {
 + double error = nextLayer->getNeuron(k)->getConnectionIn(k)->getError();
 + double weight = nextLayer->getNeuron(k)->getConnectionIn(j)->getWeight();
 +
 +
 + //subSum += pow(nextLayer.getNeuron(j).getConnectionIn(k).getError() * currentLayer.getNeuron(j).getConnectionIn(k).getWeight(),2); // Getting their backpropagated error and weight.
 + subSum += pow(nextLayer->getNeuron(k)->getConnectionIn(k)->getError() * currentLayer->getNeuron(j)->getConnectionIn(k)->getWeight(), 2); // Getting their backpropagated error and weight.
 + //subSum += pow(nextLayer.getNeuron(k).getConnectionIn(k).getError() * nextLayer.getNeuron(k).getConnectionIn(j).getWeight(),2); // Getting their backpropagated error and weight.
 + }
 +
 + double error = output*(1 - output)*(subSum);
 +
 + for (unsigned int k = 0; k<currentLayer->getNeuron(j)->getSizeIn(); k++)
 + {
 + currentLayer->getNeuron(j)->getConnectionIn(k)->setError(error);
 + double newWeight = currentLayer->getNeuron(j)->getConnectionIn(k)->getWeight();
 + newWeight += error * output;
 + currentLayer->getNeuron(j)->getConnectionIn(k)->setWeight(newWeight);
 + }
 + }
 + }
 +}
 +
 +
 +
 +
 +//void Net::printOutput(void) const 
 +void Net::printOutput(void)
 +{
 + std::cout << "***Net has [" << layers.size() << "] layers" << std::endl;
 +
 + for (unsigned int i = 0; i<layers.size(); i++)
 + {
 + pLayerX& currentLayer = layers[i];
 + currentLayer->printOutput();
 + }
 +}
 +
 +
 +void Net::printResult(void)
 +{
 + pLayerX& outputLayer = layers.back();
 +
 + for (unsigned int i = 0; i<outputLayer->getSize(); i++)
 + {
 + std::cout << "Result=" << outputLayer->getNeuron(i)->getValue() << std::endl;
 + }
 +}
 +
 +
 +void Net::setTarget(const std::vector<double>& targetVals)
 +{
 +
 +}
 +</file>
 +
 +net.h
 +
 +<file h net.h>
 +#ifndef __SHAREWIZ_NET_H__
 +#define __SHAREWIZ_NET_H__
 +
 +#include <memory>
 +#include <vector>
 +
 +// A Net class.
 +//
 +// To handle neural networks.
 +
 +// There are several things to keep in mind when applying this agent in practice :
 +//   1. If the rewards are very sparse in the environment the agent will have trouble learning.
 +//      Right now there is no priority sweeping support, but one might imagine oversampling experience that have 
 +//      high TD errors.  It's not clear how this can be done in most principled way. 
 +//      Similarly, there are no eligibility traces right now though this could be added with a few modifications 
 +//      in future versions.
 +//   2. The exploration is rather naive, since a random action is taken once in a while.
 +//      If the environment requires longer sequences of precise actions to get a reward, the agent might have a 
 +//      lot of difficulty finding these by chance, and then also learning from them sufficiently.
 +//   3. DQN only supports a set number of discrete actions and it is not obvious how one can incorporate
 +//      (high - dimensional) continuous action spaces.
 +
 +class Layer;
 +
 +typedef std::shared_ptr<Layer> pLayerX;
 +typedef std::vector<pLayerX> pLayer;
 +
 +
 +class Neuron;
 +typedef std::shared_ptr<Neuron> pNeuronX;
 +typedef std::vector<pNeuronX> pNeuron;
 +
 +
 +class Net
 +{
 +private:
 + //std::vector<double>& targetVals;
 +
 + double learning_rate; // eta.
 +                                       // Controls how much the weights are changed during a weight update.
 +                                       // The larger the value, the more the weights are changed.
 +                                       // This must be a real value between 0.0 and 10.0.
 +                                       // These values are commonly set from 0.5 to 0.7.
 + double max_error_tollerance;
 +
 +  double alpha = 0.1;                  // Learning rate.
 +                                       // Set this by trial and error.  That's Pretty much the best thing we have.
 +  double gamma = 0.4;                  // Discount factor (0 - 1).
 +                                       // If Gamma is closer to 0, the agent will tend to consider only 
 +                                       // immediate rewards.
 +                                       // If Gamma is closer to 1, the agent will consider future rewards 
 +                                       // with greater weight, willing to delay the reward.
 +  //double epsilon = 0.2;                // Initial epsilon for epsilon-greedy policy (0 - 1).
 +                                       // High epsilon(up to 1) will cause the agent to take more random actions.
 +                                       // It is a good idea to start with a high epsilon(e.g. 0.2 or even a bit higher) 
 +                                       // and decay it over time to be lower(e.g. 0.05).
 +  //double lambda = 0;                   // eligibility trace decay, [0,1). 0 = no eligibility traces.
 +
 +  double goal_amount;                  // Used by DQN networks.  The goal amount to try to obtain.
 +
 + pLayer layers;
 +
 +public:
 + Net();
 + Net(const std::vector<unsigned int>& topology);
 +
 + double getLearningRate(void);
 + void setLearningRate(const double& learning_rate);
 +
 + double getMaxErrorTollerance(void);
 + void setMaxErrorTollerance(const double& max_error_tollerance);
 +
 +  double getAlpha(void);
 +  void setAlpha(const double& _alpha_amount);
 +
 +  double getGamma(void);
 +  void setGamma(const double& _gamma_amount);
 +
 +  double getGoalAmount(void);
 +  void setGoalAmount(const double& _goal_amount);
 +
 + void setTarget(const std::vector<double>& targetVals);
 +
 + void setTest();
 +
 +  void connect(const std::vector< std::vector<double> > connections);
 +  void connect(int layerFrom, int neuronFrom, int layerTo, int neuronTo, double _R, int connection_idx = 1);
 +
 +  void connectAll();
 +  void connectForward();
 +  void connectForward2();
 +  void connectAllInLayer(const pLayerX& layer);
 +
 +  void DQN(void);
 +  double getMaxQ(pNeuronX state);
 +
 +  pNeuronX getPolicy(pNeuronX currentState);
 +  void showPolicy(void);
 +
 + void feedForward(const std::vector<double>& inputVals);
 + void backPropagate(const std::vector<double>& targetVals);
 + void backPropagate2(const std::vector<double>& targetVals);
 +
 +
 +  int randomBetween(int lowestNumber, int highestNumber);
 +
 + void printOutput(void);
 + void printResult(void);
 +};
 +
 +
 +#endif
 +</file>
 +
 +neuron.cpp
 +
 +<file cpp neuron.cpp>
 +#include <iostream>
 +#include <cassert>
 +
 +#include "neuron.h"
 +#include "connection.h"
 +
 +
 +Neuron::Neuron()
 +{
 +#ifdef DEBUG
 + std::cout << "Neuron::Neuron" << std::endl;
 +#endif
 +
 + index = -1;
 + gradient = 0;
 + value = 0;
 +
 + connections_in.reserve(10);
 + connections_out.reserve(10);
 +
 +  pActivationX tmp(new Activation());
 +  //pActivationX tmp(new Activation(ACTIVATION_SQRT));
 +  this->activation = tmp;
 +  //activation_type = ACTIVATION_SIGMOID;
 +
 + randomizeValue();
 +}
 +
 +
 +bool Neuron::operator==(Neuron& rhs) const
 +{
 + //cout << "operator overloaded == " << rhs.name;
 + if (this->index==rhs.index)
 + //if (*this == rhs)
 + return true;
 +
 + return false;
 +}
 +
 +
 +double Neuron::getGradient(void)
 +{
 +#ifdef DEBUG
 + std::cout << "Neuron::getGradient" << std::endl;
 +#endif
 +
 + return gradient;
 +}
 +
 +
 +void Neuron::setGradient(const double& gradient)
 +{
 +#ifdef DEBUG
 + std::cout << "Neuron::setGradient" << std::endl;
 +#endif
 +
 + this->gradient = gradient;
 +}
 +
 +
 +int Neuron::getIndex(void)
 +{
 +#ifdef DEBUG
 + std::cout << "Neuron::getIndex" << std::endl;
 +#endif
 +
 + return index;
 +}
 +
 +
 +void Neuron::setIndex(const int& index)
 +{
 +#ifdef DEBUG
 + std::cout << "Neuron::setIndex" << std::endl;
 +#endif
 +
 + this->index = index;
 +}
 +
 +
 +unsigned int Neuron::getSizeIn(void)
 +{
 +#ifdef DEBUG
 + std::cout << "Neuron::getSizeIn" << std::endl;
 +#endif
 +
 + return connections_in.size();
 +}
 +
 +
 +unsigned int Neuron::getSizeOut(void)
 +{
 +#ifdef DEBUG
 + std::cout << "Neuron::getSizeOut" << std::endl;
 +#endif
 +
 + return connections_out.size();
 +}
 +
 +
 +double Neuron::getValue(void)
 +{
 +#ifdef DEBUG
 + std::cout << "Neuron::getValue" << std::endl;
 +#endif
 +
 + return value;
 +}
 +
 +
 +void Neuron::setValue(const double& v)
 +{
 +#ifdef DEBUG
 + std::cout << "Neuron::setValue" << std::endl;
 +#endif
 +
 + value = v;
 +}
 +
 +
 +void Neuron::addConnectionIn(const pConnectionX& c)
 +{
 +#ifdef DEBUG
 + std::cout << "Neuron::addConnectionIn" << std::endl;
 +#endif
 +
 + connections_in.push_back(c);
 + //index++;
 +}
 +
 +
 +//void Neuron::addConnectionOut(const shared_ptr<Connection>& c)
 +void Neuron::addConnectionOut(const pConnectionX& c)
 +{
 +#ifdef DEBUG
 + std::cout << "Neuron::addConnectionOut" << std::endl;
 +#endif
 +
 + connections_out.push_back(c);
 + //index++;
 +}
 +
 +
 +// Returns a specific input connection.
 +pConnectionX &Neuron::getConnectionIn(const unsigned int& idx)
 +{
 +#ifdef DEBUG
 +  std::cout << "Neuron::getConnectionIn" << std::endl;
 +#endif
 +
 +  assert(connections_in.size() >= idx);
 +
 +  return connections_in[idx];
 +}
 +
 +
 +// Returns a specific output connection.
 +pConnectionX &Neuron::getConnectionOut(const unsigned int& idx)
 +{
 +#ifdef DEBUG
 +  std::cout << "Neuron::getConnectionOut" << std::endl;
 +#endif
 +
 +  assert(connections_out.size() >= idx);
 +
 +  return connections_out[idx];
 +}
 +
 +
 +// Remove all connections with a value below the indicated threshold.
 +//
 +// TODO: Should we consider abs value?
 +void Neuron::pruneConnectionIn(const double& threshold)
 +{
 +  for (unsigned i = connections_in.size()-1; i > 0; i--)
 +  {
 +    if (connections_in[i]->getWeight() < threshold)
 +    {
 +      // TODO. Do we need to also remove the "From" and "To" elements of Neurons manually or will these auto remove?
 +      // TODO.  Does this retain the actual Connection, which potentially we should potentially delete if not used?
 +      connections_in.erase(connections_in.begin() + i);
 +    }
 +  }
 +}
 +
 +
 +// Remove all connections with a value below the indicated threshold.
 +//
 +// TODO: Should we consider abs value?
 +void Neuron::pruneConnectionOut(const double& threshold)
 +{
 +  for (unsigned i = connections_out.size()-1; i > 0; i--)
 +  {
 +    if (connections_out[i]->getWeight() < threshold)
 +    {
 +      // TODO. Do we need to also remove the "From" and "To" elements of Neurons manually or will these auto remove?
 +      // TODO.  Does this retain the actual Connection, which potentially we should potentially delete if not used?
 +      connections_out.erase(connections_out.begin() + i);
 +    }
 +  }
 +}
 +
 +
 +void Neuron::removeConnectionIn(const unsigned int& idx)
 +{
 +#ifdef DEBUG
 + std::cout << "Neuron::removeConnectionIn" << std::endl;
 +#endif
 +
 + assert(connections_in.size() >= idx);
 +  
 +  //for (unsigned i = 0; i < connections_in.size(); i++)
 +  for (unsigned i = connections_in.size()-1; i > 0; i--)
 +  {
 +    if (connections_in[i]->getIndex() == idx)
 +    {
 +      connections_in.erase(connections_in.begin() + i);
 +      return;
 +    }
 +  }
 +}
 +
 +
 +void Neuron::removeConnectionOut(const unsigned int& idx)
 +{
 +#ifdef DEBUG
 + std::cout << "Neuron::removeConnectionOut" << std::endl;
 +#endif
 +
 +  assert(connections_out.size() >= idx);
 +
 +  //for (unsigned i = 0; i < connections_out.size(); i++)
 +  for (unsigned i = connections_out.size()-1; i > 0; i--)
 +  {
 +    if (connections_out[i]->getIndex() == idx)
 +    {
 +      connections_out.erase(connections_out.begin() + i);
 +      return;
 +    }
 +  }
 +}
 +
 +
 +double Neuron::randomizeValue(void)
 +{
 +#ifdef DEBUG
 + std::cout << "Neuron::randomizeValue" << std::endl;
 +#endif
 +
 + value = rand() / double(RAND_MAX);
 +
 + return value;
 +}
 +
 +
 +
 +pActivationX &Neuron::getActivation(void)
 +{
 +  return activation;
 +}
 +
 +
 +Activation_Types Neuron::getActivationType()
 +{
 +  return activation->getActivationType();
 +}
 +
 +
 +void Neuron::setActivationType(Activation_Types _activation_type)
 +{
 +  //activation_type = _activation_type;
 +  activation->setActivationType(_activation_type);
 +}
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +
 +/*
 +// Return random double between -0.5 and +0.5.
 +double Neuron::randf()
 +{
 +#ifdef DEBUG
 +std::cout << "Neuron::randf" << std::endl;
 +#endif
 +
 +double r = ((double)rand()) / double(RAND_MAX);
 +return r - 0.5;
 +}
 +*/
 +
 +
 +// Returns a value between 0.0 and 1.0.
 +double Neuron::sigmoid(const double& weightedSum)
 +{
 +#ifdef DEBUG
 + std::cout << "Neuron::sigmoid" << std::endl;
 +#endif
 +
 + return 1.0 / double((1.0 + exp(-weightedSum)));
 +}
 +
 +
 +double Neuron::sigmoid_derivative(const double& x)
 +{
 +#ifdef DEBUG
 + std::cout << "Neuron::sigmoid_derivative" << std::endl;
 +#endif
 +
 + return sigmoid(x) * (1.0 - sigmoid(x));
 +}
 +
 +
 +double Neuron::sigmoidX(double x)
 +{
 +#ifdef DEBUG
 + std::cout << "Neuron::sigmoidX" << std::endl;
 +#endif
 +
 + if (x < -45.0)
 + return 0.0;
 + else
 + if (x > 45.0)
 + return 1.0;
 + else
 + return 1.0 / (1.0 + exp(-x));
 +}
 +
 +
 +// Returns a value between -1.0 and +1.0.
 +double Neuron::hyperTanFunction(double& x)
 +{
 +#ifdef DEBUG
 + std::cout << "Neuron::hyperTanFunction" << std::endl;
 +#endif
 +
 + if (x < -10.0)
 + return -1.0;
 + else
 + if (x > 10.0)
 + return 1.0;
 + else
 + return tanh(x);
 +}
 +
 +
 +double Neuron::tanh_derivative(const double& x)
 +{
 +#ifdef DEBUG
 + std::cout << "Neuron::tanh_derivative" << std::endl;
 +#endif
 +
 + return (1.0 - tanh(x)) * (1.0 + tanh(x));
 +}
 +
 +/*
 +double Neuron::transferFunction(double x)
 +{
 +// tanh - output range [-1.0..1.0]
 +
 +return tanh(x);
 +}
 +
 +double Neuron::transferFunctionDerivative(double x)
 +{
 +// tanh derivative
 +return 1.0 - x * x;
 +}
 +*/
 +
 +void Neuron::printOutput(void)
 +{
 +#ifdef DEBUG
 + std::cout << "Neuron::printOutput" << std::endl;
 +#endif
 +
 + std::cout << "Neuron[" << index << "] = " << value << " . It has " << connections_in.size() << " Connections-In, and "
 + << connections_out.size() << " Connections-Out" << std::endl;
 +
 + for (unsigned int i = 0; i<connections_in.size(); i++)
 + {
 + if (!connections_in[i])
 + continue;
 +
 + std::cout << "  Connection-In[" << i << "] w=" << connections_in[i]->getWeight() << ", d=" << connections_in[i]->getDeltaWeight() << std::endl;
 + }
 +
 + for (unsigned int i = 0; i<connections_out.size(); i++)
 + {
 + if (!connections_out[i])
 + continue;
 +
 + std::cout << "  Connection-Out[" << i << "] w=" << connections_out[i]->getWeight() << ", d=" << connections_out[i]->getDeltaWeight() << std::endl;
 + }
 +}
 +</file>
 +
 +neuron.h
 +
 +<file h neuron.h>
 +#ifndef __SHAREWIZ_NEURON_H__
 +#define __SHAREWIZ_NEURON_H__
 +
 +#include <memory>
 +#include <vector>
 +
 +#include "activation.h"
 +
 +// Neuron class.
 +//
 +// Represents Synapsis within the brain.
 +
 +
 +class Connection;
 +
 +typedef std::shared_ptr<Connection> pConnectionX;
 +typedef std::vector<pConnectionX> pConnection;
 +
 +
 +class Neuron
 +{
 +private:
 + int index;
 + double value;
 + double gradient;  // How far off, and in what direction (positive or negative), local value are relative to the target outputs.
 +
 + pConnection connections_in;
 + pConnection connections_out;
 +
 +  pActivationX activation;
 +
 +public:
 + Neuron();
 +
 + bool operator==(Neuron& rhs) const;
 +
 + unsigned int getSizeIn(void);  // Returns how many connections.
 + unsigned int getSizeOut(void);  // Returns how many connections.
 +
 + void addConnectionIn(const pConnectionX& c);
 + void addConnectionOut(const pConnectionX& c);
 +
 + pConnectionX &getConnectionIn(const unsigned int& idx);
 + pConnectionX &getConnectionOut(const unsigned int& idx);
 +
 + void pruneConnectionIn(const double& threshold);  // Remove all synapses with a value below the indicated threshold.
 + void pruneConnectionOut(const double& threshold);  // Remove all synapses with a value below the indicated threshold.
 +
 +  void removeConnectionIn(const unsigned int& idx);
 +  void removeConnectionOut(const unsigned int& idx);
 +  
 +  double getGradient(void);
 + void setGradient(const double& gradient);
 +
 + int getIndex(void);
 + void setIndex(const int& index);
 +
 + double getValue(void);
 + void setValue(const double& v);
 +
 + double randomizeValue(void);
 +
 +  pActivationX &getActivation(void);
 +  Activation_Types getActivationType();
 +  void setActivationType(Activation_Types _activation_type);
 +
 + double sigmoid(const double& weightedSum);
 + double sigmoid_derivative(const double& x);
 + double sigmoidX(double x);
 +
 + double hyperTanFunction(double& x);
 + double tanh_derivative(const double& x);
 +
 + void printOutput(void);
 +};
 +
 +
 +#endif
 +</file>
 +
 +neuron_group.cpp
 +
 +<file cpp neuron_group.cpp>
 +#include <iostream>
 +#include <cassert>
 +
 +#include "neuron_group.h"
 +#include "neuron.h"
 +#include "connection.h"
 +
 +
 +NeuronGroup::NeuronGroup()
 +{
 +  index = -1;
 +
 +  neurons.reserve(10);
 +}
 +
 +
 +NeuronGroup::NeuronGroup(unsigned int num_neurons)
 +{
 +  index = -1;
 +
 +  neurons.reserve(num_neurons);
 +
 +  for (unsigned int i = 0; i < num_neurons; i++)
 +  {
 +    pNeuronX tmp(new Neuron());
 +    tmp->setIndex(i);
 +    neurons.push_back(tmp);
 +  }
 +}
 +
 +
 +int NeuronGroup::getIndex(void)
 +{
 +  return index;
 +}
 +
 +
 +void NeuronGroup::setIndex(const int& index)
 +{
 +  this->index = index;
 +}
 +
 +
 +unsigned int NeuronGroup::getSize(void)
 +{
 +  return neurons.size();
 +}
 +
 +
 +void NeuronGroup::addNeuron(const pNeuronX& n)
 +{
 +  neurons.push_back(n);
 +}
 +
 +
 +void NeuronGroup::removeNeuron(const int& idx)
 +{
 +  assert(neurons.size() >= idx);
 +
 +  for (unsigned i = neurons.size() - 1; i > 0; i--)
 +  {
 +    if (neurons[i]->getIndex() == idx)
 +    {
 +      neurons.erase(neurons.begin() + i);
 +      return;
 +    }
 +  }
 +}
 +
 +
 +pNeuronX &NeuronGroup::getNeuron(const int& idx)
 +{
 +  assert(neurons.size() >= idx);
 +
 +  return neurons[idx];
 +}
 +
 +
 +void NeuronGroup::printOutput(void)
 +{
 +  std::cout << "Layer " << index << " has " << neurons.size() << " Neurons" << std::endl;
 +
 +  for (unsigned int i = 0; i<neurons.size(); i++)
 +  {
 +    if (!neurons[i])
 +      continue;
 +
 +    std::cout << "  Neuron[" << i << "] v=" << neurons[i]->getValue() << ", g=" << neurons[i]->getGradient() << std::endl;
 +
 +    for (unsigned int j = 0; j<neurons[i]->getSizeOut(); j++)
 +    {
 +      pConnectionX& currentConnection = neurons[i]->getConnectionOut(j);
 +      if (!currentConnection)
 +        continue;
 +      currentConnection->printOutput();
 +    }
 +  }
 +}
 +</file>
 +
 +neuron_group.h
 +
 +<file h neuron_group.h>
 +#ifndef __SHAREWIZ_NEURON_GROUP_H__
 +#define __SHAREWIZ_NEURON_GROUP_H__
 +
 +#include <memory>
 +#include <vector>
 +
 +
 +class NeuronGroup;
 +class Neuron;
 +
 +
 +typedef std::shared_ptr<NeuronGroup> pNeuronGroupX;
 +typedef std::vector<pNeuronGroupX> pNeuronGroup;
 +
 +typedef std::shared_ptr<Neuron> pNeuronX;
 +typedef std::vector<pNeuronX> pNeuron;
 +
 +
 +
 +class NeuronGroup
 +{
 +private:
 +  int index;
 +  
 +  pNeuron neurons;
 +
 +public:
 +  NeuronGroup();
 +  NeuronGroup(unsigned int num_neurons);
 +
 +  unsigned int getSize(void);  // Returns how many neurons.
 +
 +  int getIndex(void);
 +  void setIndex(const int& index);
 +
 +  void addNeuron(const pNeuronX& n);
 +  void removeNeuron(const int& idx);
 +
 +  pNeuronX& getNeuron(const int& idx);
 +
 +  //void feedForward(const pLayerX& prevLayer);
 +
 +  void printOutput(void);
 +};
 +
 +
 +#endif
 +</file>
 +
 +string.cpp
 +
 +<file cpp string.cpp>
 +#include <string>
 +#include <sstream>
 +
 +#include "string.h"
 +
 +
 +// Example:
 +//   numberToString(69);
 +template <typename T>
 +std::string numberToString(T pNumber)
 +{
 + std::ostringstream oOStrStream;
 + oOStrStream << pNumber;
 + return oOStrStream.str();
 +}
 +
 +
 +#include <iostream>
 +#include <regex>
 +
 +
 +// Returns all occurences of the regex within the string.
 +//
 +// Example:
 +//   std::string regex = "([A-Z]+)([\\d]+)";
 +//   std::string ss = "aaaMAY14bbbJUNE4";
 +//
 +// Returns:
 +//   [0]=MAY14#
 +//   [1]=JUNE4#
 +std::vector<std::string> string_find(const std::string& s, const std::string& regex)
 +{
 +  std::vector<std::string> result;
 +  std::regex reg(regex);
 +  //std::sregex_token_iterator it(s.begin(), s.end(), reg, { 1, 2, 3, 4, 5, 6, 7, 8, 9 });
 +  //std::sregex_token_iterator it(s.begin(), s.end(), reg, { 1, 0 });
 +
 +  // The 4th param indicates:
 +  //   -1 would indicate to return all none-occurences.
 +  //   0 indicates to return all occurences found.
 +  //   1 would return all the 1st sub-expression occurences.
 +  //   2 would return all the 2nd sub-expression occurences.
 +  //   3...
 +  std::sregex_token_iterator it(s.begin(), s.end(), reg, 0); 
 +  std::sregex_token_iterator reg_end;
 +  for (int i=0; it != reg_end; ++it, i++) 
 +  {
 +    //std::cout << "[" << i << "]=" << it->str() << "#" << std::endl;
 +    //std::cout << "[" << i << "]=" << *it << "#" << std::endl;
 +    result.push_back(*it);
 +  }
 +
 +  return result;
 +}
 +
 +
 +// Replaces all occurences of the regex within the replacement string.
 +//
 +// Parameters:
 +//
 +//   replacement:
 +//     The replacement string may contain references of the form $n. Every such reference will be replaced by the 
 +//     text captured by the n'th parenthesized pattern. 
 +//     n can be from 0 to 99, and $0 refers to the text matched by the whole pattern.
 +//            
 +//     This may include format specifiers and escape sequences that are replaced by the characters they represent.
 +//               
 +//     For format_default, the possible specifiers are:
 +//       $n n-th backreference(i.e., a copy of the n-th matched group specified with parentheses in the regex pattern).
 +//          n must be an integer value designating a valid backreference, greater than 0, and of two digits at most.
 +//       $& A copy of the entire match
 +//       $` The prefix(i.e., the part of the target sequence that precedes the match).
 +//       $' The suffix(i.e., the part of the target sequence that follows the match).
 +//       $$ A single $ character.
 +//      
 +//   flags:
 +//     One or more of these constants can be combined (using the bitwise OR operator, |) to 
 +//     form a valid bitmask value of type regex_constants::match_flag_type:
 +//
 +//     flag effects notes
 +//     ------------------
 +//     match_default Default Default matching behavior. This constant has a value of zero**.
 +//     match_not_bol Not Beginning-Of-Line The first character is not considered a beginning of line("^" does not match).
 +//     match_not_eol Not End-Of-Line The last character is not considered an end of line("$" does not match).
 +//     match_not_bow Not Beginning-Of - Word The escape sequence "\b" does not match as a beginning-of-word.
 +//     match_not_eow Not End-Of-Word The escape sequence "\b" does not match as an end-of-word.
 +//     match_any Any match Any match is acceptable if more than one match is possible.
 +//     match_not_null Not null Empty sequences do not match.
 +//     match_continuous Continuous The expression must match a sub-sequence that begins at the first character.
 +//                                  Sub-sequences must begin at the first character to match.
 +//     match_prev_avail Previous Available One or more characters exist before the first one. (match_not_bol and match_not_bow are ignored).
 +//     format_default Default formatting Uses the standard formatting rules to replace matches(those used by ECMAScript's replace method).
 +//                                        This constant has a value of zero**.
 +//     format_sed sed formatting Uses the same rules as the sed utility in POSIX to replace matches.
 +//     format_no_copy No copy The sections in the target sequence that do not match the regular expression are not copied when replacing matches.
 +//     format_first_only First only Only the first occurrence of a regular expression is replaced.
 +//
 +//     NOTE:  ** Constants with a value of zero are ignored if some other flag is set.
 +//
 +// Example:
 +//   std::string s("This is a catfish");
 +//   std::string regex("(cat)");
 +//   std::string replacement("(dog)");
 +//
 +//   result = string_replace(ss, regex, "dog");
 +//
 +// Returns:
 +//   This is a dogfish.
 +//
 +// Example2:
 +//   std::string regex("([A-Za-z]+)&([A-Za-z]+)");  // Find word&word
 +//   std::string replacement = "$2&$1";             // Switch order.
 +//
 +//   result = string_replace(s, regex, replacement);
 +//
 +// Example3:
 +//   std::string s = "April 15, 2003";
 +//   std::string regex = "(\\w+) (\\d+), (\\d+)";
 +//   std::string result = string_replace(ss, regex, "$011,$3");
 +//
 +// Returns:
 +//   April1,2003.
 +//
 +//  NOTE:  Isolated $1 backreferences.
 +//         The $011 says to use $01, or the 1st regex match.
 +//         If $11 was used, the system would try to use the 11th regex match.
 +//         This only works because the limit of set to 99 maximum matches.
 +//
 +// Example4:
 +//   result = string_replace(ss, regex, "dog", std::regex_constants::format_first_only);
 +std::string string_replace(const std::string& s, const std::string& regex, const std::string& replacement,
 +  std::regex_constants::match_flag_type flags)
 +{
 +  std::string result = s;
 +  std::regex reg(regex);
 +
 +  // using string/c-string (3) version:
 +  result = std::regex_replace(result, reg, replacement, flags);
 +
 +
 +  /*
 +  // using string/c-string (3) version:
 +  std::cout << std::regex_replace(s3, e, "sub-$2");
 +
 +  // using range/c-string (6) version:
 +  std::string result2;
 +  std::regex_replace(std::back_inserter(result2), s3.begin(), s3.end(), e, "$2");
 +  std::cout << result2;
 +
 +  // with flags:
 +  std::cout << std::regex_replace(s3, e, "$1 and $2", std::regex_constants::format_no_copy);
 +  std::cout << std::endl;
 +  */
 +
 +
 +  return result;
 +}
 +
 +
 +// Replaces all occurences of the regex within the replacement string.
 +//
 +// Parameters:
 +//
 +//   replacement:
 +//     The replacement string may contain references of the form $n. Every such reference will be replaced by the 
 +//     text captured by the n'th parenthesized pattern. 
 +//     n can be from 0 to 99, and $0 refers to the text matched by the whole pattern.
 +//            
 +//     This may include format specifiers and escape sequences that are replaced by the characters they represent.
 +//               
 +//     For format_default, the possible specifiers are:
 +//       $n n-th backreference(i.e., a copy of the n-th matched group specified with parentheses in the regex pattern).
 +//          n must be an integer value designating a valid backreference, greater than 0, and of two digits at most.
 +//       $& A copy of the entire match
 +//       $` The prefix(i.e., the part of the target sequence that precedes the match).
 +//       $' The suffix(i.e., the part of the target sequence that follows the match).
 +//       $$ A single $ character.
 +//
 +//    retain:
 +//      If false then the replacement string completely overwrites the previous string by the replacement.
 +//
 +// Example:
 +//   std::string s = "  14MAY  15JUNE ";
 +//   result = string_replace(ss, regex, "$1 $2");
 +// 
 +// Returns:
 +//   std::string s = "  14 MAY  15 JUNE ";
 +//
 +// Example2:
 +//   result = string_replace(ss, regex, "$1 $2", std::regex_constants::format_no_copy);
 +//
 +// Returns:
 +//   std::string s = "14 MAY15 JUNE ";
 +//
 +// Example3:
 +//   result = string_replace(ss, regex, "$1 $2", false);
 +//
 +// Returns:
 +//   std::string s = "14 MAY15 JUNE ";
 +std::string string_replace(const std::string& s, const std::string& regex, const std::string& replacement,
 +  bool retain)
 +{
 +  if (retain)
 +    return string_replace(s, regex, replacement);
 +  else
 +    return string_replace(s, regex, replacement, std::regex_constants::format_no_copy);
 +}
 +
 +
 +// Returns true if the string matches the regex.
 +//
 +// Example:
 +bool string_match(const std::string& s, const std::string& regex, std::regex_constants::match_flag_type flags)
 +{
 +  std::smatch m;
 +  std::regex_search(s, m, std::regex(regex), flags);
 +  if (m.empty()) {
 +    return false;
 +  }
 +  else {
 +    return true;
 +  }
 +
 +}
 +
 +
 +// Shows all matches of the regex within the string.
 +//
 +// Example:
 +//   show_matches("abcdef", "abc|def");
 +//   show_matches("abc", "ab|abc"); // left Alernative matched first
 +//
 +//   Match of the input against the left Alternative (a) followed by the remainder of the 
 +//   regex (c|bc) succeeds, with results:
 +//     m[1]="a" and m[4]="bc".
 +//   The skipped Alternatives (ab) and (c) leave their submatches
 +//     m[3] and m[5] empty.
 +//
 +//  show_matches("abc", "((a)|(ab))((c)|(bc))");
 +void show_matches(const std::string& s, const std::string& regex)
 +{
 +  std::smatch m;
 +  std::regex_search(s, m, std::regex(regex));
 +  if (m.empty()) {
 +    std::cout << "input=[" << s << "], regex=[" << regex << "]: NO MATCH\n";
 +  }
 +  else {
 +    std::cout << "input=[" << s << "], regex=[" << regex << "]: ";
 +    std::cout << "prefix=[" << m.prefix() << "] ";
 +    for (std::size_t n = 0; n < m.size(); ++n)
 +      std::cout << " m[" << n << "]=[" << m[n] << "] ";
 +    std::cout << "suffix=[" << m.suffix() << "]\n";
 +  }
 +}
 +
 +
 +
 +// Splits a string into seperate tokens.
 +//
 +// Example:
 +//   s = "0 HEAD";
 +//   regex = "([\\d]+)[\\s]+([A-Z]*)";
 +std::vector<std::string> string_tokenize(const std::string& s, const std::string& regex)
 +{
 +  
 +  std::vector<std::string> result;
 +  std::smatch m;
 +  std::regex_search(s, m, std::regex(regex));
 +  if (m.empty()) {
 +    return result;
 +  }
 +  else {
 +    //result.push_back(m.prefix());
 +    for (std::size_t n = 0; n < m.size(); ++n)
 +      result.push_back(m[n]);
 +    //result.push_back(m.suffix());
 +  }
 +
 +  return result;
 +  
 +  /*
 +  std::vector<std::string> result;
 +  std::regex rgx(regex);
 +  std::sregex_token_iterator iter(s.begin(),
 +    s.end(),
 +    rgx,
 +    -1);
 +  std::sregex_token_iterator end;
 +  for (; iter != end; ++iter)
 +    result.push_back(*iter);
 +
 +  return result;
 +  */
 +
 +  /*
 +  std::vector<std::string> result;
 +  std::regex rgx(regex);
 +  std::sregex_token_iterator i(s.begin(), s.end(), rgx, -1);
 +  std::sregex_token_iterator j;
 +  while (i != j) {
 +    //std::cout << *i++ << " ";
 +    result.push_back(*i++);
 +  }
 +
 +  return result;
 +  */
 +}
 +</file>
 +
 +string.h
 +
 +<file h string.h>
 +#ifndef __SHAREWIZ_STRING_H__
 +#define __SHAREWIZ_STRING_H__
 +
 +#include <string>
 +#include <vector>
 +#include <regex>
 +// String class.
 +
 +
 +template <typename T>
 +std::string numberToString(T pNumber);
 +
 +std::vector<std::string> string_find(const std::string& s, const std::string& regex);
 +std::string string_replace(const std::string& s, const std::string& regex, const std::string& replacement,
 +  std::regex_constants::match_flag_type flags = std::regex_constants::match_default);
 +std::string string_replace(const std::string& s, const std::string& regex, const std::string& replacement,
 +  bool retain);
 +bool string_match(const std::string& s, const std::string& regex, 
 +  std::regex_constants::match_flag_type flags = std::regex_constants::match_default);
 +void show_matches(const std::string& s, const std::string& regex);
 +std::vector<std::string> string_tokenize(const std::string& s, const std::string& regex);
 +
 +
 +#endif
 +</file>
 +
 +verylong.cpp
 +
 +<file cpp verylong.cpp>
 +/*
 +#include <cassert>
 +#include <cctype>
 +#include <cmath>
 +#include <cstdlib>
 +#include <iomanip>
 +#include <iostream>
 +#include <limits>
 +#include <string>
 +*/
 +#include <iostream>
 +#include <cassert>
 +#include <cmath>
 +#include <string>
 +
 +#include "verylong.h"
 +
 +// Class Data
 +const Verylong Verylong::zero = Verylong("0");
 +const Verylong Verylong::one = Verylong("1");
 +const Verylong Verylong::two = Verylong("2");
 +
 +
 +// Constructors, Destructors and Conversion operators.
 +
 +Verylong::Verylong(const std::string &value = "0")
 +{
 + std::string s = (value == "") ? "0" : value;
 +
 + vlsign = (s[0] == '-') ? 1 : 0;        // check for negative sign
 + if (ispunct(s[0]))                     // if the first character
 + vlstr = s.substr(1, s.length() - 1); // is a punctuation mark.
 + else 
 + vlstr = s;
 +}
 +
 +
 +Verylong::Verylong(int n)
 +{
 + if (n < 0)                           // check for sign and convert the
 + {                                    // number to positive if it is negative
 + vlsign = 1; 
 + n = (-n); 
 + } 
 + else 
 + vlsign = 0;                        
 +
 + if (n > 0)
 +   while (n >= 1)                     // extract the number digit by digit and store 
 +   {                                  // internally
 +   vlstr = char(n % 10 + '0') + vlstr;
 +   n /= 10;
 +   }
 +   else 
 +   vlstr = std::string("0");          // else number is zero
 +}
 +
 +
 +Verylong::Verylong(const Verylong &x) : vlstr(x.vlstr), vlsign(x.vlsign) 
 +{ 
 +}
 +
 +
 +Verylong::~Verylong() 
 +{ 
 +}
 +
 +
 +Verylong::operator int() const
 +{
 + int number, factor = 1;
 + static Verylong max0(INT_MAX);
 + static Verylong min0(INT_MIN + 1);
 + std::string::const_reverse_iterator j = vlstr.rbegin();
 +
 + if (*this > max0)
 + {
 + std::cerr << "Error : Conversion Verylong->integer is not possible" << std::endl;
 + return INT_MAX;
 + }
 + else 
 + if (*this < min0)
 + {
 + std::cerr << "Error : Conversion Verylong->integer is not possible" << std::endl;
 + return INT_MIN;
 + }
 +
 + number = *j - '0';
 +
 + for (j++; j != vlstr.rend(); j++)
 + {
 + factor *= 10;
 + number += (*j - '0') * factor;
 + }
 +
 + if (vlsign) 
 + return -number;
 +
 + return number;
 +}
 +
 +
 +Verylong::operator double() const
 +{
 + double sum, factor = 1.0;
 + std::string::const_reverse_iterator i = vlstr.rbegin();
 +
 + sum = double(*i) - '0';
 +
 + for (i++; i != vlstr.rend(); i++)
 + {
 + factor *= 10.0;
 + sum += double(*i - '0') * factor;
 + }
 +
 + if (vlsign) 
 + return -sum;
 +
 + return sum;
 +}
 +
 +
 +Verylong::operator std::string() const
 +{
 + if (vlstr.length() == 0) 
 + return std::string("0");
 +
 + return vlstr;
 +}
 +
 +
 +
 +// Various member operators
 +
 +const Verylong & Verylong::operator = (const Verylong &rhs)
 +{
 + if (this == &rhs) 
 + return *this;
 +
 + vlstr = rhs.vlstr;
 + vlsign = rhs.vlsign;
 +
 + return *this;
 +}
 +
 +
 +// Unary - operator
 +Verylong Verylong::operator -() const
 +{
 + Verylong temp(*this);
 +
 + if (temp != zero)  
 + temp.vlsign = !vlsign;
 +
 + return temp;
 +}
 +
 +
 +// Prefix increment operator
 +Verylong Verylong::operator ++ ()
 +{
 + return *this = *this + one;
 +}
 +
 +
 +// Postfix increment operator
 +Verylong Verylong::operator ++ (int)
 +{
 + Verylong result(*this);
 +
 + *this = *this + one;
 + return result;
 +}
 +
 +
 +// Prefix decrement operator
 +Verylong Verylong::operator -- ()
 +{
 + return *this = *this - one;
 +}
 +
 +
 +// Postfix decrement operator
 +Verylong Verylong::operator -- (int)
 +{
 + Verylong result(*this);
 +
 + *this = *this - one;
 + return result;
 +}
 +
 +
 +Verylong Verylong::operator += (const Verylong &v)
 +{
 + return *this = *this + v;
 +}
 +
 +
 +Verylong Verylong::operator -= (const Verylong &v)
 +{
 + return *this = *this - v;
 +}
 +
 +
 +Verylong Verylong::operator *= (const Verylong &v)
 +{
 + return *this = *this * v;
 +}
 +
 +
 +Verylong Verylong::operator /= (const Verylong &v)
 +{
 + return *this = *this / v;
 +}
 +
 +
 +Verylong Verylong::operator %= (const Verylong &v)
 +{
 + return *this = *this % v;
 +}
 +
 +
 +Verylong Verylong::operator ^= (const Verylong &degree)
 +{
 + Verylong N(degree);
 + Verylong Y("1");
 +
 + if (N == Verylong::zero)
 + return Verylong::one;
 +
 + if (N < Verylong::zero)
 + return Verylong::zero;
 +
 + while (1)
 + {
 + if (N == Verylong::zero)
 + {
 + *this = Y;
 + break;
 + }
 +
 + Y = Y * *this;
 + N = N - Verylong::one;
 + }
 +
 + return *this;
 +}
 +
 +
 +
 +// Various friendship operators and functions.
 +
 +Verylong operator + (const Verylong &u, const Verylong &v)
 +{
 + char digitsum, d1, d2, carry = 0;
 + std::string temp;
 + std::string::const_reverse_iterator j, k;
 +
 + if (u.vlsign ^ v.vlsign)
 + {
 + if (u.vlsign == 0) 
 + return u - abs(v);
 + else               
 + return v - abs(u);
 + }
 +
 + for (j = u.vlstr.rbegin(), k = v.vlstr.rbegin();
 + j != u.vlstr.rend() || k != v.vlstr.rend();)
 + {
 + d1 = (j == u.vlstr.rend()) ? 0 : *(j++) - '0'; // get digit
 + d2 = (k == v.vlstr.rend()) ? 0 : *(k++) - '0'; // get digit
 + digitsum = d1 + d2 + carry;                    // add digits
 +
 + carry = (digitsum >= 10) ? 1 : 0;
 + digitsum -= 10 * carry;
 +
 + temp = char(digitsum + '0') + temp;
 + }
 +
 + if (carry) // if carry at end, last digit is 1
 + temp = '1' + temp;  
 +
 + if (u.vlsign) 
 + temp = '-' + temp;
 +
 + return Verylong(temp);
 +}
 +
 +
 +Verylong operator - (const Verylong &u, const Verylong &v)
 +{
 + char d, d1, d2, borrow = 0;
 + int negative;
 + std::string temp, temp2;
 + std::string::reverse_iterator i, j;
 +
 + if (u.vlsign ^ v.vlsign)
 + {
 + if (u.vlsign == 0) 
 + return u + abs(v);
 + else
 + return -(v + abs(u));
 + }
 +
 + Verylong w, y;
 +
 + if (u.vlsign == 0)                   // both u,v are positive
 + if (u<v) 
 + { 
 + w = v; 
 + y = u; 
 + negative = 1; 
 + }
 + else    
 + { 
 + w = u; 
 + y = v; 
 + negative = 0; 
 + }
 + else                               // both u,v are negative
 + if (u<v) 
 + { 
 + w = u; 
 + y = v; 
 + negative = 1; 
 + }
 + else    
 + { 
 + w = v; 
 + y = u; 
 + negative = 0; 
 + }
 +
 + for (i = w.vlstr.rbegin(), j = y.vlstr.rbegin();
 + i != w.vlstr.rend() || j != y.vlstr.rend();)
 + {
 + d1 = (i == w.vlstr.rend()) ? 0 : *(i++) - '0';
 + d2 = (j == y.vlstr.rend()) ? 0 : *(j++) - '0';
 +
 + d = d1 - d2 - borrow;
 + borrow = (d < 0) ? 1 : 0;
 + d += 10 * borrow;
 +
 + temp = char(d + '0') + temp;
 + }
 +
 + while (temp[0] == '0') 
 + temp = temp.substr(1);
 +
 + if (negative) 
 + temp = '-' + temp;
 +
 + return Verylong(temp);
 +}
 +
 +
 +Verylong operator * (const Verylong &u, const Verylong &v)
 +{
 + Verylong pprod("1"), tempsum("0");
 + std::string::const_reverse_iterator r = v.vlstr.rbegin();
 +
 + for (int j = 0; r != v.vlstr.rend(); j++, r++)
 + {
 + int digit = *r - '0';              // extract a digit
 +
 + pprod = u.multdigit(digit);        // multiplied by the digit
 + pprod = pprod.mult10(j);           // "adds" suitable zeros behind
 + tempsum = tempsum + pprod;         // result added to tempsum
 + }
 +
 + tempsum.vlsign = u.vlsign^v.vlsign;  // to determine sign
 + return tempsum;
 +}
 +
 +
 +//  This algorithm is the long division algorithm.
 +Verylong operator / (const Verylong &u, const Verylong &v)
 +{
 + int len = u.vlstr.length() - v.vlstr.length();
 + std::string temp;
 + Verylong w, y, b, c, d, quotient = Verylong::zero;
 +
 + if (v == Verylong::zero)
 + {
 + std::cerr << "Error : division by zero" << std::endl;
 + return Verylong::zero;
 + }
 +
 + w = abs(u); 
 + y = abs(v);
 +
 + if (w < y) 
 + return Verylong::zero;
 +
 + c = Verylong(w.vlstr.substr(0, w.vlstr.length() - len));
 +
 + for (int i = 0; i <= len; i++)
 + {
 + quotient = quotient.mult10(1);
 +
 + b = d = Verylong::zero;            // initialize b and d to 0
 +
 + while (b < c)
 + {
 + b = b + y; d = d + Verylong::one;
 + }
 +
 + if (c < b)                           // if b>c, then
 + {                                    // we have added one count too many 
 + b = b - y;
 + d = d - Verylong::one;
 + }
 +
 + quotient = quotient + d;             // add to the quotient
 +
 + if (i < len)
 + {
 + // partial remainder * 10 and add to next digit
 + c = (c - b).mult10(1);
 + c += Verylong(w.vlstr[w.vlstr.length() - len + i] - '0');
 + }
 + }
 +
 + quotient.vlsign = u.vlsign^v.vlsign;   // to determine sign
 +
 + return quotient;
 +}
 +
 +
 +Verylong operator % (const Verylong &u, const Verylong &v)
 +{
 + return (u - v*(u / v));
 +}
 +
 +
 +Verylong operator ^ (const Verylong &u, const Verylong &v)
 +{
 + //return (u - v*(u / v));
 +
 + Verylong temp(u);
 +
 + return temp ^= v;
 +}
 +
 +
 +
 +int operator == (const Verylong &u, const Verylong &v)
 +{
 + return (u.vlsign == v.vlsign && u.vlstr == v.vlstr);
 +}
 +
 +
 +int operator != (const Verylong &u, const Verylong &v)
 +{
 + return !(u == v);
 +}
 +
 +
 +int operator < (const Verylong &u, const Verylong &v)
 +{
 + if (u.vlsign < v.vlsign) 
 + return 0;
 + else 
 + if (u.vlsign > v.vlsign) 
 + return 1;
 +
 + // exclusive or (^) to determine sign
 + if (u.vlstr.length() < v.vlstr.length()) 
 + return (1 ^ u.vlsign);
 + else 
 + if (u.vlstr.length() > v.vlstr.length()) 
 + return (0 ^ u.vlsign);
 +
 + return (u.vlstr < v.vlstr && !u.vlsign) ||
 + (u.vlstr > v.vlstr && u.vlsign);
 +}
 +
 +
 +int operator <= (const Verylong &u, const Verylong &v)
 +{
 + return (u<v || u == v);
 +}
 +
 +
 +int operator >(const Verylong &u, const Verylong &v)
 +{
 + return (!(u<v) && u != v);
 +}
 +
 +
 +int operator >= (const Verylong &u, const Verylong &v)
 +{
 + return (u>v || u == v);
 +}
 +
 +
 +// Calculate the absolute value of a number
 +Verylong abs(const Verylong &v)
 +{
 + Verylong u(v);
 +
 + if (u.vlsign) 
 + u.vlsign = 0;
 +
 + return u;
 +}
 +
 +// Calculate the integer square root of a number
 +//    based on the formula (a+b)^2 = a^2 + 2ab + b^2
 +Verylong sqrt(const Verylong &v)
 +{
 + // if v is negative, error is reported
 + if (v.vlsign) 
 + { 
 + std::cerr << "NaN" << std::endl; 
 + return Verylong::zero; 
 + }
 +
 + int j, k = v.vlstr.length() + 1, num = k >> 1;
 + Verylong y, z, sum, tempsum, digitsum;
 +
 + std::string temp, w(v.vlstr);
 +
 + k = 0;
 + j = 1;
 +
 + // segment the number 2 digits by 2 digits
 + if (v.vlstr.length() % 2) 
 + digitsum = Verylong(w[k++] - '0');
 + else
 + {
 + digitsum = Verylong((w[k] - '0') * 10 + w[k + 1] - '0');
 + k += 2;
 + }
 +
 + // find the first digit of the integer square root
 + sum = z = Verylong(int(sqrt(double(digitsum))));
 +
 + // store partial result
 + temp = char(int(z) + '0');
 + digitsum = digitsum - z*z;
 +
 + for (; j<num; j++)
 + {
 + // get next digit from the number
 + digitsum = digitsum.mult10(1) + Verylong(w[k++] - '0');
 + y = z + z;        // 2*a
 + z = digitsum / y;
 + tempsum = digitsum.mult10(1) + Verylong(w[k++] - '0');
 + digitsum = -y*z.mult10(1) + tempsum - z*z;
 +
 + // decrease z by 1 and re-calculate when it is over-estimated.
 + while (digitsum < Verylong::zero)
 + {
 + --z;
 + digitsum = -y*z.mult10(1) + tempsum - z*z;
 + }
 +
 + temp = temp + char(int(z) + '0');  // store partial result
 + z = sum = sum.mult10(1) + z;       // update value of the partial result
 + }
 +
 + Verylong result(temp);
 +
 + return result;
 +}
 +
 +
 +// Raise a number X to a power of degree
 +Verylong pow(const Verylong &X, const Verylong &degree)
 +{
 + Verylong N(degree), Y("1"), x(X);
 +
 + if (N == Verylong::zero) 
 + return Verylong::one;
 +
 + if (N < Verylong::zero) 
 + return Verylong::zero;
 +
 + while (1)
 + {
 + if (N%Verylong::two != Verylong::zero)
 + {
 + Y = Y * x;
 + N = N / Verylong::two;
 + if (N == Verylong::zero) 
 + return Y;
 + }
 + else  
 + N = N / Verylong::two;
 +
 + x = x * x;
 + }
 +}
 +
 +
 +// Double division function
 +double div(const Verylong &u, const Verylong &v)
 +{
 + double qq = 0.0, qqscale = 1.0;
 + Verylong w, y, b, c;
 + int d, count,
 + decno = std::numeric_limits<double>::digits; // number of significant digits
 +
 + if (v == Verylong::zero)
 + {
 + std::cerr << "ERROR : Division by zero" << std::endl;
 + return 0.0;
 + }
 +
 + if (u == Verylong::zero) 
 + return 0.0;
 +
 + w = abs(u); 
 + y = abs(v);
 +
 + while (w<y) 
 + { 
 + w = w.mult10(1); 
 + qqscale *= 0.1; 
 + }
 +
 + int len = w.vlstr.length() - y.vlstr.length();
 + std::string temp = w.vlstr.substr(0, w.vlstr.length() - len);
 +
 + c = Verylong(temp);
 +
 + for (int i = 0; i <= len; i++)
 + {
 + qq *= 10.0;
 +
 + b = Verylong::zero; d = 0;         // initialize b and d to 0
 +
 + while (b < c)
 + {
 + b += y; d += 1;
 + }
 +
 + if (c < b)                         // if b>c, then
 + {                                  // we have added one count too many
 + b -= y; 
 + d -= 1;
 + }                                
 +
 + qq += double(d);                   // add to the quotient
 +
 + c = (c - b).mult10(1);             // the partial remainder * 10
 +
 + if (i < len)                       // and add to next digit
 + c += Verylong(w.vlstr[w.vlstr.length() - len + i] - '0');
 + }
 +
 + qq *= qqscale; count = 0;
 +
 + while (c != Verylong::zero && count < decno)
 + {
 + qqscale *= 0.1;
 +
 + b = Verylong::zero; d = 0;         // initialize b and d to 0
 +
 + while (b < c)
 + {
 + b += y; d += 1;
 + }
 +
 + if (c < b)                         // if b>c, then
 + {                                  // we have added one count too many
 + b -= y; d -= 1;
 + }           
 +
 + qq += double(d)*qqscale;
 +
 + c = (c - b).mult10(1);
 + count++;
 + }
 +
 + if (u.vlsign^v.vlsign)               // check for the sign
 + qq *= (-1.0); 
 +
 + return qq;
 +}
 +
 +
 +std::ostream & operator << (std::ostream &s, const Verylong &v)
 +{
 + if (v.vlstr.length() > 0)
 + {
 + if (v.vlsign) s << "-";
 + s << v.vlstr;
 + }
 + else 
 + s << "0";
 +
 + return s;
 +}
 +
 +
 +std::istream & operator >> (std::istream &s, Verylong &v)
 +{
 + std::string temp(10000, ' ');
 +
 + s >> temp;
 + v = Verylong(temp);
 +
 + return s;
 +}
 +
 +
 +//
 +// Private member functions: multdigit(), mult10().
 +//
 +
 +// Multiply this Verylong number by num
 +Verylong Verylong::multdigit(int num) const
 +{
 + int carry = 0;
 + std::string::const_reverse_iterator r;
 +
 + if (num)
 + {
 + std::string temp;
 +
 + for (r = vlstr.rbegin(); r != vlstr.rend(); r++)
 + {
 + int d1 = *r - '0',               // get digit and multiplied by
 + digitprod = d1*num + carry;    // that digit plus carry
 +
 + if (digitprod >= 10)             // if there's a new carry,
 + {
 + carry = digitprod / 10;        // carry is high digit
 + digitprod -= carry * 10;       // result is low digit
 + }
 + else 
 + carry = 0;                     // otherwise carry is 0
 +
 + temp = char(digitprod + '0') + temp;   // insert char in string
 + }
 +
 + if (carry) //if carry at end,
 + temp = char(carry + '0') + temp; 
 +
 + Verylong result(temp);
 + return result;
 + }
 + else 
 + return zero;
 +}
 +
 +
 +// Multiply this Verylong number by 10*num
 +Verylong Verylong::mult10(int num) const
 +{
 + int j;
 +
 + if (*this != zero)
 + {
 + std::string temp;
 +
 + for (j = 0; j<num; j++) 
 + temp = temp + '0';
 +
 + Verylong result(vlstr + temp);
 +
 + if (vlsign) 
 + result = -result;
 +
 + return result;
 + }
 + else 
 + return zero;
 +}
 +
 +
 +//template <> Verylong zero(Verylong) { return Verylong::zero; }
 +//template <> Verylong one(Verylong) { return Verylong::one; }
 +
 +</file>
 +
 +verylong.h
 +
 +<file h verylong.h>
 +#ifndef __SHAREWIZ_VERYLONG_H__
 +#define __SHAREWIZ_VERYLONG_H__
 +
 +//#include <string>
 +
 +// Very Long Integer Class
 +
 +
 +class Verylong
 +{
 +private:
 + // Data Fields
 + std::string vlstr;     // The string is stored in reverse order.
 + int    vlsign;    // Sign of Verylong: +=>0; -=>1
 +
 + // Private member functions
 + Verylong multdigit(int) const;
 + Verylong mult10(int) const;
 +
 +public:
 + // Constructors and destructor
 + Verylong(const std::string&);
 + Verylong(int);
 + Verylong(const Verylong &);
 + ~Verylong();
 +
 + // Conversion operators
 + operator int() const;
 + operator double() const;
 + operator std::string () const;
 +
 + // Arithmetic operators and Relational operators
 + const Verylong & operator = (const Verylong &);  // assignment operator
 + Verylong operator - () const;     // negate  operator
 + Verylong operator ++ ();          // prefix  increment operator
 + Verylong operator ++ (int);       // postfix increment operator
 + Verylong operator -- ();          // prefix  decrement operator
 + Verylong operator -- (int);       // postfix decrement operator
 +
 + Verylong operator += (const Verylong &);
 + Verylong operator -= (const Verylong &);
 + Verylong operator *= (const Verylong &);
 + Verylong operator /= (const Verylong &);
 + Verylong operator %= (const Verylong &);
 + Verylong operator ^= (const Verylong &);
 +
 + friend Verylong operator + (const Verylong &, const Verylong &);
 + friend Verylong operator - (const Verylong &, const Verylong &);
 + friend Verylong operator * (const Verylong &, const Verylong &);
 + friend Verylong operator / (const Verylong &, const Verylong &);
 + friend Verylong operator % (const Verylong &, const Verylong &);
 + friend Verylong operator ^ (const Verylong &, const Verylong &);
 +
 + friend int operator == (const Verylong &, const Verylong &);
 + friend int operator != (const Verylong &, const Verylong &);
 + friend int operator <  (const Verylong &, const Verylong &);
 + friend int operator <= (const Verylong &, const Verylong &);
 + friend int operator >  (const Verylong &, const Verylong &);
 + friend int operator >= (const Verylong &, const Verylong &);
 +
 + // Other functions
 + friend Verylong abs(const Verylong &);
 + friend Verylong sqrt(const Verylong &);
 + friend Verylong pow(const Verylong &, const Verylong &);
 + friend double div(const Verylong &, const Verylong &);
 +
 + // Class Data
 + static const Verylong zero;
 + static const Verylong one;
 + static const Verylong two;
 +
 + // I/O stream functions
 + friend std::ostream & operator << (std::ostream &, const Verylong &);
 + friend std::istream & operator >> (std::istream &, Verylong &);
 +};
 +
 +
 +//template <> Verylong zero(Verylong) { return Verylong::zero; }
 +//template <> Verylong one(Verylong) { return Verylong::one; }
 +
 +
 +#endif
 +</file>
 +
brain/brain.1481541534.txt.gz · Last modified: 2020/07/15 10:30 (external edit)
Donate Powered by PHP Valid HTML5 Valid CSS Driven by DokuWiki