Floats to Double and Signature
This commit is contained in:
Asrın Doğan
parent
4475fd3c7f
commit
29da090c99
184
Genetic.cpp
184
Genetic.cpp
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@ -1,3 +1,7 @@
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/*
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Author: Asrın "Syntriax" Doğan
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Mail: asrindogan99@gmail.com
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*/
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#include <iostream>
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#include <time.h>
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@ -5,7 +9,7 @@
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#define InitialSynapseValue 0.0
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#define MutationRate 0.25
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#define CrossOverRate 0.25
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#define PopCrossOverRate 0.7
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#define PopCrossOverRate 0.75
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class Synapse;
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class Neuron;
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@ -15,14 +19,14 @@ class Output;
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class NeuralNetwork;
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class Generation;
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float RandomFloat(int min, int max)
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double RandomDouble(int min, int max)
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{
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float result;
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int value;
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double result;
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long int value;
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static unsigned long int counter = time(0);
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srand(time(0) * counter++);
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value = ((rand() * counter) % ((max - min) * 100000));
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result = (float)value / 100000.0 + (float)min;
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value = ((rand() * counter) % ((max - min) * 100000000));
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result = (double)value / 100000000.0 + (double)min;
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return result;
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}
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@ -30,19 +34,19 @@ float RandomFloat(int min, int max)
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class Synapse
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{
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private:
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float weight;
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float value;
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float bias;
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double weight;
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double value;
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double bias;
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public:
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Synapse();
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~Synapse();
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void SetValue(float);
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void SetWeight(float);
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void SetBias(float);
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float GetWeight();
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float GetValue();
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float GetBias();
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float Fire();
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void SetValue(double);
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void SetWeight(double);
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void SetBias(double);
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double GetWeight();
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double GetValue();
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double GetBias();
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double Fire();
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};
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Synapse::Synapse()
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@ -54,39 +58,39 @@ float RandomFloat(int min, int max)
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{
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}
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void Synapse::SetValue(float value)
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void Synapse::SetValue(double value)
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{
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this -> value = value;
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}
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void Synapse::SetWeight(float weight)
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void Synapse::SetWeight(double weight)
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{
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this -> weight = weight;
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}
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void Synapse::SetBias(float bias)
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void Synapse::SetBias(double bias)
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{
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this -> bias = bias;
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}
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float Synapse::GetWeight()
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double Synapse::GetWeight()
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{
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return weight;
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}
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float Synapse::GetValue()
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double Synapse::GetValue()
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{
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return value;
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}
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float Synapse::GetBias()
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double Synapse::GetBias()
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{
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return bias;
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}
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float Synapse::Fire()
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double Synapse::Fire()
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{
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float result = 0.0;
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double result = 0.0;
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result = this -> value * this -> weight + this -> bias;
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@ -107,9 +111,9 @@ float RandomFloat(int min, int max)
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~Neuron();
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void ConnectIncomings(Synapse *, int);
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void ConnectForwards(Synapse *, int, int);
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void SetValue(float);
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void SetValue(double);
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void Reset();
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float GetValue();
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double GetValue();
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};
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Neuron::Neuron()
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@ -129,7 +133,7 @@ float RandomFloat(int min, int max)
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incomingsSize = forwardsSize = layerSize = 0;
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}
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void Neuron::SetValue(float value)
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void Neuron::SetValue(double value)
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{
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int i;
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for (i = 0; i < forwardsSize; i++)
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@ -149,10 +153,10 @@ float RandomFloat(int min, int max)
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this -> layerSize = layerSize;
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}
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float Neuron::GetValue()
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double Neuron::GetValue()
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{
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int i;
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float result = 0.0;
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double result = 0.0;
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if(!incomings) return result;
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@ -261,14 +265,14 @@ float RandomFloat(int min, int max)
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void Layer::RandomizeValues()
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{
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float bias;
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float weight;
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double bias;
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double weight;
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int i;
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for (i = 0; i < synapseSize; i++)
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{
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bias = RandomFloat(-RandomRange, RandomRange);
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weight = RandomFloat(-RandomRange, RandomRange);
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bias = RandomDouble(-RandomRange, RandomRange);
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weight = RandomDouble(-RandomRange, RandomRange);
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(synapses + i) -> SetBias(bias);
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(synapses + i) -> SetWeight(weight);
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}
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@ -276,18 +280,18 @@ float RandomFloat(int min, int max)
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void Layer::Mutate()
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{
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float bias = 0.0;
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float weight = 0.0;
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float mutationValue = 0.0;
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double bias = 0.0;
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double weight = 0.0;
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double mutationValue = 0.0;
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int i;
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for (i = 0; i < synapseSize; i++)
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{
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mutationValue = RandomFloat(0, 1);
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mutationValue = RandomDouble(0, 1);
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if(mutationValue <= MutationRate)
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{
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bias = RandomFloat(-RandomRange, RandomRange);
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weight = RandomFloat(-RandomRange, RandomRange);
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bias = RandomDouble(-RandomRange, RandomRange);
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weight = RandomDouble(-RandomRange, RandomRange);
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(synapses + i) -> SetBias(bias);
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(synapses + i) -> SetWeight(weight);
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}
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@ -298,7 +302,7 @@ float RandomFloat(int min, int max)
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{
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int thisCounter;
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for (thisCounter = 0; thisCounter < synapseSize; thisCounter++)
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if(RandomFloat(0, 1) < CrossOverRate)
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if(RandomDouble(0, 1) < CrossOverRate)
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_SwapSynapses((synapses + thisCounter), (other -> synapses + thisCounter));
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}
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@ -364,11 +368,11 @@ float RandomFloat(int min, int max)
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{
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public:
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Input();
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void SetValue(float, int);
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void SetValue(double, int);
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};
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Input::Input() : Layer() {}
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void Input::SetValue(float value, int index = 0)
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void Input::SetValue(double value, int index = 0)
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{
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if(index >= this -> neuronSize || index < 0)
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return;
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@ -380,13 +384,13 @@ float RandomFloat(int min, int max)
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{
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public:
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Output();
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float GetValue(int);
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double GetValue(int);
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};
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Output::Output() : Layer() {}
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float Output::GetValue(int index = 0)
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double Output::GetValue(int index = 0)
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{
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float result = 0.0;
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double result = 0.0;
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if(index >= this -> neuronSize || index < 0)
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return result;
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@ -404,7 +408,7 @@ float RandomFloat(int min, int max)
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Layer *hidden;
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Output *output;
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int hiddenSize;
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float score;
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double score;
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Input *_CreateInput();
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Layer *_CreateLayers(int);
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Output *_CreateOutput();
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@ -424,13 +428,13 @@ float RandomFloat(int min, int max)
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bool SetOutputNeurons(int);
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bool ConnectLayers();
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bool SetLayer(int);
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float GetOutput(int);
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float GetError(int, float);
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float GetPrediction(int);
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float GetScore();
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double GetOutput(int);
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double GetError(int, double);
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double GetPrediction(int);
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double GetScore();
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int GetHiddenSize();
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void SetScore(float);
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void SetInput(float, int);
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void SetScore(double);
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void SetInput(double, int);
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};
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Input *NeuralNetwork::_CreateInput()
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@ -548,7 +552,7 @@ float RandomFloat(int min, int max)
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FILE *file = fopen("Data/BestSynapses.txt", "w");
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for (i = 0; i < count; i++)
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{
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fprintf(file, "%f, %f, ", synapsePtr -> GetWeight(), synapsePtr -> GetBias());
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fprintf(file, "%lf, %lf, ", synapsePtr -> GetWeight(), synapsePtr -> GetBias());
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synapsePtr++;
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}
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@ -559,7 +563,7 @@ float RandomFloat(int min, int max)
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synapsePtr = (network -> hidden + j) -> synapses;
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for (i = 0; i < count; i++)
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{
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fprintf(file, "%f, %f, ", synapsePtr -> GetWeight(), synapsePtr -> GetBias());
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fprintf(file, "%lf, %lf, ", synapsePtr -> GetWeight(), synapsePtr -> GetBias());
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synapsePtr++;
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}
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}
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@ -570,7 +574,7 @@ float RandomFloat(int min, int max)
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std::cout << count << "\n";
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for (i = 0; i < count; i++)
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{
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fprintf(file, "%f, %f, ", synapsePtr -> GetWeight(), synapsePtr -> GetBias());
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fprintf(file, "%lf, %lf, ", synapsePtr -> GetWeight(), synapsePtr -> GetBias());
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synapsePtr++;
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}
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fclose(file);
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@ -624,24 +628,24 @@ float RandomFloat(int min, int max)
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return input && hidden && output;
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}
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float NeuralNetwork::GetOutput(int index = 0)
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double NeuralNetwork::GetOutput(int index = 0)
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{
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return output -> GetValue(index);
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}
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float NeuralNetwork::GetError(int index = 0, float target = 0.0)
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double NeuralNetwork::GetError(int index = 0, double target = 0.0)
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{
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float result = GetOutput(index) - target;
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double result = GetOutput(index) - target;
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return result < 0.0 ? -result : result;
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}
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float NeuralNetwork::GetPrediction(int index = 0)
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double NeuralNetwork::GetPrediction(int index = 0)
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{
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float result = GetOutput(index);
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double result = GetOutput(index);
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return result;
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}
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float NeuralNetwork::GetScore()
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double NeuralNetwork::GetScore()
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{
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return score;
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}
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@ -651,12 +655,12 @@ float RandomFloat(int min, int max)
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return hiddenSize;
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}
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void NeuralNetwork::SetInput(float value, int index = 0)
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void NeuralNetwork::SetInput(double value, int index = 0)
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{
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input -> SetValue(value, index);
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}
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void NeuralNetwork::SetScore(float value)
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void NeuralNetwork::SetScore(double value)
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{
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score = value;
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}
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@ -668,7 +672,7 @@ float RandomFloat(int min, int max)
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NeuralNetwork *networks;
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int size;
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int step;
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float target;
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double target;
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void _SwapNetworks(NeuralNetwork *, NeuralNetwork *);
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NeuralNetwork *_CreateNetworks(int, int);
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public:
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@ -680,8 +684,8 @@ float RandomFloat(int min, int max)
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void SortByScore();
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void DisplayScores(int);
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void DisplayBest(int);
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void SetTarget(float);
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void SetInput(float, int);
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void SetTarget(double);
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void SetInput(double, int);
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void NextGeneration();
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void WriteBestToFile();
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void UpdateScores();
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@ -691,8 +695,8 @@ float RandomFloat(int min, int max)
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bool SetInputNeurons(int);
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bool SetHiddenNeurons(int, int);
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bool SetOutputNeurons(int);
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float GetBestPrediction(int);
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float GetError(int);
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double GetBestPrediction(int);
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double GetError(int);
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int GetStep();
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};
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@ -768,7 +772,7 @@ float RandomFloat(int min, int max)
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void Generation::UpdateScores()
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{
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float scoreToAdd;
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double scoreToAdd;
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int i;
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for (i = 0; i < size; i++)
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{
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@ -784,15 +788,16 @@ float RandomFloat(int min, int max)
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(networks + i) -> SetScore(0.0);
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}
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float Generation::GetBestPrediction(int index = 0)
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double Generation::GetBestPrediction(int index = 0)
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{
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return networks -> GetPrediction(index);
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}
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float Generation::GetError(int index = 0)
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double Generation::GetError(int index = 0)
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{
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return (networks + index) -> GetError(0, target);
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}
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void Generation::SortByScore()
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{
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int i;
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@ -803,12 +808,12 @@ float RandomFloat(int min, int max)
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_SwapNetworks((networks + i), (networks + j));
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}
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void Generation::SetTarget(float target)
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void Generation::SetTarget(double target)
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{
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this -> target = target;
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}
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void Generation::SetInput(float value, int index = 0)
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void Generation::SetInput(double value, int index = 0)
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{
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int i;
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for (i = 0; i < this -> size; i++)
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@ -840,7 +845,7 @@ float RandomFloat(int min, int max)
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first -> Copy(*(networks + 0));
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second -> Copy(*(networks + 1));
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if(RandomFloat(0, 1) < 0.5)
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if(RandomDouble(0, 1) < 0.5)
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first -> CrossOverNetwork(second);
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else
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{
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@ -897,6 +902,7 @@ float RandomFloat(int min, int max)
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return false;
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return true;
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}
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int Generation::GetStep()
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{
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return step;
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@ -908,26 +914,26 @@ int main()
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FILE *inputFile;
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FILE *outputFile;
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int decision;
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float currentError;
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double currentError;
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int trainCounter;
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int inputCounter;
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int floatCounter;
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int doubleCounter;
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int groupSize;
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float trainInputs[30][5];
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float testInputs[120][5];
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double trainInputs[30][5];
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double testInputs[120][5];
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Generation generation(50, 5);
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inputFile = fopen("Data/train.data", "r");
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for (inputCounter = 0; inputCounter < 30; inputCounter++)
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for (floatCounter = 0; floatCounter < 5; floatCounter++)
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fscanf(inputFile, "%f,", &trainInputs[inputCounter][floatCounter]);
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for (doubleCounter = 0; doubleCounter < 5; doubleCounter++)
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fscanf(inputFile, "%lf,", &trainInputs[inputCounter][doubleCounter]);
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fclose(inputFile);
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inputFile = fopen("Data/test.data", "r");
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for (inputCounter = 0; inputCounter < 120; inputCounter++)
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for (floatCounter = 0; floatCounter < 5; floatCounter++)
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fscanf(inputFile, "%f,", &testInputs[inputCounter][floatCounter]);
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for (doubleCounter = 0; doubleCounter < 5; doubleCounter++)
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fscanf(inputFile, "%lf,", &testInputs[inputCounter][doubleCounter]);
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fclose(inputFile);
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std::cout << "Inputs Are Getting Set: ";
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@ -947,11 +953,13 @@ int main()
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std::cout << "Networks Are Getting Connected: ";
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std::cout << (generation.ConnectNetworks() ? "Successfull!" : "Failed!") << "\n";
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std::cout << "Networks Are Getting Randomized: ";
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generation.Randomize();
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std::cout << "Done!\n";
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do
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{
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std::cout << "[-1] Test\n[-2] Best to File\n[-3] Exit\nAny Positive Number for train count\nDecision: ";
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std::cout << "\n[-1] Test\n[-2] Best to File\n[-3] Exit\nAny Positive Number for train count\nDecision: ";
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std::cin >> decision;
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switch (decision)
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@ -971,8 +979,8 @@ int main()
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generation.ResetScores();
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for (groupSize = 0; groupSize < 3; groupSize++)
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{
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for (floatCounter = 0; floatCounter < 4; floatCounter++)
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generation.SetInput(trainInputs[inputCounter * 3 + groupSize][floatCounter], floatCounter);
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for (doubleCounter = 0; doubleCounter < 4; doubleCounter++)
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generation.SetInput(trainInputs[inputCounter * 3 + groupSize][doubleCounter], doubleCounter);
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generation.SetTarget(trainInputs[inputCounter * 3 + groupSize][4]);
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generation.Fire();
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generation.UpdateScores();
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@ -988,13 +996,13 @@ int main()
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outputFile = fopen("Data/results.data", "w");
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for (inputCounter = 0; inputCounter < 120; inputCounter++)
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{
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for (floatCounter = 0; floatCounter < 4; floatCounter++)
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generation.SetInput(testInputs[inputCounter][floatCounter], floatCounter);
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for (doubleCounter = 0; doubleCounter < 4; doubleCounter++)
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generation.SetInput(testInputs[inputCounter][doubleCounter], doubleCounter);
|
||||
generation.SetTarget(testInputs[inputCounter][4]);
|
||||
|
||||
generation.Fire();
|
||||
currentError = testInputs[inputCounter][4] - generation.GetBestPrediction() < 0 ? generation.GetBestPrediction() - testInputs[inputCounter][4] : testInputs[inputCounter][4] - generation.GetBestPrediction();
|
||||
fprintf(outputFile, "%f,%f,%f\n", testInputs[inputCounter][4], generation.GetBestPrediction(), currentError);
|
||||
fprintf(outputFile, "%lf,%lf,%lf\n", testInputs[inputCounter][4], generation.GetBestPrediction(), currentError);
|
||||
}
|
||||
fclose(outputFile);
|
||||
std::cout << "Test is Over!\n";
|
||||
|
|
|
|||
Loading…
Reference in New Issue