NeuralNetwork.cpp
4.58 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
#include <iostream>
#include <cmath>
#include <vector>
#include <fstream>
#include "../includes/NeuralNetwork.h"
double NeuralNetwork::singleNeuron(double input, double weight)
{
return 0;
}
double NeuralNetwork::multipleInputSingleOutput(std::vector<double> inputs, std::vector<double> weights, double bias)
{
return 0;
}
void NeuralNetwork::singleInputMultipleOutput(double input, std::vector<double> weights, double bias, std::vector<double>& outputs)
{
return;
}
void NeuralNetwork::multipleInputMultipleOutput(std::vector<double>& inputs, std::vector<double>& weights, std::vector<double>& biases, std::vector<double>& outputs, int inputSize, int outputSize)
{
return;
}
void NeuralNetwork::hiddenLayer(std::vector<double>& inputs, std::vector<double>& hiddenWeights, std::vector<double>& hiddenBiases, std::vector<double>& hiddenOutputs, int inputSize, int hiddenSize)
{
return;
}
void NeuralNetwork::calculateError(std::vector<double>& predictedOutput, std::vector<double>& groundTruth, std::vector<double>& error)
{
return;
}
double NeuralNetwork::calculateMSE(std::vector<double>& error)
{
return 0;
}
double NeuralNetwork::calculateRMSE(double mse) {
return 0;
}
void NeuralNetwork::bruteForceLearning(double input, double& weight, double expectedValue, double learningRate, int maxEpochs)
{
return;
}
double NeuralNetwork::relu(double x)
{
return 0;
}
double NeuralNetwork::sigmoid(double x)
{
return 0;
}
void NeuralNetwork::vectorReLU(std::vector<double>& inputVector, std::vector<double>& outputVector)
{
return;
}
void NeuralNetwork::vectorSigmoid(std::vector<double>& inputVector, std::vector<double>& outputVector)
{
return;
}
void NeuralNetwork::saveNetwork(const std::string& filename, int numOfFeatures, int numOfHiddenNodes, int numOfOutputNodes,
std::vector<std::vector<double>>& inputToHiddenWeights, std::vector<double>& hiddenLayerBias,
std::vector<std::vector<double>>& hiddenToOutputWeights, std::vector<double>& outputLayerBias)
{
std::ofstream file(filename);
if (!file.is_open()) {
std::cerr << "Error: Could not open file " << filename << " for writing.\n";
return;
}
file << "Hidden Layer Weights:\n";
for (int i = 0; i < numOfHiddenNodes; i++) {
for (int j = 0; j < numOfFeatures; j++) {
file << inputToHiddenWeights[i][j] << " ";
}
file << "\n";
}
file << "Hidden Layer Biases:\n";
for (int i = 0; i < numOfHiddenNodes; i++) {
file << hiddenLayerBias[i] << " ";
}
file << "\n";
file << "Output Layer Weights:\n";
for (int i = 0; i < numOfOutputNodes; i++) {
for (int j = 0; j < numOfHiddenNodes; j++) {
file << hiddenToOutputWeights[i][j] << " ";
}
file << "\n";
}
file << "Output Layer Biases:\n";
for (int i = 0; i < numOfOutputNodes; i++) {
file << outputLayerBias[i] << " ";
}
file << "\n";
file.close();
std::cout << "Network saved to file: " << filename << "\n";
}
void NeuralNetwork::loadNetwork(const std::string& filename, int numOfFeatures, int numOfHiddenNodes, int numOfOutputNodes,
std::vector<std::vector<double>>& inputToHiddenWeights, std::vector<double>& hiddenLayerBias,
std::vector<std::vector<double>>& hiddenToOutputWeights, std::vector<double>& outputLayerBias)
{
std::ifstream file(filename);
if (!file.is_open()) {
std::cerr << "Error: Could not open file " << filename << " for reading.\n";
return;
}
std::string temp;
file >> temp >> temp; // Skip "Hidden Layer Weights:"
for (int i = 0; i < numOfHiddenNodes; i++) {
for (int j = 0; j < numOfFeatures; j++) {
file >> inputToHiddenWeights[i][j];
}
}
file >> temp >> temp; // Skip "Hidden Layer Biases:"
for (int i = 0; i < numOfHiddenNodes; i++) {
file >> hiddenLayerBias[i];
}
file >> temp >> temp; // Skip "Output Layer Weights:"
for (int i = 0; i < numOfOutputNodes; i++) {
for (int j = 0; j < numOfHiddenNodes; j++) {
file >> hiddenToOutputWeights[i][j];
}
}
file >> temp >> temp; // Skip "Output Layer Biases:"
for (int i = 0; i < numOfOutputNodes; i++) {
file >> outputLayerBias[i];
}
file.close();
std::cout << "Network loaded from file: " << filename << "\n";
}