├── images
    ├── image1.png
    └── image2.png
├── xcode
    ├── BasicNeuralNet.xcworkspace
    │   ├── contents.xcworkspacedata
    │   └── xcuserdata
    │   │   └── kretash.xcuserdatad
    │   │       └── UserInterfaceState.xcuserstate
    └── net.xcodeproj
    │   ├── xcuserdata
    │       └── kretash.xcuserdatad
    │       │   └── xcschemes
    │       │       └── xcschememanagement.plist
    │   └── project.pbxproj
├── .gitattributes
├── vs2015
    ├── net.vcxproj.user
    ├── BasicNeuralNet.sln
    ├── net.vcxproj.filters
    └── net.vcxproj
├── src
    ├── layer.cc
    ├── link.cc
    ├── main.cc
    ├── network.cc
    └── node.cc
├── .gitignore
├── README.md
└── include
    └── network.hh


/images/image1.png:
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https://raw.githubusercontent.com/kretash/BasicNeuralNet/HEAD/images/image1.png


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/images/image2.png:
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https://raw.githubusercontent.com/kretash/BasicNeuralNet/HEAD/images/image2.png


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/xcode/BasicNeuralNet.xcworkspace/contents.xcworkspacedata:
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1 | <?xml version="1.0" encoding="UTF-8"?>
2 | <Workspace
3 | 	version = "1.0">
4 | 	<FileRef
5 | 		location = "group:net.xcodeproj">
6 | 	</FileRef>
7 | </Workspace>
8 | 


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/xcode/BasicNeuralNet.xcworkspace/xcuserdata/kretash.xcuserdatad/UserInterfaceState.xcuserstate:
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https://raw.githubusercontent.com/kretash/BasicNeuralNet/HEAD/xcode/BasicNeuralNet.xcworkspace/xcuserdata/kretash.xcuserdatad/UserInterfaceState.xcuserstate


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/.gitattributes:
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 1 | # Auto detect text files and perform LF normalization
 2 | * text=auto
 3 | 
 4 | # Custom for Visual Studio
 5 | *.cs     diff=csharp
 6 | 
 7 | # Standard to msysgit
 8 | *.doc	 diff=astextplain
 9 | *.DOC	 diff=astextplain
10 | *.docx diff=astextplain
11 | *.DOCX diff=astextplain
12 | *.dot  diff=astextplain
13 | *.DOT  diff=astextplain
14 | *.pdf  diff=astextplain
15 | *.PDF	 diff=astextplain
16 | *.rtf	 diff=astextplain
17 | *.RTF	 diff=astextplain
18 | 


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/xcode/net.xcodeproj/xcuserdata/kretash.xcuserdatad/xcschemes/xcschememanagement.plist:
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 3 | <plist version="1.0">
 4 | <dict>
 5 | 	<key>SchemeUserState</key>
 6 | 	<dict>
 7 | 		<key>net.xcscheme</key>
 8 | 		<dict>
 9 | 			<key>orderHint</key>
10 | 			<integer>0</integer>
11 | 		</dict>
12 | 	</dict>
13 | </dict>
14 | </plist>
15 | 


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/vs2015/net.vcxproj.user:
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 1 | <?xml version="1.0" encoding="utf-8"?>
 2 | <Project ToolsVersion="14.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
 3 |   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='debug|x64'">
 4 |     <DebuggerFlavor>WindowsLocalDebugger</DebuggerFlavor>
 5 |     <LocalDebuggerCommandArguments></LocalDebuggerCommandArguments>
 6 |   </PropertyGroup>
 7 |   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='release|x64'">
 8 |     <DebuggerFlavor>WindowsLocalDebugger</DebuggerFlavor>
 9 |     <LocalDebuggerCommandArguments></LocalDebuggerCommandArguments>
10 |   </PropertyGroup>
11 | </Project>
12 | 


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/src/layer.cc:
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 1 | #include <string>
 2 | 
 3 | #include "network.hh"
 4 | 
 5 | Layer::Layer( int32_t nodes, std::string name ) {
 6 | 	this->m_name = name;
 7 | 	for( int32_t i = 0; i < nodes; ++i ) {
 8 | 		std::shared_ptr<Node> n = std::make_shared<Node>( name + " Node " + std::to_string( i ) );
 9 | 		m_nodes.push_back( n );
10 | 	}
11 | }
12 | 
13 | void Layer::project( std::shared_ptr<Layer> other_layer ) {
14 | 	for( auto i_node : m_nodes ) {
15 | 		for( auto o_node : other_layer->m_nodes ) {
16 | 
17 | 			std::shared_ptr<Link> link = std::make_shared<Link>( i_node, o_node );
18 | 			i_node->add_f_link( link );
19 | 			o_node->add_b_link( link );
20 | 
21 | 		}
22 | 	}
23 | }
24 | 


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/.gitignore:
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 1 | # Windows image file caches
 2 | Thumbs.db
 3 | ehthumbs.db
 4 | 
 5 | # Folder config file
 6 | Desktop.ini
 7 | 
 8 | # Recycle Bin used on file shares
 9 | $RECYCLE.BIN/
10 | 
11 | # Windows Installer files
12 | *.cab
13 | *.msi
14 | *.msm
15 | *.msp
16 | 
17 | # Windows shortcuts
18 | *.lnk
19 | 
20 | # =========================
21 | # Operating System Files
22 | # =========================
23 | 
24 | # OSX
25 | # =========================
26 | 
27 | .DS_Store
28 | .AppleDouble
29 | .LSOverride
30 | 
31 | # Thumbnails
32 | ._*
33 | 
34 | # Files that might appear in the root of a volume
35 | .DocumentRevisions-V100
36 | .fseventsd
37 | .Spotlight-V100
38 | .TemporaryItems
39 | .Trashes
40 | .VolumeIcon.icns
41 | 
42 | # Directories potentially created on remote AFP share
43 | .AppleDB
44 | .AppleDesktop
45 | Network Trash Folder
46 | Temporary Items
47 | .apdisk
48 | 
49 | bin


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/vs2015/BasicNeuralNet.sln:
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 1 | 
 2 | Microsoft Visual Studio Solution File, Format Version 12.00
 3 | # Visual Studio 14
 4 | Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "net", "net.vcxproj", "{4C94880B-B89D-887C-4119-9F7CAD21947C}"
 5 | EndProject
 6 | Global
 7 | 	GlobalSection(SolutionConfigurationPlatforms) = preSolution
 8 | 		debug|x64 = debug|x64
 9 | 		release|x64 = release|x64
10 | 	EndGlobalSection
11 | 	GlobalSection(ProjectConfigurationPlatforms) = postSolution
12 | 		{4C94880B-B89D-887C-4119-9F7CAD21947C}.debug|x64.ActiveCfg = debug|x64
13 | 		{4C94880B-B89D-887C-4119-9F7CAD21947C}.debug|x64.Build.0 = debug|x64
14 | 		{4C94880B-B89D-887C-4119-9F7CAD21947C}.release|x64.ActiveCfg = release|x64
15 | 		{4C94880B-B89D-887C-4119-9F7CAD21947C}.release|x64.Build.0 = release|x64
16 | 	EndGlobalSection
17 | 	GlobalSection(SolutionProperties) = preSolution
18 | 		HideSolutionNode = FALSE
19 | 	EndGlobalSection
20 | 	GlobalSection(NestedProjects) = preSolution
21 | 	EndGlobalSection
22 | EndGlobal
23 | 


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/src/link.cc:
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 1 | #include "network.hh"
 2 | 
 3 | Link::Link( std::shared_ptr<Node> from, std::shared_ptr<Node> to, std::string name ) {
 4 | 	this->m_name = name;
 5 | 	m_link_from = from;
 6 | 	m_link_to = to;
 7 | 	m_weight = random( -1.0, 1.0 );
 8 | }
 9 | 
10 | void Link::_push( double value ) {
11 | 	m_link_to->_push( value * m_weight );
12 | }
13 | 
14 | void Link::_compute_error( double delta, double learning_rate ) {
15 | 
16 | 	double this_delta = delta * m_weight;
17 | 
18 | 	if (m_link_from->m_b_links.size() != 0)
19 | 		m_link_from->_compute_error( this_delta, learning_rate );
20 | }
21 | 
22 | void Link::_backpropagate( double learning_rate ) {
23 | 
24 | 	const double lamda = 0.0;
25 | 
26 | 	m_weight += 
27 | 		learning_rate
28 | 		* ( ( m_link_to->m_delta * m_link_from->m_value ) + ( lamda * m_link_from->m_value ) )
29 | 		+ ( s_alpha *  m_link_to->m_delta_weight );
30 | 
31 | 	m_link_to->m_delta_weight = learning_rate * m_link_to->m_delta * m_link_from->m_value;
32 | 
33 | 	// Stop at input nodes
34 | 	if( m_link_from->m_b_links.size() != 0 )
35 | 		m_link_from->_backpropagate( learning_rate);
36 | 
37 | }


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/vs2015/net.vcxproj.filters:
--------------------------------------------------------------------------------
 1 | <?xml version="1.0" encoding="utf-8"?>
 2 | <Project ToolsVersion="14.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
 3 |   <ItemGroup>
 4 |     <Filter Include="src">
 5 |       <UniqueIdentifier>{2DAB880B-99B4-887C-2230-9F7C8E38947C}</UniqueIdentifier>
 6 |     </Filter>
 7 |     <Filter Include="include">
 8 |       <UniqueIdentifier>{89AF369E-F58E-B539-FEA6-40106A051C9B}</UniqueIdentifier>
 9 |     </Filter>
10 |   </ItemGroup>
11 |   <ItemGroup>
12 |     <ClInclude Include="..\include\network.hh">
13 |       <Filter>include</Filter>
14 |     </ClInclude>
15 |   </ItemGroup>
16 |   <ItemGroup>
17 |     <ClCompile Include="..\src\layer.cc">
18 |       <Filter>src</Filter>
19 |     </ClCompile>
20 |     <ClCompile Include="..\src\link.cc">
21 |       <Filter>src</Filter>
22 |     </ClCompile>
23 |     <ClCompile Include="..\src\main.cc">
24 |       <Filter>src</Filter>
25 |     </ClCompile>
26 |     <ClCompile Include="..\src\network.cc">
27 |       <Filter>src</Filter>
28 |     </ClCompile>
29 |     <ClCompile Include="..\src\node.cc">
30 |       <Filter>src</Filter>
31 |     </ClCompile>
32 |   </ItemGroup>
33 | </Project>
34 | 


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/src/main.cc:
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 1 | #include <memory>
 2 | #include <iostream>
 3 | #include <time.h>
 4 | 
 5 | #include "network.hh"
 6 | 
 7 | int main( int argc, char** argv ) {
 8 | 
 9 | 	srand( ( uint32_t ) time( nullptr ) );
10 | 
11 | 	// create all the resources
12 | 	std::shared_ptr<Layer> input_layer = std::make_shared<Layer>( 2, "Input Layer" );
13 | 	std::shared_ptr<Layer> hidden_layer = std::make_shared<Layer>( 3, "Hidden Layer" );
14 | 	std::shared_ptr<Layer> output_layer = std::make_shared<Layer>( 1 , "Output Layer" );
15 | 
16 | 	input_layer->project( hidden_layer );
17 | 	hidden_layer->project( output_layer );
18 | 
19 | 	std::shared_ptr<Network> network = std::make_shared<Network>( input_layer, output_layer );
20 | 
21 | 	const	double learning_rate = 0.5;
22 | 	// train the network - learn XOR
23 | 	for( uint32_t i = 0; i < 50000; ++i ) {
24 | 
25 | 		network->activate( { 0,0 } );
26 | 		network->propagate( learning_rate, { 0 } );
27 | 
28 | 		network->activate( { 0,1 } );
29 | 		network->propagate( learning_rate, { 1 } );
30 | 
31 | 		network->activate( { 1,0 } );
32 | 		network->propagate( learning_rate, { 1 } );
33 | 
34 | 		network->activate( { 1,1 } );
35 | 		network->propagate( learning_rate, { 0 } );
36 | 	}
37 | 
38 | 	std::cout << " 0 XOR 0 -> " << network->activate( { 0,0 } )[0] << "\n";
39 | 	std::cout << " 0 XOR 1 -> " << network->activate( { 0,1 } )[0] << "\n";
40 | 	std::cout << " 1 XOR 0 -> " << network->activate( { 1,0 } )[0] << "\n";
41 | 	std::cout << " 1 XOR 1 -> " << network->activate( { 1,1 } )[0] << "\n";
42 | 
43 | 	return 0;
44 | }
45 | 


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/src/network.cc:
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 1 | #include <assert.h>
 2 | #include <iostream>
 3 | #include <random>
 4 | 
 5 | #include "network.hh"
 6 | 
 7 | #define FAKE_RANDOM 0
 8 | 
 9 | double random( double min, double max ) {
10 | #if FAKE_RANDOM
11 | 	return min + (max - min)*0.75;
12 | #else
13 |     static std::random_device rd;
14 |     static std::mt19937 gen(rd());
15 |     std::uniform_real_distribution<> dis(min, max);
16 |     return dis(gen);
17 | #endif
18 | }
19 | 
20 | std::vector<double> Network::activate( std::vector<double> inputs ) {
21 | 
22 | 	auto inodes = m_input_layer->m_nodes;
23 | 
24 | 	assert( inputs.size() == inodes.size() &&
25 | 		"INPUT MUST BE THE SIZE OF THE NODES IN THE INPUT LAYER" );
26 | 
27 | 	for( size_t i = 0; i < inodes.size(); ++i ) {
28 | 		inodes[i]->_input( inputs[i] );
29 | 	}
30 | 
31 | 	std::vector<double> outputs;
32 | 	for( auto o : m_output_layer->m_nodes ) {
33 | 		outputs.push_back( o->m_value );
34 | 	}
35 | 	return outputs;
36 | }
37 | 
38 | void Network::propagate( double learning_rate, std::vector<double> target ) {
39 | 
40 | 	auto onodes = m_output_layer->m_nodes;
41 | 
42 | 	assert( target.size() == onodes.size() &&
43 | 		"RESULTS MUST BE THE SIZE OF THE NODES IN THE OUTPUT LAYER" );
44 | 
45 | 	for( size_t i = 0; i < onodes.size(); ++i ) {
46 | 
47 | 		auto on = onodes[i];
48 | 		double this_error = target[i] - on->m_value;
49 | 		on->_start_compute_error( this_error, learning_rate );
50 | 
51 | 	}
52 | 
53 | 	for( size_t i = 0; i < onodes.size(); ++i ) {
54 | 		auto on = onodes[i];
55 | 		on->_start_backpropagate( learning_rate );
56 | 	}
57 | 
58 | }
59 | 


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/src/node.cc:
--------------------------------------------------------------------------------
  1 | #include <math.h>
  2 | #include "network.hh"
  3 | 
  4 | void Node::add_f_link( std::shared_ptr<Link> link ) {
  5 | 	link->m_name += "(" + this->m_name + " -> link forward)";
  6 | 	m_f_links.push_back( link );
  7 | }
  8 | 
  9 | void Node::add_b_link( std::shared_ptr<Link> link ) {
 10 | 	link->m_name += "(" + this->m_name + " -> <- link back)";
 11 | 	m_b_links.push_back( link );
 12 | }
 13 | 
 14 | void Node::_input( double value ){
 15 | 
 16 | 	m_value = value;
 17 | 
 18 | 	for( auto link : m_f_links ) {
 19 | 		link->_push( m_value );
 20 | 	}
 21 | 
 22 | 	m_synapse_sum = 0;
 23 | 	m_forward_load = 0;
 24 | }
 25 | 
 26 | void Node::_push( double value ) {
 27 | 	m_synapse_sum += value;
 28 | 	++m_forward_load;
 29 | 	if( m_forward_load == m_b_links.size() ) {
 30 | 		_fire();
 31 | 	}
 32 | }
 33 | 
 34 | // transfer function
 35 | double  Node::_sigmoid( double num ) {
 36 | 	return ( 1.0 / ( 1.0 + exp( -num ) ) );
 37 | }
 38 | 
 39 | void Node::_fire() {
 40 | 
 41 | 	m_value = _sigmoid( m_synapse_sum + m_bias );
 42 | 
 43 | 	m_synapse_sum = 0;
 44 | 	m_forward_load = 0;
 45 | 
 46 | 	if( m_end_node ) return;
 47 | 
 48 | 	for( auto link : m_f_links ) {
 49 | 		link->_push( m_value );
 50 | 	}
 51 | }
 52 | 
 53 | void Node::_start_compute_error( double delta, double learning_rate ) {
 54 | 
 55 | 	m_delta = ( 1.0 - m_value ) * m_value * delta;
 56 | 
 57 | 	m_error_sum = 0.0;
 58 | 	m_backward_load = 0;
 59 | 
 60 | 	for( auto blinks : m_b_links ) {
 61 | 		blinks->_compute_error( m_delta, learning_rate );
 62 | 	}
 63 | }
 64 | 
 65 | void Node::_compute_error( double delta, double learning_rate ) {
 66 | 
 67 | 	m_error_sum += delta;
 68 | 
 69 | 	++m_backward_load;
 70 | 	if( m_backward_load == m_f_links.size() ) {
 71 | 
 72 | 		m_delta = ( 1.0 - m_value ) * m_value * m_error_sum;
 73 | 
 74 | 		m_error_sum = 0.0;
 75 | 		m_backward_load = 0;
 76 | 
 77 | 		for( auto blinks : m_b_links ) {
 78 | 			blinks->_compute_error( m_delta, learning_rate );
 79 | 		}
 80 | 	}
 81 | }
 82 | 
 83 | void Node::_start_backpropagate( double learning_rate ) {
 84 | 	
 85 | 	m_bias += learning_rate * m_delta;
 86 | 
 87 | 	m_backward_load = 0;
 88 | 
 89 | 	for( auto blinks : m_b_links ) {
 90 | 		blinks->_backpropagate( learning_rate );
 91 | 	}
 92 | }
 93 | 
 94 | void Node::_backpropagate( double learning_rate ) {
 95 | 
 96 | 	++m_backward_load;
 97 | 	if( m_backward_load == m_f_links.size() ) {
 98 | 
 99 | 		m_bias += learning_rate * m_delta;
100 | 
101 | 		m_backward_load = 0;
102 | 
103 | 		for( auto blinks : m_b_links ) {
104 | 			blinks->_backpropagate( learning_rate );
105 | 		}
106 | 
107 | 	}
108 | }
109 | 


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/README.md:
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 1 | # Basic Neural Net
 2 | 
 3 | The most basic neural net you can build. Completly from scratch in C++.
 4 | 
 5 | This network currently trains and produces results for an XOR.
 6 | 
 7 | ---
 8 | 
 9 | ## Building the network
10 | 
11 | The first thing we do is create the different layers. We specify how many nodes each layer has.
12 | 
13 | ```cpp
14 |     std::shared_ptr<Layer> input_layer = std::make_shared<Layer>( 2, "Input Layer" );
15 |     std::shared_ptr<Layer> hidden_layer = std::make_shared<Layer>( 3, "Hidden Layer" );
16 |     std::shared_ptr<Layer> output_layer = std::make_shared<Layer>( 1 , "Output Layer" );
17 | ```
18 | 
19 | ![image1]
20 | 
21 | The next step is to create the links between the layers and create a network. The function **project** will connect all the nodes in a layer to the layer passed as an argument.
22 | 
23 | ```cpp
24 |     input_layer->project( hidden_layer );
25 |     hidden_layer->project( output_layer );
26 | ```
27 | 
28 | This is how our layers look now.
29 | 
30 | ![image2]
31 | 
32 |  Finally, to create the network we need pass the input and output layers.
33 |  
34 | ```cpp
35 |     std::shared_ptr<Network> network = std::make_shared<Network>( input_layer, output_layer );
36 | ```
37 | 
38 | ## Using the Network
39 | 
40 | To use the network we call the function **activate**. This function will take an std::vector as a parameter that should match in size with the number of nodes in the input layer. The return vector will be the same size as the number of output nodes. 
41 | 
42 | ```cpp
43 |     std::vector<double> output = network->activate( { 0,0 } );
44 | ```
45 | 
46 | The output values we will get the first times we use the network will be random. In order to train the network we can call **propagate** after an activate to trains the network. We want to pass in a learning rate and the target results. The learning rate is just a constant defined to 0.5 and the target results must be an std::vector that matches in size with the amount of output nodes. The learning rate can be tweaked to achieve better results.
47 | 
48 | ```cpp
49 |     network->propagate( learning_rate, { 0 } );
50 | ```
51 | 
52 | Once this is done a multitude of times with all posible values the results from the activate will start to match the target results.
53 | 
54 | ---
55 | 
56 | ## Building
57 | 
58 | There is a Visual Studio solution file and xcode project that can be use to build. There is only a small number of files, so building from the command line would be easy too.
59 | 
60 | ### License
61 | 
62 | MIT License
63 | 
64 | [image1]: https://github.com/kretash/BasicNeuralNet/raw/master/images/image1.png "Layers"
65 | [image2]: https://github.com/kretash/BasicNeuralNet/raw/master/images/image2.png "Final Network"
66 | 


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/include/network.hh:
--------------------------------------------------------------------------------
  1 | /*
  2 | 	Defines Network, Layer, Node and Link
  3 | 	Author: Carlos Martinez Romero (kretash)
  4 | 	License: MIT License
  5 | */
  6 | 
  7 | #include <memory>
  8 | #include <vector>
  9 | #include <string>
 10 | 
 11 | double random( double min, double max );
 12 | 
 13 | class Node;
 14 | 
 15 | static int32_t s_component_id_count = 0;
 16 | static const double s_alpha = 0.1; // Momentum
 17 | 
 18 | class Component {
 19 | protected:
 20 | 	Component() {
 21 | 		m_component_id = ++s_component_id_count;
 22 | 	}
 23 | public:
 24 | 	Component(std::string name) {
 25 | 		m_component_id = ++s_component_id_count;
 26 | 		m_name = name;
 27 | 	}
 28 | 	~Component() {
 29 | 
 30 | 	}
 31 | 	std::string m_name = "Nameless X";
 32 | 	int32_t m_component_id = 0;
 33 | };
 34 | 
 35 | class Link : public Component {
 36 | protected:
 37 | 	Link() {}
 38 | public:
 39 | 	Link( std::shared_ptr<Node> from, std::shared_ptr<Node> to, std::string name = "Link X" );
 40 | 	~Link() {}
 41 | 
 42 | private:
 43 | 	friend class Node;
 44 | 
 45 | 	void _push( double value );
 46 | 
 47 | 	void _compute_error(	double delta, double learning_rate ); 
 48 | 	void _backpropagate( double learning_rate );
 49 | 
 50 | 	double m_weight = 0.0;
 51 | 
 52 | 	std::shared_ptr<Node> m_link_to;
 53 | 	std::shared_ptr<Node> m_link_from;
 54 | };
 55 | 
 56 | class Node : public Component {
 57 | protected:
 58 | 	Node(){
 59 | 		// Not used
 60 | 		m_bias = random( 0.0, 1.0 );
 61 | 	}
 62 | public:
 63 | 	Node(std::string name = "Layer X" ) {
 64 | 		this->m_name = name;
 65 | 		m_bias = random( 0.0, 1.0 );
 66 | 	};
 67 | 	~Node() {};
 68 | 
 69 | 	void add_f_link( std::shared_ptr<Link> link );
 70 | 	void add_b_link( std::shared_ptr<Link> link );
 71 | 
 72 | private:
 73 | 	friend class Network;
 74 | 	friend class Link;
 75 | 
 76 | 	// Used by the first node to input values into the net.
 77 | 	void _input( double value );
 78 | 
 79 | 	double _sigmoid( double num );
 80 | 	// pushes a value to the node. When all links have been pushed
 81 | 	// the node will fire the values forward.
 82 | 	// at the beginning of the network there will be no way to start as there are no back links.
 83 | 	// once all back links have pushed the nodes will fire. If this is the first node it needs 
 84 | 	// force start to true as there will be no back nodes.
 85 | 	void _push( double value );
 86 | 	void _fire();
 87 | 
 88 | 	// computes the error. similar to push but backwards. Start function.
 89 | 	void _start_compute_error( double delta, double learning_rate );
 90 | 	// computes the error. similar to push but backwards.
 91 | 	void _compute_error( double delta, double learning_rate);
 92 | 
 93 | 	// First backpropagate
 94 | 	void _start_backpropagate( double learning_rate );
 95 | 	void _backpropagate( double learning_rate );
 96 | 
 97 | 	bool m_end_node = false;
 98 | 
 99 | 	// The last value fired forward.
100 | 	double m_value = 0.0;
101 | 	// the sum of all the values of fired to this node.
102 | 	double m_synapse_sum = 0.0;
103 | 
104 | 	double m_error_sum = 0.0;
105 | 	double m_delta = 0.0;
106 | 	double m_delta_weight = 0.0;
107 | 	double m_bias = 0.0;
108 | 
109 | 	// When the load is equal to the number of back links the node will fire.
110 | 	int32_t m_forward_load = 0;
111 | 	// When the load is equal to the number of forward the node will fire back.
112 | 	// Calculating the error made from the previous iteration.
113 | 	int32_t m_backward_load = 0;
114 | 
115 | 	std::vector<std::shared_ptr<Link>> m_f_links;
116 | 	std::vector<std::shared_ptr<Link>> m_b_links;
117 | 
118 | };
119 | 
120 | class Layer : public Component {
121 | protected:
122 | 	Layer() {}
123 | 
124 | public:
125 | 	Layer( int32_t nodes, std::string name = "Layer X" );
126 | 	~Layer() {}
127 | 
128 | 	void project( std::shared_ptr<Layer> other_layer );
129 | 
130 | private:
131 | 	friend class Network;
132 | 
133 | 	std::vector<std::shared_ptr<Node>> m_nodes;
134 | };
135 | 
136 | class Network : public Component {
137 | protected:
138 | 	Network() {}
139 | public:
140 | 
141 | 	Network( std::shared_ptr<Layer> input, std::shared_ptr<Layer> output ) :
142 | 		m_input_layer( input ), m_output_layer( output ) {
143 | 		for( auto n : m_output_layer->m_nodes ) {
144 | 			n->m_end_node = true;
145 | 		}
146 | 	}
147 | 
148 | 	~Network() {}
149 | 
150 | 	std::vector<double> activate( std::vector<double> inputs );
151 | 	void propagate( double learning_rate, std::vector<double> results );
152 | 
153 | private:
154 | 	std::shared_ptr<Layer> m_input_layer;
155 | 	std::shared_ptr<Layer> m_output_layer;
156 | 
157 | };
158 | 


--------------------------------------------------------------------------------
/vs2015/net.vcxproj:
--------------------------------------------------------------------------------
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