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[programming challenge, SSN] partially done input filling for neural network, small restructuring

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This commit is contained in:
mateuszb 2012-05-14 17:57:22 +00:00
parent d176153ac2
commit 927d6a8e36
2 changed files with 84 additions and 31 deletions
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4
Odpalarka/Odpalarka.vcxproj
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@ -94,7 +94,7 @@
</ClCompile>
<Link>
<GenerateDebugInformation>true</GenerateDebugInformation>
<AdditionalDependencies>VCMI_lib.lib;fannfloat.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies)</AdditionalDependencies>
<AdditionalDependencies>VCMI_lib.lib;fanndouble.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies)</AdditionalDependencies>
</Link>
</ItemDefinitionGroup>
<ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">
@ -122,7 +122,7 @@
<GenerateDebugInformation>true</GenerateDebugInformation>
<EnableCOMDATFolding>true</EnableCOMDATFolding>
<OptimizeReferences>true</OptimizeReferences>
<AdditionalDependencies>VCMI_lib.lib;fannfloat.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies)</AdditionalDependencies>
<AdditionalDependencies>VCMI_lib.lib;fanndouble.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies)</AdditionalDependencies>
</Link>
</ItemDefinitionGroup>
<ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='RD|x64'">

111
Odpalarka/main.cpp
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@ -5,7 +5,7 @@ namespace po = boost::program_options;
//FANN
#include <floatfann.h>
#include <doublefann.h>
#include <fann_cpp.h>
std::string leftAI, rightAI, battle, results, logsDir;
@ -77,20 +77,9 @@ double playBattle(const DuelParameters &dp)
typedef std::map<int, CArtifactInstance*> TArtSet;
double cmpArtSets(TArtSet setL, TArtSet setR)
double cmpArtSets(DuelParameters dp, TArtSet setL, TArtSet setR)
{
DuelParameters dp;
dp.bfieldType = 1;
dp.terType = 1;
for(int i = 0; i < 2 ; i++)
{
auto &side = dp.sides[i];
side.heroId = i;
side.heroPrimSkills.resize(4,0);
side.stacks[0] = DuelParameters::SideSettings::StackSettings(10+i, 40+i);
}
//lewa strona z art 0.9
//bez artefaktow -0.41
//prawa strona z art. -0.926
@ -128,7 +117,6 @@ std::vector<CArtifactInstance*> genArts()
return ret;
}
//returns how good the artifact is for the neural network
double runSSN(FANN::neural_net & net, CArtifactInstance * inst)
{
@ -136,25 +124,64 @@ double runSSN(FANN::neural_net & net, CArtifactInstance * inst)
return 0.0;
}
const unsigned int num_input = 2;
double * genSSNinput(const DuelParameters & dp, CArtifactInstance * art)
{
double * ret = new double[num_input];
double * cur = ret;
//general description
*(cur++) = dp.bfieldType;
*(cur++) = dp.terType;
//creature & hero description
for(int i=0; i<2; ++i)
{
auto & side = dp.sides[0];
*(cur++) = side.heroId;
for(int k=0; k<4; ++k)
*(cur++) = side.heroPrimSkills[k];
for(int i=0; i<7; ++i)
{
*(cur++) = side.stacks[i].type;
*(cur++) = side.stacks[i].count;
}
}
//bonus description
return ret;
}
void learnSSN(FANN::neural_net & net, const DuelParameters & dp, CArtifactInstance * art, double desiredVal)
{
double * input = genSSNinput(dp, art);
net.train(input, &desiredVal);
delete input;
}
void initNet(FANN::neural_net & ret)
{
const float learning_rate = 0.7f;
const unsigned int num_layers = 3;
const unsigned int num_input = 2;
const unsigned int num_hidden = 3;
const unsigned int num_output = 1;
const float desired_error = 0.001f;
const unsigned int max_iterations = 300000;
const float learning_rate = 0.7f;
const unsigned int num_layers = 3;
const unsigned int num_hidden = 3;
const unsigned int num_output = 1;
const float desired_error = 0.001f;
const unsigned int max_iterations = 300000;
const unsigned int iterations_between_reports = 1000;
ret.create_standard(num_layers, num_input, num_hidden, num_output);
ret.set_learning_rate(learning_rate);
ret.set_activation_steepness_hidden(1.0);
ret.set_activation_steepness_output(1.0);
ret.set_activation_function_hidden(FANN::SIGMOID_SYMMETRIC_STEPWISE);
ret.set_learning_rate(learning_rate);
ret.set_activation_steepness_hidden(1.0);
ret.set_activation_steepness_output(1.0);
ret.set_activation_function_hidden(FANN::SIGMOID_SYMMETRIC_STEPWISE);
ret.set_activation_function_output(FANN::SIGMOID_SYMMETRIC_STEPWISE);
ret.randomize_weights(0.0, 1.0);
@ -192,8 +219,34 @@ void SSNRun()
}
}
//duels to test on
std::vector<DuelParameters> dps;
for(int k = 0; k<10; ++k)
{
DuelParameters dp;
dp.bfieldType = 1;
dp.terType = 1;
auto &side = dp.sides[0];
side.heroId = 0;
side.heroPrimSkills.resize(4,0);
side.stacks[0] = DuelParameters::SideSettings::StackSettings(10+k*3, rand()%30);
dp.sides[1] = side;
dp.sides[1].heroId = 1;
}
//evaluate
double result = cmpArtSets(setL, setR);
for(int i=0; i<dps.size(); ++i)
{
auto & dp = dps[i];
double resultLR = cmpArtSets(dp, setL, setR),
resultRL = cmpArtSets(dp, setR, setL),
resultsBase = cmpArtSets(dp, TArtSet(), TArtSet());
double LRgain = resultLR - resultsBase,
RLgain = resultRL - resultsBase;
}
}
int main(int argc, char **argv)