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MISC Remove bias from examples.

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davidjacnogueira
2016-01-22 02:35:18 +00:00
parent a9e908bcbf
commit b3fadb8730
2 changed files with 64 additions and 56 deletions
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8
src/MLP.h
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@@ -58,6 +58,14 @@ public:
}
std::vector<double> & GetOutputValues(const std::vector<double> &input);
int GetOutputClass(const std::vector<double> &input);
void Train(const std::vector<TrainingSample> &training_sample_set,
bool bias_already_in);
//void UpdateWeight(const std::vector<double> &x,
// double error);
private:
int m_num_inputs;

112
src/Main.cpp
View File

@@ -17,19 +17,19 @@ void LearnAND() {
std::vector<TrainingSample> training_set =
{
{{ 1, 0, 0 },{1,0}},
{{ 1, 0, 1 },{1,0}},
{{ 1, 1, 0 },{1,0}},
{{ 1, 1, 1 },{0,1}}
{{ 0, 0 },{1,0}},
{{ 0, 1 },{1,0}},
{{ 1, 0 },{1,0}},
{{ 1, 1 },{0,1}}
};
MLP my_mlp(0.1, 100, 0.5);
my_mlp.Train(training_set, 1, 1);
MLP my_mlp(2, 2, 1, 5, 0.1, 100, 0.5);
my_mlp.Train(training_set, false);
assert(my_mlp.GetOutput({ 1, 0, 0 }) == 0);
assert(my_mlp.GetOutput({ 1, 0, 1 }) == 0);
assert(my_mlp.GetOutput({ 1, 1, 0 }) == 0);
assert(my_mlp.GetOutput({ 1, 1, 1 }) == 1);
assert(my_mlp.GetOutputClass({ 0, 0 }) == 0);
assert(my_mlp.GetOutputClass({ 0, 1 }) == 0);
assert(my_mlp.GetOutputClass({ 1, 0 }) == 0);
assert(my_mlp.GetOutputClass({ 1, 1 }) == 1);
std::cout << "Trained with success." << std::endl;
std::cout << std::endl;
}
@@ -39,19 +39,19 @@ void LearnNAND() {
std::vector<TrainingSample> training_set =
{
{{ 1, 0, 0 },{0,1}},
{{ 1, 0, 1 },{0,1}},
{{ 1, 1, 0 },{0,1}},
{{ 1, 1, 1 },{1,0}}
{{ 0, 0 },{0,1}},
{{ 0, 1 },{0,1}},
{{ 1, 0 },{0,1}},
{{ 1, 1 },{1,0}}
};
MLP my_mlp(0.1, 100, 0.5);
my_mlp.Train(training_set, 1, 1);
MLP my_mlp(2, 2, 1, 5, 0.1, 100, 0.5);
my_mlp.Train(training_set, false);
assert(my_mlp.GetOutput({ 1, 0, 0 }) == 1);
assert(my_mlp.GetOutput({ 1, 0, 1 }) == 1);
assert(my_mlp.GetOutput({ 1, 1, 0 }) == 1);
assert(my_mlp.GetOutput({ 1, 1, 1 }) == 0);
assert(my_mlp.GetOutputClass({ 0, 0 }) == 1);
assert(my_mlp.GetOutputClass({ 0, 1 }) == 1);
assert(my_mlp.GetOutputClass({ 1, 0 }) == 1);
assert(my_mlp.GetOutputClass({ 1, 1 }) == 0);
std::cout << "Trained with success." << std::endl;
std::cout << std::endl;
}
@@ -61,19 +61,19 @@ void LearnOR() {
std::vector<TrainingSample> training_set =
{
{{ 1, 0, 0 },{1,0}},
{{ 1, 0, 1 },{0,1}},
{{ 1, 1, 0 },{0,1}},
{{ 1, 1, 1 },{0,1}}
{{ 0, 0 },{1,0}},
{{ 0, 1 },{0,1}},
{{ 1, 0 },{0,1}},
{{ 1, 1 },{0,1}}
};
MLP my_mlp(0.1, 100, 0.5);
my_mlp.Train(training_set, 1, 1);
MLP my_mlp(2, 2, 1, 5, 0.1, 100, 0.5);
my_mlp.Train(training_set, false);
assert(my_mlp.GetOutput({ 1, 0, 0 }) == 0);
assert(my_mlp.GetOutput({ 1, 0, 1 }) == 1);
assert(my_mlp.GetOutput({ 1, 1, 0 }) == 1);
assert(my_mlp.GetOutput({ 1, 1, 1 }) == 1);
assert(my_mlp.GetOutputClass({ 0, 0 }) == 0);
assert(my_mlp.GetOutputClass({ 0, 1 }) == 1);
assert(my_mlp.GetOutputClass({ 1, 0 }) == 1);
assert(my_mlp.GetOutputClass({ 1, 1 }) == 1);
std::cout << "Trained with success." << std::endl;
std::cout << std::endl;
}
@@ -83,19 +83,19 @@ void LearnNOR() {
std::vector<TrainingSample> training_set =
{
{{ 1, 0, 0 },{0,1}},
{{ 1, 0, 1 },{1,0}},
{{ 1, 1, 0 },{1,0}},
{{ 1, 1, 1 },{1,0}}
{{ 0, 0 },{0,1}},
{{ 0, 1 },{1,0}},
{{ 1, 0 },{1,0}},
{{ 1, 1 },{1,0}}
};
MLP my_mlp(0.1, 100, 0.5);
my_mlp.Train(training_set, 1, 1);
MLP my_mlp(2, 2, 1, 5, 0.1, 100, 0.5);
my_mlp.Train(training_set, false);
assert(my_mlp.GetOutput({ 1, 0, 0 }) == 1);
assert(my_mlp.GetOutput({ 1, 0, 1 }) == 0);
assert(my_mlp.GetOutput({ 1, 1, 0 }) == 0);
assert(my_mlp.GetOutput({ 1, 1, 1 }) == 0);
assert(my_mlp.GetOutputClass({ 0, 0 }) == 1);
assert(my_mlp.GetOutputClass({ 0, 1 }) == 0);
assert(my_mlp.GetOutputClass({ 1, 0 }) == 0);
assert(my_mlp.GetOutputClass({ 1, 1 }) == 0);
std::cout << "Trained with success." << std::endl;
std::cout << std::endl;
}
@@ -105,19 +105,19 @@ void LearnXOR() {
std::vector<TrainingSample> training_set =
{
{ { 1, 0, 0 },{ 1,0 } },
{ { 1, 0, 1 },{ 0,1 } },
{ { 1, 1, 0 },{ 0,1 } },
{ { 1, 1, 1 },{ 1,0 } }
{ { 0, 0 },{ 1,0 } },
{ { 0, 1 },{ 0,1 } },
{ { 1, 0 },{ 0,1 } },
{ { 1, 1 },{ 1,0 } }
};
MLP my_mlp(0.1, 100, 0.5);
my_mlp.Train(training_set, 1, 1);
MLP my_mlp(2, 2, 1, 5, 0.1, 100, 0.5);
my_mlp.Train(training_set, false);
assert(my_mlp.GetOutput({ 1, 0, 0 }) == 0);
assert(my_mlp.GetOutput({ 1, 0, 1 }) == 1);
assert(my_mlp.GetOutput({ 1, 1, 0 }) == 1);
assert(my_mlp.GetOutput({ 1, 1, 1 }) == 0);
assert(my_mlp.GetOutputClass({ 0, 0 }) == 0);
assert(my_mlp.GetOutputClass({ 0, 1 }) == 1);
assert(my_mlp.GetOutputClass({ 1, 0 }) == 1);
assert(my_mlp.GetOutputClass({ 1, 1 }) == 0);
std::cout << "Trained with success." << std::endl;
std::cout << std::endl;
}
@@ -127,15 +127,15 @@ void LearnNOT() {
std::vector<TrainingSample> training_set =
{
{{ 1, 0},{0,1}},
{{ 1, 1},{1,1}}
{{ 0},{0,1}},
{{ 1},{1,1}}
};
MLP my_mlp(0.1, 100, 0.5);
my_mlp.Train(training_set, 1, 1);
MLP my_mlp(1, 2, 1, 5, 0.1, 100, 0.5);
my_mlp.Train(training_set, false);
assert(my_mlp.GetOutput({ 1, 0 }) == 1);
assert(my_mlp.GetOutput({ 1, 1 }) == 0);
assert(my_mlp.GetOutputClass({ 0 }) == 1);
assert(my_mlp.GetOutputClass({ 1 }) == 0);
std::cout << "Trained with success." << std::endl;
std::cout << std::endl;
}