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@@ -626,7 +626,7 @@ FANN::training_data * SSN::getTrainingData( const std::vector<Example> &input )
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const auto & ci = input[i];
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inputs[i] = genSSNinput(ci.dp.sides[0], ci.art, ci.dp.bfieldType, ci.dp.terType);
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outputs[i] = new double;
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- *(outputs[i]) = ci.value;
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+ *(outputs[i]) = ci.value/4;
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}
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ret->set_train_data(input.size(), num_input, inputs, 1, outputs);
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@@ -635,7 +635,7 @@ FANN::training_data * SSN::getTrainingData( const std::vector<Example> &input )
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void SSNRun()
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{
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- //buildLearningSet();
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+ //Framework::buildLearningSet();
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double percentToTrain = 0.8;
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auto trainingSet = Framework::loadExamples(false);
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@@ -661,12 +661,12 @@ void SSNRun()
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FANN::activation_function_enum possibleFuns[] = {FANN::SIGMOID_SYMMETRIC_STEPWISE, FANN::LINEAR,
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FANN::SIGMOID, FANN::SIGMOID_STEPWISE, FANN::SIGMOID_SYMMETRIC};
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-
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-// bestParams.actSteepHidden = 1.18;
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-// bestParams.actSteepnessOutput = 1.26;
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-// bestParams.hiddenActFun = FANN::SIGMOID_STEPWISE;
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+//
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+// bestParams.actSteepHidden = 0.346;
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+// bestParams.actSteepnessOutput = 0.449;
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+// bestParams.hiddenActFun = FANN::SIGMOID_SYMMETRIC;
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// bestParams.outActFun = FANN::SIGMOID_SYMMETRIC;
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-// bestParams.neuronsInHidden = 47;
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+// bestParams.neuronsInHidden = 23;
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for(int i=0; i<5000; i += 1)
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{
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mateuszb