LinearTraining2 createTrainingConfiguration2() { LinearTraining2 c = new LinearTraining2(); c.NbElements = targetTransforms.Length; c.NbInputs = 2; c.TrainingExemples = new double[c.NbElements * c.NbInputs]; c.ExpectedResults = new double[c.NbElements]; for (int i = 0; i < targetMeshRenderers.Length; ++i) { MeshRenderer mr = targetMeshRenderers[i].GetComponent <MeshRenderer>(); if (mr.material.color == redMat.color) { Transform t = targetTransforms[i].GetComponent <Transform>(); c.TrainingExemples[i * c.NbInputs] = t.position.x; c.TrainingExemples[i * c.NbInputs + 1] = t.position.z; c.ExpectedResults[i] = 1; } if (mr.material.color == blueMat.color) { Transform t = targetTransforms[i].GetComponent <Transform>(); c.TrainingExemples[i * c.NbInputs] = t.position.x; c.TrainingExemples[i * c.NbInputs + 1] = t.position.z; c.ExpectedResults[i] = -1; } } c.MaxIteration = 10000; return(c); }
void linearScenarioRegression(LinearTraining2 c, System.IntPtr model) { perceptron_regression(model, c.TrainingExemples, c.NbElements, c.NbInputs, c.ExpectedResults); adjustPlan2(model); removeModel(model); }