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It is now possible to make certain expressions more important when training SVM models.

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This commit is contained in:
Emiliana 2020-03-07 13:35:58 +01:00
parent 94d2f4fbe0
commit 9aeb4fe95d
5 changed files with 56 additions and 20 deletions
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25
Unity/OpenSeeExpression.cs
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@ -29,6 +29,8 @@ public class OpenSeeExpression : MonoBehaviour
public bool enableProbabilityTraining = false;
[Tooltip("This is the SVM training C value.")]
public float C = 2f;
[Tooltip("This map allows you to set weights for different classes.")]
public Dictionary<string, float> weightMap = null;
[Header("Toggles")]
[Tooltip("When enabled, calibration data will be collected from the given OpenSee component.")]
public bool recording = false;
@ -341,12 +343,13 @@ public class OpenSeeExpression : MonoBehaviour
List<float[]> list = new List<float[]>(expressions[key]);
list.RemoveAll(x => x.Length != colsFull);
if (list != null && list.Count == maxSamples) {
Debug.Log("[Training info] Adding expression " + key + " to training data.");
keys.Add(key);
samples += maxSamples;
samples += list.Count;
} else {
if (list != null && list.Count > 20) {
//Debug.Log("[Training warning] Expression " + key + " has little data and might be inaccurate.");
//accuracyWarnings.Add("Expression " + key + " has little data and might be inaccurate.");
Debug.Log("[Training warning] Expression " + key + " has little data and might be inaccurate.");
accuracyWarnings.Add("Expression " + key + " has little data and might be inaccurate.");
samples += list.Count;
keys.Add(key);
} else {
@ -364,9 +367,19 @@ public class OpenSeeExpression : MonoBehaviour
}
keys.Sort();
classLabels = keys.ToArray();
float[] weights = null;
if (weightMap != null) {
weights = new float[classLabels.Length];
for (int i = 0; i < classLabels.Length; i++) {
weights[i] = weightMap[classLabels[i]];
Debug.Log("[Training info] Adding weight " + weights[i] + " for " + classLabels[i] + ".");
}
}
int train_split = maxSamples * 3 / 4;
int test_split = maxSamples - train_split;
int rows_train = (classes + 2) * train_split;
int rows_train = classes * train_split;
int rows_test = classes * test_split;
float[] X_train = new float[rows_train * cols];
float[] X_test = new float[rows_test * cols];
@ -389,7 +402,7 @@ public class OpenSeeExpression : MonoBehaviour
}
int train_split_current = train_split;
if (classLabels[i] == "neutral")
train_split_current *= 3;
train_split_current *= 1;
for (int j = 0; j < train_split_current; j++) {
float factor = 1f;
float adder = 0f;
@ -416,7 +429,7 @@ public class OpenSeeExpression : MonoBehaviour
int probability = 0;
if (enableProbabilityTraining)
probability = 1;
model.TrainModel(X_train, y_train, i_train, cols, probability, C);
model.TrainModel(X_train, y_train, weights, i_train, cols, probability, C);
confusionMatrix = model.ConfusionMatrix(X_test, y_test, i_test, out accuracy);
confusionMatrixString = SVMModel.FormatMatrix(confusionMatrix, classLabels);
for (int label = 0; label < classes; label++) {