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Training with less data is now possible too.

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This commit is contained in:
Emiliana 2020-02-09 18:09:09 +01:00
parent b5706183c2
commit b5218a1eb0
2 changed files with 34 additions and 17 deletions
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43
Unity/OpenSeeExpression.cs
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@ -114,7 +114,6 @@ public class OpenSeeExpression : MonoBehaviour
recording = false;
clear = false;
train = false;
predict = false;
modelReady = false;
accuracy = 0.0f;
confusionMatrix = null;
@ -177,13 +176,24 @@ public class OpenSeeExpression : MonoBehaviour
return false;
}
List<string> keys = new List<string>();
List<string> accuracyWarnings = new List<string>();
int samples = 0;
foreach (string key in expressions.Keys) {
List<float[]> list = expressions[key];
if (list != null && list.Count == maxSamples)
if (list != null && list.Count == maxSamples) {
keys.Add(key);
else {
Debug.Log("[Training warning] Skipping expression " + key + " due to lack of collected data.");
continue;
samples += maxSamples;
} 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.");
samples += list.Count;
keys.Add(key);
} else {
Debug.Log("[Training warning] Skipping expression " + key + " due to lack of collected data.");
accuracyWarnings.Add("Skipping expression " + key + " due to lack of collected data.");
continue;
}
}
}
int classes = keys.Count;
@ -206,7 +216,9 @@ public class OpenSeeExpression : MonoBehaviour
System.Random rnd = new System.Random();
for (int i = 0; i < classes; i++) {
List<float[]> list = expressions[keys[i]];
for (int j = maxSamples; j > 1;) {
int local_train_split = list.Count * 3 / 4;
test_split = list.Count - local_train_split;
for (int j = list.Count; j > 1;) {
j--;
int k = rnd.Next(j + 1);
float[] tmp = list[k];
@ -215,12 +227,12 @@ public class OpenSeeExpression : MonoBehaviour
}
for (int j = 0; j < train_split; j++) {
for (int k = 0; k < cols; k++) {
X_train[i_train * cols + k] = list[j][k];
X_train[i_train * cols + k] = list[j % local_train_split][k];
}
y_train[i_train] = i;
i_train++;
}
for (int j = train_split; j < train_split + test_split; j++) {
for (int j = local_train_split; j < local_train_split + test_split; j++) {
for (int k = 0; k < cols; k++)
X_test[i_test * cols + k] = list[j][k];
y_test[i_test] = i;
@ -228,13 +240,18 @@ public class OpenSeeExpression : MonoBehaviour
}
}
model.TrainModel(X_train, y_train, rows_train, cols);
confusionMatrix = model.ConfusionMatrix(X_test, y_test, rows_test, out accuracy);
confusionMatrix = model.ConfusionMatrix(X_test, y_test, i_test, out accuracy);
confusionMatrixString = SVMModel.FormatMatrix(confusionMatrix, classLabels);
List<string> accuracyWarnings = new List<string>();
for (int label = 0; label < classes; label++) {
float error = 100f * (1f - ((float)confusionMatrix[label, label] / (float)test_split));
if (error > warningThreshold)
accuracyWarnings.Add("The expression \"" + classLabels[label] + "\" is misclassified with a chance of " + error.ToString("0.00") + "%.");
int total = 0;
for (int p = 0; p < classes; p++) {
total += confusionMatrix[label, p];
}
float error = 100f * (1f - ((float)confusionMatrix[label, label] / (float)total));
if (error > warningThreshold) {
accuracyWarnings.Add("[Training warning] The expression \"" + classLabels[label] + "\" is misclassified with a chance of " + error.ToString("0.00") + "%.");
Debug.Log("[Training warning] The expression \"" + classLabels[label] + "\" is misclassified with a chance of " + error.ToString("0.00") + "%.");
}
}
warnings = accuracyWarnings.ToArray();
modelReady = true;

8
Unity/Plugins/SVMModel.cs
View file

@ -122,12 +122,12 @@ public class SVMModel {
DestroyModel();
if (rows > 10000)
return false;
if (cols < 1 || rows < 1 || features.Length != rows * cols || labels.Length != rows)
if (cols < 1 || rows < 1 || features.Length < rows * cols || labels.Length < rows)
return false;
int max = -1;
HashSet<int> knownClasses = new HashSet<int>();
for (int i = 0; i < labels.Length; i++) {
for (int i = 0; i < rows; i++) {
labels[i] = (float)Math.Round(labels[i]);
int l = (int)labels[i];
if (l > max)
@ -154,9 +154,9 @@ public class SVMModel {
accuracy = 0f;
if (!haveModel)
return null;
if (rows < 1 || features.Length != rows * cols || labels.Length != rows)
if (rows < 1 || features.Length < rows * cols || labels.Length < rows)
return null;
for (int i = 0; i < labels.Length; i++) {
for (int i = 0; i < rows; i++) {
labels[i] = (float)Math.Round(labels[i]);
if (labels[i] < 0 || labels[i] >= maxClasses)
return null;