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It is now possible to train expression models with probability support. This however is a lot slower! Also, the C parameter for SVM training was set incorrectly. Now training works much better.

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This commit is contained in:
Emiliana 2020-02-13 22:24:41 +01:00
parent 9abc3029e7
commit bb845098ca
4 changed files with 46 additions and 20 deletions
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28
Unity/Plugins/SVMModel.cs
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@ -12,16 +12,16 @@ namespace OpenSee {
public class SVMModel {
#region DllImport
[DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl)]
private static extern System.IntPtr trainModel(float[] features, float[] labels, int rows, int cols, float C);
private static extern System.IntPtr trainModel(float[] features, float[] labels, int rows, int cols, int classes, int probability, float C);
[DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl)]
private static extern void predict(System.IntPtr model, float[] features, [Out] float[] predictions, int rows);
private static extern void predict(System.IntPtr model, float[] features, [Out] float[] predictions, [Out] double[] probabilities, int rows);
[DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl, CharSet = CharSet.Ansi)]
private static extern System.IntPtr loadModel([MarshalAs(UnmanagedType.LPStr)]string filename, int cols, double[] means, double[] sdevs);
private static extern System.IntPtr loadModel([MarshalAs(UnmanagedType.LPStr)]string filename, int cols, int classes, double[] means, double[] sdevs);
[DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl, CharSet = CharSet.Ansi)]
private static extern System.IntPtr loadModelString([MarshalAs(UnmanagedType.LPStr)]string modelString, int cols, double[] means, double[] sdevs);
private static extern System.IntPtr loadModelString([MarshalAs(UnmanagedType.LPStr)]string modelString, int cols, int classes, double[] means, double[] sdevs);
[DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl)]
private static extern void getScales(System.IntPtr model, [Out] double[] means, [Out] double[] sdevs);
@ -63,7 +63,7 @@ public class SVMModel {
}
cols = smr.cols;
maxClasses = smr.maxClasses;
System.IntPtr model = loadModelString(smr.modelString, smr.cols, smr.means, smr.sdevs);
System.IntPtr model = loadModelString(smr.modelString, smr.cols, smr.maxClasses, smr.means, smr.sdevs);
return model;
}
@ -118,7 +118,7 @@ public class SVMModel {
}
// features is a rows*cols long float array with rows written one after another. labels is a rows long float array with the corresponding classes as integral numbers from 0 to num_classes-1, where all classes have to exist. Training on more than 10000 rows is disabled.
public bool TrainModel(float[] features, float[] labels, int rows, int cols) {
public bool TrainModel(float[] features, float[] labels, int rows, int cols, int probability) {
DestroyModel();
if (rows > 10000)
return false;
@ -143,7 +143,7 @@ public class SVMModel {
maxClasses = max + 1;
this.cols = cols;
model = trainModel(features, labels, rows, cols, 12f);
model = trainModel(features, labels, rows, cols, maxClasses, probability, 2f);
haveModel = true;
return true;
@ -163,7 +163,8 @@ public class SVMModel {
}
int[,] confusionMatrix = new int[maxClasses, maxClasses];
float[] predictions = new float[rows];
predict(model, features, predictions, rows);
double[] probabilities = new double[rows * maxClasses];
predict(model, features, predictions, probabilities, rows);
for (int i = 0; i < rows; i++) {
predictions[i] = (float)Math.Round(predictions[i]);
if (predictions[i] >= 0 && predictions[i] < maxClasses) {
@ -219,15 +220,22 @@ public class SVMModel {
}
// features is a rows*cols long float array with rows written one after another.
public float[] Predict(float[] features, int rows) {
public float[] Predict(float[] features, out double[] probabilities, int rows) {
probabilities = null;
if (!haveModel)
return null;
if (rows < 1 || features.Length != rows * cols)
return null;
float[] predictions = new float[rows];
predict(model, features, predictions, rows);
probabilities = new double[rows * maxClasses];
predict(model, features, predictions, probabilities, rows);
return predictions;
}
public float[] Predict(float[] features, int rows) {
double[] probabilities;
return Predict(features, out probabilities, rows);
}
public bool Ready() {
return haveModel;