/// <summary>
/// To scale the test data
/// </summary>
/// <param name="orig_data">Original test data, use NaN for missing features.</param>
/// <param name="targe_min">The target min</param>
/// <param name="targe_max">The target max</param>
/// <param name="features_min">The minimums of all features</param>
/// <param name="features_max">The maximums of all features</param>
/// <returns>The scaled test data in LibSVM format.</returns>
public static LibSVMNode[] ScaleData(double[] orig_data, double targe_min, double targe_max, double[] features_min, double[] features_max)
{
int feature_n = features_min.Length;
double[] k = new double[feature_n];
for (int i = 0; i < feature_n; i++)
{
k[i] = (targe_max - targe_min) / (features_max[i] - features_min[i]);
}
List <LibSVMNode> scaled_data = new List <LibSVMNode>(); LibSVMNode node;
for (int i = 0; i < feature_n; i++)
{
if (double.IsNaN(orig_data[i]))
{
continue;
}
node = new LibSVMNode();
node.index = i + 1;
node.value = k[i] * (orig_data[i] - features_min[i]) + targe_min;
scaled_data.Add(node);
}
node = new LibSVMNode(); node.index = -1; scaled_data.Add(node);
return(scaled_data.ToArray());
}
/// <summary>
/// To scale the test data
/// </summary>
/// <param name="orig_data">Original test data, use NaN for missing features.</param>
/// <param name="targe_min">The target min</param>
/// <param name="targe_max">The target max</param>
/// <param name="features_min">The minimums of all features</param>
/// <param name="features_max">The maximums of all features</param>
/// <returns>The scaled test data in LibSVM format.</returns>
public static LibSVMNode[][] ScaleData(List <List <double> > orig_data, double targe_min, double targe_max, double[] features_min, double[] features_max)
{
int feature_n = features_min.Length;
double[] k = new double[feature_n];
for (int i = 0; i < feature_n; i++)
{
k[i] = (targe_max - targe_min) / (features_max[i] - features_min[i]);
}
int sample_n = orig_data.Count;
LibSVMNode[][] data = new LibSVMNode[sample_n][];
for (int m = 0; m < sample_n; m++)
{
List <LibSVMNode> scaled_data = new List <LibSVMNode>(); LibSVMNode node;
for (int i = 0; i < feature_n; i++)
{
if (double.IsNaN(orig_data[m][i]))
{
continue;
}
node = new LibSVMNode();
node.index = i + 1;
node.value = k[i] * (orig_data[m][i] - features_min[i]) + targe_min;
scaled_data.Add(node);
}
node = new LibSVMNode(); node.index = -1; scaled_data.Add(node);
data[m] = scaled_data.ToArray();
}
return(data);
}
/// <summary>
/// Convert sample features to LibSVM format
/// </summary>
/// <param name="sample_s">sample feature in string, i.e., one record in LibSVM file. "Target index:value index:value ..."</param>
/// <param name="sample">the converted sample feature in LibSVM format.</param>
/// <param name="label">the label</param>
public static void ToLibSVMFormat(string sample_s, out LibSVMNode[] sample, out double label)
{
string[] words = sample_s.Trim().Split();
label = double.Parse(words[0]);
sample = new LibSVMNode[words.Length];
for (int i = 1; i < words.Length; i++)
{
string[] ws = words[i].Split(new char[] { ':' });
sample[i - 1].index = int.Parse(ws[0]);
sample[i - 1].value = double.Parse(ws[1]);
}
sample[words.Length - 1].index = -1;
}
/// <summary>
/// Convert the SVM Linear model to single vector representation
/// </summary>
public unsafe float[] ToSingleVector()
{
LibSVMNode[][] support_vectors = new LibSVMNode[nrSV][];
for (int i = 0; i < nrSV; i++)
{
LibSVMNode[] sv = new LibSVMNode[nrFeature];
fixed(LibSVMNode *sv_p = sv)
{
svm_get_sv(svm_model, i, new IntPtr(sv_p));
}
support_vectors[i] = sv;
}
double rho = svm_get_rho(svm_model);
double[] coef = new double[nrSV];
fixed(double *coef_p = coef)
{
svm_get_coef(svm_model, new IntPtr(coef_p));
}
float[] single_vector = new float[nrFeature + 1]; int index; double value;
for (int i = 0; i < nrSV; i++)
{
for (int j = 0; j < support_vectors[i].Length; j++)
{
index = support_vectors[i][j].index;
if (index == -1)
{
break;
}
value = support_vectors[i][j].value;
single_vector[index - 1] += (float)(value * coef[i]);
}
}
single_vector[nrFeature] = -(float)rho;
return(single_vector);
}
/// <summary>
/// To scale the test data
/// </summary>
/// <param name="orig_data">Original test data in LibSVM format.</param>
/// <param name="targe_min">The target min</param>
/// <param name="targe_max">The target max</param>
/// <param name="features_min">The minimums of all features</param>
/// <param name="features_max">The maximums of all features</param>
/// <returns>The scaled test data in LibSVM format.</returns>
public static LibSVMNode[] ScaleData(LibSVMNode[] orig_data, double targe_min, double targe_max, double[] features_min, double[] features_max)
{
int feature_n = features_min.Length;
double[] k = new double[feature_n];
for (int i = 0; i < feature_n; i++)
{
k[i] = (targe_max - targe_min) / (features_max[i] - features_min[i]);
}
int n = orig_data.Length;
LibSVMNode[] scaled_data = new LibSVMNode[n];
for (int i = 0; i < n; i++)
{
int index = orig_data[i].index;
scaled_data[i].index = index; if (index == -1)
{
break;
}
scaled_data[i].value = k[index - 1] * (orig_data[i].value - features_min[index - 1]) + targe_min;
}
return(scaled_data);
}
