public static void Main()
{
modshogun.init_shogun_with_defaults();
double width = 2.1;
double epsilon = 1e-5;
double C = 1.0;
int mkl_norm = 2;
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
double[] trainlab = Load.load_labels("../data/label_train_twoclass.dat");
RealFeatures tfeats = new RealFeatures(traindata_real);
PolyKernel tkernel = new PolyKernel(10, 3);
tkernel.init(tfeats, tfeats);
double[,] K_train = tkernel.get_kernel_matrix();
RealFeatures pfeats = new RealFeatures(testdata_real);
tkernel.init(tfeats, pfeats);
double[,] K_test = tkernel.get_kernel_matrix();
CombinedFeatures feats_train = new CombinedFeatures();
feats_train.append_feature_obj(new RealFeatures(traindata_real));
CombinedKernel kernel = new CombinedKernel();
kernel.append_kernel(new CustomKernel(K_train));
kernel.append_kernel(new PolyKernel(10, 2));
kernel.init(feats_train, feats_train);
BinaryLabels labels = new BinaryLabels(trainlab);
MKLClassification mkl = new MKLClassification();
mkl.set_mkl_norm(1);
mkl.set_kernel(kernel);
mkl.set_labels(labels);
mkl.train();
CombinedFeatures feats_pred = new CombinedFeatures();
feats_pred.append_feature_obj(new RealFeatures(testdata_real));
CombinedKernel kernel2 = new CombinedKernel();
kernel2.append_kernel(new CustomKernel(K_test));
kernel2.append_kernel(new PolyKernel(10, 2));
kernel2.init(feats_train, feats_pred);
mkl.set_kernel(kernel2);
mkl.apply();
modshogun.exit_shogun();
}
static void Main(string[] argv)
{
modshogun.init_shogun_with_defaults();
double width = 2.1;
double epsilon = 1e-5;
double C = 1.0;
int mkl_norm = 2;
DoubleMatrix traindata_real = Load.load_numbers("../data/fm_train_real.dat");
DoubleMatrix testdata_real = Load.load_numbers("../data/fm_test_real.dat");
DoubleMatrix trainlab = Load.load_labels("../data/label_train_twoclass.dat");
RealFeatures tfeats = new RealFeatures(traindata_real);
PolyKernel tkernel = new PolyKernel(10,3);
tkernel.init(tfeats, tfeats);
DoubleMatrix K_train = tkernel.get_kernel_matrix();
RealFeatures pfeats = new RealFeatures(testdata_real);
tkernel.init(tfeats, pfeats);
DoubleMatrix K_test = tkernel.get_kernel_matrix();
CombinedFeatures feats_train = new CombinedFeatures();
feats_train.append_feature_obj(new RealFeatures(traindata_real));
CombinedKernel kernel = new CombinedKernel();
kernel.append_kernel(new CustomKernel(K_train));
kernel.append_kernel(new PolyKernel(10,2));
kernel.init(feats_train, feats_train);
Labels labels = new Labels(trainlab);
MKLClassification mkl = new MKLClassification();
mkl.set_mkl_norm(1);
mkl.set_kernel(kernel);
mkl.set_labels(labels);
mkl.train();
CombinedFeatures feats_pred = new CombinedFeatures();
feats_pred.append_feature_obj(new RealFeatures(testdata_real));
CombinedKernel kernel2 = new CombinedKernel();
kernel2.append_kernel(new CustomKernel(K_test));
kernel2.append_kernel(new PolyKernel(10, 2));
kernel2.init(feats_train, feats_pred);
mkl.set_kernel(kernel2);
mkl.apply();
modshogun.exit_shogun();
}
static void Main(string[] argv)
{
modshogun.init_shogun_with_defaults();
double width = 2.1;
double epsilon = 1e-5;
double C = 1.0;
DoubleMatrix traindata_real = Load.load_numbers("../data/fm_train_real.dat");
DoubleMatrix testdata_real = Load.load_numbers("../data/fm_test_real.dat");
DoubleMatrix trainlab = Load.load_labels("../data/label_train_twoclass.dat");
CombinedKernel kernel = new CombinedKernel();
CombinedFeatures feats_train = new CombinedFeatures();
RealFeatures tfeats = new RealFeatures(traindata_real);
PolyKernel tkernel = new PolyKernel(10,3);
tkernel.init(tfeats, tfeats);
DoubleMatrix K = tkernel.get_kernel_matrix();
kernel.append_kernel(new CustomKernel(K));
RealFeatures subkfeats_train = new RealFeatures(traindata_real);
feats_train.append_feature_obj(subkfeats_train);
PolyKernel subkernel = new PolyKernel(10,2);
kernel.append_kernel(subkernel);
kernel.init(feats_train, feats_train);
Labels labels = new Labels(trainlab);
LibSVM svm = new LibSVM(C, kernel, labels);
svm.train();
CombinedKernel kernel_pred = new CombinedKernel();
CombinedFeatures feats_pred = new CombinedFeatures();
RealFeatures pfeats = new RealFeatures(testdata_real);
PolyKernel tkernel_pred = new PolyKernel(10,3);
tkernel_pred.init(tfeats, pfeats);
DoubleMatrix KK = tkernel.get_kernel_matrix();
kernel_pred.append_kernel(new CustomKernel(KK));
RealFeatures subkfeats_test = new RealFeatures(testdata_real);
feats_pred.append_feature_obj(subkfeats_train);
PolyKernel subkernel_pred = new PolyKernel(10,2);
kernel_pred.append_kernel(subkernel_pred);
kernel_pred.init(feats_train, feats_pred);
svm.set_kernel(kernel_pred);
svm.apply();
DoubleMatrix km_train =kernel.get_kernel_matrix();
Console.WriteLine(km_train.ToString());
modshogun.exit_shogun();
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
int degree = 4;
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
PolyKernel kernel = new PolyKernel(feats_train, feats_train, degree, false);
double[,] km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
double[,] km_test = kernel.get_kernel_matrix();
// Parse and Display km_train
Console.Write("km_train:\n");
int numRows = km_train.GetLength(0);
int numCols = km_train.GetLength(1);
for (int i = 0; i < numRows; i++)
{
for (int j = 0; j < numCols; j++)
{
Console.Write(km_train[i, j] + " ");
}
Console.Write("\n");
}
// Parse and Display km_test
Console.Write("\nkm_test:\n");
numRows = km_test.GetLength(0);
numCols = km_test.GetLength(1);
for (int i = 0; i < numRows; i++)
{
for (int j = 0; j < numCols; j++)
{
Console.Write(km_test[i, j] + " ");
}
Console.Write("\n");
}
modshogun.exit_shogun();
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
int degree = 4;
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
PolyKernel kernel = new PolyKernel(feats_train, feats_train, degree, false);
double[,] km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
double[,] km_test = kernel.get_kernel_matrix();
// Parse and Display km_train
Console.Write("km_train:\n");
int numRows = km_train.GetLength(0);
int numCols = km_train.GetLength(1);
for(int i = 0; i < numRows; i++){
for(int j = 0; j < numCols; j++){
Console.Write(km_train[i,j] +" ");
}
Console.Write("\n");
}
// Parse and Display km_test
Console.Write("\nkm_test:\n");
numRows = km_test.GetLength(0);
numCols = km_test.GetLength(1);
for(int i = 0; i < numRows; i++){
for(int j = 0; j < numCols; j++){
Console.Write(km_test[i,j] +" ");
}
Console.Write("\n");
}
modshogun.exit_shogun();
}
static void Main(string[] argv)
{
modshogun.init_shogun_with_defaults();
int degree = 4;
DoubleMatrix traindata_real = Load.load_numbers("../data/fm_train_real.dat");
DoubleMatrix testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
PolyKernel kernel = new PolyKernel(feats_train, feats_train, degree, false);
DoubleMatrix km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
DoubleMatrix km_test = kernel.get_kernel_matrix();
Console.WriteLine(km_train.ToString());
Console.WriteLine(km_test.ToString());
modshogun.exit_shogun();
}
internal static ArrayList run(IList para)
{
bool reverse = false;
modshogun.init_shogun_with_defaults();
int N = (int)((int?)para[0]);
int M = (int)((int?)para[1]);
double pseudo = (double)((double?)para[2]);
int order = (int)((int?)para[3]);
int gap = (int)((int?)para[4]);
string[] fm_train_dna = Load.load_dna("../data/fm_train_dna.dat");
string[] fm_test_dna = Load.load_dna("../data/fm_test_dna.dat");
DoubleMatrix label_train_dna = Load.load_labels("../data/label_train_dna.dat");
ArrayList fm_hmm_pos_builder = new ArrayList();
ArrayList fm_hmm_neg_builder = new ArrayList();
for(int i = 0; i < label_train_dna.Columns; i++)
{
if (label_train_dna.get(i) == 1)
{
fm_hmm_pos_builder.Add(fm_train_dna[i]);
}
else
{
fm_hmm_neg_builder.Add(fm_train_dna[i]);
}
}
int pos_size = fm_hmm_pos_builder.Count;
int neg_size = fm_hmm_neg_builder.Count;
string[] fm_hmm_pos = new string[pos_size];
string[] fm_hmm_neg = new string[neg_size];
for (int i = 0; i < pos_size; i++)
{
fm_hmm_pos[i] = (string)fm_hmm_pos_builder[i];
}
for (int i = 0; i < neg_size; i++)
{
fm_hmm_pos[i] = (string)fm_hmm_neg_builder[i];
}
StringCharFeatures charfeat = new StringCharFeatures(fm_hmm_pos, DNA);
StringWordFeatures hmm_pos_train = new StringWordFeatures(charfeat.get_alphabet());
hmm_pos_train.obtain_from_char(charfeat, order-1, order, gap, reverse);
HMM pos = new HMM(hmm_pos_train, N, M, pseudo);
pos.baum_welch_viterbi_train(BW_NORMAL);
charfeat = new StringCharFeatures(fm_hmm_neg, DNA);
StringWordFeatures hmm_neg_train = new StringWordFeatures(charfeat.get_alphabet());
hmm_neg_train.obtain_from_char(charfeat, order-1, order, gap, reverse);
HMM neg = new HMM(hmm_neg_train, N, M, pseudo);
neg.baum_welch_viterbi_train(BW_NORMAL);
charfeat = new StringCharFeatures(fm_train_dna, DNA);
StringWordFeatures wordfeats_train = new StringWordFeatures(charfeat.get_alphabet());
wordfeats_train.obtain_from_char(charfeat, order-1, order, gap, reverse);
charfeat = new StringCharFeatures(fm_test_dna, DNA);
StringWordFeatures wordfeats_test = new StringWordFeatures(charfeat.get_alphabet());
wordfeats_test.obtain_from_char(charfeat, order-1, order, gap, reverse);
pos.set_observations(wordfeats_train);
neg.set_observations(wordfeats_train);
FKFeatures feats_train = new FKFeatures(10, pos, neg);
feats_train.set_opt_a(-1);
PolyKernel kernel = new PolyKernel(feats_train, feats_train, 1, true);
DoubleMatrix km_train = kernel.get_kernel_matrix();
HMM pos_clone = new HMM(pos);
HMM neg_clone = new HMM(neg);
pos_clone.set_observations(wordfeats_test);
neg_clone.set_observations(wordfeats_test);
FKFeatures feats_test = new FKFeatures(10, pos_clone, neg_clone);
feats_test.set_a(feats_train.get_a());
kernel.init(feats_train, feats_test);
DoubleMatrix km_test =kernel.get_kernel_matrix();
ArrayList result = new ArrayList();
result.Add(km_train);
result.Add(km_test);
result.Add(kernel);
modshogun.exit_shogun();
return result;
}