This page lists ready to run shogun examples for the Static Command Line interface.
To run the examples issue
shogun name_of_example.sg
% In this example a multi-class support vector machine is trained on a toy data % set and the trained classifier is used to predict labels of test examples. % The training algorithm is based on BSVM formulation (L2-soft margin % and the bias added to the objective function) which is solved by the Improved % Mitchell-Demyanov-Malozemov algorithm. The training algorithm uses the Gaussian % kernel of width 2.1 and the regularization constant C=1.2. The bias term of the % classification rule is not used. The solver stops if the relative duality gap % falls below 1e-5 and it uses 10MB for kernel cache. % % For more details on the used SVM solver see % V.Franc: Optimization Algorithms for Kernel Methods. Research report. % CTU-CMP-2005-22. CTU FEL Prague. 2005. % ftp://cmp.felk.cvut.cz/pub/cmp/articles/franc/Franc-PhD.pdf . % % GMNPSVM print GMNPSVM set_kernel GAUSSIAN REAL 10 1.2 set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier GMNPSVM svm_epsilon 1e-5 svm_use_bias 0 c 0.017 train_classifier set_features TEST ../data/fm_test_real.dat out.txt = classify ! rm out.txt
% In this example a two-class support vector machine classifier is trained on a % toy data set and the trained classifier is used to predict labels of test % examples. As training algorithm Gradient Projection Decomposition Technique % (GPDT) is used with SVM regularization parameter C=1.2 and a Gaussian % kernel of width 2.1 and 10MB of kernel cache. % % For more details on GPDT solver see http://dm.unife.it/gpdt % % % GPBTSVM print GPBTSVM set_kernel GAUSSIAN REAL 10 1.2 set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier GPBTSVM svm_epsilon 1e-5 svm_use_bias 0 c 0.017 train_classifier set_features TEST ../data/fm_test_real.dat out.txt = classify ! rm out.txt
% This example shows usage of a k-nearest neighbor (KNN) classification rule on % a toy data set. The number of the nearest neighbors is set to k=3 and the distances % are measured by the Euclidean metric. Finally, the KNN rule is applied to predict % labels of test examples. % KNN print KNN set_distance EUCLIDIAN REAL set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier KNN train_classifier 3 set_features TEST ../data/fm_test_real.dat out.txt = classify ! rm out.txt
% In this example a linear two-class classifier is trained based on the Linear % Discriminant Analysis (LDA) from a toy 2-dimensional examples. The trained % LDA classifier is used to predict test examples. Note that the LDA classifier % is optimal under the assumption that both classes are Gaussian distributed with equal % co-variance. For more details on the LDA see e.g. % http://en.wikipedia.org/wiki/Linear_discriminant_analysis % % LDA print LDA set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LDA train_classifier set_features TEST ../data/fm_test_real.dat out.txt = classify ! rm out.txt
% In this example a two-class linear support vector machine classifier is trained % on a toy data set and the trained classifier is used to predict labels of % test examples. As training algorithm the LIBLINEAR solver is used with the SVM % regularization parameter C=1 and the bias term in the classification rule % switched off. The solver iterates until it reaches epsilon-precise % (epsilon=1.e-5) solution or the maximal training time (max_train_time=60 % seconds) is exceeded. % % For more details on LIBLINEAR see % http://www.csie.ntu.edu.tw/~cjlin/liblinear/ % LibLinear print LibLinear % type can be one of LIBLINEAR_L2R_LR, LIBLINEAR_L2R_L2LOSS_SVC_DUAL, % LIBLINEAR_L2R_L2LOSS_SVC, LIBLINEAR_L2R_L1LOSS_SVC_DUAL new_classifier LIBLINEAR_L2R_LR set_features TRAIN ../data/fm_train_sparsereal.dat set_labels TRAIN ../data/label_train_twoclass.dat svm_epsilon 1e-5 svm_use_bias 0 c 0.42 train_classifier set_features TEST ../data/fm_test_sparsereal.dat out.txt = classify ! rm out.txt
% In this example a two-class support vector machine classifier is trained on a % toy data set and the trained classifier is used to predict labels of test % examples. As training algorithm LIBSVM is used with SVM regularization % parameter C=1 and a Gaussian kernel of width 1.2 and 10MB of kernel cache and % the precision parameter epsilon=1e-5. % % For more details on LIBSVM solver see http://www.csie.ntu.edu.tw/~cjlin/libsvm/ % LibSVM print LibSVM set_kernel GAUSSIAN REAL 10 1.2 set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM c 1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt = classify ! rm out.txt ! rm libsvm.model
% In this example a multi-class support vector machine classifier is trained on a % toy data set and the trained classifier is used to predict labels of test % examples. As training algorithm LIBSVM is used with SVM regularization % parameter C=1.2 and the bias in the classification rule switched off and % a Gaussian kernel of width 2.1 and 10MB of kernel cache and the precision % parameter epsilon=1e-5. % % For more details on LIBSVM solver see http://www.csie.ntu.edu.tw/~cjlin/libsvm/ % LibSVM MultiClass print LibSVMMultiClass set_kernel GAUSSIAN REAL 10 1.2 set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_multiclass.dat new_classifier LIBSVM_MULTICLASS svm_epsilon 1e-5 svm_use_bias 0 c 0.017 train_classifier set_features TEST ../data/fm_test_real.dat out.txt = classify ! rm out.txt
% In this example a one-class support vector machine classifier is trained on a % toy data set. The training algorithm finds a hyperplane in the RKHS which % separates the training data from the origin. The one-class classifier is % typically used to estimate the support of a high-dimesnional distribution. % For more details see e.g. % B. Schoelkopf et al. Estimating the support of a high-dimensional % distribution. Neural Computation, 13, 2001, 1443-1471. % % In the example, the one-class SVM is trained by the LIBSVM solver with the % regularization parameter C=1.2 and the Gaussian kernel of width 2.1 and the % precision parameter epsilon=1e-5 and 10MB of the kernel cache. % % For more details on LIBSVM solver see http://www.csie.ntu.edu.tw/~cjlin/libsvm/ . % % % LibSVM OneClass print LibSVMOneClass set_kernel GAUSSIAN REAL 10 1.2 set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM_ONECLASS svm_epsilon 1e-5 svm_use_bias 0 c 0.017 train_classifier set_features TEST ../data/fm_test_real.dat out.txt = classify ! rm out.txt
% In this example a two-class support vector machine classifier is trained on a % toy data set and the trained classifier is used to predict labels of test % examples. As training algorithm the Minimal Primal Dual SVM is used with SVM % regularization parameter C=1.2 and a Gaussian kernel of width 2.1 and 10MB of % kernel cache and the precision parameter epsilon=1e-5. % % For more details on the MPD solver see % Kienzle, W. and B. Schölkopf: Training Support Vector Machines with Multiple % Equality Constraints. Machine Learning: ECML 2005, 182-193. (Eds.) Carbonell, % J. G., J. Siekmann, Springer, Berlin, Germany (11 2005) % MPDSVM print MPDSVM set_kernel GAUSSIAN REAL 10 1.2 set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier MPDSVM svm_epsilon 1e-5 svm_use_bias 0 c 0.017 train_classifier set_features TEST ../data/fm_test_real.dat out.txt = classify
% This example shows how to use the Perceptron algorithm for training a
% two-class linear classifier, i.e. y = sign( <x,w>+b). The Perceptron algorithm
% works by iteratively passing though the training examples and applying the
% update rule on those examples which are misclassified by the current
% classifier. The Perceptron update rule reads
%
% w(t+1) = w(t) + alpha * y_t * x_t
% b(t+1) = b(t) + alpha * y_t
%
% where (x_t,y_t) is feature vector and label (must be +1/-1) of the misclassified example
% (w(t),b(t)) are the current parameters of the linear classifier
% (w(t+1),b(t+1)) are the new parameters of the linear classifier
% alpha is the learning rate.
%
% The Perceptron algorithm iterates until all training examples are correctly
% classified or the prescribed maximal number of iterations is reached.
%
% The learning rate and the maximal number of iterations can be set by
% sg('set_perceptron_parameters', alpha, max_iter);
%
% Perceptron
print Perceptron
%set_features TRAIN ../data/fm_train_real.dat
%set_labels TRAIN ../data/label_train_twoclass.dat
%new_classifier PERCEPTRON
%set_perceptron_parameters 1.6 5000
%train_classifier
%set_features TEST fm_test_real
%out.txt = classify
% In this example a two-class linear support vector machine classifier is trained % on a toy data set and the trained classifier is used to predict labels of % test examples. As training algorithm the steepest descent subgradient algorithm % is used with the SVM regularization parameter C=1.2 and the bias in the classification % rule switched off. The solver iterates until it finds epsilon-precise solution % (epsilon=1e-5) or the maximal training time (max_train_time=60 seconds) is exceeded. % % Note that this solver often has a tendency not to converge because the steepest descent % subgradient algorithm is oversensitive to rounding errors. Note also that this % is an unpublished work which was predecessor of the OCAS solver (see % classifier_svmocas). %% SubgradientSVM - often does not converge %print SubGradientSVM % %set_features TRAIN ../data/fm_train_sparsereal.dat %set_labels TRAIN ../data/label_train_twoclass.dat %new_classifier SUBGRADIENTSVM %svm_epsilon 1e-3 %svm_use_bias 0 %svm_max_train_time 10 %c 0.42 %% sometimes does not terminate %train_classifier % %set_features TEST ../data/fm_test_sparsereal.dat %out.txt = classify %! rm out.txt
% In this example a two-class support vector machine classifier is trained on a % DNA splice-site detection data set and the trained classifier is used to predict % labels on test set. As training algorithm SVM^light is used with SVM % regularization parameter C=1.2 and the Weighted Degree kernel of degree 20 and % the precision parameter epsilon=1e-5. % % For more details on the SVM^light see % T. Joachims. Making large-scale SVM learning practical. In Advances in Kernel % Methods -- Support Vector Learning, pages 169-184. MIT Press, Cambridge, MA USA, 1999. % % For more details on the Weighted Degree kernel see % G. Raetsch, S.Sonnenburg, and B. Schoelkopf. RASE: recognition of alternatively % spliced exons in C. elegans. Bioinformatics, 21:369-377, June 2005. % SVMLight %try print SVMLight set_kernel WEIGHTEDDEGREE CHAR 10 20 set_features TRAIN ../data/fm_train_dna.dat DNA set_labels TRAIN ../data/label_train_dna.dat new_classifier LIGHT svm_epsilon 1e-5 svm_use_bias 0 c 0.017 train_classifier set_features TEST ../data/fm_test_dna.dat DNA out.txt = classify ! rm out.txt %catch % print No support for SVMLight available. %end
% In this example a two-class linear support vector machine classifier is trained % on a toy data set and the trained classifier is used to predict labels of % test examples. As training algorithm the SVMLIN solver is used with the SVM % regularization parameter C=1.2 and the bias term in the classification rule % switched off. The solver iterates until it finds the epsilon-precise solution % (epsilon=1e-5) or the maximal training time (max_train_time=60 seconds) is exceeded. % % For more details on the SVMLIN solver see % V. Sindhwani, S.S. Keerthi. Newton Methods for Fast Solution of Semi-supervised % Linear SVMs. Large Scale Kernel Machines MIT Press (Book Chapter), 2007 % SVMLin print SVMLin set_features TRAIN ../data/fm_train_sparsereal.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier SVMLIN svm_epsilon 1e-5 svm_use_bias 0 c 0.42 train_classifier set_features TEST ../data/fm_test_sparsereal.dat out.txt = classify ! rm out.txt
% In this example a two-class linear support vector machine classifier is trained % on a toy data set and the trained classifier is used to predict labels of % test examples. As training algorithm the OCAS solver is used with the SVM % regularization parameter C=1.2 and the bias term in the classification rule % switched off. The solver iterates until the relative duality gap falls below % epsilon=1e-5 or the maximal training time (max_train_time=60 seconds) is % exceeded. % % For more details on the OCAS solver see % V. Franc, S. Sonnenburg. Optimized Cutting Plane Algorithm for Large-Scale Risk % Minimization.The Journal of Machine Learning Research, vol. 10, % pp. 2157--2192. October 2009. % % SVMOcas print SVMOcas set_features TRAIN ../data/fm_train_sparsereal.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier SVMOCAS svm_epsilon 1e-5 svm_use_bias 0 c 0.42 train_classifier set_features TEST ../data/fm_test_sparsereal.dat out.txt = classify ! rm out.txt
% In this example a two-class linear support vector machine classifier is trained % on a toy data set and the trained classifier is then used to predict labels of % test examples. As training algorithm the Stochastic Gradient Descent (SGD) % solver is used with the SVM regularization parameter C=1.2 and the bias term in the % classification rule switched off. The solver iterates until the maximal % training time (max_train_time=60 seconds) is exceeded. % % For more details on the SGD solver see % L. Bottou, O. Bousquet. The tradeoff of large scale learning. In NIPS 20. MIT % Press. 2008. % SVMSGD print SVMSGD % set_features TRAIN ../data/fm_train_sparsereal.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier SVMSGD svm_epsilon 1e-5 svm_use_bias 0 c 0.42 train_classifier set_features TEST ../data/fm_test_sparsereal.dat out.txt = classify ! rm out.txt
% In this example an agglomerative hierarchical single linkage clustering method % is used to cluster a given toy data set. Starting with each object being % assigned to its own cluster clusters are iteratively merged. Here the clusters % are merged that have the closest (minimum distance, here set via the Euclidean % distance object) two elements. % Hierarchical print Hierarchical set_features TRAIN ../data/fm_train_real.dat set_distance EUCLIDIAN REAL new_clustering HIERARCHICAL train_clustering 3 merge_distance.txt, pairs.txt = get_clustering
% In this example the k-means clustering method is used to cluster a given toy % data set. In k-means clustering one tries to partition n observations into k % clusters in which each observation belongs to the cluster with the nearest mean. % The algorithm class constructor takes the number of clusters and a distance to % be used as input. The distance used in this example is Euclidean distance. % After training one can fetch the result of clustering by obtaining the cluster % centers and their radiuses. % KMEANS print KMeans set_features TRAIN ../data/fm_train_real.dat set_distance EUCLIDIAN REAL new_clustering KMEANS train_clustering 3 1000 radi.txt, centers.txt = get_clustering
% An approach as applied below, which shows the processing of input data % from a file becomes a crucial factor for writing your own sample applications. % This approach is just one example of what can be done using the distance % functions provided by shogun. % % First, you need to determine what type your data will be, because this % will determine the distance function you can use. % % This example loads two stored matrices of real values (feature type 'REAL') % from different files and initializes the distance to 'BRAYCURTIS'. % Each column of the matrices corresponds to one data point. % % The target 'TRAIN' for 'set_features' controls the processing of the given % data points, where a pairwise distance matrix is computed by % 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' and % target 'TRAIN'. % % The target 'TEST' for 'set_features' controls the processing of the given % data points 'TRAIN' and 'TEST', where a pairwise distance matrix between % these two matrices is computed by 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' % and target 'TEST'. The 'TRAIN' distance matrix ceased to exist. % % For more details see doc/classshogun_1_1CBrayCurtisDistance.html. % % Obviously, using the Bray Curtis distance is not limited to this showcase % example. % BrayCurtis Distance print BrayCurtis Distance set_distance BRAYCURTIS REAL set_features TRAIN ../data/fm_train_real.dat dm_train.txt = get_distance_matrix TRAIN set_features TEST ../data/fm_test_real.dat dm_test.txt = get_distance_matrix TEST
% An approach as applied below, which shows the processing of input data % from a file becomes a crucial factor for writing your own sample applications. % This approach is just one example of what can be done using the distance % functions provided by shogun. % % First, you need to determine what type your data will be, because this % will determine the distance function you can use. % % This example loads two stored matrices of real values (feature type 'REAL') % from different files and initializes the distance to 'CANBERRA'. % Each column of the matrices corresponds to one data point. % % The target 'TRAIN' for 'set_features' controls the processing of the given % data points, where a pairwise distance (dissimilarity ratio) matrix is % computed by 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' % and target 'TRAIN'. % % The target 'TEST' for 'set_features' controls the processing of the given % data points 'TRAIN' and 'TEST', where a pairwise distance (dissimilarity ratio) % matrix between these two data sets is computed by 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' and % target 'TEST'. The 'TRAIN' distance matrix ceased to exist. % % For more details see doc/classshogun_1_1CCanberraMetric.html. % % Obviously, using the Canberra distance is not limited to this showcase % example. % Canberra Metric print CanberraMetric set_distance CANBERRA REAL set_features TRAIN ../data/fm_train_real.dat dm_train.txt = get_distance_matrix TRAIN set_features TEST ../data/fm_test_real.dat dm_test.txt = get_distance_matrix TEST
% An approach as applied below, which shows the processing of input data % from a file becomes a crucial factor for writing your own sample applications. % This approach is just one example of what can be done using the distance % functions provided by shogun. % % First, you need to determine what type your data will be, because this % will determine the distance function you can use. % % This example loads two stored data sets in 'STRING' representation % (feature type 'CHAR' with alphabet 'DNA') from different files and % initializes the distance to 'CANBERRA' with feature type 'WORD'. % % Data points in this example are defined by the transformation function % 'convert' and the preprocessing step applied afterwards (defined by % 'add_preproc' and preprocessor 'SORTWORDSTRING'). % % The target 'TRAIN' for 'set_features' controls the binding of the given % data points. In order to compute a pairwise distance matrix by % 'get_distance_matrix', we have to perform two preprocessing steps for % input data 'TRAIN'. The method 'convert' transforms the input data to % a string representation suitable for the selected distance. The individual % strings are sorted in ascending order after the execution of 'attach_preproc'. % A pairwise distance matrix is computed by 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' % and target 'TRAIN'. % % The target 'TEST' for 'set_features' controls the binding of the given % data points 'TRAIN' and 'TEST'. In order to compute a pairwise distance % matrix between these two data sets by 'get_distance_matrix', we have to % perform two preprocessing steps for input data 'TEST'. The method 'convert' % transforms the input data 'TEST' to a string representation suitable for % the selected distance. The individual strings are sorted in ascending order % after the execution of 'attach_preproc'. A pairwise distance matrix between % the data sets 'TRAIN' and 'TEST' is computed by 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' % and target 'TEST'. The 'TRAIN' distance matrix ceased to exist. % % For more details see % doc/classshogun_1_1CSortWordString.html, % doc/classshogun_1_1CPreProc.html, % doc/classshogun_1_1CStringFeatures.html (method obtain_from_char_features) and % doc/classshogun_1_1CCanberraWordDistance.html. % % Obviously, using the Canberra word distance is not limited to this showcase % example. % CanberraWord Distance print CanberraWordDistance set_distance CANBERRA WORD add_preproc SORTWORDSTRING set_features TRAIN ../data/fm_train_dna.dat DNA convert TRAIN STRING CHAR STRING WORD 3 2 0 n attach_preproc TRAIN dm_train.txt = get_distance_matrix TRAIN set_features TEST ../data/fm_test_dna.dat DNA convert TEST STRING CHAR STRING WORD 3 2 0 n attach_preproc TEST dm_test.txt = get_distance_matrix TEST
% An approach as applied below, which shows the processing of input data % from a file becomes a crucial factor for writing your own sample applications. % This approach is just one example of what can be done using the distance % functions provided by shogun. % % First, you need to determine what type your data will be, because this % will determine the distance function you can use. % % This example loads two stored matrices of real values (feature type 'REAL') % from different files and initializes the distance to 'CHEBYSHEW'. % Each column of the matrices corresponds to one data point. % % The target 'TRAIN' for 'set_features' controls the processing of the given % data points, where a pairwise distance matrix (maximum of absolute feature % dimension differences) is computed by 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' % and target 'TRAIN'. % % The target 'TEST' for 'set_features' controls the processing of the given % data points 'TRAIN' and 'TEST', where a pairwise distance matrix (maximum % of absolute feature dimension differences) between these two data sets is % computed. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' % and target 'TEST'. The 'TRAIN' distance matrix ceased to exist. % % For more details see doc/classshogun_1_1CChebyshewMetric.html. % % Obviously, using the Chebyshew distance is not limited to this showcase % example. % Chebyshew Metric print ChebyshewMetric set_distance CHEBYSHEW REAL set_features TRAIN ../data/fm_train_real.dat dm_train.txt = get_distance_matrix TRAIN set_features TEST ../data/fm_test_real.dat dm_test.txt = get_distance_matrix TEST
% An approach as applied below, which shows the processing of input data % from a file becomes a crucial factor for writing your own sample applications. % This approach is just one example of what can be done using the distance % functions provided by shogun. % % First, you need to determine what type your data will be, because this % will determine the distance function you can use. % % This example loads two stored matrices of real values (feature type 'REAL') % from different files and initializes the distance to 'CHISQUARE'. % Each column of the matrices corresponds to one data point. % % The target 'TRAIN' for 'set_features' controls the processing of the given % data points, where a pairwise distance matrix is computed by % 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' % and target 'TRAIN'. % % The target 'TEST' for 'set_features' controls the processing of the given % data points 'TRAIN' and 'TEST', where a pairwise distance matrix between % these two matrices is computed by 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' % and target 'TEST'. The 'TRAIN' distance matrix ceased to exist. % % For more details see doc/classshogun_1_1CChiSquareDistance.html. % % Obviously, using the ChiSquare distance is not limited to this showcase % example. % ChiSquare Distance print ChiSquareDistance set_distance CHISQUARE REAL set_features TRAIN ../data/fm_train_real.dat dm_train.txt = get_distance_matrix TRAIN set_features TEST ../data/fm_test_real.dat dm_test.txt = get_distance_matrix TEST
% An approach as applied below, which shows the processing of input data % from a file becomes a crucial factor for writing your own sample applications. % This approach is just one example of what can be done using the distance % functions provided by shogun. % % First, you need to determine what type your data will be, because this % will determine the distance function you can use. % % This example loads two stored matrices of real values (feature type 'REAL') % from different files and initializes the distance to 'COSINE'. % Each column of the matrices corresponds to one data point. % % The target 'TRAIN' for 'set_features' controls the processing of the given % data points, where a pairwise distance matrix is computed by % 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' and % target 'TRAIN'. % % The target 'TEST' for 'set_features' controls the processing of the given % data points 'TRAIN' and 'TEST', where a pairwise distance matrix between % these two data sets is computed by 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' % and target 'TEST'. The 'TRAIN' distance matrix ceased to exist. % % For more details see doc/classshogun_1_1CCosineDistance.html. % % Obviously, using the Cosine distance is not limited to this showcase % example. % Cosine Distance print CosineDistance set_distance COSINE REAL set_features TRAIN ../data/fm_train_real.dat dm_train.txt = get_distance_matrix TRAIN set_features TEST ../data/fm_test_real.dat dm_test.txt = get_distance_matrix TEST
% Euclidian Distance print EuclidianDistance set_distance EUCLIDIAN REAL set_features TRAIN ../data/fm_train_real.dat dm_train.txt = get_distance_matrix TRAIN set_features TEST ../data/fm_test_real.dat dm_test.txt = get_distance_matrix TEST
% An approach as applied below, which shows the processing of input data % from a file becomes a crucial factor for writing your own sample applications. % This approach is just one example of what can be done using the distance % functions provided by shogun. % % First, you need to determine what type your data will be, because this % will determine the distance function you can use. % % This example loads two stored matrices of real values (feature type 'REAL') % from different files and initializes the distance to 'GEODESIC'. % Each column of the matrices corresponds to one data point. % % The target 'TRAIN' for 'set_features' controls the processing of the given % data points, where a pairwise distance (shortest path on a sphere) matrix is % computed by 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' and % target 'TRAIN'. % % The target 'TEST' for 'set_features' controls the processing of the given % data points 'TRAIN' and 'TEST', where a pairwise distance (shortest path on % a sphere) matrix between these two data sets is computed by 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' % and target 'TEST'. The 'TRAIN' distance matrix ceased to exist. % % For more details see doc/classshogun_1_1CGeodesicMetric.html. % % Obviously, using the Geodesic distance is not limited to this showcase % example. % Geodesic Metric print GeodesicMetric set_distance GEODESIC REAL set_features TRAIN ../data/fm_train_real.dat dm_train.txt = get_distance_matrix TRAIN set_features TEST ../data/fm_test_real.dat dm_test.txt = get_distance_matrix TEST
% An approach as applied below, which shows the processing of input data % from a file becomes a crucial factor for writing your own sample applications. % This approach is just one example of what can be done using the distance % functions provided by shogun. % % First, you need to determine what type your data will be, because this % will determine the distance function you can use. % % This example loads two stored data sets in 'STRING' representation % (feature type 'CHAR' with alphabet 'DNA') from different files and % initializes the distance to 'HAMMING' with feature type 'WORD'. % % Data points in this example are defined by the transformation function % 'convert' and the preprocessing step applied afterwards (defined by % 'add_preproc' and preprocessor 'SORTWORDSTRING'). % % The target 'TRAIN' for 'set_features' controls the binding of the given % data points. In order to compute a pairwise distance matrix by % 'get_distance_matrix', we have to perform two preprocessing steps for % input data 'TRAIN'. The method 'convert' transforms the input data to % a string representation suitable for the selected distance. The individual % strings are sorted in ascending order after the execution of 'attach_preproc'. % A pairwise distance matrix is computed by 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' % and target 'TRAIN'. % % The target 'TEST' for 'set_features' controls the binding of the given % data points 'TRAIN' and 'TEST'. In order to compute a pairwise distance % matrix between these two data sets by 'get_distance_matrix', we have to % perform two preprocessing steps for input data 'TEST'. The method 'convert' % transforms the input data 'TEST' to a string representation suitable for % the selected distance. The individual strings are sorted in ascending order % after the execution of 'attach_preproc'. A pairwise distance matrix between % the data sets 'TRAIN' and 'TEST' is computed by 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' % and target 'TEST'. The 'TRAIN' distance matrix ceased to exist. % % For more details see % doc/classshogun_1_1CSortWordString.html, % doc/classshogun_1_1CPreProc.html, % doc/classshogun_1_1CStringFeatures.html (method obtain_from_char_features) and % doc/classshogun_1_1CHammingWordDistance.html. % % Obviously, using the Hamming word distance is not limited to this showcase % example. % HammingWord Distance print HammingWordDistance set_distance HAMMING WORD add_preproc SORTWORDSTRING set_features TRAIN ../data/fm_train_dna.dat DNA convert TRAIN STRING CHAR STRING WORD 3 2 0 n attach_preproc TRAIN dm_train.txt = get_distance_matrix TRAIN set_features TEST ../data/fm_test_dna.dat DNA convert TEST STRING CHAR STRING WORD 3 2 0 n attach_preproc TEST dm_test.txt = get_distance_matrix TEST
% An approach as applied below, which shows the processing of input data % from a file becomes a crucial factor for writing your own sample applications. % This approach is just one example of what can be done using the distance % functions provided by shogun. % % First, you need to determine what type your data will be, because this % will determine the distance function you can use. % % This example loads two stored matrices of real values (feature type 'REAL') % from different files and initializes the distance to 'JENSEN'. % Each column of the matrices corresponds to one data point. % % The target 'TRAIN' for 'set_features' controls the processing of the given % data points, where a pairwise distance (divergence measure based on the % Kullback-Leibler divergence) matrix is computed by 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' and % target 'TRAIN'. % % The target 'TEST' for 'set_features' controls the processing of the given % data points 'TRAIN' and 'TEST', where a pairwise distance (divergence measure % based on the Kullback-Leibler divergence) matrix between these two data sets % is computed by 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' % and target 'TEST'. The 'TRAIN' distance matrix ceased to exist. % % For more details see doc/classshogun_1_1CJensenMetric.html. % % Obviously, using the Jensen-Shannon distance/divergence is not limited to % this showcase example. % Jensen Metric print JensenMetric set_distance JENSEN REAL set_features TRAIN ../data/fm_train_real.dat dm_train.txt = get_distance_matrix TRAIN set_features TEST ../data/fm_test_real.dat dm_test.txt = get_distance_matrix TEST
% n approach as applied below, which shows the processing of input data % from a file becomes a crucial factor for writing your own sample applications. % This approach is just one example of what can be done using the distance % functions provided by shogun. % % First, you need to determine what type your data will be, because this % will determine the distance function you can use. % % This example loads two stored matrices of real values (feature type 'REAL') % from different files and initializes the distance to 'MANHATTAN'. % Each column of the matrices corresponds to one data point. % % The target 'TRAIN' for 'set_features' controls the processing of the given % data points, where a pairwise distance (sum of absolute feature % dimension differences) matrix is computed by 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' and % target 'TRAIN'. % % The target 'TEST' for 'set_features' controls the processing of the given % data points 'TRAIN' and 'TEST', where a pairwise distance (sum of absolute % feature dimension differences) matrix between these two data sets is % computed by 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' % and target 'TEST'. The 'TRAIN' distance matrix ceased to exist. % % For more details see doc/classshogun_1_1CManhattanMetric.html. % % Obviously, using the Manhattan distance is not limited to this showcase % example. % Manhattan Metric print ManhattanMetric set_distance MANHATTAN REAL set_features TRAIN ../data/fm_train_real.dat dm_train.txt = get_distance_matrix TRAIN set_features TEST ../data/fm_test_real.dat dm_test.txt = get_distance_matrix TEST
% An approach as applied below, which shows the processing of input data % from a file becomes a crucial factor for writing your own sample applications. % This approach is just one example of what can be done using the distance % functions provided by shogun. % % First, you need to determine what type your data will be, because this % will determine the distance function you can use. % % This example loads two stored data sets in 'STRING' representation % (feature type 'CHAR' with alphabet 'DNA') from different files and % initializes the distance to 'MANHATTAN' with feature type 'WORD'. % % Data points in this example are defined by the transformation function % 'convert' and the preprocessing step applied afterwards (defined by % 'add_preproc' and preprocessor 'SORTWORDSTRING'). % % The target 'TRAIN' for 'set_features' controls the binding of the given % data points. In order to compute a pairwise distance matrix by % 'get_distance_matrix', we have to perform two preprocessing steps for % input data 'TRAIN'. The method 'convert' transforms the input data to % a string representation suitable for the selected distance. The individual % strings are sorted in ascending order after the execution of 'attach_preproc'. % A pairwise distance matrix is computed by 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' % and target 'TRAIN'. % % The target 'TEST' for 'set_features' controls the binding of the given % data points 'TRAIN' and 'TEST'. In order to compute a pairwise distance % matrix between these two data sets by 'get_distance_matrix', we have to % perform two preprocessing steps for input data 'TEST'. The method 'convert' % transforms the input data 'TEST' to a string representation suitable for % the selected distance. The individual strings are sorted in ascending order % after the execution of 'attach_preproc'. A pairwise distance matrix between % the data sets 'TRAIN' and 'TEST' is computed by 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' % and target 'TEST'. The 'TRAIN' distance matrix ceased to exist. % % For more details see % doc/classshogun_1_1CSortWordString.html, % doc/classshogun_1_1CPreProc.html, % doc/classshogun_1_1CStringFeatures.html (method obtain_from_char_features) and % doc/classshogun_1_1CManhattanWordDistance.html. % % Obviously, using the Manhattan word distance is not limited to this showcase % example. % ManhattanWord Distance print ManhattanWordDistance set_distance MANHATTAN WORD add_preproc SORTWORDSTRING set_features TRAIN ../data/fm_train_dna.dat DNA convert TRAIN STRING CHAR STRING WORD 3 2 0 n attach_preproc TRAIN dm_train.txt = get_distance_matrix TRAIN set_features TEST ../data/fm_test_dna.dat DNA convert TEST STRING CHAR STRING WORD 3 2 0 n attach_preproc TEST dm_test.txt = get_distance_matrix TEST
% An approach as applied below, which shows the processing of input data % from a file becomes a crucial factor for writing your own sample applications. % This approach is just one example of what can be done using the distance % functions provided by shogun. % % First, you need to determine what type your data will be, because this % will determine the distance function you can use. % % This example loads two stored matrices of real values (feature type 'REAL') % from different files and initializes the distance to 'MINKOWSKI' with % norm 'k'. Each column of the matrices corresponds to one data point. % % The target 'TRAIN' for 'set_features' controls the processing of the given % data points, where a pairwise distance matrix is computed by % 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' and % target 'TRAIN'. % % The target 'TEST' for 'set_features' controls the processing of the given % data points 'TRAIN' and 'TEST', where a pairwise distance matrix between % these two data sets is computed by 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' % and target 'TEST'. The 'TRAIN' distance matrix ceased to exist. % % For more details see doc/classshogun_1_1CMinkowskiMetric.html. % % Obviously, using the Minkowski metric is not limited to this showcase % example. % Minkowski Metric print MinkowskiMetric set_distance MINKOWSKI REAL 3 set_features TRAIN ../data/fm_train_real.dat dm_train.txt = get_distance_matrix TRAIN set_features TEST ../data/fm_test_real.dat dm_test.txt = get_distance_matrix TEST
% An approach as applied below, which shows the processing of input data % from a file becomes a crucial factor for writing your own sample applications. % This approach is just one example of what can be done using the distance % functions provided by shogun. % % First, you need to determine what type your data will be, because this % will determine the distance function you can use. % % This example loads two stored matrices of real values (feature type 'REAL') % from different files and initializes the distance to 'TANIMOTO'. % Each column of the matrices corresponds to one data point. % % The target 'TRAIN' for 'set_features' controls the processing of the given % data points, where a pairwise distance (extended Jaccard coefficient) % matrix is computed by 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' and % target 'TRAIN'. % % The target 'TEST' for 'set_features' controls the processing of the given % data points 'TRAIN' and 'TEST', where a pairwise distance (extended % Jaccard coefficient) matrix between these two data sets is computed by % 'get_distance_matrix'. % % The resulting distance matrix can be reaccessed by 'get_distance_matrix' % and target 'TEST'. The 'TRAIN' distance matrix ceased to exist. % % For more details see doc/classshogun_1_1CTanimotoDistance.html. % % Obviously, using the Tanimoto distance/coefficient is not limited to % this showcase example. % Tanimoto Distance print TanimotoDistance set_distance TANIMOTO REAL set_features TRAIN ../data/fm_train_real.dat dm_train.txt = get_distance_matrix TRAIN set_features TEST ../data/fm_test_real.dat dm_test.txt = get_distance_matrix TEST
% In this example the Histogram algorithm object computes a histogram over all % 16bit unsigned integers in the features. % Histogram print Histogram - not yet supported %new_distribution HISTOGRAM add_preproc SORTWORDSTRING set_features TRAIN ../data/fm_train_dna.dat DNA convert TRAIN STRING CHAR STRING WORD 3 2 0 n attach_preproc TRAIN %train_distribution %histo.txt = get_histogram %get_log_likelihood %get_log_likelihood_sample
% In this example a hidden markov model with 3 states and 6 transitions is trained % on a string data set. % HMM print HMM new_hmm 3 6 set_features TRAIN ../data/fm_train_cube.dat CUBE convert TRAIN STRING CHAR STRING WORD 1 bw p.txt, q.txt, a.txt, b.txt = get_hmm %new_hmm 3 6 %set_hmm p.txt q.txt a.txt b.txt %likelihood.txt = hmm_likelihood
% Trains an inhomogeneous Markov chain of order 3 on a DNA string data set. Due to % the structure of the Markov chain it is very similar to a HMM with just one % chain of connected hidden states - that is why we termed this linear HMM. % LinearHMM print LinearHMM - not yet supported %new_distribution LinearHMM add_preproc SORTWORDSTRING set_features TRAIN ../data/fm_train_dna.dat DNA convert TRAIN STRING CHAR STRING WORD 3 2 0 n attach_preproc TRAIN %train_distribution %histo.txt = get_histogram %get_log_likelihood %get_log_likelihood_sample
% This is an example for the initialization of the chi2-kernel on real data, where % each column of the matrices corresponds to one training/test example. % CHI2 print Chi2 set_kernel CHI2 REAL 10 1.4 set_features TRAIN ../data/fm_train_real.dat km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_real.dat km_test.txt = get_kernel_matrix TEST
% This is an example for the initialization of a combined kernel, which is a weighted sum of % in this case three kernels on real valued data. The sub-kernel weights are all set to 1. % % Combined print Combined clean_features TRAIN clean_features TEST set_kernel COMBINED 10 add_kernel 1 LINEAR REAL 10 add_features TRAIN ../data/fm_train_real.dat add_features TEST ../data/fm_test_real.dat add_kernel 1 GAUSSIAN REAL 10 1. add_features TRAIN ../data/fm_train_real.dat add_features TEST ../data/fm_test_real.dat add_kernel 1 POLY REAL 10 3 0 add_features TRAIN ../data/fm_train_real.dat add_features TEST ../data/fm_test_real.dat km_train.txt = get_kernel_matrix TRAIN km_test.txt = get_kernel_matrix TEST
% This is an example for the initialization of the CommUlongString-kernel. This kernel % sums over k-mere matches (k='order'). For efficient computing a preprocessor is used % that extracts and sorts all k-mers. If 'use_sign' is set to one each k-mere is counted % only once. % Comm Ulong String print CommUlongString add_preproc SORTULONGSTRING set_kernel COMMSTRING ULONG 10 0 FULL set_features TRAIN ../data/fm_train_dna.dat DNA convert TRAIN STRING CHAR STRING ULONG 3 2 0 n attach_preproc TRAIN km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_dna.dat DNA convert TEST STRING CHAR STRING ULONG 3 2 0 n attach_preproc TEST km_test.txt = get_kernel_matrix TEST
% This is an example for the initialization of the CommWordString-kernel (aka % Spectrum or n-gram kernel; its name is derived from the unix command comm). This kernel % sums over k-mere matches (k='order'). For efficient computing a preprocessor is used % that extracts and sorts all k-mers. If 'use_sign' is set to one each k-mere is counted % only once. % Comm Word String print CommWordString add_preproc SORTWORDSTRING set_kernel COMMSTRING WORD 10 0 FULL set_features TRAIN ../data/fm_train_dna.dat DNA convert TRAIN STRING CHAR STRING WORD 3 2 0 n attach_preproc TRAIN km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_dna.dat DNA convert TEST STRING CHAR STRING WORD 3 2 0 n attach_preproc TEST km_test.txt = get_kernel_matrix TEST
% The constant kernel gives a trivial kernel matrix with all entries set to the same value % defined by the argument 'c'. % % Const print Const set_kernel CONST REAL 10 23 set_features TRAIN ../data/fm_train_real.dat km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_real.dat km_test.txt = get_kernel_matrix TEST
% This is an example for the initialization of the diag-kernel. % The diag kernel has all kernel matrix entries but those on % the main diagonal set to zero. % Diag print Diag set_kernel DIAG REAL 10 23. set_features TRAIN ../data/fm_train_real.dat km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_real.dat km_test.txt = get_kernel_matrix TEST
% With the distance kernel one can use any of the following distance metrics: % MINKOWSKI MANHATTAN HAMMING CANBERRA CHEBYSHEW GEODESIC JENSEN CHISQUARE TANIMOTO COSINE BRAYCURTIS EUCLIDIAN % Distance print Distance set_distance EUCLIDIAN REAL set_kernel DISTANCE 10 1.7 set_features TRAIN ../data/fm_train_real.dat km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_real.dat km_test.txt = get_kernel_matrix TEST
% The FixedDegree String kernel takes as input two strings of same size and counts the number of matches of length d. % Fixed Degree String print FixedDegreeString set_kernel FIXEDDEGREE CHAR 10 3 set_features TRAIN ../data/fm_train_dna.dat DNA km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_dna.dat DNA km_test.txt = get_kernel_matrix TEST
% The well known Gaussian kernel (swiss army knife for SVMs) on dense real valued features. % Gaussian print Gaussian set_kernel GAUSSIAN REAL 10 1.4 set_features TRAIN ../data/fm_train_real.dat km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_real.dat km_test.txt = get_kernel_matrix TEST
% An experimental kernel inspired by the WeightedDegreePositionStringKernel and the Gaussian kernel. % The idea is to shift the dimensions of the input vectors against eachother. 'shift_step' is the step % size of the shifts and max_shift is the maximal shift. % GaussianShift print GaussianShift set_kernel GAUSSIANSHIFT REAL 10 1.4 2 1 set_features TRAIN ../data/fm_train_real.dat km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_real.dat km_test.txt = get_kernel_matrix TEST
% This is an example for the initialization of a linear kernel on real valued % data using scaling factor 1.2. % Linear print Linear set_kernel LINEAR REAL 10 1.2 set_features TRAIN ../data/fm_train_real.dat km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_real.dat km_test.txt = get_kernel_matrix TEST
% This is an example for the initialization of a linear kernel on raw byte % data. % LinearByte is b0rked print LinearByte %set_kernel LINEAR BYTE size_cache %set_features TRAIN ../data/fm_train_byte.dat RAWBYTE %km_train.txt = get_kernel_matrix TRAIN %set_features TEST ../data/fm_test_byte.dat RAWBYTE %km_test.txt = get_kernel_matrix TEST
% This is an example for the initialization of a linear kernel on string data. The % strings are all of the same length and consist of the characters 'ACGT' corresponding % to the DNA-alphabet. Each column of the matrices of type char corresponds to % one training/test example. % Linear String print LinearString set_kernel LINEAR CHAR 10 set_features TRAIN ../data/fm_train_dna.dat DNA km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_dna.dat DNA km_test.txt = get_kernel_matrix TEST
% This is an example for the initialization of a linear kernel on word (2byte) % data. % LinearWord %print LinearWord %set_kernel LINEAR WORD 10 1.4 %set_features TRAIN ../data/fm_train_word.dat %km_train.txt = get_kernel_matrix TRAIN %set_features TEST ../data/fm_test_word.dat %km_test.txt = get_kernel_matrix TEST
% This is an example for the initialization of the local alignment kernel on % DNA sequences, where each column of the matrices of type char corresponds to % one training/test example. % Local Alignment String %print LocalAlignmentString %set_kernel LOACALALIGNMENT CHAR 10 %set_features TRAIN ../data/fm_train_dna.dat DNA %km_train.txt = get_kernel_matrix TRAIN %set_features TEST ../data/fm_test_dna.dat DNA %km_test.txt = get_kernel_matrix TEST
% This example initializes the locality improved string kernel. The locality improved string % kernel is defined on sequences of the same length and inspects letters matching at % corresponding positions in both sequences. The kernel sums over all matches in windows of % length l and takes this sum to the power of 'inner_degree'. The sum over all these % terms along the sequence is taken to the power of 'outer_degree'. % Locality Improved String print LocalityImprovedString set_kernel LIK CHAR 10 5 5 7 set_features TRAIN ../data/fm_train_dna.dat DNA km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_dna.dat DNA km_test.txt = get_kernel_matrix TEST
% This is an example initializing the oligo string kernel which takes distances % between matching oligos (k-mers) into account via a gaussian. Variable 'k' defines the length % of the oligo and variable 'w' the width of the gaussian. The oligo string kernel is % implemented for the DNA-alphabet 'ACGT'. % % Oligo String print Oligo set_kernel OLIGO CHAR 10 3 1.2 set_features TRAIN ../data/fm_train_dna.dat DNA km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_dna.dat DNA km_test.txt = get_kernel_matrix TEST
% This example initializes the polynomial kernel with real data. % If variable 'inhomogene' is 'true' +1 is added to the scalar product % before taking it to the power of 'degree'. If 'use_normalization' is % set to 'true' then kernel matrix will be normalized by the square roots % of the diagonal entries. % Poly print Poly set_kernel POLY REAL 10 4 0 1 set_features TRAIN ../data/fm_train_real.dat km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_real.dat km_test.txt = get_kernel_matrix TEST
% This is an example for the initialization of the PolyMatchString kernel on string data. % The PolyMatchString kernel sums over the matches of two stings of the same length and % takes the sum to the power of 'degree'. The strings consist of the characters 'ACGT' corresponding % to the DNA-alphabet. Each column of the matrices of type char corresponds to % one training/test example. % Poly Match String print PolyMatchString set_kernel POLYMATCH CHAR 10 3 0 set_features TRAIN ../data/fm_train_dna.dat DNA km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_dna.dat DNA km_test.txt = get_kernel_matrix TEST
% The PolyMatchWordString kernel is defined on strings of equal length. % The kernel sums over the matches of two stings of the same length and % takes the sum to the power of 'degree'. The strings in this example % consist of the characters 'ACGT' corresponding to the DNA-alphabet. Each % column of the matrices of type char corresponds to one training/test example. % Poly Match Word %print PolyMatchWord %set_kernel POLYMATCH WORD 10 2 1 1 %set_features TRAIN ../data/fm_train_word.dat %km_train.txt = get_kernel_matrix TRAIN %set_features TEST ../data/fm_test_word.dat %km_test.txt = get_kernel_matrix TEST
% The standard Sigmoid kernel computed on dense real valued features. % sigmoid print Sigmoid set_kernel SIGMOID REAL 10 1.2 1.3 set_features TRAIN ../data/fm_train_real.dat km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_real.dat km_test.txt = get_kernel_matrix TEST
% SimpleLocalityImprovedString kernel, is a ``simplified'' and better performing version of the Locality improved kernel. % Simple Locality Improved String print SimpleLocalityImprovedString set_kernel SLIK CHAR 10 5 5 7 set_features TRAIN ../data/fm_train_dna.dat DNA km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_dna.dat DNA km_test.txt = get_kernel_matrix TEST
% The well known Gaussian kernel (swiss army knife for SVMs) on sparse real valued features. % Sparse Gaussian print SparseGaussian set_kernel GAUSSIAN SPARSEREAL 10 1.4 set_features TRAIN ../data/fm_train_sparsereal.dat km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_sparsereal.dat km_test.txt = get_kernel_matrix TEST
% Computes the standard linear kernel on sparse real valued features. % Sparse Linear print SparseLinear set_kernel LINEAR SPARSEREAL 10 1.3 set_features TRAIN ../data/fm_train_sparsereal.dat km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_sparsereal.dat km_test.txt = get_kernel_matrix TEST
% Computes the standard polynomial kernel on sparse real valued features. % Sparse Poly print SparsePoly set_kernel POLY SPARSEREAL 10 3 1 1 set_features TRAIN ../data/fm_train_sparsereal.dat km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_sparsereal.dat km_test.txt = get_kernel_matrix TEST
% The class TOPFeatures implements TOP kernel features obtained from % two Hidden Markov models. % % It was used in % % K. Tsuda, M. Kawanabe, G. Raetsch, S. Sonnenburg, and K.R. Mueller. A new % discriminative kernel from probabilistic models. Neural Computation, % 14:2397-2414, 2002. % % which also has the details. % % Note that TOP-features are computed on the fly, so to be effective feature % caching should be enabled. % % It inherits its functionality from CSimpleFeatures, which should be % consulted for further reference. % % Plugin Estimate print PluginEstimate w/ HistogramWord set_features TRAIN ../data/fm_train_dna.dat DNA convert TRAIN STRING CHAR STRING WORD 3 2 0 n set_features TEST ../data/fm_test_dna.dat DNA convert TEST STRING CHAR STRING WORD 3 2 0 n new_plugin_estimator 1e-1 1e-1 set_labels TRAIN ../data/label_train_dna.dat train_estimator set_kernel HISTOGRAM WORD 10 km_train.txt = get_kernel_matrix TRAIN % not supported yet; % lab.txt = plugin_estimate_classify km_text.txt = get_kernel_matrix TEST
% The WeightedCommWordString kernel may be used to compute the weighted
% spectrum kernel (i.e. a spectrum kernel for 1 to K-mers, where each k-mer
% length is weighted by some coefficient \f$\beta_k\f$) from strings that have
% been mapped into unsigned 16bit integers.
%
% These 16bit integers correspond to k-mers. To applicable in this kernel they
% need to be sorted (e.g. via the SortWordString pre-processor).
%
% It basically uses the algorithm in the unix "comm" command (hence the name)
% to compute:
%
% k({\bf x},({\bf x'})= \sum_{k=1}^K\beta_k\Phi_k({\bf x})\cdot \Phi_k({\bf x'})
%
% where \f$\Phi_k\f$ maps a sequence \f${\bf x}\f$ that consists of letters in
% \f$\Sigma\f$ to a feature vector of size \f$|\Sigma|^k\f$. In this feature
% vector each entry denotes how often the k-mer appears in that \f${\bf x}\f$.
%
% Note that this representation is especially tuned to small alphabets
% (like the 2-bit alphabet DNA), for which it enables spectrum kernels
% of order 8.
%
% For this kernel the linadd speedups are quite efficiently implemented using
% direct maps.
%
% Weighted Comm Word String
print WeightedCommWordString
add_preproc SORTWORDSTRING
set_kernel WEIGHTEDCOMMSTRING WORD 10 0 FULL
set_features TRAIN ../data/fm_train_dna.dat DNA
convert TRAIN STRING CHAR STRING WORD 3 2 0 n
attach_preproc TRAIN
km_train.txt = get_kernel_matrix TRAIN
set_features TEST ../data/fm_test_dna.dat DNA
convert TEST STRING CHAR STRING WORD 3 2 0 n
attach_preproc TEST
km_test.txt = get_kernel_matrix TEST
% The Weighted Degree Position String kernel (Weighted Degree kernel with shifts). % % The WD-shift kernel of order d compares two sequences X and % Y of length L by summing all contributions of k-mer matches of % lengths k in 1...d, weighted by coefficients beta_k % allowing for a positional tolerance of up to shift s. % % Weighted Degree Position String print WeightedDegreePositionString set_kernel WEIGHTEDDEGREEPOS CHAR 10 20 set_features TRAIN ../data/fm_train_dna.dat DNA km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_dna.dat DNA km_test.txt = get_kernel_matrix TEST
% The Weighted Degree String kernel.
%
% The WD kernel of order d compares two sequences X and
% Y of length L by summing all contributions of k-mer matches of
% lengths k in 1...d , weighted by coefficients beta_k. It
% is defined as
%
% k(X, Y)=\sum_{k=1}^d\beta_k\sum_{l=1}^{L-k+1}I(u_{k,l}(X)=u_{k,l}(Y)).
%
% Here, $u_{k,l}(X)$ is the string of length k starting at position
% l of the sequence X and I(.) is the indicator function
% which evaluates to 1 when its argument is true and to 0
% otherwise.
%
% Weighted Degree String
print WeightedDegreeString
set_kernel WEIGHTEDDEGREE CHAR 10 20
set_features TRAIN ../data/fm_train_dna.dat DNA
km_train.txt = get_kernel_matrix TRAIN
set_features TEST ../data/fm_test_dna.dat DNA
km_test.txt = get_kernel_matrix TEST
% MKL_MultiClass print MKL_MultiClass set_labels TRAIN ../data/label_train_multiclass.dat clean_features TRAIN clean_features TEST set_kernel COMBINED 10 add_kernel 1 LINEAR REAL 10 add_features TRAIN ../data/fm_train_real.dat add_features TEST ../data/fm_test_real.dat add_kernel 1 GAUSSIAN REAL 10 1.2 add_features TRAIN ../data/fm_train_real.dat add_features TEST ../data/fm_test_real.dat add_kernel 1 POLY REAL 10 2 0 add_features TRAIN ../data/fm_train_real.dat add_features TEST ../data/fm_test_real.dat new_classifier MKL_MULTICLASS mkl_parameters 0.001 1 1.5 svm_epsilon 1e-5 c 1.2 train_classifier out.txt = classify ! rm out.txt
% In this example a kernel matrix is computed for a given real-valued data set. % The kernel used is the Chi2 kernel which operates on real-valued vectors. It % computes the chi-squared distance between sets of histograms. It is a very % useful distance in image recognition (used to detect objects). The preprocessor % LogPlusOne adds one to a dense real-valued vector and takes the logarithm of % each component of it. It is most useful in situations where the inputs are % counts: When one compares differences of small counts any difference may matter % a lot, while small differences in large counts don't. This is what this log % transformation controls for. % LogPlusOne print LogPlusOne add_preproc LOGPLUSONE set_kernel CHI2 REAL 10 1.4 set_features TRAIN ../data/fm_train_real.dat attach_preproc TRAIN km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_real.dat attach_preproc TEST km_test.txt = get_kernel_matrix TEST
% In this example a kernel matrix is computed for a given real-valued data set. % The kernel used is the Chi2 kernel which operates on real-valued vectors. It % computes the chi-squared distance between sets of histograms. It is a very % useful distance in image recognition (used to detect objects). The preprocessor % NormOne, normalizes vectors to have norm 1. % NormOne print NormOne add_preproc NORMONE set_kernel CHI2 REAL 10 1.4 set_features TRAIN ../data/fm_train_real.dat attach_preproc TRAIN km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_real.dat attach_preproc TEST km_test.txt = get_kernel_matrix TEST
% In this example a kernel matrix is computed for a given real-valued data set. % The kernel used is the Chi2 kernel which operates on real-valued vectors. It % computes the chi-squared distance between sets of histograms. It is a very % useful distance in image recognition (used to detect objects). The preprocessor % PruneVarSubMean substracts the mean from each feature and removes features that % have zero variance. % PruneVarSubMean print PruneVarSubMean add_preproc PRUNEVARSUBMEAN 1 set_kernel CHI2 REAL 10 1.4 set_features TRAIN ../data/fm_train_real.dat attach_preproc TRAIN km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_real.dat attach_preproc TEST km_test.txt = get_kernel_matrix TEST
% In this example a kernel matrix is computed for a given string data set. The % CommUlongString kernel is used to compute the spectrum kernel from strings that % have been mapped into unsigned 64bit integers. These 64bit integers correspond % to k-mers. To be applicable in this kernel the mapped k-mers have to be sorted. % This is done using the SortUlongString preprocessor, which sorts the indivual % strings in ascending order. The kernel function basically uses the algorithm in % the unix "comm" command (hence the name). Note that this representation enables % spectrum kernels of order 8 for 8bit alphabets (like binaries) and order 32 for % 2-bit alphabets like DNA. For this kernel the linadd speedups are implemented % (though there is room for improvement here when a whole set of sequences is % ADDed) using sorted lists. % SortUlongString print CommUlongString add_preproc SORTULONGSTRING set_kernel COMMSTRING ULONG 10 0 FULL set_features TRAIN ../data/fm_train_dna.dat DNA convert TRAIN STRING CHAR STRING ULONG 3 2 0 n attach_preproc TRAIN km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_dna.dat DNA convert TEST STRING CHAR STRING ULONG 3 2 0 n attach_preproc TEST km_test.txt = get_kernel_matrix TEST
% In this example a kernel matrix is computed for a given string data set. The % CommWordString kernel is used to compute the spectrum kernel from strings that % have been mapped into unsigned 16bit integers. These 16bit integers correspond % to k-mers. To be applicable in this kernel the mapped k-mers have to be sorted. % This is done using the SortWordString preprocessor, which sorts the indivual % strings in ascending order. The kernel function basically uses the algorithm in % the unix "comm" command (hence the name). Note that this representation is % especially tuned to small alphabets (like the 2-bit alphabet DNA), for which it % enables spectrum kernels of order up to 8. For this kernel the linadd speedups % are quite efficiently implemented using direct maps. % SortWordString print CommWordString add_preproc SORTWORDSTRING set_kernel COMMSTRING WORD 10 0 FULL set_features TRAIN ../data/fm_train_dna.dat DNA convert TRAIN STRING CHAR STRING WORD 3 2 0 n attach_preproc TRAIN km_train.txt = get_kernel_matrix TRAIN set_features TEST ../data/fm_test_dna.dat DNA convert TEST STRING CHAR STRING WORD 3 2 0 n attach_preproc TEST km_test.txt = get_kernel_matrix TEST
% In this example a kernelized version of ridge regression (KRR) is trained on a % real-valued data set. The KRR is trained with regularization parameter tau=1e-6 % and a gaussian kernel with width=0.8. % kernel ridge regression print KRR set_kernel GAUSSIAN REAL 10 2.1 set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_regression KRR krr_tau 1.2 c 0.017 train_regression set_features TEST ../data/fm_test_real.dat out.txt = classify ! rm out.txt
% In this example a support vector regression algorithm is trained on a % real-valued toy data set. The underlying library used for the SVR training is % LIBSVM. The SVR is trained with regularization parameter C=1 and a gaussian % kernel with width=2.1. % % For more details on LIBSVM solver see http://www.csie.ntu.edu.tw/~cjlin/libsvm/ . % LibSVR print LibSVR set_kernel GAUSSIAN REAL 10 2.1 set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_regression LIBSVR svr_tube_epsilon 1e-2 c 0.017 train_regression set_features TEST ../data/fm_test_real.dat out.txt = classify ! rm out.txt
% In this example a support vector regression algorithm is trained on a
% real-valued toy data set. The underlying library used for the SVR training is
% SVM^light. The SVR is trained with regularization parameter C=1 and a gaussian
% kernel with width=2.1.
%
% For more details on the SVM^light see
% T. Joachims. Making large-scale SVM learning practical. In Advances in Kernel
% Methods -- Support Vector Learning, pages 169-184. MIT Press, Cambridge, MA USA, 1999.
% SVR Light
%try
print SVRLight
set_kernel GAUSSIAN REAL 10 2.1
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_regression SVRLIGHT
svr_tube_epsilon 1e-2
c 0.017
train_regression
set_features TEST ../data/fm_test_real.dat
out.txt = classify
! rm out.txt
%catch
% disp('No support for SVRLight available.')
%end