Features
- load_features sg('load_features', filename, feature_class, type, target[, size[, comp_features]])
- save_features sg('save_features', filename, type, target)
- clean_features sg('clean_features', 'TRAIN|TEST')
- get_features [features]=sg('get_features', 'TRAIN|TEST')
- add_features sg('add_features', 'TRAIN|TEST', features[, DNABINFILE|<ALPHABET>])
- add_multiple_features sg('add_multiple_features', 'TRAIN|TEST', repetitions, features[, DNABINFILE|<ALPHABET>])
- add_dotfeatures sg('add_dotfeatures', 'TRAIN|TEST', features[, DNABINFILE|<ALPHABET>])
- set_features sg('set_features', 'TRAIN|TEST', features[, DNABINFILE|<ALPHABET>][, [from_position_list|slide_window], window size, [position_list|shift], skip)
- set_ref_features sg('set_ref_features', 'TRAIN|TEST')
- del_last_features sg('del_last_features', 'TRAIN|TEST')
- convert sg('convert', 'TRAIN|TEST', from_class, from_type, to_class, to_type[, order, start, gap, reversed])
- reshape sg('reshape', 'TRAIN|TEST, num_feat, num_vec)
- load_labels sg('load_labels', filename, 'TRAIN|TARGET')
- set_labels sg('set_labels', 'TRAIN|TEST', labels)
- get_labels [labels]=sg('get_labels', 'TRAIN|TEST')
Kernel
- set_kernel_normalization sg('set_kernel_normalization', IDENTITY|AVGDIAG|SQRTDIAG|FIRSTELEMENT|VARIANCE, size[, kernel-specific parameters])
- set_kernel sg('set_kernel', type, size[, kernel-specific parameters])
- add_kernel sg('add_kernel', weight, kernel-specific parameters)
- del_last_kernel sg('del_last_kernel')
- init_kernel sg('init_kernel', 'TRAIN|TEST')
- clean_kernel sg('clean_kernel')
- save_kernel sg('save_kernel', filename, 'TRAIN|TEST')
- get_kernel_matrix [K]]=sg('get_kernel_matrix', ['TRAIN|TEST')
- set_WD_position_weights sg('set_WD_position_weights', W[, 'TRAIN|TEST'])
- get_subkernel_weights [W]=sg('get_subkernel_weights')
- set_subkernel_weights sg('set_subkernel_weights', W)
- set_subkernel_weights_combined sg('set_subkernel_weights_combined', W, idx)
- get_dotfeature_weights_combined [W]=sg('get_dotfeature_weights_combined', 'TRAIN|TEST')
- set_dotfeature_weights_combined sg('set_dotfeature_weights_combined', W, idx)
- set_last_subkernel_weights sg('set_last_subkernel_weights', W)
- get_WD_position_weights [W]=sg('get_WD_position_weights')
- get_last_subkernel_weights [W]=sg('get_last_subkernel_weights')
- compute_by_subkernels [W]=sg('compute_by_subkernels')
- init_kernel_optimization sg('init_kernel_optimization')
- get_kernel_optimization [W]=sg('get_kernel_optimization')
- delete_kernel_optimization sg('delete_kernel_optimization')
- use_diagonal_speedup sg('use_diagonal_speedup', '0|1')
- set_kernel_optimization_type sg('set_kernel_optimization_type', 'FASTBUTMEMHUNGRY|SLOWBUTMEMEFFICIENT')
- set_solver sg('set_solver', 'AUTO|CPLEX|GLPK|INTERNAL')
- set_constraint_generator sg('set_constraint_generator', 'LIBSVM_ONECLASS|LIBSVM_MULTICLASS|LIBSVM|SVMLIGHT|LIGHT|GPBTSVM|MPDSVM|GNPPSVM|GMNPSVM')
- set_prior_probs sg('set_prior_probs', 'pos probs, neg_probs')
- set_prior_probs_from_labels sg('set_prior_probs_from_labels', 'labels')
- resize_kernel_cache sg('resize_kernel_cache', size)
Distance
- set_distance sg('set_distance', type, data type[, distance-specific parameters])
- init_distance sg('init_distance', 'TRAIN|TEST')
- get_distance_matrix [D]=sg('get_distance_matrix')
Classifier
- classify [result]=sg('classify')
- svm_classify [result]=sg('svm_classify')
- classify_example [result]=sg('classify_example', feature_vector_index)
- svm_classify_example [result]=sg('svm_classify_example', feature_vector_index)
- get_classifier [bias, weights]=sg('get_classifier', [index in case of MultiClassSVM])
- get_clustering [radi, centers|merge_distances, pairs]=sg('get_clustering')
- new_svm sg('new_svm', 'LIBSVM_ONECLASS|LIBSVM_MULTICLASS|LIBSVM|SVMLIGHT|LIGHT|SVMLIN|GPBTSVM|MPDSVM|GNPPSVM|GMNPSVM|SUBGRADIENTSVM|WDSVMOCAS|SVMOCAS|SVMSGD|SVMBMRM|SVMPERF|KERNELPERCEPTRON|PERCEPTRON|LIBLINEAR_LR|LIBLINEAR_L2|LDA|LPM|LPBOOST|SUBGRADIENTLPM|KNN')
- new_classifier sg('new_classifier', 'LIBSVM_ONECLASS|LIBSVM_MULTICLASS|LIBSVM|SVMLIGHT|LIGHT|SVMLIN|GPBTSVM|MPDSVM|GNPPSVM|GMNPSVM|SUBGRADIENTSVM|WDSVMOCAS|SVMOCAS|SVMSGD|SVMBMRM|SVMPERF|KERNELPERCEPTRON|PERCEPTRON|LIBLINEAR_LR|LIBLINEAR_L2|LDA|LPM|LPBOOST|SUBGRADIENTLPM|KNN')
- new_regression sg('new_regression', 'SVRLIGHT|LIBSVR|KRR')
- new_clustering sg('new_clustering', 'KMEANS|HIERARCHICAL')
- load_classifier [filename, type]=sg('load_classifier')
- save_classifier sg('save_classifier', filename)
- get_num_svms [number of SVMs in MultiClassSVM]=sg('get_num_svms')
- get_svm [bias, alphas]=sg('get_svm', [index in case of MultiClassSVM])
- set_svm sg('set_svm', bias, alphas)
- set_linear_classifier sg('set_linear_classifier', bias, w)
- get_svm_objective [objective]=sg('get_svm_objective')
- compute_svm_primal_objective [objective]=sg('compute_svm_primal_objective')
- compute_svm_dual_objective [objective]=sg('compute_svm_dual_objective')
- compute_mkl_primal_objective [objective]=sg('compute_mkl_primal_objective')
- compute_mkl_dual_objective [objective]=sg('compute_mkl_dual_objective')
- compute_relative_mkl_duality_gap [gap]=sg('compute_relative_mkl_duality_gap')
- compute_absolute_mkl_duality_gap [gap]=sg('compute_absolute_mkl_duality_gap')
- do_auc_maximization sg('do_auc_maximization', 'auc')
- set_perceptron_parameters sg('set_perceptron_parameters', learnrate, maxiter)
- train_classifier sg('train_classifier', [classifier-specific parameters])
- train_regression sg('train_regression')
- train_clustering sg('train_clustering')
- svm_train sg('svm_train', [classifier-specific parameters])
- svm_test sg('svm_test')
- svm_qpsize sg('svm_qpsize', size)
- svm_max_qpsize sg('svm_max_qpsize', size)
- svm_bufsize sg('svm_bufsize', size)
- c sg('c', C1[, C2])
- svm_epsilon sg('svm_epsilon', epsilon)
- svr_tube_epsilon sg('svr_tube_epsilon', tube_epsilon)
- svm_nu sg('svm_nu', nu)
- mkl_parameters sg('mkl_parameters', weight_epsilon, C_MKL [, mkl_norm ])
- svm_max_train_time sg('svm_max_train_time', max_train_time)
- use_shrinking sg('use_shrinking', enable_shrinking)
- use_batch_computation sg('use_batch_computation', enable_batch_computation)
- use_linadd sg('use_linadd', enable_linadd)
- svm_use_bias sg('svm_use_bias', enable_bias)
- mkl_use_interleaved_optimization sg('mkl_use_interleaved_optimization', enable_interleaved_optimization)
- krr_tau sg('krr_tau', tau)
Preprocessors
- add_preproc sg('add_preproc', preproc[, preproc-specific parameters])
- del_preproc sg('del_preproc')
- attach_preproc sg('attach_preproc', 'TRAIN|TEST', force)
- clean_preproc sg('clean_preproc')
HMM
- new_hmm sg('new_hmm', N, M)
- load_hmm sg('load_hmm', filename)
- save_hmm sg('save_hmm', filename[, save_binary])
- get_hmm [p, q, a, b]=sg('get_hmm')
- append_hmm sg('append_hmm', p, q, a, b)
- append_model sg('append_model', 'filename'[, base1, base2])
- set_hmm sg('set_hmm', p, q, a, b)
- set_hmm_as sg('set_hmm_as', POS|NEG|TEST)
- chop sg('chop', chop)
- pseudo sg('pseudo', pseudo)
- load_defs sg('load_defs', filename, init)
- hmm_classify [result]=sg('hmm_classify')
- hmm_test sg('hmm_test', output name[, ROC filename[, neglinear[, poslinear]]])
- one_class_linear_hmm_classify [result]=sg('one_class_linear_hmm_classify')
- one_class_hmm_test sg('one_class_hmm_test', output name[, ROC filename[, linear]])
- one_class_hmm_classify [result]=sg('one_class_hmm_classify')
- one_class_hmm_classify_example [result]=sg('one_class_hmm_classify_example', feature_vector_index)
- hmm_classify_example [result]=sg('hmm_classify_example', feature_vector_index)
- output_hmm sg('output_hmm')
- output_hmm_defined sg('output_hmm_defined')
- hmm_likelihood [likelihood]=sg('hmm_likelihood')
- likelihood sg('likelihood')
- save_hmm_likelihood sg('save_hmm_likelihood', filename[, save_binary])
- get_viterbi_path [path, likelihood]=sg('get_viterbi_path', dim)
- vit_def sg('vit_def')
- vit sg('vit')
- bw sg('bw')
- bw_def sg('bw_def')
- bw_trans sg('bw_trans')
- linear_train sg('linear_train')
- save_hmm_path sg('save_hmm_path', filename[, save_binary])
- convergence_criteria sg('convergence_criteria', num_iterations, epsilon)
- normalize_hmm sg('normalize_hmm', [keep_dead_states])
- add_states sg('add_states', states, value)
- permutation_entropy sg('permutation_entropy', width, seqnum)
- relative_entropy [result]=sg('relative_entropy')
- entropy [result]=sg('entropy')
- set_feature_matrix sg('set_feature_matrix', features)
- set_feature_matrix_sparse sg('set_feature_matrix_sparse', sp1, sp2)
- new_plugin_estimator sg('new_plugin_estimator', pos_pseudo, neg_pseudo)
- train_estimator sg('train_estimator')
- test_estimator sg('test_estimator')
- plugin_estimate_classify_example [result]=sg('plugin_estimate_classify_example', feature_vector_index)
- plugin_estimate_classify [result]=sg('plugin_estimate_classify')
- set_plugin_estimate sg('set_plugin_estimate', emission_probs, model_sizes)
- get_plugin_estimate [emission_probs, model_sizes]=sg('get_plugin_estimate')
Structure
- best_path sg('best_path', from, to)
- best_path_2struct [prob, path, pos]=sg('best_path_2struct', p, q, cmd_trans, seq, pos, genestr, penalties, penalty_info, nbest, content_weights, segment_sum_weights)
- set_plif_struct sg('set_plif_struct', id, name, limits, penalties, transform, min_value, max_value, use_cache, use_svm)
- get_plif_struct [id, name, limits, penalties, transform, min_value, max_value, use_cache, use_svm]=sg('get_plif_struct')
- precompute_subkernels sg('precompute_subkernels')
- precompute_content_svms sg('precompute_content_svms', sequence, position_list, weights)
- get_lin_feat [lin_feat]=sg('get_lin_feat')
- set_lin_feat sg('set_lin_feat', lin_feat)
- init_dyn_prog sg('init_dyn_prog', num_svms)
- init_intron_list sg('init_intron_list', start_positions, end_positions, quality)
- precompute_tiling_features sg('precompute_tiling_features', intensities, probe_pos, tiling_plif_ids)
- long_transition_settings sg('long_transition_settings', use_long_transitions, threshold, max_len)
- set_model sg('set_model', content_weights, transition_pointers, use_orf, mod_words)
- best_path_trans [prob, path, pos]=sg('best_path_trans', p, q, nbest, seq_path, a_trans, segment_loss)
- best_path_trans_deriv [p_deriv, q_deriv, cmd_deriv, penalties_deriv, my_scores, my_loss]=sg('best_path_trans_deriv', , my_path, my_pos, p, q, cmd_trans, seq, pos, genestr, penalties, state_signals, penalty_info, dict_weights, mod_words [, segment_loss, segmend_ids_mask])
POIM
- compute_poim_wd [W]=sg('compute_poim_wd', max_order, distribution)
- get_SPEC_consensus [W]=sg('get_SPEC_consensus')
- get_SPEC_scoring [W]=sg('get_SPEC_scoring', max_order)
- get_WD_consensus [W]=sg('get_WD_consensus')
- get_WD_scoring [W]=sg('get_WD_scoring', max_order)
Utility
- crc [crc32]=sg('crc', string)
- ! sg('!', system_command)
- exit sg('exit')
- quit sg('quit')
- exec sg('exec', filename)
- set_output sg('set_output', 'STDERR|STDOUT|filename')
- set_threshold sg('set_threshold', threshold)
- init_random sg('init_random', value_to_initialize_RNG_with)
- threads sg('threads', num_threads)
- translate_string [translation]=sg('translate_string', string, order, start)
- clear sg('clear')
- tic sg('tic')
- toc sg('toc')
- print sg('print', msg)
- echo sg('echo', level)
- loglevel sg('loglevel', 'ALL|DEBUG|INFO|NOTICE|WARN|ERROR|CRITICAL|ALERT|EMERGENCY')
- syntax_highlight sg('syntax_highlight', 'ON|OFF')
- progress sg('progress', 'ON|OFF')
- get_version [version]=sg('get_version')
- help sg('help')
- whos sg('whos')
- run_python [results]=sg('run_python', 'Var1', Var1, 'Var2', Var2,..., python_function)
- run_octave [results]=sg('run_octave', 'Var1', Var1, 'Var2', Var2,..., octave_function)
- run_r [results]=sg('run_r', 'Var1', Var1, 'Var2', Var2,..., r_function)
SHOGUN 机器学习工具包 - 项目文档