load_features filename feature_class type target[ size[ comp_features]]
save_features filename type target
clean_features TRAIN|TEST
features = get_features TRAIN|TEST
add_features TRAIN|TEST features[ DNABINFILE|<ALPHABET>]
set_features TRAIN|TEST features[ DNABINFILE|<ALPHABET>]
set_ref_features TRAIN|TEST
del_last_features TRAIN|TEST
convert TRAIN|TEST from_class from_type to_class to_type[ order start gap reversed]
from_position_list TRAIN|TEST winsize shift[ skip]
slide_window TRAIN|TEST winsize shift[ skip]
reshape TRAIN|TEST num_feat num_vec
load_labels filename TRAIN|TARGET
set_labels TRAIN|TEST labels
labels = get_labels TRAIN|TEST
set_kernel_normalization IDENTITY|AVGDIAG|SQRTDIAG|FIRSTELEMENT size[ kernel-specific parameters]
set_kernel type size[ kernel-specific parameters]
add_kernel weight kernel-specific parameters
del_last_kernel
init_kernel TRAIN|TEST
clean_kernel
save_kernel filename
load_kernel_init filename
save_kernel_init filename
K = get_kernel_matrix
set_custom_kernel kernelmatrix DIAG|FULL|FULL2DIAG
set_WD_position_weights W[ TRAIN|TEST]
W = get_subkernel_weights
set_subkernel_weights W
set_subkernel_weights_combined W idx
set_last_subkernel_weights W
W = get_WD_position_weights
W = get_last_subkernel_weights
W = compute_by_subkernels
init_kernel_optimization
W = get_kernel_optimization
delete_kernel_optimization
use_diagonal_speedup USAGE_STR0|1USAGE_STR
set_kernel_optimization_type USAGE_STRFASTBUTMEMHUNGRY|SLOWBUTMEMEFFICIENTUSAGE_STR
set_prior_probs USAGE_STRpos probs, neg_probsUSAGE_STR
set_prior_probs_from_labels USAGE_STRlabelsUSAGE_STR
resize_kernel_cache size
set_distance type data type[ distance-specific parameters]
init_distance TRAIN|TEST
D = get_distance_matrix
result = classify
result = svm_classify
result = classify_example feature_vector_index
result = svm_classify_example feature_vector_index
bias weights = get_classifier [index in case of MultiClassSVM]
radi centers|merge_distances pairs = get_clustering
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 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 SVRLIGHT|LIBSVR|KRR
new_clustering KMEANS|HIERARCHICAL
filename type = load_classifier
save_classifier filename
number of SVMs in MultiClassSVM = get_num_svms
bias alphas = get_svm [index in case of MultiClassSVM]
set_svm bias alphas
objective = get_svm_objective
do_auc_maximization auc
set_perceptron_parameters learnrate maxiter
train_classifier [classifier-specific parameters]
train_regression
train_clustering
svm_train [classifier-specific parameters]
svm_test
svm_qpsize size
svm_max_qpsize size
svm_bufsize size
c C1[ C2]
svm_epsilon epsilon
svr_tube_epsilon tube_epsilon
svm_one_class_nu nu
mkl_parameters weight_epsilon C_MKL [ mkl_norm ]
svm_max_train_time max_train_time
use_mkl enable_mkl
use_shrinking enable_shrinking
use_batch_computation enable_batch_computation
use_linadd enable_linadd
svm_use_bias enable_bias
krr_tau tau
add_preproc preproc[, preproc-specific parameters]
del_preproc
load_preproc filename
save_preproc filename
attach_preproc TRAIN|TEST force
clean_preproc
new_hmm N M
load_hmm filename
save_hmm filename[ save_binary]
p q a b = get_hmm
append_hmm p q a b
append_model filename[ base1 base2]
set_hmm p q a b
set_hmm_as POS|NEG|TEST
chop chop
pseudo pseudo
load_defs filename init
result = hmm_classify
hmm_test output name[ ROC filename[ neglinear[ poslinear]]]
result = one_class_linear_hmm_classify
one_class_hmm_test output name[ ROC filename[ linear]]
result = one_class_hmm_classify
result = one_class_hmm_classify_example feature_vector_index
result = hmm_classify_example feature_vector_index
output_hmm
output_hmm_defined
likelihood = hmm_likelihood
likelihood
save_hmm_likelihood filename[ save_binary]
path likelihood = get_viterbi_path dim
vit_def
vit
bw
bw_def
bw_trans
linear_train
save_hmm_path filename[ save_binary]
convergence_criteria num_iterations epsilon
normalize_hmm [keep_dead_states]
add_states states value
permutation_entropy width seqnum
result = relative_entropy
result = entropy
set_feature_matrix features
new_plugin_estimator pos_pseudo neg_pseudo
train_estimator
test_estimator
result = plugin_estimate_classify_example feature_vector_index
result = plugin_estimate_classify
set_plugin_estimate emission_probs model_sizes
emission_probs model_sizes = get_plugin_estimate
best_path from to
prob path pos = best_path_2struct p q cmd_trans seq pos genestr penalties penalty_info nbest content_weights segment_sum_weights
set_plif_struct id name limits penalties transform min_value max_value use_cache use_svm
id name limits penalties transform min_value max_value use_cache use_svm = get_plif_struct
precompute_subkernels
precompute_content_svms sequence position_list weights
precompute_tiling_features intensities probe_pos tiling_plif_ids
set_model content_weights transition_pointers use_orf mod_words
prob path pos = best_path_trans p q nbest seq_path a_trans segment_loss
p_deriv q_deriv cmd_deriv penalties_deriv my_scores my_loss = 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]
prob path = best_path_no_b p q a max_iter
prob path = best_path_trans_simple p q cmd_trans seq nbest
prob path = best_path_no_b_trans p q cmd_trans max_iter nbest
W = compute_poim_wd max_order distribution
W = get_SPEC_consensus
W = get_SPEC_scoring max_order
W = get_WD_consensus
W = get_WD_scoring max_order
crc32 = crc string
! system_command
exit
quit
exec filename
set_output STDERR|STDOUT|filename
set_threshold threshold
init_random value_to_initialize_RNG_with
threads num_threads
translation = translate_string string, order, start
clear
tic
toc
print msg
echo level
loglevel ALL|DEBUG|INFO|NOTICE|WARN|ERROR|CRITICAL|ALERT|EMERGENCY
syntax_highlight ON|OFF
progress ON|OFF
version = get_version
help