.. _`matindex_skl`: ==================================== CoSMoMVPA functions - skeleton files ==================================== .. toctree:: :maxdepth: 2 :hidden: matlab/cosmo_wizard_set_config_skl matlab/cosmo_align_skl matlab/cosmo_anova_feature_selector_skl matlab/cosmo_average_samples_skl matlab/cosmo_balance_dataset_skl matlab/cosmo_balance_partitions_skl matlab/cosmo_cartprod_skl matlab/cosmo_check_dataset_skl matlab/cosmo_check_external_skl matlab/cosmo_check_neighborhood_skl matlab/cosmo_check_partitions_skl matlab/cosmo_chunkize_skl matlab/cosmo_classify_knn_skl matlab/cosmo_classify_lda_skl matlab/cosmo_classify_libsvm_skl matlab/cosmo_classify_matlabcsvm_skl matlab/cosmo_classify_matlabsvm_skl matlab/cosmo_classify_matlabsvm_2class_skl matlab/cosmo_classify_meta_feature_selection_skl matlab/cosmo_classify_naive_bayes_skl matlab/cosmo_classify_nn_skl matlab/cosmo_classify_svm_skl matlab/cosmo_cluster_neighborhood_skl matlab/cosmo_clusterize_skl matlab/cosmo_config_skl matlab/cosmo_confusion_matrix_skl matlab/cosmo_convert_neighborhood_skl matlab/cosmo_corr_skl matlab/cosmo_correlation_measure_skl matlab/cosmo_cross_neighborhood_skl matlab/cosmo_crossvalidate_skl matlab/cosmo_crossvalidation_measure_skl matlab/cosmo_dataset_slice_fa_skl matlab/cosmo_dataset_slice_sa_skl matlab/cosmo_dim_find_skl matlab/cosmo_dim_generalization_measure_skl matlab/cosmo_dim_insert_skl matlab/cosmo_dim_match_skl matlab/cosmo_dim_prune_skl matlab/cosmo_dim_remove_skl matlab/cosmo_dim_rename_skl matlab/cosmo_dim_slice_skl matlab/cosmo_dim_transpose_skl matlab/cosmo_dir_skl matlab/cosmo_disp_skl matlab/cosmo_dissimilarity_matrix_measure_skl matlab/cosmo_distatis_skl matlab/cosmo_find_local_extrema_skl matlab/cosmo_flatten_skl matlab/cosmo_fmri_convert_xform_skl matlab/cosmo_fmri_dataset_skl matlab/cosmo_fmri_deoblique_skl matlab/cosmo_fmri_orientation_skl matlab/cosmo_fmri_reorient_skl matlab/cosmo_fx_skl matlab/cosmo_independent_samples_partitioner_skl matlab/cosmo_index_unique_skl matlab/cosmo_interval_neighborhood_skl matlab/cosmo_isequaln_skl matlab/cosmo_isfield_skl matlab/cosmo_make_temp_filename_skl matlab/cosmo_map2fmri_skl matlab/cosmo_map2meeg_skl matlab/cosmo_map2surface_skl matlab/cosmo_map_pca_skl matlab/cosmo_mask_dim_intersect_skl matlab/cosmo_match_skl matlab/cosmo_measure_clusters_skl matlab/cosmo_meeg_baseline_correct_skl matlab/cosmo_meeg_chan_neighborhood_skl matlab/cosmo_meeg_chan_neighbors_skl matlab/cosmo_meeg_chantype_skl matlab/cosmo_meeg_dataset_skl matlab/cosmo_meeg_find_layout_skl matlab/cosmo_meeg_layout_collection_skl matlab/cosmo_meeg_read_layout_skl matlab/cosmo_meeg_senstype2layout_mapping_skl matlab/cosmo_meeg_senstype_collection_skl matlab/cosmo_meta_feature_selection_classifier_skl matlab/cosmo_montecarlo_cluster_stat_skl matlab/cosmo_montecarlo_phase_stat_skl matlab/cosmo_naive_bayes_classifier_searchlight_skl matlab/cosmo_nchoosek_partitioner_skl matlab/cosmo_neighborhood_split_skl matlab/cosmo_nfold_partitioner_skl matlab/cosmo_normalize_skl matlab/cosmo_norminv_skl matlab/cosmo_notify_test_skipped_skl matlab/cosmo_oddeven_partitioner_skl matlab/cosmo_overlap_skl matlab/cosmo_parallel_get_nproc_available_skl matlab/cosmo_parcellfun_skl matlab/cosmo_pca_skl matlab/cosmo_pdist_skl matlab/cosmo_phase_itc_skl matlab/cosmo_phase_stat_skl matlab/cosmo_plot_slices_skl matlab/cosmo_publish_run_scripts_skl matlab/cosmo_rand_skl matlab/cosmo_randomize_targets_skl matlab/cosmo_randperm_skl matlab/cosmo_remove_useless_data_skl matlab/cosmo_run_tests_skl matlab/cosmo_sample_unique_skl matlab/cosmo_searchlight_skl matlab/cosmo_set_path_skl matlab/cosmo_show_progress_skl matlab/cosmo_singleton_neighborhood_skl matlab/cosmo_skip_test_if_no_external_skl matlab/cosmo_slice_skl matlab/cosmo_sphere_offsets_skl matlab/cosmo_spherical_neighborhood_skl matlab/cosmo_split_skl matlab/cosmo_squareform_skl matlab/cosmo_stack_skl matlab/cosmo_stat_skl matlab/cosmo_statcode_skl matlab/cosmo_strjoin_skl matlab/cosmo_strsplit_skl matlab/cosmo_structjoin_skl matlab/cosmo_surface_dataset_skl matlab/cosmo_surficial_neighborhood_skl matlab/cosmo_synthetic_dataset_skl matlab/cosmo_tail_skl matlab/cosmo_target_dsm_corr_measure_skl matlab/cosmo_tiedrank_skl matlab/cosmo_type_skl matlab/cosmo_unflatten_skl matlab/cosmo_vol_coordinates_skl matlab/cosmo_vol_grid_convert_skl matlab/cosmo_warning_skl matlab/cosmo_winner_indices_skl matlab/cosmo_wtf_skl =================================================== ================================================================================================= --------------------------------------------------- ------------------------------------------------------------------------------------------------- **All functions** --------------------------------------------------- ------------------------------------------------------------------------------------------------- :ref:`cosmo_align_skl` find permutation so that values in two inputs are matched :ref:`cosmo_anova_feature_selector_skl` find the features that show the most variance between classes :ref:`cosmo_average_samples_skl` average subsets of samples by unique combinations of sample attributes :ref:`cosmo_balance_dataset_skl` sub-sample a dataset to have an equal number of samples for each target :ref:`cosmo_balance_partitions_skl` balances a partition so that each target occurs equally often in each training and test chunk :ref:`cosmo_cartprod_skl` returns the cartesian product with all combinations of the input :ref:`cosmo_check_dataset_skl` Check consistency of a dataset. :ref:`cosmo_check_external_skl` Checks whether a certain external toolbox exists, or list citation info :ref:`cosmo_check_neighborhood_skl` check that a neighborhood is kosher :ref:`cosmo_check_partitions_skl` check whether partitions are balanced and not double-dippy :ref:`cosmo_chunkize_skl` assigns chunks that are as balanced as possible based on targets. :ref:`cosmo_classify_knn_skl` k-nearest neighbor classifier :ref:`cosmo_classify_lda_skl` linear discriminant analysis classifier - without prior :ref:`cosmo_classify_libsvm_skl` libsvm-based SVM classifier :ref:`cosmo_classify_matlabcsvm_skl` svm classifier wrapper (around fitcsvm) :ref:`cosmo_classify_matlabsvm_2class_skl` svm classifier wrapper (around svmtrain/svmclassify) :ref:`cosmo_classify_matlabsvm_skl` SVM multi-classifier using matlab's SVM implementation :ref:`cosmo_classify_meta_feature_selection_skl` meta classifier that uses feature selection on the training data :ref:`cosmo_classify_naive_bayes_skl` naive bayes classifier :ref:`cosmo_classify_nn_skl` nearest neighbor classifier :ref:`cosmo_classify_svm_skl` classifier wrapper that uses either matlab's or libsvm's SVM. :ref:`cosmo_cluster_neighborhood_skl` define neighborhood suitable for cluster-based analysis :ref:`cosmo_clusterize_skl` fast depth-first clustering based on equal values of neighbors :ref:`cosmo_config_skl` return a struc with configuration settings, or store such settings :ref:`cosmo_confusion_matrix_skl` Returns a confusion matrix :ref:`cosmo_convert_neighborhood_skl` Converts between cell, matrix and struct representations of neighborhoods :ref:`cosmo_corr_skl` Computes correlation - faster than than matlab's "corr" for Pearson. :ref:`cosmo_correlation_measure_skl` Computes a split-half correlation measure :ref:`cosmo_cross_neighborhood_skl` cross neighborhoods along dataset dimensions :ref:`cosmo_crossvalidate_skl` performs cross-validation using a classifier :ref:`cosmo_crossvalidation_measure_skl` performs cross-validation using a classifier :ref:`cosmo_dataset_slice_fa_skl` Slice a dataset by features (columns) [deprecated] :ref:`cosmo_dataset_slice_sa_skl` Slice a dataset by samples (rows) [deprecated] :ref:`cosmo_dim_find_skl` find dimension attribute in dataset :ref:`cosmo_dim_generalization_measure_skl` measure generalization across pairwise combinations over time (or any other dimension) :ref:`cosmo_dim_insert_skl` insert a dataset dimension :ref:`cosmo_dim_match_skl` return a mask indicating match of dataset dimensions with values :ref:`cosmo_dim_prune_skl` prune dataset dimension values that are not used after slicing :ref:`cosmo_dim_remove_skl` remove a dataset dimension :ref:`cosmo_dim_rename_skl` rename dimension attribute name :ref:`cosmo_dim_slice_skl` slice and prune a dataset with dimension attributes [deprecated] :ref:`cosmo_dim_transpose_skl` move a dataset dimension from samples to features or vice versa :ref:`cosmo_dir_skl` list files recursively in a directory :ref:`cosmo_disp_skl` display the input as a string representation :ref:`cosmo_dissimilarity_matrix_measure_skl` Compute a dissimilarity matrix measure :ref:`cosmo_distatis_skl` apply DISTATIS measure to each feature :ref:`cosmo_find_local_extrema_skl` find local extrema in a dataset using a neighborhood :ref:`cosmo_flatten_skl` flattens an arbitrary array to a dataset structure :ref:`cosmo_fmri_convert_xform_skl` convert xform code between numeric and string in fmri dataset :ref:`cosmo_fmri_dataset_skl` load an fmri volumetric dataset :ref:`cosmo_fmri_deoblique_skl` de-oblique a dataset :ref:`cosmo_fmri_orientation_skl` get orientation of a dataset :ref:`cosmo_fmri_reorient_skl` Change the orientation of an fmri dataset :ref:`cosmo_fx_skl` apply a function to unique combinations of .sa or .fa values :ref:`cosmo_independent_samples_partitioner_skl` Compute partitioning scheme based on dataset with independent samples :ref:`cosmo_index_unique_skl` index unique (combinations of) elements :ref:`cosmo_interval_neighborhood_skl` compute neighborhoods stretching intervals :ref:`cosmo_isequaln_skl` compares two input for equality with NaNs considered being equal :ref:`cosmo_isfield_skl` checks the presence of (possibly nested) fieldnames in a struct :ref:`cosmo_make_temp_filename_skl` give temporary filename that does not exist when this function is called :ref:`cosmo_map2fmri_skl` maps a dataset structure to a NIFTI, AFNI, or BV structure or file :ref:`cosmo_map2meeg_skl` maps a dataset to a FieldTrip or EEGlab structure or file :ref:`cosmo_map2surface_skl` maps a dataset structure to AFNI/SUMA NIML dset or BV SMP file :ref:`cosmo_map_pca_skl` normalize dataset either by estimating or applying estimated parameters :ref:`cosmo_mask_dim_intersect_skl` find intersection mask across a set of datasets :ref:`cosmo_match_skl` returns a mask indicating matching occurences in two arrays or cells relative to the second array :ref:`cosmo_measure_clusters_skl` General cluster measure :ref:`cosmo_meeg_baseline_correct_skl` correct baseline of MEEG dataset :ref:`cosmo_meeg_chan_neighborhood_skl` determine neighborhood of channels in MEEG dataset :ref:`cosmo_meeg_chan_neighbors_skl` find neighbors of MEEG channels :ref:`cosmo_meeg_chantype_skl` return channel types and optionally a feature mask matching a type :ref:`cosmo_meeg_dataset_skl` Returns a dataset structure based on MEEG data :ref:`cosmo_meeg_find_layout_skl` finds an MEEG channel layout associated with a dataset :ref:`cosmo_meeg_layout_collection_skl` return supported MEEG channel layouts :ref:`cosmo_meeg_read_layout_skl` Read FieldTrip layout :ref:`cosmo_meeg_senstype2layout_mapping_skl` return mapping from MEEG sensor types to sensor layouts :ref:`cosmo_meeg_senstype_collection_skl` return supported MEEG acquisition systems and their channel labels :ref:`cosmo_meta_feature_selection_classifier_skl` meta classifier that uses feature selection on the training data [deprecated] :ref:`cosmo_montecarlo_cluster_stat_skl` compute random-effect cluster statistics corrected for multiple comparisons :ref:`cosmo_montecarlo_phase_stat_skl` compute phase statistics based on Monte Carlo simulation :ref:`cosmo_naive_bayes_classifier_searchlight_skl` Run (fast) Naive Bayes classifier searchlight with crossvalidation :ref:`cosmo_nchoosek_partitioner_skl` partitions for into nchoosek(n,k) parititions with optional grouping schemas. :ref:`cosmo_neighborhood_split_skl` partitions a neighborhood in a cell with (smaller) neigborhoods :ref:`cosmo_nfold_partitioner_skl` generates an n-fold partition scheme :ref:`cosmo_normalize_skl` normalize dataset either by estimating or applying estimated parameters :ref:`cosmo_norminv_skl` compute inverse normal cumulative distribution function :ref:`cosmo_notify_test_skipped_skl` notify that a test in the test suite is skipped :ref:`cosmo_oddeven_partitioner_skl` generates an odd-even partition scheme :ref:`cosmo_overlap_skl` compute overlap between vectors or cellstrings in two cells :ref:`cosmo_parallel_get_nproc_available_skl` get number of processes available from Matlab parallel processing pool :ref:`cosmo_parcellfun_skl` applies a function to elements in a cell in parallel :ref:`cosmo_pca_skl` Principal Component Analysis :ref:`cosmo_pdist_skl` compute pair-wise distance between samples in a matrix :ref:`cosmo_phase_itc_skl` compute phase inter trial coherence :ref:`cosmo_phase_stat_skl` Compute phase perturbation, or opposition sum or product phase statistic :ref:`cosmo_plot_slices_skl` Plots a set of slices from a dataset, nifti image, or 3D data array :ref:`cosmo_publish_run_scripts_skl` helper function to publish example scripts (for developers) :ref:`cosmo_rand_skl` generate uniform pseudo-random numbers, optionally using a seed value :ref:`cosmo_randomize_targets_skl` provides randomized target labels :ref:`cosmo_randperm_skl` generate random permutation of integers :ref:`cosmo_remove_useless_data_skl` remove 'useless' (constant and/or non-finite) samples or features :ref:`cosmo_run_tests_skl` run unit and documentation tests :ref:`cosmo_sample_unique_skl` sample without replacement from subsets of integers in balanced manner :ref:`cosmo_searchlight_skl` Generic searchlight function returns a map of results computed at each searchlight location :ref:`cosmo_set_path_skl` set the matlab path for CoSMoMVPA :ref:`cosmo_show_progress_skl` Shows a progress bar, and time elapsed and expected to complete. :ref:`cosmo_singleton_neighborhood_skl` return neighborhood where each feature is only neighbor of itself :ref:`cosmo_skip_test_if_no_external_skl` Notify that test in the test suite is skipped if no external is present :ref:`cosmo_slice_skl` Slice a dataset by samples (the default) or features :ref:`cosmo_sphere_offsets_skl` computes sub index offsets for voxels in a sphere :ref:`cosmo_spherical_neighborhood_skl` computes neighbors for a spherical searchlight :ref:`cosmo_split_skl` splits a dataset by unique values in (a) sample or feature attribute(s). :ref:`cosmo_squareform_skl` converts pair-wise distances between matrix and vector form :ref:`cosmo_stack_skl` stacks multiple datasets to yield a single dataset :ref:`cosmo_stat_skl` compute t-test or F-test (ANOVA) statistic :ref:`cosmo_statcode_skl` Convert statcode for different analysis packages :ref:`cosmo_strjoin_skl` joins strings using a delimeter string :ref:`cosmo_strsplit_skl` splits a string based on another delimeter string :ref:`cosmo_structjoin_skl` joins values in structs or key-value pairs :ref:`cosmo_surface_dataset_skl` Returns a dataset structure based on surface mesh data :ref:`cosmo_surficial_neighborhood_skl` neighborhood definition for surface-based searchlight :ref:`cosmo_synthetic_dataset_skl` generate synthetic dataset :ref:`cosmo_tail_skl` find values in left or right tail of a vector or string :ref:`cosmo_target_dsm_corr_measure_skl` measure correlation with target dissimilarity matrix :ref:`cosmo_tiedrank_skl` Compute ranks for the input along the specified dimension :ref:`cosmo_type_skl` print or return ASCII contents of a file :ref:`cosmo_unflatten_skl` unflattens a dataset from 2 to (1+K) dimensions. :ref:`cosmo_vol_coordinates_skl` convert to and from spatial (x,y,z) coordinates :ref:`cosmo_vol_grid_convert_skl` convert between volumetric (fmri) and grid-based (meeg source) dataset :ref:`cosmo_warning_skl` show a warning message; by default just once for each message :ref:`cosmo_winner_indices_skl` Given multiple predictions, get indices that were predicted most often. :ref:`cosmo_wizard_set_config_skl` GUI-based 'wizard' to set CoSMoMVPA configuration file :ref:`cosmo_wtf_skl` return system, toolbox and externals information =================================================== =================================================================================================