< Master index Index for lib >

Index for lib

Matlab files in this directory:

 adjust_loadingsfunction L = adjust_loadings(L)
 and_prodfunction out = and_prod(memberships)
 ann_bootfunction model = ann_boot(model, data)
 ann_estimateNeeds global variable eModel
 ann_newfunction out = knn_new(name)
 ann_trainfunction model = ann_train(model, data)
 arrowARROW Draw a line with an arrowhead.
 ask_class
 ask_isolate_classdata = ask_isolate_class(data)
 ask_ld
 ask_pcaX =
 ask_pca_factors
 ask_proportion_train
 assure_connectedThis function tests the mysql connection to the database.
 assure_reference_judge_selectedThis function checks data.idjudge_ref and data.class_labels
 atrtool_loadATRTOOL_LOAD M-file for atrtool_load.fig
 average_spectradata = data_average_by_colony(data)
 bmresult_draw_lines
 bmresult_draw_markersNew biomarker analysis result
 bmresult_go_sfsfunction bmresult = bmresult_go_sfs(bmresult, ds, ms, sfs_k, sfs_max_vars)
 bmresult_intersectNew biomarker analysis result
 bmresult_intersection_report
 bmresult_newNew biomarker analysis result
 bmresultline_build_mountainfunction o = bmresultline_build_mountain(o)
 bmresultline_detect_peaksfunction o = bmresultline_build_mountain(o)
 bmresultline_new
 bmresults_calculate_drawlines
 bmresults_draw_markersfunction draw_MARKER_MARKERS(results, flag_wntext)
 bmresults_new
 bmtoolBMTOOL M-file for bmtool.fig
 bmtool_setuplinesThis function is part of BMTOOL, not particularly useful in other contexts.
 bsearchbsearch(x,var)
 build_bm3_histogramfunction histogram = build_bm3_histogram(dataset, modelset, max_vars, perc_train, no_reps)
 calc_confusionfunction cc = calc_confusion(classes_row, classes_col, flag_percentage)
 calc_predictive_valuesfunction values = calc_predictive_values(cc)
 calc_ratefunction rate = est_get_rate(est, data_test)
 calc_sens_specfunction values = calc_sens_spec(cc, flag_mean=0)
 calculate_scatters[S_B, S_W] = calculate_scatters(data, flag_modified_s_b = 0, penalty=0)
 ccafunction [A, B] = cca(X, Y, P)
 cell2class_labelfuntion cl = cell2class_label(c)
 cla_bootfunction model = cla_boot(model, data)
 cla_estimatefunction [model, est] = cla_estimate(model, data)
 cla_newfunction out = cla_new(name)
 cla_trainfunction model = cla_train(model, data)
 class_labels2cellfunction out = class_labels2cell(class_labels, new_hierarchy)
 class_loadingsProduces class loadings as in JCB2007.PDF
 classes2booleanoutput = classes2boolean(classes, no_different)
 classes2labelsfunction out = classes2labels(classes, labels)
 classify_knn[valid, no_right] = classify_knn(k, train, valid)
 cmp_new
 colors3function varargout = colors3(scheme)
 colors_markersMakes default COLORS, MARKERS and MARKERSIZES global variables
 colors_othersLBMAP Returns specified Light-Bertlein colormap.
 compare_classesfunction result = compare_classes(classes1, labels1, classes2, labels2)
 confusion_str
 connect_to_cells
 crossvalindCROSSVALIND generates cross-validation indices
 csv_from_cellfunction s = csv_from_cell(results_table)
 data_2classes_ttest
 data_average_by_colonydata = data_average_by_colony(data)
 data_calculate_3dhisto.hist_space, o.cc, o.xx, o.mm, o.yy
 data_ccaCCA according to Hastie, The elements of Statistical Learning, 2001, Algorithm 3.2, p.68
 data_classes2codesfunction codes = data_classes2codes(data)
 data_curve2peakposfunction data = data_curve2peakpos(data, map)
 data_deconv
 data_difffunction data = data_diff(data, order)
 data_diff_sgfunction data = data_diff_sg(data, order, porder, ncoeff)
 data_draw_3dhist
 data_draw_covariancefunction data = data_draw_covariance(data, which = 'c', y_ref = mean(data.X, 1))
 data_draw_cvdata_draw_loadings(data, class_origin=-1, flag_abs = 0, threshold = 1, flag_print_peaks = 0,
 data_draw_histfunction data = data_draw_hist(data, index)
 data_draw_loadingsdata_draw_loadings(data, idx_fea, flag_abs = 0, threshold = 1, flag_print_peaks = 0,
 data_draw_pca_paretofunction data_draw_pca_pareto(dataset_name, no_pcs)
 data_eliminate_outliers_by_densityvarargout = data_eliminate_outliers_by_density(data)
 data_eliminate_outliers_by_distancevarargout = data_eliminate_outliers_by_distance(data, diff_order, threshold, flag_interactive)
 data_eliminate_outliers_by_distance2varargout = data_eliminate_outliers_by_distance(data, diff_order, threshold)
 data_eliminate_outliers_by_distance3varargout = data_eliminate_outliers_by_distance3(data, no_stds)
 data_eliminate_outliers_by_distance4varargout = data_eliminate_outliers_by_distance3(data, no_stds)
 data_eliminate_var_0new = data_eliminate_var_0(data, threshold = 1e-10)
 data_get_class_labelsfunction labels = data_get_class_labels(data)
 data_get_classes_alphafunction clalpha = data_get_classes_alpha(data)
 data_get_cvCalculated the 'cluster vectors'
 data_get_feature_namesnames = data_get_feature_names(data, idx_fea)
 data_get_legendfunction legends = data_get_legend(data)
 data_get_loadings_bootstrapfunction LL = data_get_loadings_bootstrap(data, fcn, percent, no_rep)
 data_get_no_classesfunction no_classes = data_no_classes(data)
 data_get_no_coloniesfunction data_get_no_colonies(data)
 data_get_titletitle = data_get_feature_names(data, idx_fea)
 data_isolate_classIsolates a particular class, so it will be class 0 and all the other will
 data_isolate_classesIsolates a particular class, so it will be class 0 and all the other will
 data_line_correctfunction data = data_line_correct(data, wns, no_convolutions)
 data_line_correct2function data = data_line_correct(data, wns, no_convolutions)
 data_line_correct_authenticfunction data = data_line_correct_authentic(data, wnrange1, wnrange2)
 data_loadings_combofunction d = data_loadings_combo(dataa)
 data_map_rowsfunction data = data_map_rows(data, idxnew)
 data_mergedata_merged = data_merge(dataa)
 data_newfunction out = data_new()
 data_new_from_databaseLoads spectra and classification from database
 data_new_from_txtdata = data_new_from_txt(filename, range = [])
 data_normalizeoutput = data_normalize(X, params)
 data_organize_classesfunction data = data_organize_classes(data)
 data_plothtemp = plot(data.x, data.X');
 data_plot_indicator_spectrumfunction o = data_plot_indicator_spectrum(data, maxy, color)
 data_plot_means
 data_plot_scatter_1dplot_scatter_1d(data, idx_fea, flag_distr=1)
 data_plot_scatter_2ddata_plot_scatter_2d(data, idx_fea, confidences = [], flag_text=0)
 data_plot_scatter_3dfunction varargout = data_plot_scatter_3d(data, idx_fea, confidences=[], flag_text=0)
 data_plot_scatter_3d2function varargout = data_plot_scatter_3d(data, idx_fea, confidences)
 data_randomize_classesfunction data = data_randomize_classes(data)
 data_scramblefunction out = data_scramble(data)
 data_select_coloniesfunction out = data_select_colonies(data, idxs_codes)
 data_select_features[data_new, new_x] = data_select_features(data, v, v_type)
 data_select_features_paired_ttestfunction data_select_features_paired_ttest(data, idx_reference=1)
 data_select_hierarchyfunction out = data_select_hierarchy(data, hierarchy)
 data_set_Xdata = data_set_X(data, X)
 data_sort_by_colonyfunction varargout = data_sort_by_colony(data)
 data_sort_class_labelsfunction data = data_sort_class_labels(data)
 data_sort_distance[data]
 data_split_classespieces = data_split_classes(data)
 data_split_mapfunction out = data_split_map(data, map)
 data_split_proportion[data1, data2] = data_split_proportion(proportion, data)
 data_thresholdfunction data = data_threshold(data, th)
 data_transform_ldafunction d2 = data_transform_lda(data, flag_sphere, flag_modified_s_b, P)
 data_transform_lda2function d2 = data_transform_lda2(data, flag_sphere=1, flag_modified_s_b=0, min_lds=1)
 data_transform_linearfunction data = data_transform_linear(data, L, new_domain_name='lt', X = data.X)
 data_transform_linear_crossvalfunction out = data_transform_linear_crossval(data, fcn, k)
 data_transform_pcad2 = data_transform_pca(data, no_factors = 0, flag_rotate_factors = 1)
 data_transform_pcaldad2 = data_transform_pcalda(data, no_factors = 0, flag_rotate_factors = 1, flag_modified_s_b = 0)
 data_transform_plsfunction d2 = data_transform_pls(data, no_factors)
 data_transform_plsldafunction ds_combo01 = data_transform_plslda(ds01, no_factors)
 data_transform_splinebasisfunction dscoef = data_transform_splinebasis(data, no_basis)
 data_trim_negativesfunction data = data_trim_negatives(data)
 data_update_background
 data_windowfunction data_window(data, xrange, xwindowinterval)
 datelabeldatelabel(opts) Make x-axis have nice date/time format
 dbresult_draw_linesCopyright 2010 Julio Trevisan, Plamen P. Angelov & Francis L. Martin.
 detect_peakspeak_indexes = detect_peaks(y, threshold, min_dist = 1, no_max = Inf, min_height = 0)
 detect_peaks2peak_indexes = detect_peaks(y, threshold, min_dist = 1, no_max = Inf, min_height = 0, no_conv = 6)
 diff_operatorfunction diff_operator(nf, order)
 dist_bootfunction model = dist_boot(model, data)
 dist_estimateOutputs:
 dist_newfunction out = dist_new(name)
 dist_trainfunction model = dist_train(model, data)
 distribution
 draw_covariancefunction data = data_draw_covariance(data, which = 'c', y_ref = mean(data.X, 1))
 draw_directiondraw_direction(v, linescale, arrowscale)
 draw_loadings_completefunction draw_loadings_complete(data, L, legends, flag_abs, threshold, flag_print_peaks, flag_plot_peaks)
 draw_projectionsdraw_projections(data, v)
 draw_zero_line
 eig_ordered[vv, lambdas] = eig_ordered(M)
 errorMeasure
 error_ellipseERROR_ELLIPSE - plot an error ellipse, or ellipsoid, defining confidence region
 error_ellipse2ERROR_ELLIPSE - plot an error ellipse, or ellipsoid, defining confidence region
 est_calc_classesfunction est_calc_classes(est)
 est_get_confusionfunction cc = est_get_confusion(est, data, flag_percentage=0)
 est_get_ratefunction rate = est_get_rate(est, data_test)
 est_newfunction out = est_new()
 est_new_from_model_datafunction out = est_new_from_model_data(model, data_test)
 est_set_Yfunction est = est_set_Y(est, Y)
 feasel_curves_from_casesThis routine extracts the wn X performance curves from o.cases
 feasel_generate_dssfunction dss = feasel_generate_dss(o)
 feasel_get_features_rankingfunction ranking = feasel_get_features_ranking(o, mode)
 feasel_get_performance_matrixfunction M = feasel_get_performance_matrix(o)
 feasel_goCopyright 2010 Julio Trevisan, Plamen P. Angelov & Francis L. Martin.
 feasel_go_exhaustivefunction o = feasel_go_stepwise(o)
 feasel_go_exhaustive2function o = feasel_go_exhaustive2(o)
 feasel_go_stepwisefunction o = feasel_go_stepwise(o)
 feasel_newfunction o = feasel_new()
 feasel_rank_features
 feasel_validatefunction o = feasel_validate(o, idxs_candidates)
 feasellog_newCopyright 2010 Julio Trevisan, Plamen P. Angelov & Francis L. Martin.
 feaselvalidationlog_newfunction o = feaselvalidationlog_new()
 felfunction s = fel(c, n)
 find_color
 find_filenamefunction name = find_filename(prefix, suffix = '', extension = 'txt')
 find_marker
 find_marker_size
 find_peaksfunction pp = find_peaks(v)
 fisher_ld[W_star]/[W_star, lambdas] =
 format_commafunction format_comma(ax)
 format_frankformat_frank(F, scale=1, additional_handles)
 format_number_with_commas
 format_wnfunction format_wn(par)
 good_file_namefunction name = good_file_name(name)
 gui_set_positionfunction gui_set_position(hObject)
 integratefunction I = integrate(X)
 interactive_feasel_for_biomarkers
 interactive_feasel_for_biomarkers_bm3This one is maintained so it can read some result file I have saved.
 interactive_feasel_for_biomarkers_old
 interactive_feasel_for_biomarkers_setuplinesNot standalone. This is part of the interactive_feasel_for_biomarkers application
 interactive_pcacrossvalPCA cross-validation aims to determine the "best" number of PCs to use in PCA
 interactive_read_from_database% Interactive reader from database
 knn_bootfunction model = knn_boot(model, data)
 knn_estimatefunction [model, est] = knn_estimate(model, data)
 knn_newfunction out = knn_new(name)
 knn_trainfunction model = knn_train(model, data)
 levelfunction H=level(func,x0,dxmax,col,precision,kmax);
 lin_bootfunction model = lin_boot(model, data)
 lin_estimatefunction [model, est] = lin_estimate(model, data)
 lin_newfunction out = lin_new(name)
 lin_trainfunction model = lin_train(model, data)
 load_mat_filefunction load_mat_file(filename, range = [])
 load_txt_fileloads txt file into DATA global variable
 maximizeMAXIMIZE maximize figure windows
 menufunction option = menu(title, options, cancel_label)
 mf_gaussfunction mm = mf_gauss(X, centers, radii)
 mnr_bootfunction model = mnr_boot(model, data)
 mnr_estimatefunction [model, est] = mnr_estimate(model, data)
 mnr_newfunction out = mnr_new(name)
 mnr_trainfunction model = mnr_train(model, data)
 model_bootfunction model = model_boot(model, data)
 model_get_classesfunction classes = model_get_classes(model, data)
 model_get_classes_targetsfunction targets = model_get_targets(model, data)
 model_get_no_outputsfunction no_outputs = model_get_no_outputs(model)
 model_get_targetsfunction targets = model_get_targets(model, data)
 model_newfunction model_new(name)
 modelset_bootfunction a = modelset_boot(a, data)
 modelset_create_modelsfunction a = modelset_boot(a, data)
 modelset_estimatefunction a = modelset_estimate(a, data)
 modelset_getbatchfunction a = modelset_getbatch(a, param_name)
 modelset_newfunction a = modelset_new(fcn_new_model, flag_split)
 modelset_plot_mffunction a = modelset_plot_mf(a, idxs_fea)
 modelset_prepare_datafunction data = modelset_prepare_data(o, data)
 modelset_setbatchInputs
 modelset_trainfunction a = modelset_train(a, data)
 mymMYM - Interact with a MySQL database server MEX
 mysqlMYSQL - Interact with a MySQL database server MEX
 normalize_rowsfunction cc = normalize_rows(cc)
 pause2function pause2()
 peak_closestresult = peak_closest(db, a)
 peak_dbZ = peak_db(x = None)
 peak_landmarksArguments:
 penalty_matrixfunction P = penalty_matrix(nf, dcoeff)
 plot_curve_pieceshandles{} = plot_curve_pieces(x, y, varargin)
 plot_indicator_spectrumfunction o = plot_indicator_spectrum(x, y, color)
 plot_peaksprint_peaks(x, y, threshold)
 plot_reconst
 pls[scores] = pls(X, Y, no_factors)
 princomp2Principal Component Analysis (PCA)
 print_peaksprint_peaks(x, y, threshold)
 quantile_landmarksfunction T = quantile_landmarks(I, no_quants, t_range=[1, i_end])
 read_n_save
 read_spectra_with_classificationLoads spectra and classification from database
 renumber_classesfunction classes = renumber_classes(classes_orig, class_labels_orig, class_labels_ref)
 resize_legend_markersfunction resize_legend_markers(size=12, flag_line=0)
 rgbrgb.m: translates a colour from multiple formats into matlab colour format
 rotatefactors2L = rotatefactors2(L, flag_normal)
 round_randomfunction x = round_random(x)
 rows_colsnum = ceil(num/2)*2; % "rounds" num to the next integer
 save_global_variablesSaves global variables IDEXPERIMENT, IDJUDGE_REF, class_labels, DATA and
 save_txt_filefunction save_txt_file(s, name)
 select_experiment
 select_load_fileflag_selected = select_load_file()
 select_load_mat_fileflag_selected = select_load_mat_file()
 select_load_txt_fileflag_selected = select_load_txt_file()
 select_reference_judge
 str2filenamefunction files = str2files(s)
 strip_codefunction c = strip_class(params)
 svm_bootfunction model = svm_boot(model, data)
 svm_estimatefunction [model, est] = svm_estimate(model, data)
 svm_newfunction out = svm_new(name)
 svm_trainfunction model = knn_train(model, data)
 v_ind2xfunction indexes = v_x2ind(x, indexes)
 v_x2indfunction indexes = v_x2ind(v, x)
 x_despike1load_mat_file('../mat/imran_pec29_oneclassonly.mat', [2100 200]);
 ypoll_get_confusionfunction cc = est_get_confusion(est, data, flag_percentage=0)
 ypoll_get_ratefunction rate = ypoll_get_rate(ypoll, ypoll_correct)
 ypoll_newfunction out = ypoll_new()
 ypoll_new_pollfunction [ypoll] = ypoll_new_poll(colony_codes, classes1, class_labels)

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