adjust_loadings | function L = adjust_loadings(L) |
and_prod | function out = and_prod(memberships) |
ann_boot | function model = ann_boot(model, data) |
ann_estimate | Needs global variable eModel |
ann_new | function out = knn_new(name) |
ann_train | function model = ann_train(model, data) |
arrow | ARROW Draw a line with an arrowhead. |
ask_class | |
ask_isolate_class | data = ask_isolate_class(data) |
ask_ld | |
ask_pca | X = |
ask_pca_factors | |
ask_proportion_train | |
assure_connected | This function tests the mysql connection to the database. |
assure_reference_judge_selected | This function checks data.idjudge_ref and data.class_labels |
atrtool_load | ATRTOOL_LOAD M-file for atrtool_load.fig |
average_spectra | data = data_average_by_colony(data) |
bmresult_draw_lines | |
bmresult_draw_markers | New biomarker analysis result |
bmresult_go_sfs | function bmresult = bmresult_go_sfs(bmresult, ds, ms, sfs_k, sfs_max_vars) |
bmresult_intersect | New biomarker analysis result |
bmresult_intersection_report | |
bmresult_new | New biomarker analysis result |
bmresultline_build_mountain | function o = bmresultline_build_mountain(o) |
bmresultline_detect_peaks | function o = bmresultline_build_mountain(o) |
bmresultline_new | |
bmresults_calculate_drawlines | |
bmresults_draw_markers | function draw_MARKER_MARKERS(results, flag_wntext) |
bmresults_new | |
bmtool | BMTOOL M-file for bmtool.fig |
bmtool_setuplines | This function is part of BMTOOL, not particularly useful in other contexts. |
bsearch | bsearch(x,var) |
build_bm3_histogram | function histogram = build_bm3_histogram(dataset, modelset, max_vars, perc_train, no_reps) |
calc_confusion | function cc = calc_confusion(classes_row, classes_col, flag_percentage) |
calc_predictive_values | function values = calc_predictive_values(cc) |
calc_rate | function rate = est_get_rate(est, data_test) |
calc_sens_spec | function values = calc_sens_spec(cc, flag_mean=0) |
calculate_scatters | [S_B, S_W] = calculate_scatters(data, flag_modified_s_b = 0, penalty=0) |
cca | function [A, B] = cca(X, Y, P) |
cell2class_label | funtion cl = cell2class_label(c) |
cla_boot | function model = cla_boot(model, data) |
cla_estimate | function [model, est] = cla_estimate(model, data) |
cla_new | function out = cla_new(name) |
cla_train | function model = cla_train(model, data) |
class_labels2cell | function out = class_labels2cell(class_labels, new_hierarchy) |
class_loadings | Produces class loadings as in JCB2007.PDF |
classes2boolean | output = classes2boolean(classes, no_different) |
classes2labels | function out = classes2labels(classes, labels) |
classify_knn | [valid, no_right] = classify_knn(k, train, valid) |
cmp_new | |
colors3 | function varargout = colors3(scheme) |
colors_markers | Makes default COLORS, MARKERS and MARKERSIZES global variables |
colors_others | LBMAP Returns specified Light-Bertlein colormap. |
compare_classes | function result = compare_classes(classes1, labels1, classes2, labels2) |
confusion_str | |
connect_to_cells | |
crossvalind | CROSSVALIND generates cross-validation indices |
csv_from_cell | function s = csv_from_cell(results_table) |
data_2classes_ttest | |
data_average_by_colony | data = data_average_by_colony(data) |
data_calculate_3dhist | o.hist_space, o.cc, o.xx, o.mm, o.yy |
data_cca | CCA according to Hastie, The elements of Statistical Learning, 2001, Algorithm 3.2, p.68 |
data_classes2codes | function codes = data_classes2codes(data) |
data_curve2peakpos | function data = data_curve2peakpos(data, map) |
data_deconv | |
data_diff | function data = data_diff(data, order) |
data_diff_sg | function data = data_diff_sg(data, order, porder, ncoeff) |
data_draw_3dhist | |
data_draw_covariance | function data = data_draw_covariance(data, which = 'c', y_ref = mean(data.X, 1)) |
data_draw_cv | data_draw_loadings(data, class_origin=-1, flag_abs = 0, threshold = 1, flag_print_peaks = 0, |
data_draw_hist | function data = data_draw_hist(data, index) |
data_draw_loadings | data_draw_loadings(data, idx_fea, flag_abs = 0, threshold = 1, flag_print_peaks = 0, |
data_draw_pca_pareto | function data_draw_pca_pareto(dataset_name, no_pcs) |
data_eliminate_outliers_by_density | varargout = data_eliminate_outliers_by_density(data) |
data_eliminate_outliers_by_distance | varargout = data_eliminate_outliers_by_distance(data, diff_order, threshold, flag_interactive) |
data_eliminate_outliers_by_distance2 | varargout = data_eliminate_outliers_by_distance(data, diff_order, threshold) |
data_eliminate_outliers_by_distance3 | varargout = data_eliminate_outliers_by_distance3(data, no_stds) |
data_eliminate_outliers_by_distance4 | varargout = data_eliminate_outliers_by_distance3(data, no_stds) |
data_eliminate_var_0 | new = data_eliminate_var_0(data, threshold = 1e-10) |
data_get_class_labels | function labels = data_get_class_labels(data) |
data_get_classes_alpha | function clalpha = data_get_classes_alpha(data) |
data_get_cv | Calculated the 'cluster vectors' |
data_get_feature_names | names = data_get_feature_names(data, idx_fea) |
data_get_legend | function legends = data_get_legend(data) |
data_get_loadings_bootstrap | function LL = data_get_loadings_bootstrap(data, fcn, percent, no_rep) |
data_get_no_classes | function no_classes = data_no_classes(data) |
data_get_no_colonies | function data_get_no_colonies(data) |
data_get_title | title = data_get_feature_names(data, idx_fea) |
data_isolate_class | Isolates a particular class, so it will be class 0 and all the other will |
data_isolate_classes | Isolates a particular class, so it will be class 0 and all the other will |
data_line_correct | function data = data_line_correct(data, wns, no_convolutions) |
data_line_correct2 | function data = data_line_correct(data, wns, no_convolutions) |
data_line_correct_authentic | function data = data_line_correct_authentic(data, wnrange1, wnrange2) |
data_loadings_combo | function d = data_loadings_combo(dataa) |
data_map_rows | function data = data_map_rows(data, idxnew) |
data_merge | data_merged = data_merge(dataa) |
data_new | function out = data_new() |
data_new_from_database | Loads spectra and classification from database |
data_new_from_txt | data = data_new_from_txt(filename, range = []) |
data_normalize | output = data_normalize(X, params) |
data_organize_classes | function data = data_organize_classes(data) |
data_plot | htemp = plot(data.x, data.X'); |
data_plot_indicator_spectrum | function o = data_plot_indicator_spectrum(data, maxy, color) |
data_plot_means | |
data_plot_scatter_1d | plot_scatter_1d(data, idx_fea, flag_distr=1) |
data_plot_scatter_2d | data_plot_scatter_2d(data, idx_fea, confidences = [], flag_text=0) |
data_plot_scatter_3d | function varargout = data_plot_scatter_3d(data, idx_fea, confidences=[], flag_text=0) |
data_plot_scatter_3d2 | function varargout = data_plot_scatter_3d(data, idx_fea, confidences) |
data_randomize_classes | function data = data_randomize_classes(data) |
data_scramble | function out = data_scramble(data) |
data_select_colonies | function 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_ttest | function data_select_features_paired_ttest(data, idx_reference=1) |
data_select_hierarchy | function out = data_select_hierarchy(data, hierarchy) |
data_set_X | data = data_set_X(data, X) |
data_sort_by_colony | function varargout = data_sort_by_colony(data) |
data_sort_class_labels | function data = data_sort_class_labels(data) |
data_sort_distance | [data] |
data_split_classes | pieces = data_split_classes(data) |
data_split_map | function out = data_split_map(data, map) |
data_split_proportion | [data1, data2] = data_split_proportion(proportion, data) |
data_threshold | function data = data_threshold(data, th) |
data_transform_lda | function d2 = data_transform_lda(data, flag_sphere, flag_modified_s_b, P) |
data_transform_lda2 | function d2 = data_transform_lda2(data, flag_sphere=1, flag_modified_s_b=0, min_lds=1) |
data_transform_linear | function data = data_transform_linear(data, L, new_domain_name='lt', X = data.X) |
data_transform_linear_crossval | function out = data_transform_linear_crossval(data, fcn, k) |
data_transform_pca | d2 = data_transform_pca(data, no_factors = 0, flag_rotate_factors = 1) |
data_transform_pcalda | d2 = data_transform_pcalda(data, no_factors = 0, flag_rotate_factors = 1, flag_modified_s_b = 0) |
data_transform_pls | function d2 = data_transform_pls(data, no_factors) |
data_transform_plslda | function ds_combo01 = data_transform_plslda(ds01, no_factors) |
data_transform_splinebasis | function dscoef = data_transform_splinebasis(data, no_basis) |
data_trim_negatives | function data = data_trim_negatives(data) |
data_update_background | |
data_window | function data_window(data, xrange, xwindowinterval) |
datelabel | datelabel(opts) Make x-axis have nice date/time format |
dbresult_draw_lines | Copyright 2010 Julio Trevisan, Plamen P. Angelov & Francis L. Martin. |
detect_peaks | peak_indexes = detect_peaks(y, threshold, min_dist = 1, no_max = Inf, min_height = 0) |
detect_peaks2 | peak_indexes = detect_peaks(y, threshold, min_dist = 1, no_max = Inf, min_height = 0, no_conv = 6) |
diff_operator | function diff_operator(nf, order) |
dist_boot | function model = dist_boot(model, data) |
dist_estimate | Outputs: |
dist_new | function out = dist_new(name) |
dist_train | function model = dist_train(model, data) |
distribution | |
draw_covariance | function data = data_draw_covariance(data, which = 'c', y_ref = mean(data.X, 1)) |
draw_direction | draw_direction(v, linescale, arrowscale) |
draw_loadings_complete | function draw_loadings_complete(data, L, legends, flag_abs, threshold, flag_print_peaks, flag_plot_peaks) |
draw_projections | draw_projections(data, v) |
draw_zero_line | |
eig_ordered | [vv, lambdas] = eig_ordered(M) |
errorMeasure | |
error_ellipse | ERROR_ELLIPSE - plot an error ellipse, or ellipsoid, defining confidence region |
error_ellipse2 | ERROR_ELLIPSE - plot an error ellipse, or ellipsoid, defining confidence region |
est_calc_classes | function est_calc_classes(est) |
est_get_confusion | function cc = est_get_confusion(est, data, flag_percentage=0) |
est_get_rate | function rate = est_get_rate(est, data_test) |
est_new | function out = est_new() |
est_new_from_model_data | function out = est_new_from_model_data(model, data_test) |
est_set_Y | function est = est_set_Y(est, Y) |
feasel_curves_from_cases | This routine extracts the wn X performance curves from o.cases |
feasel_generate_dss | function dss = feasel_generate_dss(o) |
feasel_get_features_ranking | function ranking = feasel_get_features_ranking(o, mode) |
feasel_get_performance_matrix | function M = feasel_get_performance_matrix(o) |
feasel_go | Copyright 2010 Julio Trevisan, Plamen P. Angelov & Francis L. Martin. |
feasel_go_exhaustive | function o = feasel_go_stepwise(o) |
feasel_go_exhaustive2 | function o = feasel_go_exhaustive2(o) |
feasel_go_stepwise | function o = feasel_go_stepwise(o) |
feasel_new | function o = feasel_new() |
feasel_rank_features | |
feasel_validate | function o = feasel_validate(o, idxs_candidates) |
feasellog_new | Copyright 2010 Julio Trevisan, Plamen P. Angelov & Francis L. Martin. |
feaselvalidationlog_new | function o = feaselvalidationlog_new() |
fel | function s = fel(c, n) |
find_color | |
find_filename | function name = find_filename(prefix, suffix = '', extension = 'txt') |
find_marker | |
find_marker_size | |
find_peaks | function pp = find_peaks(v) |
fisher_ld | [W_star]/[W_star, lambdas] = |
format_comma | function format_comma(ax) |
format_frank | format_frank(F, scale=1, additional_handles) |
format_number_with_commas | |
format_wn | function format_wn(par) |
good_file_name | function name = good_file_name(name) |
gui_set_position | function gui_set_position(hObject) |
integrate | function I = integrate(X) |
interactive_feasel_for_biomarkers | |
interactive_feasel_for_biomarkers_bm3 | This one is maintained so it can read some result file I have saved. |
interactive_feasel_for_biomarkers_old | |
interactive_feasel_for_biomarkers_setuplines | Not standalone. This is part of the interactive_feasel_for_biomarkers application |
interactive_pcacrossval | PCA cross-validation aims to determine the "best" number of PCs to use in PCA |
interactive_read_from_database | % Interactive reader from database |
knn_boot | function model = knn_boot(model, data) |
knn_estimate | function [model, est] = knn_estimate(model, data) |
knn_new | function out = knn_new(name) |
knn_train | function model = knn_train(model, data) |
level | function H=level(func,x0,dxmax,col,precision,kmax); |
lin_boot | function model = lin_boot(model, data) |
lin_estimate | function [model, est] = lin_estimate(model, data) |
lin_new | function out = lin_new(name) |
lin_train | function model = lin_train(model, data) |
load_mat_file | function load_mat_file(filename, range = []) |
load_txt_file | loads txt file into DATA global variable |
maximize | MAXIMIZE maximize figure windows |
menu | function option = menu(title, options, cancel_label) |
mf_gauss | function mm = mf_gauss(X, centers, radii) |
mnr_boot | function model = mnr_boot(model, data) |
mnr_estimate | function [model, est] = mnr_estimate(model, data) |
mnr_new | function out = mnr_new(name) |
mnr_train | function model = mnr_train(model, data) |
model_boot | function model = model_boot(model, data) |
model_get_classes | function classes = model_get_classes(model, data) |
model_get_classes_targets | function targets = model_get_targets(model, data) |
model_get_no_outputs | function no_outputs = model_get_no_outputs(model) |
model_get_targets | function targets = model_get_targets(model, data) |
model_new | function model_new(name) |
modelset_boot | function a = modelset_boot(a, data) |
modelset_create_models | function a = modelset_boot(a, data) |
modelset_estimate | function a = modelset_estimate(a, data) |
modelset_getbatch | function a = modelset_getbatch(a, param_name) |
modelset_new | function a = modelset_new(fcn_new_model, flag_split) |
modelset_plot_mf | function a = modelset_plot_mf(a, idxs_fea) |
modelset_prepare_data | function data = modelset_prepare_data(o, data) |
modelset_setbatch | Inputs |
modelset_train | function a = modelset_train(a, data) |
mym | MYM - Interact with a MySQL database server  |
mysql | MYSQL - Interact with a MySQL database server  |
normalize_rows | function cc = normalize_rows(cc) |
pause2 | function pause2() |
peak_closest | result = peak_closest(db, a) |
peak_db | Z = peak_db(x = None) |
peak_landmarks | Arguments: |
penalty_matrix | function P = penalty_matrix(nf, dcoeff) |
plot_curve_pieces | handles{} = plot_curve_pieces(x, y, varargin) |
plot_indicator_spectrum | function o = plot_indicator_spectrum(x, y, color) |
plot_peaks | print_peaks(x, y, threshold) |
plot_reconst | |
pls | [scores] = pls(X, Y, no_factors) |
princomp2 | Principal Component Analysis (PCA) |
print_peaks | print_peaks(x, y, threshold) |
quantile_landmarks | function T = quantile_landmarks(I, no_quants, t_range=[1, i_end]) |
read_n_save | |
read_spectra_with_classification | Loads spectra and classification from database |
renumber_classes | function classes = renumber_classes(classes_orig, class_labels_orig, class_labels_ref) |
resize_legend_markers | function resize_legend_markers(size=12, flag_line=0) |
rgb | rgb.m: translates a colour from multiple formats into matlab colour format |
rotatefactors2 | L = rotatefactors2(L, flag_normal) |
round_random | function x = round_random(x) |
rows_cols | num = ceil(num/2)*2; % "rounds" num to the next integer |
save_global_variables | Saves global variables IDEXPERIMENT, IDJUDGE_REF, class_labels, DATA and |
save_txt_file | function save_txt_file(s, name) |
select_experiment | |
select_load_file | flag_selected = select_load_file() |
select_load_mat_file | flag_selected = select_load_mat_file() |
select_load_txt_file | flag_selected = select_load_txt_file() |
select_reference_judge | |
str2filename | function files = str2files(s) |
strip_code | function c = strip_class(params) |
svm_boot | function model = svm_boot(model, data) |
svm_estimate | function [model, est] = svm_estimate(model, data) |
svm_new | function out = svm_new(name) |
svm_train | function model = knn_train(model, data) |
v_ind2x | function indexes = v_x2ind(x, indexes) |
v_x2ind | function indexes = v_x2ind(v, x) |
x_despike1 | load_mat_file('../mat/imran_pec29_oneclassonly.mat', [2100 200]); |
ypoll_get_confusion | function cc = est_get_confusion(est, data, flag_percentage=0) |
ypoll_get_rate | function rate = ypoll_get_rate(ypoll, ypoll_correct) |
ypoll_new | function out = ypoll_new() |
ypoll_new_poll | function [ypoll] = ypoll_new_poll(colony_codes, classes1, class_labels) |