plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | CReSIS/cresis-toolbox-master | emquest_txt_reader.m | .m | cresis-toolbox-master/cresis-toolbox/hardware/emquest_txt_reader.m | 4,845 | utf_8 | cf00d9aff71d8ca18c277c884a03efb5 | function [param,data] = emquest_txt_reader(fn)
fid = fopen(fn,'r');
% Read param header
line = 0;
[param,line] = emquest_txt_reader_param(fid, line);
% Read data
format_line = fgets(fid); line = line + 1;
if ~strcmp(sprintf('Format\r\n'),format_line)
warning('%d: Should be "Format", was %s', line, format_line);
en... |
github | CReSIS/cresis-toolbox-master | Anichoic_Chamber_Interface.m | .m | cresis-toolbox-master/cresis-toolbox/hardware/Anichoic_Chamber_Interface.m | 50,724 | utf_8 | 28f455a9b17ee33da76a672774a4ea6f | function varargout = Anichoic_Chamber_Interface(varargin)
% ANICHOIC_CHAMBER_INTERFACE MATLAB code for Anichoic_Chamber_Interface.fig
% ANICHOIC_CHAMBER_INTERFACE, by itself, creates a new ANICHOIC_CHAMBER_INTERFACE or raises the existing
% singleton*.
%
% H = ANICHOIC_CHAMBER_INTERFACE returns the ha... |
github | CReSIS/cresis-toolbox-master | SXPWrite.m | .m | cresis-toolbox-master/cresis-toolbox/hardware/sbox/SXPWrite.m | 3,338 | utf_8 | 1a9f16baa0b51c3388a472e7128b774c | function SXPWrite(freq, s, FileName, unit_adj, comment)
% SXPWrite(freq, S, FileName, unit_adj, COMMENT)
%
% writes multiport parameter data S to an .sxp file data
% using the MDIF format (a.k.a. HPEEsof format); for a detailed
% description of data format see SXPParse.m
%
% freq is multiplied by unit_adj and written ... |
github | CReSIS/cresis-toolbox-master | SXPParse.m | .m | cresis-toolbox-master/cresis-toolbox/hardware/sbox/SXPParse.m | 14,027 | utf_8 | 9a1a2a17a6c185e0630e9710aab8b447 | function [freq, data, freq_noise, data_noise, Zo] = SXPParse(DataFileName, fid_log)
% reads .sxp file data in MDIF (a.k.a. Touchstone / HPEEsof format)
%
% EXAMPLE :
% [freq, data, freq_noise, data_noise, Zo] = SXPParse(DataFileName, fid_log);
%
% freq, freq_noise - 1xF arrays
% data - PxPxF matrix, P- n... |
github | CReSIS/cresis-toolbox-master | icards_data_ignore_list.m | .m | cresis-toolbox-master/cresis-toolbox/icards/icards_data_ignore_list.m | 1,364 | utf_8 | a4025544dab80beb28050edce69a4b15 |
function [valid_data_file]=icards_data_ignore_list(fns,full_dir);
% This function is for the convenience to ignore some wrong file of certain
% days-----Qi Shi
switch full_dir
case 'Z:\ICARDS\2002\may24\'%to ignore "fiberdly" of 20020524
valid_data_file=[2:205];
case 'Z:\ICARDS\2002\may20\'%to ignore "... |
github | CReSIS/cresis-toolbox-master | icards_burst_noise_detection.m | .m | cresis-toolbox-master/cresis-toolbox/icards/icards_burst_noise_detection.m | 2,740 | utf_8 | 99146b4a6296ad1de7659da81c8088e4 | % This function is used to detect and cancel burst noise
% (i.e.those thin vertical stripes in echogram )---qishi
function [data_out]=icards_burst_noise_detection(data_in)
time_start=tic;
test_data=data_in;
filter_length=51;%FIR and median filter length
noise_threshold=7;%threshold in dB
consecutive_ones=5;%d... |
github | CReSIS/cresis-toolbox-master | icards_monthANDday.m | .m | cresis-toolbox-master/cresis-toolbox/icards/icards_monthANDday.m | 1,240 | utf_8 | 254494bdeb1bfc6cba376d5fed40b0ca | %extract the day and month value from a 8 digits date
%today:8 digits data e.g.20160712
%month1:interger value of month
%month2: English expression of month (char)
%day1:interger value of day with no "0" take the place of second digit if
% month value is only 1 digit number e.g. 5 instead of 05
%day2:characte... |
github | CReSIS/cresis-toolbox-master | create_records_icards_interpolation.m | .m | cresis-toolbox-master/cresis-toolbox/icards/create_records_icards_interpolation.m | 1,697 | utf_8 | dc421771db63836a0333e70fc6b755d3 | % this function is used to correct and interpolate unreasonable time
% sequence---qishi
function time_after=interpolation(time_before,previous_mark,previous_time)
time=time_before;
if previous_mark
second_valid_idx=find(time>time(1),1,'first');
time(1:second_valid_idx)=interp1([0 second_valid_idx],[previous_... |
github | CReSIS/cresis-toolbox-master | load_icards_data.m | .m | cresis-toolbox-master/cresis-toolbox/icards/load_icards_data.m | 16,693 | utf_8 | 5e9d8ce4fcb2c06311b5cc8768ba1d74 | function [param] = load_icards_data(param,whole_param)
if ~isfield(param.proc,'raw_data')
param.proc.raw_data = false;
end
if ~isfield(param.load,'wf_adc_comb')
param.load.wf_adc_comb.en = 0;
end
sample_size=2; %the file type of icards sample is "int16"---qishi
global g_data;
wfs = param.wfs;
phy... |
github | CReSIS/cresis-toolbox-master | LSMObject.m | .m | cresis-toolbox-master/cresis-toolbox/+tomo/LSMObject.m | 9,196 | utf_8 | 8cd8d6a48e6cc571fca9bf261000556d | classdef LSMObject <handle
%LSMObject constructor
%requires a list of images (full file paths), file type, and image resize rate
% LSMObject(listOfFiles,fileType,resizeRate)
% Detailed explanation goes here
properties (Access=protected)
imds
% fileType='png'
phi
resizeRate
initiArgs
... |
github | CReSIS/cresis-toolbox-master | freezeColors.m | .m | cresis-toolbox-master/cresis-toolbox/+tomo/freezeColors.m | 9,815 | utf_8 | 2068d7a4f7a74d251e2519c4c5c1c171 | function freezeColors(varargin)
% freezeColors Lock colors of plot, enabling multiple colormaps per figure. (v2.3)
%
% Problem: There is only one colormap per figure. This function provides
% an easy solution when plots using different colomaps are desired
% in the same figure.
%
% freezeColors freeze... |
github | CReSIS/cresis-toolbox-master | viterbi_tests.m | .m | cresis-toolbox-master/cresis-toolbox/+tomo/viterbi_tests.m | 3,149 | utf_8 | eb555da8edc53789c0b2958dde84c9e0 | % Standalone test environment for viterbi implementation
% Author: Reece Mathews
function viterbi_tests()
global matrix layer layers;
% CONSTANTS
rows = 20;
cols = 15;
surf = 5;
mult = 10;
grnd = 17;
matrix = zeros(rows, cols);
matrix(surf, :) = ones(1, cols) * 30; % Surface
matrix... |
github | CReSIS/cresis-toolbox-master | LSMObject_tuning.m | .m | cresis-toolbox-master/cresis-toolbox/+tomo/LSMObject_tuning.m | 11,000 | utf_8 | 466bac55b9f3ee8ac0a8199cb90b9123 | classdef LSMObject_tuning <handle
%LSMObject constructor
%requires a list of images (full file paths), file type, and image resize rate
% LSMObject(listOfFiles,fileType,resizeRate)
% Detailed explanation goes here
properties (Access=protected)
imds
% fileType='png'
phi
resizeRate
init... |
github | CReSIS/cresis-toolbox-master | viterbi_tests2.m | .m | cresis-toolbox-master/cresis-toolbox/+tomo/viterbi_tests2.m | 2,257 | utf_8 | ee7c5ba0eec345153dcf49680a6f931e | % Standalone test environment for viterbi implementation
% Author: Reece Mathews
function viterbi_tests2()
global matrix layer elevation;
% CONSTANTS
rows = 20;
cols = 20;
flatness = 100;
along_track_weight = 100;
down_shift = rows;
matrix = zeros(rows, cols);
shift_matrix = 1;
shift_elev = 1;
... |
github | CReSIS/cresis-toolbox-master | tomo_quick_loader.m | .m | cresis-toolbox-master/cresis-toolbox/+tomo/tomo_quick_loader.m | 5,179 | utf_8 | e1b343384cb4466f49773da8c0907a29 | function tomo_quick_loader
% tomo_quick_loader
%
% Simple script for viewing 3D imagery.
%
% Author: John Paden
%% User Settings
% 1. Specify filename in fn
% 2. Specify start range bin to plot (rbin) and how many range bins(rbins) of
% leave blank.
% Example of multipass datasets
if 0
% Camp Centur... |
github | CReSIS/cresis-toolbox-master | result_make_data_matrix.m | .m | cresis-toolbox-master/cresis-toolbox/+tomo/+parameter_tuning/+helper_functions/result_make_data_matrix.m | 1,343 | utf_8 | cdb4ced08b49f4b8165663d57a1c9ea9 | function [data_matrix] = result_make_data_matrix(cluster_result)
%Summary of this function goes here
% format the data after running the tests on the cluster
%% preprocessed the data
reverseStr = '';
for idx = 1:length(cluster_result)
cluster_result{idx}.argsout{1} = compute_hit_ratios(cluster_result{idx}.... |
github | CReSIS/cresis-toolbox-master | result_find_optimal_param_2D.m | .m | cresis-toolbox-master/cresis-toolbox/+tomo/+parameter_tuning/+helper_functions/result_find_optimal_param_2D.m | 3,875 | utf_8 | f92efbb0bc2f38d7b73c36da3a077953 | function [ result ] = result_find_optimal_param_2D( data_matrix, params, param_override, options, geotiff_fn, geotiff2_fn,...
OPS_Surface, OPS_Bottom, OPS_data, OPS_crossover_data)
%RESULT_FIND_OPTIMAL_PARAM_2D
% Process the result from the test and find the optimal parameters using
% fmincon
% Input:
% d... |
github | CReSIS/cresis-toolbox-master | result_find_optimal_param_3D.m | .m | cresis-toolbox-master/cresis-toolbox/+tomo/+parameter_tuning/+helper_functions/result_find_optimal_param_3D.m | 8,137 | utf_8 | 54db4e86941cf620adceccfcc678171f | function [ result ] = result_find_optimal_param_3D(data_matrix, algo_param, sources, references, num_slices, algorithm_name, data_type)
%RESULT_FIND_OPT_PARAM
% Process the result from the test and find the optimal parameters using
% fmincon
% Input:
% data_matrix: a matrix; result we got from the grid/rand... |
github | CReSIS/cresis-toolbox-master | result_visualize.m | .m | cresis-toolbox-master/cresis-toolbox/+tomo/+parameter_tuning/+helper_functions/result_visualize.m | 3,643 | utf_8 | 1844fb734773e31a461bf1a42cfa7e48 | function [] = result_visualize( data_matrix, test, algo_name, data_type, best_point)
%DATA_STATISTICS Summary of this function goes here
% statistical analysis and data visualization on the processed data
[~,rank_indices] = sort(data_matrix(:,end-2), 'ascend'); % rank by rmse in ascending order
data_matrix = ... |
github | CReSIS/cresis-toolbox-master | setup_parameters.m | .m | cresis-toolbox-master/cresis-toolbox/+tomo/+parameter_tuning/+helper_functions/setup_parameters.m | 7,237 | utf_8 | 520f64e24a68e03267c6280e39561910 | function [ detect_params_array, stats_array, num_combinations ] = setup_parameters( algo_param, num_images, method, algorithm, data_type, number_of_trials)
%SETUP_3D_PARAMETERS Summary of this function goes here
% Setup the combinations of parameters to feed in to the tests later on
if strcmp(data_type, '3D')
if strc... |
github | CReSIS/cresis-toolbox-master | physicalOpticsSmallSlopeUlaby.m | .m | cresis-toolbox-master/cresis-toolbox/em_model/physicalOpticsSmallSlopeUlaby.m | 11,087 | utf_8 | 1d50b46f7eed46905beaf241d3b4b0d5 | function scatter = physicalOpticsSmallSlopeUlaby(er1,er2,ur1,ur2,corrLen,h,pdf,freq, ...
thetaInc,phiInc,thetaScat,phiScat,etaInc,etaScat)
% scatter = physicalOpticsSmallSlopeUlaby(er1,er2,ur1,ur2,s,pdf,freq, ...
% thetaInc,phiInc,thetaScat,phiScat,etaInc,etaScat)
%
% er1,ur1: relative permittivity and permeabili... |
github | CReSIS/cresis-toolbox-master | physicalOpticsLargeUlaby.m | .m | cresis-toolbox-master/cresis-toolbox/em_model/physicalOpticsLargeUlaby.m | 8,646 | utf_8 | 68342b0cb5e0aef9930e90b1b845f18f | function scatter = physicalOpticsLargeUlaby(er1,er2,ur1,ur2,h,s,pdf,thetaInc,phiInc,thetaScat,phiScat,etaInc,etaScat)
% scatter = physicalOpticsLargeUlaby(er1,er2,ur1,ur2,h,s,pdf,thetaInc,phiInc,thetaScat,phiScat,etaInc,etaScat)
% er1,ur1: relative permittivity and permeability of medium 1 (radar located in this medium... |
github | CReSIS/cresis-toolbox-master | physicalOpticsRuck.m | .m | cresis-toolbox-master/cresis-toolbox/em_model/physicalOpticsRuck.m | 8,427 | utf_8 | ddea1263a07a545265f395eddd984425 | function scatter = physicalOpticsRuck(er1,er2,ur1,ur2,h,s,pdf,thetaInc,phiInc,thetaScat,phiScat,etaInc,etaScat)
% scatter = physicalOpticsRuck(er1,er2,ur1,ur2,h,s,pdf,thetaInc,phiInc,thetaScat,phiScat,etaInc,etaScat)
%
% er1,ur1: relative permittivity and permeability of medium 1 (radar located in this medium)
% er2,ur... |
github | CReSIS/cresis-toolbox-master | smallPerturbation.m | .m | cresis-toolbox-master/cresis-toolbox/em_model/smallPerturbation.m | 7,674 | utf_8 | dc53330df1501780699b276f98349ca2 | function scatter = smallPerturbation(er1,er2,ur1,ur2,corrLen,h,pdf,freq,thetaInc,phiInc,thetaScat,phiScat,etaInc,etaScat)
% scatter = smallPerturbation(er1,er2,ur1,ur2,corrLen,h,pdf,freq,thetaInc,phiInc,thetaScat,phiScat,etaInc,etaScat)
% er1,ur1: relative permittivity and permeability of medium 1 (radar located in thi... |
github | CReSIS/cresis-toolbox-master | genReflTranFromPerm.m | .m | cresis-toolbox-master/cresis-toolbox/em_model/genReflTranFromPerm.m | 11,094 | utf_8 | 86b2df4d944dfb6ad41c3a66b22fba19 | function [refl,tran,tx_angle] = genReflTranFromPerm(thick,ur,er,freq,inc)
% [refl,tran,tx_angle] = genReflTranFromPerm(thick,ur,er,freq,inc)
%
% Generates the reflection coefficient, transmission coefficient and
% transmitted angle for arbitrary incidence angle plane waves incident
% on layered media.
% WARNING: CURREN... |
github | CReSIS/cresis-toolbox-master | Debye.m | .m | cresis-toolbox-master/cresis-toolbox/em_model/Debye.m | 846 | utf_8 | ee4a5e6966f90017b7225d7c94b4a88f | %Returns dielectric of wet snow according to Hallikainen
%freq = frequency in Hz
%mv = % of liquid water content
% roh = density in g/cc
% mv=0:0.1:12;
% f=6e9;
% roh=0.3;
% [er_r,er_i]=Debye(mv,freq,roh);
% delta=er_r-1-1.832*roh;
% figure(1); clf;
% f1=plot(mv,delta,'k-');
% ylim([0,1.6]);
% xlim([0.0,13]);
% title(... |
github | CReSIS/cresis-toolbox-master | modified_debyelike_model.m | .m | cresis-toolbox-master/cresis-toolbox/em_model/modified_debyelike_model.m | 984 | utf_8 | 787b0168aa6514ee8f48a485e89c0ced | %Modified Debye like model
% Returns dielectric of wet snow according to Hallikainen
% freq = frequency in GHz
% clear;
% clc;
% f=3:1:37;
% mv=2:2:12;
% roh=0.25;
% for r = 1:1:length(mv)
% [er_r(:,r),er_i(:,r)]=modified_debyelike_model(mv(r),f,roh);
% end
% figure(1); clf;
% f1=plot(f,er_r,'k-');
% title('Modified De... |
github | CReSIS/cresis-toolbox-master | sort_clicks.m | .m | cresis-toolbox-master/cresis-toolbox/+imb/sort_clicks.m | 666 | utf_8 | fc72908305412d00e1c5a7355774fe93 | % =====================================================================
% Support function for finding min and max x/y coords from init/final click
% Also ensures that mins/maxes don't exceed current axis limits
% =====================================================================
function [x_min x_max y_min y_max] =... |
github | CReSIS/cresis-toolbox-master | get_google_map.m | .m | cresis-toolbox-master/cresis-toolbox/+imb/@mapwin/get_google_map.m | 775 | utf_8 | 3f1260a137e4cb7b5955145edbddf600 | % get_google_map returns a Google Static Map depending on the map_zone
% selected and centered at the default lat lon set for the map_zone
function A = get_google_map(obj)
%% Setting default lat lon depending on the map_zone
if strcmpi('arctic', obj.map_pref.settings.map_zone)
c_lat = 73.82177;
c_lo... |
github | CReSIS/cresis-toolbox-master | redraw_google_map.m | .m | cresis-toolbox-master/cresis-toolbox/+imb/@mapwin/redraw_google_map.m | 3,058 | utf_8 | 9a0a331bf4b040839386362b3ceab177 | % Called by imb/mapwin/key_press to pan around the map or zoom in/out in the picker
% Updates the Map in the figure and the axis data
function redraw_google_map(obj, x_min, x_max, y_min, y_max)
%% Updating map depending on whether user wants to pan or zoom
if ~obj.googleObj.zoom_in_out == 0
% If zoom ... |
github | CReSIS/cresis-toolbox-master | push.m | .m | cresis-toolbox-master/cresis-toolbox/+imb/@undo_stack/push.m | 512 | utf_8 | b57700358ce7008d9900a808b23876d4 |
function push(obj,cmds)
% Pushes a set of commands onto the stack
obj.last_pointer = obj.pointer;
obj.pointer = obj.pointer + 1;
obj.stack{obj.pointer} = cmds;
% Since the stack may contain cmds for indices > obj.pointer (e.g. because
% the user chose to undo some commands and then pushed this command), we
... |
github | CReSIS/cresis-toolbox-master | cmds_execute.m | .m | cresis-toolbox-master/cresis-toolbox/+imb/@echowin/cmds_execute.m | 14,341 | utf_8 | 0a87572819b55ad5017c1e8351be4b43 | function cmds_execute(obj,cmds_list,cmds_direction)
% cmds_execute(obj,cmds_list,cmds_direction)
%
% Executes tool commands
if strcmpi(cmds_direction,'redo')
%% Redo commands
for cmd_idx = 1:length(cmds_list)
for sub_idx = 1:length(cmds_list{cmd_idx})
if strcmpi(cmds_list{cmd_idx}(sub_idx).redo_cmd,'inse... |
github | CReSIS/cresis-toolbox-master | cmds_convert_units.m | .m | cresis-toolbox-master/cresis-toolbox/+imb/@echowin/cmds_convert_units.m | 7,487 | utf_8 | ced87e0917c4d2b097316897d5d6ba0b | function cmds = cmds_convert_units(obj,cmds)
% cmds = convert_cmd_units(obj,cmds)
%
% Converts tool commands from current units to gps-time and twtt
for cmd_idx = 1:length(cmds)
if strcmpi(cmds(cmd_idx).undo_cmd,'insert')
cmds(cmd_idx).undo_args = cmds_convert_units_insert(obj,cmds(cmd_idx).undo_args);
elseif ... |
github | CReSIS/cresis-toolbox-master | left_click_and_drag.m | .m | cresis-toolbox-master/cresis-toolbox/+imb/@picktool_viterbi/left_click_and_drag.m | 9,029 | utf_8 | 4b43c7ed35efaf68113591756e761650 | function cmds = left_click_and_drag(obj,param)
% cmds = left_click_and_drag(obj,param)
%
% Detect tool
%
% Compile with
% mex -largeArrayDims viterbi.cpp
physical_constants;
image_x = param.image_x;
image_y = param.image_y;
image_c = param.image_c;
cur_layers = param.cur_layers;
x = param.x;
y = param.y;
cmds = [];... |
github | CReSIS/cresis-toolbox-master | ascopeCM_callback.m | .m | cresis-toolbox-master/cresis-toolbox/+imb/@ascopewin/ascopeCM_callback.m | 4,494 | utf_8 | c7f1f61acf11879a7fe71bb5e7de9e4d | function ascopeCM_callback(obj,source,event)
% ascopeCM_callback(obj,source,event)
% Ensure focus stays on figure to prevent hotkeys registering with this
% uicontrol.
uicontrol(obj.right_panel.status_panel.statusText);
if source == obj.left_panel.ascopeCM_visible || source == obj.left_panel.ascopeCM_hide
%% ascope... |
github | CReSIS/cresis-toolbox-master | records_bit_mask.m | .m | cresis-toolbox-master/cresis-toolbox/ct_support/records_bit_mask.m | 8,393 | utf_8 | 10955d189ab0e4a0ad4486af29ad51ae | function records_bit_mask(param,param_override)
%
% GPR profiles usually contain stops, 270+ deg loop turns, etc that may be
% undesirable for SAR processing. Use this script to help find the data
% records associated with these maneuvers and mask them for SAR processing.
% Usually it is not necessary to do this unless... |
github | CReSIS/cresis-toolbox-master | basic_load.m | .m | cresis-toolbox-master/cresis-toolbox/ct_support/basic_load.m | 22,186 | utf_8 | e9885293360148c21723c4792186de66 | function [hdr,data] = basic_load(fn,param)
% [hdr,data] = basic_load(fn, param)
%
% This is the only function which loads raw data directly. This is for
% files which follow the convention:
% Bytes 0-3 UINT32 FRAME_SYNC 0x1ACFFC1D
% Byte 24-25 UINT16 FILE_VERSION https://wiki.cresis.ku.edu/cresis/Raw_File_Guide#Overvie... |
github | CReSIS/cresis-toolbox-master | plot_vectors.m | .m | cresis-toolbox-master/cresis-toolbox/ct_support/plot_vectors.m | 14,083 | utf_8 | 9e2309837c21964c33a0c2d994c0d4f9 | function plot_vectors(filename,bbox,geoTiff)
% plot_vectors(filename,bbox,geoTiff)
%
% filename of file created by create_seasonVector
% (can also be a cell array of filenames to load)
% bbox = bounding boxes (4 by N). These are two (lat/lon) pairs in deg.
% where N is the number of bounding boxes you want display... |
github | CReSIS/cresis-toolbox-master | insert_param_xls.m | .m | cresis-toolbox-master/cresis-toolbox/ct_support/insert_param_xls.m | 19,447 | utf_8 | 7619e4bc63ff4fdf513d009271f4f7d6 | % This program will not clear the entire worksheet before writing
% If a field were deleted or elements of a nested field(wfs) were reduced
% for a date, unwanted columns may remain on the worksheet% struct & double rows
% This program will not create a new worksheet
% If nested structure contains only 1 element, i... |
github | CReSIS/cresis-toolbox-master | passwordEntryDialog.m | .m | cresis-toolbox-master/cresis-toolbox/OPS-MATLAB/utility/passwordEntryDialog.m | 14,919 | utf_8 | 001f9a9506fa34da196207748087843a | function [Password, UserName] = passwordEntryDialog(varargin)
% PASSWORDENTRYDIALOG
% [Password, UserName] = passwordEntryDialog(varargin)
%
% Create a password entry dialog for entering a password that is visibly
% hidden. Java must be enabled for this function to work properly.
%
% It has only been tested on the Win... |
github | CReSIS/cresis-toolbox-master | savejson.m | .m | cresis-toolbox-master/cresis-toolbox/OPS-MATLAB/conversion/jsonlab/savejson.m | 14,346 | utf_8 | fe9bea866e3ee533c8f6963dd8dae95f | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | CReSIS/cresis-toolbox-master | loadjson.m | .m | cresis-toolbox-master/cresis-toolbox/OPS-MATLAB/conversion/jsonlab/loadjson.m | 16,524 | ibm852 | dbcaf53ab19256aece438d7a423ea8d1 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% date: 2011/09/09
% Nedialko Krouchev: http:... |
github | CReSIS/cresis-toolbox-master | loadubjson.m | .m | cresis-toolbox-master/cresis-toolbox/OPS-MATLAB/conversion/jsonlab/loadubjson.m | 14,326 | utf_8 | aa6e3470cfceb97e02c8cab1a13d76a5 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% date: 2013/08/01
%
% $Id: loadubjson.m 417 20... |
github | CReSIS/cresis-toolbox-master | saveubjson.m | .m | cresis-toolbox-master/cresis-toolbox/OPS-MATLAB/conversion/jsonlab/saveubjson.m | 15,180 | utf_8 | ce632cb877e9531c6824b47b7ba64de4 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | CReSIS/cresis-toolbox-master | load_mcrds_data.m | .m | cresis-toolbox-master/cresis-toolbox/mcrds/load_mcrds_data.m | 15,567 | utf_8 | 7e9c0ceb462af5119e679bcf8ff3c4b4 | function [param] = load_mcrds_data(param)
% [param] = load_mcrds_data(param)
%
% Function for loading MCRDS data. Supports loading over multiple files,
% pulse compression and fast decimation and presumming while loading
% to minimize memory usage. It supports loading arbitrary channels,
% waveform pairs and combining.... |
github | cbaldassano/Voxel-Level-Functional-Connectivity-master | learnConnectivity.m | .m | Voxel-Level-Functional-Connectivity-master/learnConnectivity.m | 17,473 | utf_8 | 4e9e17e85aea0d335dbfe7d5c582f6cc | function connWeights = learnConnectivity(varargin)
%learnConnectivity Learns connectivity maps over one or two regions
% learnConnectivity estimates function connectivity maps from fMRI data,
% as described in these two papers:
%
% C. Baldassano, M.C. Iordan, D.M. Beck, L. Fei-Fei. "Voxel-Level
% Functional Con... |
github | cbaldassano/Voxel-Level-Functional-Connectivity-master | connectivityDemo.m | .m | Voxel-Level-Functional-Connectivity-master/connectivityDemo.m | 7,437 | utf_8 | fa93d54fe6373552f3ffc03eb4712b4f | function connectivityDemo()
%connectivityDemo Gives two examples of using learnConnectivity
if (~exist('cvx_version'))
error('learnConnectivity requires CVX - see http://cvxr.com/cvx/');
end
% Example One: Learn a connectivity map over hV4, using the mean PPA
% timecourse as the seed. The result shows that hV4 v... |
github | supratimray/GammaLengthProjectCodes-master | platformSpecificName.m | .m | GammaLengthProjectCodes-master/platformSpecificName.m | 259 | utf_8 | 9bdd09a7081f02866ca42bdabf87f6eb | % This program changes the file delimiters to slash or backslash depending
% on the platform. No changes are made if the file already has the correct delimiter
function fn = platformSpecificName(fn)
if ispc
fn(fn=='/')='\';
else
fn(fn=='\')='/';
end |
github | supratimray/GammaLengthProjectCodes-master | generateBurstData.m | .m | GammaLengthProjectCodes-master/generateBurstData.m | 6,580 | utf_8 | 7798889be367c27cfd2e935fae63f5ae | % To generate synthetic data, we use two conditions: real data when no
% stilumus is presented (contrast=0; called baseline data) and when a
% stimulus of contrast cVal is presented, which generates gamma
% oscillations (called stimulus data). The goal is to inject gamma bursts
% of a specified length (burstLen) in the... |
github | supratimray/GammaLengthProjectCodes-master | getStochasticDictionaryMP3p1.m | .m | GammaLengthProjectCodes-master/getStochasticDictionaryMP3p1.m | 2,028 | utf_8 | e7980d063ac061da0fa04bb51079a013 | % This program uses MP version 3.1 by Piotr Durka's group. This is the
% original stochastic dictionary code used in their 2001 paper.
function [gaborInfo,header] = getStochasticDictionaryMP3p1(data,timeVals,maxIteration,adaptiveDictionaryParam,dictionarySize)
if ~exist('maxIteration','var'); maxIteration=5... |
github | supratimray/GammaLengthProjectCodes-master | getSEMedian.m | .m | GammaLengthProjectCodes-master/getSEMedian.m | 253 | utf_8 | 96f7d88aca47a9ca918689404f8da252 | % [se,bs] = getSEMedian(X,N) returns the standard error of the median of
% the values in X.
function [se,bs] = getSEMedian(X,N)
if ~exist('N','var'); N=length(X); end
bs = bootstrp(N,@median,X);
se = std(bs);
end |
github | supratimray/GammaLengthProjectCodes-master | getWavelet.m | .m | GammaLengthProjectCodes-master/getWavelet.m | 1,194 | utf_8 | 934214651ff85025ae5d62214a6a6838 | % This function computes the scalogram (Wavelet Transform (WT)) of a signal
% Input - sig : Signal in one row
% timeVals : Time Values in a row (seconds)
% fRange : Frequency range for which the Wavelet transform should be computed
% fRes : Frequency resolution
% Output - cw1 ... |
github | supratimray/GammaLengthProjectCodes-master | gabor.m | .m | GammaLengthProjectCodes-master/gabor.m | 966 | utf_8 | 5f6503b3d6baa265f91f76e6a55e34c0 | %%% This function is used by mp31. However, the original program appears to
%%% have a couple of small bugs. Once you download mp31, replace the
%%% gabor.m in the mat4mp folder with this one.
function s=gabor(signal_size, signal_sampling, width, frequency, position, amplitude, phase)
%s=gabor(signal_size, signal_samp... |
github | supratimray/GammaLengthProjectCodes-master | getBurstLengthMP.m | .m | GammaLengthProjectCodes-master/getBurstLengthMP.m | 5,557 | utf_8 | 412d6f0b16fd3354113efd4caf59025d | function [lengthList,freqList,timeList,gaborInfo,header,modList] = getBurstLengthMP(analogData,timeVals,thresholdFactor,displayFlag,stimulusPeriodS,baselinePeriodS,burstFreqRangeHz,maxIteration,adaptiveDictionaryParam,dictionarySize,gaborInfo,header)
if ~exist('displayFlag','var'); displayFlag=1; ... |
github | supratimray/GammaLengthProjectCodes-master | getBurstLengthHilbert.m | .m | GammaLengthProjectCodes-master/getBurstLengthHilbert.m | 3,792 | utf_8 | 98c045ec63ee088f81ee386313aa5b14 | function burstLengthS = getBurstLengthHilbert(analogData,timeVals,thresholdFactor,displayFlag,stimulusPeriodS,baselinePeriodS,burstFreqRangeHz,filterOrder)
if ~exist('displayFlag','var'); displayFlag=1; end
if ~exist('stimulusPeriodS','var'); stimulusPeriodS=[0.5 1.5]; end
if ~exist('... |
github | supratimray/GammaLengthProjectCodes-master | getBurstLengthCGT.m | .m | GammaLengthProjectCodes-master/getBurstLengthCGT.m | 6,725 | utf_8 | 68e4666ed992437ace62eb321ee1a854 | function [burstLengthS,burstFreqList] = getBurstLengthCGT(analogData,timeVals,thresholdFactor,displayFlag,stimulusPeriodS,baselinePeriodS,burstFreqRangeHz,cgtGaborSDS,cgtFreqResolutionHz,searchRangeFreqHz,phaseThreshold,useMaxPowerSeedFlag)
if ~exist('thresholdFactor','var'); thresholdFactor=[]; end
if... |
github | supratimray/GammaLengthProjectCodes-master | plotFigure2.m | .m | GammaLengthProjectCodes-master/plotFigure2.m | 3,962 | utf_8 | dc49a0f8c68abc85e1fa241675c6866d | % CV is calculated for each trial separately, by calculating the std and
% mean of power or amplitude values across time.
function plotCVDifferentMethods
subjectName = 'alpa'; expDate = '120316'; protocolName = 'GRF_001';
gridType = 'Microelectrode'; folderSourceString = '';
electrodeNum=83;cVal=100;
folderName = fu... |
github | supratimray/GammaLengthProjectCodes-master | getCGT.m | .m | GammaLengthProjectCodes-master/getCGT.m | 979 | utf_8 | 7405f8a41c77e887a0fa0dd511e754e4 | % This function computes the CGT (Continuous Gabor Transform) of a signal
% Input - sig : Signal in one row
% timeVals : Time Values in a row (seconds)
% fRange : Frequency range for which the CGT should be computed
% fRes : Frequency resolution
% sd : Standard devi... |
github | supratimray/GammaLengthProjectCodes-master | testPerformanceSynthData.m | .m | GammaLengthProjectCodes-master/testPerformanceSynthData.m | 6,045 | utf_8 | 3ee36364a1815aaf5015c4aa5513fe04 | function testPerformanceSynthData(thresholdFractionList,electrodeNum,numMeanBursts)
subjectName = 'alpa'; expDate = '120316'; protocolName = 'GRF_001';
gridType = 'Microelectrode'; folderSourceString = ''; cVal=100;
numThresholds = length(thresholdFractionList);
% BurstDataParameters
burstLenList = [0.05 0.1:0.1:1];... |
github | supratimray/GammaLengthProjectCodes-master | getBurstLengthWavelet.m | .m | GammaLengthProjectCodes-master/getBurstLengthWavelet.m | 6,352 | utf_8 | 0d5d403bcd94ffc4b3c807ab135e23fa | function [burstLengthS,burstFreqList] = getBurstLengthWavelet(analogData,timeVals,thresholdFactor,displayFlag,stimulusPeriodS,baselinePeriodS,burstFreqRangeHz,waveletFreqResolutionHz,searchRangeFreqHz,useMaxPowerSeedFlag)
if ~exist('thresholdFactor','var'); thresholdFactor=[]; end
if ~exist('displa... |
github | supratimray/GammaLengthProjectCodes-master | getBurstLengthFeingold.m | .m | GammaLengthProjectCodes-master/getBurstLengthFeingold.m | 3,767 | utf_8 | ae53821cbbdb197e755e3f410b003df4 | function burstLengthS = getBurstLengthFeingold(analogData,timeVals,thresholdFactor,displayFlag,stimulusPeriodS,baselinePeriodS,burstFreqRangeHz,filterOrder)
if ~exist('displayFlag','var'); displayFlag=1; end
if ~exist('stimulusPeriodS','var'); stimulusPeriodS=[0.5 1.5]; end
if ~exist(... |
github | supratimray/GammaLengthProjectCodes-master | getChangeInPower.m | .m | GammaLengthProjectCodes-master/getChangeInPower.m | 960 | utf_8 | c0e46b60e9e6fb308dd7661a21dbd9d7 | % This program finds the increase in power in the stimulus period relative
% to baseline in the gamma band. This is used to find the appropriate
% threshold for each electrode.
function diffPower = getChangeInPower(analogData,timeVals,stimulusPeriod,baselinePeriod,gammaRange)
if diff(stimulusPeriod) ~= diff(baselineP... |
github | supratimray/GammaLengthProjectCodes-master | plotFigure1.m | .m | GammaLengthProjectCodes-master/plotFigure1.m | 2,567 | utf_8 | 8d962572a37d1bc3ba3f0b8e19aff28e | function plotFigure1
subjectName = 'alpa'; expDate = '120316'; protocolName = 'GRF_001';
gridType = 'Microelectrode'; folderSourceString = '';
electrodeNum=83;cVal=100;trialNum=2;
folderName = fullfile(folderSourceString,'data',subjectName,gridType,expDate,protocolName,'segmentedData','LFP');
st = load(fullfile(folde... |
github | supratimray/GammaLengthProjectCodes-master | getHilbertPower.m | .m | GammaLengthProjectCodes-master/getHilbertPower.m | 1,078 | utf_8 | ae4da0369ce2e7129b0ef1f7174d52ef | % This function computes the instantaneous power at gamma band using
% Hilbert transform. It uses a Butterworth filter to bandpass filter the
% signal in the gamma range
% Input - signal : Signal in one row
% timeVals : Time Values in a row (seconds)
% gammaFreqRangeHz : Freq... |
github | supratimray/GammaLengthProjectCodes-master | makeDirectory.m | .m | GammaLengthProjectCodes-master/makeDirectory.m | 425 | utf_8 | 2059ebbffcb6ba5938134a09a7a84fd4 | % makeDirectoryMPP(foldername)
% Makes the folder if is does not exist.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Supratim Ray, 2008
% Distributed under the General Public License.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function makeDirectory(f... |
github | juangpc/fiff_anonymizer-master | fiff_anonymizer.m | .m | fiff_anonymizer-master/fiff_anonymizer.m | 22,431 | utf_8 | 5c74516e2f38b8396f9c5ce8a91ca5cc | function fiff_anonymizer(inFile, varargin)
% FIFF_ANONYMIZER Anonymizes fiff files.
% FIFF_ANONYMIZER('filename.fif') anonymizes filename.fif
% Functional Image File Format (FIFF) specifies how information inside a
% fif file is built into a linked list of tags. Different information
% fields typically are allocate... |
github | JonasNygaard/brp-survey-master | progressbar.m | .m | brp-survey-master/progressbar.m | 9,921 | utf_8 | 41f24eea35f0380ff0ee3334ca1802e1 | function upd = progressbar(n, varargin)
% UPD = TEXTPROGRESSBAR(N) initializes a text progress bar for monitoring a
% task comprising N steps (e.g., the N rounds of an iteration) in the
% command line. It returns a function handle UPD that is used to update and
% render the progress bar. UPD takes a single argument i <... |
github | CSAILVision/sceneparsing-master | convertFromADE.m | .m | sceneparsing-master/convertFromADE/convertFromADE.m | 1,211 | utf_8 | 6a2f5a3f48d2ac6f4263720d28d51793 | % This function converts an annotation image from ADE dataset format to
% scene parsing challenge format
% input args: filename_label_input, filename_label_output
% example: convertFromADE('ADE_train_00000970_raw.png','ADE_train_00000970_challenge.png')
function convertFromADE(fileLabIn, fileLabOut)
% load in map... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | submit.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex2/ex2/submit.m | 1,605 | utf_8 | 9b63d386e9bd7bcca66b1a3d2fa37579 | function submit()
addpath('./lib');
conf.assignmentSlug = 'logistic-regression';
conf.itemName = 'Logistic Regression';
conf.partArrays = { ...
{ ...
'1', ...
{ 'sigmoid.m' }, ...
'Sigmoid Function', ...
}, ...
{ ...
'2', ...
{ 'costFunction.m' }, ...
'Logistic R... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | submitWithConfiguration.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | savejson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex2/ex2/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | loadjson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex2/ex2/lib/jsonlab/loadjson.m | 18,744 | ibm852 | 58ededaffbb9f3194b9292f48fef212f | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | loadubjson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex2/ex2/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | saveubjson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex2/ex2/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | submit.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex4/ex4/submit.m | 1,635 | utf_8 | ae9c236c78f9b5b09db8fbc2052990fc | function submit()
addpath('./lib');
conf.assignmentSlug = 'neural-network-learning';
conf.itemName = 'Neural Networks Learning';
conf.partArrays = { ...
{ ...
'1', ...
{ 'nnCostFunction.m' }, ...
'Feedforward and Cost Function', ...
}, ...
{ ...
'2', ...
{ 'nnCostFunct... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | submitWithConfiguration.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex4/ex4/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | savejson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex4/ex4/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | loadjson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex4/ex4/lib/jsonlab/loadjson.m | 18,744 | ibm852 | 58ededaffbb9f3194b9292f48fef212f | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | loadubjson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex4/ex4/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | saveubjson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex4/ex4/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | submit.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex6/ex6/submit.m | 1,318 | utf_8 | bfa0b4ffb8a7854d8e84276e91818107 | function submit()
addpath('./lib');
conf.assignmentSlug = 'support-vector-machines';
conf.itemName = 'Support Vector Machines';
conf.partArrays = { ...
{ ...
'1', ...
{ 'gaussianKernel.m' }, ...
'Gaussian Kernel', ...
}, ...
{ ...
'2', ...
{ 'dataset3Params.m' }, ...
... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | porterStemmer.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex6/ex6/porterStemmer.m | 9,902 | utf_8 | 7ed5acd925808fde342fc72bd62ebc4d | function stem = porterStemmer(inString)
% Applies the Porter Stemming algorithm as presented in the following
% paper:
% Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,
% no. 3, pp 130-137
% Original code modeled after the C version provided at:
% http://www.tartarus.org/~martin/PorterStemmer/c.tx... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | submitWithConfiguration.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex6/ex6/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | savejson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex6/ex6/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | loadjson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex6/ex6/lib/jsonlab/loadjson.m | 18,744 | ibm852 | 58ededaffbb9f3194b9292f48fef212f | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | loadubjson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex6/ex6/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | saveubjson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex6/ex6/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | submit.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex7/ex7/submit.m | 1,438 | utf_8 | 665ea5906aad3ccfd94e33a40c58e2ce | function submit()
addpath('./lib');
conf.assignmentSlug = 'k-means-clustering-and-pca';
conf.itemName = 'K-Means Clustering and PCA';
conf.partArrays = { ...
{ ...
'1', ...
{ 'findClosestCentroids.m' }, ...
'Find Closest Centroids (k-Means)', ...
}, ...
{ ...
'2', ...
... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | submitWithConfiguration.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex7/ex7/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | savejson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex7/ex7/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | loadjson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex7/ex7/lib/jsonlab/loadjson.m | 18,744 | ibm852 | 58ededaffbb9f3194b9292f48fef212f | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | loadubjson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex7/ex7/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | saveubjson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex7/ex7/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | submit.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex5/ex5/submit.m | 1,765 | utf_8 | b1804fe5854d9744dca981d250eda251 | function submit()
addpath('./lib');
conf.assignmentSlug = 'regularized-linear-regression-and-bias-variance';
conf.itemName = 'Regularized Linear Regression and Bias/Variance';
conf.partArrays = { ...
{ ...
'1', ...
{ 'linearRegCostFunction.m' }, ...
'Regularized Linear Regression Cost Fun... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | submitWithConfiguration.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex5/ex5/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | savejson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex5/ex5/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | loadjson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex5/ex5/lib/jsonlab/loadjson.m | 18,745 | ibm852 | 215ac3a4c4a4402f2e2d710dd99da99a | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | loadubjson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex5/ex5/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | saveubjson.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex5/ex5/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | AvaisP/machine-learning-programming-assignments-coursera-andrew-ng-master | submit.m | .m | machine-learning-programming-assignments-coursera-andrew-ng-master/machine-learning-ex3/ex3/submit.m | 1,567 | utf_8 | 1dba733a05282b2db9f2284548483b81 | function submit()
addpath('./lib');
conf.assignmentSlug = 'multi-class-classification-and-neural-networks';
conf.itemName = 'Multi-class Classification and Neural Networks';
conf.partArrays = { ...
{ ...
'1', ...
{ 'lrCostFunction.m' }, ...
'Regularized Logistic Regression', ...
}, ..... |
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