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value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | multiscalePb_Luminance.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/Main/MATLAB&Precompiledmex/multiscalePb_Luminance.m | 5,270 | utf_8 | 7eca65a028583fae7da2def54dceb114 | function [mPb_nmax, mPb_nmax_rsz, bg1, bg2, bg3, cga1, cga2, cga3, cgb1, cgb2, cgb3, tg1, tg2, tg3, textons] = multiscalePb_Luminance(im, rsz)
%function [mPb_nmax, mPb_nmax_rsz, bg1, bg2, bg3, cga1, cga2, cga3, cgb1, cgb2, cgb3, tg1, tg2, tg3, textons] = multiscalePb(im, rsz)
%
% description:
% compute local contour cu... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | contour.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/include/revised_gPb/gpb_src/matlab/segmentation/contour.m | 2,084 | utf_8 | ca81ae92efc656fc124537b346e45754 | % extract contours and neighboring regions given non-max suppressed edge map
function contours = contour(nmax)
% extract contours
tic;
[skel, labels, is_v, is_e, assign, vertices, edges, ...
v_left, v_right, e_left, e_right, c_left, c_right, ...
edge_equiv_ids, is_compl, e_x_coords, e_y_coords] = ...
mex_contour_... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | disp_contours.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/include/revised_gPb/gpb_src/matlab/segmentation/disp_contours.m | 3,323 | utf_8 | b784d85ef4730c0d5f9b04c2df8a348a | % interactively display contours and neighboring regions
function disp_contours(contours, im)
% get image size
im_size = size(contours.skel);
% get vertex and edge indices
v_inds = find(contours.is_v);
e_inds = find(contours.is_e);
% initialize region indices
r_inds_left = [];
r_inds_right = [];
r_inds_left_ext = ... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | contour_export.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/include/revised_gPb/gpb_src/matlab/segmentation/contour_export.m | 686 | utf_8 | 09999cd3a413dd3712232a13a21e934d | % extract contours and neighboring regions given non-max suppressed edge map
function contour_export(filename, nmax)
% extract contours
[skel, labels, is_v, is_e, assign, vertices, edges, e_x, e_y] = mex_contour(nmax);
n_vertices = size(vertices,1)
n_edges = size(edges,1)
% flip x,y
vertices = vertices(:,2:-1:1);
% ... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | load_exemplar.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/include/revised_gPb/gpb_src/matlab/recognition/load_exemplar.m | 632 | utf_8 | 8251b2328da8a1af19830dbe2baf8487 | % load exemplar data from the given file
function [features, x_pos, y_pos] = load_exemplar(filename, ftype)
% check whether to use pca features
if (strcmp(ftype,'no_pca'))
use_pca = 0;
elseif (strcmp(ftype,'pca'))
use_pca = 1;
else
error('ftype must be no_pca or pca');
end
% load features
data = load(filenam... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | create_train_test.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/include/revised_gPb/gpb_src/matlab/recognition/create_train_test.m | 2,270 | utf_8 | 0bafdbaa82a68d85647621e51a96e3e1 | % split the dataset into train and test sets
function [train, test] = create_train_test( ...
dirname, ...
dirname_img, ...
n_train_per_class, ...
n_test_per_class)
classdirs = dir(dirname);
classdirs = classdirs(3:end);
classdirs = {classdirs.name};
n_classes = length(classdirs);
train_filenames = cell([1... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | build_vocab_shapeme.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/include/revised_gPb/gpb_src/matlab/recognition/build_vocab_shapeme.m | 529 | utf_8 | 70d6c99b2faba3482f9f4b14382bdfea | % read training set into shape vocabulary
function build_vocab_shapeme( ...
train, ...
ftype)
% read features, add to database
for n = 1:length(train.filenames)
msg = ['added training file ' num2str(n) ' of ' num2str(length(train.filenames)) ' to shape vocab'];
% load exemplar
[features, x_pos, y_pos] =... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | build_db.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/include/revised_gPb/gpb_src/matlab/recognition/build_db.m | 602 | utf_8 | 2b36d9df68fe89b74c31db6e91aa6bcf | % read training set into memory and create search structure
function build_db( ...
train, ...
ftype)
% read features, add to database
for n = 1:length(train.filenames)
msg = ['added training file ' num2str(n) ' of ' num2str(length(train.filenames))];
% load exemplar
[features, x_pos, y_pos] = load_exemp... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | build_db_shapeme.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/include/revised_gPb/gpb_src/matlab/recognition/build_db_shapeme.m | 671 | utf_8 | 07887697e4f59f82498880f199ea06ce | % read training set into memory and create search structure
function build_db_shapeme( ...
train, ...
ftype, ...
r)
% read features, add to database
for n = 1:length(train.filenames)
msg = ['added training file ' num2str(n) ' of ' num2str(length(train.filenames)) ' to db'];
% load exemplar
[features,... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | run_test_shapeme.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/include/revised_gPb/gpb_src/matlab/recognition/run_test_shapeme.m | 1,112 | utf_8 | 15f262e1832ff5cbb13b5812c25e4533 | % run test on the full dataset using shapemes
function [cmx, score] = run_test_shapeme( ...
train, ...
test, ...
ftype)
% compute total number of classes
n_test = length(test.filenames);
n_classes = n_test/test.n_test_per_class;
% initialize results
cmx = zeros([n_classes n_classes]);
n_correct = 0;... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | run_test.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/include/revised_gPb/gpb_src/matlab/recognition/run_test.m | 1,094 | utf_8 | f1cb89b9bd45d5bfc7a8c02750ac3012 | % run test on the full dataset using the bigram model
function [cmx, score] = run_test( ...
train, ...
test, ...
ftype, ...
num_nn, ...
item_limit)
% compute total number of classes
n_test = length(test.filenames);
n_classes = n_test/test.n_test_per_class;
% initialize results
cmx = zeros([n_c... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | query_db.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/include/revised_gPb/gpb_src/matlab/recognition/query_db.m | 289 | utf_8 | d91c20c79fe9dde9372a09e86425e88a | % query database for an exemplar
function id_vec = query_db( ...
features, ...
x_pos, ...
y_pos, ...
num_nn, ...
item_limit)
id_vec = mex_category_db( ...
'query_db', ...
features', ...
x_pos, ...
y_pos, ...
num_nn, ...
item_limit);
id_vec = id_vec + 1;
|
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | add_exemplar.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/include/revised_gPb/gpb_src/matlab/recognition/add_exemplar.m | 246 | utf_8 | 7fa51fcb2b507670be94aa78f9185e7d | % add an exemplar to the database
function add_exemplar( ...
features, ...
x_pos, ...
y_pos, ...
class_id)
% add exemplar
mex_category_db( ...
'add_exemplar', ...
features', ...
x_pos, ...
y_pos, ...
class_id - 1);
|
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | Miji.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/include/Fiji.app/scripts/Miji.m | 2,806 | utf_8 | 398de9861d5e29c50ace6a092d0ad4c2 | function Miji(open_imagej)
%% This script sets up the classpath to Fiji and optionally starts MIJ
% Author: Jacques Pecreaux, Johannes Schindelin, Jean-Yves Tinevez
if nargin < 1
open_imagej = true;
end
%% Get the Fiji directory
fiji_directory = fileparts(fileparts(mfilename('full... |
github | ApuliaMonitoringNetwork/Coastal-Video-Monitoring-Tool-master | bfopen.m | .m | Coastal-Video-Monitoring-Tool-master/src/Shoreline_extraction/include/Fiji.app/scripts/bfopen.m | 10,040 | utf_8 | 083c9e818055469f652e7e3a05858669 | function [result] = bfopen(id)
% A script for opening microscopy images in MATLAB using Bio-Formats.
%
% The function returns a list of image series; i.e., a cell array of cell
% arrays of (matrix, label) pairs, with each matrix representing a single
% image plane, and each inner list of matrices representing an image
... |
github | Le-Pelley-Lab/saccade-analysis-master | ProcessSaccades.m | .m | saccade-analysis-master/ProcessSaccades.m | 15,671 | utf_8 | 95d914d6a7ad95106f2929897182bee2 | function ProcessSaccades(subjectlist, ROIs, varargin)
% ProcessSaccades: I-VT Algorithm for determining saccade direction and
% latency from Tobii eyetracker data
%
% ProcessSaccades(subjectlist, ROIs, [fixationCoords], [discardAnticipatorySaccades], [discardOutsideFixationSaccades])
% subjectlist is a vector of partic... |
github | jbrzusto/sensorgnome-R-package-master | gain2sg.m | .m | sensorgnome-R-package-master/gain_pattern_src/gain2sg.m | 4,231 | utf_8 | 6a541645dcdba5e165e01de6e31601e9 | %% svn: $Id: gain2sg.m 8 2010-01-13 16:34:13Z john $
%%
%% gain2sg.m - globally normalized gain of 2D array of parallel dipoles with sinusoidal currents
%%
%% Usage: [ge,gh,th] = gain2sg(L,d,I,N,ph0)
%% [ge,gh,th] = gain2sg(L,d,I,N) (equivalent to ph0=0)
%%
%% L = antenna l... |
github | gbacco5/femm33-master | cogging.m | .m | femm33-master/3F/plot/cogging.m | 875 | utf_8 | 774db6d614958233ef1cd4aff0b5a6d7 | clear all;
close all;
clc;
m = 3;
% results filename
res_fn = '../output/results_20160812_090941.out';
read_results % self-explaining
% create transformation matrices
T = 2/m*[cos([0:m-1]*2*pi/m)' , sin([0:m-1]*2*pi/m)'];
U = [cos([0:m-1]*2*pi/m)' , sin([0:m-1]*2*pi/m)'];
function [d,q] = dq2ab(a,b,x)
d = a.*cos(... |
github | vonway/teamtalk-mac-master | FMSearchTokenField.m | .m | teamtalk-mac-master/TeamTalk/interface/mainWindow/FMSearchTokenField.m | 4,519 | utf_8 | 2a89df28133e0c91280b5daf58944c94 | //
// FMSearchTokenField.m
// Duoduo
//
// Created by zuoye on 13-12-23.
// Copyright (c) 2013年 zuoye. All rights reserved.
//
#import "FMSearchTokenField.h"
#import "FMSearchTokenFieldCell.h"
@implementation FMSearchTokenField
@synthesize sendActionWhenEditing=_sendActionWhenEditing;
@synthesize alwaysSendAction... |
github | vonway/teamtalk-mac-master | DDNinePartImage.m | .m | teamtalk-mac-master/TeamTalk/interface/mainWindow/searchField/DDNinePartImage.m | 6,722 | utf_8 | 6dac0c29b80d07b31ccfd0b48ec932de | //
// DDNinePartImage.m
// Duoduo
//
// Created by zuoye on 14-1-20.
// Copyright (c) 2014年 zuoye. All rights reserved.
//
#import "DDNinePartImage.h"
@implementation DDNinePartImage
-(id)initWithNSImage:(NSImage *)image leftPartWidth:(CGFloat)leftWidth rightPartWidth:(CGFloat)rightWidth topPartHeight:(CGFloat)t... |
github | phoenixnn/RGBD-object-propsal-master | get_synched_frames.m | .m | RGBD-object-propsal-master/ext/toolbox_nyu_depth_v2/get_synched_frames.m | 3,249 | utf_8 | 79faf399cbd01562241647f30ea6dddf | % Returns a struct with synchronized RGB and depth frames, as well as the
% accelerometer data. Note that this script considers the depth frames as
% 'primary' in the sense that it keeps every depth frame and matches the
% nearest RGB frame.
%
% Args:
% sceneDir - the directory containing the raw kinect dump for a
% ... |
github | phoenixnn/RGBD-object-propsal-master | get_rgb_depth_overlay.m | .m | RGBD-object-propsal-master/ext/toolbox_nyu_depth_v2/get_rgb_depth_overlay.m | 988 | utf_8 | 54b0bb7754e798fb96feb33dff54e800 | % Returns an overlay of RGB and Depth frames to evaluate the alignment.
%
% Args:
% imgRgb - the RGB image, an HxWx3 matrix of type uint8.
% imgDepthAbs - the absolute-depth image, an HxW matrix of type double
% whose values indicate depth in meters.
%
% Returns:
% imgOverlay - an image visualizin... |
github | phoenixnn/RGBD-object-propsal-master | get_projection_mask.m | .m | RGBD-object-propsal-master/ext/toolbox_nyu_depth_v2/get_projection_mask.m | 482 | utf_8 | b0d8ee3ef3d70d419116843c1a355e23 | % Gets a mask for the projected images that is most conservative with
% respect to the regions that maintain the kinect depth signal following
% projection.
%
% Returns:
% mask - HxW binary image where the projection falls.
% sz - the size of the valid region.
function [mask sz] = get_projection_mask()
mask = fa... |
github | phoenixnn/RGBD-object-propsal-master | rgb_world2rgb_plane.m | .m | RGBD-object-propsal-master/ext/toolbox_nyu_depth_v2/rgb_world2rgb_plane.m | 580 | utf_8 | 80162374cb60208c0ee551cdc7f67de0 | % Performs the camera projection from the RGB-world coordinate frame onto
% the RGB plane.
%
% Args:
% points3d - Nx3 matrix of (X,Y,Z) points in the RGB-world coordinate
% frame.
%
% Returns:
% X_plane - the X coordinates in the RGB plane.
% Y_plane - the Y coordiantes in the RGB plane.
function [X... |
github | phoenixnn/RGBD-object-propsal-master | depth_rel2depth_abs.m | .m | RGBD-object-propsal-master/ext/toolbox_nyu_depth_v2/depth_rel2depth_abs.m | 880 | utf_8 | 4a8cfad34135a694eb4f2178f0c82eff | % Projects the given depth image to world coordinates. Note that this 3D
% coordinate space is defined by a horizontal plane made from the X and Z
% axes and the Y axis points up.
%
% Args:
% imgDepthOrig - 480x640 raw depth image from the Kinect. Note that the
% bytes of the original uint16 image mu... |
github | phoenixnn/RGBD-object-propsal-master | fill_depth_cross_bf.m | .m | RGBD-object-propsal-master/ext/toolbox_nyu_depth_v2/fill_depth_cross_bf.m | 1,846 | utf_8 | f4523471d4c1ec290db238a35cafbfb3 | % In-paints the depth image using a cross-bilateral filter. The operation
% is implemented via several filterings at various scales. The number of
% scales is determined by the number of spacial and range sigmas provided.
% 3 spacial/range sigmas translated into filtering at 3 scales.
%
% Args:
% imgRgb - the RGB im... |
github | phoenixnn/RGBD-object-propsal-master | crop_image.m | .m | RGBD-object-propsal-master/ext/toolbox_nyu_depth_v2/crop_image.m | 507 | utf_8 | cc39cc66283ed2cb954ec31f1294b6ce | % Crops the given image to use only the portion where the projected depth
% image exists.
%
% Args:
% img - either a HxW image or a HxWxD image.
%
% Returns:
% img - a cropped version of the image.
function img = crop_image(img)
[mask, sz] = get_projection_mask();
switch ndims(img)
case 2
img = reshap... |
github | phoenixnn/RGBD-object-propsal-master | get_scene_type_from_scene.m | .m | RGBD-object-propsal-master/ext/toolbox_nyu_depth_v2/get_scene_type_from_scene.m | 361 | utf_8 | faaa10765488df4919cddd6120345c1e | % Returns the scene type (living room, starbucks, subway, etc) from the
% scene (living_room_0002k, office_0013, etc).
%
% Args:
% scene - the scene name: [sceneType]_[sceneNumber]
%
% Returns:
% sceneType - the name of the scene type.
function sceneType = get_scene_type_from_scene(scene)
ind = regexp(scene, '\d... |
github | phoenixnn/RGBD-object-propsal-master | get_instance_masks.m | .m | RGBD-object-propsal-master/ext/toolbox_nyu_depth_v2/get_instance_masks.m | 889 | utf_8 | a8a5d11c9118b70fac97d887366eaf9a | % Returns a series of masks for each object instance in the given scene.
%
% Args:
% imgObjectLabels - HxW label map. 0 indicates a missing label.
% imgInstances - HxW instance map.
%
% Returns:
% instanceMasks - binary masks of size HxWxN where N is the number of
% total objects in the room.
% ... |
github | phoenixnn/RGBD-object-propsal-master | undistort.m | .m | RGBD-object-propsal-master/ext/toolbox_nyu_depth_v2/undistort.m | 2,856 | utf_8 | 59ff94b420c82f87d4a248a343cd3718 | % Undistorts the given image using a set of intrinsic parameters.
%
% Note that this code was taken from Jean-Yves Bouguet's excellent Camera
% Calibration Toolbox for matlab which can be found in its entirety here:
% http://www.vision.caltech.edu/bouguetj/calib_doc/
%
% Args:
% I - the distorted image, an HxW doub... |
github | phoenixnn/RGBD-object-propsal-master | apply_distortion.m | .m | RGBD-object-propsal-master/ext/toolbox_nyu_depth_v2/apply_distortion.m | 1,823 | utf_8 | 7473df3959f60b2c7e7ba6ff9f92cb58 | % Applies distortion to the given image.
%
% Note that this code was taken from Jean-Yves Bouguet's excellent Camera
% Calibration Toolbox for matlab which can be found in its entirety here:
% http://www.vision.caltech.edu/bouguetj/calib_doc/
function [xd,dxddk] = apply_distortion(x,k)
% Complete the distortion vec... |
github | phoenixnn/RGBD-object-propsal-master | get_timestamp_from_filename.m | .m | RGBD-object-propsal-master/ext/toolbox_nyu_depth_v2/get_timestamp_from_filename.m | 761 | utf_8 | 478731dc26def77c1b550697331241ce | % Extracts the timestamp from the filename.
%
% Example usage:
% filename = [CLIPS_DIR '/r-1339729868.166858-2965701968.ppm']
% matlabTime = get_timestamp_from_filename(filename);
% disp(datestr(matlabTime, 'mm/dd/yy HH:MM:SS.FFF'));
%
%
%
% Args:
% filename - the path to the raw kinect output file.
%
% Return... |
github | phoenixnn/RGBD-object-propsal-master | undistort_depth.m | .m | RGBD-object-propsal-master/ext/toolbox_nyu_depth_v2/undistort_depth.m | 3,294 | utf_8 | 63a586f41762878ab10b3b27e301b5df | % Undistorts the given image using a set of intrinsic parameters.
%
% Note that this code was taken from Jean-Yves Bouguet's excellent Camera
% Calibration Toolbox for matlab which can be found in its entirety here:
% http://www.vision.caltech.edu/bouguetj/calib_doc/
%
% Args:
% I - the distorted image, an HxW doub... |
github | phoenixnn/RGBD-object-propsal-master | fill_depth_colorization.m | .m | RGBD-object-propsal-master/ext/toolbox_nyu_depth_v2/fill_depth_colorization.m | 2,851 | utf_8 | 5336fb5c8da4925aa5ebe75e32da6c6c | % Preprocesses the kinect depth image using a gray scale version of the
% RGB image as a weighting for the smoothing. This code is a slight
% adaptation of Anat Levin's colorization code:
%
% See: www.cs.huji.ac.il/~yweiss/Colorization/
%
% Args:
% imgRgb - HxWx3 matrix, the rgb image for the current frame. This must... |
github | phoenixnn/RGBD-object-propsal-master | depth_world2rgb_world.m | .m | RGBD-object-propsal-master/ext/toolbox_nyu_depth_v2/depth_world2rgb_world.m | 577 | utf_8 | bfb699309fb2f698d05c8eb3e6fb0df7 | % Performs the affine transformation between the Depth-world coordinate
% frame and the RGB-world coordinate frame.
%
% Args:
% points3d - the 3D points in the depth camera's world coordinate frame,
% an Nx3 matrix where N=480*640.
%
% Returns:
% points3d - the 3D points in the RGB camera's wor... |
github | phoenixnn/RGBD-object-propsal-master | depth_plane2depth_world.m | .m | RGBD-object-propsal-master/ext/toolbox_nyu_depth_v2/depth_plane2depth_world.m | 697 | utf_8 | c2b0377e454dd16e784e4b625c3fed80 | % Projects the given depth image to world coordinates. Note that this 3D
% coordinate space is defined by a horizontal plane made from the X and Z
% axes and the Y axis points up.
%
% Args:
% imgDepthAbs - 480x640 depth image whose values indicate depth in
% meters.
%
% Returns:
% points3d ... |
github | phoenixnn/RGBD-object-propsal-master | project_depth_map.m | .m | RGBD-object-propsal-master/ext/toolbox_nyu_depth_v2/project_depth_map.m | 2,301 | utf_8 | 7b56d3f0db3888e58fbe69212bd58c17 | % Projects the depth values onto the RGB image.
%
% Usage:
% imgDepth = imread('raw_clips/bedroom_0001/d-12942.665769-31455701.pgm');
% imgDepth = swapbytes(imgDepth);
% rgb = imread('raw_clips/bedroom_0001/r-12941.6324869-2938947.ppm');
%
% [depthOut, rgbOut] = project_depth_map(imgDepth, rgb);
%
%
%
% Arg... |
github | phoenixnn/RGBD-object-propsal-master | bvecs_read.m | .m | RGBD-object-propsal-master/ext/YAEL/bvecs_read.m | 1,520 | utf_8 | 977df50a2a45c709849888179f2f1e39 | % Read a set of vectors stored in the bvec format (int + n * float)
% The function returns a set of output uint8 vector (one vector per column)
%
% Syntax:
% v = bvecs_read (filename) -> read all vectors
% v = bvecs_read (filename, n) -> read n vectors
% v = bvecs_read (filename, [a b]) -> read the ... |
github | phoenixnn/RGBD-object-propsal-master | bvecs_size.m | .m | RGBD-object-propsal-master/ext/YAEL/bvecs_size.m | 494 | utf_8 | f740fcd630ac853920781a4a1a4f742c | % Return the number of vectors contained in a bvecs files and their dimension
%
% Syntax: [n,d] = bvecs_size (filename)
function [n, d] = bvecs_size (filename)
% open the file and count the number of descriptors
fid = fopen (filename, 'rb');
if fid == -1
error ('I/O error : Unable to open the file %s\n', filena... |
github | phoenixnn/RGBD-object-propsal-master | fvecs_write.m | .m | RGBD-object-propsal-master/ext/YAEL/fvecs_write.m | 635 | utf_8 | 69a30552f6ea46eddf3605642048b76b | % This function reads a vector of float vectors
%
% Usage: fvecs_write (filename, v)
% where v is a set of vector (stored columnwise)
function fvecs_write (filename, v)
% open the file and count the number of descriptors
fid = fopen (filename, 'wb');
d = size (v, 1);
n = size (v, 2);
for i = 1:n
% first write... |
github | phoenixnn/RGBD-object-propsal-master | bvecs_write.m | .m | RGBD-object-propsal-master/ext/YAEL/bvecs_write.m | 561 | utf_8 | 6cec679cf64f52656b11ffacfefce339 | % This function reads a vector from a file in the libit format
function bvecs_write (filename, v)
% open the file and count the number of descriptors
fid = fopen (filename, 'wb');
d = size (v, 1);
n = size (v, 2);
for i = 1:n
% first write the vector size
count = fwrite (fid, d, 'int');
if count ~= 1
... |
github | phoenixnn/RGBD-object-propsal-master | yael_kmin.m | .m | RGBD-object-propsal-master/ext/YAEL/yael_kmin.m | 1,031 | utf_8 | 626bb4abc8061664cbc15247e0d9383d | % This function returns the k smallest values of a vector
%
% Usage: [val, idx] = yael_kmin (v,k)
%
% Parameters:
% v the vector to be normalized. If v is a matrix, then the k smallest values
% of each column are returned (similar to the min function)
% k the number of neighbors to be returned. Mus... |
github | phoenixnn/RGBD-object-propsal-master | yael_L2sqr.m | .m | RGBD-object-propsal-master/ext/YAEL/yael_L2sqr.m | 860 | utf_8 | 81282dfbf310860445f74620ccb58948 | % Compute all the distances between two sets of vectors
%
% Usage: [dis] = dis_L2sqr(q, v)
%
% Parameters:
% q, v sets of vectors (1 vector per column)
%
% Returned values
% dis the corresponding *square* distances
% vectors of q corresponds to row, and columns for v
function dis = dis_... |
github | phoenixnn/RGBD-object-propsal-master | fvec_write.m | .m | RGBD-object-propsal-master/ext/YAEL/fvec_write.m | 401 | utf_8 | 0eb88a1df37dd296f56f52331ed8e54d | % This function reads a vector from a file in the libit format
function fvec_write (fid, v)
% first read the vector size
count = fwrite (fid, length(v), 'int');
if (count ~= 1)
error ('Unable to write vector dimension: count !=1 \n');
end
% write the vector components
count = fwrite (fid, v, 'float');
if (count... |
github | phoenixnn/RGBD-object-propsal-master | ivecs_write.m | .m | RGBD-object-propsal-master/ext/YAEL/ivecs_write.m | 561 | utf_8 | 72a8ceae9a93322b5941701735a89289 | % This function writes a vector from a file in the libit format
function ivecs_write (filename, v)
% open the file and count the number of descriptors
fid = fopen (filename, 'wb');
d = size (v, 1);
n = size (v, 2);
for i = 1:n
% first write the vector size
count = fwrite (fid, d, 'int');
if count ~= 1
... |
github | phoenixnn/RGBD-object-propsal-master | fvecs_size.m | .m | RGBD-object-propsal-master/ext/YAEL/fvecs_size.m | 498 | utf_8 | 6d37c7e1e4d00bd9fa8dc774eaf3ca1c | % Return the number of vectors contained in a fvecs files and their dimension
%
% Syntax: [n,d] = fvecs_size (filename)
function [n, d] = fvecs_size (filename)
% open the file and count the number of descriptors
fid = fopen (filename, 'rb');
if fid == -1
error ('I/O error : Unable to open the file %s\n', filena... |
github | phoenixnn/RGBD-object-propsal-master | uint8tobit.m | .m | RGBD-object-propsal-master/ext/YAEL/uint8tobit.m | 326 | utf_8 | 1074f77e291a69382ecc5b7d60c2c334 | % This function translates a uint8 vector into a binary vector
% Usage: b = uint8tobit (v)
% The vectors are column-stored
function b = uint8tobit (v)
n = size (v, 2);
dbytes = size (v, 1);
d = dbytes * 8;
b = zeros(d, n, 'uint8');
for i = 1:n
for j = 1:dbytes
b((j-1)*8+1:j*8 ,i) = bitget (v(j, i), 1:8);
end... |
github | phoenixnn/RGBD-object-propsal-master | yael_kmax.m | .m | RGBD-object-propsal-master/ext/YAEL/yael_kmax.m | 1,039 | utf_8 | daa209681728acb7d88e86bb3c636e5e | % This function returns the k largest values of a vector
%
% Usage: [val, idx] = yael_kmax (v,k)
%
% Parameters:
% v the vector to be normalized. If v is a matrix, then the k largest values
% of each column are returned (similar to the min function)
% k the number of neighbors to be returned. Must... |
github | phoenixnn/RGBD-object-propsal-master | fvec_read.m | .m | RGBD-object-propsal-master/ext/YAEL/fvec_read.m | 213 | utf_8 | 363d7d860bcd190414ecad7c6a887f8d | % This function reads a vector from a file in the libit format
function [v,d] = fvec_read (fid)
% first read the vector size
d = fread (fid, 1, 'int');
% read the elements
v = fread (fid, d, 'float=>single');
|
github | phoenixnn/RGBD-object-propsal-master | gmm_read.m | .m | RGBD-object-propsal-master/ext/YAEL/gmm_read.m | 637 | utf_8 | 4a76011f253e15048c463832a1fdf432 | % This function reads the parameters of a gmm file
%
% Usage: [w, mu, sigma] = gmm_read (filename)
function [w, mu, sigma] = gmm_read (filename)
% open the file and count the number of descriptors
fid = fopen (filename, 'rb');
if fid == -1
error ('I/O error : Unable to open the file %s\n', filename)
end
% first... |
github | phoenixnn/RGBD-object-propsal-master | yael_nn.m | .m | RGBD-object-propsal-master/ext/YAEL/yael_nn.m | 1,592 | utf_8 | db7335e3e3c87ffe223b83bb9b7f167a | % Return the k nearest neighbors of a set of query vectors
%
% Usage: [ids,dis] = nn(v, q, k, distype)
% v the dataset to be searched (one vector per column)
% q the set of queries (one query per column)
% k (default:1) the number of nearest neigbors we want
% distype d... |
github | phoenixnn/RGBD-object-propsal-master | ivecs_read.m | .m | RGBD-object-propsal-master/ext/YAEL/ivecs_read.m | 1,446 | utf_8 | bccc2d5437bc4c9f3372086f6f6b6d6d | % Read a set of vectors stored in the ivec format (int + n * int)
% The function returns a set of output vector (one vector per column)
%
% Syntax:
% v = ivecs_read (filename) -> read all vectors
% v = ivecs_read (filename, n) -> read n vectors
% v = ivecs_read (filename, [a b]) -> read the vectors from a ... |
github | phoenixnn/RGBD-object-propsal-master | ivecs_size.m | .m | RGBD-object-propsal-master/ext/YAEL/ivecs_size.m | 496 | utf_8 | 10a6970b9239b5eee1b6831b3665e93a | % Return the number of vectors contained in a ivecs file and their dimension
%
% Syntax: [n,d] = ivecs_size (filename)
function [n, d] = ivecs_size (filename)
% open the file and count the number of descriptors
fid = fopen (filename, 'rb');
if fid == -1
error ('I/O error : Unable to open the file %s\n', filename... |
github | phoenixnn/RGBD-object-propsal-master | ivec_write.m | .m | RGBD-object-propsal-master/ext/YAEL/ivec_write.m | 409 | utf_8 | cc96e4c56543e2c74419151a3823e9d4 | % This function writes a vector from a file in the libit format
function [v,d] = ivec_write (fid, v)
% first write the vector size
count = fwrite (fid, length(v), 'int');
if count ~= 1
error ('Unable to write vector dimension: count !=1 \n');
end
% write the vector components
count = fwrite (fid, v, 'int');
... |
github | phoenixnn/RGBD-object-propsal-master | ivec_read.m | .m | RGBD-object-propsal-master/ext/YAEL/ivec_read.m | 203 | utf_8 | 68cb23cdec0879a0f09f0773c5ff6c95 | % This function reads a vector from a file in the libit format
function [v,d] = ivec_read (fid)
% first read the vector size
d = fread (fid, 1, 'int');
% read the elements
v = fread (fid, d, 'int');
|
github | phoenixnn/RGBD-object-propsal-master | yael_fvecs_normalize.m | .m | RGBD-object-propsal-master/ext/YAEL/yael_fvecs_normalize.m | 688 | utf_8 | f919d0d4fadf4184c476451a6afc4fcc | % This function normalize a set of vectors
% Parameters:
% v the set of vectors to be normalized (column stored)
% nr the norm for which the normalization is performed (Default: Euclidean)
%
% Output:
% vout the normalized vector
% vnr the norms of the input vectors
%
% Remark: the function return Na... |
github | phoenixnn/RGBD-object-propsal-master | yael_cross_distances.m | .m | RGBD-object-propsal-master/ext/YAEL/yael_cross_distances.m | 825 | utf_8 | af5bf753183dea59cc7b4a0e4b367958 | % Compute all the distances between two sets of vectors
%
% Usage: [dis] = dis_cross_distances(q, v, distype, nt)
%
% Parameters:
% q, v sets of vectors (1 vector per column)
% distype distance type: 1=L1,
% 2=L2 -> Warning: return the square L2 distance
% ... |
github | phoenixnn/RGBD-object-propsal-master | fvecs_read.m | .m | RGBD-object-propsal-master/ext/YAEL/fvecs_read.m | 1,457 | utf_8 | 0318bd7f465153c725687e87ddd7c3ca | % Read a set of vectors stored in the fvec format (int + n * float)
% The function returns a set of output vector (one vector per column)
%
% Syntax:
% v = fvecs_read (filename) -> read all vectors
% v = fvecs_read (filename, n) -> read n vectors
% v = fvecs_read (filename, [a b]) -> read the vectors from ... |
github | phoenixnn/RGBD-object-propsal-master | b2fvecs_read.m | .m | RGBD-object-propsal-master/ext/YAEL/b2fvecs_read.m | 1,609 | utf_8 | 58b9445ea5835d74f5186f5fddef4b21 | % Read a set of vectors stored in the bvec format (int + n * float)
% The function returns a set of output floating point vector (one vector per column)
%
% Syntax:
% v = b2fvecs_read (filename) -> read all vectors
% v = b2fvecs_read (filename, n) -> read n vectors
% v = b2fvecs_read (filename, [a b... |
github | phoenixnn/RGBD-object-propsal-master | m_GrabCut_GUI_3D.m | .m | RGBD-object-propsal-master/ext/m_Grabcut_3D/m_GrabCut_GUI_3D.m | 10,134 | utf_8 | cadc2b4ff99a8b1e01eebe90f24b32f2 | function m_GrabCut_GUI_3D
close all
m_create_components();
end
function m_create_components()
% Create and hide the UI as it is being constructed.
f = figure('Visible','on','Position',[360,500,1320,750]);
% Construct the pushbuttons
h_load = uicontrol('Style','pushbutton','String','Color','Position', ...
... |
github | phoenixnn/RGBD-object-propsal-master | m_assignGMM2pixels_3D.m | .m | RGBD-object-propsal-master/ext/m_Grabcut_3D/m_assignGMM2pixels_3D.m | 1,049 | utf_8 | 01fc79535da062aad4a61d3197372d39 | function [fgkids, bgkids] = m_assignGMM2pixels_3D(examples, fgGMMs, bgGMMs, fgIds, bgIds)
% Assign GMMs component id to each pixel by choosing the component which
% has the minimum negative log likelihood of producing the pixel's color.
% (do not consider the component weight here)
%
% Inputs:
% examples : N x 3 in ... |
github | phoenixnn/RGBD-object-propsal-master | GraphCut.m | .m | RGBD-object-propsal-master/ext/m_Grabcut_3D/GraphCut.m | 15,582 | utf_8 | 61f31e82e80e219ed6be8e2e3155233d | function [gch, varargout] = GraphCut(mode, varargin)
%
% Performing Graph Cut energy minimization operations on a 2D grid.
%
% Usage:
% [gch ...] = GraphCut(mode, ...);
%
%
% Inputs:
% - mode: a string specifying mode of operation. See details below.
%
% Output:
% - gch: A handle to ... |
github | phoenixnn/RGBD-object-propsal-master | m_Unary_LogPL_3D.m | .m | RGBD-object-propsal-master/ext/m_Grabcut_3D/m_Unary_LogPL_3D.m | 1,686 | utf_8 | 2b433d413d0d4bf954fc404699ba1217 | function [fgLogPL, bgLogPL] = m_Unary_LogPL_3D(examples, fgGMMs, bgGMMs, ...
mask_u, mask_fixed_fg, mask_fixed_bg, lambda)
% compute date terms for graph cut
% Inputs:
% examples: N x 3 color image (double)
% fgGMMs, bgGMMs : GMMs model for fg/bg
% mask_u: initial unknown region
% mask... |
github | phoenixnn/RGBD-object-propsal-master | m_GrabCut_GUI.m | .m | RGBD-object-propsal-master/ext/m_Grabcut/m_GrabCut_GUI.m | 2,220 | utf_8 | a3c5952e6ae141ac9886c8607ed69e9c | function m_GrabCut_GUI
close all
% Create and hide the UI as it is being constructed.
f = figure('Visible','on','Position',[360,500,1320,350]);
% Construct the components
h_load = uicontrol('Style','pushbutton',...
'String','Image','Position',[410,250,70,25], ...
'Callback', @loadImage_Ca... |
github | phoenixnn/RGBD-object-propsal-master | m_Unary_LogPL.m | .m | RGBD-object-propsal-master/ext/m_Grabcut/m_Unary_LogPL.m | 1,475 | utf_8 | ca9af26ed81fdae16a00db2632e217e6 | function [fgLogPL, bgLogPL] = m_Unary_LogPL(examples, fgGMMs, bgGMMs, ...
mask_u, mask_fixed_fg, mask_fixed_bg, lambda)
% compute date terms for graph cut
% Inputs:
% examples: N x 3 color image (double)
% fgGMMs, bgGMMs : GMMs model for fg/bg
% mask_u: initial unknown region
% mask_fi... |
github | phoenixnn/RGBD-object-propsal-master | GraphCut.m | .m | RGBD-object-propsal-master/ext/m_Grabcut/GraphCut.m | 15,582 | utf_8 | 61f31e82e80e219ed6be8e2e3155233d | function [gch, varargout] = GraphCut(mode, varargin)
%
% Performing Graph Cut energy minimization operations on a 2D grid.
%
% Usage:
% [gch ...] = GraphCut(mode, ...);
%
%
% Inputs:
% - mode: a string specifying mode of operation. See details below.
%
% Output:
% - gch: A handle to ... |
github | phoenixnn/RGBD-object-propsal-master | m_assignGMM2pixels.m | .m | RGBD-object-propsal-master/ext/m_Grabcut/m_assignGMM2pixels.m | 1,219 | utf_8 | b32e30fa2e2193b195d9184f45dd3645 | function [fgkids, bgkids] = m_assignGMM2pixels(examples, fgGMMs, bgGMMs, fgIds, bgIds)
% Assign GMMs component id to each pixel by choosing the component which
% has the minimum negative log likelihood of producing the pixel's color.
% (do not consider the component weight here)
%
% Inputs:
% examples : N x 3 in col... |
github | phoenixnn/RGBD-object-propsal-master | igraphseg.m | .m | RGBD-object-propsal-master/ext/EGBS3D/igraphseg.m | 2,176 | utf_8 | fcaa8627c5d0b6f552f682cd8c25b3a7 | %%%%MatlabWrapperFromRoboticsVisionMatlabToolboxes
%IGRAPHSEG Graph-based image segmentation
%
% L = IGRAPHSEG(IM, K, MIN) is a graph-based segmentation of the color
% image IM (HxWx3). L (HxW) is an image where each element is the label
% assigned to the corresponding pixel in IM. K is the scale parameter,
... |
github | phoenixnn/RGBD-object-propsal-master | m_SEG_VS_GT.m | .m | RGBD-object-propsal-master/src/eval/m_SEG_VS_GT.m | 865 | utf_8 | 63fad5be4d3c5472f65e3687101bf095 | function Jmat = m_SEG_VS_GT( segCell, GtMasks)
% compare segments with ground truth
% segCell -- segments stored in cell
% GtMasks -- ground truth masks
% Jmat
nobjs = numel(segCell);
[h, w, nGt] = size(GtMasks);
% Jmat
Jmat = zeros(nGt, nobjs);
% care
instancesMap = zeros(h, w);
for i = 1 : nG... |
github | phoenixnn/RGBD-object-propsal-master | PlanesDet.m | .m | RGBD-object-propsal-master/src/planeDet/PlanesDet.m | 9,099 | utf_8 | fc4e67813a04582687f715c53b478d76 | function Pinfo = PlanesDet( points, rawDepth)
% fitting multiple planes to point clouds
%
% Inputs:
% points: organized 3d points m x n x 3 (unit cm)
% rawDepth: original depth m x n (unit meter)
%
% Outputs:
% planesMap: m x n, 0 means uncertain area
% planes: N x 4, plane parameters.
%% initialize planes
[h, w... |
github | phoenixnn/RGBD-object-propsal-master | mat2PCDfile.m | .m | RGBD-object-propsal-master/src/vis/mat2PCDfile.m | 3,348 | utf_8 | 6879dd29633686dc2acdfdd05c139932 | function mat2PCDfile(fileName, points, mode)
% zhuo deng
% temple university
% 20140918
% convert the 3d points represented by matlab matrix into .pcd file
% PCD v.7 file format
% =========================
% VERSION .7
% FIELDS x y z rgb
% SIZE 4 4 4 4
% TYPE F F F F
% COUNT 1 1 1 1
% WIDTH 213
% HEIGHT 1
% VIEWPOI... |
github | phoenixnn/RGBD-object-propsal-master | WatershedSegmentation.m | .m | RGBD-object-propsal-master/src/segmentations/WatershedSegmentation.m | 2,103 | utf_8 | 9075afed7b4925c425897e41296351b4 | function masksWS_cell = WatershedSegmentation(I, rawDepth, D)
% generate segments based on watershed from different signal channels
%
th_L = 0.1;
th_rD = 0.3;
th_d = 0.2;
th_N = 0.1;
G1 = fspecial('gaussian',[9 9],1);
%% process RGB info (intensity)
[L,~,~] = Rgb2Lab(I);
L = imfilter(L,G1,'same','replicate');
gradien... |
github | phoenixnn/RGBD-object-propsal-master | Rgb2Lab.m | .m | RGBD-object-propsal-master/src/segmentations/Rgb2Lab.m | 222 | utf_8 | cd426c19cf8a277bfc76d56050f24928 | % 20130604 Zhuo Deng Temple University
% Convert a rgb image into Lab space
function [L,A,B,lab] = Rgb2Lab(img)
cform = makecform('srgb2lab');
lab = applycform(img,cform);
L=lab(:,:,1);
A=lab(:,:,2);
B=lab(:,:,3);
end |
github | phoenixnn/RGBD-object-propsal-master | m_mask2bbox.m | .m | RGBD-object-propsal-master/src/segmentations/m_mask2bbox.m | 1,479 | utf_8 | e9ce06bef0eb994cc573969274b0ad3d | function bbox = m_mask2bbox(masks, scale)
% covert masks into bounding boxes
% Inputs:
% masks: m x n x d object masks
% scale: scale ratio for the bounding box
%
% outputs:
% bbox: d x 4 [col, row, width, height]
if nargin < 2
scale = 1.0;
end
[h, w, d] = size(masks);
bbox = zeros(d, 4);
for i = 1 : d
... |
github | phoenixnn/RGBD-object-propsal-master | RemoveDupSeg.m | .m | RGBD-object-propsal-master/src/segmentations/RemoveDupSeg.m | 1,859 | utf_8 | 855375228fcb7f92065d08b273f9ccf5 | function out = RemoveDupSeg( segCells, sz )
% remove duplicated segments from multiple sources
N = numel(segCells);
th = 1;
care = true(sz);
% precompute area, centroid
Area = zeros(N, 1);
Centers = zeros(N, 1);
for i = 1 : N
Area(i) = numel(segCells{i});
Centers(i) = mean(segCells{i});
end
%
cpmat = zeros(... |
github | phoenixnn/RGBD-object-propsal-master | HierClustering.m | .m | RGBD-object-propsal-master/src/segmentations/HierClustering.m | 2,821 | utf_8 | a3e756ff60f3eacce1bc46b5826fa664 | function [bbox, segMasks] = HierClustering(points, clusterTolerance, inliers, isV, isH, isB, pid)
% spatial pcd partition by euclidean clustering
% note plane points are removed
%
% Inputs:
% points: mxnx3 pcd
% clusterTolerance: Lx1 (cm)
% inliers: NX1 cell for plane inliers
% isV, isH, isB: plane types
... |
github | phoenixnn/RGBD-object-propsal-master | m_Normalize.m | .m | RGBD-object-propsal-master/src/segmentations/m_Normalize.m | 342 | utf_8 | bfaa47d3db27ed8f1814c3936f0d1413 | % 20130604 Zhuo Deng Temple University
% normalize an input matrix of which values fall in [0,1]
% currently Input is a 1D or 2D matrix
function Mat_norm = m_Normalize(Matrix)
M = max(Matrix(:));
N = min(Matrix(:));
diff = double(M-N);
if diff == 0
diff = diff + eps;
end
Mat_norm = (M... |
github | phoenixnn/RGBD-object-propsal-master | BBfromMPRs.m | .m | RGBD-object-propsal-master/src/segmentations/BBfromMPRs.m | 2,096 | utf_8 | 80bae2e5c8c67e5399b115d71e5f3591 | function [ bbox, Masks ] = BBfromMPRs( inliers, points )
% propose object bounding box by merging plane regions
%
% Inputs:
% inliers: N x 1 cell
% points: n x m x 3 pcd
%
% Outputs:
% bbox: bounding boxes
% Masks: corresponding regions for bbox
[h, w, ~] = size(points);
N = numel(inliers);
% cc for each plane r... |
github | phoenixnn/RGBD-object-propsal-master | Mask2Bbox.m | .m | RGBD-object-propsal-master/src/segmentations/Mask2Bbox.m | 1,531 | utf_8 | a8cc264c2377b5013f5984d96614c1bd | function bbox = Mask2Bbox(masks, sz, scale)
% covert masks into bounding boxes
% Inputs:
% masks: d x 1 cell object masks
% sz: [h, w]
% scale: scale ratio for the bounding box
%
% outputs:
% bbox: d x 4 [col, row, width, height]
if nargin < 2
scale = 1.0;
end
h = sz(1);
w = sz(2);
d = numel(masks);
bbox = ... |
github | phoenixnn/RGBD-object-propsal-master | BBfromPRs.m | .m | RGBD-object-propsal-master/src/segmentations/BBfromPRs.m | 3,108 | utf_8 | 267db5871cc6bbef0b05336529e4ae63 | function [ bbox, segMasks ] = BBfromPRs (segMasks,sz, inliers)
% propose bounding box on planar regions
%
% Inputs:
% segMasks: L x 1 cell segment masks from segmentation
% inliers: N x 1 cell plane points
%
% Outputs:
% bbox: bounding boxes on planes
debug = false;
h = sz(1);
w = sz(2);
num_seg = numel(segMas... |
github | Ziyi-Guo/ml_coursera-master | submit.m | .m | ml_coursera-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 | Ziyi-Guo/ml_coursera-master | submitWithConfiguration.m | .m | ml_coursera-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 | Ziyi-Guo/ml_coursera-master | savejson.m | .m | ml_coursera-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 | Ziyi-Guo/ml_coursera-master | loadjson.m | .m | ml_coursera-master/machine-learning-ex2/ex2/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | 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 | Ziyi-Guo/ml_coursera-master | loadubjson.m | .m | ml_coursera-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 | Ziyi-Guo/ml_coursera-master | saveubjson.m | .m | ml_coursera-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 | Ziyi-Guo/ml_coursera-master | submit.m | .m | ml_coursera-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 | Ziyi-Guo/ml_coursera-master | submitWithConfiguration.m | .m | ml_coursera-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 | Ziyi-Guo/ml_coursera-master | savejson.m | .m | ml_coursera-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 | Ziyi-Guo/ml_coursera-master | loadjson.m | .m | ml_coursera-master/machine-learning-ex4/ex4/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | 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 | Ziyi-Guo/ml_coursera-master | loadubjson.m | .m | ml_coursera-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 | Ziyi-Guo/ml_coursera-master | saveubjson.m | .m | ml_coursera-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 | Ziyi-Guo/ml_coursera-master | submit.m | .m | ml_coursera-master/machine-learning-ex6/mlclass-ex6/submit.m | 16,836 | utf_8 | bbf9b999a1dae2f9a208e9edbfb6981a | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | Ziyi-Guo/ml_coursera-master | porterStemmer.m | .m | ml_coursera-master/machine-learning-ex6/mlclass-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 | Ziyi-Guo/ml_coursera-master | submitWeb.m | .m | ml_coursera-master/machine-learning-ex6/mlclass-ex6/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | Ziyi-Guo/ml_coursera-master | submit.m | .m | ml_coursera-master/machine-learning-ex5/ex5/submit.m | 17,211 | utf_8 | 13a9995decf628307987cfb3364dd6a1 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | Ziyi-Guo/ml_coursera-master | submitWeb.m | .m | ml_coursera-master/machine-learning-ex5/ex5/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | Ziyi-Guo/ml_coursera-master | submit.m | .m | ml_coursera-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', ...
}, ..... |
github | Ziyi-Guo/ml_coursera-master | submitWithConfiguration.m | .m | ml_coursera-master/machine-learning-ex3/ex3/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 | Ziyi-Guo/ml_coursera-master | savejson.m | .m | ml_coursera-master/machine-learning-ex3/ex3/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... |
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