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 | mmedvin/dpm--matlab-master | mi__tryRangeCompression2D.m | .m | dpm--matlab-master/Radar/+SAR/+minus_i/mi__tryRangeCompression2D.m | 4,100 | utf_8 | 1b59b754f6657aeb6754ce8c8d103da0 | function mi__tryRangeCompression2D
set(0, 'defaultLineLineWidth', 2);
set(0, 'defaultLineMarkerSize', 15);
set(0, 'defaultAxesFontSize', 20);
testSixDirections();
testHalfLambda()
end
function testSixDirections()
k_band = 50:0.1:55;
pointScattererSep = 2.3;
pointS... |
github | mmedvin/dpm--matlab-master | mi__tryRangeCompression1D.m | .m | dpm--matlab-master/Radar/+SAR/+minus_i/mi__tryRangeCompression1D.m | 3,075 | utf_8 | 3802f0e12fd395fff5b2b84421efdf2a | function mi__tryRangeCompression1D
set(0, 'defaultLineLineWidth', 2);
set(0, 'defaultLineMarkerSize', 15);
set(0, 'defaultAxesFontSize', 20);
y_r = -4:0.02:4;
k_band = 50:0.1:55;
pointSourceSep = 2.3;
pointSourceAngle = deg2rad(40);
pointSources(1) = mi__SARUtils.c... |
github | mmedvin/dpm--matlab-master | mi__trySARfromVaryingSector.m | .m | dpm--matlab-master/Radar/+SAR/+minus_i/mi__trySARfromVaryingSector.m | 2,906 | utf_8 | 0f555cde779b30121ad98dc433029a39 | function mi__trySARfromVaryingSector
set(0, 'defaultLineLineWidth', 2);
set(0, 'defaultLineMarkerSize', 15);
set(0, 'defaultAxesFontSize', 20);
phi_range = pi * (1 + (-0.12 : 0.004 : 0.08));
k_band = 50 : 1 : 55;
R = 5.4;
rCenter = [1.2; 1.7];
% ??? sharp transiti... |
github | mmedvin/dpm--matlab-master | mi__tryFFPfromSector.m | .m | dpm--matlab-master/Radar/+SAR/+minus_i/mi__tryFFPfromSector.m | 3,442 | utf_8 | 83037ac4afa8abf0a8701402d3922c5a | function mi__tryFFPfromSector
set(0, 'defaultLineLineWidth', 2);
set(0, 'defaultLineMarkerSize', 15);
set(0, 'defaultAxesFontSize', 20);
k = 50;
% TODO: what is this? -phi_inc?
phi_xhat = 1.15 * pi;
R = 5.4;
rCenter = [1.2; 1.7];
% ??? sharp transition about endx = 0... |
github | mmedvin/dpm--matlab-master | AnalyticData.m | .m | dpm--matlab-master/Radar/TryExactOnEllipse/AnalyticData.m | 1,837 | utf_8 | ccd23cad4f7a12112da24710d0576567 | function AnalyticData(AR,ispec)
% AR is an aspect ratio of an ellipse
if nargin==0, AR=2; end
if nargin < 2
spec = getSpec();
else
spec=ispec;
end
ellipse.a=1;%major axis
ellipse.b=ellipse.a/AR;%minor axis
theta = spec.theta; % elliptical angle
x = ellips... |
github | sc1991327/scatnet-0.2-master | GetPixelFeature.m | .m | scatnet-0.2-master/GetPixelFeature.m | 866 | utf_8 | 1e6382a0c395303f55c4228305185206 | % get the pixel feature
function [featureS, featureU] = GetPixelFeature(Sx, Ux, Pi, Pj, rows, cols)
featureS = [];
featureU = [];
% get feature s
level_s = length(Sx);
for lev = 1:level_s
ssl = length(Sx{lev}.signal);
for sig = 1:ssl
ti = ceil(Pi / 16);
... |
github | sc1991327/scatnet-0.2-master | check_options_white_list.m | .m | scatnet-0.2-master/utils/check_options_white_list.m | 794 | utf_8 | 8a9a5b128254dc78332f85ad3e838df5 | % CHECK_OPTIONS_WHITE_LIST Check that all fields of a struct are valid
%
% Usage
% CHECK_OPTIONS_WHITE_LIST(options, white_list)
%
% Input
% options (struct): a structure with optional fields
% white_list (cell of string): containing the valid fields
%
% Ouput
% none
%
% Description
% Will crash with an ... |
github | sc1991327/scatnet-0.2-master | sub_options.m | .m | scatnet-0.2-master/utils/sub_options.m | 700 | utf_8 | 62b5e164b4aa85e467c38ef68c864db9 | % SUB_OPTIONS Copy optional fields among a specified valid list
%
% Usage
% options2 = SUB_OPTIONS(options, field_list)
%
% Input
% options (struct): an input struct of field
% field_list (cell of string): the list of optional fields to copy
%
% Outpout
% options2 (struct): containing a copy of all field of o... |
github | sc1991327/scatnet-0.2-master | imreadBW.m | .m | scatnet-0.2-master/utils/imreadBW.m | 469 | utf_8 | d3771e6ccfc10aed38a53e0c13fc50c8 | % function [f] = imreadBW (filename)
% This function reads any image, convert it in black and white and rescale
% it to range 0..1
function f = imreadBW (filename)
fcol= imread(filename);
%do it only if the image is in color
if numel(size(fcol))==3
fcold=double(fcol);
if (size(size(fcol),2)==3)
f=1/255*(0... |
github | sc1991327/scatnet-0.2-master | fill_struct.m | .m | scatnet-0.2-master/utils/fill_struct.m | 797 | utf_8 | 5ac7c92aacc741e58e54014498ddba9b | % FILL_STRUCT Sets default values of a structure
%
% Usage
% s = FILL_STRUCT(s, field, value, ...)
%
% Input
% s (struct): Structure whose fields are to be set.
% field (char): The name of the field to set.
% value: The default value of the field.
%
% Output
% s (struct): The structure with the default v... |
github | sc1991327/scatnet-0.2-master | func_output.m | .m | scatnet-0.2-master/utils/func_output.m | 843 | utf_8 | 94fe087d9f046dd522fd0f43e9f81177 | % FUNC_OUTPUT Extracts multiple outputs of a function
%
% Usage
% out = FUNC_OUTPUT(func, output_ind, ...)
%
% Input
% func (function handle): The function whose outputs are to be extracted.
% output_ind (int): The indices of the outputs.
%
% Output
% out: The desired output of func specified by output_ind.... |
github | sc1991327/scatnet-0.2-master | rgb_fun.m | .m | scatnet-0.2-master/utils/rgb_fun.m | 404 | utf_8 | d52e9d6dc9576285a214c1c1814760b1 | %function feat = rgb_fun(filename, fun)
% apply function handler fun separately to all rgb channel of image located
% in filename
function feat = rgb_fun(filename, fun)
rgbx = imread(filename);
feat = [];
if (numel(size(rgbx)) == 3)
for c = 1:3
cx = single(squeeze(rgbx(:,:,c)));
feat = [feat, fun(cx)];
end... |
github | sc1991327/scatnet-0.2-master | data_read.m | .m | scatnet-0.2-master/utils/data_read.m | 1,349 | utf_8 | 3ae1652f4c3512bd7054d6de9f9a08f6 | % DATA_READ Read data (audio, image) file
%
% Usage
% x = DATA_READ(filename, ...)
%
% Input
% filename (char): The file to read.
%
% Output
% x (numeric): The contents of the file. In the case of audio, this is a
% one-dimensional vector, while for images, it is a two-dimensional
% array.
%
% De... |
github | sc1991327/scatnet-0.2-master | yuv_fun.m | .m | scatnet-0.2-master/utils/yuv_fun.m | 393 | utf_8 | 337e11a0baca2c6aca2c179c40105dc6 | %function feat = rgb_fun(filename, fun)
% apply function handler fun separately to all rgb channel of image located
% in filename
function feat = yuv_fun(filename, fun)
rgbx = imread(filename);
feat = [];
if (numel(size(rgbx)) ~= 3)
rgbx = repmat(rgbx,[1,1,3]);
end
yuvx = rgb2yuv(rgbx);
feat = [];
for c = 1:3
... |
github | sc1991327/scatnet-0.2-master | upsample.m | .m | scatnet-0.2-master/utils/upsample.m | 627 | utf_8 | 76236130a4ff7ce61c66dc4f6f0a84cf | % UPSAMPLE Upsamples a signal using cubic spline interpolation
%
% Usage
% y = upsample(x, N)
%
% Input
% x (numeric): The signal to be upsampled.
% N (numeric): The number of points in the upsampled signal.
%
% Output
% y (numeric): The upsampled signal.
%
% Description
% The input signal x is interpola... |
github | sc1991327/scatnet-0.2-master | sphere_read.m | .m | scatnet-0.2-master/utils/sphere_read.m | 2,885 | utf_8 | f0e867d0b90717029e0fe5da7ea9dade | % SPHERE_READ Read a NIST SPHERE audio file
%
% Usages
% [y, fs] = SPHERE_READ(filename, N)
%
% sz = SPHERE_READ(filename, 'size')
%
% Input
% filename (char): The name of the file to read.
% N (int, optional): The number of samples to read.
%
% Output
% y (numeric): The audio data in the file.
% fs (... |
github | sc1991327/scatnet-0.2-master | orientation_avg_scat.m | .m | scatnet-0.2-master/scatutils/orientation_avg_scat.m | 2,113 | utf_8 | 13ab5a45bd1a5d3bb2a92472cd2e02ab | % ORIENTATION_AVG_SCAT Average 2d scattering along orientations
%
% Usage
% Sx_avg = orientation_avg_scat(Sx)
%
% Input
% Sx (cell): the output of 2d scattering
%
% Output
% Sx_avg (cell): the scattering averaged along its orientation parameter
%
% Description
% Order 1 coefficient are average along there uniqu... |
github | sc1991327/scatnet-0.2-master | concatenate_freq.m | .m | scatnet-0.2-master/scatutils/concatenate_freq.m | 5,260 | utf_8 | 6682f5a32d005a0ecf5c883d72818872 | % CONCATENATE_FREQ Concatenates first frequencies into tables
%
% Usage
% Y = CONCATENATE_FREQ(X, fmt)
%
% Input
% X (struct or cell): The scattering layer to process, or a cell array of
% such scattering layers. Often S or U outputs of SCAT.
% fmt (char, optional): Either 'table' or 'cell'. Describes ho... |
github | sc1991327/scatnet-0.2-master | scatfun_layer.m | .m | scatnet-0.2-master/scatutils/scatfun_layer.m | 283 | utf_8 | b5a9cb3ad774e5d315f509c45fea0c6d | % scatfun_layer : applies fun to all signal of a ScatNet layer
%
% Usage
% layer_out = scatfun_layer(fun, layer);
function layer_out = scatfun_layer(fun, layer)
layer_out = layer;
for p = 1:numel(layer.signal)
layer_out.signal{p} = fun(layer.signal{p});
end
end
|
github | sc1991327/scatnet-0.2-master | aggregate_scat.m | .m | scatnet-0.2-master/scatutils/aggregate_scat.m | 1,810 | utf_8 | 0a83505d4722e34f26817d20c6286787 | % AGGREGATE_SCAT Aggregate successive frames of a scattering transform
%
% Usage
% S = AGGREGATE_SCAT(S, N)
%
% Input
% S: A scattering transform.
% N: The length of the window with which to aggregate.
%
% Output
% S: The scattering transform with successive frames within a window of
% length N aggreg... |
github | sc1991327/scatnet-0.2-master | hanning_standalone.m | .m | scatnet-0.2-master/scatutils/hanning_standalone.m | 638 | utf_8 | 31e86db164e0c7539626918aaff90ded | % HANNING_STANDALONE Hanning window.
%
% Usage
% window = HANNING_STANDALONE(window_length)
%
% Input
% window_length (integer): length of the desired window.
%
% Output
% window (numeric): Hanning window vector
%
% Description
% This function generates a symmetric Hanning window of arbitrary
% length, n... |
github | sc1991327/scatnet-0.2-master | scat_energy.m | .m | scatnet-0.2-master/scatutils/scat_energy.m | 901 | utf_8 | d7c7b6d8c21553722389cf41d2150ff5 | % SCAT_ENERGY Calculate scattering energy
%
% Usage
% energy = scat_energy(S, U)
%
% Input
% S (cell): The scattering transform.
% U (cell): The wavelet modulus coefficients (optional).
%
% Output
% energy (numeric): The energy of the scattering transform (the sum of the
% squares of the coefficients... |
github | sc1991327/scatnet-0.2-master | renorm_scat.m | .m | scatnet-0.2-master/scatutils/renorm_scat.m | 944 | utf_8 | a10ce64595dae9be139acb5138ef59b0 | % RENORM_SCAT Renormalize a scattering coefficients by their parents
%
% Usage
% S = renorm_scat(S)
%
% Input
% S: A scattering transform.
%
% Output
% S: The scattering transform with second- and higher-order coefficients
% divided by their parent coefficients.
function X = renorm_scat(X, epsilon, min_... |
github | sc1991327/scatnet-0.2-master | mean_renorm_scat.m | .m | scatnet-0.2-master/scatutils/mean_renorm_scat.m | 1,275 | utf_8 | d4a1652674b31db981cab3f447d15007 | % mean_renorm_scat: Renormalize scattering coefficients by the mean over
% coefficients with the same parent.
% Usage
% S = mean_renorm_scat(S)
% Input
% S: A scattering transform.
% Output
% S: The renormalized scattering transform.
function X = mean_renorm_scat(X,p)
if nargin < 2
p = 1;
end
for m ... |
github | sc1991327/scatnet-0.2-master | renorm_wavelet_layer_factory_1d.m | .m | scatnet-0.2-master/scatutils/renorm_wavelet_layer_factory_1d.m | 931 | utf_8 | 642ef432d9c0f4736f45b355715aa7d0 | % wavelet_factory_1d: Create wavelet cascade from filters
% Usage
% [Wop, filters] = wavelet_factory_1d(N, filter_options, scat_options)
% Input
% N: The size of the signals to be transformed.
% filter_options: The filter options, same as for filter_bank.
% scat_options: General options to be passed to wave... |
github | sc1991327/scatnet-0.2-master | map_meta.m | .m | scatnet-0.2-master/scatutils/map_meta.m | 1,375 | utf_8 | 27fe2061c686a829fa6521216791015b | % MAP_META Copy meta fields
%
% Usage
% to_meta = MAP_META(from_meta, from_ind, to_meta, to_ind, except)
%
% Input
% from_meta (struct): The meta structure to copy from.
% from_ind (int): The range of columns to copy from.
% to_meta (struct): The meta struture to copy to.
% to_ind (int): The range of col... |
github | sc1991327/scatnet-0.2-master | log_scat.m | .m | scatnet-0.2-master/scatutils/log_scat.m | 1,351 | utf_8 | c71de1fa81e955ef25fe43338edd0978 | % LOG_SCAT Calculate the logarithm of a scattering transform.
%
% Usages
% S = LOG_SCAT(S)
%
% S = LOG_SCAT(S, epsilon)
%
% Input
% S (cell): A scattering transform.
% epsilon (real): An small constant added to each component of S in
% order to reduce contribution of noise (default 2^-20).
%
% Output
% ... |
github | sc1991327/scatnet-0.2-master | average_scat.m | .m | scatnet-0.2-master/scatutils/average_scat.m | 2,088 | utf_8 | 2508620ce6a31c3344d4b24e5aa575fe | % AVERAGE_SCAT Average successive frames of a scattering transform
%
% Usage
% S = AVERAGE_SCAT(S, T, step, window_fun)
%
% Input
% S (cell): A scattering transform.
% T (int): The length of the window with which to average.
% step (int, optional): The stepping of the successive windows (default
% T... |
github | sc1991327/scatnet-0.2-master | renorm_parent_3d.m | .m | scatnet-0.2-master/scatutils/renorm_parent_3d.m | 807 | utf_8 | 35278ce79b237997948600cc9d3c580b | % RENORM_PARENT_3D Renormalization for roto-translation scattering
%
% Usage
% Sx_rn = renorm_parent_3d(Sx)
%
% Input
% Sx (cell): output of roto-translation scattering
%
% Output
% Sx_rn (cell): the renormalized roto-translation scattering
%
% Description
% This function will renormalize every 2nd order node b... |
github | sc1991327/scatnet-0.2-master | format_scat.m | .m | scatnet-0.2-master/scatutils/format_scat.m | 2,864 | utf_8 | 39f12f72ffb40603c18e0747fe178ad4 | % FORMAT_SCAT Formats a scattering representation
%
% Usages
% [out,meta] = FORMAT_SCAT(S)
%
% [out, meta] = FORMAT_SCAT(S, fmt)
%
% Input
% S (cell): The scattering representation to be formatted.
% fmt (string): The desired format. Can be either 'raw',
% 'order_table' or 'table' (default 'table').
%
... |
github | sc1991327/scatnet-0.2-master | renorm_parent_2d.m | .m | scatnet-0.2-master/scatutils/renorm_parent_2d.m | 849 | utf_8 | 1cde7f38fae6ef282c204fa78701785f | % RENORM_PARENT_2D Renormalization for 2d scattering
%
% Usage
% Sx_rn = renorm_parent_2d(Sx)
%
% Input
% Sx (cell): output of 2d scattering
%
% Output
% Sx_rn (cell): the renormalized 2d scattering
%
% Description
% This function will renormalize every 2nd order node by its parent
% (hence his name renorm_... |
github | sc1991327/scatnet-0.2-master | flatten_scat.m | .m | scatnet-0.2-master/scatutils/flatten_scat.m | 1,511 | utf_8 | 02b6e9178cec98d0fac7e1c7d24fe34c | % FLATTEN_SCAT Put scattering coefficients of all layers together
%
% Usage
% S = flatten_scat(S)
%
% Input
% S (cell): A scattering representation.
%
% Output
% S (cell): The same scattering representation, but flattened into one layer. As
% a result, meta fields from different orders are concatenated.
... |
github | sc1991327/scatnet-0.2-master | renorm_low_pass_layer_1d.m | .m | scatnet-0.2-master/scatutils/renorm_low_pass_layer_1d.m | 389 | utf_8 | 77a298a0702801d9bac55727de26a97a |
function [A,Utilde] = renorm_low_pass_layer_1d(A, U, epsilon)
if nargin < 3
epsilon = 1e-6; % ~1e-6
end
Utilde = U;
for p = 1:length(U.signal)
sub_multiplier = 2^(U.meta.resolution(p)/2);
denom = interpft(A.signal{p},size(U.signal{p},1));
Utilde.signal{p... |
github | sc1991327/scatnet-0.2-master | remove_margin.m | .m | scatnet-0.2-master/scatutils/remove_margin.m | 612 | utf_8 | 190efb29728fede782f416f2f9d20f1e | % remove_margin : remove margins along specified dimensions
%
% Input
% - x (numeric) : the array (up to 3d) for which to remove the margin
% - margins (margins) : the margins
%
% Output
% - x2 (numeric) : the array with removed margin
%
% Usage
% x2 = remove_margin(x, [0,0,1,1,1,1])
% will remove 1 e... |
github | sc1991327/scatnet-0.2-master | scatfun.m | .m | scatnet-0.2-master/scatutils/scatfun.m | 213 | utf_8 | 59e39f8936754fccacb77770d1fa48ac | % scatfun: applies fun to all signal of a scattering
%
% Usage
% Sx_out = scatfun(fun, Sx);
function Sx_out = scatfun(fun, Sx)
for m = 1:numel(Sx)
Sx_out{m} = scatfun_layer(fun, Sx{m});
end
end
|
github | sc1991327/scatnet-0.2-master | pad_signal.m | .m | scatnet-0.2-master/convolution/pad_signal.m | 3,439 | utf_8 | fa0ea782a05bb6d20bc9b5932e3383f5 | % PAD_SIGNAL Pad a signal
%
% Usage
% y = PAD_SIGNAL(x, Npad, boundary, center)
%
% Input
% x (numeric): The signal to be padded.
% Npad (numeric): The desired size of the padded output.
% boundary (string): The boundary condition of the signal, one of:
% 'symm': Symmetric boundary condition with hal... |
github | sc1991327/scatnet-0.2-master | unpad_signal.m | .m | scatnet-0.2-master/convolution/unpad_signal.m | 2,018 | utf_8 | 9222a11a3db0e49b523c88221257cbdc | % UNPAD_SIGNAL Remove de padding from PAD_SIGNAL
%
% Usage
% x = UNPAD_SIGNAL(y, resolution, target_sz, center)
%
% Input
% y (numeric): The signal to be unpadded.
% resolution (int): The resolution of the signal (as a power of 2), with
% respect to the original, unpadded version.
% target_sz (numeri... |
github | sc1991327/scatnet-0.2-master | conv_sub_2d.m | .m | scatnet-0.2-master/convolution/conv_sub_2d.m | 2,293 | utf_8 | 6aea3591de96780fd4770d7f2654a785 | % CONV_SUB_2D Two dimension convolution and downsampling
%
% Usage
% y_ds = conv_sub_2d(in, filter, ds, offset)
%
% Input
% in (numeric) : ! WARNING ! varies depending on the filter type used :
% for 'fourier_multires', in = 2d fourier transform of input signal
% for 'spatial_support', in = original i... |
github | sc1991327/scatnet-0.2-master | conv_sub_1d.m | .m | scatnet-0.2-master/convolution/conv_sub_1d.m | 4,390 | utf_8 | 3fca26a3128ca5993b945df8f508a26c | % CONV_SUB_1D One-dimensional convolution and downsampling.
%
% Usage
% y_ds = CONV_SUB_1D(xf, filter, ds)
%
% Input
% xf (numeric): The Fourier transform of the signal to be convolved.
% filter (*numeric OR struct*): The analysis filter in the frequency
% domain. Either Fourier transform of the filter o... |
github | sc1991327/scatnet-0.2-master | extract_block.m | .m | scatnet-0.2-master/convolution/extract_block.m | 1,390 | utf_8 | be3a9c30057d67f690dd5bd8e06284e5 | % EXTRACT_BLOCK extract sub block of a 2d matrix
%
% Usage
% x_block = EXTRACT_BLOCK(x, nb_block)
%
% Input
% x (numeric): the 2d input matrix
% nb_block (2x1 int): the vertical and horizontal number of block
%
% Output
% x_block (numeric): a 3d block matrix whos third dimension corresponds
% to block in... |
github | sc1991327/scatnet-0.2-master | pad_size.m | .m | scatnet-0.2-master/convolution/pad_size.m | 1,023 | utf_8 | ba6f36fa3f36d46b4feddf9178cbce1f | % PAD_SIZE Compute the optimal size for padding
%
% Usage
% sz_padded = PAD_SIZE(sz, min_margin, max_ds)
%
% Input
% sz (int): The size of the original signal.
% min_margin (int): The minimum margin for padding.
% max_ds (int): The maximum downsampling factor.
%
% Output
% sz_padded (int): The minimum size of... |
github | sc1991327/scatnet-0.2-master | srcfun.m | .m | scatnet-0.2-master/papers/RSDS/srcfun.m | 595 | utf_8 | f928b61828148bd40c7c273af2b46415 | % SRCFUN Apply function to each filenames of a ScatNet-compatible src
%
% Usage
% cell_out = SRCFUN(fun, src);
function cell_out = srcfun(fun, src)
time_start = clock;
for k = 1:length(src.files)
db.indices{k} = k;
filename = src.files{k};
cell_out{k} = fun(filename);
tm0 = tic;
time_elapsed = etime(clo... |
github | sc1991327/scatnet-0.2-master | uiuc_src.m | .m | scatnet-0.2-master/papers/RSDS/uiuc_src.m | 683 | utf_8 | d0fbf23840c08fe4cf89f3ba4110aae4 | % UIUC_SRC Creates a source for the UIUC Texture dataset
%
% Usage
% src = UIUC_SRC(directory)
%
% Input
% directory: The directory containing the UIUC Texture dataset.
%
% Output
% src: The UIUC source.
%
% Note
% The dataset is available at http://www-cvr.ai.uiuc.edu/ponce_grp/data/
function src = uiuc_src... |
github | sc1991327/scatnet-0.2-master | kthtips_src.m | .m | scatnet-0.2-master/papers/RSDS/kthtips_src.m | 770 | utf_8 | cee2a25acddf376aa655fdffd6bcde9a | % KTHTIPS_SRC Creates a source for the KTH TIPS Texture dataset
%
% Usage
% src = KTHTIPS_SRC(directory)
%
% Input
% directory: The directory containing the KTH TIPS Texture dataset.
%
% Output
% src: The KTH TIPS source.
%
% Note
% The dataset is available at http://www.nada.kth.se/cvap/databases/kth-tips/
f... |
github | sc1991327/scatnet-0.2-master | umd_src.m | .m | scatnet-0.2-master/papers/RSDS/umd_src.m | 619 | utf_8 | ec34e20e8581bc83179e2f24423533de | % UMD_SRC Creates a source for the UMD Texture dataset
%
% Usage
% src = UMD_SRC(directory)
%
% Input
% directory: The directory containing the UMD Texture dataset.
%
% Output
% src: The UMD source.
%
function src = umd_src(directory)
if (nargin<1)
directory = '/Users/laurentsifre/TooBigForDropbox/Databas... |
github | sc1991327/scatnet-0.2-master | cellsrc2db.m | .m | scatnet-0.2-master/papers/RSDS/cellsrc2db.m | 302 | utf_8 | a624426e71165bb63a4869b912b93ae7 | % CELLSRC2DB Constitute a database compatible with the ScatNet
% convention with a cell of one object per image and a src
%
% Usage
% db = cellsrc2db(cell, src);
function db = cellsrc2db(cell, src)
db.src = src;
db.features = cell2mat(cell);
for i = 1:numel(cell)
db.indices{i} = i;
end
end
|
github | sc1991327/scatnet-0.2-master | cellfun_monitor.m | .m | scatnet-0.2-master/papers/RSDS/cellfun_monitor.m | 926 | utf_8 | 55e4da313e14dc383e95f8f16cb344cc | % CELLFUN_MONITOR apply a function to each element of a cell array and monitor
% the estimated time left every second
%
% Usage
% cell_out = CELLFUN_MONITOR(fun, cell_in);
%
% Input
% fun (function_handle) : the function handle to be applied
% cell_in (cell) : the cell whose element will be applied fun to
%
% ... |
github | sc1991327/scatnet-0.2-master | rsds_classif.m | .m | scatnet-0.2-master/papers/RSDS/rsds_classif.m | 1,390 | utf_8 | f251eeabb5420272a2135d00a269ea9c | % rsds_classif : a function to reproduce classification experiments of paper
%
% ``Rotation, Scaling and Deformation Invariant Scattering
% for Texture Discrimination"
% Laurent Sifre, Stephane Mallat
% Proc. IEEE CVPR 2013 Portland, Oregon
%
function error = rsds_classif(db, db_name, feature_name, grid_train,... |
github | sc1991327/scatnet-0.2-master | equalize_first_order_scattering.m | .m | scatnet-0.2-master/papers/ISCV/equalize_first_order_scattering.m | 2,607 | utf_8 | efd67d75f10d909d78490358e07bbf59 | function [out,l1norms,l1orig,l1eq]=equalize_first_order_scattering(f,g,psi,phi,lp)
%this function equalizes the process g such that
%it has the same first order scattering coefficients as f
J=size(psi,2);
L=size(psi{end},2);
options.null=1;
% calculate dual wavelets
[dpsi,dphi]=dualwavelets(psi,phi,lp{1});
... |
github | sc1991327/scatnet-0.2-master | legacy_reshape_filters.m | .m | scatnet-0.2-master/papers/ISCV/legacy_reshape_filters.m | 616 | utf_8 | 376ac4664713be80dfaf0d07ae89f0d0 | function [psi,phi,lp]=legacy_reshape_filters(filters,sizein)
J=max(filters.psi.meta.j)+1;
L=max(filters.psi.meta.theta);
r=1;
for j=1:J
for l=1:L
tmp = filters.psi.filter{r}.coefft{1};
tmp = fftshift(ifft2(tmp));
tmp = fftshift(cropc(tmp,sizein));
psi{j}{l} = fft2(tmp);
r=r+1;
end
end
tmp=filters.phi.filter.coefft{1}... |
github | sc1991327/scatnet-0.2-master | recover_meta.m | .m | scatnet-0.2-master/papers/ISCV/recover_meta.m | 690 | utf_8 | 1a8f6e884ed17e6bf1ebfe71edd0bb93 | function meta=recover_meta(in, dirac)
%meta.order
%meta.scale
%meta.orientation
%meta.dirac_norm
%meta.ave
J=in{1}.meta.j;
L=max(in{2}.meta.theta);
meta.order(1)=1;
meta.scale(1)=-1;
meta.orientation(1)=0;
meta.dirac_norm(1)=norm(dirac{1}.signal{1}(:));
meta.ave(1)=mean(in{1}.signal{1}(:));
r=2;
for m=2:size(in,2)
f... |
github | sc1991327/scatnet-0.2-master | scat_display.m | .m | scatnet-0.2-master/papers/ISCV/scat_display.m | 9,879 | utf_8 | af46a9adb60c6c0a47291a5da39206bc | function [imout,orderout,cimout,meta,thetaout]=scat_display(in,dirac,options,scatt)
%this function constructs the scattering logpolar display, presented
%in the paper 'Invariant Scattering Convolution Networks'.
%scatt=array of scattering coefficients to be displayed
%meta=additional information produced by the scatter... |
github | sc1991327/scatnet-0.2-master | gtzan_src.m | .m | scatnet-0.2-master/papers/DSS/gtzan_src.m | 966 | utf_8 | 8219fe8a20b267728d0f82b000466f41 | % GTZAN_SRC Creates a source for the GTZAN dataset
%
% Usage
% src = GTZAN_SRC(directory, N)
%
% Input
% directory (char): The directory containing the GTZAN dataset.
% N (int): The size to which the audio files are to be truncated (default
% 5*2^17).
%
% Output
% src (struct): The GTZAN source.
%
% ... |
github | sc1991327/scatnet-0.2-master | phone_src.m | .m | scatnet-0.2-master/papers/DSS/phone_src.m | 5,214 | utf_8 | af522a97115277f42ee3cc8e960f0544 | % PHONE_SRC Creates a source for the phones in the TIMIT dataset
%
% Usage
% src = PHONE_SRC(directory)
%
% Input
% directory (char): The directory containing the TIMIT dataset.
%
% Output
% src (struct): The TIMIT phone source.
%
% Description
% The function searches through the directory structure, indexi... |
github | sc1991327/scatnet-0.2-master | phone_partition.m | .m | scatnet-0.2-master/papers/DSS/phone_partition.m | 1,241 | utf_8 | 38f55f703e08412238f07aabafebd7ef | % PHONE_PARTITION Specifies training, testing and development sets for TIMIT
%
% Usage
% [train_set, test_set, dev_set] = PHONE_PARTITION(src)
%
% Input
% src (struct): The TIMIT phone source, as obtained from PHONE_SRC.
%
% Output
% train_set (int): The standard training set (TRAIN).
% test_set (int): The ... |
github | sc1991327/scatnet-0.2-master | wavelet_factory_1dave.m | .m | scatnet-0.2-master/papers/IPASM/wavelet_factory_1dave.m | 2,115 | utf_8 | 8ef1a1710084675c32637132295dca60 | % WAVELET_FACTORY_1D Create wavelet cascade
%
% Usage
% [Wop, filters] = WAVELET_FACTORY_1D(N)
%
% [Wop, filters] = WAVELET_FACTORY_1D(N, filt_opt)
%
% [Wop, filters] = WAVELET_FACTORY_1D(N, filt_opt, scat_opt)
%
% Input
% N (int): The size of the signals to be transformed.
% filt_opt (struct): The filte... |
github | sc1991327/scatnet-0.2-master | wavelet_layer_1dave.m | .m | scatnet-0.2-master/papers/IPASM/wavelet_layer_1dave.m | 2,688 | utf_8 | 5b70d4ebdb7a6cccf0216067c9ac71cf | % WAVELET_LAYER_1D Compute the one-dimensional wavelet transform from
% the modulus wavelet coefficients of the previous layer.
%
% Usages
% [U_phi , U_psi] = wavelet_layer_1d(U, filters)
%
% [U_phi , U_psi] = wavelet_layer_1d(U, filters, scat_opt)
%
% [U_phi , U_psi] = wavelet_layer_1d(U, filters, scat_opt, w... |
github | sc1991327/scatnet-0.2-master | stblrnd.m | .m | scatnet-0.2-master/papers/IPASM/stblrnd.m | 3,976 | utf_8 | 9d0d79b97da6753024c6002400a3f4ca | % source : http://math.bu.edu/people/mveillet/research.html
function r = stblrnd(alpha,beta,gamma,delta,varargin)
%STBLRND alpha-stable random number generator.
% R = STBLRND(ALPHA,BETA,GAMMA,DELTA) draws a sample from the Levy
% alpha-stable distribution with characteristic exponent ALPHA,
% skewness BETA, sc... |
github | sc1991327/scatnet-0.2-master | MRWsimu.m | .m | scatnet-0.2-master/papers/IPASM/MRWsimu.m | 1,338 | utf_8 | e675868b90594bcea7be5cab28bd04fb | % Simulates a log-normal multifractal random walk
% Returns a vector X_n[tau] - X_n[0], ... , X_n[N*tau]-X_n[(N-1) tau]
% where X_n goes to a log-normal MRW with intermittency coefficient lambda^2 and integral scale T when n is large.
% The parameter n is such that n>=16 should yield a good approximation.
% Note th... |
github | sc1991327/scatnet-0.2-master | fGnsimu.m | .m | scatnet-0.2-master/papers/IPASM/fGnsimu.m | 332 | utf_8 | 4eeab1b7caf24f28379236598248ec18 | % Simulates a fractional gaussian noise of size N and Hurst exponent H
% Returns the vector B^H(1) - B^H(0), ..., B^H(size) - B^H(size-1) where B^H is a fractional Brownian motion with Hurst exponent H
function x = fGnsimu(N,H)
correl = .5*((0:N-1).^(2*H) + (2:N+1).^(2*H) - 2*(1:N).^(2*H));
x =gaussprocess([1 c... |
github | sc1991327/scatnet-0.2-master | scattergram.m | .m | scatnet-0.2-master/display/scattergram.m | 1,418 | utf_8 | 08f776f2e5dd5f91d34cdd2fd658656e | % SCATTERGRAM Displays temporal evolution of scattering coefficients
%
% Usage
% img = SCATTERGRAM(X{m+1}, [j_1 j_2 ... j_(m-1)]);
%
% img = SCATTERGRAM(X{m1+1}, [j_1 j_2 ... j_(m1-1)], ...
% X{m2+1}, [j_1 j_2 ... j_(m2-1)]);
%
% Input
% X (struct): Scattering layer. Output S or U from SCA... |
github | sc1991327/scatnet-0.2-master | display_littlewood_paley_2d.m | .m | scatnet-0.2-master/display/display_littlewood_paley_2d.m | 546 | utf_8 | 56fc0a478674772473238a5f13f268ec | % DISPLAY_LITTLEWOOD_PALEY_2D Display Littlewood-Paley sum of a filter bank
%
% Usage
% littlewood = display_littlewood_paley_2d(filters);
%
% Input
% filters (struct): filter bank (see FILTER_BANK)
%
% Description
% The function computes the Littlewood-Paley sum of the filter bank and
% displays it. It al... |
github | sc1991327/scatnet-0.2-master | scatter_meta.m | .m | scatnet-0.2-master/display/scatter_meta.m | 582 | utf_8 | 0e9b71b5c70e8a0ada8a162f2a963b47 | % scatter_meta : Scatter two different meta field of scattering
%
% Usage :
% scatter_meta(S{3}, 'j', 1, 'j', 2) will draw a scatter plot
% where each point corresponds to a scattering path, the x axis
% corresponds to j1, the y axis corresponds to j2
function scatter_meta(meta, field_name_1, id1, field_name_2, id2)
... |
github | sc1991327/scatnet-0.2-master | image_scat_layer_order.m | .m | scatnet-0.2-master/display/image_scat_layer_order.m | 3,153 | utf_8 | 07ccbd760195237c58d4ae777d3a2ca2 | % image_scat_layer_order
function big_img = image_scat_layer_order(S, var_x, var_y, renorm)
margin = 3;
margin_inter = 20;
big_img = [];
% compute the range of each variable
for n_var_x = 1:numel(var_x)
cur_var_name = var_x{n_var_x}.name;
cur_var_index = var_x{n_var_x}.index;
string_var = sprintf... |
github | sc1991327/scatnet-0.2-master | display_slice.m | .m | scatnet-0.2-master/display/display_slice.m | 3,120 | utf_8 | eeef79b4fa67d138a3895a0062ad809b | % DISPLAY_SLICE Display a scattering transform slice
%
% Usage
% [sc1, sc2] = DISPLAY_SLICE(S, t, scale, options)
%
% Input
% S (cell): A scattering transform.
% t (int): The time index for which coefficients are to be displayed.
% scale (int, optional): The j-prefix of the coefficients to be displayed
% ... |
github | sc1991327/scatnet-0.2-master | plot_meta_layer.m | .m | scatnet-0.2-master/display/plot_meta_layer.m | 815 | utf_8 | e5ac5ef6e0aa8ccd49f53cf8b72a005a | % PLOT_META_LAYER : Plot the meta variables of a layer of scattering
%
% Usage
% PLOT_META_LAYER(meta, m, nb_row, nb_column)
%
% Input
% meta (struct): structure with array of value
% m (numerical): indice of the layer
% nb_row (numerical): number of row for the subplot
% nb_column (numerical): number ... |
github | sc1991327/scatnet-0.2-master | image_scat.m | .m | scatnet-0.2-master/display/image_scat.m | 941 | utf_8 | 1531c3aaa1cdefd1ea5a6ab102ecfb67 | % IMAGE_SCAT return scattering outputs images next to each other
%
% Usage
% IMAGE_SCAT(S, renorm, dsp_legend)
%
% Input
% Scatt (cell): layers of scattering (either U or S)
% renorm (boolean): if 1 renormalize each path by its max. Default
% value is set to 1.
% dsp_legend (boolean): if set to 1, display legend. D... |
github | sc1991327/scatnet-0.2-master | display_filter_bank_2d.m | .m | scatnet-0.2-master/display/display_filter_bank_2d.m | 2,917 | utf_8 | 4d50d7e23dcb2050affacda971493416 | % DISPLAY_FILTER_BANK_2D Display all the fine-resolution filters of the filter
% bank and returns the result to display
%
% Usage
% big_img = DISPLAY_FILTER_BANK_2D(filters, renorm, n)
%
% Input
% filters (cell of struct): filter banke from a wavelet factory (for
% instance).
% r (bool): renormalize the f... |
github | sc1991327/scatnet-0.2-master | plot_meta.m | .m | scatnet-0.2-master/display/plot_meta.m | 386 | utf_8 | e0f7fd9ed3c2c239496423e2e0bc3b93 | % PLOT_META plot all the meta of all orders of the scattering
%
% Usage
% plot_meta(S)
%
% Input
% S (cell): the output of scat
function plot_meta(S)
% compute number of subplot
M = numel(S);
nb_row = 0;
for m = 1:M
fn = fieldnames(S{m}.meta);
nb_row = max(nb_row, numel(fn));
end
nb_column = M;
for m ... |
github | sc1991327/scatnet-0.2-master | display_with_layer_order.m | .m | scatnet-0.2-master/display/display_with_layer_order.m | 3,334 | utf_8 | 06272bead0d1706760946c8de5ddc9f7 | % DISPLAY_WITH_LAYER_ORDER displays scattering outputs with a structured
% visualization.
%
% Usage
% DISPLAY_WITH_LAYER_ORDER(S,renorm)
%
% Input
% S (cell): layers of scattering
% renorm (boolean): if 1 renormalize each path by its max. Default
% value is set to 1.
%
% Description
% Display the scattering coeffi... |
github | sc1991327/scatnet-0.2-master | plot_lognorm_scat2_1d.m | .m | scatnet-0.2-master/display/plot_lognorm_scat2_1d.m | 1,463 | utf_8 | 8224aa3f7756ee9a9410de74c21c122a | % plot_lognorm_scat2_1d: Plots the log normalized 2nd order scattering
% coefficients for 1d signals.
% Usage
% plot_lognorm_scat2_1d(S)
% Input
% S: The log normalized scattering coefficients.
% Output
% N/A
% Description
% Plots the second order log normalized scattering coefficients as a
% function of j... |
github | sc1991327/scatnet-0.2-master | scattergram_layer.m | .m | scatnet-0.2-master/display/scattergram_layer.m | 977 | utf_8 | e9fdb5e8f72d1791197ba8ccb52e62ac | %SCATTERGRAM_LAYER Formats a one-dimensional layer as an image
% Usages
% img = scattergram_layer(X,[])
%
% img = scattergram_layer(X,j)
%
% Input
% X (struct): a scattering representation layer
% j (integer array): a vector of scale indexes
%
% Output
% img (numeric): a two-dimensional array of scatterin... |
github | sc1991327/scatnet-0.2-master | plot_littlewood_paley_1d.m | .m | scatnet-0.2-master/display/plot_littlewood_paley_1d.m | 1,284 | utf_8 | 76f8d51c8f8cd39b1fc99c76952dcfdc | % PLOT_LITTLEWOOD_PALEY_1D Plot Littlewood-Paley sum of a filter bank
%
% Usage
% littlewood = plot_littlewood_paley_1d(filters);
%
% Input
% filters (struct): filter bank (see FILTER_BANK)
%
% Description
% This function computes, at every frequency, the Littlewood-Paley sum
% of the filter bank, i.e. the ... |
github | sc1991327/scatnet-0.2-master | plot_diracnorm_scat.m | .m | scatnet-0.2-master/display/plot_diracnorm_scat.m | 813 | utf_8 | 72056d7ed622bfff65ecc0844ee4acf3 | % PlotNormScatt(x,Scatt)
% Color plot of a normalized scattering representation
% x gives the horizontal axis
% nScatt is the normalized scattering tranform which is piecewise constant
% The color on each interval depends upon the path length:
% 0 is yellow, 1 is red, 2 is green 3 is blue, 4 is magenta
function plot... |
github | sc1991327/scatnet-0.2-master | plot_spect_scat.m | .m | scatnet-0.2-master/display/plot_spect_scat.m | 930 | utf_8 | e39d776c74e1c45a3120821c26d4207a | % PlotSpectScat(x,nScatt)
% Color plot of the scattering power spectrum
% x gives the horizontal axis
% nScatt is the normalized scattering tranform which is piecewise constant
% The color on each interval depends upon the path length:
% 0 is yellow, 1 is red, 2 is green 3 is blue, 4 is magenta
function plot_spect_sca... |
github | sc1991327/scatnet-0.2-master | display_filter_2d.m | .m | scatnet-0.2-master/display/display_filter_2d.m | 1,135 | utf_8 | d4344bc6a83ac0b7ba1de4aa30c7e412 | % DISPLAY_FILTER_2D Display and return the spatially centered cropped
% version of a filter
%
% Usage
% filt_for_disp = DISPLAY_FILTER_2D(filter, rorim, n)
%
% Input
% filter (struct): a filter from a wavelet factory (for instance).
% rorim (string): 'r' real part, 'i' imaginary part
% n (numeric): size of ... |
github | sc1991327/scatnet-0.2-master | image_scat_layer.m | .m | scatnet-0.2-master/display/image_scat_layer.m | 5,189 | utf_8 | da8b36e3d595caa28d42d0876eeb7240 | % IMAGE_SCAT_LAYER engine function of IMAGE_SCAT
%
% Usage
% big_img = image_scat_layer(Scatt, renorm, dsp_legend)
%
% Input
% Scatt (struct): a layer of scattering (either U or S)
% renorm (boolean): if 1 renormalize each path by its max. Default
% value is set to 1.
% dsp_legend (boolean): if set to 1, display ... |
github | sc1991327/scatnet-0.2-master | prepare_database.m | .m | scatnet-0.2-master/classification/prepare_database.m | 5,690 | utf_8 | 47e834846eab843cbe7ff7a360f6704c | % PREPARE_DATABASE Calculates the features from objects in a source
%
% Usage
% database = PREPARE_DATABASE(src, feature_fun, options)
%
% Input
% src (struct): The source specifying the objects.
% feature_fun (cell): The feature functions applied to each object.
% options (struct): Options for calculating ... |
github | sc1991327/scatnet-0.2-master | svm_test.m | .m | scatnet-0.2-master/classification/svm_test.m | 7,788 | utf_8 | c89ca0db84bd1d4215bd65a82d72484f | % SVM_TEST Calculate labels for an SVM model
%
% Usage
% [labels, votes, feature_labels] = SVM_TEST(db, model, test_set)
%
% Input
% db (struct): The database containing the feature vector.
% model (struct): The affine space model obtained from svm_train.
% test_set (int): The object indices of the testing ... |
github | sc1991327/scatnet-0.2-master | classif_recog.m | .m | scatnet-0.2-master/classification/classif_recog.m | 1,254 | utf_8 | 7484e3f87000989d4c930e466c97668c | % CLASSIF_RECOG Calculates the average recognition rate
%
% Usage
% [rr_mean,recog_rate] = CLASSIF_RECOG(labels,test_set,truth)
%
% Input
% labels (int): The labels attributed to the testing instances.
% test_set (int): The object indices of the testing instances.
% truth: the actual labels of the testing ... |
github | sc1991327/scatnet-0.2-master | duration_feature.m | .m | scatnet-0.2-master/classification/duration_feature.m | 428 | utf_8 | 0dc9f02e57e02036608746d1a01201de | % DURATION_FEATURE Calculate the log-duration of an object
%
% Usage
% duration = DURATION_FEATURE(x, object)
%
% Input
% x (numeric): The file data (not used).
% object (struct): The objects contained in the data.
%
% Output
% duration (numeric): The log-duration of the objects.
%
% See also
% PREPARE_D... |
github | sc1991327/scatnet-0.2-master | feature_wrapper.m | .m | scatnet-0.2-master/classification/feature_wrapper.m | 4,046 | utf_8 | 8e8994e436e0273c1f2616f2d036e5bc | % FEATURE_WRAPPER Wrapper for feature functions
%
% Usage
% feature = FEATURE_WRAPPER(x, objects, feature_fun, options)
%
% Input
% x (numeric): The file data.
% object (struct): The objects contained in the data.
% feature_fun (function handle): The real feature function handle, takes as
% input one ... |
github | sc1991327/scatnet-0.2-master | svm_param_search.m | .m | scatnet-0.2-master/classification/svm_param_search.m | 2,252 | utf_8 | 5d4e6eae14e5cc97920ec42cfc73e9d2 | % SVM_PARAM_SEARCH Parameter search for SVM classifier
%
% Usage
% [err, C, gamma] = SVM_PARAM_SEARCH(db, train_set, valid_set, options)
%
% Input
% db (struct): The database containing the feature vector.
% train_set (int): The object indices of the training instances.
% valid_set (int): The object indices... |
github | sc1991327/scatnet-0.2-master | affine_train.m | .m | scatnet-0.2-master/classification/affine_train.m | 2,039 | utf_8 | bf8418f5670fb1d84fbbf91b596c821f | % AFFINE_TRAIN Train an affine space classifier
%
% Usage
% model = AFFINE_TRAIN(db, train_set, options)
%
% Input
% db (struct): The database containing the feature vector.
% train_set (int): The object indices of the training instances.
% options (struct): The training options. options.dim specifies the d... |
github | sc1991327/scatnet-0.2-master | svm_adaptive_param_search.m | .m | scatnet-0.2-master/classification/svm_adaptive_param_search.m | 1,875 | utf_8 | 553e3f0c0ef90a3c7efbcc975d9c7c14 | % SVM_ADAPTIVE_PARAM_SEARCH Adaptive parameter search for SVM classifier
%
% Usage
% [err, C, gamma] = SVM_ADAPTIVE_PARAM_SEARCH(db, train_set, valid_set, ...
% options)
%
% Input
% db (struct): The database containing the feature vector.
% train_set (int): The object indices of the training instances.
%... |
github | sc1991327/scatnet-0.2-master | create_partition.m | .m | scatnet-0.2-master/classification/create_partition.m | 1,727 | utf_8 | cef4cdfe0f55696f4ae46199af666ab7 | % CREATE_PARTITION Creates a train/test partition
%
% Usage
% [train_set, test_set] = CREATE_PARTITION(src, ratio, shuffle)
%
% Input
% src (struct): The source structure describing the objects.
% ratio (numeric): The proportion of all instances selected for training
% (default 0.8).
% shuffle (boole... |
github | sc1991327/scatnet-0.2-master | next_fold.m | .m | scatnet-0.2-master/classification/next_fold.m | 1,677 | utf_8 | 3aeb4a45050283f125b19c164e0ab792 | % NEXT_FOLD Calculates the next fold in an N-fold cross validation
%
% Usage
% [train_set, test_set] = NEXT_FOLD(train_set, test_set, obj_class)
%
% Input
% train_set (int): The training set of the current fold.
% test_set (int): The testing set of the current fold.
% obj_class (int): The classes of the obj... |
github | sc1991327/scatnet-0.2-master | svm_extract_w.m | .m | scatnet-0.2-master/classification/svm_extract_w.m | 1,144 | utf_8 | 059380fcfdcfb6d03f97c59b891c9fcb | % SVM_EXTRACT_W Calculates the discriminant vector for a linear SVM
%
% Usage
% [w,rho] = SVM_EXTRACT_W(db, model)
%
% Input
% db (struct): The database from which the model is calculated.
% model (struct): The linear SVM model.
%
% Output
% w (numeric): The discriminant vectors for each pair of classes in ... |
github | sc1991327/scatnet-0.2-master | create_src.m | .m | scatnet-0.2-master/classification/create_src.m | 2,387 | utf_8 | d14e3f9421132a79f68ee867e56370e2 | % CREATE_SRC Create a source of files & objects
%
% Usage
% src = CREATE_SRC(directory, objects_fun)
%
% Input
% directory (char): The directory in which the files are found.
% objects_fun (function handle): Given a filename, objects_fun returns its
% constituent objects and their respective classes.
%
... |
github | sc1991327/scatnet-0.2-master | affine_test.m | .m | scatnet-0.2-master/classification/affine_test.m | 3,001 | utf_8 | 418d4a1ea85b592da08e491803427371 | % AFFINE_TEST Calculate labels for an affine space model
%
% Usage
% [labels, err, feature_err] = AFFINE_TEST(db, model, test_set)
%
% Input
% db (struct): The database containing the feature vector.
% model (struct): The affine space model obtained from affine_train.
% test_set (int): The object indices of... |
github | sc1991327/scatnet-0.2-master | fmd_src.m | .m | scatnet-0.2-master/classification/fmd_src.m | 708 | utf_8 | 1c5e65b4b04a6f02fe1d5c0439d773cd | % FMD_SRC Creates a source for the FMD Texture dataset
%
% Usage
% src = FMD_SRC(directory)
%
% Input
% directory: The directory containing the FMD Texture dataset.
%
% Output
% src: The FMD source.
%
% Note
% The dataset is available at http://people.csail.mit.edu/celiu/CVPR2010/FMD/
function src = fmd_src(... |
github | sc1991327/scatnet-0.2-master | classif_err.m | .m | scatnet-0.2-master/classification/classif_err.m | 694 | utf_8 | 77c092b3fac2e78fa5cbdbc49ecf6268 | % CLASSIF_ERR Calculates the classification error.
%
% Usage
% err = CLASSIF_ERR(labels, test_set, src)
%
% Input
% labels (int): The predicted labels corresponding to the testing in-
% stances.
% test_set (int): The object indices of the testing instances.
% src (struct): The source from which the ob... |
github | sc1991327/scatnet-0.2-master | affine_param_search.m | .m | scatnet-0.2-master/classification/affine_param_search.m | 1,420 | utf_8 | cfa0f7c01b27eccd17f27250a8fb748c | % AFFINE_PARAM_SEARCH Parameter search for affine classifier
%
% Usage
% [err, dim] = AFFINE_PARAM_SEARCH(db, train_set, valid_set, options)
%
% Input
% db (struct): The database containing the feature vector.
% train_set (int): The object indices of the training instances.
% valid_set (int): The object ind... |
github | sc1991327/scatnet-0.2-master | svm_calc_kernel.m | .m | scatnet-0.2-master/classification/svm_calc_kernel.m | 4,267 | utf_8 | 5003e9f20526f8d7d7fa5592803eef76 | % SVM_CALC_KERNEL Precalculate SVM kernel
%
% Usage
% database = SVM_CALC_KERNEL(database, kernel_type, kernel_format, ...
% kernel_set)
%
% Input
% database (struct): The database containing the feature vectors.
% kernel_type (char): The type of kernel: 'linear', or 'gaussian' (default
% 'gaussi... |
github | sc1991327/scatnet-0.2-master | svm_train.m | .m | scatnet-0.2-master/classification/svm_train.m | 7,577 | utf_8 | cfaf8aaa03ff87938f9c326e24edc967 | % SVM_TRAIN Train an SVM classifier
%
% Usage
% model = SVM_TRAIN(db, train_set, options)
%
% Input
% db (struct): The database containing the feature vector.
% train_set (int): The object indices of the training instances.
% options (struct): The training options:
% options.kernel_type (char): The... |
github | sc1991327/scatnet-0.2-master | wavelet_2d.m | .m | scatnet-0.2-master/core/wavelet_2d.m | 2,523 | utf_8 | 2f54f71b92dae3e1b956df2419db12a4 | % WAVELET_2D Compute the wavelet transform of a signal x
%
% Usage
% [x_phi, x_psi] = WAVELET_2D(x, filters, options)
%
% Input
% x (numeric): the input signal
% filters (cell): cell containing the filters
% options (structure): options of the wavelet transform
%
% Output
% x_phi (numeric): Low pass part... |
github | sc1991327/scatnet-0.2-master | wavelet_factory_2d.m | .m | scatnet-0.2-master/core/wavelet_factory_2d.m | 2,372 | utf_8 | b62bd9b0128be4fd10056cb7bc91e063 | % WAVELET_FACTORY_2D Create wavelet cascade from morlet filter bank
%
% Usage
% [Wop, filters] = WAVELET_FACTORY_2D(size_in, filt_opt, scat_opt)
%
% Input
% size_in (numeric): The size of the signals to be transformed.
% filt_opt (structure): The filter options, same as for
% MORLET_FILTER_BANK_2D or SH... |
github | sc1991327/scatnet-0.2-master | wavelet_factory_3d_pyramid.m | .m | scatnet-0.2-master/core/wavelet_factory_3d_pyramid.m | 2,503 | utf_8 | af8de9b297f24ed9f80099fbd6b244c9 | % WAVELET_FACTORY_3D_PYRAMID Build roto-translation wavelet operators
%
% Usage
% [Wop, filters, filters_rot] = WAVELET_FACTORY_3D_PYRAMID(filt_opt, filt_rot_opt, scat_opt)
%
% Input
% filt_opt (struct): the filter options, same as for MORLET_FILTER_BANK_2D_PYRAMID
% filt_rot_opt (struct): the filter options fo... |
github | sc1991327/scatnet-0.2-master | wavelet_2d_pyramid.m | .m | scatnet-0.2-master/core/wavelet_2d_pyramid.m | 3,429 | utf_8 | f4601c4a9599a2742d2c46e395cfa87c | % WAVELET_2D_PYRAMID Compute the scattering transform
%
% Usage
% [x_phi, x_psi] = WAVELET_2D_PYRAMID(x, filters, options)
%
% Input
% x (numeric): the input signal
% filters (struct): a 2d pyramid filter bank, typically obtained with
% MORLET_FILTER_BANK_2D_PYRAMID
% options (struct): containing the foll... |
github | sc1991327/scatnet-0.2-master | wavelet_3d_pyramid.m | .m | scatnet-0.2-master/core/wavelet_3d_pyramid.m | 9,812 | utf_8 | 74fdd5e21c4075f8d86553d4d1f79eb6 | % WAVELET_3D_PYRAMID Compute the roto-translation wavelet transfrom
%
% Usage
% [y_Phi, y_Psi, meta_Phi, meta_Psi] = wavelet_3d_pyramid(y, filters, filters_rot, options)
%
% Input
% y (numeric): a 3d matrix whose first two dimension corresponds to spatial
% postion and third dimension corresponds to orientati... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.