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github | ZijingMao/baselineeegtest-master | hlp_flattensearch.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/helpers/hlp_flattensearch.m | 4,984 | utf_8 | 862f3f897f7991f789931d500395318c | function x = hlp_flattensearch(x,form)
% Flatten search() clauses in a nested data structure into a flat search() clause.
% Result = hlp_flattensearch(Expression, Output-Form)
%
% Internal tool used by utl_gridsearch to enable the specification of search parameters using
% search() clauses.
%
% In:
% Expression : s... |
github | ZijingMao/baselineeegtest-master | hlp_tostring.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/helpers/hlp_tostring.m | 9,007 | utf_8 | 27a2805eb3c352fa66ade79a1bff3974 | function str = hlp_tostring(v,stringcutoff,prec)
% Get an human-readable string representation of a data structure.
% String = hlp_tostring(Data,StringCutoff)
%
% The resulting string representations are usually executable, but there are corner cases (e.g.,
% certain anonymous function handles and large data sets), whi... |
github | ZijingMao/baselineeegtest-master | hlp_config.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/helpers/hlp_config.m | 13,648 | utf_8 | 63022cdbc72bbfdfcf73c317df53f400 | function result = hlp_config(configname, operation, varargin)
% helper function to process human-readable config scripts.
% Result = hlp_config(FileName,Operation,VariableName,Value,NVPs...)
%
% Config scripts consist of assignments of the form name = value; to set configuration options. In
% addition, there may be any... |
github | ZijingMao/baselineeegtest-master | hlp_deserialize.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/helpers/hlp_deserialize.m | 13,494 | utf_8 | bb9c30239d4a0cee13a59fbc98798cd3 | function v = hlp_deserialize(m)
% Convert a serialized byte vector back into the corresponding MATLAB data structure.
% Data = hlp_deserialize(Bytes)
%
% In:
% Bytes : a representation of the original data as a byte stream
%
% Out:
% Data : some MATLAB data structure
%
%
% See also:
% hlp_serialize
%
% Examples:
... |
github | ZijingMao/baselineeegtest-master | hlp_aggregatestructs.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/helpers/hlp_aggregatestructs.m | 8,572 | utf_8 | c7ca0758ab30ea7e668808cb89ef00a1 | function res = hlp_aggregatestructs(structs,defaultop,varargin)
% Aggregate structs (recursively), using the given combiner operations.
% Result = hlp_aggregatestructs(Structs,Default-Op,Field-Ops...)
%
% This results in a single 1x1 struct which has aggregated values in its fields (e.g., arrays,
% averages, etc.). Fo... |
github | ZijingMao/baselineeegtest-master | hlp_matlab_version.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/helpers/hlp_matlab_version.m | 1,410 | utf_8 | ca4cb09e51849db00730a297a71d0869 | function v = hlp_matlab_version()
% Get the MATLAB version in a numeric format that can be compared with <, >, etc.
% Copyright (C) Christian Kothe, SCCN, 2010, christian@sccn.ucsd.edu
%
% This program is free software; you can redistribute it and/or modify it under the terms of the GNU
% General Public License as pub... |
github | ZijingMao/baselineeegtest-master | hlp_diskcache.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/helpers/hlp_diskcache.m | 21,219 | utf_8 | f71772574ae6ef73a7d234bf721dfe94 | function varargout = hlp_diskcache(options, f, varargin)
% Cache results of function invocations.
% Results... = hlp_diskcache(Settings, Function, Arguments...)
%
% This function maintains a disk cache of function results in a user-specified folder and
% if a result had already been computed before, it will be immedia... |
github | ZijingMao/baselineeegtest-master | hlp_trycompile.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/helpers/hlp_trycompile.m | 51,457 | utf_8 | 23414826acf0e2e771679cbd7296fb01 | function ok = hlp_trycompile(varargin)
% Try to auto-compile a set of binary files in a folder, and return the status.
% OK = hlp_trycompile(Options...)
%
% This function tries to ensure that a given set of functions or classes (specified by their
% MATLAB identifier), whose source files are assumed to be located in a ... |
github | ZijingMao/baselineeegtest-master | hlp_serialize.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/helpers/hlp_serialize.m | 16,643 | utf_8 | 3fb4a9c53a5cb6059f6ad2653c028e4b | function m = hlp_serialize(v)
% Convert a MATLAB data structure into a compact byte vector.
% Bytes = hlp_serialize(Data)
%
% The original data structure can be recovered from the byte vector via hlp_deserialize.
%
% In:
% Data : some MATLAB data structure
%
% Out:
% Bytes : a representation of the original data as... |
github | ZijingMao/baselineeegtest-master | hlp_varargin2struct.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/helpers/hlp_varargin2struct.m | 6,991 | utf_8 | c99a9bbc7d04cae15828bc517e8e27fe | function res = hlp_varargin2struct(args, varargin)
% Convert a list of name-value pairs into a struct with values assigned to names.
% struct = hlp_varargin2struct(Varargin, Defaults)
%
% In:
% Varargin : cell array of name-value pairs and/or structs (with values assigned to names)
%
% Defaults : optional list of n... |
github | ZijingMao/baselineeegtest-master | hlp_superimposedata.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/helpers/hlp_superimposedata.m | 6,265 | utf_8 | 57e525f9b2247bd13dcfe2e4a88519fc | function res = hlp_superimposedata(varargin)
% Merge multiple partially populated data structures into one fully populated one.
% Result = hlp_superimposedata(Data1, Data2, Data3, ...)
%
% The function is applicable when you have cell arrays or structs/struct arrays with non-overlapping
% patterns of non-empty entries,... |
github | ZijingMao/baselineeegtest-master | utl_check_fingerprint.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/utils/utl_check_fingerprint.m | 1,935 | utf_8 | ec068f208d9bfb62e93003b17b4e37c4 | function x = utl_check_fingerprint(x,opts,ctx,exp)
% Check whether the given argument is an inconsistent impure expression.
% Data = utl_check_fingerprint(Data,Options,Context,Expressions)
%
% The remaining arguments are used only when the function is used as an argstep in exp_beginfun.
% There, it serves as an argstep... |
github | ZijingMao/baselineeegtest-master | utl_memoize_lookup.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/utils/utl_memoize_lookup.m | 8,255 | utf_8 | d560d4554b490a97703199eacfbc418b | function [action,result] = utl_memoize_lookup(exp,memo_ctrl,ctx)
% Check for memoizability and/or availability of the given expression.
% [Action,Result] = utl_memoize_lookup(Key-Expression,Memo-Locations)
%
% In:
% Key-Expression : an expression that uniquely identifies the object to be looked up;
% ... |
github | ZijingMao/baselineeegtest-master | utl_check_dataset.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/utils/utl_check_dataset.m | 5,228 | utf_8 | 9e99691540285666e5f5ec5dc35f01b4 | function sig = utl_check_dataset(sig,opts,ctx,exp)
% Check whether the given argument is an imporperly tracked data set and fix.
% Data = utl_check_dataset(Data,Options,Context,Expressions)
%
% The remaining arguments are used only when the function is used as an argstep in exp_beginfun.
% There, it serves as an argste... |
github | ZijingMao/baselineeegtest-master | utl_crossval.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/utils/utl_crossval.m | 18,571 | utf_8 | 62c4543edf1543c8940dfdd6fa1c3662 | function [measure,stats] = utl_crossval(data, varargin)
% Run a generic cross-validation over indexable data.
% [Measure, Stats] = utl_crossval(Data, Arguments...)
%
% Cross-validation [1] is a data resampling technique in which per each iteration (or "fold"), a
% model is formed given a subset of the data (called the ... |
github | ZijingMao/baselineeegtest-master | utl_prune_handles.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/utils/utl_prune_handles.m | 1,215 | utf_8 | 693a58479eb9a012c85a785e96e5a1ff | function x = utl_prune_handles(x)
% Prune unreferenced workspace variables from anonymous functions in the given argument.
%
% In:
% Data : an arbitrary data structure
%
% Out:
% Data : the data structure with hidden references in anonymous functions removed
%
% Christian Kothe, Swart... |
github | ZijingMao/baselineeegtest-master | utl_resolve_streams.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/utils/utl_resolve_streams.m | 16,798 | utf_8 | 38280b0e6e9771ff30e132ca3877ac2d | function pip = utl_resolve_streams(pip,streams,chanlocs)
% resolve the stream that each rawdata node in the given filter chain requires, given a list
% of workspace stream names (or stream structs), and a subset of channels needed by above pipelines (where empty means 'all')
%
% In:
% Pipeline : a filter chain or fil... |
github | ZijingMao/baselineeegtest-master | utl_collection_partition.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/utils/utl_collection_partition.m | 14,933 | utf_8 | ac43428cc40e2b8adf3e76d0eb985dfc | function res = utl_collection_partition(collection,inds,settings)
% Partition a dataset collection according to some settings
% Result = utl_collection_partition(Collection,IndexSet,Settings)
%
% In:
% Collection : a dataset collection, i.e. cell array of structs with meta-data
%
% IndexSet : the index set to use f... |
github | ZijingMao/baselineeegtest-master | utl_timeseries_fields.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/utils/utl_timeseries_fields.m | 2,603 | utf_8 | 09b9960261401f01fc406f35b8985890 | function fields = utl_timeseries_fields(signal)
% Get the time-series fields of te given signal.
% function Fields = utl_timeseries_fields(Signal)
%
% This function returns the field names in the given signal that are
% carrying time-series information (and which therefore should be filtered,
% buffered, etc.). The re... |
github | ZijingMao/baselineeegtest-master | utl_printapproach.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/utils/utl_printapproach.m | 2,158 | utf_8 | f2d75a0e24733c93a2b3841475b3a743 | function string = utl_printapproach(app,strip_direct,indent,indent_incr)
% Convert an approach to a string representation
% String = utl_printapproach(Approach)
%
% In:
% Approach : a BCI approach, either designed in the GUI or constructed in a script
%
% StripDirect : strip arg_direct flags (default: true)
%
% ... |
github | ZijingMao/baselineeegtest-master | utl_match.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/utils/utl_match.m | 3,245 | utf_8 | 31c0021a79888d0261036c7eff8d1feb | function [r,dict] = utl_match(x,f,dict)
% Check whether an expression is matched by a pattern.
% [Equals,Assignment] = utl_match(Expression, Pattern, Assignment)
%
% The pattern may contain blanks, conditional expressions, and named sub-patterns.
% Assignment is an optional output which contains sub-expressions matche... |
github | ZijingMao/baselineeegtest-master | arg_guidialog.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/arguments/arg_guidialog.m | 11,737 | utf_8 | 37a7d0aadf53e47998dd759d4f19867c | function varargout = arg_guidialog(func,varargin)
% Create an input dialog that displays input fields for a Function and Parameters.
% Parameters = arg_guidialog(Function, Options...)
%
% The Parameters that are passed to the function can be used to override some of its defaults. The
% function must declare its argumen... |
github | ZijingMao/baselineeegtest-master | arg_define.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/arguments/arg_define.m | 32,685 | utf_8 | 8d8bd4577decde884d4bcb63072bb9e1 | function res = arg_define(vals,varargin)
% Declare function arguments with optional defaults and built-in GUI support.
% Struct = arg_define(Values, Specification...)
% Struct = arg_define(Format, Values, Specification...)
%
% This is essentially an improved replacement for the parameter declaration line of a function.... |
github | ZijingMao/baselineeegtest-master | invoke_arg_internal.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/arguments/invoke_arg_internal.m | 5,224 | utf_8 | 2784cfbfe50f9ab2d84cdd3e0fa13f95 | function spec = invoke_arg_internal(reptype,varargin) %#ok<INUSL>
% same type of invoke function as in arg_sub, arg_subswitch, etc. - but shared between
% arg, arg_norep, and arg_nogui
% Copyright (C) Christian Kothe, SCCN, 2010, christian@sccn.ucsd.edu
%
% This program is free software; you can redistribute it and/or... |
github | ZijingMao/baselineeegtest-master | arg_report.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/arguments/arg_report.m | 7,719 | utf_8 | 8c31f7700b53f8cc14f0f913ba6aaf20 | function res = arg_report(type,func,args)
% Report information of a certain Type from the given Function.
% Result = arg_report(Type,Function,Arguments)
%
% Functions that declare their arguments via arg_define() make their parameter specification
% accessible to outside functions. This can be used to display auto-gene... |
github | ZijingMao/baselineeegtest-master | arg_subswitch.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/arguments/arg_subswitch.m | 18,030 | utf_8 | 2b99bc728471da69b824b4c58fbcdcd3 | function res = arg_subswitch(varargin)
% Specify a function argument that can be one of several alternative structs.
% Spec = arg_subswitch(Names,Defaults,Alternatives,Help,Options...)
%
% The correct struct is chosen according to a selection rule (the mapper). Accessible to the
% function as a struct, and visible in t... |
github | ZijingMao/baselineeegtest-master | arg_sub.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/arguments/arg_sub.m | 12,903 | utf_8 | d2d222baf398ec763c607d5b9168fd97 | function res = arg_sub(varargin)
% Specify an argument of a function which is a structure of sub-arguments.
% Spec = arg_sub(Names,Defaults,Source,Help,Options...)
%
% Delivered to the function as a struct, and visible in the GUI as a an expandable sub-list of
% arguments. A function may have an argument which itself c... |
github | ZijingMao/baselineeegtest-master | arg_subtoggle.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/arguments/arg_subtoggle.m | 16,104 | utf_8 | 7e89df9d3f60a509a08809550a8266e1 | function res = arg_subtoggle(varargin)
% Specify an argument of a function which is a struct of sub-arguments that can be disabled.
% Spec = arg_subtoggle(Names,Default,Source,Help,Options...)
%
% Accessible to the function as a struct, and visible in the GUI as a an expandable sub-list of
% arguments (with a checkbox ... |
github | ZijingMao/baselineeegtest-master | arg_guipanel.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/arguments/arg_guipanel.m | 4,253 | utf_8 | f1f39d58a10b31916e58237bd50d0811 | function result = arg_guipanel(varargin)
% Create a uipanel that displays an argument property inspector for a Function.
% Result = arg_guipanel(Options ...)
% Result = arg_guipanel(Parent, Options ...)
%
% The handle supports the method .GetPropertySpecification(), by means of which the edited argument
% specification... |
github | ZijingMao/baselineeegtest-master | arg_tovals.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/arguments/arg_tovals.m | 3,298 | utf_8 | 4c53983211063113b2ef46011720a192 | function res = arg_tovals(spec,direct)
% Convert a 'rich' argument report into a 'vals' report.
% Vals = arg_tovals(Rich)
%
% In:
% Rich : a 'rich' argument report, as obtained via arg_report('rich',some_function)
%
% Direct : whether to endow the result with an 'arg_direct' flag set to true, which indicates to
%... |
github | ZijingMao/baselineeegtest-master | arg_specifier.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/arguments/arg_specifier.m | 3,766 | utf_8 | d9f9eaf9bd9514675f5ba5e7832bf577 | function spec = arg_specifier(varargin)
% Internal: create a base specifier struct for an argument.
% Specifier = arg_specifier(Overrides...)
%
% In:
% Overrides... : name-value pairs of fields that should be overridden
%
% Out:
% A specifier that is recognized by arg_define.
%
% See also:
% arg_define
%
% ... |
github | ZijingMao/baselineeegtest-master | is_needing_search.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/queries/is_needing_search.m | 808 | utf_8 | b4e0ea02b09d80b71d7e2970b648578f | function res = is_needing_search(argform,args)
% test whether some argument pack requires a search or not (according to the specified argument format)
if strcmp(argform,'direct')
% a search is specified by multielement arguments
res = prod(max(1,cellfun(@length,args))) > 1;
elseif strcmp(argform,'clauses')
... |
github | ZijingMao/baselineeegtest-master | is_raw_dataset.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/queries/is_raw_dataset.m | 184 | utf_8 | 6bcaef74ea534a299898aa10b68af85a | % determine whether some object is a raw EEGLAB data set with no BCILAB constituents
function res = is_raw_dataset(x)
res = all(isfield(x,{'data','srate'})) && ~isfield(x,'tracking');
|
github | ZijingMao/baselineeegtest-master | asr_process.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/ASR/asr_process.m | 10,842 | utf_8 | 2b9444373fa0587a1378382eea5850ec | function [outdata,outstate] = asr_process(data,srate,state,windowlen,lookahead,stepsize,maxdims,maxmem,usegpu)
% Processing function for the Artifact Subspace Reconstruction (ASR) method.
% [Data,State] = asr_process(Data,SamplingRate,State,WindowLength,LookAhead,StepSize,MaxDimensions,MaxMemory,UseGPU)
%
% This functi... |
github | ZijingMao/baselineeegtest-master | asr_calibrate.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/ASR/asr_calibrate.m | 20,719 | utf_8 | 8fae81e23ca90db07857571dd5142fbb | function state = asr_calibrate(X,srate,cutoff,blocksize,B,A,window_len,window_overlap,max_dropout_fraction,min_clean_fraction)
% Calibration function for the Artifact Subspace Reconstruction (ASR) method.
% State = asr_calibrate(Data,SamplingRate,Cutoff,BlockSize,FilterB,FilterA,WindowLength,WindowOverlap,MaxDropoutFra... |
github | ZijingMao/baselineeegtest-master | set_merge.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/dataset_editing/set_merge.m | 1,974 | utf_8 | 755faf243ef5fb29eb8933010d7985cd | function [data,idxmap] = set_merge(varargin)
% Merge epoched EEGLAB data sets across trials or time in a fault-tolerant way.
% [Merged,IndexMap] = set_merge(Set-#1, Set-#2, ...)
%
% In:
% Set-#k : data set #k
%
% Out:
% Merged : The merged epoched data set
%
% IndexMap : a mapping from trial index (in the mer... |
github | ZijingMao/baselineeegtest-master | set_gettarget.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/dataset_editing/set_gettarget.m | 4,587 | utf_8 | cc8abafa68a3a656c951cd1a88a51321 | function targ = set_gettarget(signal)
% Generic function to extract the target values from a data set (epoched/continuous).
% Target = set_gettarget(Signal)
%
% Data sets may have associated "target values" in their meta-data (usually one per epoch and/or per
% event). These target values encode what outputs a cognitiv... |
github | ZijingMao/baselineeegtest-master | set_insert_markers.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/dataset_editing/set_insert_markers.m | 20,491 | utf_8 | 2fbcb099d528aa6023ef9d7caba9cee8 | function newsignal = set_insert_markers(varargin)
% Inject runs of markers into specific segments of a continuous data set.
% Signal = set_insert_markers(Signal, Options...)
%
% Almost all real-time inference in BCIs is done on the basis of epochs, and epochs are most
% conveniently created relative to certain ("time-l... |
github | ZijingMao/baselineeegtest-master | set_concat.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/dataset_editing/set_concat.m | 3,110 | utf_8 | 46fd9077c3b6a6e2c007ff4991a9712c | function result = set_concat(varargin)
% Concatenate continuous signals across time.
% Result = set_joinepos(Set1, Set2, ...)
%
% In:
% SetK : The k'th data set to concatenate.
%
% Out:
% Result : A new data set that is the concatenation of all input sets. The following changes are made:
% * .data and ... |
github | ZijingMao/baselineeegtest-master | set_fit_dipoles.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/dataset_editing/set_fit_dipoles.m | 13,316 | utf_8 | 5ca3bc4f74abcc0a542211d0525687ba | function signal = set_fit_dipoles(varargin)
% Fit dipoles for each independent component of an IC-decomposed dataset.
%
% This function uses the Dipfit plugin for EEGLAB to automatically derive dipole locations
% for all IC's in the data set. The returned coordinates are in the MNI coordinate system.
% In addition, (pr... |
github | ZijingMao/baselineeegtest-master | set_joinepos.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/dataset_editing/set_joinepos.m | 3,572 | utf_8 | 86418e7bbad408ccff0c1f5d02c48478 | function result = set_joinepos(varargin)
% Join epoched signals across epochs.
% Result = set_joinepos(Set1, Set2, ...)
%
% In:
% SetK : The k'th data set to join.
%
% Out:
% Result : A new data set with trials from all sets joined. The following changes are made:
% * .data and all other time-series fi... |
github | ZijingMao/baselineeegtest-master | exp_eval_optimized.m | .m | baselineeegtest-master/BigEEGConsortium-ESS/Ess_tools/dependency/expressions/exp_eval_optimized.m | 5,336 | utf_8 | ebfd9f7b2500706eaefa02a690f4c01d | function varargout = exp_eval_optimized(x,iters)
% Evaluate the given expression using optimizations.
% Out-Args = exp_eval_optimized(exp)
%
% This function should produce the same result as exp_eval(), however it may cache intermediate
% results in memory or on disk (and reuse them later), or perform some (known-to-be... |
github | minogame/caffe-qhconv-master | prepare_batch.m | .m | caffe-qhconv-master/matlab/caffe/prepare_batch.m | 1,298 | utf_8 | 68088231982895c248aef25b4886eab0 | % ------------------------------------------------------------------------
function images = prepare_batch(image_files,IMAGE_MEAN,batch_size)
% ------------------------------------------------------------------------
if nargin < 2
d = load('ilsvrc_2012_mean');
IMAGE_MEAN = d.image_mean;
end
num_images = length... |
github | minogame/caffe-qhconv-master | matcaffe_demo_vgg.m | .m | caffe-qhconv-master/matlab/caffe/matcaffe_demo_vgg.m | 3,036 | utf_8 | f836eefad26027ac1be6e24421b59543 | function scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
%
% Demo of the matlab wrapper using the networks described in the BMVC-2014 paper "Return of the Devil in the Details: Delving Deep into Convolutional... |
github | minogame/caffe-qhconv-master | matcaffe_demo.m | .m | caffe-qhconv-master/matlab/caffe/matcaffe_demo.m | 3,344 | utf_8 | 669622769508a684210d164ac749a614 | function [scores, maxlabel] = matcaffe_demo(im, use_gpu)
% scores = matcaffe_demo(im, use_gpu)
%
% Demo of the matlab wrapper using the ILSVRC network.
%
% input
% im color image as uint8 HxWx3
% use_gpu 1 to use the GPU, 0 to use the CPU
%
% output
% scores 1000-dimensional ILSVRC score vector
%
% You m... |
github | minogame/caffe-qhconv-master | matcaffe_demo_vgg_mean_pix.m | .m | caffe-qhconv-master/matlab/caffe/matcaffe_demo_vgg_mean_pix.m | 3,069 | utf_8 | 04b831d0f205ef0932c4f3cfa930d6f9 | function scores = matcaffe_demo_vgg_mean_pix(im, use_gpu, model_def_file, model_file)
% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file)
%
% Demo of the matlab wrapper based on the networks used for the "VGG" entry
% in the ILSVRC-2014 competition and described in the tech. report
% "Very Deep Convo... |
github | minogame/caffe-qhconv-master | classification_demo.m | .m | caffe-qhconv-master/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | PSLmodels/Geo-DICE-master | T_Utility.m | .m | Geo-DICE-master/CTP/T_Utility.m | 1,456 | utf_8 | 925d74c7c2c30029e45997ddcc1ee234 | %Algorithm for calculating the social utility of each state under ceratin
%action (a3)
function [ U ] = T_Utility( S4, a4, g4, Wm4, t4)
%Social Utility for a given state St
% state = S4
% action = a4, g4
% time = t4
global theta1 theta2 theta3 L alpha Gcoeff deltarf Fex etha1
global thetaGE sai2temp s... |
github | PSLmodels/Geo-DICE-master | Rep_Utility.m | .m | Geo-DICE-master/CTP/MHE-DWI/Rep_Utility.m | 745 | utf_8 | d33cbd5b2f47256faebac97e812f7a35 | %Algorithm for calculating the social utility of each state under ceratin
%action (a3)
function [ U ] = Rep_Utility( S4, a4, Wm4, t4)
%Social Utility for a given state St
% state = S4
% action = a4
% time = t4
global theta1 theta2 L alpha
global sai1 sai2 sai3
% Damage cost function
Damage = 1 /... |
github | PSLmodels/Geo-DICE-master | T_G_Utility.m | .m | Geo-DICE-master/CTP/MHE-DWI/T_G_Utility.m | 1,460 | utf_8 | 94eca70573405582478bde2db91a5d39 | %Algorithm for calculating the social utility of each state under ceratin
%action (a3)
function [ U ] = T_G_Utility( S4, a4, g4, Wm4, t4)
%Social Utility for a given state St
% state = S4
% action = a4, g4
% time = t4
global theta1 theta2 theta3 L alpha Gcoeff deltarf Fex etha1
global thetaGE sai2temp... |
github | PSLmodels/Geo-DICE-master | Utility.m | .m | Geo-DICE-master/SGEUPC/Utility.m | 1,448 | utf_8 | b544380a9a72033fabc965a1f78de0f6 | %Algorithm for calculating the social utility of each state under ceratin
%action (a3)
function [ U ] = Utility( S4, a4, g4, Wm4, t4)
%Social Utility for a given state St
% state = S4
% action = a4, g4
% time = t4
global theta1 theta2 theta3 L alpha Gcoeff deltarf Fex etha1
global thetaGE sai2temp sai... |
github | uday1201/MLworkshop-master | submitWithConfiguration.m | .m | MLworkshop-master/Unsolved/linear regression/ex1/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 | uday1201/MLworkshop-master | savejson.m | .m | MLworkshop-master/Unsolved/linear regression/ex1/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 | uday1201/MLworkshop-master | loadjson.m | .m | MLworkshop-master/Unsolved/linear regression/ex1/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 | uday1201/MLworkshop-master | loadubjson.m | .m | MLworkshop-master/Unsolved/linear regression/ex1/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 | uday1201/MLworkshop-master | saveubjson.m | .m | MLworkshop-master/Unsolved/linear regression/ex1/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 | uday1201/MLworkshop-master | submitWithConfiguration.m | .m | MLworkshop-master/Unsolved/handwriting recognition/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 | uday1201/MLworkshop-master | savejson.m | .m | MLworkshop-master/Unsolved/handwriting recognition/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... |
github | uday1201/MLworkshop-master | loadjson.m | .m | MLworkshop-master/Unsolved/handwriting recognition/ex3/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 | uday1201/MLworkshop-master | loadubjson.m | .m | MLworkshop-master/Unsolved/handwriting recognition/ex3/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 | uday1201/MLworkshop-master | saveubjson.m | .m | MLworkshop-master/Unsolved/handwriting recognition/ex3/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 | uday1201/MLworkshop-master | submitWithConfiguration.m | .m | MLworkshop-master/Unsolved/logistic regression/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 | uday1201/MLworkshop-master | savejson.m | .m | MLworkshop-master/Unsolved/logistic regression/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 | uday1201/MLworkshop-master | loadjson.m | .m | MLworkshop-master/Unsolved/logistic regression/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 | uday1201/MLworkshop-master | loadubjson.m | .m | MLworkshop-master/Unsolved/logistic regression/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 | uday1201/MLworkshop-master | saveubjson.m | .m | MLworkshop-master/Unsolved/logistic regression/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 | uday1201/MLworkshop-master | submit.m | .m | MLworkshop-master/Unsolved/Anomaly Detection and Recommender
Systems/ex8/submit.m | 2,064 | utf_8 | 7c4fcf60df3a7e09d05a74f7772fed3b | function submit()
addpath('./lib');
conf.assignmentSlug = 'anomaly-detection-and-recommender-systems';
conf.itemName = 'Anomaly Detection and Recommender Systems';
conf.partArrays = { ...
{ ...
'1', ...
{ 'estimateGaussian.m' }, ...
'Estimate Gaussian Parameters', ...
}, ...
{ ...... |
github | uday1201/MLworkshop-master | submitWithConfiguration.m | .m | MLworkshop-master/Unsolved/Anomaly Detection and Recommender
Systems/ex8/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 | uday1201/MLworkshop-master | savejson.m | .m | MLworkshop-master/Unsolved/Anomaly Detection and Recommender
Systems/ex8/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 | uday1201/MLworkshop-master | loadjson.m | .m | MLworkshop-master/Unsolved/Anomaly Detection and Recommender
Systems/ex8/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 | uday1201/MLworkshop-master | loadubjson.m | .m | MLworkshop-master/Unsolved/Anomaly Detection and Recommender
Systems/ex8/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 | uday1201/MLworkshop-master | saveubjson.m | .m | MLworkshop-master/Unsolved/Anomaly Detection and Recommender
Systems/ex8/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 | uday1201/MLworkshop-master | submitWithConfiguration.m | .m | MLworkshop-master/solved/linear regression/ex1/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 | uday1201/MLworkshop-master | savejson.m | .m | MLworkshop-master/solved/linear regression/ex1/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 | uday1201/MLworkshop-master | loadjson.m | .m | MLworkshop-master/solved/linear regression/ex1/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 | uday1201/MLworkshop-master | loadubjson.m | .m | MLworkshop-master/solved/linear regression/ex1/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 | uday1201/MLworkshop-master | saveubjson.m | .m | MLworkshop-master/solved/linear regression/ex1/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 | uday1201/MLworkshop-master | submit.m | .m | MLworkshop-master/solved/anomaly detection and reccomendor system/ex8/submit.m | 2,064 | utf_8 | 7c4fcf60df3a7e09d05a74f7772fed3b | function submit()
addpath('./lib');
conf.assignmentSlug = 'anomaly-detection-and-recommender-systems';
conf.itemName = 'Anomaly Detection and Recommender Systems';
conf.partArrays = { ...
{ ...
'1', ...
{ 'estimateGaussian.m' }, ...
'Estimate Gaussian Parameters', ...
}, ...
{ ...... |
github | uday1201/MLworkshop-master | submitWithConfiguration.m | .m | MLworkshop-master/solved/anomaly detection and reccomendor system/ex8/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 | uday1201/MLworkshop-master | savejson.m | .m | MLworkshop-master/solved/anomaly detection and reccomendor system/ex8/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 | uday1201/MLworkshop-master | loadjson.m | .m | MLworkshop-master/solved/anomaly detection and reccomendor system/ex8/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 | uday1201/MLworkshop-master | loadubjson.m | .m | MLworkshop-master/solved/anomaly detection and reccomendor system/ex8/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 | uday1201/MLworkshop-master | saveubjson.m | .m | MLworkshop-master/solved/anomaly detection and reccomendor system/ex8/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 | uday1201/MLworkshop-master | submit.m | .m | MLworkshop-master/solved/handwriting recognition/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 | uday1201/MLworkshop-master | submitWithConfiguration.m | .m | MLworkshop-master/solved/handwriting recognition/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 | uday1201/MLworkshop-master | savejson.m | .m | MLworkshop-master/solved/handwriting recognition/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... |
github | uday1201/MLworkshop-master | loadjson.m | .m | MLworkshop-master/solved/handwriting recognition/ex3/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 | uday1201/MLworkshop-master | loadubjson.m | .m | MLworkshop-master/solved/handwriting recognition/ex3/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 | uday1201/MLworkshop-master | saveubjson.m | .m | MLworkshop-master/solved/handwriting recognition/ex3/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 | uday1201/MLworkshop-master | submit.m | .m | MLworkshop-master/solved/logistic regression/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 | uday1201/MLworkshop-master | submitWithConfiguration.m | .m | MLworkshop-master/solved/logistic regression/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 | uday1201/MLworkshop-master | savejson.m | .m | MLworkshop-master/solved/logistic regression/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 | uday1201/MLworkshop-master | loadjson.m | .m | MLworkshop-master/solved/logistic regression/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 | uday1201/MLworkshop-master | loadubjson.m | .m | MLworkshop-master/solved/logistic regression/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 | uday1201/MLworkshop-master | saveubjson.m | .m | MLworkshop-master/solved/logistic regression/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 | marcotcr/BlackBoxAuditing-master | SLIMCoefficientConstraints.m | .m | BlackBoxAuditing-master/MATLAB_code/src/SLIMCoefficientConstraints.m | 17,125 | utf_8 | d66e1a62518673dd6497f25601e4c046 | classdef SLIMCoefficientConstraints
%Helper class to store, access, and change information used for
%coefficients in a SLIM model.
%
%Fields include:
%
%variable_name: names of each feature, X_1...X_d by default
%
%ub: upperbound on coefficient set, 10 by default
%... |
github | kartik-nighania/Coursera-Machine-Learning-Course-by-Stanford-master | submit.m | .m | Coursera-Machine-Learning-Course-by-Stanford-master/5 bias vs variance linear regression/ex5/submit.m | 1,765 | utf_8 | b1804fe5854d9744dca981d250eda251 | function submit()
addpath('./lib');
conf.assignmentSlug = 'regularized-linear-regression-and-bias-variance';
conf.itemName = 'Regularized Linear Regression and Bias/Variance';
conf.partArrays = { ...
{ ...
'1', ...
{ 'linearRegCostFunction.m' }, ...
'Regularized Linear Regression Cost Fun... |
github | kartik-nighania/Coursera-Machine-Learning-Course-by-Stanford-master | submitWithConfiguration.m | .m | Coursera-Machine-Learning-Course-by-Stanford-master/5 bias vs variance linear regression/ex5/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | kartik-nighania/Coursera-Machine-Learning-Course-by-Stanford-master | savejson.m | .m | Coursera-Machine-Learning-Course-by-Stanford-master/5 bias vs variance linear regression/ex5/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | kartik-nighania/Coursera-Machine-Learning-Course-by-Stanford-master | loadjson.m | .m | Coursera-Machine-Learning-Course-by-Stanford-master/5 bias vs variance linear regression/ex5/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
%
% ... |
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