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 | terrykong/Phase-Vocoder-master | serial_connect.m | .m | Phase-Vocoder-master/Version Final/FinalDemo/serial_connect.m | 2,661 | utf_8 | 622ee4b26d7b8fa543dc82d9902b3855 | % SERIAL_CONNECT Stablishes a serial communication protocol.
%
% [S, CONNECTED] = SERIAL_CONNECT(COMPORT, BAUD) sets up serial
% communicaiton on COMPORT at given BAUD rate. Returns the serial object S
% and the CONNECTED status.
%
% The communication protocol is as follows:
% - The DSP Shield will transmit a... |
github | terrykong/Phase-Vocoder-master | serial_cmd.m | .m | Phase-Vocoder-master/Version Final/FinalDemo/serial_cmd.m | 1,131 | utf_8 | afcc68b0388cf917aea4d4d8978892cd | % SERIAL_CMD Send a command/data to the DSP Shield.
%
% SERIAL_CMD(S, CMD, DATA) send command CMD and DATA over the serial
% object S assuming INT8 data type. CMD is an integer.
%
% SERIAL_CMD(S, CMD, DATA, TYPE) send command CMD and DATA over the
% serial object S assuming INT8 data type. Valid ... |
github | terrykong/Phase-Vocoder-master | serial_recv_array.m | .m | Phase-Vocoder-master/Version Final/FinalDemo/serial_recv_array.m | 1,681 | utf_8 | 2fd718de02842c86e5a9f13f871236a6 | % SERIAL_RECV_ARRAY This function receives a vector from the DSP Shield.
%
% X = SERIAL_RECV_ARRAY(S) receives data vector X over the serial
% object S assuming INT8 data type.
%
% X = SERIAL_RECV_ARRAY(S, TYPE) receives data vector X over the serial
% object S assuming TYPE as data type. Valid da... |
github | terrykong/Phase-Vocoder-master | complex2RealImag.m | .m | Phase-Vocoder-master/Version Final/FinalDemo/complex2RealImag.m | 496 | utf_8 | c1b8304e2e28e21adbb977b411070490 | % COMPLEX2REALIMAG Converts from complex to real-imag interleaved format.
%
% OUTPUT = COMPLEX2REALIMAG(INPUT) converts INPUT vector in complex
% format to a vector with real and imaginary values interleaved. E.g., if
% INPUT = [1 + 2 j; 3 + 4 j; 5 + 6 j; 7 + 8 j] the output is
% OUTPUT = [1; 2; 3; 4; 5; 6; 7;... |
github | terrykong/Phase-Vocoder-master | serial_send_array.m | .m | Phase-Vocoder-master/Version Final/FinalDemo/serial_send_array.m | 3,686 | utf_8 | 16b60c3dfaf9a0af3ba6f52318d08ae0 | % SERIAL_SEND_ARRAY Sends a vector of binary data to the DSP Shield.
%
% SERIAL_SEND_ARRAY(S, X) send data vector X over the serial
% object S assuming INT8 data type.
%
% SERIAL_SEND_ARRAY(S, X, TYPE) send data vector X over the serial
% object S assuming TYPE as data type. Valid data types are '... |
github | spmallick/learnopencv-master | make.m | .m | learnopencv-master/ImageMetrics/Python/libsvm/matlab/make.m | 888 | utf_8 | 4a2ad69e765736f8cca8e3b721fb7ebd | % This make.m is for MATLAB and OCTAVE under Windows, Mac, and Unix
function make()
try
% This part is for OCTAVE
if (exist ('OCTAVE_VERSION', 'builtin'))
mex libsvmread.c
mex libsvmwrite.c
mex -I.. svmtrain.c ../svm.cpp svm_model_matlab.c
mex -I.. svmpredict.c ../svm.cpp svm_model_matlab.c
% This part is fo... |
github | bdiazdeastarloa/ek-matlab-master | bounds.m | .m | ek-matlab-master/bounds.m | 291 | utf_8 | 4030aec6da0c6d6219fb403cf9e0cc48 | %% Calculate price bounds implied by the model
function pbounds = bounds(w)
global b theta t d n g
temp1 = g*t.*(w.^(-theta*b));
temp2 = repmat(temp1,1,n)';
G = d.*temp2;
Gmax = max(sum(G,2));
pmax = repmat(Gmax^(1/b),n,1);
pmin = (diag(G)).^(1/b);
pbounds = [pmax,pmin];
|
github | NareshPeshwe/CASA495-Python-Code-master | decompose_kernel.m | .m | CASA495-Python-Code-master/nmf/decompose_kernel.m | 6,611 | utf_8 | 291001d43251aec7a2797b476a6883bb | function [k1,kn,err] = decompose_kernel(h_orig)
% This function does the decomposition of a separable nD kernel into
% its 1D components, such that a convolution with each of these
% components yields the same result as a convolution with the full nD
% kernel, at a drastic reduction in computational cost.
%
% SYNTAX:
%... |
github | tntrung/sdm_face_alignment-master | mean_covariance_of_data.m | .m | sdm_face_alignment-master/common/manifold/mean_covariance_of_data.m | 161 | utf_8 | eba64c3a6e320b95d72604e752d626d5 | %data has nx2
function [mu,cov] = mean_covariance_of_data ( data )
n = size(data,1);
mu = (1/n)*sum(data);
z = data - repmat(mu,n,1);
cov = (1/(n))*z'*z;
end
|
github | tntrung/sdm_face_alignment-master | load_all_data.m | .m | sdm_face_alignment-master/common/io/load_all_data.m | 4,494 | utf_8 | f9010a3ebc8aa907fd59603f3bf24534 | function [Data] = load_all_data ( dbpath_img, dbpath_pts, options )
%% output format
%{
DATA.
- width_orig: the width of the original image.
- height_orig: the height of the original image.
- img_gray: the crop image.
- height: the height of crop image.
- wdith: the width of crop image.
- shape_gt: ground-truth landma... |
github | tntrung/sdm_face_alignment-master | load_all_data2.m | .m | sdm_face_alignment-master/common/io/load_all_data2.m | 4,499 | utf_8 | a6fb5a9348c715501f0f39856aaf7a81 | function [Data] = load_all_data2 ( dbpath_img, dbpath_pts, options )
%% output format
%{
DATA.
- width_orig: the width of the original image.
- height_orig: the height of the original image.
- img_gray: the crop image.
- height: the height of crop image.
- wdith: the width of crop image.
- shape_gt: ground-truth landm... |
github | bobye/20newsgroups-master | experiments.m | .m | 20newsgroups-master/matlab/experiments.m | 2,557 | utf_8 | f8a23abc7a90a02a3081ccad75b2a9ae | function [] = experiments(vocab_name, version)
db=load_data(['../vocab/' vocab_name], version);
addpath('liblinear-1.96/matlab');
train_d = diag(1./(sum(db.train_vec>0,2)+eps))* (db.train_vec>0) * db.vocab_mat(1:size(db.train_vec,2),:);
test_d = diag(1./(sum(db.test_vec>0,2)+eps)) * (db.test_vec>0) * db.vo... |
github | bobye/20newsgroups-master | pdist2.m | .m | 20newsgroups-master/matlab/pdist2.m | 5,461 | utf_8 | 173103b09eefbe457c081a3d41cddd3d | % This function belongs to Piotr Dollar's Toolbox
% http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html
% Please refer to the above web page for definitions and clarifications
%
% Calculates the distance between sets of vectors.
%
% Let X be an m-by-p matrix representing m points in p-dimensional space
% and Y be an... |
github | bobye/20newsgroups-master | ami.m | .m | 20newsgroups-master/matlab/ami.m | 5,532 | utf_8 | 73330a501dd96da36f3db9775b05a300 | %Program for calculating the Adjusted Mutual Information (AMI) between
%two clusterings, tested on Matlab 7.0 (R14)
%(C) Nguyen Xuan Vinh 2008-2010
%Contact: n.x.vinh@unsw.edu.au
% vthesniper@yahoo.com
%--------------------------------------------------------------------------
%**Input: a contingency tab... |
github | bobye/20newsgroups-master | kmeans.m | .m | 20newsgroups-master/matlab/kmeans.m | 26,099 | utf_8 | e909fd5b0214a2943a9e711bcd586437 | function [idx, C, sumD, D] = kmeans(X, k, varargin)
%KMEANS K-means clustering.
% IDX = KMEANS(X, K) partitions the points in the N-by-P data matrix
% X into K clusters. This partition minimizes the sum, over all
% clusters, of the within-cluster sums of point-to-cluster-centroid
% distances. Rows of X c... |
github | mriphysics/optimal-control-EPG-master | obj_EPG13.m | .m | optimal-control-EPG-master/obj_EPG13.m | 12,538 | utf_8 | 2d16f9491de5f4fc6d59d5dbcff5b554 | %% EPG forward for FSE
%% objective function. maximizes the signal
function [obj, grad,FF] = obj_EPG13(params,ESP,T1,T2,c,B1,target,frequencies,klim)
% klim limit maximum k coefficient
%
% parameters are with respect to frequencies
%
% efficient implementation of blockdiagonal matrix vec multiplication
% w.r.t... |
github | mriphysics/optimal-control-EPG-master | obj_EPG11.m | .m | optimal-control-EPG-master/obj_EPG11.m | 11,477 | utf_8 | d5eb055471590eec899ddbaae64146b0 | %% EPG forward for FSE
%% objective function. maximizes the signal
function [obj, grad,FF] = obj_EPG11(params,ESP,T1,T2,c,B1,target)
% efficient implementation of blockdiagonal matrix vec multiplication
% w.r.t. obj_EPG8
%
% c is vector of {0,1} samplings, for instance: c(t) = 1 counts, c(t) = 0
% does not coun... |
github | mriphysics/optimal-control-EPG-master | EPG_forward1.m | .m | optimal-control-EPG-master/EPG_forward1.m | 7,164 | utf_8 | 1ec38671f1d4cfa7850007f95001109b | %% EPG forward for FSE
function [F, grad] = EPG_forward1(theta,varargin)
% F0 is the FID/Z0 created by the flipback pulse. Initialize in case not set
F0 = 0;
np = length(theta);
kmax = 2*np - 1; % up to just before the next RF pulse
N = 3*(kmax-1)/2; % number of states in total
% split magnitude and ... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | exportfig.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/exportfig/exportfig.m | 33,826 | utf_8 | e23c2da3e0d6fff7c7c65e76364d3d0e | function varargout = exportfig(varargin)
%EXPORTFIG Export a figure.
% EXPORTFIG(H, FILENAME) writes the figure H to FILENAME. H is
% a figure handle and FILENAME is a string that specifies the
% name of the output file.
%
% EXPORTFIG(H, FILENAME, OPTIONS) writes the figure H to FILENAME
% with optio... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | samplemusigma.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/attic/samplemusigma.m | 5,376 | utf_8 | 8392c5a284ab63d4b3af6a68ef7279c9 |
%************************************************************************************************************************************************************
%Will Sample a mean value for each state based upon the observations assigned to that state
%A sigma will be directly Calculated and used to sample mean
%TODO: ... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | state_merge_sort.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/attic/state_merge_sort.m | 731 | utf_8 | d3bcd243d59cfafc4b2436fec4bc1a4c | %FUNCTION TAKES A LIST OF INDEX VALUES AND SORTS THEM ACCORDING TO THE RULES IN COMPARE STATES IN ASCENDING ORDER
function sorted_states = state_merge_sort(state_indices, mu, sigma, transition)
if size(state_indices,2) <= 1,
sorted_states = state_indices;
else
len = size(state_indices,2);
middle = int32(... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | state_merge.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/attic/state_merge.m | 760 | utf_8 | 013f7a6cfa989ab0c8d4afbe5cc1d51f | %MERGES TWO LISTS OF STATE INDICES IN ASCENDING SORTED ORDER BASED ON COMPARE STATES SEE http://en.wikipedia.org/wiki/Merge_sort
function merged_indices = state_merge(left, right, mu, sigma, transition)
merged_indices = zeros(1);
merged_indices(1,:) = [];
while size(left,2) > 0 && size(right, 2) > 0,
... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | normalprobability.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/attic/normalprobability.m | 139 | utf_8 | 4aaf9bb58ab1f5ea6e398320b14bb15d |
function probability = normalprobability(value, mu, sigma)
probability = 1/((2 * pi)^(1/2)*sigma) * exp(- (value - mu)^2/(2*sigma^2));
|
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | makefrets.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/attic/makefrets.m | 2,358 | utf_8 | 800ea510488326f240be12ebe4d898b8 | % THIS FILE GENERATES SAMPLE OBSERVATIONS ASSUMING THERE ARE N STATES AND AN NxN TRANSITION MATRIX. EACH STATE IS DEFINED BY A NORMAL DISTRIBUTION WITH MEAN MU AND STANDARD DEVIATION SIGMA
%accepts d_length = length of fret trajectory
%accepts data_index = index of data file to save to
function makefretdata(d_lengt... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | exportfig.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/attic/exportfig.m | 33,872 | utf_8 | a76997b3382d465dac9e5bb587722cb1 | function varargout = exportfig(varargin)
%EXPORTFIG Export a figure.
% EXPORTFIG(H, FILENAME) writes the figure H to FILENAME. H is
% a figure handle and FILENAME is a string that specifies the
% name of the output file.
%
% EXPORTFIG(H, FILENAME, OPTIONS) writes the figure H to FILENAME
% with optio... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | sample.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/attic/sample.m | 9,434 | utf_8 | 1dfb8e570d383f31375ea60ede4f7c2c | %************************************************************************************************************************************************************
%sample.m will run sampling on the specified data trajectory for a specified number of steps
%parameters are as follows
%sample_file_index: the index number of a ... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | initialize.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/attic/initialize.m | 8,099 | utf_8 | b3eb9a953ccae493b504c90582da3d8a |
%************************************************************************************************************************************************************
%FUNCTION WILL INITIALIZE A SAMPLING TRAJECTORY AND GRANT IT A UNIQUE ID NUMBER, ALL TRAJECTORIES WILL BE STORED IN trajectories.m
%takes in data, initializes t... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | analyze.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/attic/analyze.m | 7,958 | utf_8 | 4ae46255d88490eed6c7091026a7f467 |
%***************************************************************************************************************************
%TAKES THE DATA AND MAKES PRETTY PICTURES AND STUFF
%PROVIDE AS AN ARGUMENT THE TRAJECTORY NUMBER, FOR EXAMPLE, to analyze trajectory 19 type 'analyze(10)'
%************************************... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | filterfrets.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/attic/filterfrets.m | 1,132 | utf_8 | 8976941a9ce174d9c4d576be91aec26a |
%***********************************************************************************************************************************************************
%Function will accept a data list and eliminate ludicrous fret values
%that is all fret data that is less or equal to zero or greater then or equal to one THAT IS... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | sampletransitionmatrix.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/attic/sampletransitionmatrix.m | 1,189 | utf_8 | 609cf4df39c13bd538769134ef9711ab |
%************************************************************************************************************************************************************
%Arguments:
%counts: counts matrix with each cell corresponding the number of times a transition between each indexed state is observed
%************************... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | order_states.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/attic/order_states.m | 653 | utf_8 | f1e362934b8e5db95097bb5e9841331b | %GIVEN NEW DEFFINITIONS ORDERING OF STATES, RESORTS THE MU, SIGMA, AND TRANSITION MATRIX TO REFLECT.
function [sorted_mu sorted_sigma sorted_transition] = order_states(new_indices, mu, sigma, transition)
numstates = size(new_indices,2);
%sort mu values, sigma values, and columns of transition matrix
for i = 1... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | comparestates.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/attic/comparestates.m | 1,418 | utf_8 | f10f65976d41b4d1b028e406bb73061e | %FUNCTION WILL COMPARE TWO STATES AND RETURN -1 IF THE FIRST (STATE 0) IS GREATER THEN THE SECOND (STATE 1) RETURN 1 IF THE SECOND STATE (STATE 1) IS GREATER THEN THE FIRST (STATE 0)
%RETURN 0 IF ALL COMPARABLE VALUES ARE WITHIN THRESHOLDS
function truth_value = comparestates(mu_0, sigma_0, self_transition_0, mu_1, s... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | hummer_rate_matrix_update.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/attic/ratematrix/hummer_rate_matrix_update.m | 3,827 | utf_8 | e130c8c44cd5f9f69bee44cc5b5d6138 | function Tij = hummer_rate_matrix_update(Tij, Nij, tau)
% Produce a (correlated) sample of the transition matrix corresponding to a true rate matrix that satisfies detailed balance using scheme of Gerhard Hummer.
%
% ARGUMENTS
% Tij (MxM matrix) - current transition matrix sample
% This transition matrix must corr... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | generate_transition_matrix_guess.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/attic/ratematrix/generate_transition_matrix_guess.m | 3,343 | utf_8 | d914531356693d33a9c97c6325c12ba8 | function Tij = generate_transition_matrix_guess(Nij, tau)
% Generate an initial row-stochastic transition matrix from the specified count matrix.
% The transition matrix is guaranteed to satisfy
%
% Tij = exp(Kij * tau)
%
% where Kij is some rate matrix that satisfies:
%
% Kij(i,j) > 0 for i \ne j
% sum(K(i,:)) = 0 for... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | reversible_transition_matrix_update.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/bhmm/reversible_transition_matrix_update.m | 4,258 | utf_8 | 3dd6d804138aa36ebfc1fd6dffefa1d6 | function Tij = reversible_transition_matrix_update(Tij, Nij, tau)
% Produce a (correlated) sample of the transition matrix corresponding to a true rate matrix that satisfies detailed balance using scheme of Gerhard Hummer.
%
% Tij = hummer_rate_matrix_update(Tij, Nij, tau)
%
% ARGUMENTS
% Tij (MxM matrix) - current tr... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | hummer_rate_matrix_update.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/bhmm/hummer_rate_matrix_update.m | 4,248 | utf_8 | 8b61eaa2c6edbcbb2a3e0f347f9e58b0 | function Tij = hummer_rate_matrix_update(Tij, Nij, tau)
% Produce a (correlated) sample of the transition matrix corresponding to a true rate matrix that satisfies detailed balance using scheme of Gerhard Hummer.
%
% Tij = hummer_rate_matrix_update(Tij, Nij, tau)
%
% ARGUMENTS
% Tij (MxM matrix) - current transition m... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | plot_fractional_state_assignments.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/bhmm/plot_fractional_state_assignments.m | 1,404 | utf_8 | 72bf88ebc41214cdf027e2b09227905d | % Plot traces of observables with state assignments.
function plot_fractional_state_assignments(data, models, options)
% PARAMETERS
markersize = 5; % size of points in plot
colors = hsv(models(1).nstates); % colors for states
alpha = 0.2;
% Get number of traces.
ntraces = length(data);
% Determine number of plot pan... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | exportfig.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/bhmm/exportfig.m | 33,872 | utf_8 | a76997b3382d465dac9e5bb587722cb1 | function varargout = exportfig(varargin)
%EXPORTFIG Export a figure.
% EXPORTFIG(H, FILENAME) writes the figure H to FILENAME. H is
% a figure handle and FILENAME is a string that specifies the
% name of the output file.
%
% EXPORTFIG(H, FILENAME, OPTIONS) writes the figure H to FILENAME
% with optio... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | rate_matrix_sample.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/bhmm/rate_matrix_sample.m | 5,973 | utf_8 | 65c2e9702cb9d77508815cb4f10cc5bb | function model = rate_matrix_sample(model, Nij, mode)
% Generate an uncorrelated transition matrix sample using the method of Gerhard Hummer.
%
% Tij = sample_transition_matrix(Tij, Nij, tau)
%
% ARGUMENTS
% Tij (MxM matrix) - current transition matrix sample
% This transition matrix must correspond to a valid rat... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | plot_state_assignments.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/bhmm/plot_state_assignments.m | 4,446 | utf_8 | c7adccf44706a20ba1b86b77ca1b2377 | % Plot traces of observables with state assignments.
function plot_state_assignments(data, model, options)
% PARAMETERS
markersize = 5; % size of points in plot
colors = hsv(model.nstates); % colors for states
stddev_alpha = 0.2;
mean_alpha = 0.4;
% Get number of traces.
ntraces = length(data);
% Determine number of... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | noe_transition_matrix_update.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/bhmm/noe_transition_matrix_update.m | 4,251 | utf_8 | ca7f268a11975dba08bd3b76ca020ce0 | function Tij = noe_transition_matrix_update(Tij, Nij, tau)
% Produce a (correlated) sample of the transition matrix corresponding to a true rate matrix that satisfies detailed balance using scheme of Gerhard Hummer.
%
% Tij = hummer_rate_matrix_update(Tij, Nij, tau)
%
% ARGUMENTS
% Tij (MxM matrix) - current transitio... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | update_state_trajectories.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/bhmm/update_state_trajectories.m | 4,240 | utf_8 | 081164749cd4570e264d757e48a99e7e | function model = update_state_trajectories(data, model, options)
% Update state trajectories given emission probabilities and transition matrix.
%
% model = update_state_trajectories(data, model, options)
%
% ARGUMENTS
% data (cell array of 1D arrays) - observed trajectories of some real-valued signal
% model (stru... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | transition_matrix_mle_optimize.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/bhmm/transition_matrix_mle_optimize.m | 4,863 | utf_8 | 42090496036ceeb1635c23ef6b00ea4b | function Tij = transition_matrix_mle_optimize(Nij, options)
% Generate maximum-likelihood estimate of row-stochastic transition matrix by optimization procedure.
%
% WARNING
% There is currently no constraint ensuring sum(Pi(1:N-1)) < 1 during likelihood maximization. This needs to be fixed before this code is usable... |
github | bhmm/legacy-bhmm-force-spectroscopy-manuscript-master | generate_transition_matrix_guess.m | .m | legacy-bhmm-force-spectroscopy-manuscript-master/matlab/bhmm/generate_transition_matrix_guess.m | 3,431 | utf_8 | 7301222f585d064eb25e867f5faeff2a | function Tij = generate_transition_matrix_guess(Nij, tau)
% Generate an initial row-stochastic transition matrix from the specified count matrix.
% The transition matrix is guaranteed to satisfy
%
% Tij = exp(Kij * tau)
%
% where Kij is some rate matrix that satisfies:
%
% Kij(i,j) > 0 for i \ne j
% sum(K(i,:)) = 0 for... |
github | subangstrom/superAngle-master | SuperAngle.m | .m | superAngle-master/SuperAngle.m | 79,302 | utf_8 | 6f5a1c6bb662c88a483f90c53bd1be9d | function varargout = SuperAngle(varargin)
% SUPERANGLE MATLAB code for SuperAngle.fig
% SUPERANGLE, by itself, creates a new SUPERANGLE or raises the existing
% singleton*.
%
% H = SUPERANGLE returns the handle to a new SUPERANGLE or the handle to
% the existing singleton*.
%
% SUPERANGLE('CALL... |
github | subangstrom/superAngle-master | terrorbar.m | .m | superAngle-master/Display/terrorbar.m | 10,679 | utf_8 | 1456a9ace1c5416f2d9e8a80fc0445d1 | function herrorbars=terrorbar(varargin)
%function herrorbars=terrorbar(x,val,lowererror,uppererror,errorbarwidth,errorbarunits)
%
%=========================
% terrorbar.m
% Draws error bars (just the error bars, not the lines) whose size can be
% controlled (which was otherwise a challenge in versions 2014b onwa... |
github | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/problem_sets/ex8_solution/submit.m | 9,318 | utf_8 | c79c5087bd1a0ecd8339200134b58334 | function submit(partId)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isempty(partId)... |
github | Blz-Galaxy/Machine-Learning-master | submitWeb.m | .m | Machine-Learning-master/problem_sets/ex8_solution/submitWeb.m | 10,923 | utf_8 | 73518c1a5543c7cf2d29ca05290a62c2 | function submitWeb(partId)
%SUBMITWEB Generates a base64 encoded string for web-based submissions
% SUBMITWEB() will generate a base64 encoded string so that you can submit your
% solutions via a web form
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id(... |
github | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/problem_sets/ex8/submit.m | 17,509 | utf_8 | 11676b36395cc2443b2fdf3ad55b562e | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | Blz-Galaxy/Machine-Learning-master | submitWeb.m | .m | Machine-Learning-master/problem_sets/ex8/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/problem_sets/ex6/submit.m | 16,830 | utf_8 | 6844114ab6410b81b00d62f25f193d97 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | Blz-Galaxy/Machine-Learning-master | porterStemmer.m | .m | Machine-Learning-master/problem_sets/ex6/porterStemmer.m | 9,902 | utf_8 | 7ed5acd925808fde342fc72bd62ebc4d | function stem = porterStemmer(inString)
% Applies the Porter Stemming algorithm as presented in the following
% paper:
% Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,
% no. 3, pp 130-137
% Original code modeled after the C version provided at:
% http://www.tartarus.org/~martin/PorterStemmer/c.tx... |
github | Blz-Galaxy/Machine-Learning-master | submitWeb.m | .m | Machine-Learning-master/problem_sets/ex6/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/problem_sets/ex3_solution/submit.m | 8,845 | utf_8 | 5caa0b85df1ed19046b0ac3c62264812 | function submit(partId)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isempty(partId)... |
github | Blz-Galaxy/Machine-Learning-master | submitWeb.m | .m | Machine-Learning-master/problem_sets/ex3_solution/submitWeb.m | 10,449 | utf_8 | fe5b717ff9313a3436522e6afd43662f | function submitWeb(partId)
%SUBMITWEB Generates a base64 encoded string for web-based submissions
% SUBMITWEB() will generate a base64 encoded string so that you can submit your
% solutions via a web form
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id(... |
github | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/problem_sets/ex7_solution/submit.m | 8,761 | utf_8 | 8dd7a1d9d8b7d974e21a54431b37414c | function submit(partId)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isempty(partId)... |
github | Blz-Galaxy/Machine-Learning-master | submitWeb.m | .m | Machine-Learning-master/problem_sets/ex7_solution/submitWeb.m | 10,365 | utf_8 | a89cfa35b3558108eca010fbcf026415 | function submitWeb(partId)
%SUBMITWEB Generates a base64 encoded string for web-based submissions
% SUBMITWEB() will generate a base64 encoded string so that you can submit your
% solutions via a web form
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id(... |
github | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/problem_sets/ex4_solution/submit.m | 8,931 | utf_8 | e59fd944f064fbb5a7d1b8af88e7ed2f | function submit(partId)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isempty(partId)... |
github | Blz-Galaxy/Machine-Learning-master | submitWeb.m | .m | Machine-Learning-master/problem_sets/ex4_solution/submitWeb.m | 10,536 | utf_8 | 3ae9f31fed1f04ebebd8ee816ae4c58b | function submitWeb(partId)
%SUBMITWEB Generates a base64 encoded string for web-based submissions
% SUBMITWEB() will generate a base64 encoded string so that you can submit your
% solutions via a web form
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id(... |
github | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/problem_sets/ex1_solution/submit.m | 9,092 | utf_8 | 9728b3f1a4a51c12fae3a07a387c8136 | function submit(part)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('part', 'var') || isempty(part)
p... |
github | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/problem_sets/ex7/submit.m | 16,952 | utf_8 | bc03673b87f8ab399ff79b67b7f30f73 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | Blz-Galaxy/Machine-Learning-master | submitWeb.m | .m | Machine-Learning-master/problem_sets/ex7/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/problem_sets/ex6_solution/submit.m | 8,638 | utf_8 | df402bdf5b2c32cd37caecce9b0698b7 | function submit(partId)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isempty(partId)... |
github | Blz-Galaxy/Machine-Learning-master | porterStemmer.m | .m | Machine-Learning-master/problem_sets/ex6_solution/porterStemmer.m | 9,902 | utf_8 | 7ed5acd925808fde342fc72bd62ebc4d | function stem = porterStemmer(inString)
% Applies the Porter Stemming algorithm as presented in the following
% paper:
% Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,
% no. 3, pp 130-137
% Original code modeled after the C version provided at:
% http://www.tartarus.org/~martin/PorterStemmer/c.tx... |
github | Blz-Galaxy/Machine-Learning-master | submitWeb.m | .m | Machine-Learning-master/problem_sets/ex6_solution/submitWeb.m | 10,243 | utf_8 | e4833e11a0a4d3e0e96ad641ee57efbc | function submitWeb(partId)
%SUBMITWEB Generates a base64 encoded string for web-based submissions
% SUBMITWEB() will generate a base64 encoded string so that you can submit your
% solutions via a web form
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id(... |
github | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/problem_sets/ex2/submit.m | 17,080 | utf_8 | 64dff98a31cec6eb759506bbed3364fe | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | Blz-Galaxy/Machine-Learning-master | submitWeb.m | .m | Machine-Learning-master/problem_sets/ex2/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/problem_sets/ex2_solution/submit.m | 8,888 | utf_8 | c99474aec005044575d5716c1e2fdbe6 | function submit(partId)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isempty(partId)... |
github | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/problem_sets/ex4/submit.m | 17,123 | utf_8 | b9d8a27e8fe9b6f74252918dc255947d | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | Blz-Galaxy/Machine-Learning-master | submitWeb.m | .m | Machine-Learning-master/problem_sets/ex4/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/problem_sets/ex5_solution/submit.m | 9,014 | utf_8 | b198c96129e23041ef7fe68fc68b0858 | function submit(partId)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isempty(partId)... |
github | Blz-Galaxy/Machine-Learning-master | submitWeb.m | .m | Machine-Learning-master/problem_sets/ex5_solution/submitWeb.m | 10,617 | utf_8 | cd4ecd582b72a5ea8159f6e438faeac1 | function submitWeb(partId)
%SUBMITWEB Generates a base64 encoded string for web-based submissions
% SUBMITWEB() will generate a base64 encoded string so that you can submit your
% solutions via a web form
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id(... |
github | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/problem_sets/ex3/submit.m | 17,035 | utf_8 | 7188fec680ac9e8561d12c6acba27e13 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | Blz-Galaxy/Machine-Learning-master | submitWeb.m | .m | Machine-Learning-master/problem_sets/ex3/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/problem_sets/ex1/submit.m | 15,593 | utf_8 | d718bd2b3f48972e91120193823816c5 | function submit(partId)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isempty(partId)... |
github | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/problem_sets/ex5/submit.m | 17,205 | utf_8 | 3ec3e311dc8ee1f8ee36bc04f8e89804 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | Blz-Galaxy/Machine-Learning-master | submitWeb.m | .m | Machine-Learning-master/problem_sets/ex5/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex2/ex2/submit.m | 1,605 | utf_8 | 9b63d386e9bd7bcca66b1a3d2fa37579 | function submit()
addpath('./lib');
conf.assignmentSlug = 'logistic-regression';
conf.itemName = 'Logistic Regression';
conf.partArrays = { ...
{ ...
'1', ...
{ 'sigmoid.m' }, ...
'Sigmoid Function', ...
}, ...
{ ...
'2', ...
{ 'costFunction.m' }, ...
'Logistic R... |
github | Blz-Galaxy/Machine-Learning-master | submitWithConfiguration.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | Blz-Galaxy/Machine-Learning-master | savejson.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex2/ex2/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | Blz-Galaxy/Machine-Learning-master | loadjson.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex2/ex2/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | Blz-Galaxy/Machine-Learning-master | loadubjson.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex2/ex2/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | Blz-Galaxy/Machine-Learning-master | saveubjson.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex2/ex2/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex4/ex4/submit.m | 1,635 | utf_8 | ae9c236c78f9b5b09db8fbc2052990fc | function submit()
addpath('./lib');
conf.assignmentSlug = 'neural-network-learning';
conf.itemName = 'Neural Networks Learning';
conf.partArrays = { ...
{ ...
'1', ...
{ 'nnCostFunction.m' }, ...
'Feedforward and Cost Function', ...
}, ...
{ ...
'2', ...
{ 'nnCostFunct... |
github | Blz-Galaxy/Machine-Learning-master | submitWithConfiguration.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex4/ex4/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | Blz-Galaxy/Machine-Learning-master | savejson.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex4/ex4/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | Blz-Galaxy/Machine-Learning-master | loadjson.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex4/ex4/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | Blz-Galaxy/Machine-Learning-master | loadubjson.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex4/ex4/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | Blz-Galaxy/Machine-Learning-master | saveubjson.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex4/ex4/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex6/ex6/submit.m | 1,318 | utf_8 | bfa0b4ffb8a7854d8e84276e91818107 | function submit()
addpath('./lib');
conf.assignmentSlug = 'support-vector-machines';
conf.itemName = 'Support Vector Machines';
conf.partArrays = { ...
{ ...
'1', ...
{ 'gaussianKernel.m' }, ...
'Gaussian Kernel', ...
}, ...
{ ...
'2', ...
{ 'dataset3Params.m' }, ...
... |
github | Blz-Galaxy/Machine-Learning-master | porterStemmer.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex6/ex6/porterStemmer.m | 9,902 | utf_8 | 7ed5acd925808fde342fc72bd62ebc4d | function stem = porterStemmer(inString)
% Applies the Porter Stemming algorithm as presented in the following
% paper:
% Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,
% no. 3, pp 130-137
% Original code modeled after the C version provided at:
% http://www.tartarus.org/~martin/PorterStemmer/c.tx... |
github | Blz-Galaxy/Machine-Learning-master | submitWithConfiguration.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex6/ex6/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | Blz-Galaxy/Machine-Learning-master | savejson.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex6/ex6/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | Blz-Galaxy/Machine-Learning-master | loadjson.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex6/ex6/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 | Blz-Galaxy/Machine-Learning-master | loadubjson.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex6/ex6/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | Blz-Galaxy/Machine-Learning-master | saveubjson.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex6/ex6/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | Blz-Galaxy/Machine-Learning-master | submit.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex7/ex7/submit.m | 1,438 | utf_8 | 665ea5906aad3ccfd94e33a40c58e2ce | function submit()
addpath('./lib');
conf.assignmentSlug = 'k-means-clustering-and-pca';
conf.itemName = 'K-Means Clustering and PCA';
conf.partArrays = { ...
{ ...
'1', ...
{ 'findClosestCentroids.m' }, ...
'Find Closest Centroids (k-Means)', ...
}, ...
{ ...
'2', ...
... |
github | Blz-Galaxy/Machine-Learning-master | submitWithConfiguration.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex7/ex7/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | Blz-Galaxy/Machine-Learning-master | savejson.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex7/ex7/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | Blz-Galaxy/Machine-Learning-master | loadjson.m | .m | Machine-Learning-master/Solution_KC.Mei/machine-learning-ex7/ex7/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
%
% ... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.