code stringlengths 1 2.01M | repo_name stringlengths 3 62 | path stringlengths 1 267 | language stringclasses 231
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function tf = gr_is_connected(G)
%GR_IS_CONNECTED Test graph connectivity
%
% tf = GR_IS_CONNECTED(G);
% Returns whether G is a connected graph. Here, G should be
% an undirected graph.
%
% Created by Dahua Lin, on Jan 25, 2012
%
%% verify input arguments
if ~(is_gr(G) && isequal(G.dty, 'u') && G.has_n... | zzhangumd-smitoolbox | graph/algs/gr_is_connected.m | MATLAB | mit | 482 |
function [edges, ccs] = gr_kruskal(G, w, K)
%GR_KRUSKAL Kruskal's Algorithm for minimum spanning tree/forest
%
% edges = GR_KRUSKAL(G, w);
% edges = GR_KRUSKAL(G, w, K);
%
% Finds the minimum spanning tree/forest of the given graph G.
%
% Input arguments:
% - G: The input graph, which shoul... | zzhangumd-smitoolbox | graph/algs/gr_kruskal.m | MATLAB | mit | 2,226 |
function [Q, H] = hmm_infer(Pi, T, LL)
%HMM_INFER HMM E-step inference
%
% Q = HMM_INFER(Pi, T, LL);
%
% Infers the (posterior) marginal distributions of all states in
% an Hidden Markov Model.
%
% Suppose there are K distinct state values, and n steps along
% the chain.
%
% Input argu... | zzhangumd-smitoolbox | pmodels/markov/hmm_infer.m | MATLAB | mit | 1,412 |
function [x, v] = chain_viterbi(A, B, w)
%CHAIN_VITERBI Viterbi algorithm on a Hidden Markov chain
%
% [x, v] = CHAIN_VITERBI(A, B);
% [x, v] = CHAIN_VITERBI(A, B, w);
%
% Performs the Viterbi algorithm along a chain that finds a sequence
% of states that maximizes the following objective
%
% ... | zzhangumd-smitoolbox | pmodels/markov/chain_viterbi.m | MATLAB | mit | 2,134 |
function [x, v] = hmm_decode(Pi, T, LL)
%HMM_DECODE Find the most probable state sequence for HMM
%
% [x, v] = HMM_DECODE(Pi, T, LL);
%
% Finds the state sequence based on an Hidden Markov Model using
% viterbi algorithm.
%
% Suppose there are K states and n nodes of the chain.
%
% Input argu... | zzhangumd-smitoolbox | pmodels/markov/hmm_decode.m | MATLAB | mit | 1,613 |
function [Alpha, Lc] = hmm_forward(Pi, T, LL)
%HMM_FORWARD HMM Forward recursion
%
% [Alpha, Lp] = HMM_FORWARD(Pi, T, LL);
%
% Performs forward recursion to evaluate the alpha values for
% Hidden Markov Model based inference.
%
% Input arguments:
% - Pi: The initial distribution of state... | zzhangumd-smitoolbox | pmodels/markov/hmm_forward.m | MATLAB | mit | 1,692 |
function Beta = hmm_backward(T, LL, Lc)
%HMM_BACKWARD HMM Backward recursion
%
% Beta = HMM_BACKWARD(T, LL, Lc);
%
% Performs forward recursion to evaluate the alpha values for
% Hidden Markov Model based inference.
%
% Input arguments:
% - T: The transition probability matrix [K x K]
%... | zzhangumd-smitoolbox | pmodels/markov/hmm_backward.m | MATLAB | mit | 1,467 |
/**********************************************************
*
* chain_viterbi_cimp.cpp
*
* The C++ mex implementation of chain_viterbi
*
* Created by Dahua Lin, on Feb 1, 2012
*
**********************************************************/
#include <bcslib/matlab/bcs_mex.h>
#include <cmath>
using namespace ... | zzhangumd-smitoolbox | pmodels/markov/private/chain_viterbi_cimp.cpp | C++ | mit | 5,153 |
/**********************************************************
*
* chain_bp_cimp.cpp
*
* The C++ mex implementation of chain_bp
*
* Created by Dahua Lin, on Feb 1, 2012
*
**********************************************************/
#include <bcslib/matlab/bcs_mex.h>
#include <cmath>
using namespace bcs;
using ... | zzhangumd-smitoolbox | pmodels/markov/private/chain_bp_cimp.cpp | C++ | mit | 5,691 |
/********************************************************************
*
* hmm_backward_cimp.cpp
*
* The C++ mex implementation of hmm_backward
*
* Created by Dahua Lin, on Feb 1, 2012
*
********************************************************************/
#include <bcslib/matlab/bcs_mex.h>
#include <cmath>
... | zzhangumd-smitoolbox | pmodels/markov/private/hmm_backward_cimp.cpp | C++ | mit | 2,472 |
/********************************************************************
*
* hmm_forward_cimp.cpp
*
* The C++ mex implementation of hmm_forward
*
* Created by Dahua Lin, on Feb 1, 2012
*
********************************************************************/
#include <bcslib/matlab/bcs_mex.h>
#include <cmath>
u... | zzhangumd-smitoolbox | pmodels/markov/private/hmm_forward_cimp.cpp | C++ | mit | 2,548 |
function T = markov_tpm(K, seqs, w, pri_c)
%MARKOV_TPM Estimation of Markov Transition Probability Matrix
%
% T = MARKOV_TPM(K, seqs);
% T = MARKOV_TPM(K, seqs, w);
% T = MARKOV_TPM(K, seqs, [], pri_c);
% T = MARKOV_TPM(K, seqs, w, pri_c);
%
% Estimates the transition probability from observed sequences
%... | zzhangumd-smitoolbox | pmodels/markov/markov_tpm.m | MATLAB | mit | 3,184 |
function [mu, fmsg, bmsg] = chain_bp(LA, LB)
%CHAIN_BP Discrete Belief Propagation along a chain
%
% [mu, fmsg, bmsg] = CHAIN_BP(LA, LB);
%
% Performs belief propagation along a chain based on the following
% Markov model
%
% p(x) = (1/Z) *
% prod_{i=1}^n a_i(x_i) *
% pro... | zzhangumd-smitoolbox | pmodels/markov/chain_bp.m | MATLAB | mit | 1,959 |
function v = ddentropy(P)
% Compute the entropy of discrete distribution
%
% v = ddentropy(P);
% computes the entropies of the discrete distributions represented
% by columns of P.
%
% If P is a vector (row or column), it returns the entropy of
% the discrete distribution whose probab... | zzhangumd-smitoolbox | pmodels/ddistr/ddentropy.m | MATLAB | mit | 776 |
function v = ddkldiv(P, Q)
% Compute Kullback Leibler divergence
%
% v = ddkldiv(P, Q);
% computes the Kullback Leibler divergence between the discrete
% distributions represented by the columns of P and Q.
%
% If P and Q are both vectors, then it returns the K-L divergence
% between t... | zzhangumd-smitoolbox | pmodels/ddistr/ddkldiv.m | MATLAB | mit | 844 |
function [P, H] = ddestimate(V, w, c0)
%DDESTIMATE Estimates discrete distribution(s)
%
% P = ddestimate(V);
% P = ddestimate(V, w);
%
% performs maximum likelihood estimation of discrete distribution(s)
% given (weighted) observations.
%
% Input arguments:
% - V: the observed histograms,... | zzhangumd-smitoolbox | pmodels/ddistr/ddestimate.m | MATLAB | mit | 3,142 |
function Y = nrmexp(X, dim)
% Compute normalized exponentiation
%
% Y = nrmexp(X);
% Y = nrmexp(X, dim);
% computes normalized exponentiation along the specified dimension.
% If dim is omitted, the normalization is performed along the
% first non-singleton dimension.
%
% Given a ve... | zzhangumd-smitoolbox | pmodels/ddistr/nrmexp.m | MATLAB | mit | 1,404 |
function Q = ddposterior(pri, lik, op)
%DDPOSTERIOR Compute posterior discrete distribution
%
% Q = DDPOSTERIOR(pri, lik);
% Q = DDPOSTERIOR(pri, loglik, 'LL');
%
% Compute posterior discrete distributions, given prior and
% likelihood (or log-likelihood).
%
% Suppose there are K classes and n (ob... | zzhangumd-smitoolbox | pmodels/ddistr/ddposterior.m | MATLAB | mit | 2,391 |
/********************************************************************
*
* ddsample_cimp.cpp
*
* The C++ mex implementation for ddsample: sampling from
* discrete distributions
*
* Created by Dahua Lin, on Nov 7, 2010
*
********************************************************************/
#include <bcslib/... | zzhangumd-smitoolbox | pmodels/ddistr/private/ddsample_cimp.cpp | C++ | mit | 2,934 |
function X = ddsample(P, n)
% Draw samples from discrete distribution(s)
%
% X = ddsample(P, n);
% draw n samples from each of the discrete distributions in P.
%
% Let P be an K x m matrix, then P represents m distinct discrete
% distributions over K classes.
%
% The output X will be an n x m... | zzhangumd-smitoolbox | pmodels/ddistr/ddsample.m | MATLAB | mit | 848 |
classdef tsuite_wishartd
% Test suite for wishartd and wishart_sample
%
% Created by Dahua Lin, on Sep 3, 2011
%
%% Properties
properties
dims = [1 2 3 5];
types = {'s', 'd', 'f'};
deg = 8.5;
end
%% Test cases
methods
... | zzhangumd-smitoolbox | pmodels/tests/tsuite_wishartd.m | MATLAB | mit | 4,967 |
classdef tsuite_ppca
% A test suite for PPCA functions
%
% Created by Dahua Lin, on Dec 27, 2011
%
%% properties
properties
dims = {[2 1], [5, 3], [10, 4]};
end
%% Test cases
methods
function test_basics(obj)
ru... | zzhangumd-smitoolbox | pmodels/tests/tsuite_ppca.m | MATLAB | mit | 6,035 |
classdef tsuite_dirichletd
% Test suite for dirichlet distribution functions
%
% History
% -------
% - Create by Dahua Lin, on Sep 1, 2011
% - Modified by Dahua Lin, on Sep 3, 2011
%
%% Properties
properties
dims = [2 5];
nu... | zzhangumd-smitoolbox | pmodels/tests/tsuite_dirichletd.m | MATLAB | mit | 4,972 |
classdef tsuite_gammad
% Test suite for gamma distribution functions
%
% History
% -------
% - Create by Dahua Lin, on Sep 1, 2011
% - Modified by Dahua Lin, on Sep 3, 2011
%
%% Properties
properties
dims = { [1 1 1], [1 1 3], [1 3 ... | zzhangumd-smitoolbox | pmodels/tests/tsuite_gammad.m | MATLAB | mit | 4,188 |
classdef tsuite_vscatter
% A test suite for vscatter function
%
% Created by Dahua Lin, on Sep 28, 2011
%
%% Test cases
methods
function test_scatter_vec(obj) %#ok<MANU>
tsuite_vscatter.run_multi(@tsuite_vscatter.do_test_scatter_vec);
end
... | zzhangumd-smitoolbox | pmodels/tests/tsuite_vscatter.m | MATLAB | mit | 2,938 |
classdef tsuite_invgammad
% Test suite for inverse gamma distribution functions
%
% History
% -------
% - Create by Dahua Lin, on Sep 1, 2011
% - Modified by Dahua Lin, on Sep 3, 2011
%
%% Properties
properties
dims = { [1 1 1], [1 ... | zzhangumd-smitoolbox | pmodels/tests/tsuite_invgammad.m | MATLAB | mit | 4,280 |
classdef tsuite_gaussd
% A test suite for gaussd class
%
% History
% -------
% - Created by Dahua Lin, on Jun 19, 2010
% - Modified by Dahua Lin, on Aug 17, 2011
% - Modified by Dahua Lin, on Aug 25, 2011
% - Modified by Dahua Lin, on Sep 3, 2011
%
... | zzhangumd-smitoolbox | pmodels/tests/tsuite_gaussd.m | MATLAB | mit | 15,274 |
classdef tsuite_gauss_models
% The test suite for various Gauss based generative models
%
% Created by Dahua Lin, on Dec 13, 2011
%
%% configurations
properties
Jx_forms = {'s', 'd', 'f'};
dims = {1, 2, 5, [3, 2]};
Ks = [1, 3];
end
%% main test... | zzhangumd-smitoolbox | pmodels/tests/tsuite_gauss_models.m | MATLAB | mit | 7,206 |
function [W, idf] = tf_idf_weight(C, op)
%TF_IDF_WEIGHT Computes TF-IDF Weights
%
% W = TF_IDF_WEIGHT(C);
%
% calculates the TF-IDF weights for each pair of word and document
% in a corpus.
%
% Input arguments:
% - C: The word-count table of the corpus. Suppose there
% ... | zzhangumd-smitoolbox | pmodels/topics/tf_idf_weight.m | MATLAB | mit | 1,539 |
function LL = topic_loglik(Beta, C, Q)
%TOPIC_LOGLIK Document log-likelihood for Topic model
%
% LL = TOPIC_LOGLIK(Beta, C, Q);
%
% Evaluates the log-likelihood of each document based on a
% topic model.
%
% Suppose there are K topics, n documents and V words in the
% vocabulary.
%
% ... | zzhangumd-smitoolbox | pmodels/topics/topic_loglik.m | MATLAB | mit | 1,632 |
function topiclda_demo()
%TOPICLDA_DEMO A simple program to demo the use of LDA
%
% R = TOPICLDA_DEMO;
%
%% Configuration
V = 50; % the number of words in the vocabulary
K = 3; % the number of topics
n0 = 500; % the number of training documents
n1 = 1000; % the number of testing documents
nw = 1000; %... | zzhangumd-smitoolbox | pmodels/topics/topiclda_demo.m | MATLAB | mit | 2,694 |
function [sol, objVs] = topiclda_em_update(C, w, eta, sol, eiters, objVs)
%TOPICLDA_EM_UPDATE Performs E-M update of a Topic-LDA solution
%
% [sol, objVs] = TOPICLDA_EM_UPDATE(C, w, eta, sol, eiters);
%
% Performs E-M update for Topic-LDA estimation.
%
% Input arguments:
% - C: The word-count... | zzhangumd-smitoolbox | pmodels/topics/topiclda_em_update.m | MATLAB | mit | 2,092 |
/**********************************************************
*
* topic_loglik_cimp.cpp
*
* C++ mex implementation of topic_loglik
*
* Created by Dahua Lin, on Feb 18, 2012
*
**********************************************************/
#include <bcslib/matlab/bcs_mex.h>
#include "specfuncs.h"
#include "corpus.... | zzhangumd-smitoolbox | pmodels/topics/private/topic_loglik_cimp.cpp | C++ | mit | 2,866 |
// The header file for the class Corpus
#ifndef SMI_CORPUS_H
#define SMI_CORPUS_H
namespace smi
{
struct Doc
{
Doc()
: nwords(0), words(0), counts(0)
{}
int nwords;
const int32_t *words;
const double *counts;
};
class Corpus
{
public:
// I and J are zero-based indices
... | zzhangumd-smitoolbox | pmodels/topics/private/corpus.h | C++ | mit | 1,846 |
/**********************************************************
*
* topiclda_varinfer_cimp.cpp
*
* C++ mex implementation of topiclda_varinfer
*
* Created by Dahua Lin, on Feb 5, 2012
*
**********************************************************/
#include <bcslib/matlab/bcs_mex.h>
#include "specfuncs.h"
#includ... | zzhangumd-smitoolbox | pmodels/topics/private/topiclda_varinfer_cimp.cpp | C++ | mit | 10,797 |
// Some special function codes
#ifndef SMI_SPECFUNCS_H
#define SMI_SPECFUNCS_H
#include <cmath>
namespace smi
{
double gammaln(double x)
{
const double M_lnSqrt2PI = 0.91893853320467274178;
const double s0 = 76.18009172947146;
const double s1 = -86.50532032941677... | zzhangumd-smitoolbox | pmodels/topics/private/specfuncs.h | C++ | mit | 2,055 |
function v = topiclda_ppx(Beta, C, Gamma)
%TOPICLDA_PPX Evaluation of Perplexity for Topic LDA
%
% v = topiclda_ppx(Beta, C, Gamma);
%
% Evaluates the perplexity over a corpus for a Topic LDA model.
%
% Input arguments:
% - Beta: The word-distributions of topics
% - C: The w... | zzhangumd-smitoolbox | pmodels/topics/topiclda_ppx.m | MATLAB | mit | 679 |
classdef topiclda_em < smi_state
% Topic Latent Dirichlet allocation E-M algorithm state
%
% Created by Dahua Lin, on Feb 18, 2012
%
%% properties
properties(GetAccess='public', SetAccess='private')
vsize; % the size of vocabulary (V)
prico... | zzhangumd-smitoolbox | pmodels/topics/topiclda_em.m | MATLAB | mit | 7,733 |
function [Gamma, W, objVs, cvg] = topiclda_varinfer(Beta, alpha, C, w, Gamma, maxIter, tol)
%TOPICLDA_VARINFER Variational Inference Step of Latent Dirichlet Allocation
%
% [Gamma, W] = TOPICLDA_VARINFER(Beta, alpha, C, w, Gamma, maxIter, tol);
% [Gamma, W, objVs] = TOPICLDA_VARINFER( ... );
% [Gamma, W, objVs, c... | zzhangumd-smitoolbox | pmodels/topics/topiclda_varinfer.m | MATLAB | mit | 3,818 |
function v = topic_ppx(Beta, C, Q)
%TOPIC_PPX Corpus Perplexity for Topic model
%
% v = TOPIC_PPX(Beta, C, Q);
%
% Evaluates the perplexity of a topic model on a corpus.
%
% Suppose there are K topics, n documents and V words in the
% vocabulary.
%
% Input arguments:
% - Beta: ... | zzhangumd-smitoolbox | pmodels/topics/topic_ppx.m | MATLAB | mit | 856 |
classdef gauss_lingen < genmodel_base
% The class that implements a linear Gaussian generative model
%
% The linear Gaussian generative model, with parameter u, is
% formulated as
%
% x ~ N(A * u, Cx), or x ~ N(u, Cx) if A is identity.
%
% Here, let d be the dimension o... | zzhangumd-smitoolbox | pmodels/gauss/gauss_lingen.m | MATLAB | mit | 13,623 |
function X = ppca_ft(M, Z)
% Performs forward transform w.r.t. PPCA model
%
% X = ppca_ft(M, Z);
% Transforms the latent vectors in Z to the observed space, with
% respect to the PPCA model M, as
%
% x <- W * z.
%
% Here, Z should be a q x n matrix, and X is a d x n matrix.
%
% Created by Da... | zzhangumd-smitoolbox | pmodels/gauss/ppca_ft.m | MATLAB | mit | 559 |
function M = ppca_model(B, s, se, mu)
% Constructs a Probabilistic PCA model struct
%
% M = ppca_model(B, s, se);
% M = ppca_model(B, s, se, mu);
%
% constructs a PPCA model strict.
%
% Input arguments:
%
% - B: the basis matrix. The size of U is d x q, where
% d is the ob... | zzhangumd-smitoolbox | pmodels/gauss/ppca_model.m | MATLAB | mit | 2,119 |
function M = ppca_mle(X, w, q, varargin)
% Performs Maximum Likelihood estimation of PPCA from data
%
% M = ppca_mle(X, [], q, ...);
% M = ppca_mle(X, w, q, ...);
%
% performs Maximum Likelihood estimation of the PPCA
% model from data.
%
% Input arguments:
% - X: The data matrix of size ... | zzhangumd-smitoolbox | pmodels/gauss/ppca_mle.m | MATLAB | mit | 4,833 |
function gaussd_ellipse(G, r, n, varargin)
% Draws an Ellipse to represent a Gaussian model
%
% gaussd_ellipse(G, r, n, ...);
%
% Input arguments:
% - G: A gaussd struct to represent the Gaussian model(s)
% - r: the relative radius
% - n: the number of points on each ellipse
%... | zzhangumd-smitoolbox | pmodels/gauss/gaussd_ellipse.m | MATLAB | mit | 1,349 |
function C = ppca_cov(M)
% Get the covariance matrix of a PPCA model
%
% C = ppca_cov(M);
% computes the equivalent covariance matrix of the input PPCA model.
%
% Created by Dahua Lin, on Dec 27, 2011
%
%% verify input
if ~is_ppca(M)
error('ppca_cov:invalidarg', 'M should be a PPCA struct.');
end
%% mai... | zzhangumd-smitoolbox | pmodels/gauss/ppca_cov.m | MATLAB | mit | 368 |
function [ca, cb] = gaussd_const(G)
% Calculate two useful constants of Gaussian distributions
%
% ca = gaussd_const(G);
% [ca, cb] = gaussd_const(G);
%
% This function calculates two useful constants for Gaussian
% pdf evaluation.
%
% ca = mu' * h = mu' * inv(C) * mu = h' * inv(J) * h
% cb ... | zzhangumd-smitoolbox | pmodels/gauss/gaussd_const.m | MATLAB | mit | 1,453 |
function R = gaussd_mapest(G, S)
% Performs MAP estimation w.r.t. Gaussian prior
%
% R = gaussd_mapest(G, S);
%
% Performs MAP estimation with the Gaussian prior G, and the
% updates to the canonical param of the prior.
%
% Input arguments:
% - G: the Gaussian prior (G.ty == 'c' && ... | zzhangumd-smitoolbox | pmodels/gauss/gaussd_mapest.m | MATLAB | mit | 694 |
function W = ppca_W(M)
% Get the factor matrix W of a PPCA model
%
% W = ppca_W(M);
% computes the factor matrix of the input PPCA model.
%
% Created by Dahua Lin, on Dec 27, 2011
%
%% main
W = bsxfun(@times, M.B, M.s);
| zzhangumd-smitoolbox | pmodels/gauss/ppca_W.m | MATLAB | mit | 232 |
classdef ppca_gm < genmodel_base
% The class implementing PPCA as a genmodel_base subclass
%
% Created by Dahua Lin, on Nov 6, 2011
%
properties(GetAccess='public', SetAccess='private')
dim; % the observed space dimension
ldim; % the latent space dimens... | zzhangumd-smitoolbox | pmodels/gauss/ppca_gm.m | MATLAB | mit | 3,960 |
classdef gausspri < prior_base
% Gaussian prior distribution
%
% This class wrap a gaussd object to prior_base interface
%
% Created by Dahua Lin, on Dec 27, 2011
%
properties(GetAccess='public', SetAccess='private')
dim; % the space dimension
gdistr; %... | zzhangumd-smitoolbox | pmodels/gauss/gausspri.m | MATLAB | mit | 3,707 |
function G = agmrf(W, a, y)
% Constructs an attractive Gaussian MRF
%
% An attractive Gaussian MRF is defined to be a Gaussian model, as
%
% p(x) = exp(E(x)) / Z,
%
% with
%
% E(x) = 1/2 * sum_{(i,j) \in E} W(i,j) (x(i) - x(j))^2
% + 1/2 * a (x(i) - y(i))^2
%
% This can be written in a matr... | zzhangumd-smitoolbox | pmodels/gauss/agmrf.m | MATLAB | mit | 1,790 |
function v = gaussd_entropy(C, op)
% Compute the entropy of a Gaussian distribution
%
% v = gaussd_entropy(C);
% v = gaussd_entropy(C, 'm');
%
% computes the entropy of Gaussian distribution(s) based on
% its covariance matrix.
%
% C should be a pdmat struct. v will be a scalar (if C.n == 1),
% ... | zzhangumd-smitoolbox | pmodels/gauss/gaussd_entropy.m | MATLAB | mit | 1,712 |
function tf = is_gaussd(G)
% Tests whether the input argument is a gaussd struct.
%
% tf = is_gaussd(G);
%
% Created by Dahua Lin, on Dec 5, 2011
%
tf = isstruct(G) && isscalar(G) && isfield(G, 'tag') && ...
strcmp(G.tag, 'gaussd');
| zzhangumd-smitoolbox | pmodels/gauss/is_gaussd.m | MATLAB | mit | 241 |
classdef gaussgm < genmodel_base
% The class to implement a basic Gaussian generative model
%
% x ~ N(mu, Sigma);
%
% Two parameters:
% - mu: the mean vector
% - Sigma: the covariance
%
% They together are encapsulated in a gaussd struct.
%
%% propert... | zzhangumd-smitoolbox | pmodels/gauss/gaussgm.m | MATLAB | mit | 4,988 |
function X = ppca_sample(M, n)
% Draws n samples from a PPCA model
%
% X = ppca_sample(M, n);
% draws n samples from a PPCA model M.
%
% Created by Dahua Lin, on Dec 27, 2011
%
%% verify input
if ~is_ppca(M)
error('ppca_sample:invalidarg', 'M should be a ppca struct.');
end
if nargin < 2
n = 1;
else... | zzhangumd-smitoolbox | pmodels/gauss/ppca_sample.m | MATLAB | mit | 544 |
function D = gaussd_sqmahdist(G, X, ca)
% Evaluate the squared Mahalanobis distances to Gaussian centers
%
% D = gaussd_sqmahdist(G, X);
% D = gaussd_sqmahdist(G, X, ca)
%
% computes the Gaussian Mahalanobis distance to the centers of
% Gaussian distribution in G.
%
% Given a Gaussian distributio... | zzhangumd-smitoolbox | pmodels/gauss/gaussd_sqmahdist.m | MATLAB | mit | 4,523 |
function Z = ppca_bt(M, X)
% Performs backward transform w.r.t. PPCA model
%
% X = ppca_ft(M, Z);
% Transforms the observed vectors in X to the latent space, with
% respect to the PPCA model M, as
%
% Here, X should be a d x n matrix, and Z is a q x n matrix.
%
% Created by Dahua Lin, on Dec 27, 2... | zzhangumd-smitoolbox | pmodels/gauss/ppca_bt.m | MATLAB | mit | 545 |
function J = ppca_icov(M)
% Get the inverse covariance matrix of a PPCA model
%
% J = ppca_icov(M);
% computes the inverse covariance matrix of the input PPCA model.
%
% Created by Dahua Lin, on Dec 27, 2011
%
%% verify input
if ~is_ppca(M)
error('ppca_cov:invalidarg', 'M should be a PPCA struct.');
end
... | zzhangumd-smitoolbox | pmodels/gauss/ppca_icov.m | MATLAB | mit | 449 |
function dists = ppca_sqmahdist(M, X)
% Compute squared Mahalanobis distances to the center of PPCA model
%
% dists = ppca_sqmahdist(M, X);
% computes the squared Mahalanobis distances from the samples
% in X to the center of the PPCA model M, which is defined by
%
% (x - mu)' * inv(C) * (x - m... | zzhangumd-smitoolbox | pmodels/gauss/ppca_sqmahdist.m | MATLAB | mit | 981 |
classdef gmrf_blk_gibbs
% The class that implements a block gibbs sampler for Gaussian MRF
%
% Created by Dahua Lin, on Dec 9, 2011
%
%% properties
properties(GetAccess='public', SetAccess='private')
gmrf; % the underlying gaussian model
blocks;... | zzhangumd-smitoolbox | pmodels/gauss/gmrf_blk_gibbs.m | MATLAB | mit | 5,279 |
function X = ellip_slice_sample(x0, llikfun, V, intv, n)
%ELLIP_SLICE_SAMPLE Elliptical Slice Sampling
%
% x = ELLIP_SLICE_SAMPLE(x0, llikfun, v);
%
% Draws the next sample from the posterior formulated as below
%
% x ~ N(x | 0, Sigma) * llik( x );
%
% Here, Sigma is the prior covariance matri... | zzhangumd-smitoolbox | pmodels/gauss/ellip_slice_sample.m | MATLAB | mit | 3,413 |
function tf = is_ppca(M)
% Tests whether the input argument is a PPCA model struct
%
% tf = is_ppca(M);
%
% Created by Dahua Lin, on Dec 27, 2011
%
%% main
tf = isstruct(M) && numel(M) == 1 && isfield(M, 'tag') && ...
strcmp(M.tag, 'ppca');
| zzhangumd-smitoolbox | pmodels/gauss/is_ppca.m | MATLAB | mit | 251 |
function Gpos = gaussd_conjupdate(Gpri, S)
% Computes the posterior Gaussian distribution via conjuate update
%
% Gpos = gaussd_conjupdate(Gpri, S);
%
% Estimates the posterior distribution via conjugate updates on
% a given priro.
%
% Input arguments:
% - G: the Gaussian prior (G.ty... | zzhangumd-smitoolbox | pmodels/gauss/gaussd_conjupdate.m | MATLAB | mit | 1,251 |
function G = gaussd_mle(X, W, cform, tie_cov)
% Performs Maximum likelihood estimation of Gaussian distributions
%
% G = gaussd_mle(X);
% G = gaussd_mle(X, W);
% G = gaussd_mle(X, W, cform);
% G = gaussd_mle(X, W, cform, tie_cov);
%
% performs maximum likelihood estimation of Gaussian models based on
% ... | zzhangumd-smitoolbox | pmodels/gauss/gaussd_mle.m | MATLAB | mit | 4,342 |
function LP = gaussd_logpdf(G, X, ca_cb)
% Evaluate the log-pdf of Gaussian models
%
% LP = gaussd_logpdf(G, X);
% LP = gaussd_logpdf(G, X, {ca, cb})
%
% computes the log-pdf of the samples given as columns of X,
% with respect to the Gaussian distributions in G.
%
% Inputs:
% - G: ... | zzhangumd-smitoolbox | pmodels/gauss/gaussd_logpdf.m | MATLAB | mit | 1,385 |
function G = gaussd(op, param1, param2)
% Constructs a Gauss distribution struct
%
% G = gaussd('m', mu, C);
%
% constructs a Gaussian distribution struct with mean and
% covariance.
%
% Suppose you are to construct a struct G comprised of n
% Gaussian distributions on a d-dimensional space.... | zzhangumd-smitoolbox | pmodels/gauss/gaussd.m | MATLAB | mit | 6,026 |
function demo_gmrf_sample(imsiz, w, n, intv)
% Demos the use of Gaussian MRF sampling
%
% demo_gmrf_sample(imsiz, w, n, intv);
%
% demonstrates the sampling of images from a Gaussian MRF.
%
% Input:
% - imsiz: the image size, in form of [height, width]
% - w: the weights of ... | zzhangumd-smitoolbox | pmodels/gauss/demo_gmrf_sample.m | MATLAB | mit | 1,764 |
function L = ppca_logpdf(M, X)
% Evaluate log pdf values at given samples
%
% L = ppca_logpdf(M, X);
% evaluates the log proability density values at the samples given
% by X, with respect to the PPCA model.
%
% Suppose X has n columns (each column is sample), then L
% is a vector of size 1 x... | zzhangumd-smitoolbox | pmodels/gauss/ppca_logpdf.m | MATLAB | mit | 459 |
function Gs = gaussd_sub(G, idx)
% Get a subset of Gaussian models
%
% Gs = gaussd_sub(G, idx);
% Get a subset of Gaussian models (specified by the index or
% index vector idx).
%
% Created by Dahua Lin, on Dec 20, 2011
%
%% main
if G.ty == 'm'
mu = G.mu(:, idx);
Gs.tag = G.tag;
Gs.ty... | zzhangumd-smitoolbox | pmodels/gauss/gaussd_sub.m | MATLAB | mit | 729 |
function X = gaussd_sample(G, n, Z)
% Samples from (multivariate) Gaussian distributions
%
% X = gaussd_sample(G);
% X = gaussd_sample(G, n);
% Draws n samples from a Gaussian distribution G whose mean and
% covariance are respectively given by mu and C.
%
% Input arguments:
% - G: a... | zzhangumd-smitoolbox | pmodels/gauss/gaussd_sample.m | MATLAB | mit | 2,827 |
function v = fmm_em_objective(pri, LL, w, c0, s)
%FMM_EM_OBJECTIVE Evaluate the objective of an FMM-EM solution
%
% v = FMM_EM_OBJECTIVE(pri, LL, w, c0, s);
%
% Evaluates the objective function value for EM-estimation of
% a finite mixture model.
%
% Created by Dahua Lin, on Feb 3, 2012
%
%% main
if ~i... | zzhangumd-smitoolbox | pmodels/mixmodel/fmm_em_objective.m | MATLAB | mit | 924 |
function S = fmm_init(pri, gm, X, w, method, arg)
%FMM_INIT Initialize an FMM solution
%
% S = FMM_INIT(pri, gm, X, w, 'params', A);
%
% Initializes a finite mixture model solution with initial
% parameters (given by A).
%
% Other inputs:
% - pri: The prior object
% - gm: The ... | zzhangumd-smitoolbox | pmodels/mixmodel/fmm_init.m | MATLAB | mit | 2,910 |
classdef fmm_std < smi_state
% The class that implements standard finite mixture model
%
% History
% -------
% - Created by Dahua Lin, on Sep 27, 2011
% - Modified by Dahua Lin, on Dec 26, 2011
%
%% Properties
% configurations
properties(GetAcc... | zzhangumd-smitoolbox | pmodels/mixmodel/fmm_std.m | MATLAB | mit | 7,521 |
function R = glmm_demo(method, K)
% A script to demo the inference over mixture of Gaussian linear models
%
% R = glmm_demo(method, K);
%
% Input arguments:
% - method: either of the following strings
% - 'em': expectation-maximization
% - 'gibbs': Gibb... | zzhangumd-smitoolbox | pmodels/mixmodel/glmm_demo.m | MATLAB | mit | 2,687 |
function [S, L] = fmm_em_update(pri, gm, X, w, c0, S, L)
%FMM_EM_UPDATE Expectation-Maximization Update for Finite Mixture Model
%
% [S, L] = FMM_EM_UPDATE(pri, gm, X, w, S, L);
%
% Updates finite mixture model solution as an E-M iteration.
%
% Input arguments:
% - pri: The prior object
% ... | zzhangumd-smitoolbox | pmodels/mixmodel/fmm_em_update.m | MATLAB | mit | 1,182 |
function s = fmm_merge_samples(pri, gm, c0, X, w, ss)
%FMM_MERGE_SAMPLES Merge multiple FMM solutions into a single one
%
% s = FMM_MERGE_SAMPLES(pri, gm, X, w, ss);
%
% Creates a single FMM that best represents all input FMM samples.
%
% Input arguments:
% - pri: the prior object
% -... | zzhangumd-smitoolbox | pmodels/mixmodel/fmm_merge_samples.m | MATLAB | mit | 1,261 |
function params = fmm_est_params(pri, gm, X, w, Z)
% Estimate mixture model given sample assignment
%
% params = fmm_est_params(pri, gm, X, w, Z);
%
% Estimates component model parameters given sample assignment.
%
% Input arguments:
% - gm: the generative model of observations
% - pri: ... | zzhangumd-smitoolbox | pmodels/mixmodel/fmm_est_params.m | MATLAB | mit | 1,193 |
function R = gmm_demo(cf, op)
% A script to demo the inference over Gaussian mixture model
%
% R = gmm_demo(cf);
% R = gmm_demo(cf);
%
% Runs a Gaussian mixture model demo using the specified covariance
% matrix form, which can be either 's' (scale form),
% 'd' (diagonal form), or 'f' (full matri... | zzhangumd-smitoolbox | pmodels/mixmodel/gmm_demo.m | MATLAB | mit | 2,542 |
function [S, L] = fmm_gibbs_update(pri, gm, X, w, c0, S, L)
%FMM_GIBBS_UPDATE Gibbs Sampling Update for Finite Mixture Model
%
% [S, L] = FMM_GIBBS_UPDATE(pri, gm, X, w, S, L);
%
% Updates finite mixture model solution as an E-M iteration.
%
% Input arguments:
% - pri: The prior object
% ... | zzhangumd-smitoolbox | pmodels/mixmodel/fmm_gibbs_update.m | MATLAB | mit | 1,415 |
function [R, state] = gmm_fit(X, w, K, varargin)
%GMM_FIT Fits a Gaussian Mixture Model
%
% R = gmm_fit(X, [], K, ...);
% R = gmm_fit(X, w, K, ...);
%
% [R, state] = gmm_fit( ... );
%
% Fits a Gaussian mixture model to a (weighted) set of data.
%
% Suppose there are n samples on a d dimensional space.... | zzhangumd-smitoolbox | pmodels/mixmodel/gmm_fit.m | MATLAB | mit | 6,333 |
function gsm_demo(method)
% A simple demo of Gaussian scale mixture fitting
%
% gsm_demo(method);
%
% Here method can be either 'ip' or 'em'.
%
% Created by Dahua Lin, on Nov 2, 2011
%
%% configure model
K = 4;
sigma = [1, 4, 12, 36].';
p = [0.4, 0.2, 0.25, 0.15].';
ext = 100;
%% generate data
n = 1e5;
z = dd... | zzhangumd-smitoolbox | pmodels/mixmodel/gsm_demo.m | MATLAB | mit | 1,051 |
function [p, s, converged] = gsm_fit(x, w, K, varargin)
% Fits a Gaussian scale mixture to data
%
% The pdf of a Gaussian scale mixture (GSM) model is formulated as
%
% f(x) = \sum_{k=1}^K p_k/sqrt(2 *pi * s_k^2) *
% exp(- x^2 / (2 * s_k^2) );
%
% Here, p_1, ..., p_K are the prior w... | zzhangumd-smitoolbox | pmodels/mixmodel/gsm_fit.m | MATLAB | mit | 9,730 |
function mixppca_demo(n, K)
% A program to demonstrate the use of mixture of PPCA
%
% mixppca_demo(n, K);
%
% Here, n is the number of sample points, K is the number of
% mixture components to be used
%
% By default n = 50000, and K = 8;
%
% Created by Dahua Lin, on Nov 6, 2011
%
%% get input
if... | zzhangumd-smitoolbox | pmodels/mixmodel/mixppca_demo.m | MATLAB | mit | 1,099 |
function c = invgammad_const(alpha, beta)
% Compute the constant term for gamma distribution logpdf
%
% c = invgammad_const(alpha, beta);
%
% Evaluates the following constant term for the evaluation of
% inverse gamma distribution logpdf:
%
% c = - (alpha log(beta) + gammaln(alpha))
%
% Th... | zzhangumd-smitoolbox | pmodels/gamma/invgammad_const.m | MATLAB | mit | 811 |
function L = gammad_logpdf(alpha, beta, X, c)
%GAMMAD_LOGPDF Evaluate log probability density of gamma distribution
%
% log f(x; alpha, beta) =
% (alpha - 1) log(x) - x / beta + const
%
% with const = - (alpha log(beta) + gammaln(alpha))
%
% L = gammad_logpdf(alpha, beta, X);
% L = gammad_logpdf(alpha, b... | zzhangumd-smitoolbox | pmodels/gamma/gammad_logpdf.m | MATLAB | mit | 3,474 |
function v = dird_entropy(alpha, d)
%DIRD_ENTROPY Evaluates the entropy of Dirichlet distribution
%
% v = dird_entropy(alpha, d);
%
% Evaluates the entropy of Dirichlet distribution(s).
%
% Here, alpha should be a matrix of size d x n, and in the output,
% the size of v is 1 x n. Particularly... | zzhangumd-smitoolbox | pmodels/gamma/dird_entropy.m | MATLAB | mit | 1,029 |
function [alpha, objv] = dird_mle(X, w, alpha0, varargin)
%DIRD_MLE Maximum-likelihood Estimation of Dirichlet Distribution
%
% [alpha, objv] = DIRD_MLE(X, w, alpha0);
% [alpha, objv] = DIRD_MLE(X, w, alpha0, ...);
%
% Performs maximum-likelihood estimation of Dirichlet distribution.
%
% Input arguments... | zzhangumd-smitoolbox | pmodels/gamma/dird_mle.m | MATLAB | mit | 5,728 |
function L = wishartd_logpdf(Sigma, df, V, c, op)
%WISHARTD_LOGPDF Evaluates the log pdf of Wishart distribution
%
% L = wishartd_logpdf(Sigma, df, V);
% L = wishartd_logpdf(Sigma, df, V, 0);
% L = wishartd_logpdf(Sigma, df, V, c);
%
% Evaluates the log pdf at the matrices given in V, w.r.t. the
% Wis... | zzhangumd-smitoolbox | pmodels/gamma/wishartd_logpdf.m | MATLAB | mit | 2,266 |
function h = visdird3(alpha, form)
% Visualize a Dirichlet distribution with K == 3
%
% visdird3(alpha);
% visdird3(alpha, 'image');
% visdird3(alpha, 'mesh');
%
% visualizes a Dirichlet distribution with K = 3 as a colored
% image or mesh. (image by default);
%
% alpha is either a scalar or a ... | zzhangumd-smitoolbox | pmodels/gamma/visdird3.m | MATLAB | mit | 2,091 |
function c = wishartd_const(J, df)
% Compute the constant term for Wishart distribution log-pdf
%
% c = wishartd_const(J, df);
%
% Evaluates the constant term for Wishart distribution log-pdf
% or inverse Wishart distribution log-pdf.
%
% Inputs:
% - J: inverse scale matrix in form of p... | zzhangumd-smitoolbox | pmodels/gamma/wishartd_const.m | MATLAB | mit | 974 |
function v = gammad_entropy(alpha, beta)
% Compute the entropy for gamma distribution
%
% v = gammad_entropy(alpha, beta);
%
% Evaluates the entropy of gamma distribution.
%
% The sizes of alpha and beta should be compatible in the bsxfun
% sense.
%
% If either size(alpha, 1) > 1 or size(beta,... | zzhangumd-smitoolbox | pmodels/gamma/gammad_entropy.m | MATLAB | mit | 665 |
function X = dird_sample(alpha, n)
%DIRD_SAMPLE Samples from a Dirichlet distribution
%
% X = dird_sample(alpha);
% X = dird_sample(alpha, n);
% X = dird_sample(alpha, [d, n]);
%
% Draws n samples from a Dirichlet distribution over the
% (d-1)-dimensional probability simplex, whose parameter
% ... | zzhangumd-smitoolbox | pmodels/gamma/dird_sample.m | MATLAB | mit | 1,818 |
function X = gammad_sample(alpha, beta, n)
%GAMMAD_SAMPLE Samples from a Gamma distribution
%
% X = gammad_sample(alpha, beta);
% X = gammad_sample(alpha, beta, n);
%
% draws n samples from a gamma distribution with shape parameter
% alpha and scale parameter beta.
%
% Input arguments:
% - ... | zzhangumd-smitoolbox | pmodels/gamma/gammad_sample.m | MATLAB | mit | 2,351 |
function L = invwishartd_logpdf(Phi, df, V, c)
%INVWISHARTD_LOGPDF Evaluates the log pdf of Inverse Wishart distribution
%
% L = invwishartd_logpdf(Phi, df, V);
% L = invwishartd_logpdf(Phi, df, V, 0);
% L = invwishartd_logpdf(Phi, df, V, c);
%
% Evaluates the log pdf at the matrices given in V, w.r.t. the
... | zzhangumd-smitoolbox | pmodels/gamma/invwishartd_logpdf.m | MATLAB | mit | 1,822 |
function v = invgammad_entropy(alpha, beta)
% Compute the entropy for inverse gamma distribution
%
% v = invgammad_entropy(alpha, beta);
%
% Evaluates the entropy of inverse gamma distribution.
%
% The sizes of alpha and beta should be compatible in the bsxfun
% sense.
%
% If either size(alpha... | zzhangumd-smitoolbox | pmodels/gamma/invgammad_entropy.m | MATLAB | mit | 687 |
function v = mvbetaln(alpha, d)
%MVBETALN Logarithm of multivariate beta function
%
% A multivariate beta function is defined to be
%
% f(a) = prod(gamma(a)) / gamma(sum(a)).
%
%
% v = mvbetaln(alpha);
%
% Evaluates the logarithm of multivariate beta function.
%
% Here, alpha should be a ma... | zzhangumd-smitoolbox | pmodels/gamma/mvbetaln.m | MATLAB | mit | 1,478 |
function V = mvgammaln(d, X)
% Evaluate Multivariate Log-Gamma function
%
% In math, the multivariate gamma function with dim d, is defined by
%
% Gamma_d(x) = pi^(d * (d-1) / 4)
% * prod_{j=1}^d Gamma(x + (1 - j)/2)
%
% In particular, when d == 1, Gamma_d(x) = Gamma(x)
%
% Then, i... | zzhangumd-smitoolbox | pmodels/gamma/mvgammaln.m | MATLAB | mit | 1,496 |
function c = gammad_const(alpha, beta)
% Compute the constant term for gamma distribution logpdf
%
% c = gammad_const(alpha, beta);
%
% Evaluates the following constant term for the evaluation of
% gamma distribution logpdf:
%
% c = - (alpha log(beta) + gammaln(alpha))
%
% The sizes of alp... | zzhangumd-smitoolbox | pmodels/gamma/gammad_const.m | MATLAB | mit | 802 |
/********************************************************************
*
* wishartd_sample_cimp.cpp
*
* The C++ mex implementation for pieces
*
* Created by Dahua Lin, on Mar 22, 2011
*
********************************************************************/
#include <bcslib/matlab/bcs_mex.h>
#include <cmath>
... | zzhangumd-smitoolbox | pmodels/gamma/private/wishartd_sample_cimp.cpp | C++ | mit | 2,226 |