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github | UCL-SML/pilco-matlab-master | dynamics_cdp.m | .m | pilco-matlab-master/scenarios/cartDoublePendulum/dynamics_cdp.m | 2,891 | utf_8 | 8e34721756a5a3b7a6f8558a2e93b4b4 | %% dynamics_cdp.m
% *Summary:* Implements ths ODE for simulating the cart-double pendulum
% dynamics.
%
% function dz = dynamics_cdp(t,z,f)
%
%
% *Input arguments:*
%
% t current time step (called from ODE solver)
% z state [6 x 1]
% f (optional):... |
github | UCL-SML/pilco-matlab-master | dynamics_pendulum.m | .m | pilco-matlab-master/scenarios/pendulum/dynamics_pendulum.m | 1,302 | utf_8 | 093b761dfe36df40ca96d15de3e6232d | %% dynamics_pendulum.m
% *Summary:* Implements ths ODE for simulating the pendulum dynamics, where
% an input torque f can be applied
%
% function dz = dynamics_pendulum(t,z,u)
%
%
% *Input arguments:*
%
% t current time step (called from ODE solver)
% z state ... |
github | UCL-SML/pilco-matlab-master | loss_pendulum.m | .m | pilco-matlab-master/scenarios/pendulum/loss_pendulum.m | 4,270 | utf_8 | 919853b11632e5c459d540abf96e2219 | %% loss_pendulum.m
% *Summary:* Pendulum loss function; the loss is
% $1-\exp(-0.5*d^2*a)$, where $a>0$ and $d^2$ is the squared difference
% between the actual and desired position of the tip of the pendulum.
% The mean and the variance of the loss are computed by averaging over the
% Gaussian distribution of the st... |
github | UCL-SML/pilco-matlab-master | draw_pendulum.m | .m | pilco-matlab-master/scenarios/pendulum/draw_pendulum.m | 2,290 | utf_8 | c50e71f7b37fb137cfc5459d39c5c495 | %% draw_pendulum.m
% *Summary:* Draw the pendulum system with reward, applied torque,
% and predictive uncertainty of the tips of the pendulums
%
% function draw_pendulum(theta, torque, cost, text1, text2, M, S)
%
%
% *Input arguments:*
%
% theta1 angle of inner pendulum
% theta2 angle of outer pendulum... |
github | UCL-SML/pilco-matlab-master | gp0d.m | .m | pilco-matlab-master/gp/gp0d.m | 6,121 | utf_8 | d73ef5c95a55519268da282ffbc627ca | %% gp0d.m
% *Summary:* Compute joint predictions and derivatives for multiple GPs
% with uncertain inputs. Predictive variances contain uncertainty about the
% function, but no noise.
% If gpmodel.nigp exists, individial noise contributions are added.
%
%
% function [M, S, V, dMdm, dSdm, dVdm, dMds, dSds, dVds] = g... |
github | UCL-SML/pilco-matlab-master | gp2d.m | .m | pilco-matlab-master/gp/gp2d.m | 13,617 | utf_8 | 25edf637a6e3ad389ac32a3c5547aa74 | %% gp2d.m
% *Summary:* Compute joint predictions and derivatives for multiple GPs
% with uncertain inputs. Does not consider the uncertainty about the underlying
% function (in prediction), hence, only the GP mean function is considered.
% Therefore, this representation is equivalent to a regularized RBF
% network.
% I... |
github | UCL-SML/pilco-matlab-master | hypCurb.m | .m | pilco-matlab-master/gp/hypCurb.m | 2,378 | utf_8 | 2aa8259c63afdfece72ccc8a31ef5e82 | %% hypCurb.m
% *Summary:* Wrapper for GP training (via gpr.m), penalizing large SNR and
% extreme length-scales to avoid numerical instabilities
%
% function [f df] = hypCurb(lh, covfunc, x, y, curb)
%
% *Input arguments:*
%
% lh log-hyper-parameters [D+2 x E ]
% covfun... |
github | UCL-SML/pilco-matlab-master | gp1.m | .m | pilco-matlab-master/gp/gp1.m | 4,916 | utf_8 | d5d2ccda21c8686f5c72a3b7dcba2d62 | %% gp1.m
% *Summary:* Compute joint predictions for the FITC sparse approximation to
% multiple GPs with uncertain inputs.
% Predictive variances contain uncertainty about the function, but no noise.
% If gpmodel.nigp exists, individual noise contributions are added.
%
% function [M, S, V] = gp1d(gpmodel, m, s)
% ... |
github | UCL-SML/pilco-matlab-master | covSEard.m | .m | pilco-matlab-master/gp/covSEard.m | 1,734 | utf_8 | 1f400b4ffc10975b4572164e889e177c | %% covSEard.m
% Squared Exponential covariance function with Automatic Relevance Detemination
% (ARD) distance measure. The covariance function is parameterized as:
%
% k(x^p,x^q) = sf2 * exp(-(x^p - x^q)'*inv(P)*(x^p - x^q)/2)
%
% where the P matrix is diagonal with ARD parameters ell_1^2,...,ell_D^2, where
% D is the... |
github | UCL-SML/pilco-matlab-master | gp1d.m | .m | pilco-matlab-master/gp/gp1d.m | 7,391 | utf_8 | b53a266575ac4ef1f2678674efe61708 | %% gp1d.m
% *Summary:* Compute joint predictions (and derivatives) for the FITC sparse
% approximation to multiple GPs with uncertain inputs.
% Predictive variances contain uncertainty about the function, but no noise.
% If gpmodel.nigp exists, individual noise contributions are added.
%
% function [M, S, V, dMdm, dS... |
github | UCL-SML/pilco-matlab-master | covSum.m | .m | pilco-matlab-master/gp/covSum.m | 2,403 | utf_8 | bf6228b9460e36949e8a52e49b67666e | %% covSum.m
% *Summary:* Compose a covariance function as the sum of other covariance
% functions. This function doesn't actually compute very much on its own, it
% merely does some bookkeeping, and calls other covariance functions to do the
% actual work.
%
% function [A, B] = covSum(covfunc, logtheta, x, z)
%
% (... |
github | UCL-SML/pilco-matlab-master | gp2.m | .m | pilco-matlab-master/gp/gp2.m | 3,808 | utf_8 | 5483b52dac200a108b03fb1d9e43b9a8 | %% gp2.m
% *Summary:* Compute joint predictions and derivatives for multiple GPs
% with uncertain inputs. Does not consider the uncertainty about the underlying
% function (in prediction), hence, only the GP mean function is considered.
% Therefore, this representation is equivalent to a regularized RBF
% network.
% If... |
github | UCL-SML/pilco-matlab-master | gp0.m | .m | pilco-matlab-master/gp/gp0.m | 3,761 | utf_8 | 335c986e61bbf92aae2cc328b0d56a18 | %% gp0.m
% *Summary:* Compute joint predictions for multiple GPs with uncertain inputs.
% If gpmodel.nigp exists, individial noise contributions are added.
% Predictive variances contain uncertainty about the function, but no noise.
%
% function [M, S, V] = gp0(gpmodel, m, s)
%
% *Input arguments:*
%
% gpmodel G... |
github | UCL-SML/pilco-matlab-master | gpr.m | .m | pilco-matlab-master/gp/gpr.m | 2,833 | utf_8 | 3f1c270a5d7daccb9115e4d97f87bb61 | %% gpr.m
% *Summary:* Gaussian process regression, with a named covariance function. Two
% modes are possible: training and prediction: if no test data are given, the
% function returns minus the log likelihood and its partial derivatives with
% respect to the hyperparameters; this mode is used to fit the hyperparamete... |
github | UCL-SML/pilco-matlab-master | fitc.m | .m | pilco-matlab-master/gp/fitc.m | 4,699 | utf_8 | 388c5581c035c02a82aa9b9526160dd8 | %% fitc.m
% *Summary:* Compute the FITC negative log marginal likelihood and its
% derivatives with respect to the inducing inputs (we don't compute the
% derivatives with respect to the GP hyper-parameters)
%
% function [nml dnml] = fitc(induce, gpmodel)
%
% *Input arguments:*
%
% induce matrix of induci... |
github | UCL-SML/pilco-matlab-master | train.m | .m | pilco-matlab-master/gp/train.m | 4,025 | utf_8 | 951ec2e880bdaaee127eb29562311025 | %% train.m
% *Summary:* Train a GP model with SE covariance function (ARD). First, the
% hyper-parameters are trained using a full GP. Then, if gpmodel.induce exists,
% indicating sparse approximation, if enough training exmples are present,
% train the inducing inputs (hyper-parameters are taken from the full GP). ... |
github | UCL-SML/pilco-matlab-master | covNoise.m | .m | pilco-matlab-master/gp/covNoise.m | 1,086 | utf_8 | 40d4e24e7127f8134528c2c8f8772626 | %% covNoise.m
% Independent covariance function, ie "white noise", with specified variance.
% The covariance function is specified as:
%
% k(x^p,x^q) = s2 * \delta(p,q)
%
% where s2 is the noise variance and \delta(p,q) is a Kronecker delta function
% which is 1 iff p=q and zero otherwise. The hyperparameter is
%
% log... |
github | UCL-SML/pilco-matlab-master | minimize.m | .m | pilco-matlab-master/util/minimize.m | 13,669 | utf_8 | 4532d014f593db54fa59610e074f6261 | %% minimize.m
% minimize.m - minimize a smooth differentiable multivariate function using
% LBFGS (Limited memory LBFGS) or CG (Conjugate Gradients)
% Usage: [X, fX, i] = minimize(X, F, p, other, ... )
% where
% X is an initial guess (any type: vector, matrix, cell array, struct)
% F is the objective function... |
github | UCL-SML/pilco-matlab-master | gSat.m | .m | pilco-matlab-master/util/gSat.m | 2,628 | utf_8 | fef6d775a326be1ea04c570d514a6c10 | %% gSat.m
% *Summary:* Compute moments of the saturating function
% $e*(9*\sin(x(i))+\sin(3*x(i)))/8$,
% where $x \sim\mathcal N(m,v)$ and $i$ is a (possibly empty) set of $I$
% indices. The optional scaling factor $e$ is a vector of length $I$.
% Optionally, compute derivatives of the moments.
%
% function [M, S, ... |
github | UCL-SML/pilco-matlab-master | sq_dist.m | .m | pilco-matlab-master/util/sq_dist.m | 2,207 | utf_8 | 682c4b378252a4c8eeefa4439c364fc2 | %% sq_dist.m
% sq_dist - a function to compute a matrix of all pairwise squared distances
% between two sets of vectors, stored in the columns of the two matrices, a
% (of size D by n) and b (of size D by m). If only a single argument is given
% or the second matrix is empty, the missing matrix is taken to be identical... |
github | UCL-SML/pilco-matlab-master | unwrap.m | .m | pilco-matlab-master/util/unwrap.m | 902 | utf_8 | 3856da2312d3091678383d3130b7479d | %% unwrap.m
% *Summary:* Extract the numerical values from $s$ into the column vector $v$.
% The variable $sS can be of any type, including struct and cell array.
% Non-numerical elements are ignored. See also the reverse rewrap.m.
%
% v = unwrap(s)
%
% *Input arguments:*
%
% s structure, cell, or numeric valu... |
github | UCL-SML/pilco-matlab-master | trigSquash_old.m | .m | pilco-matlab-master/util/trigSquash_old.m | 4,316 | utf_8 | ea7d2aed20d2f2142502765686cf0a6a | % Augment a Gaussian with e*sin(x(i)), where i is a (possibly
% empty) set of I indices. The optional e scaling factor is a vector of length
% I. Optionally, compute derivatives of the parameters of the new Gaussian.
%
% Copyright (C) 2007, 2008 & 2009 by Carl Edward Rasmussen, 2009-07-07.
function [m, v, dmda, dmdb, ... |
github | UCL-SML/pilco-matlab-master | gTrig.m | .m | pilco-matlab-master/util/gTrig.m | 4,963 | utf_8 | 997f08390a915af387fa08125f79a454 | %% gTrig.m
% *Summary:* Compute moments of the saturating function $e*sin(x(i))$ and $
% e*cos(x(i))$, where $x \sim\mathcal N(m,v)$ and $i$ is a (possibly empty)
% set of $I$ indices. The optional scaling factor $e$ is a vector of
% length $I$. Optionally, compute derivatives of the moments.
%
% [M, V, C, dMdm, ... |
github | UCL-SML/pilco-matlab-master | error_ellipse.m | .m | pilco-matlab-master/util/error_ellipse.m | 8,229 | utf_8 | 9f67e27c9f6218404167e7eb24c0cf57 | %% error_ellipse.m
% ERROR_ELLIPSE - plot an error ellipse, or ellipsoid, defining confidence region
% ERROR_ELLIPSE(C22) - Given a 2x2 covariance matrix, plot the
% associated error ellipse, at the origin. It returns a graphics handle
% of the ellipse that was drawn.
%
% ERROR_ELLIPSE(C33) - Given a 3x3 co... |
github | UCL-SML/pilco-matlab-master | rewrap.m | .m | pilco-matlab-master/util/rewrap.m | 1,351 | utf_8 | af00755de2984f738e21e52712a59fc0 | %% rewrap.m
% *Summary:* Map the numerical elements in the vector $v$ onto the variables
% $s$, which can be of any type. The number of numerical elements must match;
% on exit, $v$ should be empty. Non-numerical entries are just copied.
% See also the reverse unwrap.m.
%
% [s v] = rewrap(s, v)
%
% *Input arguments:... |
github | UCL-SML/pilco-matlab-master | solve_chol.m | .m | pilco-matlab-master/util/solve_chol.m | 1,014 | utf_8 | 675d515fffa67a72cb9cce4cc8c6374e | %% solve_chol.m
% solve_chol - solve linear equations from the Cholesky factorization.
% Solve A*X = B for X, where A is square, symmetric, positive definite. The
% input to the function is R the Cholesky decomposition of A and the matrix B.
% Example: X = solve_chol(chol(A),B);
%
% NOTE: The program code is written in... |
github | UCL-SML/pilco-matlab-master | gaussian.m | .m | pilco-matlab-master/util/gaussian.m | 793 | utf_8 | bc62e952d89ae7dfe8860f37a4f807b9 | %% gaussian.m
% *Summary:* Generate n samples from a Gaussian $p(x)=\mathcal N(m,S).
% Sampling is based on the Cholesky factorization of the covariance matrix S
%
% function x = gaussian(m, S, n)
%
% *Input arguments:*
%
% m mean of Gaussian [D x 1]
% S covarian... |
github | UCL-SML/pilco-matlab-master | gSin.m | .m | pilco-matlab-master/util/gSin.m | 3,391 | utf_8 | a002d13d7d48d976738abdba11c9344d | %% gSin.m
% *Summary:* Compute moments of the saturating function $e*sin(x(i))$,
% where $x \sim\mathcal N(m,v)$ and $i$ is a (possibly empty) set of $I$
% indices. The optional scaling factor $e$ is a vector of length $I$.
% Optionally, compute derivatives of the moments.
%
% function [M, V, C, dMdm, dVdm, dCdm, d... |
github | UCL-SML/pilco-matlab-master | maha.m | .m | pilco-matlab-master/util/maha.m | 943 | utf_8 | 78f272e79b5be527d48c9dad4abb8d5d | %% maha.m
% *Summary:* Point-wise squared Mahalanobis distance (a-b)*Q*(a-b)'.
% Vectors are row-vectors
%
% function K = maha(a, b, Q)
%
% *Input arguments:*
%
% a matrix containing n row vectors [n x D]
% b matrix containing n row vectors ... |
github | UCL-SML/pilco-matlab-master | lossSat.m | .m | pilco-matlab-master/loss/lossSat.m | 2,984 | utf_8 | 19c52ddda1502850d6c1cbe4af044594 | %% lossSat.m
% *Summary:* Compute expectation and variance of a saturating cost
% $1 - \exp(-(x-z)^T*W*(x-z)/2)$
% and their derivatives, where x ~ N(m,S), z is a (target state), and W
% is a weighting matrix
%
% function [L, dLdm, dLds, S, dSdm, dSds, C, dCdm, dCds] = lossSat(cost, m, s)
%
% *Input arguments:*
%
% ... |
github | UCL-SML/pilco-matlab-master | lossLin.m | .m | pilco-matlab-master/loss/lossLin.m | 2,032 | utf_8 | 420a72edfbc56bc050d2edb16c94c45e | %% lossLin.m
% *Summary:* Function to compute the expected loss and its derivatives, given an
% input distribution, under a linear loss function: L = a^T(x - b). Note, this
% loss function can return negative loss.
%
% [L dLdm dLds S dSdm dSds C dCdm dCds] = lossLin(cost,m,s)
%
% *Input arguments:*
%
% cost
% ... |
github | UCL-SML/pilco-matlab-master | reward.m | .m | pilco-matlab-master/loss/reward.m | 2,084 | utf_8 | 8908699b1c46fed8e1d75d6b2ebb4177 | %% reward.m
% *Summary:* Compute expectation, variance, and their derivatives of an
% exponentiated negative quadratic cost $\exp(-(x-z)'W(x-z)/2)$,
% where $x\sim\mathcal N(m,S)$
%
% *Input arguments:*
%
% m: D-by-1 mean of the state distribution
% S: D-by-D covariance matrix of the state distri... |
github | UCL-SML/pilco-matlab-master | lossAdd.m | .m | pilco-matlab-master/loss/lossAdd.m | 3,774 | utf_8 | 52c5dfbd0b0cd5caa9c3dd7423fe832e | %% lossAdd.m
% *Summary:* Utility function to add a number of loss functions together, each of which
% can be using a different loss function and operating on a different part of
% the state.
%
% function [L, dLdm, dLds, S, dSdm, dSds, C, dCdm, dCds] = lossAdd(cost, m, s)
%
% *Input arguments:*
%
% cost ... |
github | UCL-SML/pilco-matlab-master | lossHinge.m | .m | pilco-matlab-master/loss/lossHinge.m | 3,596 | utf_8 | 8065572dbe7ba938d78649a31e3aa0b1 | %% lossHinge.m
% *Summary:* Function to compute the moments and derivatives of the loss of a
% Gaussian distributed point under a double hinge loss function. The loss
% function has slope -/+a and corners b1 and b2. The function also calculates
% derivatives of the loss w.r.t. the state distribution.
%
% Graph:
% ... |
github | UCL-SML/pilco-matlab-master | lossQuad.m | .m | pilco-matlab-master/loss/lossQuad.m | 2,562 | utf_8 | d196478052135d8a9bd1201fdcdd8fa7 | %% lossQuad.m
% *Summary:* Compute expectation and variance of a quadratic cost
% $(x-z)'*W*(x-z)$
% and their derivatives, where $x \sim N(m,S)$
%
%
% function [L, dLdm, dLds, S, dSdm, dSds, C, dCdm, dCds] = lossQuad(cost, m, S)
%
%
%
% *Input arguments:*
%
% cost
% .z: target state ... |
github | UCL-SML/pilco-matlab-master | congp.m | .m | pilco-matlab-master/control/congp.m | 3,929 | utf_8 | 8a137598e0f056778e7c3ebeb2981c4a | %% congp.m
% *Summary:* Implements the mean-of-GP policy (equivalent to a regularized RBF
% network. Compute mean, variance and input-output covariance of
% the control $u$ using a mean-of-GP policy function, when the input $x$ is
% Gaussian. The GP is parameterized using a pseudo training set size N.
% Optionally, com... |
github | UCL-SML/pilco-matlab-master | conCat.m | .m | pilco-matlab-master/control/conCat.m | 4,305 | utf_8 | e7135e1b0710f40b0ac1151ee1a6d88d | %% concat.m
% *Summary:* Compute a control signal $u$ from a state distribution
% $x\sim\mathcal N(x|m,s)$. Here, the predicted control distribution
% and its derivatives are computed by concatenating a controller "con" with
% a saturation function "sat", such as gSat.m.
%
% function [M, S, C, dMdm, dSdm, dCdm, dMds, ... |
github | UCL-SML/pilco-matlab-master | conlin.m | .m | pilco-matlab-master/control/conlin.m | 3,409 | utf_8 | 3c0856129195fa4c0ec010e933b345f9 | %% conlin.m
% *Summary:* Affine controller $u = Wx + b$ with input dimension D and
% control dimension E.
% Compute mean and covariance of the control distribution $p(u)$ from a
% Gaussian distributed input $x\sim\mathcal N(x|m,s)$.
% Moreover, the $s^{-1}cov(x,u)$ is computed.
%
%
% function [M, S, V, dMdm, dSdm, dV... |
github | UCL-SML/pilco-matlab-master | propagated.m | .m | pilco-matlab-master/base/propagated.m | 6,815 | utf_8 | e9669f64759cea3c9fcef4e9f7e6ded3 | %% propagated.m
% *Summary:* Propagate the state distribution one time step forward
% with derivatives
%
% function [Mnext, Snext, dMdm, dSdm, dMds, dSds, dMdp, dSdp] = ...
% propagated(m, s, plant, dynmodel, policy)
%
% *Input arguments:*
%
% m mean of the state distribution at time t ... |
github | UCL-SML/pilco-matlab-master | predcost.m | .m | pilco-matlab-master/base/predcost.m | 1,226 | utf_8 | 33b93e42bfaaba81a29a0106348021c5 | %% predcost.m
% *Summary:* Compute trajectory of expected costs for a given set of
% state distributions
%
% inputs:
% m0 mean of states, D-by-1 or D-by-K for multiple means
% S covariance matrix of state distributions
% dynmodel (struct) for dynamics model (GP)
% plant (struct) of system p... |
github | UCL-SML/pilco-matlab-master | propagate.m | .m | pilco-matlab-master/base/propagate.m | 3,751 | utf_8 | 981110574e60d83f95424a9b9489c04d | %% propagate.m
% *Summary:* Propagate the state distribution one time step forward.
%
% [Mnext, Snext] = propagate(m, s, plant, dynmodel, policy)
%
% *Input arguments:*
%
% m mean of the state distribution at time t [D x 1]
% s covariance of the state distribution at time ... |
github | UCL-SML/pilco-matlab-master | calcCost.m | .m | pilco-matlab-master/base/calcCost.m | 1,329 | utf_8 | 3f9d3c6f6845a8b673f95345f81c56b9 | %% calcCost.m
% *Summary:* Function to calculate the incurred cost and its standard deviation,
% given a sequence of predicted state distributions and the cost struct
%
% [L sL] = calcCost(cost, M, S)
%
% *Input arguments:*
%
% cost cost structure
% M mean vectors of state... |
github | UCL-SML/pilco-matlab-master | simulate.m | .m | pilco-matlab-master/base/simulate.m | 3,877 | utf_8 | 2dfb96f96725ecbb27b0ec86d2aedc63 | %% simulate.m
% *Summary:* Simulate dynamics using a given control scheme.
%
% function next = simulate(x0, f, plant)
%
% *Input arguments:*
%
% x0 start state (with additional control states if required)
% f the control setpoint for this time step
% plant plant structure
% .dt time discret... |
github | UCL-SML/pilco-matlab-master | pred.m | .m | pilco-matlab-master/base/pred.m | 1,166 | utf_8 | 542ef9cd484a7b32fbcd49e6771eae0e | %% pred.m
% *Summary:* Compute predictive (marginal) distributions of a trajecory
%
% [M S] = pred(policy, plant, dynmodel, m, s, H)
%
% *Input arguments:*
%
% policy policy structure
% plant plant structure
% dynmodel dynamics model structure
% m D-by-1 mea... |
github | UCL-SML/pilco-matlab-master | value.m | .m | pilco-matlab-master/base/value.m | 2,645 | utf_8 | bce8a1d93f7a603513a4b3820ac4e5a7 | %% value.m
% *Summary:* Compute expected (discounted) cumulative cost for a given (set of) initial
% state distributions
%
% function [J, dJdp] = value(p, m0, S0, dynmodel, policy, plant, cost, H)
%
% *Input arguments:*
%
% p policy parameters chosen by minimize
% policy policy structure
% ... |
github | UCL-SML/pilco-matlab-master | rollout.m | .m | pilco-matlab-master/base/rollout.m | 4,299 | utf_8 | b1d2fdc6eb88a4e15beaa61e61a33041 | %% rollout.m
% *Summary:* Generate a state trajectory using an ODE solver (and any additional
% dynamics) from a particular initial state by applying either a particular
% policy or random actions.
%
% function [x y L latent] = rollout(start, policy, H, plant, cost)
%
% *Input arguments:*
%
% start vecto... |
github | UCL-SML/pilco-matlab-master | valueT.m | .m | pilco-matlab-master/test/valueT.m | 1,378 | utf_8 | 3adc202d8e07edc1ea870c160bfe7451 | %% valueT.m
% *Summary:* Test derivatives of the propagate function, which computes the
% mean and the variance of the successor state distribution, assuming that the
% current state is Gaussian distributed with mean m and covariance matrix
% s.
%
% [d dy dh] = valueT(p, delta, m, s, dynmodel, policy, plant, cost, H... |
github | UCL-SML/pilco-matlab-master | lossT.m | .m | pilco-matlab-master/test/lossT.m | 4,104 | utf_8 | 1d3ad31b497f7aa08f0bfb60ed4af10b | %% lossT.m
% *Summary:* Test derivatives of cost functions. It is assumed that
% the cost function computes (at least) the mean and the variance of the
% cost for a Gaussian distributed input $x\sim\mathcal N(m,s)$
%
%
% function [dd dy dh] = lossT(deriv, policy, m, s, delta)
%
%
% *Input arguments:*
%
% deriv d... |
github | UCL-SML/pilco-matlab-master | conT.m | .m | pilco-matlab-master/test/conT.m | 6,499 | utf_8 | ed7e152cffd95ed992cd6686223561e1 | %% conT.m
% *Summary:* Test derivatives of controller functions. It is assumed that
% the controller function computes the mean and the variance of the
% control signal for a Gaussian distributed input $x\sim\mathcal N(m,s)$
%
%
% function [dd dy dh] = conT(deriv, policy, m, s, delta)
%
%
% *Input arguments:*
%
% d... |
github | UCL-SML/pilco-matlab-master | gTrigT.m | .m | pilco-matlab-master/test/gTrigT.m | 4,317 | utf_8 | 903d6b160be13bf92b4b1a5231164f10 | %% gTrigT.m
% *Summary:* Test the gTrig function, which computes (at least) the mean and
% the variance of the transformed variable for a Gaussian distributed input
% $x\sim\mathcal N(m,v)$. Check the outputs using Monte Carlo, and the
% derivatives using finite differences.
%
%
% function gTrigT(m, v, i, e)
%
%
%... |
github | UCL-SML/pilco-matlab-master | checkgrad.m | .m | pilco-matlab-master/test/checkgrad.m | 2,523 | utf_8 | 2f83ec025d217dd2aa54a4df46e074c0 | %% checkgrad.m
% *Summary:* checkgrad checks the derivatives in a function, by comparing them
% to finite differences approximations. The partial derivatives and the
% approximation are printed and the norm of the difference divided by the
% norm of the sum is returned as an indication of accuracy.
%
% function [... |
github | UCL-SML/pilco-matlab-master | propagateT.m | .m | pilco-matlab-master/test/propagateT.m | 6,891 | utf_8 | d716216e47aca62720d858232988f61b | %% propagateT.m
% *Summary:* Test derivatives of the propagate function, which computes the
% mean and the variance of the successor state distribution, assuming that the
% current state is Gaussian distributed with mean m and covariance matrix
% s.
%
% [dd dy dh] = propagateT(deriv, plant, dynmodel, policy, m, s, d... |
github | UCL-SML/pilco-matlab-master | gSinSatT.m | .m | pilco-matlab-master/test/gSinSatT.m | 4,434 | utf_8 | 1aaa7fdf4505735d6c5f4dc9e5936884 | %% gSinSatT.m
% *Summary:* Test the gSin and gSat functions.
% Check the predictions using Monte Carlo and the derivatives by
% finite differences.
%
%
% function gSinSatT(fcn, m, v, i, e)
%
%
% *Input arguments:*
%
% fcn 'gSin' or 'gSat'
% m mean of input distribution [D x... |
github | UCL-SML/pilco-matlab-master | gpT.m | .m | pilco-matlab-master/test/gpT.m | 6,610 | utf_8 | f6bfd962ef044e7a89f5209e24aa9617 | %% gpT.m
% *Summary:* Test derivatives of gp*-family of functions. It is assumed that
% the gp* function computes the mean and the variance of a GP prediction
% for a Gaussian distributed input $x\sim\mathcal N(m,s)$.
% The GP-family of functions is located in <rootDir>/gp and is called gp*.m
%
%
% function [dd dy dh... |
github | iuriivoitenko/simpleMANET-master | ini2struct.m | .m | simpleMANET-master/functions/ini2struct.m | 3,371 | utf_8 | 3ca01220c3135789fd6a8393f0dd9233 | % Copyright (c) 2014, freeb
% Copyright (c) 2008, Andriy Nych
% Copyright (c) 2009-2010, Evgeny Prilepin aka Iroln
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are
% met:
%
% * Redistributions of ... |
github | DeepCognition/Segmentation-Demo-master | classification_demo.m | .m | Segmentation-Demo-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 | DeepCognition/Segmentation-Demo-master | MyVOCevalseg.m | .m | Segmentation-Demo-master/matlab/my_script/MyVOCevalseg.m | 4,625 | utf_8 | 128c24319d520c2576168d1cf17e068f | %VOCEVALSEG Evaluates a set of segmentation results.
% VOCEVALSEG(VOCopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = VOCEV... |
github | DeepCognition/Segmentation-Demo-master | MyVOCevalsegBoundary.m | .m | Segmentation-Demo-master/matlab/my_script/MyVOCevalsegBoundary.m | 4,415 | utf_8 | 1b648714e61bafba7c08a8ce5824b105 | %VOCEVALSEG Evaluates a set of segmentation results.
% VOCEVALSEG(VOCopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = VOCEV... |
github | wangsuyuan/Imageprocesingtoobox-master | LocalWeightedMeanTransformation2D.m | .m | Imageprocesingtoobox-master/+geotrans/LocalWeightedMeanTransformation2D.m | 9,665 | utf_8 | fb03604c551b06668ad51f81b45db77c | %images.geotrans.LocalWeightedMeanTransformation2D 2-D Local Weighted Mean Geometric Transformation
%
% An images.geotrans.LocalWeightedMeanTransformation2D object encapsulates a 2-D local weighted mean geometric transformation.
%
% images.geotrans.LocalWeightedMeanTransformation2D properties:
% Dimensionality... |
github | wangsuyuan/Imageprocesingtoobox-master | PiecewiseLinearTransformation2D.m | .m | Imageprocesingtoobox-master/+geotrans/PiecewiseLinearTransformation2D.m | 12,449 | utf_8 | d20232ab7aabeb3674c5ce7ca9145ace | %images.geotrans.PiecewiseLinearTransformation2D 2-D Piecewise Linear Geometric Transformation
%
% An images.geotrans.PiecewiseLinearTransformation2D object encapsulates a 2-D piecewise linear geometric transformation.
%
% images.geotrans.PiecewiseLinearTransformation2D properties:
% Dimensionality - Dimensio... |
github | wangsuyuan/Imageprocesingtoobox-master | iradon.m | .m | Imageprocesingtoobox-master/@gpuArray/iradon.m | 12,101 | utf_8 | 8bb1ec9847c30edc4795f7a6f6060c30 | function [img,H] = iradon(varargin)
%IRADON Inverse Radon transform.
% I = iradon(R,THETA) reconstructs the image I from projection data in
% the 2-D gpuArray R. The columns of R are parallel beam projection
% data. IRADON assumes that the center of rotation is the center point of
% the projections, which is d... |
github | wangsuyuan/Imageprocesingtoobox-master | corr2.m | .m | Imageprocesingtoobox-master/@gpuArray/corr2.m | 2,249 | utf_8 | 697b0fa98d14ca82f9891dd9fee48123 | function r = corr2(varargin)
%CORR2 2-D correlation coefficient.
% R = CORR2(A,B) computes the correlation coefficient between A
% and B, where A and B are 2-D gpuArrays of the same size.
%
% Class Support
% -------------
% A and B must be real, 2-D gpuArrays. If either one of A or B is not a
% gpuArray, it... |
github | wangsuyuan/Imageprocesingtoobox-master | padarray.m | .m | Imageprocesingtoobox-master/@gpuArray/padarray.m | 3,889 | utf_8 | 81f12ae408cd7788076ab6e2de0efe89 | function b = padarray(varargin)
%PADARRAY Pad array.
% B = PADARRAY(A,PADSIZE) pads gpuArray A with PADSIZE(k) number of zeros
% along the k-th dimension of A. PADSIZE should be a vector of
% nonnegative integers.
%
% B = PADARRAY(A,PADSIZE,PADVAL) pads gpuArray A with PADVAL (a scalar)
% instead of with zer... |
github | wangsuyuan/Imageprocesingtoobox-master | rgb2gray.m | .m | Imageprocesingtoobox-master/@gpuArray/rgb2gray.m | 2,907 | utf_8 | 2bb015f8740348907dd5af4650c9ca6c | function I = rgb2gray(X)
%RGB2GRAY Convert RGB gpuArray image or colormap to grayscale.
% RGB2GRAY converts RGB gpuArray images to grayscale by eliminating the
% hue and saturation information while retaining the
% luminance.
%
% I = RGB2GRAY(RGB) converts the truecolor gpuArray image RGB to the
% grayscale i... |
github | wangsuyuan/Imageprocesingtoobox-master | imgradientxy.m | .m | Imageprocesingtoobox-master/@gpuArray/imgradientxy.m | 7,437 | utf_8 | 1879c6d1684e8f81b5079775dcb44db8 | function [Gx, Gy] = imgradientxy(varargin)
%IMGRADIENTXY Find the directional gradients of an image.
% [Gx, Gy] = IMGRADIENTXY(I) takes a grayscale or binary gpuArray image I
% as input and returns the gradient along the X axis, Gx, and the Y axis,
% Gy. X axis points in the direction of increasing column subscri... |
github | wangsuyuan/Imageprocesingtoobox-master | imrotate.m | .m | Imageprocesingtoobox-master/@gpuArray/imrotate.m | 8,201 | utf_8 | aa5b7a3a9d5251ee694943abefe820f7 | function varargout = imrotate(varargin)
%IMROTATE Rotate image.
% B = IMROTATE(A, ANGLE) rotates the image in gpuArray A by ANGLE degrees
% in a counterclockwise direction around its center point. To rotate the
% image clockwise, specify a negative value for ANGLE. IMROTATE makes the
% output gpuArray B large e... |
github | wangsuyuan/Imageprocesingtoobox-master | imnoise.m | .m | Imageprocesingtoobox-master/@gpuArray/imnoise.m | 11,566 | utf_8 | 5c907a664971c69a29354a8539437f37 | function b = imnoise(varargin)
%IMNOISE Add noise to gpuArray image.
% J = IMNOISE(I,TYPE,...) Add noise of a given TYPE to the gpuArray
% intensity image I. TYPE is a string that can have one of these values:
%
% 'gaussian' Gaussian white noise with constant
% mean and variance
%... |
github | wangsuyuan/Imageprocesingtoobox-master | normxcorr2.m | .m | Imageprocesingtoobox-master/@gpuArray/normxcorr2.m | 7,447 | utf_8 | 83737fbfe2ae6f1fbd16a1bf0a0c4392 | function C = normxcorr2(varargin)
%NORMXCORR2 Normalized two-dimensional cross-correlation.
% C = NORMXCORR2(TEMPLATE,A) computes the normalized cross-correlation of
% gpuArray TEMPLATE and A. The gpuArray A must be larger than the
% gpuArray TEMPLATE for the normalization to be meaningful. The values of
% TEMP... |
github | wangsuyuan/Imageprocesingtoobox-master | im2uint8.m | .m | Imageprocesingtoobox-master/@gpuArray/im2uint8.m | 4,843 | utf_8 | e110587eea6c78c6845e415550224b50 | function u = im2uint8(img, varargin)
%IM2UINT8 Convert gpuArray image to 8-bit unsigned integers.
% IM2UINT8 takes a gpuArray image as input, and returns a gpuArray image
% of underying class uint8. If the input gpuArray is of class uint8, the
% output gpuArray is identical to it. If the input gpuArray is not
%... |
github | wangsuyuan/Imageprocesingtoobox-master | im2int16.m | .m | Imageprocesingtoobox-master/@gpuArray/im2int16.m | 4,101 | utf_8 | b82817abcde17bfebd5fe10f09ce4a93 | function J = im2int16(I)
%IM2INT16 Convert gpuArray image to 16-bit signed integers.
% IM2INT16 takes a gpuArray image I as input, and returns a gpuArray
% image J of underlying class int16. If I is int16, then J is identical
% to it. If I is not int16 then IM2INT16 returns the equivalent gpuArray
% image J of... |
github | wangsuyuan/Imageprocesingtoobox-master | imabsdiff.m | .m | Imageprocesingtoobox-master/@gpuArray/imabsdiff.m | 3,734 | utf_8 | c96891b3d506862c1b4e86aaf541d921 | function Z = imabsdiff(varargin)
%IMABSDIFF Absolute difference of two images.
% Z = IMABSDIFF(X,Y) subtracts each element in gpuArray Y from the
% corresponding element in gpuArray X and returns the absolute difference
% in the corresponding element of the output array Z. X and Y are real,
% nonsparse, numeri... |
github | wangsuyuan/Imageprocesingtoobox-master | imlincomb.m | .m | Imageprocesingtoobox-master/@gpuArray/imlincomb.m | 6,507 | utf_8 | 6adf6a3d5cb2c9c40c860bffc30868b8 | function Z = imlincomb(varargin)
%IMLINCOMB Linear combination of images.
% Z = IMLINCOMB(K1,A1,K2,A2, ..., Kn,An) computes K1*A1 + K2*A2 + ... +
% Kn*An. A1, A2, ..., An are gpuArray's with the same class and size,
% and K1, K2, ..., Kn are real double scalars. Z has the same size and
% class as A1 unless A1... |
github | wangsuyuan/Imageprocesingtoobox-master | imfilter.m | .m | Imageprocesingtoobox-master/@gpuArray/imfilter.m | 9,498 | utf_8 | f661bcffae73194f86e2efb63c4ec74c | function b = imfilter(varargin)
%IMFILTER N-D filtering of multidimensional images.
% B = IMFILTER(A,H) filters the multidimensional array A with the
% filter H. A can be logical or it can be a nonsparse numeric
% array of any class and dimension. The result, B, has the same
% size and class as A. When A is ... |
github | wangsuyuan/Imageprocesingtoobox-master | bwmorph.m | .m | Imageprocesingtoobox-master/@gpuArray/bwmorph.m | 16,608 | utf_8 | 096f4eb02067e14b48c5853808f0bfa3 | function bwout = bwmorph(bwin,opStr,n)
%BWMORPH Morphological operations on binary image.
% BW2 = BWMORPH(BW1,OPERATION) applies a specific
% morphological operation to the binary gpuArray image BW1.
%
% BW2 = BWMORPH(BW1,OPERATION,N) applies the operation N
% times. N can be Inf, in which case the operation i... |
github | wangsuyuan/Imageprocesingtoobox-master | imgradient.m | .m | Imageprocesingtoobox-master/@gpuArray/imgradient.m | 6,769 | utf_8 | d569bf9873ebf2e6d435c7558ab2ae1c | function [Gmag, Gdir] = imgradient(varargin)
%IMGRADIENT Find the gradient magnitude and direction of an image.
% [Gmag, Gdir] = IMGRADIENT(I) takes a grayscale or binary gpuArray image
% I as input and returns the gradient magnitude, Gmag, and the gradient
% direction, Gdir as gpuArray's. Gmag and Gdir are the s... |
github | wangsuyuan/Imageprocesingtoobox-master | histeq.m | .m | Imageprocesingtoobox-master/@gpuArray/histeq.m | 7,854 | utf_8 | ae69fd5e7b0c95431671b9653bb91967 | function [out,T] = histeq(varargin)
%HISTEQ Enhance contrast using histogram equalization.
% HISTEQ enhances the contrast of images by transforming the values in an
% intensity image so that the histogram of the output image approximately
% matches a specified histogram.
%
% J = HISTEQ(I,HGRAM) transforms the g... |
github | wangsuyuan/Imageprocesingtoobox-master | stretchlim.m | .m | Imageprocesingtoobox-master/@gpuArray/stretchlim.m | 3,803 | utf_8 | 092c68e2e1f03b7f2edd0bef10399c08 | function lowhigh = stretchlim(varargin)
%STRETCHLIM Find limits to contrast stretch a gpuArray image.
% LOW_HIGH = STRETCHLIM(I,TOL) returns a pair of gray values that can be
% used by IMADJUST to increase the contrast of a gpuArray image.
%
% TOL = [LOW_FRACT HIGH_FRACT] specifies the fraction of gpuArray image ... |
github | wangsuyuan/Imageprocesingtoobox-master | imfill.m | .m | Imageprocesingtoobox-master/@gpuArray/imfill.m | 9,187 | utf_8 | 63688d92d55590f72ada05025b677cb1 | function [I2,locations] = imfill(varargin)
%IMFILL Fill image regions and holes.
% BW2 = IMFILL(BW1,LOCATIONS) performs a flood-fill operation on
% background pixels of the 2-D input binary gpuArray image BW1, starting
% from the points specified in LOCATIONS. LOCATIONS can be a P-by-1
% vector, in which case ... |
github | wangsuyuan/Imageprocesingtoobox-master | im2uint16.m | .m | Imageprocesingtoobox-master/@gpuArray/im2uint16.m | 4,853 | utf_8 | 2c8a1a3f84d6d73ff8553d05927578d8 | function u = im2uint16(img, varargin)
%IM2UINT16 Convert gpuArray image to 16-bit unsigned integers.
% IM2UINT16 takes a gpuArray image as input, and returns a gpuArray image
% of underlying class uint16. If the input gpuArray image is of class
% uint16, the output gpuArray is identical to it. If the input gpuArr... |
github | wangsuyuan/Imageprocesingtoobox-master | std2.m | .m | Imageprocesingtoobox-master/@gpuArray/std2.m | 1,043 | utf_8 | 13e5093bb1ad70d0e551119e820649a5 | function s = std2(a)
%STD2 Standard deviation of matrix elements.
% B = STD2(A) computes the standard deviation of the values in
% gpuArray A.
%
% Class Support
% -------------
% A can be a numeric or logical gpuArray. B is a scalar double gpuArray.
%
% Example
% -------
% I = gpuArray(imread('lifti... |
github | wangsuyuan/Imageprocesingtoobox-master | edge.m | .m | Imageprocesingtoobox-master/@gpuArray/edge.m | 13,182 | utf_8 | a4f38fd1dac4f529479a1b113102769a | function [eout,thresh,gv_45,gh_135] = edge(varargin)
%EDGE Find edges in intensity image.
% EDGE takes an intensity or a binary gpuArray image I as its input, and
% returns a binary gpuArray image BW of the same size as I, with 1's
% where the function finds edges in I and 0's elsewhere.
%
% EDGE supports five ... |
github | wangsuyuan/Imageprocesingtoobox-master | morphopAlgo.m | .m | Imageprocesingtoobox-master/@gpuArray/private/morphopAlgo.m | 5,840 | utf_8 | d007ed841fa1001d56781cac5ae81054 | function B = morphopAlgo(A,se,padfull,unpad,op_type)
%MORPHOPALGO Algorithmic core for gpuArray image dilation/erosion. Intended
%for use with morphopInputParser in functions like IMDILATE, IMERODE,
%IMOPEN, IMCLOSE, IMTOPHAT and IMBOTHAT.
% Copyright 2013 The MathWorks, Inc.
num_strels = length(se);
ndims_A = ndi... |
github | wangsuyuan/Imageprocesingtoobox-master | morphopInputParser.m | .m | Imageprocesingtoobox-master/@gpuArray/private/morphopInputParser.m | 3,065 | utf_8 | 7bea034bd4ddfe28d541ef78b5578b38 | function [A,se,padfull,unpad,op_type] = morphopInputParser(A,se,op_type,func_name,varargin)
%MORPHOPINPUTPARSER Parse and validate inputs to morphology family of
%functions and determine padding requirements. Intended for use with
%morphopAlgo in functions IMDILATE, IMERODE, IMOPEN, IMCLOSE, IMTOPHAT and
%IMBOTHAT.
% ... |
github | wangsuyuan/Imageprocesingtoobox-master | iptcheckmap.m | .m | Imageprocesingtoobox-master/@gpuArray/private/iptcheckmap.m | 1,759 | utf_8 | 3366775bec56f6d92f2168538604b0b1 | function iptcheckmap(map, function_name, variable_name, argument_position)
%IPTCHECKMAP Check validity of colormap.
% IPTCHECKMAP(MAP,FUNC_NAME,VAR_NAME,ARG_POS) checks to see if
% MAP is a valid MATLAB colormap and issues a formatted error
% message if it is invalid.
%
% FUNC_NAME is a string that specifies t... |
github | wangsuyuan/Imageprocesingtoobox-master | intlut.m | .m | Imageprocesingtoobox-master/@gpuArray/private/intlut.m | 2,267 | utf_8 | 42bf30d9a236bfee8fa4717e1f2be9a7 | function B = intlut(varargin)
%INTLUT Convert integer values using lookup table.
% B = INTLUT(A,LUT) converts values in array A based on lookup table
% LUT and returns these new values in array B.
%
% For example, if A is a uint8 vector whose kth element is equal
% to alpha, then B(k) is equal to the LUT value ... |
github | wangsuyuan/Imageprocesingtoobox-master | padarray_algo.m | .m | Imageprocesingtoobox-master/@gpuArray/private/padarray_algo.m | 1,870 | utf_8 | f4489e1c4fa1070e1f9750171d3f64bf | function b = padarray_algo(a, padSize, method, padVal, direction)
%PADARRAY_ALGO Pad array.
% B = PADARRAY_AGLO(A,PADSIZE,METHOD,PADVAL,DIRECTION) internal helper
% function for PADARRAY, which performs no input validation. See the
% help for PADARRAY for the description of input arguments, class
% support, an... |
github | wangsuyuan/Imageprocesingtoobox-master | getPaddingIndices.m | .m | Imageprocesingtoobox-master/@gpuArray/private/getPaddingIndices.m | 2,951 | utf_8 | 5d150a985dc5ac9e143c045184767e14 | function aIdx = getPaddingIndices(aSize,padSize,method,direction)
%getPaddingIndices is used by padarray and blockproc.
% Computes padding indices of input image. This is function is used to
% handle padding of in-memory images (via padarray) as well as
% arbitrarily large images (via blockproc).
%
% aSize : ... |
github | wangsuyuan/Imageprocesingtoobox-master | conformalShowCircles.m | .m | Imageprocesingtoobox-master/imdemos/conformalShowCircles.m | 1,286 | utf_8 | 1e66322d244ab24885284ee330cab774 | function conformalShowCircles(axIn, axOut, t1, t2)
% conformalShowCircles Plot packed circles before/after transformation.
%
% Supports conformal transformation example, ConformalMappingImageExample.m
% ("Exploring a Conformal Mapping").
% Copyright 2005-2013 The MathWorks, Inc.
sep = 0.002; % Separation between c... |
github | wangsuyuan/Imageprocesingtoobox-master | conformalShowLines.m | .m | Imageprocesingtoobox-master/imdemos/conformalShowLines.m | 1,812 | utf_8 | 5d8b9afc8b7fae13318411bd1a2022cd | function conformalShowLines(axIn, axOut, t1, t2)
% conformalShowLines Plot a grid of lines before/after transformation.
%
% Supports conformal transformation example, ConformalMappingImageExample.m
% ("Exploring a Conformal Mapping").
% Copyright 2005-2013 The MathWorks, Inc.
d = 1/16;
u1 = [-5/4 : d : -d, -1e-6];
u... |
github | wangsuyuan/Imageprocesingtoobox-master | rgb2ycbcr.m | .m | Imageprocesingtoobox-master/colorspaces/rgb2ycbcr.m | 4,738 | utf_8 | 854b189ad37b73ba4dcf25e79dd82052 | function ycbcr = rgb2ycbcr(varargin)
%RGB2YCBCR Convert RGB color values to YCbCr color space.
% YCBCRMAP = RGB2YCBCR(MAP) converts the RGB values in MAP to the YCBCR
% color space. MAP must be a M-by-3 array. YCBCRMAP is a M-by-3 matrix
% that contains the YCBCR luminance (Y) and chrominance (Cb and Cr) color
% ... |
github | wangsuyuan/Imageprocesingtoobox-master | iccwrite.m | .m | Imageprocesingtoobox-master/colorspaces/iccwrite.m | 50,643 | utf_8 | 6bb53c9105cc85db41e22336563abc3e | function p_new = iccwrite(p, filename)
%ICCWRITE Write ICC color profile.
% P_NEW = ICCWRITE(P, FILENAME) writes the International Color Consortium
% (ICC) color profile data from the profile structure specified by P to
% the file specified by FILENAME.
%
% P is a structure representing an ICC profile in the da... |
github | wangsuyuan/Imageprocesingtoobox-master | ntsc2rgb.m | .m | Imageprocesingtoobox-master/colorspaces/ntsc2rgb.m | 2,732 | utf_8 | 54f4fadb9dc61419dff96920bfaa7a8f | function varargout = ntsc2rgb(varargin)
%NTSC2RGB Convert NTSC color values to RGB color space.
% RGBMAP = NTSC2RGB(YIQMAP) converts the M-by-3 NTSC
% (television) values in the colormap YIQMAP to RGB color
% space. If YIQMAP is M-by-3 and contains the NTSC luminance
% (Y) and chrominance (I and Q) color compon... |
github | wangsuyuan/Imageprocesingtoobox-master | makecform.m | .m | Imageprocesingtoobox-master/colorspaces/makecform.m | 40,214 | utf_8 | cc693b52b96528419278ee0160c717a7 | function c = makecform(varargin)
%MAKECFORM Create a color transformation structure.
% C = MAKECFORM(TYPE) creates the color transformation structure, C,
% that defines the color space conversion specified by TYPE. To
% perform the transformation, pass the color transformation structure
% as an argument to the... |
github | wangsuyuan/Imageprocesingtoobox-master | rgb2ntsc.m | .m | Imageprocesingtoobox-master/colorspaces/rgb2ntsc.m | 2,155 | utf_8 | c1817a124ab55d7f48380db742b39eda | function varargout = rgb2ntsc(varargin)
%RGB2NTSC Convert RGB color values to NTSC color space.
% YIQMAP = RGB2NTSC(RGBMAP) converts the M-by-3 RGB values in RGBMAP to NTSC
% colorspace. YIQMAP is an M-by-3 matrix that contains the NTSC luminance
% (Y) and chrominance (I and Q) color components as columns that ar... |
github | wangsuyuan/Imageprocesingtoobox-master | iccfind.m | .m | Imageprocesingtoobox-master/colorspaces/iccfind.m | 2,347 | utf_8 | c8d16b35ecb58d37c771a8ad23299421 | function [profiles, descriptions] = iccfind(directory, pattern)
%ICCFIND Search for ICC profiles by description.
% [PROFILES, DESCRIPTIONS] = ICCFIND(DIRECTORY, PATTERN) searches for all
% of the ICC profiles in the specified DIRECTORY with a given PATTERN in
% their Description fields. PROFILES is a cell arra... |
github | wangsuyuan/Imageprocesingtoobox-master | ycbcr2rgb.m | .m | Imageprocesingtoobox-master/colorspaces/ycbcr2rgb.m | 4,415 | utf_8 | bd91bb3c97671f4fc9756eca73a1f0c9 | function rgb = ycbcr2rgb(varargin)
%YCBCR2RGB Convert YCbCr color values to RGB color space.
% RGBMAP = YCBCR2RGB(YCBCRMAP) converts the YCbCr values in the colormap
% YCBCRMAP to the RGB color space. If YCBCRMAP is M-by-3 and contains the
% YCbCr luminance (Y) and chrominance (Cb and Cr) color values as columns,... |
github | wangsuyuan/Imageprocesingtoobox-master | applycform.m | .m | Imageprocesingtoobox-master/colorspaces/applycform.m | 5,745 | utf_8 | 58e0ec6842bc76e39002893354a82217 | function out = applycform(in,c)
%APPLYCFORM Apply device-independent color space transformation.
% B = APPLYCFORM(A, C) converts the color values in A to the color space
% specified in the color transformation structure, C. The color
% transformation structure specifies various parameters of the
% transformati... |
github | wangsuyuan/Imageprocesingtoobox-master | iccread.m | .m | Imageprocesingtoobox-master/colorspaces/iccread.m | 54,080 | utf_8 | f0e7a81484e12539e0c6849c6ab93e62 | function s = iccread(filename)
%ICCREAD Read ICC color profile.
% P = ICCREAD(FILENAME) reads the International Color Consortium (ICC)
% color profile data from the file specified by FILENAME. The file can
% be either an ICC profile file or a TIFF file containing an embedded
% ICC profile. ICCREAD returns the... |
github | wangsuyuan/Imageprocesingtoobox-master | rgb2ycbcr.m | .m | Imageprocesingtoobox-master/colorspaces/@gpuArray/rgb2ycbcr.m | 5,800 | utf_8 | ed436581c4079fe3ce18c0bea32f2ec2 | function ycbcr = rgb2ycbcr(varargin)
%RGB2YCBCR Convert RGB color values to YCbCr color space.
% YCBCRMAP = RGB2YCBCR(MAP) converts the RGB values in MAP to the YCBCR
% color space. MAP must be a M-by-3 gpuArray. YCBCRMAP is a M-by-3
% gpuArray that contains the YCBCR luminance (Y) and chrominance
% (Cb and Cr)... |
github | wangsuyuan/Imageprocesingtoobox-master | ycbcr2rgb.m | .m | Imageprocesingtoobox-master/colorspaces/@gpuArray/ycbcr2rgb.m | 6,012 | utf_8 | 69a2cde70d14e4eda4fe22a4929b40e4 | function rgb = ycbcr2rgb(varargin)
%YCBCR2RGB Convert YCbCr color values to RGB color space.
% RGBMAP = YCBCR2RGB(YCBCRMAP) converts the YCbCr values in the colormap
% YCBCRMAP to the RGB color space. If YCBCRMAP is an M-by-3 gpuArray and
% contains the YCbCr luminance (Y) and chrominance (Cb and Cr) color
% va... |
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