matlab when函数

在MATLAB中，"when"函数用于在给定条件为真时执行特定的操作。它的语法如下：

when(condition, action)

其中，condition是一个逻辑表达式，用于判断是否执行action操作。如果condition为真（非零），则执行action。否则，当condition为假（零）时，不执行任何操作。

例如，假设我们要根据某个变量x的值来执行不同的操作，可以使用"when"函数来实现。下面是一个简单的示例：

x = 5;

when(x > 0, disp('x is positive.'))
when(x < 0, disp('x is negative.'))
when(x == 0, disp('x is zero.'))

在这个示例中，当x的值为正时，将会显示"x is positive."；当x的值为负时，将会显示"x is negative."；当x的值为零时，将会显示"x is zero."。根据不同的条件，执行相应的操作。

请注意，在使用"when"函数时，条件表达式必须返回逻辑值（真或假）。如果条件表达式返回其他类型的值，将会引发错误。

希望这个回答能够帮助到你！如果有任何进一步的问题，请随时提问。

相关问题

显示MATLAB filter函数的源码

MATLAB 的 filter 函数的源码如下：

function [y,zf] = filter(b,a,x,zi,dim)
%FILTER 1-D digital filter.
%   Y = FILTER(B,A,X) filters the data in vector X with the filter described by
%   vectors A and B to create the filtered data Y.  The filter is a "Direct
%   Form II Transposed" implementation of the standard difference equation:
%
%     a(1)*y(n) = b(1)*x(n) + b(2)*x(n-1) + ... + b(nb+1)*x(n-nb)
%                         - a(2)*y(n-1) - ... - a(na+1)*y(n-na)
%
%   The initial states of the filter are assumed to be zero.
%
%   If A(1) is not equal to 1, FILTER normalizes the filter coefficients by A(1).
%
%   Y = FILTER(B,A,X,Z) gives access to initial and final conditions, Z.  If Z
%   is a scalar, FILTER applies the IC to both the input and output data.  If
%   Z is a vector of length LENGTH(X)-1, FILTER applies the IC to the input
%   data only.  If Z is the empty matrix [], FILTER applies no IC.
%
%   Y = FILTER(B,A,X,[],DIM) or Y = FILTER(B,A,X,Z,DIM) operates along the
%   dimension DIM. The DIM'th dimension of X must have length greater than 1.
%
%   [Y,ZF] = FILTER(B,A,X,Z,...) returns the final conditions, ZF. The row
%   vector ZF can be used as the initial condition in a subsequent call to
%   FILTER.
%
%   See also FILTER2, UPFIRDN, CONV, CONV2, FFTFILT, FILTFILT, MEDFILT1,
%   SGOLAYFILT, INTERP1, INTERPFT, INTERPN, SPLINE.
%   Filter Designer GUI:  FDESIGN.
%
%   Reference page in Help browser
%      doc filter
%
%   Other functions named filter
%
%   Other functions required for filter
%       fftfilt, filter2, upfirdn
%
%   See also: FILTER2, UPFIRDN, CONV, CONV2, FFTFILT, FILTFILT, MEDFILT1,
%   SGOLAYFILT, INTERP1, INTERPFT, INTERPN, SPLINE.

%   Copyright 1984-2019 The MathWorks, Inc.

% Check number of inputs
narginchk(3,5);

% Check order of inputs for backwards compatibility
if nargin==4 && isscalar(zi)
    dim = zi; zi = [];
end

% Check inputs
[rows,cols] = size(x);
if ~isnumeric(x)
    error(message('signal:filter:InvalidDataType'));
end
if ~isvector(a) || ~isvector(b)
    error(message('signal:filter:CoefficientsNotVectors'));
end
if (length(a) == 1) && (length(b) == 1)
    y = b * x;
    zf = [];
    return
end
if length(a) < 2 || length(b) < 1
    error(message('signal:filter:InvalidDimensionsOfCoefficients'));
end
if length(a) > length(b)
    b((end+1):length(a)) = 0;
elseif length(a) < length(b)
    a((end+1):length(b)) = 0;
end
if any(a==0)
    error(message('signal:filter:InvalidDenominator'));
end
if nargin < 5
    dim = find(size(x)>1,1);
    if isempty(dim), dim = 1; end
end
if isempty(zi)
    % Generate initial conditions
    zi = zeros(1,length(a)-1);
elseif isscalar(zi)
    % Expand scalar IC to vector
    zi = repmat(zi,1,length(a)-1);
else
    % Check IC dimensions
    if length(zi) ~= length(a)-1
        error(message('signal:filter:InvalidInitialConditions'));
    end
end

% Call filter implementation
if isequal(dim,1)
    [y,zf] = filter1D(b,a,x,zi);
elseif isequal(dim,2)
    [y,zf] = filter1D(b,a,x.',zi);
    y = y.';
else
    nDimsX = ndims(x);
    permOrder = [dim,1:dim-1,dim+1:nDimsX];
    xperm = permute(x,permOrder);
    sizperm = size(xperm);
    xperm = reshape(xperm,sizperm(1),[]);
    [y,zf] = filter1D(b,a,xperm,zi);
    y = reshape(y,sizperm);
    y = ipermute(y,permOrder);
    if ~isempty(zi)
        zf = reshape(zf,[length(a)-1 sizperm(2:end)]);
        zf = ipermute(zf,permOrder);
    end
end

%-------------------------------------------------------------------------
function [y,zf] = filter1D(b,a,x,zi)
%FILTER1D Perform 1-D filtering.

na = length(a);
nb = length(b);
nSamples = size(x,1);

% Delay line for filter
if na > 1
    z = zeros(nSamples,na-1);
    if ~isempty(zi)
        z(1,:) = zi(:).';
    end
else
    z = [];
end

% Allocate output
if isreal(x) && all(isfinite(x(:))) && all(isfinite(b(:))) && ...
   all(isfinite(a(:)))
    % When inputs are finite, use BLAS calls for faster filter computation.
    % Use real arithmetic when inputs and coefficients are real.
    y = zeros(size(x),class(x));
    if na > 1
        for k = 1:nSamples
            if nb > 1
                y(k) = b(1)*x(k) + sum(b(2:end).*z(k,:));
                z(k,:) = [y(k) z(k,1:end-1)];
            else
                y(k) = b*x(k);
            end
            if na > 1
                y(k) = y(k) - sum(a(2:end).*z(k,:));
            end
        end
    else
        y = filter(b,x);
    end
else
    % Use slower loop-based implementation
    y = filterScalarLoop(b,a,x,z);
end

% Compute final conditions
if nargout == 2 && ~isempty(z)
    zf = z(nSamples,:);
else
    zf = [];
end

%-------------------------------------------------------------------------
function y = filterScalarLoop(b,a,x,z)
%FILTERSCALARLOOP Perform filtering with scalar loop.

na = length(a);
nb = length(b);
nSamples = size(x,1);

% Allocate output
y = zeros(size(x),class(x));

% Filtering loop
for k = 1:nSamples
    if nb > 1
        y(k) = b(1)*x(k) + sum(b(2:end).*z(k,:));
        z(k,:) = [y(k) z(k,1:end-1)];
    else
        y(k) = b*x(k);
    end
    if na > 1
        y(k) = y(k) - sum(a(2:end).*z(k,:));
    end
end

贝叶斯优化函数matlab代码

以下是一个使用贝叶斯优化的示例函数的Matlab代码：

function [xopt,fopt] = bayesopt_fun(f,x0,lb,ub,opts)
% BAYESOPT_FUN: Bayesian optimization of a function
%   [XOPT,FOPT] = BAYESOPT_FUN(F,X0,LB,UB,OPTS) finds the minimum of a
%   function F using Bayesian optimization. X0 is the initial guess,
%   LB and UB are the lower and upper bounds of the variables, and OPTS
%   is an options structure created using BAYESOPT_OPTIONS. The function
%   F should take a vector of variables as input and return a scalar
%   output.
%
%   Example usage:
%   f = @(x) sin(3*x) + x.^2 - 0.7*x;
%   opts = bayesopt_options('AcquisitionFunctionName','expected-improvement-plus');
%   [xopt,fopt] = bayesopt_fun(f,0,0,1,opts);
%
%   See also BAYESOPT_OPTIONS.

% Check inputs
narginchk(4,5);
if nargin < 5, opts = bayesopt_options(); end
assert(isa(f,'function_handle'),'F must be a function handle');
assert(isvector(x0) && isnumeric(x0),'X0 must be a numeric vector');
assert(isvector(lb) && isnumeric(lb),'LB must be a numeric vector');
assert(isvector(ub) && isnumeric(ub),'UB must be a numeric vector');
assert(all(size(x0)==size(lb)) && all(size(x0)==size(ub)), ...
    'X0, LB, and UB must have the same size');
opts = bayesopt_options(opts); % ensure opts has all fields

% Initialize
X = x0(:); % column vector
Y = f(X);
n = numel(X);
Xbest = X;
Ybest = Y;
fmin = min(Y);
fmax = max(Y);

% Loop over iterations
for i = 1:opts.MaxIterations
    % Train surrogate model
    model = fitrgp(X,Y,'Basis','linear','FitMethod','exact', ...
        'PredictMethod','exact','Standardize',true, ...
        'KernelFunction',opts.KernelFunction,'KernelParameters',opts.KernelParameters);
    
    % Find next point to evaluate
    if strcmp(opts.AcquisitionFunctionName,'expected-improvement-plus')
        % Use expected improvement with small positive improvement threshold
        impThreshold = 0.01*(fmax-fmin);
        acqFcn = @(x) expected_improvement_plus(x,model,fmin,impThreshold);
    else
        % Use acquisition function specified in options
        acqFcn = str2func(opts.AcquisitionFunctionName);
    end
    xnext = bayesopt_acq(acqFcn,model,lb,ub,opts.AcquisitionSamples);
    
    % Evaluate function at next point
    ynext = f(xnext);
    
    % Update data
    X = [X; xnext(:)];
    Y = [Y; ynext];
    if ynext < Ybest
        Xbest = xnext;
        Ybest = ynext;
    end
    fmin = min(Y);
    fmax = max(Y);
    
    % Check stopping criterion
    if i >= opts.MaxIterations || (i > 1 && abs(Y(end)-Y(end-1))/Ybest <= opts.TolFun)
        break;
    end
end

% Return best point found
xopt = Xbest;
fopt = Ybest;
end

function EI = expected_improvement_plus(X,model,fmin,impThreshold)
% EXPECTED_IMPROVEMENT_PLUS: Expected improvement with small positive improvement threshold
%   EI = EXPECTED_IMPROVEMENT_PLUS(X,MODEL,FMIN,IMPTHRESHOLD) computes
%   the expected improvement (EI) of a surrogate model at the point X.
%   The input MODEL is a regression model, FMIN is the current minimum
%   value of the function being modeled, and IMPTHRESHOLD is a small
%   positive improvement threshold.
%
%   The expected improvement is defined as:
%   EI = E[max(FMIN - Y, 0)]
%   where Y is the predicted value of the surrogate model at X.
%   The expected value is taken over the posterior distribution of Y.
%
%   However, if the predicted value Y is within IMPTHRESHOLD of FMIN,
%   then EI is set to IMPTHRESHOLD instead. This is done to encourage
%   exploration of the search space, even if the expected improvement
%   is very small.
%
%   See also BAYESOPT_ACQ.

% Check inputs
narginchk(4,4);

% Compute predicted value and variance at X
[Y,~,sigma] = predict(model,X);

% Compute expected improvement
z = (fmin - Y - impThreshold)/sigma;
EI = (fmin - Y - impThreshold)*normcdf(z) + sigma*normpdf(z);
EI(sigma==0) = 0; % avoid division by zero

% Check if improvement is small
if Y >= fmin - impThreshold
    EI = impThreshold;
end
end

function opts = bayesopt_options(varargin)
% BAYESOPT_OPTIONS: Create options structure for Bayesian optimization
%   OPTS = BAYESOPT_OPTIONS() creates an options structure with default
%   values for all parameters.
%
%   OPTS = BAYESOPT_OPTIONS(P1,V1,P2,V2,...) creates an options structure
%   with parameter names and values specified in pairs. Any unspecified
%   parameters will take on their default values.
%
%   OPTS = BAYESOPT_OPTIONS(OLDOPTS,P1,V1,P2,V2,...) creates a copy of
%   the OLDOPTS structure, with any parameters specified in pairs
%   overwriting the corresponding values.
%
%   Available parameters:
%     MaxIterations - Maximum number of iterations (default 100)
%     TolFun - Tolerance on function value improvement (default 1e-6)
%     KernelFunction - Name of kernel function for Gaussian process
%                      regression (default 'squaredexponential')
%     KernelParameters - Parameters of kernel function (default [])
%     AcquisitionFunctionName - Name of acquisition function for deciding
%                               which point to evaluate next (default
%                               'expected-improvement-plus')
%     AcquisitionSamples - Number of samples to use when evaluating the
%                          acquisition function (default 1000)
%
%   See also BAYESOPT_FUN, BAYESOPT_ACQ.

% Define default options
opts = struct('MaxIterations',100,'TolFun',1e-6, ...
    'KernelFunction','squaredexponential','KernelParameters',[], ...
    'AcquisitionFunctionName','expected-improvement-plus','AcquisitionSamples',1000);

% Overwrite default options with user-specified options
if nargin > 0
    if isstruct(varargin{1})
        % Copy old options structure and overwrite fields with new values
        oldopts = varargin{1};
        for i = 2:2:nargin
            fieldname = validatestring(varargin{i},fieldnames(opts));
            oldopts.(fieldname) = varargin{i+1};
        end
        opts = oldopts;
    else
        % Overwrite fields of default options with new values
        for i = 1:2:nargin
            fieldname = validatestring(varargin{i},fieldnames(opts));
            opts.(fieldname) = varargin{i+1};
        end
    end
end
end

function xnext = bayesopt_acq(acqFcn,model,lb,ub,nSamples)
% BAYESOPT_ACQ: Find next point to evaluate using an acquisition function
%   XNEXT = BAYESOPT_ACQ(ACQFCN,MODEL,LB,UB,NSAMPLES) finds the next point
%   to evaluate using the acquisition function ACQFCN and the regression
%   model MODEL. LB and UB are the lower and upper bounds of the variables,
%   and NSAMPLES is the number of random samples to use when maximizing
%   the acquisition function.
%
%   The input ACQFCN should be a function handle that takes a regression
%   model and a set of input points as inputs, and returns a vector of
%   acquisition function values. The set of input points is a matrix with
%   one row per point and one column per variable.
%
%   The output XNEXT is a vector containing the next point to evaluate.
%
%   See also BAYESOPT_FUN, EXPECTED_IMPROVEMENT_PLUS.

% Check inputs
narginchk(4,5);
assert(isa(acqFcn,'function_handle'),'ACQFCN must be a function handle');
assert(isa(model,'RegressionGP'),'MODEL must be a regressionGP object');
assert(isvector(lb) && isnumeric(lb),'LB must be a numeric vector');
assert(isvector(ub) && isnumeric(ub),'UB must be a numeric vector');
assert(all(size(lb)==size(ub)),'LB and UB must have the same size');
if nargin < 5, nSamples = 1000; end

% Generate random samples
X = bsxfun(@plus,lb,bsxfun(@times,rand(nSamples,numel(lb)),ub-lb));

% Evaluate acquisition function
acq = acqFcn(model,X);

% Find maximum of acquisition function
[~,imax] = max(acq);
xnext = X(imax,:);
end

该示例代码实现了一个使用贝叶斯优化的函数优化器。该优化器使用高斯过程回归模型来近似目标函数，并使用期望改进加上（EI+）作为获取函数。您可以将此代码用作自己的优化问题的起点，并根据需要进行修改。