sample_discrete函数matlab

在 Matlab 中，可以使用 `randsample` 函数来实现从离散分布中抽样的功能。该函数的语法如下：

s = randsample(values, n, replace, weights)

其中，`values` 是一个向量，表示离散分布的取值；`n` 表示要抽取的样本数量；`replace` 是一个布尔值，表示是否允许重复抽样；`weights` 是一个向量，表示每个取值对应的权重。如果不指定 `weights`，则默认每个取值的权重都为 1。

例如，假设我们有一个离散分布，包含三个取值 {1, 2, 3}，对应的概率分别为 {0.3, 0.5, 0.2}。那么可以使用以下代码进行抽样：

values = [1, 2, 3];
weights = [0.3, 0.5, 0.2];
n = 10;
s = randsample(values, n, true, weights);

上述代码中，`true` 表示允许重复抽样，`s` 将是一个长度为 10 的向量，表示抽取的 10 个样本。可以通过 `histcounts` 函数来统计样本的分布情况：

histcounts(s, [1, 2, 3, 4])

该函数返回一个向量，表示每个取值的出现次数。例如，上述代码的输出可能为：

ans =
     3     5     2

表示样本中有 3 个 1，5 个 2，2 个 3。

相关问题

matlab spline函数用法

### Matlab Spline Function Usage

In MATLAB, the `spline` function provides an easy way to perform cubic spline interpolation. This method fits a piecewise cubic polynomial between each pair of data points while ensuring that the resulting curve is smooth at these points.

#### Basic Syntax and Description
The basic syntax for using the `spline` function involves providing vectors or arrays representing the x-coordinates (`x`) and corresponding y-values (`y`). The output can be evaluated at query points specified by another vector (`xi`).

yi = spline(x,y,xi);

Here,
- `x`: A vector specifying the coordinates of known data points.
- `y`: Corresponding values associated with those coordinates.
- `xi`: Points where interpolated values will be computed.
- `yi`: Interpolated values obtained through cubic spline fitting[^1].

#### Example Code Demonstrating Spline Interpolation in MATLAB

Below demonstrates how one might apply this technique within MATLAB:

% Define original sample points
x = linspace(0, 4*pi, 9); % Original sampling locations
y = sin(x);               % Values sampled from sine wave

% Create finer grid for plotting purposes
xfine = linspace(min(x), max(x), 1e3);

% Perform cubic spline interpolation
y_splined = spline(x, y, xfine);

% Plotting both original samples alongside interpolated result
figure;
plot(x, y, 'o', 'MarkerFaceColor', 'r'); hold on;
plot(xfine, y_splined, '-b');
title('Cubic Spline Interpolation Demonstration');
xlabel('X-axis'); ylabel('Y-axis');
legend({'Original Samples','Interpolated Curve'});
grid minor;
hold off;

This script first defines several discrete points along a sinusoidal waveform before applying cubic splines to interpolate new intermediate positions across a denser interval. Finally, it visualizes both sets together for comparison.

--related questions--

1. How does MATLAB handle boundary conditions during cubic spline interpolation?
2. What alternatives exist besides cubic splines for achieving smoother curves in MATLAB?
3. Can users customize knot placement when performing spline interpolations in MATLAB?
4. Is there any performance difference between built-in MATLAB spline functions versus custom implementations?
5. Are there specific applications better suited for linear vs. cubic spline methods in MATLAB?

matlab s函数的模板

function [sys,x0,str,ts] = myfun(t,x,u,flag)

switch flag

case 0 % Initialization
sizes = simsizes; % Get the simulation sizes
sizes.NumContStates = 0; % Number of continuous states
sizes.NumDiscStates = 0; % Number of discrete states
sizes.NumOutputs = 1; % Number of outputs
sizes.NumInputs = 1; % Number of inputs
sizes.DirFeedthrough = 0; % Direct feedthrough flag
sizes.NumSampleTimes = 1; % Number of sample times
sys = simsizes(sizes); % Return simulation sizes

x0 = []; % Initialize the initial states
str = []; % Initialize the state ordering string
ts = [0 0]; % Initialize the sample times

case 1 % Derivatives
sys = [];

case 2 % Discrete states
sys = [];

case 3 % Outputs
sys = u; % Return the output

case 4 % Update
sys = [];

case 9 % Terminate
sys = [];

otherwise
error(['Unhandled flag = ',num2str(flag)]);
end
end