mat % 初始化绘图参数 data = [x,y]; % 密度计算 radius = 1.5; % 定义半径 density_2D = density2D_KD(data(:,1:2),radius); % 2D平面密度 % 回归趋势线生成 xq = min(data(:,1)):0.1:max(data(:,1)); p = polyfit(data(:,1),data(:,2),1); x1 = linspace(min(data(:,1)),...
1.数据准备 此部分主要是读取原始数据并初始化绘图参数,然后计算生成回归线参数(本文采用多项式回归)。 %读取数据 load data.mat %初始化绘图参数 data = [x,y]; %密度计算 radius = 1.5; %定义半径 density_2D = density2D_KD(data(:,1:2),radius); % 2D平面密度 %回归趋势线生成 xq = min(data(:...
SCATTERCLOUD(X,Y) creates a scatterplot of X and Y, displayed over a surface representing the smoothed density of the points. The density is determined with a 2D histogram, using 25 equally spaced bins in both directions. SCATTERCLOUD(X,Y,N) uses N equally spaced bins. ...
Making a 2D or 3D Histogram to Visualize Data Density (Originally posted on Doug's MATLAB Video Tutorials blog.) This short video makes a 2D histogram as an alternative to plotting data points and visually estimating where the most data is. Recorded: 22 Jan 2010Related...
2D histogram and/or 2D Kernel Density 1 Answer Get parameters for normal distributions in Kernel fit of probability distribution 1 Answer Visualizing Dense Areas in cartesian plane 1 Answer Categories AI and StatisticsStatistics and Machine Learning ToolboxProbability DistributionsContinuous DistributionsKernel...
MATLAB实现的二维核密度估计。 输入平面样本点,得到概率密度函数。 2D Kernel Density Estimation。 2DKDE2016-01-26 上传大小:4KB 二维非参数核密度估计 基于二维非参数核密度估计,以及带宽的优化计算的代码 上传者:zhanghong2017时间:2017-11-13 核密度估计 ...
functiondensity_2D=density2D_KD(data,radius)% 功能:利用KD树提取离散点2D密度特征% 输入:data - 原始数据(m*3)% 输出:planes - 拟合所得平面参数M=size(data,1);density_2D=zeros(M,1);idx=rangesearch(data(:,1:2),data(:,1:2),radius,'Distance','euclidean','NSMethod','kdtree');fori=1:...
function density_2D = density2D_KD(data,radius) % 功能:利用KD树提取离散点2D密度特征 % 输入:data - 原始数据(m*3) % 输出:planes - 拟合所得平面参数 M = size(data,1); density_2D = zeros(M,1); idx = rangesearch(data(:,1:2),data(:,1:2),radius,'Distance','euclidean','NSMethod'...
Example (Sinusoidal density): clear all X=rand(1000,1); Y=sin(X*10*pi)+randn(size(X))/3; data=[X,Y]; % apply routine [bandwidth,density,X,Y]=kde2d(data); % plot the data and the density estimate surf(X,Y,density,'LineStyle','none'), view([0,70]) colormap hot...
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