Question

我在MATLAB中使用k-means。我正在尝试创建绘图/图形，但我的数据有三维数组。这是我的k-means代码：

clc
clear all
close all
load cobat.txt;  % read the file

k=input('Enter a number: ');        % determine the number of cluster
isRand=0;   % 0 -> sequeantial initialization
            % 1 -> random initialization

[maxRow, maxCol]=size(cobat);
if maxRow<=k, 
    y=[m, 1:maxRow];
elseif k>7
    h=msgbox('cant more than 7');
else
    % initial value of centroid
    if isRand,
        p = randperm(size(cobat,1));      % random initialization
        for i=1:k
            c(i,:)=cobat(p(i),:);  
        end
    else
        for i=1:k
           c(i,:)=cobat(i,:);        % sequential initialization
        end
    end

    temp=zeros(maxRow,1);   % initialize as zero vector
    u=0;
    while 1,
        d=DistMatrix3(cobat,c);   % calculate the distance 
        [z,g]=min(d,[],2);      % set the matrix g group

        if g==temp,             % if the iteration doesn't change anymore
            break;              % stop the iteration
        else
            temp=g;             % copy the matrix to the temporary variable
        end
        for i=1:k
            f=find(g==i);
            if f                % calculate the new centroid 
                c(i,:)=mean(cobat(find(g==i),:),1);
            end
        end
        c
        [B,index] = sortrows( c );  % sort the centroids
        g = index(g); % arrange the labels based on centroids
    end
    y=[cobat,g]

    hold off;    

   %This plot is actually placed in plot 3D code (last line), but I put it into here, because I think this is the plotting line
   f = PlotClusters(cobat,g,y,Colors) %Here is the error
   if Dimensions==2
    for i=1:NumOfDataPoints %plot data points    
        plot(cobat(i,1),cobat(i,2),'.','Color',Colors(g(i),:))
        hold on
    end
    for i=1:NumOfCenters %plot the centers
        plot(y(i,1),y(i,2),'s','Color',Colors(i,:))
    end
else
    for i=1:NumOfDataPoints %plot data points 
        plot3(cobat(i,1),cobat(i,2),cobat(i,3),'.','Color',Colors(g(i),:)) 
        hold on
    end
    for i=1:NumOfCenters %plot the centers
        plot3(y(i,1),y(i,2),y(i,3),'s','Color',Colors(i,:))
    end 

   end
end

这是情节3D代码：

%This function plots clustering data, for example the one provided by
%kmeans. To be able to plot, the number of dimensions has to be either 2 or
%3. 
%Inputs:
%       Data - an m-by-d matrix, where m is the number of data points to
%              cluster and d is the number of dimensions. In my code, it is cobat
%       IDX - an m-by-1 indices vector, where each element gives the
%             cluster to which the corresponding data point in Data belongs. In my file, it is 'g'
%       Centers y - an optional c-by-d matrix, where c is the number of
%             clusters and d is the dimensions of the problem. The matrix
%             gives the location of the cluster centers. If this is not
%             given, the centers will be calculated. In my file, I think, it is 'y'
%       Colors - an optional color scheme generated by hsv. If this is not
%             given, a color scheme will be generated.
%
function f = PlotClusters(cobat,g,y,Colors)
%Checking inputs
switch nargin
    case 1 %Not enough inputs
        error('Clustering data is required to plot clusters. Usage: PlotClusters(Data,IDX,Centers,Colors)')
    case 2 %Need to calculate cluster centers and color scheme
        [NumOfDataPoints,Dimensions]=size(cobat);
        if Dimensions~=2 && Dimensions~=3 %Check ability to plot
            error('It is only possible to plot in 2 or 3 dimensions.')
        end
        if length(g)~=NumOfDataPoints %Check that each data point is assigned to a cluster
            error('The number of data points in Data must be equal to the number of indices in IDX.')
        end
        NumOfClusters=max(g);
        Centers=zeros(NumOfClusters,Dimensions);
        NumOfCenters=NumOfClusters;
        NumOfPointsInCluster=zeros(NumOfClusters,1);
        for i=1:NumOfDataPoints
            Centers(g(i),:)=y(g(i),:)+cobat(i,:);
            NumOfPointsInCluster(g(i))=NumOfPointsInCluster(g(i))+1;
        end
        for i=1:NumOfClusters
            y(i,:)=y(i,:)/NumOfPointsInCluster(i);
        end
        Colors=hsv(NumOfClusters);        
    case 3 %Need to calculate color scheme        
        [NumOfDataPoints,Dimensions]=size(cobat);
        if Dimensions~=2 && Dimensions~=3 %Check ability to plot
            error('It is only possible to plot in 2 or 3 dimensions.')
        end
        if length(g)~=NumOfDataPoints %Check that each data point is assigned to a cluster
            error('The number of data points in Data must be equal to the number of indices in IDX.')
        end
        NumOfClusters=max(g);
        [NumOfCenters,Dims]=size(y);
        if Dims~=Dimensions
            error('The number of dimensions in Data should be equal to the number of dimensions in Centers')
        end
        if NumOfCenters<NumOfClusters %Check that each cluster has a center
            error('The number of cluster centers is smaller than the number of clusters.')
        elseif NumOfCenters>NumOfClusters %Check that each cluster has a center
            disp('There are more centers than clusters, all will be plotted')
        end
        Colors=hsv(NumOfCenters);
    case 4 %All data is given just need to check consistency        
        [NumOfDataPoints,Dimensions]=size(cobat);
        if Dimensions~=2 && Dimensions~=3 %Check ability to plot
            error('It is only possible to plot in 2 or 3 dimensions.')
        end
        if length(g)~=NumOfDataPoints %Check that each data point is assigned to a cluster
            error('The number of data points in Data must be equal to the number of indices in IDX.')
        end
        NumOfClusters=max(g);
        [NumOfCenters,Dims]=size(y);
        if Dims~=Dimensions
            error('The number of dimensions in Data should be equal to the number of dimensions in Centers')
        end
        if NumOfCenters<NumOfClusters %Check that each cluster has a center
            error('The number of cluster centers is smaller than the number of clusters.')
        elseif NumOfCenters>NumOfClusters %Check that each cluster has a center
            disp('There are more centers than clusters, all will be plotted')
        end
        [NumOfColors,RGB]=size(Colors);
        if RGB~=3 || NumOfColors<NumOfCenters
            error('Colors should have at least the same number of rows as number of clusters and 3 columns')
        end            
end
%Data is ready. Now plotting

end

这是错误：

??? Undefined function or variable 'Colors'.

Error in ==> clustere at 69
    f = PlotClusters(cobat,g,y,Colors)

我错了这样的功能吗？我该怎么办？非常感谢您的帮助。

Answer 1

你的代码非常混乱，而且不必要很长......

这是做同样事情的小例子。您需要使用统计工具箱来运行它（对于kmeans函数和Iris数据集）：

%# load dataset of 150 instances and 3 dimensions
load fisheriris
X = meas(:,1:3);
[numInst,numDims] = size(X);

%# K-means clustering
%# (K: number of clusters, G: assigned groups, C: cluster centers)
K = 3;
[G,C] = kmeans(X, K, 'distance','sqEuclidean', 'start','sample');

%# show points and clusters (color-coded)
clr = lines(K);
figure, hold on
scatter3(X(:,1), X(:,2), X(:,3), 36, clr(G,:), 'Marker','.')
scatter3(C(:,1), C(:,2), C(:,3), 100, clr, 'Marker','o', 'LineWidth',3)
hold off
view(3), axis vis3d, box on, rotate3d on
xlabel('x'), ylabel('y'), zlabel('z')

Answer 2

你可以简单地选择scatter()：

enter image description here

从图像中可以看出，您可以区分颜色和簇的大小。有关详细信息，请查看文档中的示例。

Answer 3

这是我们如何获取3d图形的示例代码。

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt


fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

x =[1,2,3,4,5,6,7,8,9,10]
y =[5,6,2,3,13,4,1,2,4,8]
z =[2,3,3,3,5,7,9,11,9,10]


ax.scatter(x, y, z, c='r', marker='o')

ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')

plt.show()

K-means绘制三维数据

3 个答案: