How to plot multi data set in matlab?

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Kwan Chak Yin 2018-5-2

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https://ww2.mathworks.cn/matlabcentral/answers/398673-how-to-plot-multi-data-set-in-matlab

编辑： Kwan Chak Yin 2018-5-2

Hello, I am facing a problem with plotting the graph. I wish to plot the graph in the iteration as for loop is using in my coding. I had a data set,a with 8 variables, which made to 300x8 matrix, while another set of data,w is 1x8 matrix and w is updating for every iteration. But sometimes the w may update or increase more than 1 row in the coding. I just want to plot the a and w for every iteration in same plot. Can the w that changes for every iteration plot as line graph. Here is my full coding, maybe can have a look. When I run the code, it encounter error that the matrix length is different. I wish to run it in live script because need to record video. Thank you.

clc
index=0;
load ('S1.mat')
load ('var.dat','-mat');
learning_rate=0.001;
data_size=size(Untitled);
for i=1:data_size(1)%%indicating size of matrix S1 row
      current_reading=Untitled(i,:);
      index=index+1;
      neuron_matrix_size=size(w);
      for j=1:neuron_matrix_size(1)%%indicating size of matrix w row
          for k=1:neuron_matrix_size(2)%%indicating size of matrix w column
              square_distance=((current_reading(k)-w(j,k)).^2);
          end
          euc_distance_neuronj=sqrt(square_distance);
          if j==1 
              euc_distance = [euc_distance_neuronj];%%this is the first time create this matrix 
          else
              euc_distance = [euc_distance euc_distance_neuronj];%%this is when the matrix has been created
          end
      end
      %%find the best matching neuron and determine which neuron win
      [minimum_distance,winning_neuron] = min(euc_distance);
      %%disp(euc_distance,'euc_distance')
      %%disp(minimum_distance,'minimum_distance = ')
      if minimum_distance<0.05
          h(winning_neuron)=h(winning_neuron)+1;
          %%update weight
          for k=1:neuron_matrix_size(1)
              w(k,winning_neuron)=w(k,winning_neuron)+learning_rate*(current_reading(k)-w(j,k));
          end
      else
          %%disp(w,sensor_reading)
          h = [h 1];
          w = [w;current_reading];
      end
      plot(i,Untitled);
      hold on;
      plot(i,w);
  end