left and right sides have a different number of elements

Getting error with this line, please advise,

Thanks!

dA(i)=(rate_a(i).*F.*Q)-(rate_b(i).*F.*L)-(rate_c(i).*F);

left and right sides have a different number of elements

[madhan ravi]

Without knowing the rest of the code it’s useless, no matter how many times you delete and ask the same question.

[william Smith]

Sorry I deleted the previous question becuase I thought I figured it out ,

but I ran to the same issue again when plotting:

i=1;
while  (i)<100 && dA(i)<= T && dA(i)>1
   
 dA(i)=(rate_a(i).*A.*B)-(rate_b(i).*A.*C)-(rate_c(i).*A);
 dB(i)=(rate_b(i).*A.*C)-(rate_a(i).*A.*B)-(rate_d(i).*B);
 dC(i)=rate_d(i).*B;
 
 dAdt(i)=x.*A(i); %where A is set as zeros vector
 dBdt(i)=y.*B(i);%where B is set as zeros vector
 dCdt(i)=z.*C(i);%where C is set as zeros vector
 
 A(i+1)=A(i)+i.*(dAdt(i)+dAdt(i+1))/2;
 B(i+1)=B(i)+i.*(dBdt(i)+dBdt(i+1))/2;
 C(i+1)=C(i)+i.*(dCdt(i)+dCdt(i+1))/2;

 
 i=i+1;
 
end

[madhan ravi]

Provide the values of missing datas.

Answer 1

[Star Strider]

My guess is that ‘A’, ‘B’ and ‘C’ are vectors, or you would not be subscripting them.

You need to subscript them here as well:

 dA(i)=(rate_a(i).*A.*B)-(rate_b(i).*A.*C)-(rate_c(i).*A);
 dB(i)=(rate_b(i).*A.*C)-(rate_a(i).*A.*B)-(rate_d(i).*B);
 dC(i)=rate_d(i).*B;

or you will continue to have a dimension mismatch.

[william Smith]

Thank you,

Correct A,B and C are zero vectors,

when I try to plot C vs i (which is a counting variable representing time from 1:100 seconds) I am getting:

Index exceeds the number of array elements (0).

[Star Strider]

Your ‘i’ counter needs to be a vector. One way to do that is to create another variable, for example ‘iv’ to store the values, then plot against it:

i=1;
while  (i)<100 && dA(i)<= T && dA(i)>1
    . . . 
    i = 1 + 1;
    iv(i) = i;
end

although a more efficient way (since you’re incrementing by 1 in each iteration) is:

iv = 1:numel(C);

That should work.

[william Smith]

Thanks again, no luck with it, would this get me any closer? tried to run it and getting error:

Index exceeds the number of array elements (1).

i=1;
j=1
while  i(j)<100 && dA(j)<= T && dA(j)>1
   
 dA(j)=(rate_a(j).*A(j).*B(j))-(rate_b(j).*A(j).*C)-(rate_c(j).*A(j));
 dB(j)=(rate_b(j).*A(j).*C(j))-(rate_a(j).*A(j).*B(j))-(rate_d(j).*B(j));
 dC(j)=rate_d(j).*B(j);
 
 dAdt(j)=x.*A(j); %where A is set as zeros vector
 dBdt(j)=y.*B(j);%where B is set as zeros vector
 dCdt(j)=z.*C(j);%where C is set as zeros vector
 
 A(j+1)=A(j)+i.*(dAdt(j)+dAdt(j+1))/2;
 B(j+1)=B(j)+i.*(dBdt(j)+dBdt(j+1))/2;
 C(j+1)=C(j)+i.*(dCdt(j)+dCdt(j+1))/2;
 
 i(j+1)=i(j)+1; 

 j=j+1;
 
end

[Star Strider]

I don’t have the rest of your code.

Use your original while loop:

i=1;
while  (i)<100 && dA(i)<= T && dA(i)>1
   
 dA(i)=(rate_a(i).*A.*B)-(rate_b(i).*A.*C)-(rate_c(i).*A);
 dB(i)=(rate_b(i).*A.*C)-(rate_a(i).*A.*B)-(rate_d(i).*B);
 dC(i)=rate_d(i).*B;
 
 dAdt(i)=x.*A(i); %where A is set as zeros vector
 dBdt(i)=y.*B(i);%where B is set as zeros vector
 dCdt(i)=z.*C(i);%where C is set as zeros vector
 
 A(i+1)=A(i)+i.*(dAdt(i)+dAdt(i+1))/2;
 B(i+1)=B(i)+i.*(dBdt(i)+dBdt(i+1))/2;
 C(i+1)=C(i)+i.*(dCdt(i)+dCdt(i+1))/2;
 
 i=i+1;
 
end

and then just do this:

iv = 1:numel(C);

so the plot call is then:

plot(iv, C)

That should work, if ‘C’ is a vector.

Answer 2

[kevin harianto]

I also have the same problem with it being from location(:) = [pointCloud.Location];

classdef LidarSemanticSegmentation < lidar.labeler.AutomationAlgorithm
    % LidarSemanticSegmentation Automation algorithm performs semantic
    % segmentation in the point cloud.
    %   LidarSemanticSegmentation is an automation algorithm for segmenting
    %   a point cloud using SqueezeSegV2 semantic segmentation network
    %   which is trained on Pandaset data set.
    %
    %   See also lidarLabeler, groundTruthLabeler
    %   lidar.labeler.AutomationAlgorithm.
    %   Copyright 2021 The MathWorks, Inc.
    % ----------------------------------------------------------------------
    % Step 1: Define the required properties describing the algorithm. This
    % includes Name, Description, and UserDirections.
    properties(Constant)
        % Name Algorithm Name
        %   Character vector specifying the name of the algorithm.
        Name = 'Lidar Semantic Segmentation';
        % Description Algorithm Description
        %   Character vector specifying the short description of the algorithm.
        Description = 'Segment the point cloud using SqueezeSegV2 network.';
        % UserDirections Algorithm Usage Directions
        %   Cell array of character vectors specifying directions for
        %   algorithm users to follow to use the algorithm.
        UserDirections = {['ROI Label Definition Selection: select one of ' ...
            'the ROI definitions to be labeled'], ...
            'Run: Press RUN to run the automation algorithm. ', ...
            ['Review and Modify: Review automated labels over the interval ', ...
            'using playback controls. Modify/delete/add ROIs that were not ' ...
            'satisfactorily automated at this stage. If the results are ' ...
            'satisfactory, click Accept to accept the automated labels.'], ...
            ['Accept/Cancel: If the results of automation are satisfactory, ' ...
            'click Accept to accept all automated labels and return to ' ...
            'manual labeling. If the results of automation are not ' ...
            'satisfactory, click Cancel to return to manual labeling ' ...
            'without saving the automated labels.']};
    end
    % ---------------------------------------------------------------------
    % Step 2: Define properties you want to use during the algorithm
    % execution.
    properties
        % AllCategories
        % AllCategories holds the default 'unlabelled', 'Vegetation',
        % 'Ground', 'Road', 'RoadMarkings', 'SideWalk', 'Car', 'Truck',
        % 'OtherVehicle', 'Pedestrian', 'RoadBarriers', 'Signs',
        % 'Buildings' categorical types.
        AllCategories = {'unlabelled'};
        % PretrainedNetwork
        %   PretrainedNetwork saves the pretrained SqueezeSegV2 network.
        PretrainedNetwork
    end
    %----------------------------------------------------------------------
    % Note: this method needs to be included for lidarLabeler app to
    % recognize it as using pointcloud
    methods (Static)
        % This method is static to allow the apps to call it and check the
        % signal type before instantiation. When users refresh the
        % algorithm list, we can quickly check and discard algorithms for
        % any signal that is not support in a given app.
        function isValid = checkSignalType(signalType)
            isValid = (signalType == vision.labeler.loading.SignalType.PointCloud);
        end
    end
    %----------------------------------------------------------------------
    % Step 3: Define methods used for setting up the algorithm.
    methods
        function isValid = checkLabelDefinition(algObj, labelDef)
            % Only Voxel ROI label definitions are valid for the Lidar
            %  semantic segmentation algorithm.
            isValid = labelDef.Type == lidarLabelType.Voxel;
            if isValid
                algObj.AllCategories{end+1} = labelDef.Name;
            end
        end
        function isReady = checkSetup(algObj)
            % Is there one selected ROI Label definition to automate.
            isReady = ~isempty(algObj.SelectedLabelDefinitions);
        end
    end
    %----------------------------------------------------------------------
    % Step 4: Specify algorithm execution. This controls what happens when
    %         the user presses RUN. Algorithm execution proceeds by first
    %         executing initialize on the first frame, followed by run on
    %         every frame, and terminate on the last frame.
    methods
        function initialize(algObj,~)
            % Load the pretrained SqueezeSegV2 semantic segmentation network.
            outputFolder = fullfile(tempdir, 'Pandaset');
            pretrainedSqueezeSeg = load(fullfile(outputFolder,'trainedSqueezeSegV2PandasetNet.mat'));
            % Store the network in the 'PretrainedNetwork' property of this object.
            algObj.PretrainedNetwork = pretrainedSqueezeSeg.net;
        end
        function autoLabels = run(algObj, pointCloud)
            % Setup categorical matrix with categories including
            % 'Vegetation', 'Ground', 'Road', 'RoadMarkings', 'SideWalk',
            % 'Car', 'Truck', 'OtherVehicle', 'Pedestrian', 'RoadBarriers',
            % and 'Signs'.
            autoLabels = categorical(zeros(size(pointCloud.Location,1), size(pointCloud.Location,2)), ...
                0:12,algObj.AllCategories);
            %A = zeros(10000,10000);
            %filling in the minimum required resolution
            % to meet the neural network's specification.
  %(first iteration failed)   pointCloud.Location = zeros(65,1856,5);
            %Due to an error we must append the various point cloud data
            %first.
           Location =  zeros(64,1856,5);
            %next we can add in the ptCloud locations
       %     Location(:,:,1) = pointCloud.Location;
         %   Location = zeros(65,1856,5);
         Location(:) = [pointCloud.Location]
            %
            ptCloud=pointCloud(Location);
            %This will also be applied to the pointCloud Intensity levels
            % as these are also analyzed by the machine learning algorithm.
  %(Pushed aside for later modifications)          pointCloud.Intensity = zeros(64,1865);
           
            % Convert the input point cloud to five channel image.
            I = helperPointCloudToImage(pointCloud);
            % Predict the segmentation result.
            predictedResult = semanticseg(I, algObj.PretrainedNetwork);
            autoLabels(:) = predictedResult;
            %using this area we would be able to continuously update the latest file on
            % sending the output towards the CAN Network or atleast ensure that the
            % item is obtainable
            % This area would work the best.
            %first we must
        end
    end
end
function helperDisplayLabelOverlaidPointCloud(I,predictedResult)
    % helperDisplayLabelOverlaidPointCloud Overlay labels over point cloud object.
    %  helperDisplayLabelOverlaidPointCloud(I,predictedResult)
    %  displays the overlaid pointCloud object. I is the 5 channels organized
    %  input image. predictedResult contains pixel labels.
    ptCloud = pointCloud(I(:,:,1:3),Intensity = I(:,:,4));
    cmap = helperPandasetColorMap;
    B = ...
        labeloverlay(uint8(ptCloud.Intensity),predictedResult,Colormap = cmap,Transparency = 0.4);
    pc = pointCloud(ptCloud.Location,Color = B);
    ax = pcshow(pc);
    set(ax,XLim = [-70 70],YLim = [-70 70])
    zoom(ax,3.5)
end
function cmap = helperPandasetColorMap
    cmap = [[30 30 30];  % Unlabeled
        [0 255 0];       % Vegetation
        [255 150 255];   % Ground
        [237 117 32];    % Road
        [255 0 0];       % Road Markings
        [90 30 150];     % Sidewalk
        [255 255 30];    % Car
        [245 150 100];   % Truck
        [150 60 30];     % Other Vehicle
        [255 255 0];     % Pedestrian
        [0 200 255];     % Road Barriers
        [170 100 150];   % Signs
        [255 0 255]];    % Building
    cmap = cmap./255;
end
function image = helperPointCloudToImage(ptcloud)
% helperPointCloudToImage converts the point cloud to 5 channel image
image = ptcloud.Location;
image(:,:,5) = ptcloud.Intensity;
rangeData = iComputeRangeData(image(:,:,1),image(:,:,2),image(:,:,3));
image(:,:,4) = rangeData;
index = isnan(image);
image(index) = 0;
end
function rangeData = iComputeRangeData(xChannel,yChannel,zChannel)
rangeData = sqrt(xChannel.*xChannel+yChannel.*yChannel+zChannel.*zChannel);
end