Problem in Navie Bayes theorem code

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Ali Asghar
Ali Asghar 2019-12-12
评论： saurabh kumar 2023-5-26
Dear all
I used the below code on 160x5 file and found it work correctly but now i change the file from 160x5 to 160x17 then it show error. Kindly look into it.
clear all, close all, clc
load('FeatureAngle000Musscle1.mat') % 160x17 size
X = FeaturesAngle000Muscle1(:,1:16);
Y = FeaturesAngle000Muscle1(:,17);
rng(1); % For reproducibility
Mdl2 = fitcnb(X,Y,...
'DistributionNames',{'normal','normal','kernel','kernel' 'normal','normal','kernel','kernel' 'normal','normal','kernel','kernel' 'normal','normal','kernel','kernel'},...
'ClassNames',{'HandGrip','HandOpen','HandRest','WristExtension','WristFlexion'});
Mdl2.DistributionParameters{1,2}
isLabels2 = resubPredict(Mdl2); % it generate the output/response of model
ConfusionMat2 = confusionchart(table2array(Y),isLabels2); % Y need to convert
showing error
Error using ClassificationNaiveBayes/fitNonMNDists (line 222)
A normal distribution cannot be fit for the combination of class HandGrip and predictor LD. The data has zero variance.
Error in ClassificationNaiveBayes (line 104)
this.DistributionParameters = fitNonMNDists(this);
Error in classreg.learning.FitTemplate/fit (line 258)
[varargout{1:nargout}] = this.MakeFitObject(X,Y,W,this.ModelParams,fitArgs{:});
Error in ClassificationNaiveBayes.fit (line 132)
this = fit(temp,X,Y);
Error in fitcnb (line 252)
this = ClassificationNaiveBayes.fit(X,Y,RemainingArgs{:});

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回答（2 个）

Hiro Yoshino
Hiro Yoshino 2019-12-12
'ClassNames',{'HandGrip','HandOpen','HandRest','WristExtension','WristFlexion'});
the number of class labels is 5 but I guess yours is 17?
  1 个评论
Ali Asghar
Ali Asghar 2019-12-12
In 1-16 Columns there are different features values of emg signals like RMS, VAR etc and in 17th column there is respective output like handopen, handclose etc
In 1-32 rows the 17th columns have Handopen
in 33-64 the the 17th columns have Handclose
etc

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Hiro Yoshino
Hiro Yoshino 2019-12-12
I guess one of the distributions does not match what it is, i.e., variance is zero. In this case, Gaussian distributions cannot be fit anyway...
  2 个评论
Ali Asghar
Ali Asghar 2019-12-14
What i can i do now, other than Gaussian...
saurabh kumar
saurabh kumar 2023-5-26
The error is due to the wrong distribution in the data sample. It occurs when the variance between the feature value is repeatedly 0 and gaussian distribution will find itself helpless in order to draw a distribution diagram. As for example, look into the attached screen shot. A dataset in my work contained 0s in most of the columns and I was validating the features via Naive Bayes algorithm, I got the same error .....
"A normal distribution cannot be fit for the combination of class 1 and predictor x3. The data has zero variance." .
There are two ways to solve the problem
a) Change your classifier (Use knn or M-SVM instead)
b) Use the following code to reduce your variance
function [odata] = reducevariance(data)
%REDUCEVARIANCE Summary of this function goes here
% Detailed explanation goes here
[rows,cols]=size(data); % find size of the data
odata=[]; % create updated data matrix
colcount=0; % column counter for new data
for j=1:cols
current_col_values=data(:,j); % take current col values
total_samples=numel(current_col_values); % find total number of samples
f=find(current_col_values==0); % find 0s in total samples
percentageofzeros=(numel(f)/total_samples)*100; % find % of existance
if percentageofzeros>20 % if it is greater than 20% remove the col
else
colcount=colcount+1; % else add the col to updated data
odata(1:rows,colcount)=data(1:rows,j);
end
end
end
For any other query, mail me at director.smarttech@gmail.com

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