not enough input arguments
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Iam trying to run function but it give me an error in ' d{i} = pdist2(F{i}.', testSample, 'mahalanobis');' not enough input arguments
function [Ps, winnerClass] = classifyKNN_D_Multi(F, testSample, k, NORMALIZE, useL1distance )
% function [Ps, winnerClass] = classifyKNN_D_Multi(F, testSample, k, NORMALIZE, useL1distance);
%
% This function is used for classifying an unknown sample using the kNN
% algorithm, in its multi-class form.
%
% ARGUMENTS:
% - F: an CELL array that contains the feature values for each class. I.e.,
% F{1} is a matrix of size numOfDimensions x numofSamples FOR THE FIRST
% CLASS, etc.
%
% - testSample: the input sample to be classified
% - k: the kNN parameter
% - NORMALIZE: use class priors to weight results
% - useL1distance: use L1 instead of L2 distance
%
% RETURNS:
% - Ps: an array that contains the classification probabilities for each class
% - winnerClass: the label of the winner class
%%error(nargchk(4,5,nargin))
switch nargin
case 4
useL1distance = '1'; % euclidean distance if 4 variables included
case 5
useL1distance = '0'; % mahalanobis distance if 5 variables included
otherwise
disp('error')
end
numOfClasses = length(F);
if (size(testSample, 2)==1)
testSample = testSample';
end
% initilization of distance vectors:
numOfDims = zeros( 1, numOfClasses );
numOfTrainSamples = zeros( 1, numOfClasses );
d = cell(numOfClasses,1);
% d{i} is a vector, whose elements represent the distance of the testing
% sample from all the samples of i-th class
testSample(isnan(testSample)) = 0.0;
for i=1:numOfClasses
[ numOfDims(i), numOfTrainSamples(i) ] = size( F{i} );
d{i} = inf*ones(max(numOfTrainSamples), 1); % we fill it with inf values
F{i}(isnan(F{i})) = 0.0;
end
if (length(testSample)>1)
for i=1:numOfClasses % for each class:
if (numOfTrainSamples(i)>0)
if ( useL1distance == 1 )
% d{i} = sum( abs(repmat(testSample, [numOfTrainSamples(i) 1]) - F{i}'),2); % L1
d{i} = pdist2(F{i}.', testSample, 'euclidean');
else
%[size(repmat(testSample, [numOfTrainSamples(i) 1])) size(F{i}')]
%sum(sum(isnan(F{i}')))
% d{i} = sum( ((repmat(testSample, [numOfTrainSamples(i) 1]) - F{i}').^2 ),2); % L2
d{i} = pdist2(F{i}.', testSample, 'mahalanobis'); %56
end
d{i} = sort(d{i});
d{i}(end+1:max(numOfTrainSamples)) = inf;
else
d{i} = inf;
end
end
else % single dimension (NO SUM required!!!)
for i=1:numOfClasses
if (numOfTrainSamples(i)>0)
d{i} = (abs(repmat(testSample, [numOfTrainSamples(i) 1]) - F{i}')');
d{i} = sort(d{i});
d{i}(end+1:max(numOfTrainSamples)) = inf;
else
d{i} = inf;
end
end
end
kAll = zeros(numOfClasses, 1);
for j=1:k
curArray = zeros(numOfClasses, 1);
for i=1:numOfClasses
curArray(i) = d{i}(kAll(i)+1);
end
[MIN, IMIN] = min(curArray);
kAll(IMIN) = kAll(IMIN) + 1;
end
if ( NORMALIZE == 0 )
Ps = (kAll ./ k);
else
Ps = kAll ./ numOfTrainSamples';
Ps = Ps / sum(Ps);
end
[MAX, IMAX] = max(Ps);
winnerClass = IMAX;
>> classifyKNN_D_Multi
error
Not enough input arguments.
Error in classifyKNN_D_Multi (line 30)
numOfClasses = length(F);
this is the function Iam classifying with variable Feature is 2 class and has same column size Can anyone tell me where the problem is ?
function [label, P, classNames] = ...
fileClassification(wavFileName, kNN, modelFileName)
%
% function [label, P, classNames] = ...
% fileClassification(wavFileName, kNN, modelFileName)
%
% This function demonstrates the classification of an audio segment,
% stored in a wav file.
%
% ARGUMENTS:
% - wavFileName: the path of the wav file to be classified
% - kNN: the k parameter of the kNN algorithm
% - modelFileName: the path of the kNN classification model
%
% RETURNS:
% - label: the label of the winner class
% - P: a vector that contains all estimated probabilities
% for each audio class contained in the model
% - classNames: a cell array that contains the names of the
% audio classes of the classification model
%
% NOTE: This function classifies the WHOLE audio file, i.e., we
% assume that the file contains a homogeneous audio segment.
% For mid-term classification, please use mtFileClassification().
%
% load classification model:
[Features, classNames, MEAN, STD, Statistics, ...
stWin, stStep, mtWin, mtStep] = kNN_model_load(modelFileName);
[x, fs] = audioread(wavFileName); % read wav file
% short-term feature extraction:
stF = stFeatureExtraction(x, fs, stWin, stStep);
mtWinRatio = mtWin / stWin; mtStepRatio = mtStep / stStep;
% mid-term feature statistic calculation:
[mtFeatures] = mtFeatureExtraction(...
stF, mtWinRatio, mtStepRatio, Statistics);
% long term averaging of the mid-term statistics:
mtFeatures = mean(mtFeatures,2);
% kNN classification
[P, label] = classifyKNN_D_Multi(Features, ...
(mtFeatures - MEAN') ./ STD', kNN,1);
Answers (2)
Walter Roberson
on 19 Nov 2020
>> classifyKNN_D_Multi
You are trying to run a function that requires either 4 or 5 inputs, with no inputs at all.
case 4
useL1distance = '1'; % euclidean distance if 4 variables included
case 5
useL1distance = '0'; % mahalanobis distance if 5 variables included
The code sets useL1distance to a character regardless of what was passed in as the fifth parameter. Just the fact that you have five parameters is enough to assign the character '0' to useL1distance
if ( useL1distance == 1 )
It is never going to equal the number 1 because it was assigned the character '0' or the charater '1'
4 Comments
Kamil Kacer
on 19 Nov 2020
Edited: Kamil Kacer
on 19 Nov 2020
Walter Roberson
on 20 Nov 2020
function [Ps, winnerClass] = classifyKNN_D_Multi(F, testSample, k, NORMALIZE, distancetype )
% function [Ps, winnerClass] = classifyKNN_D_Multi(F, testSample, k, NORMALIZE, distancetype);
%
% This function is used for classifying an unknown sample using the kNN
% algorithm, in its multi-class form.
%
% ARGUMENTS:
% - F: an CELL array that contains the feature values for each class. I.e.,
% F{1} is a matrix of size numOfDimensions x numofSamples FOR THE FIRST
% CLASS, etc.
%
% - testSample: the input sample to be classified
% - k: the kNN parameter
% - NORMALIZE: use class priors to weight results
% - useL1distance: use L1 instead of L2 distance
%
% RETURNS:
% - Ps: an array that contains the classification probabilities for each class
% - winnerClass: the label of the winner class
%%error(nargchk(4,5,nargin))
switch nargin
case 4
distancetype = 1; % euclidean distance if 4 variables included
case 5
if ~isscalar(distancetype) || ~isnumeric(distancetype) || ~ismember(distancetype, 1:3);
error('invalid distance type');
end
otherwise
disp('error')
end
numOfClasses = length(F);
if (size(testSample, 2)==1)
testSample = testSample';
end
% initilization of distance vectors:
numOfDims = zeros( 1, numOfClasses );
numOfTrainSamples = zeros( 1, numOfClasses );
d = cell(numOfClasses,1);
% d{i} is a vector, whose elements represent the distance of the testing
% sample from all the samples of i-th class
testSample(isnan(testSample)) = 0.0;
for i=1:numOfClasses
[ numOfDims(i), numOfTrainSamples(i) ] = size( F{i} );
d{i} = inf*ones(max(numOfTrainSamples), 1); % we fill it with inf values
F{i}(isnan(F{i})) = 0.0;
end
if (length(testSample)>1)
for i=1:numOfClasses % for each class:
if (numOfTrainSamples(i)>0)
if ( distancetype == 1 )
% d{i} = sum( abs(repmat(testSample, [numOfTrainSamples(i) 1]) - F{i}'),2); % L1
d{i} = pdist2(F{i}.', testSample, 'euclidean');
elseif ( distancetype == 2)
d{i} = pdist2{F{i}.', testSample, 'cityblock'); %L1
else
d{i} = pdist2(F{i}.', testSample, 'mahalanobis'); %56
end
d{i} = sort(d{i});
d{i}(end+1:max(numOfTrainSamples)) = inf;
else
d{i} = inf;
end
end
else % single dimension (NO SUM required!!!)
for i=1:numOfClasses
if (numOfTrainSamples(i)>0)
d{i} = (abs(repmat(testSample, [numOfTrainSamples(i) 1]) - F{i}')');
d{i} = sort(d{i});
d{i}(end+1:max(numOfTrainSamples)) = inf;
else
d{i} = inf;
end
end
end
kAll = zeros(numOfClasses, 1);
for j=1:k
curArray = zeros(numOfClasses, 1);
for i=1:numOfClasses
curArray(i) = d{i}(kAll(i)+1);
end
[MIN, IMIN] = min(curArray);
kAll(IMIN) = kAll(IMIN) + 1;
end
if ( NORMALIZE == 0 )
Ps = (kAll ./ k);
else
Ps = kAll ./ numOfTrainSamples';
Ps = Ps / sum(Ps);
end
[MAX, IMAX] = max(Ps);
winnerClass = IMAX;
Now, to use Euclidean distance use
[P, label] = classifyKNN_D_Multi(Features, ...
(mtFeatures - MEAN') ./ STD', kNN,1);
or
[P, label] = classifyKNN_D_Multi(Features, ...
(mtFeatures - MEAN') ./ STD', kNN,1,1);
This is a change from before. Now the final 1 is for distance type #1, Euclidean.
To use L1 distance use distance type #2, L1
[P, label] = classifyKNN_D_Multi(Features, ...
(mtFeatures - MEAN') ./ STD', kNN, 1, 2);
To use mahalonobis use distance type #3
[P, label] = classifyKNN_D_Multi(Features, ...
(mtFeatures - MEAN') ./ STD', kNN, 1, 3);
Kamil Kacer
on 20 Nov 2020
Walter Roberson
on 20 Nov 2020
'cityblock' is another name for L1 norm.
Can i make it work li this ?
Why? If you are going to use a numeric parameter, then make it mean something.
When you go to write a GUI to allow the user to choose which distance type they want, are you going to want to code
if handles.distance_type_button.Value == 0
[P, label] = classifyKNN_D_Multi(Features, ...
(mtFeatures - MEAN') ./ STD', kNN, 1);
else
[P, label] = classifyKNN_D_Multi(Features, ...
(mtFeatures - MEAN') ./ STD', kNN, 1, 1);
end
or are you going to want to have a single call to the function with a parameter that indicates which distance type the user wanted?
Kamil Kacer
on 20 Nov 2020
10 Comments
Walter Roberson
on 20 Nov 2020
"cityblock distance" is another name for L1 norm, which is the distance measure your code used to use, the inspiration for the variable name "useL1distance" . It is also called "taxicab distance" https://en.wikipedia.org/wiki/Taxicab_geometry
There is no point in deliberately removing it -- you might want it later for further tests.
why is this orring everything
This is a common code pattern when you are trying to ensure that a variable has some specific form. Another equivalent way of writing the same code would be
if ~(isscalar(distancetype) && isnumeric(distancetype) && ismember(distancetype, 1:3))
This is mathematically equivalent: ((~A) or (~B)) is the same as ~(A and B) . Which form you write becomes a matter of style. The ~A || ~B form is easier to add more cases to.
The tests we are doing here are:
- distancetype must be a scalar
- distancetype must be numeric
- distance type must be one of 1, 2, or 3.
Kamil Kacer
on 20 Nov 2020
Walter Roberson
on 20 Nov 2020
function [Ps, winnerClass] = classifyKNN_D_Multi(F, testSample, k, NORMALIZE, distancetype )
% function [Ps, winnerClass] = classifyKNN_D_Multi(F, testSample, k, NORMALIZE, distancetype);
%
% This function is used for classifying an unknown sample using the kNN
% algorithm, in its multi-class form.
%
% ARGUMENTS:
% - F: an CELL array that contains the feature values for each class. I.e.,
% F{1} is a matrix of size numOfDimensions x numofSamples FOR THE FIRST
% CLASS, etc.
%
% - testSample: the input sample to be classified
% - k: the kNN parameter
% - NORMALIZE: use class priors to weight results
% - useL1distance: use L1 instead of L2 distance
%
% RETURNS:
% - Ps: an array that contains the classification probabilities for each class
% - winnerClass: the label of the winner class
%%error(nargchk(4,5,nargin))
switch nargin
case 4
distancetype = 1; % euclidean distance if 4 variables included
case 5
distancetype = 3;
otherwise
disp('error')
end
numOfClasses = length(F);
if (size(testSample, 2)==1)
testSample = testSample';
end
% initilization of distance vectors:
numOfDims = zeros( 1, numOfClasses );
numOfTrainSamples = zeros( 1, numOfClasses );
d = cell(numOfClasses,1);
% d{i} is a vector, whose elements represent the distance of the testing
% sample from all the samples of i-th class
testSample(isnan(testSample)) = 0.0;
for i=1:numOfClasses
[ numOfDims(i), numOfTrainSamples(i) ] = size( F{i} );
d{i} = inf*ones(max(numOfTrainSamples), 1); % we fill it with inf values
F{i}(isnan(F{i})) = 0.0;
end
if (length(testSample)>1)
for i=1:numOfClasses % for each class:
if (numOfTrainSamples(i)>0)
if ( distancetype == 1 )
% d{i} = sum( abs(repmat(testSample, [numOfTrainSamples(i) 1]) - F{i}'),2); % L1
d{i} = pdist2(F{i}.', testSample, 'euclidean');
elseif ( distancetype == 2)
d{i} = pdist2{F{i}.', testSample, 'cityblock'); %L1
else
d{i} = pdist2(F{i}.', testSample, 'mahalanobis'); %56
end
d{i} = sort(d{i});
d{i}(end+1:max(numOfTrainSamples)) = inf;
else
d{i} = inf;
end
end
else % single dimension (NO SUM required!!!)
for i=1:numOfClasses
if (numOfTrainSamples(i)>0)
d{i} = (abs(repmat(testSample, [numOfTrainSamples(i) 1]) - F{i}')');
d{i} = sort(d{i});
d{i}(end+1:max(numOfTrainSamples)) = inf;
else
d{i} = inf;
end
end
end
kAll = zeros(numOfClasses, 1);
for j=1:k
curArray = zeros(numOfClasses, 1);
for i=1:numOfClasses
curArray(i) = d{i}(kAll(i)+1);
end
[MIN, IMIN] = min(curArray);
kAll(IMIN) = kAll(IMIN) + 1;
end
if ( NORMALIZE == 0 )
Ps = (kAll ./ k);
else
Ps = kAll ./ numOfTrainSamples';
Ps = Ps / sum(Ps);
end
[MAX, IMAX] = max(Ps);
winnerClass = IMAX;
Kamil Kacer
on 20 Nov 2020
Walter Roberson
on 21 Nov 2020
function [Ps, winnerClass] = classifyKNN_D_Multi(F, testSample, k, NORMALIZE, distancetype )
% function [Ps, winnerClass] = classifyKNN_D_Multi(F, testSample, k, NORMALIZE, distancetype);
%
% This function is used for classifying an unknown sample using the kNN
% algorithm, in its multi-class form.
%
% ARGUMENTS:
% - F: an CELL array that contains the feature values for each class. I.e.,
% F{1} is a matrix of size numOfDimensions x numofSamples FOR THE FIRST
% CLASS, etc.
%
% - testSample: the input sample to be classified
% - k: the kNN parameter
% - NORMALIZE: use class priors to weight results
% - useL1distance: use L1 instead of L2 distance
%
% RETURNS:
% - Ps: an array that contains the classification probabilities for each class
% - winnerClass: the label of the winner class
%%error(nargchk(4,5,nargin))
switch nargin
case 4
distancetype = 1; % euclidean distance if 4 variables included
case 5
distancetype = 3;
otherwise
disp('error')
end
numOfClasses = length(F);
if (size(testSample, 2)==1)
testSample = testSample';
end
% initilization of distance vectors:
numOfDims = zeros( 1, numOfClasses );
numOfTrainSamples = zeros( 1, numOfClasses );
d = cell(numOfClasses,1);
% d{i} is a vector, whose elements represent the distance of the testing
% sample from all the samples of i-th class
testSample(isnan(testSample)) = 0.0;
for i=1:numOfClasses
[ numOfDims(i), numOfTrainSamples(i) ] = size( F{i} );
d{i} = inf*ones(max(numOfTrainSamples), 1); % we fill it with inf values
F{i}(isnan(F{i})) = 0.0;
end
if (length(testSample)>1)
for i=1:numOfClasses % for each class:
if (numOfTrainSamples(i)>0)
if ( distancetype == 1 )
% d{i} = sum( abs(repmat(testSample, [numOfTrainSamples(i) 1]) - F{i}'),2); % L1
d{i} = pdist2(F{i}.', testSample, 'euclidean');
elseif ( distancetype == 2)
d{i} = pdist2(F{i}.', testSample, 'cityblock'); %L1
else
d{i} = pdist2(F{i}.', testSample, 'mahalanobis'); %56
end
d{i} = sort(d{i});
d{i}(end+1:max(numOfTrainSamples)) = inf;
else
d{i} = inf;
end
end
else % single dimension (NO SUM required!!!)
for i=1:numOfClasses
if (numOfTrainSamples(i)>0)
d{i} = (abs(repmat(testSample, [numOfTrainSamples(i) 1]) - F{i}')');
d{i} = sort(d{i});
d{i}(end+1:max(numOfTrainSamples)) = inf;
else
d{i} = inf;
end
end
end
kAll = zeros(numOfClasses, 1);
for j=1:k
curArray = zeros(numOfClasses, 1);
for i=1:numOfClasses
curArray(i) = d{i}(kAll(i)+1);
end
[MIN, IMIN] = min(curArray);
kAll(IMIN) = kAll(IMIN) + 1;
end
if ( NORMALIZE == 0 )
Ps = (kAll ./ k);
else
Ps = kAll ./ numOfTrainSamples';
Ps = Ps / sum(Ps);
end
[MAX, IMAX] = max(Ps);
winnerClass = IMAX;
Kamil Kacer
on 21 Nov 2020
Walter Roberson
on 21 Nov 2020
Are you still using
[P, label] = classifyKNN_D_Multi(Features, ...
(mtFeatures - MEAN') ./ STD', kNN,1);
Kamil Kacer
on 21 Nov 2020
Walter Roberson
on 21 Nov 2020
You told me before that you were passing in only 4 arguments but were still having mahalonobis being invoked, so that is the problem that I am trying to debug. This code shows you passing in 5 arguments. You specifically asked that passing in 5 arguments should always mean mahalonobis. I argued against that but you insisted so I changed it back for you.
Kamil Kacer
on 21 Nov 2020
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