How to make cosine Distance classify

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Kong 2020-3-13

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评论： Kong 2020-3-17

采纳的回答： Ameer Hamza

Hello! I am a beginner in Matlab.

I have dataset that consisted of 90 data (10 label x 9 data).

Can I get an idea to make classify based on cosine distance or euclidean distance, etc?

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Ameer Hamza 2020-3-13

Can you show an example of your dataset. For example, attach a small dataset and describe what is your expected output.

Kong 2020-3-13

geo01_KTH.csv

Hello.

I attached the file. The dataset is consisted of 120 x 2353 (column 2353 is label, 0~6).

I want to calculate each rows using cosine distance or euclidean distance and classify the result.

Thank you!

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Ameer Hamza 2020-3-14

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If you want to classify a new vector by using the Euclidean or cosine distance between the rows of your matrix and the new vector the try this

data = readmatrix('geo01_KTH.csv');
predictors = data(:, 1:end-1);
labels = data(:, end);
predictors = normalize(predictors, 2, 'range'); % normalize each row to be in range 0-1
x = rand(1, 2352); % generate a random vector
euclidean_dist = pdist2(predictors, x, 'euclidean');
cosine_dist = pdist2(predictors, x, 'cosine');
[~, euclidean_index] = min(euclidean_dist);
[~, cosine_index] = min(cosine_dist);
euclidean_prediction = labels(euclidean_index);
cosine_prediction = labels(cosine_index);

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Ameer Hamza 2020-3-17

What is the size of predictors_train and x?

Kong 2020-3-17

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I am sorry that I was mistaken.

predictors_train : 80 x 2856, predictors_test : 10 x 2856,

When I modify the code as below, I got this value.

How can I compare this prediction with real labels to calculate accuracy?

for i = 1:10
euclidean_dist{i} = pdist2(predictors_train, predictors_test(i,:), 'euclidean');
[~, euclidean_index{i}] = min(euclidean_dist{i});
euclidean_prediction{i} = labels(euclidean_index{i});
end

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