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@ -1,7 +1,7 @@
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function centroids = computeCentroids(X, idx, K)
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function centroids = computeCentroids(X, idx, K)
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%COMPUTECENTROIDS returs the new centroids by computing the means of the
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%COMPUTECENTROIDS returs the new centroids by computing the means of the
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%data points assigned to each centroid.
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%data points assigned to each centroid.
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% centroids = COMPUTECENTROIDS(X, idx, K) returns the new centroids by
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% centroids = COMPUTECENTROIDS(X, idx, K) returns the new centroids by
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% computing the means of the data points assigned to each centroid. It is
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% computing the means of the data points assigned to each centroid. It is
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% given a dataset X where each row is a single data point, a vector
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% given a dataset X where each row is a single data point, a vector
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% idx of centroid assignments (i.e. each entry in range [1..K]) for each
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% idx of centroid assignments (i.e. each entry in range [1..K]) for each
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@ -19,18 +19,27 @@ centroids = zeros(K, n);
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% ====================== YOUR CODE HERE ======================
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% ====================== YOUR CODE HERE ======================
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% Instructions: Go over every centroid and compute mean of all points that
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% Instructions: Go over every centroid and compute mean of all points that
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% belong to it. Concretely, the row vector centroids(i, :)
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% belong to it. Concretely, the row vector centroids(k, :)
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% should contain the mean of the data points assigned to
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% should contain the mean of the data points assigned to
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% centroid i.
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% centroid k.
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%
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%
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% Note: You can use a for-loop over the centroids to compute this.
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% Note: You can use a for-loop over the centroids to compute this.
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%
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%
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for k = 1:K
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count = 0;
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% XXX vectorize
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for i = 1:m
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if idx(i) == k
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centroids(k, :) += X(i, :);
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count += 1;
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end
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end
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if count > 0
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centroids(k, :) /= count;
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end
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end
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% =============================================================
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% =============================================================
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