| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748 |
- function centroids = computeCentroids(X, idx, K)
- %COMPUTECENTROIDS returns the new centroids by computing the means of the
- %data points assigned to each centroid.
- % centroids = COMPUTECENTROIDS(X, idx, K) returns the new centroids by
- % computing the means of the data points assigned to each centroid. It is
- % given a dataset X where each row is a single data point, a vector
- % idx of centroid assignments (i.e. each entry in range [1..K]) for each
- % example, and K, the number of centroids. You should return a matrix
- % centroids, where each row of centroids is the mean of the data points
- % assigned to it.
- %
- % Useful variables
- [m n] = size(X);
- % You need to return the following variables correctly.
- centroids = zeros(K, n);
- % ====================== YOUR CODE HERE ======================
- % Instructions: Go over every centroid and compute mean of all points that
- % belong to it. Concretely, the row vector centroids(i, :)
- % should contain the mean of the data points assigned to
- % centroid i.
- %
- % Note: You can use a for-loop over the centroids to compute this.
- %
- for k = 1:K
- % obtain a vector that contains only k in the idx vector
- idx_konly = (idx == k);
- % count the number of examples that are assigned centroid k
- num_ex_assigned_k = sum(idx_konly);
- % compute the mean value for centroid k
- % multiplying idx_konly with each element in X results in a matrix that have all the
- % unwanted values zeroed out, leaving the ones that are assigned k
- centroids(k,:) = 1 / abs(num_ex_assigned_k) * sum(idx_konly .* X);
- endfor
- % =============================================================
- end
|
