I am currently using a dataset of over 2.5 million images, of which I use the image itself as a comparison to eachother, for use in a content-based recommendati
Suppose I have a numpy matrix like the following: array([array([ 0.0072427 , 0.00669255, 0.00785213, 0.00845336, 0.01042869]), array([ 0.00710799, 0.00
I have a code to calculate cosine similarity between two matrices: def cos_cdist_1(matrix, vector): v = vector.reshape(1, -1) return sp.distance.cdist(
I am trying to create matrices for cosine and euclidean distances of a document. not too sure how I would approach this question. Any advice would be appreciate