Hi everyone, I'm working with VNFeaturePrintObservation in Swift to compute the similarity between images. The computeDistance function allows me to calculate the distance between two images, and I want to cluster similar images based on these distances. Current Approach Right now, I'm using a brute-force approach where I compare every image against every other image in the dataset. This results in an O(n^2) complexity, which quickly becomes a bottleneck. With 5000 images, it takes around 10 seconds to complete, which is too slow for my use case. Question Are there any efficient algorithms or data structures I can use to improve performance? If anyone has experience with optimizing feature vector clustering or has suggestions on how to scale this efficiently, I'd really appreciate your insights. Thanks!

is forKey:fileSize considered accessing non-public API? has your app been rejected at review stage due to this? let resources = PHAssetResource.assetResources(for: asset) if let resource = resources.first { if let fileSize = resource.value(forKey: "fileSize") as? Int { return fileSize } }

I have been struggling to test the IAP response but it is returning empty. I am now in the very beginning of one app, and I don't want to submit the contacts and banking and tax stuff that early. are these necessary for even testing IAP results locally? I think it does not make sense if I have to.