In that case, the cosine similarity will have a value of 0 this means that the two vectors are orthogonal or perpendicular to each other.Īs the cosine similarity measurement gets closer to 1, then the angle between the two vectors A and B is smaller. Suppose the angle between the two vectors was 90 degrees. The similarity measurement is a measure of the cosine of the angle between the two non-zero vectors A and B. ![]() func cosineSimilarity(A:, B: ) -> CGFloat Ĭosine Similarity is a value that is bound by a constrained range of 0 and 1. I’ll also provide several applications and domains where cosine similarity is leveraged, and finally, there will be a code snippet of the algorithm in the Swift programming language.Ĭosine similarity is a commonly used similarity measurement technique that can be found in widely used libraries and tools such as Matlab, SciKit-Learn, TensorFlow etc.īelow is a quick implementation of the cosine similarity logic in Swift. In this article, I will explain the basics of cosine similarity. I was intrigued by the simplicity of this implementation, and more importantly, it was straightforward to understand. I stumbled upon a similarity measurement called Cosine Similarity. ![]() Trying to solve this problem led me to a world of maths - I’m not a massive fan of maths. The objects in this project were two images that contained humans performing certain poses the objective was to quantify how similar the poses in the images were. IntroductionĪ specific requirement within a project I’m currently undertaking required me to explore methods of quantifying the similarity between two or more objects. Learn the basics of AI and Deep Learning with TensorFlow and Keras in this Live Training Session hosted by Me. All rights reserved.Understand the basics behind a technique that is used across different fields and domains of machine learning. The improved cosine measures of SNSs based on cosine function can overcome some drawbacks of existing cosine similarity measures of SNSs in vector space, and then their diagnosis method is very suitable for handling the medical diagnosis problems with simplified neutrosophic information and demonstrates the effectiveness and rationality of medical diagnoses.Ĭosine similarity measure Interval neutrosophic set Medical diagnosis Simplified neutrosophic set Single valued neutrosophic set.Ĭopyright © 2014 Elsevier B.V. By two medical diagnoses problems, the medical diagnoses using various similarity measures of SNSs indicated the identical diagnosis results and demonstrated the effectiveness and rationality of the diagnosis method proposed in this paper. Two numerical examples all demonstrated that the improved cosine similarity measures of SNSs based on the cosine function can overcome the shortcomings of the existing cosine similarity measures between two vectors in some cases. Then, the medical diagnosis method based on the improved cosine similarity measures was applied to two medical diagnosis problems to show the applications and effectiveness of the proposed method. ![]() In the medical diagnosis method, we can find a proper diagnosis by the cosine similarity measures between the symptoms and considered diseases which are represented by SNSs. Then, we compared the improved cosine similarity measures of SNSs with existing cosine similarity measures of SNSs by numerical examples to demonstrate their effectiveness and rationality for overcoming some shortcomings of existing cosine similarity measures of SNSs in some cases. The improved cosine similarity measures between SNSs were introduced based on cosine function. Further, a medical diagnosis method using the improved cosine similarity measures was proposed to solve medical diagnosis problems with simplified neutrosophic information. Then, weighted cosine similarity measures of SNSs were introduced by taking into account the importance of each element. To overcome some disadvantages of existing cosine similarity measures of simplified neutrosophic sets (SNSs) in vector space, this paper proposed improved cosine similarity measures of SNSs based on cosine function, including single valued neutrosophic cosine similarity measures and interval neutrosophic cosine similarity measures. In pattern recognition and medical diagnosis, similarity measure is an important mathematical tool.
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