kosinukseta

Kosinukseta, also known as the "cosine similarity," is a measure used to determine the similarity between two non-zero vectors in an inner product space. It is defined as the cosine of the angle between the two vectors, which is the dot product of the vectors divided by the product of their magnitudes. The cosine similarity ranges from -1 to 1, where 1 indicates that the vectors are identical, 0 indicates that the vectors are orthogonal (i.e., they have no similarity), and -1 indicates that the vectors are diametrically opposed. This metric is widely used in various fields, including information retrieval, machine learning, and natural language processing, to compare the similarity between documents, sentences, or other types of data. The cosine similarity is particularly useful when dealing with high-dimensional data, as it is less affected by the magnitude of the vectors compared to other similarity measures, such as the Euclidean distance.