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- type VectorFunction = (xVector: number[], yVector: number[]) => number;
- /**
- * Apply a row-wise function between two matrices with the same number of columns.
- *
- * @param {number[][]} X - The first matrix.
- * @param {number[][]} Y - The second matrix.
- * @param {VectorFunction} func - The function to apply.
- *
- * @throws {Error} If the number of columns in X and Y are not the same.
- *
- * @returns {number[][] | [[]]} A matrix where each row represents the result of applying the function between the corresponding rows of X and Y.
- */
- export declare function matrixFunc(X: number[][], Y: number[][], func: VectorFunction): number[][];
- export declare function normalize(M: number[][], similarity?: boolean): number[][];
- /**
- * This function calculates the row-wise cosine similarity between two matrices with the same number of columns.
- *
- * @param {number[][]} X - The first matrix.
- * @param {number[][]} Y - The second matrix.
- *
- * @throws {Error} If the number of columns in X and Y are not the same.
- *
- * @returns {number[][] | [[]]} A matrix where each row represents the cosine similarity values between the corresponding rows of X and Y.
- */
- export declare function cosineSimilarity(X: number[][], Y: number[][]): number[][];
- export declare function innerProduct(X: number[][], Y: number[][]): number[][];
- export declare function euclideanDistance(X: number[][], Y: number[][]): number[][];
- /**
- * This function implements the Maximal Marginal Relevance algorithm
- * to select a set of embeddings that maximizes the diversity and relevance to a query embedding.
- *
- * @param {number[]|number[][]} queryEmbedding - The query embedding.
- * @param {number[][]} embeddingList - The list of embeddings to select from.
- * @param {number} [lambda=0.5] - The trade-off parameter between relevance and diversity.
- * @param {number} [k=4] - The maximum number of embeddings to select.
- *
- * @returns {number[]} The indexes of the selected embeddings in the embeddingList.
- */
- export declare function maximalMarginalRelevance(queryEmbedding: number[] | number[][], embeddingList: number[][], lambda?: number, k?: number): number[];
- export {};
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