import { logger } from "./logger";
import { MapDocument } from "../controllers/v2/types";

export function performCosineSimilarity(links: string[], searchQuery: string) {
  try {
    // Function to calculate cosine similarity
    const cosineSimilarity = (vec1: number[], vec2: number[]): number => {
      const dotProduct = vec1.reduce((sum, val, i) => sum + val * vec2[i], 0);
      const magnitude1 = Math.sqrt(
        vec1.reduce((sum, val) => sum + val * val, 0),
      );
      const magnitude2 = Math.sqrt(
        vec2.reduce((sum, val) => sum + val * val, 0),
      );
      if (magnitude1 === 0 || magnitude2 === 0) return 0;
      return dotProduct / (magnitude1 * magnitude2);
    };

    // Function to convert text to vector
    const textToVector = (text: string): number[] => {
      const words = searchQuery.toLowerCase().split(/\W+/);
      return words.map(word => {
        const count = (text.toLowerCase().match(new RegExp(word, "g")) || [])
          .length;
        return count / text.length;
      });
    };

    // Calculate similarity scores
    const similarityScores = links.map(link => {
      const linkVector = textToVector(link);
      const searchVector = textToVector(searchQuery);
      return cosineSimilarity(linkVector, searchVector);
    });

    // Sort links based on similarity scores and print scores
    const a = links
      .map((link, index) => ({ link, score: similarityScores[index] }))
      .sort((a, b) => b.score - a.score);

    links = a.map(item => item.link);
    return links;
  } catch (error) {
    logger.error(`Error performing cosine similarity: ${error}`);
    return links;
  }
}

export function performCosineSimilarityV2(
  links: MapDocument[],
  searchQuery: string,
) {
  try {
    // Function to calculate cosine similarity
    const cosineSimilarity = (vec1: number[], vec2: number[]): number => {
      const dotProduct = vec1.reduce((sum, val, i) => sum + val * vec2[i], 0);
      const magnitude1 = Math.sqrt(
        vec1.reduce((sum, val) => sum + val * val, 0),
      );
      const magnitude2 = Math.sqrt(
        vec2.reduce((sum, val) => sum + val * val, 0),
      );
      if (magnitude1 === 0 || magnitude2 === 0) return 0;
      return dotProduct / (magnitude1 * magnitude2);
    };

    // Function to convert text to vector
    const textToVector = (text: string): number[] => {
      const words = searchQuery.toLowerCase().split(/\W+/);
      return words.map(word => {
        const count = (text.toLowerCase().match(new RegExp(word, "g")) || [])
          .length;
        return count / text.length;
      });
    };

    // Calculate similarity scores
    const similarityScores = links.map(link => {
      const linkVector = textToVector(link.url); // TODO: we can use title/description here as well!
      const searchVector = textToVector(searchQuery);
      return cosineSimilarity(linkVector, searchVector);
    });

    // Sort links based on similarity scores and print scores
    const a = links
      .map((link, index) => ({ link, score: similarityScores[index] }))
      .sort((a, b) => b.score - a.score);

    links = a.map(item => item.link);
    return links;
  } catch (error) {
    logger.error(`Error performing cosine similarity: ${error}`);
    return links;
  }
}