"use strict";
/**
 * Graph Algorithms - MinCut, Spectral Clustering, Community Detection
 *
 * Provides graph partitioning and clustering algorithms for:
 * - Code module detection
 * - Dependency clustering
 * - Architecture analysis
 * - Refactoring suggestions
 */
Object.defineProperty(exports, "__esModule", { value: true });
exports.buildGraph = buildGraph;
exports.minCut = minCut;
exports.spectralClustering = spectralClustering;
exports.louvainCommunities = louvainCommunities;
exports.calculateModularity = calculateModularity;
exports.findBridges = findBridges;
exports.findArticulationPoints = findArticulationPoints;
/**
 * Build adjacency representation from edges
 */
function buildGraph(nodes, edges) {
    const adjacency = new Map();
    for (const node of nodes) {
        adjacency.set(node, new Map());
    }
    for (const { from, to, weight = 1 } of edges) {
        if (!adjacency.has(from))
            adjacency.set(from, new Map());
        if (!adjacency.has(to))
            adjacency.set(to, new Map());
        // Undirected graph - add both directions
        adjacency.get(from).set(to, weight);
        adjacency.get(to).set(from, weight);
    }
    return { nodes, edges, adjacency };
}
/**
 * Minimum Cut (Stoer-Wagner algorithm)
 *
 * Finds the minimum weight cut that partitions the graph into two parts.
 * Useful for finding loosely coupled module boundaries.
 */
function minCut(graph) {
    const n = graph.nodes.length;
    if (n < 2) {
        return { groups: [graph.nodes], cutWeight: 0, modularity: 0 };
    }
    // Copy adjacency for modification
    const adj = new Map();
    for (const [node, neighbors] of graph.adjacency) {
        adj.set(node, new Map(neighbors));
    }
    let minCutWeight = Infinity;
    let bestPartition = [];
    const merged = new Map(); // Track merged nodes
    for (const node of graph.nodes) {
        merged.set(node, [node]);
    }
    let remaining = [...graph.nodes];
    // Stoer-Wagner phases
    while (remaining.length > 1) {
        // Maximum adjacency search
        const inA = new Set([remaining[0]]);
        const weights = new Map();
        for (const node of remaining) {
            if (!inA.has(node)) {
                weights.set(node, adj.get(remaining[0])?.get(node) || 0);
            }
        }
        let lastAdded = remaining[0];
        let beforeLast = remaining[0];
        while (inA.size < remaining.length) {
            // Find node with maximum weight to A
            let maxWeight = -Infinity;
            let maxNode = '';
            for (const [node, weight] of weights) {
                if (!inA.has(node) && weight > maxWeight) {
                    maxWeight = weight;
                    maxNode = node;
                }
            }
            if (!maxNode)
                break;
            beforeLast = lastAdded;
            lastAdded = maxNode;
            inA.add(maxNode);
            // Update weights
            for (const [neighbor, w] of adj.get(maxNode) || []) {
                if (!inA.has(neighbor)) {
                    weights.set(neighbor, (weights.get(neighbor) || 0) + w);
                }
            }
        }
        // Cut of the phase
        const cutWeight = weights.get(lastAdded) || 0;
        if (cutWeight < minCutWeight) {
            minCutWeight = cutWeight;
            const lastGroup = merged.get(lastAdded) || [lastAdded];
            const otherNodes = remaining.filter(n => n !== lastAdded).flatMap(n => merged.get(n) || [n]);
            bestPartition = [lastGroup, otherNodes];
        }
        // Merge last two nodes
        if (remaining.length > 1) {
            // Merge lastAdded into beforeLast
            const mergedNodes = [...(merged.get(beforeLast) || []), ...(merged.get(lastAdded) || [])];
            merged.set(beforeLast, mergedNodes);
            // Update adjacency
            for (const [neighbor, w] of adj.get(lastAdded) || []) {
                if (neighbor !== beforeLast) {
                    const current = adj.get(beforeLast)?.get(neighbor) || 0;
                    adj.get(beforeLast)?.set(neighbor, current + w);
                    adj.get(neighbor)?.set(beforeLast, current + w);
                }
            }
            // Remove lastAdded
            remaining = remaining.filter(n => n !== lastAdded);
            adj.delete(lastAdded);
            for (const [, neighbors] of adj) {
                neighbors.delete(lastAdded);
            }
        }
    }
    const modularity = calculateModularity(graph, bestPartition);
    return {
        groups: bestPartition.filter(g => g.length > 0),
        cutWeight: minCutWeight,
        modularity,
    };
}
/**
 * Spectral Clustering (using power iteration)
 *
 * Uses graph Laplacian eigenvectors for clustering.
 * Good for finding natural clusters in code dependencies.
 */
function spectralClustering(graph, k = 2) {
    const n = graph.nodes.length;
    const nodeIndex = new Map(graph.nodes.map((node, i) => [node, i]));
    const clusters = new Map();
    if (n === 0) {
        return { clusters, eigenvalues: [], coordinates: new Map() };
    }
    // Build Laplacian matrix (D - A)
    const degree = new Float64Array(n);
    const laplacian = Array(n).fill(null).map(() => Array(n).fill(0));
    for (const [node, neighbors] of graph.adjacency) {
        const i = nodeIndex.get(node);
        let d = 0;
        for (const [neighbor, weight] of neighbors) {
            const j = nodeIndex.get(neighbor);
            laplacian[i][j] = -weight;
            d += weight;
        }
        degree[i] = d;
        laplacian[i][i] = d;
    }
    // Normalized Laplacian: D^(-1/2) L D^(-1/2)
    for (let i = 0; i < n; i++) {
        for (let j = 0; j < n; j++) {
            if (degree[i] > 0 && degree[j] > 0) {
                laplacian[i][j] /= Math.sqrt(degree[i] * degree[j]);
            }
        }
    }
    // Power iteration to find eigenvectors
    const eigenvectors = [];
    const eigenvalues = [];
    for (let ev = 0; ev < Math.min(k, n); ev++) {
        let vector = new Float64Array(n);
        for (let i = 0; i < n; i++) {
            vector[i] = Math.random();
        }
        normalize(vector);
        // Deflation: orthogonalize against previous eigenvectors
        for (const prev of eigenvectors) {
            const dot = dotProduct(vector, new Float64Array(prev));
            for (let i = 0; i < n; i++) {
                vector[i] -= dot * prev[i];
            }
        }
        normalize(vector);
        // Power iteration
        for (let iter = 0; iter < 100; iter++) {
            const newVector = new Float64Array(n);
            for (let i = 0; i < n; i++) {
                for (let j = 0; j < n; j++) {
                    newVector[i] += laplacian[i][j] * vector[j];
                }
            }
            // Deflation
            for (const prev of eigenvectors) {
                const dot = dotProduct(newVector, new Float64Array(prev));
                for (let i = 0; i < n; i++) {
                    newVector[i] -= dot * prev[i];
                }
            }
            normalize(newVector);
            vector = newVector;
        }
        // Compute eigenvalue
        let eigenvalue = 0;
        for (let i = 0; i < n; i++) {
            let sum = 0;
            for (let j = 0; j < n; j++) {
                sum += laplacian[i][j] * vector[j];
            }
            eigenvalue += vector[i] * sum;
        }
        eigenvectors.push(Array.from(vector));
        eigenvalues.push(eigenvalue);
    }
    // K-means clustering on eigenvector coordinates
    const coordinates = new Map();
    for (let i = 0; i < n; i++) {
        coordinates.set(graph.nodes[i], eigenvectors.map(ev => ev[i]));
    }
    // Simple k-means
    const clusterAssignment = kMeans(graph.nodes.map(node => coordinates.get(node)), k);
    for (let i = 0; i < n; i++) {
        clusters.set(graph.nodes[i], clusterAssignment[i]);
    }
    return { clusters, eigenvalues, coordinates };
}
/**
 * Louvain Community Detection
 *
 * Greedy modularity optimization for finding communities.
 * Good for detecting natural module boundaries.
 */
function louvainCommunities(graph) {
    const communities = new Map();
    let communityId = 0;
    // Initialize: each node in its own community
    for (const node of graph.nodes) {
        communities.set(node, communityId++);
    }
    // Total edge weight
    let m = 0;
    for (const { weight = 1 } of graph.edges) {
        m += weight;
    }
    m /= 2; // Undirected
    if (m === 0)
        return communities;
    // Node weights (sum of edge weights)
    const nodeWeight = new Map();
    for (const node of graph.nodes) {
        let w = 0;
        for (const [, weight] of graph.adjacency.get(node) || []) {
            w += weight;
        }
        nodeWeight.set(node, w);
    }
    // Community weights
    const communityWeight = new Map();
    for (const node of graph.nodes) {
        const c = communities.get(node);
        communityWeight.set(c, (communityWeight.get(c) || 0) + (nodeWeight.get(node) || 0));
    }
    // Iterate until no improvement
    let improved = true;
    while (improved) {
        improved = false;
        for (const node of graph.nodes) {
            const currentCommunity = communities.get(node);
            const ki = nodeWeight.get(node) || 0;
            // Calculate modularity gain for moving to neighbor communities
            let bestCommunity = currentCommunity;
            let bestGain = 0;
            const neighborCommunities = new Set();
            for (const [neighbor] of graph.adjacency.get(node) || []) {
                neighborCommunities.add(communities.get(neighbor));
            }
            for (const targetCommunity of neighborCommunities) {
                if (targetCommunity === currentCommunity)
                    continue;
                // Calculate edge weight to target community
                let ki_in = 0;
                for (const [neighbor, weight] of graph.adjacency.get(node) || []) {
                    if (communities.get(neighbor) === targetCommunity) {
                        ki_in += weight;
                    }
                }
                const sumTot = communityWeight.get(targetCommunity) || 0;
                const gain = ki_in / m - (ki * sumTot) / (2 * m * m);
                if (gain > bestGain) {
                    bestGain = gain;
                    bestCommunity = targetCommunity;
                }
            }
            // Move node if beneficial
            if (bestCommunity !== currentCommunity) {
                communities.set(node, bestCommunity);
                // Update community weights
                communityWeight.set(currentCommunity, (communityWeight.get(currentCommunity) || 0) - ki);
                communityWeight.set(bestCommunity, (communityWeight.get(bestCommunity) || 0) + ki);
                improved = true;
            }
        }
    }
    // Renumber communities to be contiguous
    const renumber = new Map();
    let newId = 0;
    for (const [node, c] of communities) {
        if (!renumber.has(c)) {
            renumber.set(c, newId++);
        }
        communities.set(node, renumber.get(c));
    }
    return communities;
}
/**
 * Calculate modularity of a partition
 */
function calculateModularity(graph, partition) {
    let m = 0;
    for (const { weight = 1 } of graph.edges) {
        m += weight;
    }
    m /= 2;
    if (m === 0)
        return 0;
    let modularity = 0;
    for (const group of partition) {
        const groupSet = new Set(group);
        // Edges within group
        let inGroup = 0;
        let degreeSum = 0;
        for (const node of group) {
            for (const [neighbor, weight] of graph.adjacency.get(node) || []) {
                if (groupSet.has(neighbor)) {
                    inGroup += weight;
                }
                degreeSum += weight;
            }
        }
        inGroup /= 2; // Count each edge once
        modularity += inGroup / m - Math.pow(degreeSum / (2 * m), 2);
    }
    return modularity;
}
/**
 * Find bridges (edges whose removal disconnects components)
 */
function findBridges(graph) {
    const bridges = [];
    const visited = new Set();
    const discovery = new Map();
    const low = new Map();
    const parent = new Map();
    let time = 0;
    function dfs(node) {
        visited.add(node);
        discovery.set(node, time);
        low.set(node, time);
        time++;
        for (const [neighbor] of graph.adjacency.get(node) || []) {
            if (!visited.has(neighbor)) {
                parent.set(neighbor, node);
                dfs(neighbor);
                low.set(node, Math.min(low.get(node), low.get(neighbor)));
                if (low.get(neighbor) > discovery.get(node)) {
                    bridges.push({ from: node, to: neighbor });
                }
            }
            else if (neighbor !== parent.get(node)) {
                low.set(node, Math.min(low.get(node), discovery.get(neighbor)));
            }
        }
    }
    for (const node of graph.nodes) {
        if (!visited.has(node)) {
            parent.set(node, null);
            dfs(node);
        }
    }
    return bridges;
}
/**
 * Find articulation points (nodes whose removal disconnects components)
 */
function findArticulationPoints(graph) {
    const points = [];
    const visited = new Set();
    const discovery = new Map();
    const low = new Map();
    const parent = new Map();
    let time = 0;
    function dfs(node) {
        visited.add(node);
        discovery.set(node, time);
        low.set(node, time);
        time++;
        let children = 0;
        for (const [neighbor] of graph.adjacency.get(node) || []) {
            if (!visited.has(neighbor)) {
                children++;
                parent.set(neighbor, node);
                dfs(neighbor);
                low.set(node, Math.min(low.get(node), low.get(neighbor)));
                // Root with 2+ children or non-root with low[v] >= disc[u]
                if ((parent.get(node) === null && children > 1) ||
                    (parent.get(node) !== null && low.get(neighbor) >= discovery.get(node))) {
                    if (!points.includes(node)) {
                        points.push(node);
                    }
                }
            }
            else if (neighbor !== parent.get(node)) {
                low.set(node, Math.min(low.get(node), discovery.get(neighbor)));
            }
        }
    }
    for (const node of graph.nodes) {
        if (!visited.has(node)) {
            parent.set(node, null);
            dfs(node);
        }
    }
    return points;
}
// Helper functions
function normalize(v) {
    let sum = 0;
    for (let i = 0; i < v.length; i++) {
        sum += v[i] * v[i];
    }
    const norm = Math.sqrt(sum);
    if (norm > 0) {
        for (let i = 0; i < v.length; i++) {
            v[i] /= norm;
        }
    }
}
function dotProduct(a, b) {
    let sum = 0;
    for (let i = 0; i < a.length; i++) {
        sum += a[i] * b[i];
    }
    return sum;
}
function kMeans(points, k, maxIter = 100) {
    const n = points.length;
    if (n === 0 || k === 0)
        return [];
    const dim = points[0].length;
    // Random initialization
    const centroids = [];
    const used = new Set();
    while (centroids.length < Math.min(k, n)) {
        const idx = Math.floor(Math.random() * n);
        if (!used.has(idx)) {
            used.add(idx);
            centroids.push([...points[idx]]);
        }
    }
    const assignment = new Array(n).fill(0);
    for (let iter = 0; iter < maxIter; iter++) {
        // Assign points to nearest centroid
        let changed = false;
        for (let i = 0; i < n; i++) {
            let minDist = Infinity;
            let minC = 0;
            for (let c = 0; c < centroids.length; c++) {
                let dist = 0;
                for (let d = 0; d < dim; d++) {
                    dist += Math.pow(points[i][d] - centroids[c][d], 2);
                }
                if (dist < minDist) {
                    minDist = dist;
                    minC = c;
                }
            }
            if (assignment[i] !== minC) {
                assignment[i] = minC;
                changed = true;
            }
        }
        if (!changed)
            break;
        // Update centroids
        const counts = new Array(k).fill(0);
        for (let c = 0; c < centroids.length; c++) {
            for (let d = 0; d < dim; d++) {
                centroids[c][d] = 0;
            }
        }
        for (let i = 0; i < n; i++) {
            const c = assignment[i];
            counts[c]++;
            for (let d = 0; d < dim; d++) {
                centroids[c][d] += points[i][d];
            }
        }
        for (let c = 0; c < centroids.length; c++) {
            if (counts[c] > 0) {
                for (let d = 0; d < dim; d++) {
                    centroids[c][d] /= counts[c];
                }
            }
        }
    }
    return assignment;
}
exports.default = {
    buildGraph,
    minCut,
    spectralClustering,
    louvainCommunities,
    calculateModularity,
    findBridges,
    findArticulationPoints,
};