Go: package main

import (
	"bufio"
	"fmt"
	"os"
	"strconv"
)

const (
	MAX_N      = 200
	MAX_WEIGHT = 2000
)

type Edge struct {
	start  int
	end    int
	weight int
}

type ShortestInfo struct {
	edges    []Edge
	shortest int
}

func main() {
	sc := bufio.NewScanner(os.Stdin)
	sc.Split(bufio.ScanWords)

	n, err := nextInt(sc)
	if err != nil {
		panic(err)
	}

	shortestList := make([]ShortestInfo, n)
	for i := 1; i < n; i++ {
		shortestList[i] = ShortestInfo{edges: []Edge{}, shortest: MAX_N*MAX_WEIGHT + 1}
	}

	adjacentMatrix := make([][]int, n)
	for i := 0; i < n; i++ {
		adjacentMatrix[i] = make([]int, n)
		for j := 0; j < n; j++ {
			v, err := nextInt(sc)
			if err != nil {
				panic(err)
			}
			adjacentMatrix[i][j] = v
		}
	}

	mst([]int{1}, shortestList, adjacentMatrix)
	fmt.Println(shortestList)
	fmt.Println(calculateMinWeight(shortestList))
}

func calculateMinWeight(shortestList []ShortestInfo) int {
	visitedList := make([]bool, len(shortestList))
	visitedList[0] = true
	sumWeight := 0
	for !visitedAll(visitedList) {
		shortestInfo := shortestList[0]
		shortestList = shortestList[1:]
		edges := shortestInfo.edges
		for _, edge := range edges {
			if !visitedList[edge.end-1] {
				visitedList[edge.end-1] = true
				sumWeight += edge.weight
			}
		}
	}
	return sumWeight
}

func visitedAll(visitedList []bool) bool {
	for _, v := range visitedList {
		if !v {
			return false
		}
	}
	return true
}

func mst(nodeStack []int, shortestList []ShortestInfo, adjacentMatrix [][]int) {
	for len(nodeStack) > 0 {
		currentNode := nodeStack[0]
		nodeStack = nodeStack[1:]
		currentShortest := getShortest(currentNode, shortestList)
		adjacents := findAdjacents(currentNode, adjacentMatrix)
		for i := 0; i < len(adjacents); i++ {
			adj := adjacents[i]
			s := getShortest(adj.end, shortestList)
			if currentShortest.shortest+adj.weight < s.shortest || (currentShortest.shortest+adj.weight == s.shortest && len(currentShortest.edges)+1 > len(s.edges)) {
				setShortest(adj.end, adj, currentShortest, shortestList)
				nodeStack = append(nodeStack, adj.end)
			}
		}
	}
}

func getShortest(node int, shortestList []ShortestInfo) ShortestInfo {
	return shortestList[node-1]
}

func setShortest(node int, edge Edge, currentShortest ShortestInfo, shortestList []ShortestInfo) {
	newEdges := make([]Edge, len(currentShortest.edges)+1)
	for i := 0; i < len(newEdges)-1; i++ {
		newEdges[i] = currentShortest.edges[i]
	}
	newEdges[len(newEdges)-1] = edge
	shortestInfo := ShortestInfo{edges: newEdges, shortest: currentShortest.shortest + edge.weight}
	shortestList[node-1] = shortestInfo
}

func findAdjacents(currentNode int, adjacentMatrix [][]int) []Edge {
	adjacents := adjacentMatrix[currentNode-1]
	edges := []Edge{}
	for i, v := range adjacents {
		if v > 0 {
			edges = append(edges, Edge{start: currentNode, end: i + 1, weight: v})
		}
	}
	return edges
}

func nextInt(sc *bufio.Scanner) (int, error) {
	sc.Scan()
	return strconv.Atoi(sc.Text())
}

Python: class UnionFindTree:
    """
        Disjoint set data structure
        Union-Find tree
        complexity:
            init: O(n)
            find, unite, same: O(alpha(n))
    """
    def __init__(self, n):
        self.par = list(range(n))
        self.rank = [0] * n

    def find(self, x):
        if self.par[x] == x:
            return x
        else:
            self.par[x] = self.find(self.par[x])
            return self.par[x]

    def unite(self, x, y):
        x, y = self.find(x), self.find(y)
        if x == y:
            return
        elif self.rank[x] < self.rank[y]:
            self.par[x] = y
        else:
            self.par[y] = x
            if self.rank[x] == self.rank[y]:
                self.rank[x] += 1

    def same(self, x, y):
        return self.find(x) == self.find(y)


class Kruskal:
    """
        Kruskal's algorithm: find Minimum Spanning Tree
        complexity:
            O(E logV)
    """
    def __init__(self, V, E, start=0, INF=10**9):
        """
        :param V: the number of vertices
        :param E:  adjacency list (undirected graph)
        :param start:
        :param INF:
        """
        self.kruskal(V, E)

    def kruskal(self, V, E):
        edges = []
        for v1 in range(V):
            for v2, cost in E[v1]:
                if v1 < v2:
                    edges.append((cost, v1, v2))
        edges.sort(reverse=True)
        self.mincost = 0
        self.min_spanning_tree = []
        uf = UnionFindTree(V)
        while len(self.min_spanning_tree) < V - 1:
            cost, v1, v2 = edges.pop()
            if not uf.same(v1, v2):
                self.mincost += cost
                uf.unite(v1, v2)
                self.min_spanning_tree.append((v1, v2, cost))

    def min_cost(self):
        return self.mincost

    def get_min_spanning_tree(self):
        return sorted(self.min_spanning_tree)


V = int(input())
edges = [[] for _ in range(V)]
for i in range(V):
    for j, d in enumerate(map(int, input().split())):
        if d != -1:
            edges[i].append((j, d))
mst = Kruskal(V, edges)
print(mst.min_cost())

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#!/usr/bin/env python
from __future__ import division, print_function
from sys import stdin, maxint
 
 
def main():
    num = int(stdin.readline())
 
    M = []
    for _ in xrange(num):
        M.append([int(s) for s in stdin.readline().split()])
 
    g = set(xrange(1, num+1))
    index = g.pop()
    h = {index}
    weight = 0
 
    while g:
        minimum = maxint
        for j in h:
            mj = M[j-1]
            for i in g:
                if 0 <= mj[i-1] < minimum:
                    index = i
                    minimum = mj[i-1]
        h.add(index)
        g.remove(index)
        weight += minimum
 
    print(weight)

Python: #!/usr/bin/python3
import itertools

n = int(input())
A = []
for i in range(n):
    src = input()
    dst = src.replace('-1', '3000')
    ai = list(map(int, dst.split()))
    A.append(ai)
# rand
i = 0
S = 0
G = set()
T = set(range(n))

T.remove(i)
G.add(i)

while len(T) > 0:
    m = 3000
    mg = None
    mt = None
    for g, t in itertools.product(G, T):
        if A[g][t] < m:
            mg = g
            mt = t
            m = A[g][t]
    S += m
    G.add(mt)
    T.remove(mt)

print(S)

Python: import array
import collections
import heapq

INF = 1e9

N = int(input())

AdjacentVertex = collections.namedtuple('AdjacentVertex', 'vertex cost')
adjacency_list = collections.defaultdict(set)

NO_VERTEX = -1
 
def compute_mst_prim(max_v, adj_list):
    pred = collections.defaultdict(lambda: NO_VERTEX)
    key = collections.defaultdict(lambda: INF)
    key[0] = 0
    pq = [(key[v], v) for v in range(max_v)]
    heapq.heapify(pq)
    in_pq = array.array("B", (True for _ in range(max_v)))
    while pq:
        _, u = heapq.heappop(pq)
        in_pq[u] = False
        for v, v_cost in adj_list[u]:
            if in_pq[v]:
                weight = v_cost
                if weight < key[v]:
                    pred[v] = u
                    key[v] = weight
                    heapq.heappush(pq, (weight, v))
                    in_pq[v] = True
    return (pred, key)

for i in range(N):
    u = list(map(int, input().split()))
    for j in range(N):
        if u[j] == -1:
            adjacency_list[i].add(AdjacentVertex(j, INF))
            adjacency_list[j].add(AdjacentVertex(i, INF))
        else:
            adjacency_list[i].add(AdjacentVertex(j, u[j]))
            adjacency_list[j].add(AdjacentVertex(i, u[j]))

(_, key) = compute_mst_prim(N, adjacency_list)

print(sum(key.values()))