please show that the solution has the same output as given.
Thank you!
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from typing import List
class MinHeap:
def __init__(self) -> None:
self.size = 0
self.keys = [None] # other
elements start at index 1
self.values = [None]
def __str__(self) -> str:
return f"keys: {self.keys}, values:
{self.values}"
def add(self, item: int, value: int) ->
None:
self.keys.append(item)
self.values.append(value)
self.size += 1
idx = self.size
parent_idx = idx//2
while (idx > 1) and
(self.values[parent_idx] > value):
self._swap_nodes(parent_idx, idx)
idx = parent_idx
parent_idx = idx//2
def _swap_nodes(self, idx1: int, idx2: int):
# Swap keys
self.keys[idx1], self.keys[idx2] =
self.keys[idx2], self.keys[idx1]
# Swap values
self.values[idx1], self.values[idx2] =
self.values[idx2], self.values[idx1]
def get_smallest(self) -> int:
"""Return the key of the Node with
smallest value"""
return self.keys[1]
def remove_smallest(self) -> int:
"""Remove and return the key of Node
with smallest value"""
key_of_min = self.keys[1]
end_key = self.keys.pop()
end_value = self.values.pop()
self.size -= 1
if self.size:
self.keys[1] =
end_key
self.values[1] =
end_value
# Reorder the heap to fix
order
self._reorder_heap_from_root()
return key_of_min
def _reorder_heap_from_root(self):
parent_idx = 1
while True:
right_idx = parent_idx*2
+ 1
left_idx =
parent_idx*2
# Get index with
minimum value among parent and children
min_idx =
parent_idx
if left_idx <=
self.size:
if
self.values[left_idx] < self.values[min_idx]:
min_idx = left_idx
if right_idx <=
self.size:
if
self.values[right_idx] < self.values[min_idx]:
min_idx = right_idx
# If parent already
minimum value, heap already ok.
# Otherwise, swap and
continue
if min_idx ==
parent_idx:
break
self._swap_nodes(min_idx,
parent_idx)
parent_idx = min_idx
class WeightedQuickUnion:
def __init__(self, size: int) -> None:
"""
Initializes the union-find ds
size: number of elements to
initialize
"""
self.size = size
self.parent = [-1]*self.size
# create a list where all elements are roots
self.group_size = [1]*self.size #
create separate list to indicate the size for each element
# (useful for knowing the size in root's
perspective)
def __str__(self) -> str:
return f"parent: {self.parent}\nsize:
{self.group_size}\n"
def get_root(self, p: int) -> int:
"""Find the root of p"""
idx = p
while self.parent[idx] >= 0:
# root should have a negative value
idx =
self.parent[idx]
return idx
def is_connected(self, p1: int, p2:int) ->
bool:
return self.get_root(p1) ==
self.get_root(p2)
def connect(self, p1: int, p2: int) ->
None:
"""Connects p1 and p2"""
r1 = self.get_root(p1)
r2 = self.get_root(p2)
r1_size = self.group_size[r1]
r2_size = self.group_size[r2]
if r1_size > r2_size:
self.parent[r2] =
r1
self.group_size[r1] +=
r2_size # increase r1 group size by r2 size
else:
self.parent[r1] =
r2
self.group_size[r2] +=
r1_size # increase r2 group size by r1 size
class GraphUndirected:
def __init__(self, num_vertices: int) ->
None:
self.num_vertices = num_vertices
self.num_edges = 0
self.adj_list = []
for _ in range(num_vertices):
self.adj_list.append({})
def __str__(self) -> str:
return f"adj: {self.adj_list}"
def add_edge(self, v: int, w: int, weight: int)
-> None:
self.adj_list[v][w] = weight
self.adj_list[w][v] = weight
self.num_edges += 1
def adj(self, v: int) -> List[int]:
return self.adj_list[v] # returns
vertices adjacent to v
def has_edge(self, v: int, w: int) ->
bool:
if w in self.adj_list[v]:
return True
return False
class PrimMST:
def __init__(self, graph: GraphUndirected, init_v:
int = 0) -> None:
self.graph = graph
self.source = init_v
self.dist_to = [float("inf")] *
graph.num_vertices
self.dist_to[init_v] = 0
self.edge_to = [None] *
graph.num_vertices
self.is_marked = [False] *
graph.num_vertices
self.mst = []
self.prim(self.graph)
def get_mst(self) -> List[set]:
return self.mst
def prim(self, graph: GraphUndirected):
pq = MinHeap()
pq.add(self.source, 0) # Set
distance to source as 0
# Set distance to other vertices to
be inf in PQ
for v in
range(graph.num_vertices):
if v !=
self.source:
pq.add(v,
float("inf"))
p1 = pq.remove_smallest()
self.scan(graph, pq, p1)
while pq.size > 0:
p1 =
pq.remove_smallest()
if
self.is_marked[p1]:
continue
self.scan(graph, pq,
p1)
def scan(self, graph: GraphUndirected, pq:
MinHeap,point: int):
self.is_marked[point] = True
for neighbor, weight in
graph.adj(point).items():
if
self.is_marked[neighbor]:
continue
if self.dist_to[neighbor]
> weight:
self.dist_to[neighbor] = weight
self.edge_to[neighbor] = point
pq.add(neighbor, weight)
class KruskalMST:
def __init__(self, graph: GraphUndirected) ->
None:
self.graph = graph
self.edge_to =
[None]*graph.num_vertices
self.mst = []
self.kruskal(self.graph)
def get_mst(self) -> List[set]:
return self.mst
def kruskal(self, graph: GraphUndirected):
uf =
WeightedQuickUnion(graph.num_vertices)
pq = MinHeap()
# Create MinHeap containing all
edges and their weights
for pt in
range(graph.num_vertices):
for neighbor, weight in
graph.adj(pt).items():
edge = {pt,
neighbor}
if edge not
in pq.keys:
pq.add(edge, weight)
while (pq.size > 0):
p1, p2 =
pq.remove_smallest()
if not
uf.is_connected(p1, p2):
uf.connect(p1,p2)
self.edge_to[p2] = p1
if __name__=="__main__":
# Initialize the graph with a given number of
vertices
num_vert = int(input())
g = GraphUndirected(num_vertices=num_vert)
# Add edges to the graph
num_edges = int(input())
for x in range(num_edges):
input_line = input().split(',')
g.add_edge(
v=int(input_line[0]),
w=int(input_line[1]),
weight=int(input_line[2])
)
# Get the MST using both Prim's and Kruskal's
algorithm
prim = PrimMST(g)
print(prim.get_mst())
kruskal = KruskalMST(g)
print(kruskal.get_mst())
Modify the given code implementation for the Min Heap such that: • Instead of having separate lists for keys and values (i.e., keys and values), you will instead have a single list called heap. This heap variable should contain Node class objects. Each Node object should contain a key and a value as its attribute. Original Expected modification def class MinHeap: def __init__(self) -> None: self.size = 0 self.keys = [None] self.values = [None] class MinHeap: _init__(self) -> None: self.size = 0 self.heap = [None] • Modify all the necessary methods and lines of code given that you will only have a list of Node objects • You can also choose to add/remove methods as you see fit Hint: make sure to define the __repr__ method for Node class to make it easier to debug Modify the implementation of Prim's and Kruskal's algorithms such that: • Both classes (PrimMST and KruskalMST) would each have an mst attribute (i.e., a variable mst) that stores a list of sets, where each set represents an edge of the Minimum Spanning Tree. • Optional but highly recommended: Change the condition for Kruskal's algorithm to be when the number of edges in the mst is equal to the number of nodes - 1) instead of when the PQ is empty
Input Format • The first line contains the number N which is the number of vertices. Graph will then have nodes/vertices from o to N-1 • The second line contains the number E which is the number of edges • Each of the next E lines contains 3 numbers separated by commas: V,w,weight. This means there is an undirected edge connecting v and w with weight=weight Constraints •v and w are not equal • N, E >0 • weight > 0 v and w are guaranteed to be valid nodes/vertices Output Format • First line of the output should be the edges of the MST from Prim's algorithm • Second line of the output should be the edges of the MST from Kruskal's algorithm Both outputs should be a list of sets, where each set is an edge of the MST. For example, the MST contains edges 1-2 and 2-3. The output should then be [{1,2}, {2,3}]. Note that the checker is order agnostic (i.e., it doesn't matter what order you output the edges; it will check the presence/absence of edges)
Sample Input 0 7 12 0,1,2 ,2,1 1,2,5 1,4,3 1,3,11 4,2,1 4,5,4 4,6,3 2,5,15 3,4,2 6,3,1 6,5,1 Sample Output 0 [{0, 2}, {2, 4}, {0, 1}, {3, 4}, {3, 6}, {5, 6}] [{4, 2}, {2, 0}, {3, 6}, {5, 6}, {0, 1}, {3, 4}]
Sample Input 1 7 11 0,1,7 0,3,5 1,3,9 1,2,8 1,4,7 2,4,5 3,4,15 3,5,6 4,5,8 4,6,9 5,6,11 Sample Output 1 [{0, 3}, {3, 5}, {0, 1}, {1, 4}, {2, 4}, {4, 6}] [{0, 3}, {2, 4}, {3, 5}, {0, 1}, {1, 4}, {4, 6}]
from typing import List class MinHeap: def __init__(self) -> None: self.size = 0 self.keys [None] # other elements start at index 1 self.values [None] = def -_str__(self) -> str: return f"keys: {self.keys), values: {self.values}" def add(self, item: int, value: int) -> None: self.keys.append(item) self.values.append(value) self.size += 1 idx = self.size parent_idx = idx//2 while (idx > 1) and (self.values [parent_idx] > value): self._swap_nodes (parent_idx, idx) idx = parent_idx parent_idx = idx//2 def _swap_nodes (self, idxl: int, idx2: int): # Swap keys self.keys[idxl], self.keys [idx2] self.keys[idx2], self.keys[idx1] # Swap values self.values[idxl], self.values[idx2] = self.values[idx2], self.values[idxl] def get_smallest (self) -> int: Return the key of the Node with smallest value" return self.keys[1]
= def remove_smallest (self) -> int: "Remove and return the key of Node with smallest value"! key_of_min = self.keys[1] end_key self.keys.pop() end_value self.values.pop() self.size -= 1 if self.size: self.keys [1] = end_key self.values[1] = end_value # Reorder the heap to fix order self._reorder_heap_from_root() return key_of_min def _reorder_heap_from_root(self): parent_idx = 1 while True: right_idx = parent_idx*2 + 1 left_idx = parent_idx*2 # Get index with minimum value among parent and children min_idx = parent_idx if left_idx <= self.size: if self.values[left_idx] < self.values[min_idx]: min_idx = left_idx if right_idx <= self.size: if self.values[right_idx] < self.values[min_idx]: min_idx = right_idx =
# If parent already minimum value, heap already ok. # Otherwise, swap and continue if min_idx == parent_idx: break self._swap_nodes (min_idx, parent_idx) parent_idx = min_idx class WeightedQuickUnion: def __init__(self, size: int) -> None: Initializes the union-find ds size: number of elements to initialize self.size = size self.parent = (-1]*self.size # create a list where all elements are roots self.group_size [1]*self.size # create separate list to indicate the size for each element # (useful for knowing the size in root's perspective) = def __str__(self) -> str: return f"parent: {self.parent}\nsize: {self.group_size}\n" def get_root(self, p: int) -> int: "Find the root of p" idx = p while self.parent[idx] >= C: idx = self.parent[idx] return idx # root should have a negative value def is_connected(self, pl: int, p2:int) -> bool: return self.get_root(pl) == self.get_root(p2)
def connect(self, pl: int, p2: int) -> None: "Connects pl and p21 rl = self.get_root(pl) r2 = self.get_root(p2) ri_size = self.group_size[rl] r2_size = self.group_size[r2] if ri_size > r2_size: self.parent[r2] = rl self.group_size[rl] += r2_size # increase rl group size by r2 size else: self.parent[rl] = r2 self.group_size[r2] += ri_size # increase r2 group size by ri size class GraphUndirected: def __init__(self, num_vertices: int) -> None: self.num_vertices = num_vertices self.num_edges = 0 self.adj_list = [] for - in range (num_vertices): self.adj_list.append({}) def __str__(self) -> str: return f"adj: {self.adj_list)" def add_edge (self, v: int, w: int, weight: int) -> None: self.adj.list[v] [w] weight self.adj.list[w][v] = weight self.num_edges += 1 = def adj(self, v: int) -> List[int]: return self.adj_list[v] # returns vertices adjacent to v def has_edge (self, v: int, w: int) -> bool: if w in self.adj.list[v]: return True return false
) -> None: class PrimMST: def __init__(self, graph: GraphUndirected, init_v: int self.graph = graph self. source = init_v self.dist to [float("inf")] * graph.num_vertices self.dist_to[init_v] = 0 self.edge_to = [None] * graph.num_vertices self.is_marked [False] graph.num_vertices self.mst = [] self.prim(self.graph) def get_mst (self) -> List[set]: return self.mst def prim(self, graph: GraphUndirected): pq = MinHeap O pq.add(self.source, o) # Set distance to source as o # Set distance to other vertices to be inf in PQ for v in range (graph.num_vertices): if v != self.source: pg.add(v, float("inf")) pl = pq.remove_smallest self.scan(graph, pq, pl) while pq.size > 0: pl = pq.remove_smallest if self.is_marked[pl]: continue self.scan(graph, pg, pl) = def scan(self, graph: GraphUndirected, pq: MinHeap, point: int): self.is_marked [point] = True for neighbor, weight in graph. adj (point).items(): if self.is_marked[neighbor]: continue
if self.dist_to[neighbor] > weight: self.dist_to[neighbor] = weight self.edge_to [neighbor] = point pq.add(neighbor, weight) class KruskalMST: def __init__(self, graph: GraphUndirected) -> None: self.graph = graph self.edge_to - [None] *graph.num_vertices self.mst = [] self.kruskal(self.graph) def get_mst(self) -> List[set]: return self.mst def kruskal(self, graph: GraphUndirected): uf - WeightedQuickUnion (graph.num_vertices) pq = MinHeap # Create Minheap containing all edges and their weights for pt in range (graph.num_vertices): for neighbor, weight in graph.adj (pt). items(): edge = {pt, neighbor} if edge not in pq.keys: pq.add(edge, weight) while (pg.size > 0): pl, p2 = pq.remove_smallest() if not uf.is_connected (pl, p2): uf.connect(pl,p2) self.edge_to[22] = pl if __name__ =="__main__": # Initialize the graph with a given number of vertices num_vert = int(input) g = GraphUndirected(num_vertices=num_vert)
# Add edges to the graph num_edges = int(input) for x in range(num_edges) : input_line = input().split(',') g.add_edge v=int(input_line[o]), w=int(input_line[1]), weight=int(input_line[2]) ) # Get the MST using both Prim's and Kruskal's algorithm prim PrimMST (9) print(prim.get_msto) kruskal = KruskalMST(S) print (kruskal.get_msto)