hamidgasmi · hamidgasmi · Jul 2, 2022 · Jun 30, 2022 · Jul 1, 2022 · Jul 2, 2022
diff --git a/10-algo-ds-implementations/graph.py b/10-algo-ds-implementations/graph.py
@@ -1,11 +1,28 @@
+from typing import List
+from collections import namedtuple
 
+'''
+    Build Adjacency List
+    Input: Eges List
+    Output: 
+        - Graph implemented with an adjacency list
+
+    Time and Space Complexity:
+        - Time Complexity: O(|E|) 
+        - Space Complexity: O(|V| + |E|)
+
+'''
 
-class Weighted_Edge:
-    def __init__(self, source: int, sink: int, weight: int):
-        self.source = source
-        self.sink = sink
-        self.weight = weight
+Edge = namedtuple('Edge', ['source', 'sink', 'weight'])
 
-class Positively_Weighted_Edge(Weighted_Edge):
-    def __init__(self, source: int, sink: int, weight: int):
-        assert(weight >= 0)
+class Graph_Adjacency_List:
+    def __init__(self, vertices_count: int, edges: List[Edge]):
+        self.vertices_count = vertices_count
+        self.adjacency_list = self.__build_adjacency_list(edges) # O(|E|)
+
+    def __build_adjacency_list(self, edges: List[Edge]) -> List[List[Edge]]:
+        adjacency_list = [ [] for _ in range(self.__vertices_count) ]
+        for edge in edges:
+            adjacency_list[edge.source] = edge
+
+        return adjacency_list
diff --git a/10-algo-ds-implementations/graph_fastest_path_dijkstra_array.py b/10-algo-ds-implementations/graph_fastest_path_dijkstra_array.py
@@ -0,0 +1,88 @@
+from typing import List
+from graph import Graph_Adjacency_List
+
+'''
+    Dijkstra's Algorithm
+    Input: Weighted Graph: G(V, E, W), s ∈ V
+        - Undirected or Directed
+        - We >= 0 for all e in E
+
+    Output: 
+        - fastest path from s to any v in V
+        - fastest path distances from s to any v in V
+
+    Time and Space Complexity:
+        - Time Complexity: O(|V|^2)
+            - T(Initialization) + T(Compute Min Distance) = O(|V|) + O(|V|^2 + |E|)
+            - Dense graph (|E| = O(|V|^2)) --> T = O(|V|^2 + |V|^2) = O(|V|^2)
+            - Sparce graph (|E| ~ |V|)     --> T = O(|V|^2 + |V|) = O(|V|^2) 
+        - Space Complexity: O(|V|)
+            - S(Parent list) + S(distance list) + S(visited nodes set) = O(|V| + |V| + |V|) = O(|V|)
+
+'''
+
+Candidate = namedtuple('Candidate', ['distance', 'node'])
+
+class Dijkstra_Heap_Queue:
+    def __init__(self, graph: Graph_Adjacency_List, s: int):
+        self.__max_distance = 10**7
+
+        self.__graph = graph
+
+        self.__path_source_node = s
+        self.__parent = []
+        self.__distance = []
+
+    def fastest_distance_to(self, dest: int) -> int:
+        if len(self.__distance) == 0:
+            self.__compute_fastest_path()
+
+        return self.__distance[dest]
+
+    def fastest_path_to(self, dest: int) -> List[int]:
+        if len(self.__distance) == 0:
+            self.__compute_fastest_path()
+
+        v = dest
+        reversed_path = []
+        while v != -1:
+            reversed_path.append(v)
+            v = self.__parent[v]
+
+        return reversed_path[::-1]
+
+    # O(|V|)
+    def __get_closest_node(self, visited_nodes: set) -> int:
+        closest_node = -1
+        min_distance = self.__max_distance
+        for candidate in range(self.__graph.vertices_count):
+            if candidate not in visited_nodes and self.__distance[candidate] < min_distance:
+                closest_node = candidate
+                min_distance = self.__distance[candidate]
+
+        return closest_node
+
+    def __compute_fastest_path (self):
+        # Initialization
+        self.__parent = [ -1 for _ in range(self.__graph.vertices_count) ]                       # O(|V|)
+        self.__distance = [ self.__max_distance for _ in range(self.__graph.vertices_count) ]    # O(|V|)
+
+        self.__distance[self.__path_source_node] = 0
+        visted_nodes = set()
+
+        # Computing min distance for each v in V
+        closest_node = 0
+        while closest_node != -1:                                  # |V| * T(self.__get_closest_node) + Sum(indegree(closest_node)) = |V|^2 + |E|
+            distance =  self.__distance[closest_node]
+            visted_nodes.add(closest_node)
+
+            for edge in self.__graph.adjacency_list[closest_node]: # O(indegree(closest_node))
+                adjacent = edge.sink
+                candidate_distance = distance + edge.weight
+                if candidate_distance < self.__distance[ adjacent ]:
+                    self.__parent[ adjacent ] = closest_node
+                    self.__distance[ adjacent ] = candidate_distance
+
+            closest_node = self.__get_closest_node(visted_nodes)   # O(|V|)
+
+
diff --git a/10-algo-ds-implementations/graph_fastest_path_dijkstra_heapq.py b/10-algo-ds-implementations/graph_fastest_path_dijkstra_heapq.py
@@ -0,0 +1,84 @@
+import heapq
+from typing import List
+from graph import Graph_Adjacency_List
+
+'''
+    Dijkstra's Algorithm
+    Input: Weighted Graph: G(V, E, W), s ∈ V
+        - Undirected or Directed
+        - We >= 0 for all e in E
+
+    Output: 
+        - fastest path from s to any v in V
+        - fastest path distances from s to any v in V
+
+    Time and Space Complexity:
+        - Time Complexity: O(|V| + |E|log|E|)
+            - T(Initialization) + T(Compute Min Distance) = O(|V|) + P(|E|*log|E|)
+            - Dense graph (|E| = O(|V|^2)) --> T = O(|V| + |V|^2 * log|V|^2) = O(|V|^2 * log|V|)
+            - Sparce graph (|E| ~ |V|)     --> T = O(|V| + |V|log|V|) = O(|V|log|V|) 
+        - Space Complexity: O(|V| + |E|)
+            - S(Parent list) + S(distance list) + S(Heap queue) = O(|V| + |V| + |E|) = O(|V| + |E|)
+
+    Implementation Assumptions:
+        - Directed graph
+        - Edges number starts from 0 (zero-based numbering)
+        - Edges list is valid
+            - 0 <= edge.source, edge.sink < vertices_count for any edge in edges list
+            - It does not have duplicates
+
+'''
+
+class Dijkstra_Heap_Queue:
+    def __init__(self, graph: Graph_Adjacency_List, s: int):
+        self.__max_distance = 10**7
+
+        self.__graph = graph
+
+        self.__path_source_node = s
+        self.__parent = []
+        self.__distance = []
+
+    def fastest_distance_to(self, dest: int) -> int:
+        if len(self.__distance) == 0:
+            self.__compute_fastest_path()
+
+        return self.__distance[dest]
+
+    def fastest_path_to(self, dest: int) -> List[int]:
+        if len(self.__distance) == 0:
+            self.__compute_fastest_path()
+
+        v = dest
+        reversed_path = []
+        while v != -1:
+            reversed_path.append(v)
+            v = self.__parent[v]
+
+        return reversed_path[::-1]
+
+    def __compute_fastest_path (self):
+        # Initialization
+        self.__parent = [ -1 for _ in range(self.__graph.vertices_count) ]                       # O(|V|)
+        self.__distance = [ self.__max_distance for _ in range(self.__graph.vertices_count) ]    # O(|V|)
+
+        self.__distance[self.__path_source_node] = 0
+        closest_nodes_queue = [ (0,  self.__path_source_node) ]
+
+        # Computing min distance for each v in V
+        while closest_nodes_queue:                                                      # T(Push to hq) + T(Pop from hq) = O(2*|E|*log|E|) = O(|E|*log|E|):
+            (distance, node) = heapq.heappop(closest_nodes_queue)                       # we can pop at most |E| edges from the hq
+
+            for edge in self.__graph.adjacency_list[node]:                                    
+                adjacent = edge.sink
+                candidate_distance = distance + edge.weight                             
+                if candidate_distance < self.__distance[ adjacent ]:
+                    self.__parent[ adjacent ] = node
+                    self.__distance[ adjacent ] = candidate_distance
+
+                    # we can push at most |E| edges into the hq
+                    heapq.heappush(closest_nodes_queue, (candidate_distance, adjacent))
+                    # We could build a custom minheap that can return the position of an element in O(1).
+                    # We can then change its priority in O(log|V|): heapq.changepriority(closest_nodes_queue, adjacent, candidate_distance)
+                    # The heap queue will contain then at most: |V| elements (instead of |E| element as it's implemented here) 
+