Issue #136 is completed with Prim's Algorithm

Author: Hamid Gasmi

3-graph-algorithms/3_spanning_trees/clustering.py

@@ -6,7 +6,6 @@ def clustering(x, y, k):
     #write your code here
     return -1.
 
-
 if __name__ == '__main__':
     input = sys.stdin.read()
     data = list(map(int, input.split()))

3-graph-algorithms/3_spanning_trees/connecting_points.py

@@ -1,12 +1,44 @@
 #Uses python3
 import sys
 import math
+import queue
+from collections import namedtuple
+
+Point  = namedtuple('Point', ['len', 'x', 'y', 'index'])
 
 def minimum_distance(x, y):
+    
+    n = len(x)
+    visited = [False] * n
+    parents = [-1] * n
+
+    infinity = 3 * 10**3
+    distances = [infinity] * n
+    distances[0] = 0
+
+    hq = queue.PriorityQueue()
+    for i in range(1, n):
+        hq.put(Point(infinity, x[i], y[i], i))
+    hq.put(Point(0, x[0], y[0], 0))
+
+    while not hq.empty():
+        p = hq.get()
+        visited[p.index] = True
+        #print("(i, x, y, distances): ", p.index, p.x, p.y, distances)
+        #print("hq.empty", "empty" if hq.empty() else "not empty")
+        for i in range(n):
+            if not visited[i]:
+                candidate_distance = math.sqrt((p.x - x[i])**2 + (p.y - y[i])**2)
+                if distances[i] > candidate_distance:
+                    distances[i] = candidate_distance
+                    parents[i] = p.index
+                    hq.put(Point(distances[i], x[i], y[i], i))
+
     result = 0.
-    #write your code here
-    return result
+    for d in distances:
+        result += d
 
+    return result
 
 if __name__ == '__main__':
     input = sys.stdin.read()

3-graph-algorithms/3_spanning_trees/connecting_points_tests/01

@@ -0,0 +1,5 @@
+4
+0 0
+0 1
+1 0
+1 1

3-graph-algorithms/3_spanning_trees/connecting_points_tests/02

@@ -0,0 +1,6 @@
+5
+0 0
+0 2
+1 1
+3 0
+3 2