Merge pull request #1 from dmschauer/leet/973

solution for 973. K Closest Points to Origin

Author: dmschauer

algorithms/973_k_Closest_Points_to_Origin_heap.py

@@ -0,0 +1,19 @@
+import heapq
+
+class Solution:
+    def kClosest(self, points: List[List[int]], k: int) -> List[List[int]]:
+        minHeap = []
+        
+        for x,y in points:
+            minHeap.append([x**2 + y**2, x ,y]) # no need for sqrt for comparing distances
+        
+        # sorts elements in dist by first index (calculated distance)
+        heapq.heapify(minHeap)
+        
+        result = []
+        
+        for i in range(0,k):
+            d, x, y = heapq.heappop(minHeap)
+            result.append([x,y])
+            
+        return result

algorithms/973_k_Closest_Points_to_Origin_sorting.py

@@ -0,0 +1,20 @@
+import math
+
+class Solution:
+    def kClosest(self, points: List[List[int]], k: int) -> List[List[int]]:
+        
+        dist = []        
+        
+        for i in range(0,len(points)):
+            dist.append(math.sqrt(points[i][0]**2 + points[i][1]**2))
+            
+        dist_sorted = dist.copy()
+        dist_sorted.sort()  
+        max_dist = dist_sorted[k-1]
+                             
+        result = []
+        for i in range(0,len(points)):
+            if(dist[i] <= max_dist):
+                result.append(points[i])
+                                
+        return result