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Author: lmizner

Top_K_Elements/min_cost.py

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+import heapq
+
+class Solution:
+    def min_cost_to_hire_workers(self, quality, wage, k):
+
+        # Create list of tuples with wage/quality ratio 
+        workers = sorted([(w/q, q) for w, q in zip(wage, quality)])
+
+        # Initialize max heap
+        heap = []
+
+        # Sum of qualities of selected workers
+        total_quality = 0
+
+        # Initialize minimum cost
+        min_cost = float('inf')
+
+        # Iterate through workers sorted by wage-to-quality ratio
+        for ratio, q in workers:
+
+            # Add worker quality to heap
+            heapq.heappush(heap, -q)
+            total_quality += q
+
+            # More than k workers, remove largest quality
+            if len(heap) > k:
+                total_quality += heapq.heappop(heap)
+
+            # Exactly k workers, calculate cost
+            if len(heap) == k:
+                min_cost = min(min_cost, ratio * total_quality)
+
+        return min_cost
+
+
+
+# Time Complexity = O(nlogk)
+# Space Complexity = O(k)
+
+
+
+############################################################################
+
+
+
+# Driver code
+def main():
+    qualities = [
+        [10, 20, 5],
+        [3, 1, 10, 10, 1],
+        [4, 5, 6],
+        [2, 3, 1],
+        [10, 10, 10]
+    ]
+    
+    wages = [
+        [70, 50, 30],
+        [4, 8, 2, 2, 7],
+        [8, 10, 12],
+        [5, 6, 2],
+        [50, 60, 70]
+    ]
+    
+    k_values = [2, 3, 2, 2, 2]
+    sol = Solution()
+    for i in range(len(qualities)):
+        print(i+1, ".\tqualities:", qualities[i])
+        print("\twages:", wages[i])
+        print("\tk:", k_values[i])
+        result = sol.min_cost_to_hire_workers(qualities[i], wages[i], k_values[i])
+        print("\n\tMinimum cost to hire", k_values[i], "workers =", result)
+        print("-"*100)
+
+if __name__ == "__main__":
+    main()
+