Evaluate Division
You are given an array of variable pairs equations
and an array of real numbers values
, where equations[i] = [Ai, Bi]
and values[i]
represent the equation Ai / Bi = values[i]
. Each Ai
or Bi
is a string that represents a single variable.
You are also given some queries
, where queries[j] = [Cj, Dj]
represents the jth
query where you must find the answer for Cj / Dj = ?
.
Return the answers to all queries. If a single answer cannot be determined, return -1.0
.
Note: The input is always valid. You may assume that evaluating the queries will not result in division by zero and that there is no contradiction.
Example 1:
Input: equations = [["a","b"],["b","c"]], values = [2.0,3.0], queries = [["a","c"],["b","a"],["a","e"],["a","a"],["x","x"]]
Output: [6.00000,0.50000,-1.00000,1.00000,-1.00000]
Explanation:
Given: a / b = 2.0, b / c = 3.0
queries are: a / c = ?, b / a = ?, a / e = ?, a / a = ?, x / x = ?
return: [6.0, 0.5, -1.0, 1.0, -1.0 ]
Example 2:
Input: equations = [["a","b"],["b","c"],["bc","cd"]], values = [1.5,2.5,5.0], queries = [["a","c"],["c","b"],["bc","cd"],["cd","bc"]]
Output: [3.75000,0.40000,5.00000,0.20000]
Example 3:
Input: equations = [["a","b"]], values = [0.5], queries = [["a","b"],["b","a"],["a","c"],["x","y"]]
Output: [0.50000,2.00000,-1.00000,-1.00000]
Constraints:
1 <= equations.length <= 20
equations[i].length == 2
1 <= Ai.length, Bi.length <= 5
values.length == equations.length
0.0 < values[i] <= 20.0
1 <= queries.length <= 20
queries[i].length == 2
1 <= Cj.length, Dj.length <= 5
Ai, Bi, Cj, Dj
consist of lower case English letters and digits.
Solution
To solve the problem, we observe that
- given an equation
a/b = n > 0
we also knowb/a = 1/n
. - given two equations
a/b=n
b/c=m
, by transitivity,a/c=(a/b)(b/c)=nm
.
For each query (a/b
), we wish to find a path from dividend
(a
) to divisor
(b
) and multiply the quotients along the way to find the final answer.
If no such path exists, then we can return -1.0
.
We will use aggregated backtracking to find the answer to each query.
Implementation
def calcEquation(self, equations: List[List[str]], values: List[float], queries: List[List[str]]) -> List[float]:
graph = defaultdict(list)
for i, [dividend, divisor] in enumerate(equations):
graph[dividend].append((divisor, values[i]))
graph[divisor].append((dividend, 1/values[i]))
def divide(dividend, divisor, visited):
if dividend == divisor:
return 1
for new_dividend, multiplier in graph[dividend]:
if new_dividend not in visited:
visited.add(new_dividend)
ans = divide(new_dividend, divisor, visited)
if ans > 0:
return ans*multiplier
visited.remove(new_dividend)
return -1
res = []
for query in queries:
if graph[query[0]] == [] or graph[query[1]] == []:
res.append(-1)
else:
visited = set()
visited.add(query[0])
res.append(divide(query[0], query[1], visited))
return res
Ready to land your dream job?
Unlock your dream job with a 2-minute evaluator for a personalized learning plan!
Start EvaluatorWhat does the following code do?
1def f(arr1, arr2):
2 i, j = 0, 0
3 new_arr = []
4 while i < len(arr1) and j < len(arr2):
5 if arr1[i] < arr2[j]:
6 new_arr.append(arr1[i])
7 i += 1
8 else:
9 new_arr.append(arr2[j])
10 j += 1
11 new_arr.extend(arr1[i:])
12 new_arr.extend(arr2[j:])
13 return new_arr
14
1public static List<Integer> f(int[] arr1, int[] arr2) {
2 int i = 0, j = 0;
3 List<Integer> newArr = new ArrayList<>();
4
5 while (i < arr1.length && j < arr2.length) {
6 if (arr1[i] < arr2[j]) {
7 newArr.add(arr1[i]);
8 i++;
9 } else {
10 newArr.add(arr2[j]);
11 j++;
12 }
13 }
14
15 while (i < arr1.length) {
16 newArr.add(arr1[i]);
17 i++;
18 }
19
20 while (j < arr2.length) {
21 newArr.add(arr2[j]);
22 j++;
23 }
24
25 return newArr;
26}
27
1function f(arr1, arr2) {
2 let i = 0, j = 0;
3 let newArr = [];
4
5 while (i < arr1.length && j < arr2.length) {
6 if (arr1[i] < arr2[j]) {
7 newArr.push(arr1[i]);
8 i++;
9 } else {
10 newArr.push(arr2[j]);
11 j++;
12 }
13 }
14
15 while (i < arr1.length) {
16 newArr.push(arr1[i]);
17 i++;
18 }
19
20 while (j < arr2.length) {
21 newArr.push(arr2[j]);
22 j++;
23 }
24
25 return newArr;
26}
27
Recommended Readings
LeetCode Patterns Your Personal Dijkstra's Algorithm to Landing Your Dream Job The goal of AlgoMonster is to help you get a job in the shortest amount of time possible in a data driven way We compiled datasets of tech interview problems and broke them down by patterns This way we
Recursion Recursion is one of the most important concepts in computer science Simply speaking recursion is the process of a function calling itself Using a real life analogy imagine a scenario where you invite your friends to lunch https algomonster s3 us east 2 amazonaws com recursion jpg You first
Runtime Overview When learning about algorithms and data structures you'll frequently encounter the term time complexity This concept is fundamental in computer science and offers insights into how long an algorithm takes to complete given a certain input size What is Time Complexity Time complexity represents the amount of time
Want a Structured Path to Master System Design Too? Don’t Miss This!