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Formatted question description: https://leetcode.ca/all/1570.html

 1570. Dot Product of Two Sparse Vectors

 Given two sparse vectors, compute their dot product.

 Implement class SparseVector:

 SparseVector(nums) Initializes the object with the vector nums
 dotProduct(vec) Compute the dot product between the instance of SparseVector and vec

 A sparse vector is a vector that has mostly zero values,
 you should store the sparse vector efficiently and compute the dot product between two SparseVector.

 Follow up: What if only one of the vectors is sparse?


 Example 1:

 Input: nums1 = [1,0,0,2,3], nums2 = [0,3,0,4,0]
 Output: 8
 Explanation: v1 = SparseVector(nums1) , v2 = SparseVector(nums2)
 v1.dotProduct(v2) = 1*0 + 0*3 + 0*0 + 2*4 + 3*0 = 8


 Example 2:

 Input: nums1 = [0,1,0,0,0], nums2 = [0,0,0,0,2]
 Output: 0
 Explanation: v1 = SparseVector(nums1) , v2 = SparseVector(nums2)
 v1.dotProduct(v2) = 0*0 + 1*0 + 0*0 + 0*0 + 0*2 = 0


 Example 3:

 Input: nums1 = [0,1,0,0,2,0,0], nums2 = [1,0,0,0,3,0,4]
 Output: 6


 Constraints:
     n == nums1.length == nums2.length
     1 <= n <= 10^5
     0 <= nums1[i], nums2[i] <= 100

Algorithm

Store in hashmap for only indexes with values.

Code

  • class SparseVector {
    
        private Map<Integer, Integer> v;
    
        SparseVector(int[] nums) {
            v = new HashMap<>();
            for (int i = 0; i < nums.length; ++i) {
                if (nums[i] != 0) {
                    v.put(i, nums[i]);
                }
            }
        }
    
        // Return the dotProduct of two sparse vectors
        public int dotProduct(SparseVector vec) {
            int res = 0;
            if (v.size() > vec.v.size()) {
                Map<Integer, Integer> t = v;
                v = vec.v;
                vec.v = t;
            }
            for (Map.Entry<Integer, Integer> entry : v.entrySet()) {
                int i = entry.getKey(), num = entry.getValue();
                res += num * vec.v.getOrDefault(i, 0);
            }
            return res;
        }
    }
    
    // Your SparseVector object will be instantiated and called as such:
    // SparseVector v1 = new SparseVector(nums1);
    // SparseVector v2 = new SparseVector(nums2);
    // int ans = v1.dotProduct(v2);
    
  • class SparseVector:
        def __init__(self, nums: List[int]):
            self.v = {}
            for i, num in enumerate(nums):
                if num != 0:
                    self.v[i] = num
    
        # Return the dotProduct of two sparse vectors
        def dotProduct(self, vec: 'SparseVector') -> int:
            res = 0
            if len(self.v) > len(vec.v):
                self.v, vec.v = vec.v, self.v
            for i, num in self.v.items():
                if i not in vec.v:
                    continue
                res += num * vec.v[i]
            return res
    
    
    # Your SparseVector object will be instantiated and called as such:
    # v1 = SparseVector(nums1)
    # v2 = SparseVector(nums2)
    # ans = v1.dotProduct(v2)
    
    

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