1570. Dot Product of Two Sparse Vectors

JIAKAOBO

LeetCode

感谢赞助!

• ㊗️
• 大家
• offer
• 多多！

Problem

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

Code

class SparseVector {
    Map<Integer, Integer> indexMap = new HashMap<>();
    int n = 0;

    SparseVector(int[] nums) {
        n = nums.length;
         for (int i = 0; i < nums.length; i++)
            if (nums[i] != 0)
                indexMap.put(i, nums[i]);
    }
    
	// Return the dotProduct of two sparse vectors
    public int dotProduct(SparseVector vec) {
        if (indexMap.size() == 0 || vec.indexMap.size() == 0) return 0;
        if (indexMap.size() > vec.indexMap.size())
            return vec.dotProduct(this);
            
        int productSum = 0;

        for (Map.Entry<Integer, Integer> entry : indexMap.entrySet()) {
            int index = entry.getKey();
            Integer vecValue = vec.indexMap.get(index);
            if (vecValue == null) continue; 
            productSum += (entry.getValue() * vecValue);
        }

        return productSum;
    }
}

// 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);