Dot product of two sparse vectors

class SparseVector:
    def __init__(self, nums: List[int]):
        self.length = len(nums)
        self.data = {}
        for index, val in enumerate(nums):
            if val != 0:
                self.data[index] = val

    # Return the dotProduct of two sparse vectors
    def dotProduct(self, vec: 'SparseVector') -> int:
        res = 0
        for i in range(self.length):
            res += self.data.get(i, 0) * vec.data.get(i, 0)
        return res




# Your SparseVector object will be instantiated and called as such:
# v1 = SparseVector(nums1)
# v2 = SparseVector(nums2)
# ans = v1.dotProduct(v2)

Dot Product of Two Sparse Vectors

Difficulty: Medium

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