Sync LeetCode submission Runtime - 1320 ms (39.86%), Memory - 21.7 MB (19.73%)

Author: jimit105

1713-dot-product-of-two-sparse-vectors/README.md

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+<p>Given two sparse vectors, compute their dot product.</p>
+
+<p>Implement class <code>SparseVector</code>:</p>
+
+<ul data-indent="0" data-stringify-type="unordered-list">
+	<li><code>SparseVector(nums)</code>&nbsp;Initializes the object with the vector <code>nums</code></li>
+	<li><code>dotProduct(vec)</code>&nbsp;Compute the dot product between the instance of <em>SparseVector</em> and <code>vec</code></li>
+</ul>
+
+<p>A <strong>sparse vector</strong> is a vector that has mostly zero values, you should store the sparse vector&nbsp;<strong>efficiently </strong>and compute the dot product between two <em>SparseVector</em>.</p>
+
+<p><strong>Follow up:&nbsp;</strong>What if only one of the vectors is sparse?</p>
+
+<p>&nbsp;</p>
+<p><strong class="example">Example 1:</strong></p>
+
+<pre>
+<strong>Input:</strong> nums1 = [1,0,0,2,3], nums2 = [0,3,0,4,0]
+<strong>Output:</strong> 8
+<strong>Explanation:</strong> v1 = SparseVector(nums1) , v2 = SparseVector(nums2)
+v1.dotProduct(v2) = 1*0 + 0*3 + 0*0 + 2*4 + 3*0 = 8
+</pre>
+
+<p><strong class="example">Example 2:</strong></p>
+
+<pre>
+<strong>Input:</strong> nums1 = [0,1,0,0,0], nums2 = [0,0,0,0,2]
+<strong>Output:</strong> 0
+<strong>Explanation:</strong> v1 = SparseVector(nums1) , v2 = SparseVector(nums2)
+v1.dotProduct(v2) = 0*0 + 1*0 + 0*0 + 0*0 + 0*2 = 0
+</pre>
+
+<p><strong class="example">Example 3:</strong></p>
+
+<pre>
+<strong>Input:</strong> nums1 = [0,1,0,0,2,0,0], nums2 = [1,0,0,0,3,0,4]
+<strong>Output:</strong> 6
+</pre>
+
+<p>&nbsp;</p>
+<p><strong>Constraints:</strong></p>
+
+<ul>
+	<li><code>n == nums1.length == nums2.length</code></li>
+	<li><code>1 &lt;= n &lt;= 10^5</code></li>
+	<li><code>0 &lt;= nums1[i], nums2[i]&nbsp;&lt;= 100</code></li>
+</ul>

1713-dot-product-of-two-sparse-vectors/solution.py

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+# Approach 2: Hash Table
+
+# n = length of input array, L = no. of non-zeros elements
+# Time: O(n) for creating hash table, O(L) for calculating dot product
+# Space: O(L) for creating hash map, O(1) for calculating dot product
+
+class SparseVector:
+    def __init__(self, nums: List[int]):
+        self.nonzeros = {}
+        for i, num in enumerate(nums):
+            if num != 0:
+                self.nonzeros[i] = num
+        
+
+    # Return the dotProduct of two sparse vectors
+    def dotProduct(self, vec: 'SparseVector') -> int:
+        result = 0
+        for i, num in self.nonzeros.items():
+            if i in vec.nonzeros:
+                result += num * vec.nonzeros[i]
+
+        return result
+        
+
+# Your SparseVector object will be instantiated and called as such:
+# v1 = SparseVector(nums1)
+# v2 = SparseVector(nums2)
+# ans = v1.dotProduct(v2)