Greedy Approach to Cover Points with Minimum Rectangles (Easy Solution)
Here\u2019s how you can complete the minRectanglesToCoverPoints method in the Solution class to match the previous logic:\n\npython\nclass Solution(object):\n  
Here\u2019s how you can complete the `minRectanglesToCoverPoints` method in the `Solution` class to match the previous logic:\n\n```python\nclass Solution(object):\n    def minRectanglesToCoverPoints(self, points, w):\n        """\n        :type points: List[List[int]]\n        :type w: int\n        :rtype: int\n        """\n        # Sort points based on their x-coordinate\n        points.sort()\n        \n        count = 0\n        i = 0\n        n = len(points)\n        \n        while i < n:\n            count += 1\n            # Start covering from the current point\n            x_start = points[i][0]\n            \n            # Skip over all points that can be covered by the rectangle\n            while i < n and points[i][0] <= x_start + w:\n                i += 1\n        \n        return count\n```\n\n### Explanation:\n1. **Sorting**: The points are sorted based on their x-coordinates.\n2. **Count Rectangles**: \n   - We initialize `count` to store the number of rectangles needed.\n   - We iterate over the points using `i`. Each time we place a rectangle starting from the current point `points[i]`, and move `i` forward until we reach a point that can\'t be covered by the current rectangle (i.e., it has an x-coordinate greater than `x_start + w`).\n3. **Return**: After covering all points, we return the number of rectangles counted.\n\n### Example Usage:\n\n```python\nsol = Solution()\npoints = [[2,1],[1,0],[1,4],[1,8],[3,5],[4,6]]\nw = 1\nprint(sol.minRectanglesToCoverPoints(points, w))  # Output: 2\n```\n\nThis method follows the logic of using a greedy approach to minimize the number of rectangles needed to cover all points.
['Python']