编写一个算法，计算二维坐标系中任意两点之间的距离，并找到距离最近的两点坐标。

为了计算二维坐标系中任意两点之间的欧氏距离，并找到距离最近的两个点，我们可以创建一个简单的算法。首先，我们需要定义一个函数来计算两点之间的距离，然后遍历所有点，寻找最小距离对。

这里是一个Python示例：

import math

# 定义一个点的数据结构
class Point:
    def __init__(self, x, y):
        self.x = x
        self.y = y

# 计算两点间距离的函数
def distance(point1, point2):
    return math.sqrt((point2.x - point1.x)**2 + (point2.y - point1.y)**2)

# 找出最接近的两点
def find_nearest_points(points):
    min_distance = float('inf')
    closest_pair = None

    for i in range(len(points)):
        for j in range(i+1, len(points)):
            current_distance = distance(points[i], points[j])
            if current_distance < min_distance:
                min_distance = current_distance
                closest_pair = (points[i].x, points[i].y), (points[j].x, points[j].y)

    return closest_pair, min_distance

# 测试例子
points = [(0, 0), (3, 4), (-1, -3), (2, 5)]
nearest_points, nearest_distance = find_nearest_points(points)
print(f"Distance between the two closest points is {nearest_distance} at coordinates ({nearest_points[0][0]}, {nearest_points[0][1]}) and ({nearest_points[1][0]}, {nearest_points[1][1]})")

在这个例子中，`find_nearest_points`函数通过双层循环比较每对点的距离，更新最小距离和对应的点对。