Python数据处理之临近匹配的实现详解

def find_nearest_element(arr, target):
    nearest = arr[0]
    min_diff = abs(nearest - target)
    
    for element in arr:
        diff = abs(element - target)
        if diff < min_diff:
            min_diff = diff
            nearest = element
    
    return nearest

def find_nearest_element(arr, target):
    return min(arr, key=lambda x: abs(x - target))

import numpy as np
 
def find_nearest_element(arr, target):
    arr = np.array(arr)
    idx = np.abs(arr - target).argmin()
    return arr[idx]

def binary_search_nearest(arr, target):
    left, right = 0, len(arr) - 1
    nearest = None
    
    while left <= right:
        mid = (left + right) // 2
        if arr[mid] == target:
            return arr[mid]
        elif arr[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
        
        if nearest is None or abs(arr[mid] - target) < abs(nearest - target):
            nearest = arr[mid]
    
    return nearest

def find_elements_in_range(arr, target, radius):
    result = []
    
    for element in arr:
        if abs(element - target) <= radius:
            result.append(element)
    
    return result

import bisect
 
class BST:
    def __init__(self):
        self.data = []
 
    def insert(self, value):
        bisect.insort(self.data, value)
 
    def find_nearest(self, target):
        index = bisect.bisect_left(self.data, target)
        
        if index == 0:
            return self.data[0]
        if index == len(self.data):
            return self.data[-1]
        
        left = self.data[index - 1]
        right = self.data[index]
        
        if abs(left - target) < abs(right - target):
            return left
        else:
            return right

# 示例列表
data = [1, 3, 5, 7, 9, 11, 13]
 
# 目标值和半径
target = 6
radius = 2
 
# 使用各种方法找到最接近的元素或范围内的元素
result4 = find_elements_in_range(data, target, radius)
 
bst = BST()
for element in data:
    bst.insert(element)
result5 = bst.find_nearest(target)
 
print("临近匹配范围方法:", result4)
print("二叉搜索树方法:", result5)