Day 37 C. Kefa and Park

Kefa打算用他的第一份大薪水去餐厅庆祝。他住在一个由n个顶点组成的根树公园里,树的根节点是1,也是Kefa的家。公园里有些地方有猫,Kefa已经知道了哪些顶点有猫。他想选择一家餐厅,但如果从餐厅到他家的路径上连续超过m个有猫的顶点,他就会感到害怕。任务是帮助Kefa计算他可以去的餐厅数量。

Problem
Kefa decided to celebrate his first big salary by going to the restaurant.

He lives by an unusual park. The park is a rooted tree consisting of n vertices with the root at vertex 1. Vertex 1 also contains Kefa’s house. Unfortunaely for our hero, the park also contains cats. Kefa has already found out what are the vertices with cats in them.

The leaf vertices of the park contain restaurants. Kefa wants to choose a restaurant where he will go, but unfortunately he is very afraid of cats, so there is no way he will go to the restaurant if the path from the restaurant to his house contains more than m consecutive vertices with cats.

Your task is to help Kefa count the number of restaurants where he can go.

Input
The first line contains two integers, n and m (2 ≤ n ≤ 10^5, 1 ≤ m ≤ n) — the number of vertices of the tree and the maximum number of consecutive vertices with cats that is still ok for Kefa.

The second line contains n integers a1, a2, …, an, where each ai either equals to 0 (then vertex i has no cat), or equals to 1 (then vertex i has a cat).

Next n - 1 lines contains the edges of the tree in the format “xi yi” (without the quotes) (1 ≤ xi, yi ≤ n, xi ≠ yi), where xi and yi are the vertices of the tree, connected by an edge.

It is guaranteed that the given set of edges specifies a tree.

Output
A single integer — the number of distinct leaves of a tree the path to which from Kefa’s home contains at most m consecutive vertices with cats.

Examples
input
4 1
1 1 0 0
1 2
1 3
1 4
output
2

input
7 1
1 0 1 1 0 0 0
1 2
1 3
2 4
2 5
3 6
3 7
output
2

#include<iostream>
#include<algorithm>
#include<string.h>
#inc
我现在要使用stata进行实证分析,实证模型如下gtfp1it=α+βkefa+γXit+μi+λt*ρp+ϵi,gtfp1为被解释变量,kefa为核心解释变量,政策冲击年份policy_year分别为2011年和2017年,代表第t年县级行政区i是否被纳入重点生态功能区,x为控制变量集,ϵ为随机干扰项;i表示市级行政区,t表示时间,p表示省级行政区,为提高因果推断的可靠性,本文控制了市级行政区固定效应id和省份—年份联合固定效应provinceid#year,将标准误聚类至市级行政区—年份层面,id_year,控制变量包括,1. 经济发展水平ed 2.产业结构is 3.创新能力creat 4.环境规制强度er 5.政府干预程度gover地6.财政压力pre 7.植被覆盖率plant 8.二氧化碳排放量co2,9,y ,现在我需要构建多时点双重差分模型,要求1.进行描述性统计并输出表格,2进行基准回归并输出表格,表格需要标注是否控制个体固定效应id和省份—年份联合固定效应provinceid#year 3进行创新cr中介机制分析并输出表格,进行环境规制er机制分析并输出表格 4,以2011和2017作为政策冲击时点进行多期did平行趋势检验,要求在进行均值计算后再进行平行趋势检验并输出表格 5,进行地区zone异质性分析,地区包括东中西,输出表格 6,进行安慰剂检验,要求根据kefa相关系数0.76输出结果图, 请确保所有命令完全契合我的模型和我的需求,要求命令必须真实可执行,需要调整的地方请做标注
最新发布
04-20
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