天津大学2019ACM新生赛

Descripsion:
  In deep learning framework, auto-grad is a useful feature. Auto-grad can make back propagation more convenience when you are buiding your own neural network.
 Now, Doge Head is learning deep learning, he thinks that he can achieve a simple auto-grad module: polynomial auto-grad. For example, in your network, the polynomial is “$3x^4+0x^3+1x^2+5x^1+1$”, the auto-grad result is"$12x^3+0x^2+2x^1+5$".
Input:
 Multi test case. First line contains a single number T indicates cases number. For each test case:
 First line contains a single number N, indicates the maximum exponent of polynomial.
 Second line contains (N + 1) numbers $a_1...a_n$ $a_{n+1}$, indicates coefficient of polunomial.

$1\le T\le 10$
$1\le N\le 100000$
$0\le$ $a_i$ $\le 100000$

Output:
 For each test case, output N numbers in one line, separated by spaces, indicates the result of auto-grad for the given polynomial. (You should output all coefficients even some coefficients are 0.)

Sample Input	Sample Output
2 4 0 1 1 1 1 8 1 1 1 1 2 2 2 2 2	0 3 2 1 8 7 6 5 8 6 4 2

Hints:
None

Answer:

#include 
using namespace std;

int num, e;
bool Bool = true;
int num1, e1;
int a[100001];
int b[100001];

int main(){
	while (1){
		cin >> num1;
		if (num1 >= 1 && num1 <= 10){
			num = num1;
			break;
		}
	}

	while(1){
		Bool = true;
		while(1){
			//cout <<"e1"<> e1;
			if(e1 >= 1 && e1 <= 100000){
				e = e1;
				break;
			}
		}

		while(Bool){

			for (int i = 0; i < e + 1; i++){
				cin >> b[i];
			}

			for (int i = 0; i < e + 1;i ++){
				if (b[i] < 0 || b[i] > 100000)
					Bool = true;
				else
					for(int i = 0; i < e + 1; i ++)
						a[i] = b[i];
					Bool = false;
			}

			for (int m = 0; m < e ; m ++){
				cout << b[m] * (e - m) << " ";
			}

			}		
	}
	return 0;
}