Deep neural networks (DNN) have shown significant improvements in several application domains including computer vision and speech recognition. In computer vision, a particular type of DNN, known as Convolutional Neural Networks (CNN), have demonstrated state-of-the-art results in object recognition and detection.
Convolutional neural networks show reliable results on object recognition and detection that are useful in real world applications. Concurrent to the recent progress in recognition, interesting advancements have been happening in virtual reality (VR by Oculus), augmented reality (AR by HoloLens), and smart wearable devices. Putting these two pieces together, we argue that it is the right time to equip smart portable devices with the power of state-of-the-art recognition systems. However, CNN-based recognition systems need large amounts of memory and computational power. While they perform well on expensive, GPU-based machines, they are often unsuitable for smaller devices like cell phones and embedded electronics.
In order to simplify the networks, Professor Zhang tries to introduce simple, efficient, and accurate approximations to CNNs by binarizing the weights. Professor Zhang needs your help.
More specifically, you are given a weighted vector X=(x1,x2,...,xn)).
Convolutional neural networks show reliable results on object recognition and detection that are useful in real world applications. Concurrent to the recent progress in recognition, interesting advancements have been happening in virtual reality (VR by Oculus), augmented reality (AR by HoloLens), and smart wearable devices. Putting these two pieces together, we argue that it is the right time to equip smart portable devices with the power of state-of-the-art recognition systems. However, CNN-based recognition systems need large amounts of memory and computational power. While they perform well on expensive, GPU-based machines, they are often unsuitable for smaller devices like cell phones and embedded electronics.
In order to simplify the networks, Professor Zhang tries to introduce simple, efficient, and accurate approximations to CNNs by binarizing the weights. Professor Zhang needs your help.
More specifically, you are given a weighted vector X=(x1,x2,...,xn)).
InputThere are multiple test cases. The first line of input contains an integer q>0.Sample Input
3 4 1 2 3 4 4 2 2 2 2 5 5 6 2 3 4
Sample Output
5/1 0/1 10/1
一个公式推导题
最后的是式子为n* （x1^2+x2^2+…….+xn^2） - sum*sum
1 #include<stdio.h> 2 #include<string.h> 3 #include<algorithm> 4 #include <vector> 5 #include<math.h> 6 using namespace std; 7 long long gcd(long long a,long long b) 8 { 9 if (a==0) return b; 10 else gcd(b%a,a); 11 } 12 int main() 13 { 14 int t; 15 while(scanf("%d",&t)!=EOF){ 16 while(t--){ 17 int n,a; 18 scanf("%d",&n); 19 long long ans=0,cnt=0,sum=0; 20 for (int i=0 ;i<n ;i++){ 21 scanf("%d",&a); 22 a=abs(a); 23 ans+=a; 24 cnt+=a*a; 25 } 26 ans=ans*ans; 27 sum=n*cnt-ans; 28 long long b=gcd(sum,n); 29 printf("%lld/%lld\n",sum/b,n/b); 30 } 31 } 32 return 0; 33 }