Computes all eigenvalues and eigenvectors of a real symmetric matrix a, ! which is of size n by n, stored in a physical np by np array. ! On output, elements of a above the diagonal are destroyed. ! d returns the eigenvalues of a in its first n elements. ! v is a matrix with the same logical and physical dimensions as a, ! whose columns contain, on output, the normalized eigenvectors of a. ! nrot returns the number of Jacobi rotations that were required. ! Please notice that the eigenvalues are not ordered on output. ! If the sorting is desired, the addintioal routine "eigsrt" ! can be invoked to reorder the output of jacobi.
上傳時(shí)間： 2016-06-04
上傳用戶：1512313
1．Describe a Θ(n lg n)-time algorithm that, given a set S of n integers and another integer x, determines whether or not there exist two elements in S whose sum is exactly x. (Implement exercise 2.3-7.)
上傳時(shí)間： 2017-04-01
上傳用戶：糖兒水嘻嘻
1．Describe a Θ(n lg n)-time algorithm that, given a set S of n integers and another integer x, determines whether or not there exist two elements in S whose sum is exactly x. (Implement exercise 2.3-7.) #include<stdio.h> #include<stdlib.h> void merge(int arr[],int low,int mid,int high){ int i,k; int *tmp=(int*)malloc((high-low+1)*sizeof(int)); int left_low=low; int left_high=mid; int right_low=mid+1; int right_high=high; for(k=0;left_low<=left_high&&right_low<=right_high;k++) { if(arr[left_low]<=arr[right_low]){ tmp[k]=arr[left_low++]; } else{ tmp[k]=arr[right_low++]; } } if(left_low<=left_high){ for(i=left_low;i<=left_high;i++){ tmp[k++]=arr[i]; } } if(right_low<=right_high){ for(i=right_low;i<=right_high;i++) tmp[k++]=arr[i]; } for(i=0;i<high-low+1;i++) arr[low+i]=tmp[i]; } void merge_sort(int a[],int p,int r){ int q; if(p<r){ q=(p+r)/2; merge_sort(a,p,q); merge_sort(a,q+1,r); merge(a,p,q,r); } } int main(){ int a[8]={3,5,8,6,4,1,1}; int i,j; int x=10; merge_sort(a,0,6); printf("after Merging-Sort:\n"); for(i=0;i<7;i++){ printf("%d",a[i]); } printf("\n"); i=0;j=6; do{ if(a[i]+a[j]==x){ printf("exist"); break; } if(a[i]+a[j]>x) j--; if(a[i]+a[j]<x) i++; }while(i<=j); if(i>j) printf("not exist"); system("pause"); return 0; }
上傳時(shí)間： 2017-04-01
上傳用戶：糖兒水嘻嘻
In this paper we present a classifier called bi-density twin support vector machines (BDTWSVMs) for data classification. In the training stage, BDTWSVMs first compute the relative density degrees for all training points using the intra-class graph whose weights are determined by a local scaling heuristic strategy, then optimize a pair of nonparallel hyperplanes through two smaller sized support vector machine (SVM)-typed problems. In the prediction stage, BDTWSVMs assign to the class label depending on the kernel density degree-based distances from each test point to the two hyperplanes. BDTWSVMs not only inherit good properties from twin support vector machines (TWSVMs) but also give good description for data points. The experimental results on toy as well as publicly available datasets indicate that BDTWSVMs compare favorably with classical SVMs and TWSVMs in terms of generalization
標(biāo)簽： recognition Bi-density machines support pattern vector twin for
上傳時(shí)間： 2019-06-09
上傳用戶：lyaiqing
A kinematically redundant manipulator is a serial robotic arm that has more independently driven joints than are necessary to define the desired pose (position and orientation) of its end-effector. With this definition, any planar manipulator (a manipulator whose end-effector motion is restrained in a plane) with more than three joints is a redundant manipulator. Also, a manipulator whose end-effector can accept aspatialposeisaredundant manipulator ifithas morethan sixindependently driven joints. For example, the manipulator shown in Fig. 1.1 has two 7-DOF arms mounted on a torso with three degrees of freedom (DOFs). This provides 10 DOFs for each arm. Since the end-effector of each arm can have a spatial motion with six DOFs, the arms are redundant.
標(biāo)簽： Autonomous Modeling Planning Robots Path
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
Artificial Intelligence (AI) is a big field, and this is a big book. We have tried to explore the full breadth of the field, which encompasses logic, probability, and continuous mathematics; perception, reasoning, learning, and action; and everything from microelectronic devices to robotic planetary explorers. The book is also big because we go into some depth. The subtitle of this book is “A Modern Approach.” The intended meaning of this rather empty phrase is that we have tried to synthesize what is now known into a common frame- work, rather than trying to explain each subfield of AI in its own historical context. We apologize to those whose subfields are, as a result, less recognizable.
標(biāo)簽： A-Modern-Approach Intelligence
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
This edition of Digital Image Processing is a major revision of the book. As in the 1977 and 1987 editions by Gonzalez and Wintz, and the 1992, 2002, and 2008 editions by Gonzalez and Woods, this sixth-generation edition was prepared with students and instructors in mind. The principal objectives of the book continue to be to provide an introduction to basic concepts and methodologies applicable to digital image processing, and to develop a foundation that can be used as the basis for further study and research in this field. To achieve these objectives, we focused again on material that we believe is fundamental and whose scope of application is not limited to the solution of specialized problems. The mathematical complexity of the book remains at a level well within the grasp of college seniors and first-year graduate students who have introductory preparation in mathematical analysis, vectors, matrices, probability, statistics, linear systems, and computer programming. The book website provides tutorials to support readers needing a review of this background material
標(biāo)簽： Processing Digital Image
上傳時(shí)間： 2021-02-20
上傳用戶：
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