how to round to nearest whole number

The common ancestor of any two nodes given a treeTarjan off-line for LCA the idea is to first save all the queries, and then DFS over the tree when in judgment. If the current node is one of the two nodes required, then judge whether the other one has been accessed, and if so, then its nearest public ancestor is the ancestor of the current node.Here is the Tarjan offline Template:#include #include#includeusing namespacestd;Const intMAXN =10010;structE

given an array of integers, find a sub-array that is closest to zero. Returns the first and most of the indices. your code should return the starting and ending positions of the subarray that meet the requirementsSample ExampleGiven [ -3, 1, 1, -3, 5], return [0, 2],[1, 3], [1, 1], [2, 2] or [0, 4]challengesO (NLOGN) Time complexityAnalysis: First O (n^2) algorithm is very good to think, the direct lifting point on the line, see the complexity of the challenge, want to be sure to sort or two poi

Background In the clustering of large samples, because of the computational cost of K-means, some samples are randomly selected for clustering, and the clustering centers are obtained. However, it is often needed to get the nearest cluster center for each sample, which is commonly used in index construction, eg. OPQ (Pami 2014), inverted Multi-Index (Pami 2014). algorithm Steps Set a eigenvector p (1*2000), 2000 is a feature dimension. The cluster c

typedef xpixel PIXELCOLORRGB;Double Sinxx (Double value){if (value if (Value Float TEMP = value * value;Return 0.5 * Temp * value-temp + 2.0/3.0;}else if (value value = 2.0-value;Value *= value * value;return value/6.0;}else {return 0.0;}}int BOUND (int value, int min, int max){Chassert (min if (value {return min;}else if (value >= max){return Max;}Else{return value;}}Enum Imageqaulitye{Image_geometry_nearest_neighbor_interpolate = 0,Image_geometry_bilinear_interpolate,Image_geometry_three_order

Title Link: http://poj.org/problem?id=1330Test instructions: Given a root tree of n nodes, as well as the two nodes in the tree u,v, the nearest public ancestor of U,v is obtained.Data range: N [2, 10000]Idea: Starting from the root of the post-depth priority traversal, set the VIS array real-time records have been accessed.After each traversal of a subtrees tree R, it is incorporated into the set of the parent node P of R as the representative of the

Python automatically logs on to Renren and accesses the nearest visitor instance. python automatically logs on to Renren. This article describes how to use python to automatically log on to Renren and access the nearest visitor. The specific method is as follows: #-*-Coding: gbk-*-# In import OS from xml. dom import minidom import re import urllib import urllib2 import cookielib import datetime import time

Use the K-nearest neighbor algorithm to divide a point [0.6, 0.6] into a class (A, B). fromNumPyImport*Importoperatordefclassify0 (InX, DataSet, labels, k): Datasetsize= Dataset.shape[0]#number of array rowsDiffmat = Tile (InX, (datasetsize, 1))-DataSet Sqdiffmat= Diffmat * * 2sqdistances= Sqdiffmat.sum (Axis=1) Distances= sqdistances * * 0.5sorteddistindicies=distances.argsort () ClassCount= {} forIinc

Series article: "Machine learning combat" study notesThis chapter introduces the first machine learning algorithm in the Book of Machine Learning: the K-nearest neighbor algorithm, which is very effective and easy to master. First, we will explore the basic theory of K-nearest neighbor algorithm, and how to use distance measurement method to classify items; Secondly, we will use Python to import and parse t

:", end="") Print(sortedclasscount[0][0])returnSORTEDCLASSCOUNT[0][0]if __name__= ="__main__": start ()Output Result: Dataset.shape[0] Returns the number of rows in the matrix:4Dataset.shape[1] Returns the number of columns of a matrix:2(4, 2)dataset.shape Type:diffmat:[[2 1][1 0][2 2][ -1-2]]sqdiffmat:[[4 1][1 0][4 4][1 4]]sqdistances:[5 1 8 5]distance from unknown point to each known po

K Nearest Neighbor Density Estimation is a classification method instead of a clustering method. Not the optimal method, which is quite popular in practice. The common but not necessarily understandable rules are: 1. Calculate the distance (Euclidean or Markov) between the data to be classified and each data in different classes ). 2. Select the smallest distance of the first K data. Here we use the sort method. 3. Compare the first K distances to fin

K-Nearest Neighbors (KNN) is a well-understood classification algorithm. Simply put, it is to find K samples with the closest similarity from the training samples, then, if there are many samples in the K samples, the value to be determined (or sampling) belongs to this category.KNN algorithm steps Calculates the distance between each vertex and the current vertex in a known data set; Select K points with the minimum distance from the current poin

Given an array S of n integers, find three integers in S such so the sum is closest to a give n number, target. Return the sum of the three integers. You may assume this each input would has exactly one solution. For example, given array S = {-1 2 1-4}, and target = 1. The sum is closest to the target is 2. (-1 + 2 + 1 = 2).The sum sum of three numbers is the smallest difference between sums and target. This question actually sits with the front

Probe into the acceleration of numpy vector operation by K nearest neighbor algorithmAnise Bean's "anise" word has ...The k nearest neighbor algorithm is implemented using three ways to calculate the image distance:1. The most basic double cycle2. Using the BROADCA mechanism of numpy to realize single cycle3. Using the mathematical properties of broadcast and matrices to achieve non-cyclicThe picture is str

In the n points on the two-dimensional plane, how to quickly find the nearest pair of points is the nearest point to the problem.At first glance, it may feel a bit complicated.Scheme One: Brute force method. The array contains a total of n numbers, so we can sort all the points in the plane by the x-axis, then calculate the distance from the last coordinate to the left of the previous one, and then use Min

(This article is original, do not reprint without permission)ObjectiveHandwritten character recognition is an introduction to machine learning, and the K-Nearest neighbor algorithm (KNN algorithm) is an entry-point algorithm for machine learning. This paper introduces the principle of K-Nearest neighbor algorithm, the analysis of handwritten character recognition, the KNN realization and test of handwritten

-p[j].x) * (p[i].x-p[j].x) + (P[I].Y-P[J].Y) * (p[i].y-p[j].y)); - } - - DoubleMergeintLintR) - { in DoubleDd=1e8; - if(L==R)returnDD; to if(L +1==R)returndis (l,r); + intMid= (l+r) >>1; - DoubleDl=merge (L,mid); the DoubleDr=merge (mid+1, R); *Dd=min (dl,dr); $ Panax Notoginseng intpos=0; - for(inti=l;i) the if(Fabs (p[mid].x-p[i].x) i; +Sort (que+1, que+1+pos,cmp2); A for(intI=1; i) the for(intj=i+1; j) + { - if(Di

The use of some functions in 1.numpy learning Shape () Usage: shape : Tuple of INTs The elements of the shape tuple give the lengths of the corresponding array dimensions. Shape returns a tuple, which in turn is the length of each dimension. SHAPE[0]: First dimension length , Shape[1]: second dimension length.　　 Tile () Usage: numpy.tile(A, reps) Construct an array by repeating A, the number of tim

-Sortedclasscount = sorted (Classcount.items (), Key=operator.itemgetter (1), reverse=True) - returnSortedclasscount[0][0] - + - defimg2vector (filename): +f =open (filename) AReturnvect = Zeros ((1,1024)) at forIinchRange (32): -line =F.readline () - forJinchRange (32): -RETURNVECT[0,I*32+J] =Int (line[j]) - returnReturnvect - in - defhandwritingclasstest (): toFileList = Os.listdir ('trainingdigits') +m =Len (fileList) -Traingmat = Zeros ((M, 1024)) theHwlabels = []

First introduce the principle of KNN:KNN is classified by calculating the distance between the different eigenvalue values.The overall idea is that if a sample is in the K most similar in the feature space (that is, the nearest neighbor in the feature space) Most of the samples belong to a category, then the sample belongs to that category as well.K is usually an integer that is not greater than 20. In the KNN algorithm, the selected neighbors are the

: Label VectorDef classfy0 (datasets, dataSet, labels, k ):# Distance CalculationDataSetSize = dataSet. shape [0] # obtain the number of rows in the array. I know that there are several training data sets.DiffMat = tile (partition, (dataSetSize, 1)-dataSet # tile: Functions in numpy. Tile expands the original array into four identical arrays. DiffMat obtains the difference between the target and the training value.SqDiffMat = diffMat ** 2 # each eleme