round up to nearest hundred

right side of the line L, respectively, and S=S1∪S2. Because M is the median of the x-coordinate values of the points in S, the points in S1 and S2 are roughly equal. We get the minimum distance δ1 and δ2 in S1 and S2, respectively, on the S1 and S2 to the nearest point to the problem. Δ=min (Δ1,Δ2) is now established.If the distance between the nearest point pair (p,q) of S is At this point, a pair of poi

Reprint please indicate source: http://www.cnblogs.com/lighten/p/7593656.html1. PrincipleThis chapter introduces the first algorithm of machine learning--k nearest neighbor algorithm (k Nearest Neighbor), also known as KNN. When it comes to machine learning, it is generally thought to be very complex, very advanced content, but in fact, its learning Gate bar is not high, with basic advanced mathematics know

In this article, we briefly introduce the principle of the K-nearest neighbor algorithm and a simple example, today we will introduce a simple application, because the principle of use is roughly the same, there is no too much explanation.To be descriptive, convert the handwritten numbers image to a TXT file, as shown in (three graphs are 5, 6, 8, respectively):To use the K-nearest neighbor algorithm, you n

Before writing the K-nearest neighbor algorithm (http://boytnt.blog.51cto.com/966121/1569629), the test data is not attached, this time to find a, test the effect of the algorithm. Data from http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/ Breast-cancer-wisconsin.data, a sample of breast cancer, attribute description see Breast-cancer-wisconsin.names.The approximate form of the sample is as follows:1000025,5,1,1,1,2,1,

Vi. more hyper-parameters in grid search and K-nearest algorithmVii. Normalization of data Feature ScalingSolution: Map all data to the same scaleViii. the Scaler in Scikit-learnpreprocessing.pyImportNumPy as NPclassStandardscaler:def __init__(self): Self.mean_=None Self.scale_=NonedefFit (self, X):"""get the mean and variance of the data based on the training data set X""" assertX.ndim = = 2,"The dimension of X must be 2"Self.mean_= Np.array (

first, the basic principle There is a collection of sample data (also called a training sample set), and there is a label for each data in the sample set. After entering new data without a label, each feature of the new data is compared to the feature in the sample set, and then the algorithm extracts the category label of the most similar data (nearest neighbor) in the sample set. We generally select the most similar data for the first K (k is usua

1. Overview 1.1 Principle: (Measure the distance between different eigenvalues to classify)There is a collection of sample data, which is the training sample set, and each data in the sample set has multiple features and labels, that is, we know the sample data and its classification, and when we enter new data without labels, we compare each feature of the new data with the characteristics of the data in the sample set. Then, according to the corresponding algorithm (the Euclidean distance chos

belongs to which sub-tree, and update the corresponding centroid coordinates. After completion is the search, for a given point to go to the tree to find topk nearest neighbor, the most basic idea is to start from the root, according to the point of the vector information and each tree node segmentation of the super-plane comparison decide which tree traversal. As shown in the figure However, there are still some problems, that is, the

Python finds the nearest color from a set of colors. This example describes how to find the nearest color from a set of colors in python. Share it with you for your reference. The specific analysis is as follows: This code is very useful. You can find a color similar to the specified color. For example, there is a group of eight colors. Now, given an rgb format demonstration, you can find out which one of t

This article describes how to obtain the nearest number in a specified range in php. you can divide each interval based on the length of a given interval and find the number closest to the given number, for more information about how to obtain the nearest number in a specified range, see the example in this article. Share it with you for your reference. The specific implementation method is as follows: //

I haven't used Binary Trees for a long time. Recently I have used binary trees and found that a lot of knowledge needs to be consolidated. An algorithm involved in the middle is to find the nearest ancestor of any two nodes. Through my review and calculation, I finally proposed the following method. There are also many other ways to achieve it on the Internet. Once again, I only recorded and accumulated my work for several hours! The program is writte

POJ 1330 Nearest Common AncestorsTest instructions: Nude topic of the recent public ancestorIdea: LCA and St We are already familiar with, but here f[i][j] have similar but different meanings. F[I][J] Represents the number of 2j fathers of the I nodeThis code is not mine, transfer from Bingbin Blog1 /* ***********************************************2 Author:kuangbin3 Created time:2013-9-5 9:45:174 File NAME:F:\2013ACM exercises \ Thematic Learning \lc

KNN algorithm of ten Algorithms for machine learningThe previous period of time has been engaged in tkinter, machine learning wasted a while. Now want to re-write one, found a lot of problems, but eventually solved. We hope to make progress together with you.Gossip less, get to the point.KNN algorithm, also called nearest neighbor algorithm, is a classification algorithm.The basic idea of the algorithm: Assume that there is already a data set, the dat

topic links >Main topic:Give a tree and ask the number of the nearest public ancestor of any two points.Problem Solving Analysis:LCA template problem, the following is the online multiplication algorithm solution.1#include 2#include 3#include 4#include 5 using namespacestd;6 7 Const intN = 1e4+Ten;8 Const intINF =0x3f3f3f3f;9 structedge{Ten intTo,next; One}edge[n1]; A intCnt,head[n]; - intdep[n],f[n][ *]; - intNinch[N]; the voidAddedge (intUintv)