round to nearest million calculator

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)

Planar nearest point pair, refers to the n points on the plane to find the minimum distance between the pointsFirst you can sort by X for the first keyword, and then each time according to the X division, the left to find a shortest distance D1, the right also find a shortest distance D2, then take D=min (D1, D2)Then just consider the point across the left and right side, and enumerate the points on the right piThen it is obvious that if the pi distan

Find the nearest common parent node of any two nodes on the binary tree. It is required that the nearest common parent node (LCA, lowest common ancestor) of the two nodes can be traversed in descending order of the tree. If the two nodes are not on the same line, they must be on the left and right trees of node A respectively, node A is required to traverse to the first node that meets this condition in

Reference: http://blog.csdn.net/tjusxh/article/details/51052319K-Nearest Neighbor algorithm: Simply speaking, it is the method to classify the distance between different eigenvalues.Three basic elements: selection of K-value, distance measurement, classification decision ruleAdvantages: High precision, insensitive to outliers, no data input assumptions.Disadvantages: High computational complexity and high spatial complexity.The general flow of the KNN

search, direct processing), and then node 7 will see all queries (7, X) node pair, if present (7, 5), because node 5 has been dyed black, so it can be concluded (7, 5 The nearest public ancestor is find (5). Ancestor, that is, Node 1 (since the 2 subtree is processed, subtree 2 and Node 1 are union,find (5) to return the root 1 of the merged tree, at which point the value of the root's ancestor is 1). Someone will ask if there is no (7, 5), but ther

Question: There are several points in the plane to find the closest two points. Analysis: Method 1: compare two vertices to find the nearest two vertices. The complexity is O (n ^ 2). The advantage code is simple and error-prone. Method 2: Observe the two-to-two comparison methods and find that there are many useless comparisons. For each vertex, you only need to calculate the distance from the closest vertex, and enumerate all vertices, finally, we c

Forest　　In order to prevent overfitting, a random forest is equivalent to several decision trees.Four, KNN nearest neighborSince KNN has to traverse all the remaining points each time it looks for the next closest point to it, the algorithm is expensive.V. Naive BayesTo push the probability that the occurrence of event a occurs under B (where events A and B can be decomposed into multiple events), you can calculate the probability of event a occurrin

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