round up to nearest hundred

English Learning app- Hundred Word chop1. Introduction :Hundred Word chop is by the Chengdu Super Love Technology Co., Ltd. for English learning and development of a "picture back word software." The software provides interesting maps and sample sentences for each word, making it a pleasure to remember words.Hundred words cut covered from the first high school, 46, postgraduate, to IELTS, TOEFL, SAT, GMAT,

Buddha said that five hundred years of looking back was just worth passing through this life. This sentence comes from this story:There was a young and beautiful girl who was born from a great family. She had a rich family and a versatile family. She had a good time.The matchmaker also broke her family's door, but she never wanted to get married because she thought she hadn't seen the boy she really wanted to marry. One day, she went to a temple

First, Concept significanceFind and test all training samples that are relatively close to the sample properties.Using the most recent pro to determine the rationality of the class label, with the following words to best illustrate:"If you walk like a duck, and you look like a duck, it's probably a duck," he said.Second, the calculation steps:1. Distance: Given the test object, calculate its distance from each object in the training set 2, looking for neighbors: delimit the

Find the nearest point to the problem descriptionThe coordinates of n points on a given plane to find the closest two points.Analysis and SolutionLet's take a look at a one-dimensional situation: how to quickly find the minimum value of the 22 difference in N number in an array containing n numbers? One-dimensional situation is equivalent to all points in a straight line. Although it is a degenerate situation, it can still get some inspiration from it

Recently, the company needs to check the customer's delivery address to find out which stores are closest to the customer's address, and the customer can go to the nearest store to pick up the goods. So how do we figure out which stores are within 1000 meters of the customer's address? We can calculate it from the following sections. 1. Obtain the latitude and longitude of the customer's address, we can get through the interface provided by Baidu Map

]=classcount.get (voteIl abel,0) +1 #排序 sortedclasscount=sorted (Classcount.iteritems (), Key=operator.itemgetter (1), reverse=true) return sort Edclasscount[0][0]def img2vector (filename): Returnvect=zeros ((1,1024)) Fr=open (filename) for I in range (32): Linestr=fr.readline () For j in Range (+): Returnvect[0,32*i+j]=int (Linestr[j]) return Returnvect Test the Img2vector function by entering the following command on the Python command line, and then compare it to the file opened

1. K-Nearest Neighbor algorithm1 #Coding=utf-82 fromNumPyImport*3 Importoperator4 5 defCreateDataSet ():6Group = Array ([[[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])7Labels = ['A','A','B','B']8 returnGroup,labels9 Ten defclassify (inx,dataset,labels,k): OneDatasetsize =Dataset.shape[0] ADiffmat = Tile (InX, (datasetsize,1))-DataSet -Sqdiffmat = diffmat**2 -Sqdistances = Sqdiffmat.sum (Axis=1) thedistances = sqdistances**0.5 - #the index where the ret

Overview of the K- neighbor algorithmthe K-nearest algorithm is classified by measuring the distance between different eigenvalue values.Advantages: High accuracy, insensitive to outliers, no data input assumptionsCons: High computational complexity, high spatial complexityUse data range: Numeric and nominalHow it works : There is a collection of sample data (also known as a training sample set), and each data in the sample set has a label, that is, w

Objective:Recently in the study of machine learning, the process of experience will be recorded in the blog, the article and code are original. The turtle will be updated at irregular intervals. Note that this is not a tutorial, but it is estimated to help some students who are just getting started.------------------------I'm a split line------------------------k Nearest neighbor (K-nearest neighbor,knn) al

One. An overview of the K-Nearest neighbor algorithm (KNN)The simplest initial-level classifier is a record of all the classes corresponding to the training data, which can be categorized when the properties of the test object and the properties of a training object match exactly. But how is it possible that all the test objects will find the exact match of the training object, followed by the existence of a test object at the same time with more than

1. The core idea of the algorithm:By calculating the distance from each training sample to the sample to be classified, the nearest K training sample to the sample to be classified, and the majority of the training samples in that category in the K sample, indicate which category the sample to classify belongs to.The KNN algorithm is only associated with a very small number of adjacent samples in the decision of the class. As a result, KNN is more sui

1. Brief description: To put it simply, the valley nearest neighbor algorithm uses the distance method to measure different eigenvalues to classify. Advantages: High precision, insensitive to outliers, no data input assumptions. Disadvantages: High computational complexity and high spatial complexity. Applicable data range: Numerical and nominal type. 2. working principle is There is a collection of sample numbers, also known as the training sample se

classificationAlgorithm General Flow(1) Collection of data: Any method of data collection(2) Prepare data: Organize the collected data into structured data formats that meet the requirements of the algorithm(3) Analyzing data: Any method(4) Training algorithm: not applicable and K nearest neighbor algorithm(5) Test algorithm: Calculate error rate(6) Using algorithms: Data classification for dating sites, handwritten digit recognitionPrepare data: Imp

A series of articles on postgraduate courses see the Basic principles of those courses in the Faith section Assuming that two data sets P and Q have been given, the space transformation F of the two point sets is given to enable them to perform spatial matching. The problem here is that f is an unknown function, and the points in the two-point set are not necessarily the same. The most common way to solve this problem is to iterate over the nearest po

Morning head a bit of pain, suddenly thought can use KD Tree solution plane nearest point to the problem, found a way to test, the result can, although inefficient, but still AC ~Title Link: http://acm.hdu.edu.cn/showproblem.php?pid=1007The topic requires half of the distance between the closest points on the plane.The idea is to set up a tree first, a little bit into the tree, and then query its nearest po

image is generated. The Pixel matrix is shown as follows:234 38 22 2267 44 12 1289 65 63 6389 65 63 63 This method of enlarging an image is called the nearest interpolation algorithm. This is the most basic and simple image scaling algorithm, and the effect is also the worst. The enlarged image has a very serious mosaic, the reduced image has serious distortion. The root cause of poor effects is that its simple n