round up to nearest hundred

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

1 #-*-coding:utf-8-*-2 """3 Created on Thu June 17:16:19 20184 5 @author: Zhen6 """7 fromSklearn.model_selectionImportTrain_test_split8 ImportMglearn9 ImportMatplotlib.pyplot as PltTenX, y =Mglearn.datasets.make_forge () One X_train, X_test, y_train, y_test = Train_test_split (x, Y, random_state=0) # Generate training and test set data A - fromSklearn.neighborsImportKneighborsclassifierCLF = Kneighborsclassifier (n_neighbors=3) # call K nearest Nei

Code:1 #-*-coding:utf-8-*-2 """3 Created on Thu Jul 09:36:49 20184 5 @author: Zhen6 """7 """8 The influence of the size of n_neighbors on the predictive precision and generalization ability of K-nearest neighbor algorithm9 """Ten fromSklearn.datasetsImportLoad_breast_cancer One A fromSklearn.model_selectionImportTrain_test_split - - fromSklearn.neighborsImportKneighborsclassifier the - ImportMatplotlib.pyplot as Plt - -Cancer =Load_breast_cance

1 " "2 Created on Nov3 knn:k Nearest Neighbors4 5 Input:inX:vector to compare to existing dataset (1xN)6 dataset:size m data set of known vectors (NxM)7 Labels:data Set labels (1xM vector)8 K:number of neighbors to use for comparison (should is an odd number)9 Ten output:the Most Popular class label One A @author: Liu Chuanfeng - " " - Importoperator the ImportNumPy as NP - ImportMatplotlib.pyplot as Plt - - defclassify0 (InX, DataSet, labels, k): +

ObjectiveIf you think of an online dating site looking for a date, you're likely to classify all users of the dating site as three categories:1. Do not like the2. A bit of glamour3. Very attractiveHow do you decide which category A user belongs to? Presumably you will analyze the user's information to get a conclusion, such as the user "frequent flyer miles per year", "playing online games consumes more time than", "consumption of ice cream litres per week."The K-

The k~ nearest neighbor algorithm is the simplest machine learning algorithm. It works by comparing each feature of the new data with the characteristics of the data in the sample set, and then extracting the classification label of the data with the most similar characteristics in the sample set. In general, only the first k most similar data in a sample dataset is extracted. Usually k is not greater than 20, and the most recent classification of K m

(1) Collect data: Provide text file(2) Preparing data: Parsing text files with Python(3) Analyzing data: Using Matpltlib to draw two-dimensional diffusion graphs(4) Training algorithm: This step does not apply K-nearest neighbor algorithm(5) test algorithm: Using some of the data provided by Helen as a test sample, the difference between the test sample and the non-test sample is that the test sample is the data that has been sorted, and if the foreca

-16, -1.11022302e-16, 1.00000000e+00, 2.22044605e-16], [ -4.44089210e-16, -2.22044605e-16, -2.22044605e-16, 1.00000000e+00 ]]) >>> myeye=randmat*invarandmat #>>> Myeye-eye (4) #求误差值, eye (4) generates 4*4 unit matrix matrices ([[ -4.44089210e-16 , 0.00000000e+00, 0.00000000e+00, 2.22044605e-16], [ -2.22044605e-16, -1.11022302e-16, 1.24900090e -16, 2.49800181e-16], [ -2.22044605e-16, -1.11022302e-16, 0.00000000e+00, 2.22044605e-16], [ -4.44089210e-16, -2.22044605e-16, -2.22044605e-16, 4.44

Blog home: http://blog.csdn.net/minna_d Topic: Give a linear table A of n elements, for each number AI, find the number before it, and the nearest number. That is, for each I, the calculation Ci = min{| ai-aj| | 1 In fact, given an array, in a[0....i-1] to find the nearest value a[i], in fact, there is a bug, that is, if the same as for the 6 5,7, then the output who? n long do not write C, feel strange, w

When the number of nodes tends to infinity, the average distance of the nearest neighbor connected network with an average of 4 is deduced.Solution: Set the number of nodes to N, define% as the take-up operator, set r:= (N-1)%4 to represent (N-1)/4 remainder.According to the nature of the nearest neighbor connection network, each node has homogeneity, so we just work out one of the nodes (set to I) and the

Algorithm steps for KNN nearest neighbor algorithm KNN nearest Neighbor algorithm Should be the best understanding of the classification algorithm, but the computation is particularly large, and can not train the model (only to train the best K value). algorithm Steps 1, seeking European distanceD=sqrt (∑ (xi1-xi2) ^) I=1,2..NHere I is the various attributes, and each validation data and n training data

"016-3 Sum Closest (nearest three number sum)""leetcode-Interview algorithm classic-java Implementation" "All topics Directory Index"Original QuestionGiven an array S of n integers, find three integers in S such, the sum was closest to a Given 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.

Give you a tree, and the last line asks the nearest common ancestor of vertex a and vertex B. Tarjan: When a new vertex is found, the temporary ancestor of This vertex is the vertex of the previous layer. It is not until the leaves are found that the tracing starts. From this point, the temporary ancestor of the vertex searched is merged into the top vertex of the vertex. // Final LCA offline Algorithm for Finding the

Problem description: Given N points in a two-dimensional plane, look for the two nearest points in these points; Ideas: We found that if we want to compare all vertices by two, we need at least O (N ^ 2). Therefore, our idea is to ask for a point nearest to a point, you do not need to compare each point; We need to use the divide and conquer method to solve the problem; First, we need to define some variab