how to round to nearest whole number

K-Nearest-neighbor algorithm for machine learning (KNN algorithm) first, the conceptK-Nearest Neighbor algorithm is a simple machine learning method based on the distance between different eigenvalues. This paper simply introduces the next KNN algorithm and uses it to realize handwritten digit recognition. working principle:There is a set of sample data, also known as the training sample set, and each dat

time to find a number of points around the nearest point, but also to find out the nearest points around these points, and then find the boundaries of these points, how to find the boundary. Use PtInRegion to determine whether a point is in each face (a polygon connected to the nearest point around it), so that the tw

"y_predict= [Self._predict (x) forXinchX_predict]returnNp.array (y_predict)def_predict (self, x):"""returns the predicted result value of x given a single data to be predicted x""" assertX.shape[0] = = self._x_train.shape[1], "The feature number of x must is equal to X_train"Distances= [sqrt (np.sum (x_train-x) * * 2)) forX_traininchSelf._x_train] Nearest=Np.argsort (

K-means IntroductionThe K-means algorithm is one of the most widely used algorithms in cluster analysis. It divides n objects into K-clusters according to their attributes to satisfy the obtained clusters: the similarity of objects in the same cluster is higher, while the similarity of objects in different clusters is small. The clustering process can be represented by the following diagram: As shown in the figure, the data sample is represented by a dot, and the center point of each cluster i

of the initial center point is random, so each cluster result is not the same, the best case can be fully clustered correctly, the worst case of two cluster cluster is not separate, according to the majority of votes to determine the category, is marked the same category. KNN VS K-means The same point of the two:-The choice of K is similar-Similar thinking: Judging the properties of a sample based on a recent sample The difference between the two: The application scenario is different: The form

C ++ Implementation of k-Nearest Neighbor Method: kd tree1. the idea of the k-Nearest Neighbor algorithm is given a training set. For new input instances, find the k instances closest to the instance in the training set. Most of the k instances belong to a certain class, the input instance is divided into this class. To find the nearest k instances, it is critica

(x, (4, 1)) Out[5]: Array ([[[0, 0], [0, 0], [0, 0] , [0, 0]])You see, 4 expands the number of arrays (originally 1, now 4), and 1 expands on the number of elements per array (originally 2, now two).To confirm the above conclusion,In [6]: Tile (x, (4,2)) out[6]: Array ([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]])AndIn [7]: Tile (x, (2,2)) out[7]: Array ([[[0

}; Double meant[3]={0,0,0}; int nt=0; for (Itp=p.begin (), Itq=q.begin (); Itp!=p.end (); itp++,itq++) { Double tmpp[3]={itp->x,itp->y,itp->z}; Double tmpq[3]={itq->x,itq->y,itq->z}; Double tmpmul[3]; Matrixmul (R1, Mean_p, Tmpmul, 3, 3, 3, 1); Matrixdiv (tmpq,tmpmul,3,1); Matrixadd (meant,tmpq,3,1); nt++; } For (int i=0; i T1[i] = meant[i]/(double) (NT); The matrix of one rotation calculation is as follows:The effect shows the iteration

in the use of numpy, but after all, this is a good opportunity to learn python, the following is the code implementation of the algorithm:#-*-Coding:utf-8-*-"" " Created on Sat 14:36:02 2015input:data:vector of test sample (1xN) sample:size m data set of KN Own vectors (NxM) Labels:labels of the sample (1xM vector) K:number of neighborsoutput:the class label @author: peng__000 "" " fromNumPyImport*Importoperator fromOsImportListdir# Training SamplesSample = Array ([[1.0,1.1], [1.0,1.0], [0,0],

, ClasslabelvectorBriefly interpret the code: first open the file, read the number of rows of the file, and then initialize the two matrices (Returnmat, classlabelsvector) to be returned, then go into the loop and assign each row's data to the Returnmat and Classlabelsvector. ----3. Design Algorithm Analysis dataThe purpose of the K-nearest neighbor algorithm is to find the first k neighbors of the new data

Using the Python language to learn the K-nearest neighbor Classifier APIWelcome to my Git. View Source: Https://github.com/linyi0604/kaggle1 fromSklearn.datasetsImportLoad_iris2 fromSklearn.cross_validationImportTrain_test_split3 fromSklearn.preprocessingImportStandardscaler4 fromSklearn.neighborsImportKneighborsclassifier5 fromSklearn.metricsImportClassification_report6 7 " "8 k Nearest Neighbor class

number of rows of the matrix M = dataset.shape[0]# Matrix Operations: Implementing the Oldvalue-min step in the normalization formula Normdataset = Dataset-tile (Minvals, (M,1))# Matrix Division: Implement the Division in the normalization formula Normdataset = Normdataset/tile (ranges, (M,1))# Returns the normalized data, the data range and the minimum value matrixReturn normdataset, ranges, minvals# KNN Algorithm ImplementationDefClassify0(InX, Dat

, the selected neighbors are the objects that have been correctly categorized. This method determines the category to which the sample is to be divided based on the category of the nearest one or several samples in the categorical decision-making. KNN algorithm itself is simple and effective, it is a lazy-learning algorithm, classifier does not need to use training set for training, training time complexity of 0. The computational complexity of the KN

rule: Majority voting (majority voting) rule is the loss function is the 0-1 loss function is the experience of risk minimization2.2 kd Tree: A binary tree that solves a fast search for K-nearest neighbors, and the construction of KD trees is equivalent to continuously dividing the K-dimensional space with the super-plane perpendicular to the axis, constituting a series of k-dimensional hyper-matrix regions; Each node corresponds to a K-dimensional h

I. OverviewThe K-Nearest neighbor algorithm is classified by measuring the distance between different eigenvalues.How it works: first there is a collection of sample data (the training sample set), and each data in the sample data set has a label (classification), that is, we know that each data in the sample data corresponds to the owning category, after entering the data with no label, Compare each feature of the new data with the characteristics of

K Nearest neighbor algorithm is called KNN algorithm, this algorithm is a relatively classical machine learning algorithm, the overall KNN algorithm is relatively easy to understand the algorithm. The k represents the closest to their own K data samples. The KNN algorithm and the K-means algorithm are different, the K-means algorithm is used to cluster, to determine what is a relatively similar type, and the KNN algorithm is used to do the classificat

example of movie classification. Some have counted the fights and kissing footage of many movies, showing the number of fights and kissing shots in 6 movies. If there is a film not seen, how to determine whether it is a love movie or action movie?① first need to count the number of fights and kissing scenes in this unknown movie, where the question mark position is how many shots the unknown movie appea