how to round to nearest whole number

1: Simple Algorithm Description Given the training data samples and labels, select the nearest K training samples for a sample data in a test, the class with the largest category among the K training samples is the prediction label of the test sample. KNN for short. Generally, K is an integer not greater than 20. The distance here is generally a Euclidean distance. 2: Python code implementation Create a KNN. py file and put the core code in it. (1) cr

points in the known category DataSet and the current point2. Order by distance increment number order3. Select the K points with the minimum distance from the current point4. Determine the frequency of the category where the first K points are present5. Return to the category with the highest frequency of the first K points as the current point of the forecast classification4 Parameters:A.inx: Input vectors for classificationB.dataset: Training Sampl

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

Recently work used a bit of annoy, so took the time to read the code, record the following: The annoy supports three distance measurement modes, cos distance, European distance and Manhattan distance. The following are mainly seen through the simplest Euclidean distances. First, look at the structure of node nodes. N_descendants records the number of nodes under the node, children[2] records the left and right sub-tree, V and a will be detailed, firs

1. Brief Introduction Specify the coordinates of N points on the plane to locate the two closest points. 2. IdeasThe brute force method is the complexity of C (n, 2) and N ^ 2. The binary method, for example, first sorts all vertices according to the abscissa and recursively Splits them. Termination condition: returns the result when only one vertex or two vertices exist. If one vertex is null, the two vertices are returned. Recursive process: Calculate the closest point between n/2 points on th

Given a non-empty binary search tree and a target value, find K values in the BST that is closest to the target.Note: Given target value is a floating point. Assume k is always valid, which is: k ≤total nodes. You is guaranteed to a unique set of K values in the BST is closest to the target. Follow up:Assume that the BST are balanced, could you solve it in less than O(n) runtime (where n = Total nodes)?Hint:1. Consider implement these the helper functions:I. Getpredecessor

1: The algorithm is a simple narrative descriptionBecause of the training data samples and labels, for example of the test data, from the nearest distance K training sample, this K practice sample in the category of the most class is the measured sample of the pre-measured label.Referred to as KNN. Usually k is an integer not greater than 20, where the distance is usually the European distance.2:python Code ImplementationCreate a knn.py file and put t

KNN is one of the simplest classification methods. The idea of this method is: if most of the k most similar samples in the feature space (that is, the most adjacent samples in the feature space) belong to a certain category, the sample also belongs to this category. In KNN algorithm, the selected neighbors are objects that have been correctly classified. This method only determines the category of the samples to be classified based on the class of one or more adjacent samples. Reference, Http:/

the efficiency was too low, making it useless to occupy resources (memory. Because our main data is the more than 1 million restaurants, not the more than 1 million cities, some cities may have a large number of restaurants in the southeast corner, but none of them in the northwest corner. If we follow the principle of geohash, it is necessary to maintain two keys in the Southeast and northwest corners (assuming they are far away ). The key in the no

Title: PortalAnswer: the direct brute force solution will definitely time out. The core of this problem is to find out the nearest pair of points between the distance, that is, the nearest point to the algorithm.In brief, the idea of divide and conquer is used to compare the minimum distance of the left half with the minimum distance of the right half to get a mindist. Then traverse the boundary between the

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 (