python nearest neighbor

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 (

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

The first question is the choice of K-value? How to quickly find a K-neighbor, especially if the feature space dimension is large and the training data capacity is large. (1) K-value problem: When the K value is very small, it is equivalent to a small field of training examples to predict, the approximate error of learning will be reduced, only the training instance closer to the input instance will work on the predicted results (in turn, the clo

:", end="") Print(sortedclasscount[0][0])returnSORTEDCLASSCOUNT[0][0]if __name__= ="__main__": start ()Output Result: Dataset.shape[0] Returns the number of rows in the matrix:4Dataset.shape[1] Returns the number of columns of a matrix:2(4, 2)dataset.shape Type:diffmat:[[2 1][1 0][2 2][ -1-2]]sqdiffmat:[[4 1][1 0][4 4][1 4]]sqdistances:[5 1 8 5]distance from unknown point to each known point: [2.23606798 1.2.82842712 2.23606798]index Position: [1 0 3 2]label 0:a1th visit, Clas

#-*-coding:utf-8-*-__author__= ' Ghostviper ' "" "K Nearest neighbor Collation Algorithm" "" fromnumpy Import*importoperatordefcreatedataset (): group=array ([ [1.0,1.1], [1.0,1.0],[0,0], [0,0.1]]) labels=[' A ' , ' A ', ' B ', ' B ']returngroup,labelsdefclassify0 (InX, dataset,labels,k): #shape Get the size of the array in the dataset datasetsize=dataset.shape[0]# Copy the matrix based on the input elemen

Algorithms based on nearest neighbor, often used in various situations,For example, 100,000 users, for each user to find the most similar users,When N is particularly large, the efficiency is not very high, such as when the n=10^5, it is not very good, because the violence of the time complexity of the law is O (n^2).This requires special means, there are two commonly used methods, one is the Kdt tree (and

Working principle:Given a training dataset, for a new input instance, find the nearest K-instance to the instance in the training dataset (that is, the K-neighbor above), where the majority of the K-instances belong to a class, and the input instance is categorized into this class.code example:knn.py from Import *import operatordef CreateDataSet (): = Array ([[[1.0,1.1],[ 1.0,1.0],[0,0],[0,0.1]]) la

Use the K-nearest neighbor algorithm to divide a point [0.6, 0.6] into a class (A, B). fromNumPyImport*Importoperatordefclassify0 (InX, DataSet, labels, k): Datasetsize= Dataset.shape[0]#number of array rowsDiffmat = Tile (InX, (datasetsize, 1))-DataSet Sqdiffmat= Diffmat * * 2sqdistances= Sqdiffmat.sum (Axis=1) Distances= sqdistances * * 0.5sorteddistindicies=distances.argsort () ClassCount= {} forIinc

Taiwan Big machine skill and cornerstone are finished, but no programming has been, now intends to combine Zhou Zhihua "machine learning", the machine to learn the actual combat, the original book is Python2, but I feel python3 better use some, so plan to use Python3 write it again. Python3 and Python2 different places will be in the program in the bid.Code and data: HTTPS://GITHUB.COM/ZLE1992/MACHINELEARNINGINACTION/TREE/MASTER/CH2K-Nearest

Simply put, the K-nearest neighbor algorithm is classified by measuring the distance between different eigenvalue values.Advantages and Disadvantages Advantages High accuracy, insensitive to outliers, no data input assumptions. Disadvantages High computational complexity and high spatial complexity. Working with data ranges Numerical and nominal t

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

Introduction to K-Proximity algorithm: K-Neighbor algorithm is to calculate the distance between the data to be classified and the sample data, get the first k (usually not more than 20) and the most similar data to be classified data, then classify the K data, and classify the data to the category with the most occurrences. It is to be noted that 1, sometimes need to be based on the characteristics of the data in the classification of contribution si