python nearest neighbor

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

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-

would sort an array. Perform an indirect sort along the given axis using the algorithm specified by the kind keyword. It returns an array of indices of the same shape as a , the index data along the given axis in sorted order. Returns an array of subscripts after a small to large order. Axis represents the dimension to compare, which defaults to the last dimension.　Some function learning in 2.pythonThe reload () function, which needs to be introduced from the IMP module: from IMP impor

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 th

K Nearest neighbor (K-nearestneighbor) algorithm is abbreviated as KNN. The basic idea is simple and straightforward, for a data instance x that needs to be categorized, calculates the distance between x and all known categories of sample points in the feature space. Take the nearest K-sample point to the X-distance, and count the categories with the largest perc

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

is possible that when a new sample is input, the samples with a large capacity class in the K neighbors of the sample account for the majority. Therefore, we can use the method of weight (a large neighbor weight with a small distance from the sample) to improve. Another disadvantage of this method is that the calculation workload is large, because the distance from the text to all known samples must be calculated for each text to be classified before

(), key = Op Erator.itemgetter (1), reverse =false) #将值按从小到大进行排序print (sorted_d) #[(' B ', 1), (' A ', 3)]print ( Sorted_d[0][0]) #b get category 4. Advantages and disadvantages of the algorithm:?The parameters of KNN in Scikit-learn:neighbors.KNeighborsClassifier(n_neighbors=5, weights=’uniform’, algorithm=’auto’, leaf_size=30, p=2, metric=’minkowski’, metric_params=None, n-jobs=1)1. N_neighbors is the K in KNN, that is, when doing the classification, we select the number of points closest

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

() forJinchRange (32): Returnresult[0,32*I+J] =Int (liststr[j])returnReturnresultdefhandwritingclasstest (): Trainingfilelist= Listdir ('trainingdigits') M=Len (trainingfilelist) #Get Training DataTrainingmat = Zeros ((m,1024)) Hwlabels= [] forIinchRange (m): Filenamestr=Trainingfilelist[i] Filestr= Filenamestr.split ('.') [0] Classnumstr= Int (Filestr.split ('_') [0]) hwlabels.append (CLASSNUMSTR) trainingmat[i,:]= Image2vector ('trainingdigits/%s'%filenamestr) Testfilelist= Listdir (

2. When predicting data classification, the ' Dict ' object has no attribute ' Iteritems 'Such as:The most common workaround is to change the order of environment variablesSuch asNote: Which version is above, and who is the Python version in CMD.Such asAnother example:Then you can do this by predicting the classification of your data:Implementation of KNN classification algorithm based on K-nearest

) - returnSORTEDCLASSCOUNT[0][0]knn-classify03.3 Using KNN in the above data and calculating the rate of miscarriage1 defdatingclasstest ():2HoRatio = 0.50#Hold out 10%3Datingdatamat,datinglabels = File2matrix ('DatingTestSet2.txt')#Load Data setfrom file4Normmat, ranges, minvals =autonorm (Datingdatamat)5m =Normmat.shape[0]6numtestvecs = Int (m*hoRatio)7Errorcount = 0.08 forIinchRange (numtestvecs):9Classifierresult = Classify0 (normmat[i,:],normmat[numtestvecs:m,:],datinglabels[numtes

"%(Classifierresult, datinglabels[i]))if(Classifierresult! = Datinglabels[i]): Errorcount + = 1.0Print("The total error rate is:%f"% (errorcount/float (numtestvecs))) Print(Errorcount)defimg2vector (filename): Returnvect= Zeros ((1,1024)) Fr=open (filename) forIinchRange (32): Linestr=Fr.readline () forJinchRange (32): Returnvect[0,32*I+J] =Int (linestr[j])returnReturnvectdefhandwritingclasstest (): Hwlabels=[] trainingfilelist= Listdir ('trainingdigits')#load the training setm =Len (trainingfil

Radicals theSorteddistindicies = Distances.argsort ()#sort in ascending order, return the original subscript -ClassCount = {} - forIinchRange (k): -Voteilabel =Labels[sorteddistindicies[i]] +Classcount[voteilabel] = classcount.get (Voteilabel, 0) + 1#get是字典中的方法, preceded by the value to be obtained, followed by the default value if the value does not exist -Sortedclasscount = sorted (Classcount.items (), Key=operator.itemgetter (1), reverse=True)　　　　#在python3中没有iteritems, Key here is sorte

Working principle:Classification algorithm.When a new unlabeled sample is entered, the algorithm extracts the K-category labels for the nearest neighbor of the sample in the training sample set and the samples to be sorted (for example, there are only two characteristics of the sample, the point in the two-dimensional coordinate system is used to represent a sample, and the

Knn Simple thinking Less knowledge required for mathematics (nearly 0) Good effect Explains many of the details of the machine learning algorithm using the process A more complete process for characterizing machine learning applications K Nearest Neighbor Essence: If two samples are similar enough, they may fall into the same category.e.g. Green Dot is the newly added point, take its

Machine learning is divided into two major categories, supervised learning (supervised learning) and unsupervised learning (unsupervised learning). Supervised learning can be divided into two categories: classification (classification.) and regression (regression), the task is to classify a sample into a known category, each sample of the class information in training needs to be given, such as face recognition, behavioral recognition, target detection are classified. The task of regression is t

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 con

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-

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