text classification python

classifiersPlt.figure (facecolor= ' W ')Plt.pcolormesh (x1, x2, Y_hat, Cmap=cm_light) # Display of predictive values(5) Show classification effect of classifier (add sample points)Plt.scatter (x[:, 0], x[:, 1], s=30, c=y, edgecolors= ' K ', cmap=cm_light) # sample DisplaySummarize:Draw the classification interface of the classifier first, form the grid data with Np.meshgrid (), then draw with the help of P

Today read the Python language written using SMO in SVM optimization, using the RBF function for handwriting recognition, the following simple collation of the whole process and ideas, and then detailed the various parts.(1) Acquiring training data sets Trainingmat and Labelmat;(2) Optimization parameters Alphas and B are optimized using SMO, this step is to train to obtain the optimal parameters(3) using Alphas and B into the RBF Gaussian kernel func

The first thing to understand is that crawling pages are actually:Find a list of URLs (URLs) that contain the information we needDownload the page back via the HTTP protocolParse the required information from the HTML of the pageFind out more about this URL and go back to 2 to continueSecond, we must understand:A good list should:URLs that contain enough moviesThrough the page, you can traverse all the moviesA list sorted by update time to catch the latest updated movies fasterThe final simulati

Author:headsen ChenDate:2018-03-21 17:42:13Notice:this article created by Headsen Chen himself and don't allowed to copy,or you'll count law question and to pay 10 000$!!!1, the execution of the function name is called directly:2, execution in return:The results of the implementation are as follows:3, executed in print 1: single function without parameters execution: print (f ())The results are as follows:4, executed in print 2: Higher order function with parameters: print (f (a))The results are

)#Creating a new cluster centerdefNew_dis_center (x,z_1,z_2,n1,n2): Z_1_1=[0,0] Z_2_1=[0,0] forIinchRange (0, (Len (N1))): Z_1_1=z_1_1+x[n1[i]]/Len (N1) forIinchRange (0, (Len (N2))): Z_2_1=z_2_1+x[n2[i]]/Len (n2)returnZ_1_1,z_2_1#iterate function input data and initial cluster centerdefIteration_fun (x,z_1,z_2):#Initialize the intermediate amount of the iterative cluster centerz_1_1=[0,0] Z_2_1=[0,0] N1=[] N2=[] Distribution (X,Z_1,Z_2,N1,N2) (z_1_1,z_2_1)=New_dis_center (X,Z_1,Z_2,N1,N2)#Itera

, y_pred, beta[, labels, ...]) Hamming_loss (Y_true, y_pred[, classes]) Jaccard_similarity_score (Y_true, y_pred[, ...]) Log_loss (Y_true, y_pred[, EPS, normalize, ...]) Precision_recall_fscore_support (Y_true, y_pred) Precision_score (Y_true, y_pred[, labels, ...]) Recall_score (Y_true, y_pred[, labels, ...]) Zero_one_loss (Y_true, y_pred[, normalize, ...]) There are also some issues that can be used for both two-label and multi-label (not multi-

Small task: Achieve picture classification1. Picture materialPython bulk compress jpg images: PiL library resizehttp://blog.csdn.net/u012234115/article/details/502484092. Environment ConstructionInstallation version of Python under Windows comparison 2.7 vs 3.6Https://pypi.python.org/pypiInstallation of the PIL Library under WindowsHttps://pypi.python.org/pypiInstallation of the PIL Library under Windowshttp://zjfsharp.iteye.com/blog/2311523Installati

AssertionError异常将执行except下面的代码块 print ("AThe above example output is aExample 2: Catching exceptions using multiple except#!/usr/bin/python#coding:utf8#try is used in conjunction with multiple except to execute sequentially in a try code block, as long as an exception is caught to stop execution A = 1b = 2c = "1" Try:assert a The above results are unsupported operand type (s) for +: ' int ' and ' str ' do not support integral type and string type addi

A supervised KNN neighbor algorithm:(1) Calculate the distance between the points in a well-known category dataset and the current point(2) Sorting in ascending order of distance(3) Select K points with a minimum distance from the current point(4) Determine the frequency of the category in which the first K points are present(5) Return to the category with the highest frequency of the first K points as the forecast classification of the current point#

There are four main parameters for defining methods in Python:1. General Parameters:Common parameters are very common.def F1 (name, age): Print('My name is%s, I am%s yearsold' % (name, age))Name and age are two common parameters. When calling method F1, be sure to pass in the name and age two arguments in the order in which they were defined.F1 ('Andy', 21)2. Default parameters:The default parameter refers to a default value in the definition meth