np4

Generate entities based on vertex cacheOriginal (English link) http://ogldev.atspace.co.uk/www/tutorial03/tutorial03.html1 __author__="WSX"2 3 ImportNumPy as NP4 fromOpengl.glutImport*5 fromOpengl.glImport*6 ImportcTYPES7 defDraw ():8 glclear (gl_color_buffer_bit)9 Glenablevertexattribarray (0)Ten Glbindbuffer (Gl_array_buffer, VBO) OneGlvertexattribpointer (0, 3, gl_float, Gl_false, 0, None)#None of this here can be written as 0. AGldrawarrays (gl_

About Python data analysis in the Pandas module in the output, the middle of each line will have ellipses appear, and lines and lines in the middle of the ellipsis .... Problem, most of the other sites (Baidu) are written blindly, is simply copy paste the previous version, you want to know the answer to other questions you have to read the official documents.1 #!/usr/bin/python2 #-*-coding:utf-8-*-3 ImportNumPy as NP4 ImportPandas as PD5 ImportMySQLdb

is, unpickling. Python provides two modules for serialization: cPickle and pickle. The functions of these two modules are the same. The difference is that cPickle is written in C language and is fast. pickle is written in pure Python and is slow. It is similar to cStringIO and StringIO. When using the command, first try to import cPickle. If it fails, then import the pickle: try: import cPickle as pickleexcept ImportError: import pickle First, we try to serialize an object and write it into t

in the memory and can be modified at any time. However, once the program ends, all the memory occupied by the variables will be reclaimed by the operating system.The process of changing variables from memory to storage or transmission is called serialization and picking in python. After serialization, The serialized content can be written to the disk or transmitted to another machine over the network.In turn, it is called deserialization to re-read the variable content from the serialized objec

associated file of the calling disk;• Use Pickle's dumps () and loads () to operate directly in memory:>>> acc1{' 8908223631 ': [' Rachel ', 9000, 9000], ' 8906143632 ': [' alex3714 ', 15000, 15000]}>>> Pickle.dumps (ACC1) "(Dp0\ns ' 8908223631 ' \np1\n (lp2\ns ' Rachel ' \np3\nai9000\nai9000\nass ' 8906143632 ' \np4\n (Lp5\nS ') alex3714 ' \np6\nai15000\nai15000\nas. " >>> C = pickle.dumps (ACC1) >>> pickle.loads (c) {' 8908223631 ': [' Rachel ', 90

This paper mainly introduces the sbx of the crossover operation of the genetic algorithm (real coding), and simulates the binary crossover.First of all, give the personal code implemented by python2.7, the specific module has been uploaded to:Https://github.com/guojun007/sbx_cross1 #!/usr/bin/env python2 #Encoding:utf-83 ImportNumPy as NP4 ImportRandom5 6 """7 SBX analog binary crossover8 9 Input:Ten Population population Matrix One Alfa Crossover Pro

Online see about the use of pandas, although practiced a lot, but still some can not remember very clearly. So it was written down.Chapter1 is talking about reading a CSV file. The following code:1 #%%2 ImportPandas as PD3 ImportNumPy as NP4 ImportMatplotlib.pyplot as Plt5 #Make the graphs a bit prettier6Pd.set_option ('Display.mpl_style','default')7plt.rcparams['figure.figsize'] = (15,5)8 9 #%%TenBROKEN_DF = Pd.read_csv ('C:\Users\rui\Desktop\pandas-

Note that this library is based on python2.7, so the syntax details are different from beforeCv2 and NumPy depth fit, the picture read into is Numpy.array, but dtype comparison is not used only, support all array method (nonsense ...)Cv2.cvtcolor (Img,cv2. COLOR_BAYER_BG2BGR)Img.item (0,0)Img.itemset ((0,0), 0)Cv2.imshow (' My Image ', IMG)Cv2.waitkey ()Cv2.destroyallwindows ()1 #Coding=utf-82 ImportCv23 ImportNumPy as NP4 5 #Array Generation6img = Np

1 __author__="WSX"2 ImportCv2 as CV3 ImportNumPy as NP4 5 defLocal_threshold (IMG):#Local threshold value6Gray = Cv.cvtcolor (IMG, CV. Color_bgr2gray)#change to Grayscale first7binary = Cv.adaptivethreshold (gray, 255, CV. Adaptive_thresh_gaussian_c, CV. Thresh_binary, 25, 10,)#255 Maximum Value8 #There are two ways to adaptive_thresh_gaussian_c (mean with weights) and adaptive_thresh_mean_c (and mean comparison)9 #blockSize must be an odd num

optimal point, called the Metropolis Criterion, specifically:Here the E is y,t is the current temperature, the delta is less than 0 is the new value, whether it is accepted by this probability, when the iteration of the time, each right to move the step up to 1 times he will be able to find the final optimal solution, the step is cumulative but the probability is tired, This means that the probability is small, but once the number of iterations is sure to run out to the optimal solution.Optimal

‘,9000,9000],‘8906143632‘:[‘alex3714‘,15000,15000]} • Multiple states (dump) can be saved in one file, but it is not recommended to have one-to-one state (load);Other instructions and use of 2.Pickle• Serialization allows the reading of interactive data between different programs;· Dump () in Pickle can only store the serialization of the data structure on disk, then load () and then deserialize the associated file of the calling disk;• Use Pickle's dumps () and loads () to operate direc

Newton's Method1 #Coding:utf-82 ImportMatplotlib.pyplot as Plt3 ImportNumPy as NP4 5 defDatan (length):#Generate Data6x = np.ones (shape = (length,3))7y =np.zeros (length)8 forIinchNp.arange (0,length/100,0.02):9X[100*i][0]=1Tenx[100*i][1]=I OneX[100*i][2]=i + 1 + np.random.uniform (0,1.2) AY[100*i]=1 -X[100*i+1][0]=1 -x[100*i+1][1]=i+0.01 thex[100*i+1][2]=i+0.01 + np.random.uniform (0,1.2) -y[100*i+1]=0 - returnx, y - + defSigmoid (x):#sim

network. In turn, it is called deserialization to re-read the variable content from the serialized object to the memory, that is, unpickling. Python provides two modules for serialization: cPickle and pickle. The functions of these two modules are the same. The difference is that cPickle is written in C language and is fast. pickle is written in pure Python and is slow. it is similar to cStringIO and StringIO. When using the command, first try to import cPickle. if it fails, then import the pic

Goal• Learn to read video files, display videos, save video files• Learn to get and display video from the camera• You will learn these functions: Cv2. Videocapture (), Cv2. Videowrite ()Capturing video with the camera Use the camera to capture a video and convert it into grayscale video. You should first create a Videocapture object, which can be the index number of the device, or a video file. The device index number is the camera that you specify to use. Typical laptops have

1. Build the OPENCV environment under PythonThe most recent version of Python is 2.7.3, so this is a python-based learning OpenCV, and just add some other packages to it.For example, the first program requires a package, such asNumPy: The module of scientific calculation, contains the rich function, the configuration OpenCV also need this modulecv2:opencv2.x version, here choose opencv2.4.7Install the NumPy module only need to use PIP or Easy_install, here is also recommended with PIP, a command

Proximity algorithmOr, K nearest neighbor (Knn,k-nearestneighbor) classification algorithm is one of the simplest methods in data mining classification. The so-called K nearest neighbor is the meaning of K's closest neighbour, saying that each sample can be represented by its nearest K-neighbor.About k nearest neighbor algorithm, a very good article: KNN algorithm understandingIndustry applications: Customer churn prediction, fraud detection, etc. (more suitable for classification of rare events

serialization: cPickle and pickle . The two modules function is the same, the difference cPickle is written in C language, fast, pickle is pure python writing, slow, cStringIO and StringIO a reason. When using, try the import first cPickle , if it fails, then import pickle :try: import cPickle as pickleexcept ImportError: import pickleFirst, we try to serialize and write an object to the file:>>> d = dict(name=‘Bob‘, age=20, score=88)>>> pickle.dumps(d)"(dp0\nS‘age‘\np1\nI20\nsS‘score‘\np

extracted and the target is captured.1 # Find Outlines 2 _,contours, hierarchy = cv2.findcontours (binary,cv2. Retr_tree,cv2. chain_approx_simple) 3# draw Contour 4 cv2.drawcontours (Img,contours,-1, ( 0,0,255), 3) 5# Draw result 6 cv2.imshow (" Result", IMG)The entire code is as follows1 #Coding=utf-82 ImportCv23 ImportNumPy as NP4 5img = Cv2.imread ("temp.jpg")#Loading Images6H, W = img.shape[:2]#Get the height and width of the image7Cv2.imshow

1 #Pivot Tables Pivot Table2 #pd.pivot_table (Data,values=none,index=none,columns=none,3 ImportNumPy as NP4 ImportPandas as PD aggfunc='mean', fill_value=none,margins=false,dropna=true,margins_name=' All')5Date = ['2017-5-1','2017-5-2','2017-5-3']*36RNG =pd.to_datetime (date)7DF = PD. DataFrame ({'Date': RNG,8 'Key': List ('ABCDABCDA'),9 'Values': Np.random.rand (9) *10})Ten Print(DF) One Print('-----') A - Print

, the stereo is the n*2 matrix point, then the information processing of the future file is realized by a matrix.WAV file waveformIn order to draw the waveform diagram, the required parameters have time and amplitude information, the complete steps are as follows:1. Import the WAV file into the python work environment.2. Set the parameters, sound signal (time, amplitude, frequency).3. Draw this information through the interface provided by Matplotlib.pyplot.You can get a function that changes th