facilitate the processes adjusting related parameters by easy.py

facilitate the processes adjusting related parameters by easy.py

execute the easy.py in Linux

reference link

f=open("a1a.t","r", encoding = 'utf-8',errors='ignore') cout_1 = 0 cout = 0 for line in f: cout += 1 if (line.split(' ')[0] == "+1"): cout_1 += 1 print("cout = %d" % (cout-cout_1) + "\\n") print("cout_1 = %d " % cout_1 + "\\n")

above codes could let you know the different labels amount with “+1” or “-1”

f=open("a1a.t.predict","r", encoding = 'utf-8',errors='ignore') cout_1 = 0 cout = 30956 lines = f.readlines() cout_1 = lines.count('1\\n') print("cout_2 = {}".format(cout -cout_1) + "\\n") print("cout_1 = %d " % cout_1 + "\\n")

obtain the prediction classfication numbers

step_1 just download two database files svmguide1 and svmguide1.t into a file containing easy.py liking following:

step_2 use command python3 easy.py svmguide1 svmguide1.t and I just got the followign mistakes:

the solutions to handle the above problems: sudo apt install python-is-python3 but as follows is also a mistake:

it’s very mysterious for me. When I just restart my computer and I run this codespython easy.py a1a a1a.t python easy.py a1a a1a.t Scaling training data... WARNING: original #nonzeros 22249 > new #nonzeros 181365 If feature values are non-negative and sparse, use -l 0 rather than the default -l -1 Cross validation... Best c=512.0, g=3.0517578125e-05 CV rate=83.3022 Training... Output model: a1a.model Scaling testing data... WARNING: feature index 12 appeared in file a1a.t was not seen in the scaling factor file a1a.range. The feature is scaled to 0. WARNING: feature index 60 appeared in file a1a.t was not seen in the scaling factor file a1a.range. The feature is scaled to 0. WARNING: feature index 89 appeared in file a1a.t was not seen in the scaling factor file a1a.range. The feature is scaled to 0. WARNING: feature index 96 appeared in file a1a.t was not seen in the scaling factor file a1a.range. The feature is scaled to 0. WARNING: feature index 111 appeared in file a1a.t was not seen in the scaling factor file a1a.range. The feature is scaled to 0. WARNING: feature index 116 appeared in file a1a.t was not seen in the scaling factor file a1a.range. The feature is scaled to 0. WARNING: feature index 120 appeared in file a1a.t was not seen in the scaling factor file a1a.range. The feature is scaled to 0. WARNING: feature index 121 appeared in file a1a.t was not seen in the scaling factor file a1a.range. The feature is scaled to 0. WARNING: feature index 122 appeared in file a1a.t was not seen in the scaling factor file a1a.range. The feature is scaled to 0. WARNING: feature index 123 appeared in file a1a.t was not seen in the scaling factor file a1a.range. The feature is scaled to 0. WARNING: original #nonzeros 429343 > new #nonzeros 3498028 If feature values are non-negative and sparse, use -l 0 rather than the default -l -1 Testing... Accuracy = 84.3358% (26107/30956) (classification) Output prediction: a1a.t.predict execute the file easy.py in windows

you could watch this blog to see how to use libsvm in windows

there is a mistake I made when I used the easy.py

"gnuplot executable not found" I have to say this is a tricky question. Because after I have installed gun plot into the computer. I still cannot avoid this mistake. Finally, I found that the original test in the pgnuplot.exe instead of gnuplot.exe.

Now I could use the easy.py, but the executation time is so long I cannot know what’s the problem in here. I was ready to run it in night !.

I am ready to show my result with grid.py to find the suitable parameters for my database.

I just the command python grid.py ..\\heart_scalethe following is the results:

maybe it is not fast enough. Therefore, what I could do is wait it.

The following is the content for my assignemnt of ML

svm-train a1a and I got the following results:

* optimization finished, #iter = 537 nu = 0.460270 obj = -673.031415, rho = 0.628337 nSV = 754, nBSV = 722 Total nSV = 754

.\\svm-predict .\\a1a.t .\\a1a.model a1a.t.predict and got the following accuracy:

Accuracy = 83.5864% (25875/30956) (classification)