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by: Rocketseo2019
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How can i make the run time of Multi-Class Classification faster?

I’m trying to train and run Multi-Class classifiers for Random Forest and Logistic Regression. As of now on my machine which has an 8GB RAM and an i5 core, it’s taking quite some time to run inspite of the datasize being hardly 34K records. Is there any way in which i can speed up the current existing run time by tweaking a few parameters?

I’m just giving an example for the Logistic Regression Randomized Search below.

X.shape Out[9]: (34857, 18) 
Y.shape Out[10]: (34857,) 
Y.unique() Out[11]: array([7, 3, 8, 6, 1, 5, 9, 2, 4], dtype=int64) 
params_logreg={'C':[0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0],             'solver':['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],             'penalty':['l2'],             'max_iter':[100,200,300,400,500],             'multi_class':['multinomial']} folds = 2 n_iter = 2 scoring= 'accuracy' n_jobs= 1  model_logregression=LogisticRegression() model_logregression = RandomizedSearchCV(model_logregression,X,Y,params_logreg,folds,n_iter,scoring,n_jobs)  
[CV] solver=newton-cg, penalty=l2, multi_class=multinomial, max_iter=100, C=0.9  [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [CV]  solver=newton-cg, penalty=l2, multi_class=multinomial, max_iter=100, C=0.9, score=0.5663798049340218, total= 2.7min [CV] solver=newton-cg, penalty=l2, multi_class=multinomial, max_iter=100, C=0.9  [Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  2.7min remaining:    0.0s  [CV]  solver=newton-cg, penalty=l2, multi_class=multinomial, max_iter=100, C=0.9, score=0.5663625408848338, total= 4.2min [CV] solver=sag, penalty=l2, multi_class=multinomial, max_iter=400, C=0.8  [Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:  7.0min remaining:    0.0s  [CV]  solver=sag, penalty=l2, multi_class=multinomial, max_iter=400, C=0.8, score=0.5663798049340218, total=  33.9s [CV] solver=sag, penalty=l2, multi_class=multinomial, max_iter=400, C=0.8  [CV]  solver=sag, penalty=l2, multi_class=multinomial, max_iter=400, C=0.8, score=0.5664773053308085, total=  26.6s [Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:  8.0min finished```   It's taking about 8 mins to run for Logistic Regression. In contrast RandomForestClassifier takes only about 52 seconds.  Is there any way in which I can make this run faster by tweaking the parameters? 

If the Intel Pentium processors, was not made compatible to programs written for its predecessor, it could have been designed to be a faster processor

I find this question while solving some government job question bank. If someone could provide the answer along with a little explanation it would be very helpful.

Ques:- If the Intel Pentium processors, was not made compatible to programs written for its predecessor, it could have been designed to be a faster processor.

  1. The statement is true
  2. The statement is false
  3. The speed cannot be predicted
  4. Speed has nothing to do with the compatibility

(I did not find any tag as microprocessor or something so i have to keep it under the tag computer architecture, sorry for that, but i did not have sufficient reputation to create a tag.)

How to make this “disable touchpad while typing” python script enable the touchpad faster?

Long story short: I have a keyboard with a touchpad which is recognized as a “pointer” on xinput (in opposition to a touchpad), so I can’t use any Ubuntu embedded solution for disabling the touchpad while typing.

Reading through lots of related questions here and elsewhere, I’ve managed to adapt this python script to my problem. Here’s my version of it:

import os import time  import subprocess import threading  def main():     touch = os.popen("xinput list --id-only 'pointer:SINO WEALTH USB KEYBOARD'").read()[:-1]     keyboard = os.popen("xinput list --id-only 'keyboard:SINO WEALTH USB KEYBOARD'").read()[:-1]     subprocess.call('xinput set-prop '+touch+' 142 1', shell=True)     p = subprocess.Popen('xinput test '+keyboard, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)     clickTime = [0, 0]     def checkTime():         keys = [37, 50, 62, 64, 105, 108, 133]         while True:             out = p.stdout.readline()             if len(out) < 1:                 break             key = int(out.split()[-1])             if key not in keys:                 clickTime[0] = time.time()      t = threading.Thread(target=checkTime)     t.start()      lastTime = 0     touchpad = True     while True:         inactive = time.time() - clickTime[0]         # print ('inactive for', inactive)         if inactive > 1:                         if not touchpad:                 print ('Enable touchpad')                 subprocess.call('xinput set-prop '+touch+' 142 1', shell=True)             touchpad = True         else:             if touchpad:                 print ('Disable touchpad')                 subprocess.call('xinput set-prop '+touch+' 142 0', shell=True)             touchpad = False         time.sleep(0.5)      retval = p.wait()  if __name__ == '__main__':     main() 

The script works just fine. As soon as I start typing the touchpad is disabled. The only problem is that it takes about 1s for the touchpad to get enabled back, which is kinda long, and I haven’t found no way to make this delay smaller. Setting “time.sleep(0.5)” to a smaller number seems like an obvious choice, but setting it to 0.05 for example, only seems to make the script more cpu-hungry, but it makes no visible change on the delay between me stop typing and the touchpad getting reactivated.

My goal precisely is to able to deactivate the touchpad while typing and get the touchpad activated back around 300ms after I stop typing.

I don’t need to solve this problem using python necessarily, but that’s the only way I was able to address it on the first place. As answers, I can accept suggestions for changing this very python script, or maybe guidance on how to solve this with a bash script, or really any idea that guide me to solve this (thinking outside the box is welcome also).

Running Ubuntu 19.04.

Which representation of a 2D matrix is faster

which way is faster and compiler/cache friendlier, use M[a][b] or M[a*b] when working with matrices?

I tried writing both ways on compiler explorer in a function that allocates, initialises and returns a matrix but I don’t know assembly and how much time each instruction takes

int **M = malloc(sizeof(int*)*m) for(i=0; i<m; ++i) {   *M = malloc(sizeof(int)*n);   for(int j = 0; j < n; ++j){     M[j] = j;   } 

vs

int *M = malloc(m*n*sizeof(int)); for(i = 0; i < m*n; ++i) M[i] = i; 

I expect the second way to be faster.

Kano Model and Faster Horses

I should probably mention one thing before I start: When judging if a solution is “right”, I rather believe in seeing people’s actual behaviour than asking them about it.

Now let’s move on to the question: I have used the Kano model a few times in the past and there was always one thing lingering in my head: We’re basically asking people what they like instead of watching what works for them. Usually you don’t get to a state where you can show people a prototype of a feature when running a Kano study, instead you often show them just a sketch or a mock up. I decided to run a little experiment lately and it made me question the Kano model as a whole.

I did build an interactive prototype with certain features in order to find out what works for people who use the product. In that prototype I had Feature A, Feature B and Feature C. All of the features were relatively new to them and it’s unlikely that they have seen them in a product before. I gave them realistic tasks and watched how they used the prototype. Watching them, almost everybody used Feature A heavily and Feature B & C only a little bit or not at all.

After they finished the tasks, I asked them the questions from the Kano model and was shocked by the results:

  1. Feature A turned out to be Indifferent.
  2. Feature B was a Performance feature.
  3. Feature C was Attractive.

I ran the study with eight people and the results were pretty clear, there wasn’t much deviation. People basically said that they don’t really need the feature that they used the most to finish a task. They also said that they would love the features that they barely used.

Did anyone run into that problem before? Did I miss something in my setup/observations?

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by: monkeyseo
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Category: Link Building
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