Target Leakage in mentioned steps in Data Preprocessing. Train/test split needs to be before missing value imputation. Else you will have a bias in test/eval/serve.

Hi I would like to propose a better implementation for 'test_indices':

We can remove the unneeded np.array casting:

Cleaner/New:
test_indices = list(set(range(len(texts))) - set(train_indices))

Old:
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))

MemoryError Traceback (most recent call last)
in ()
3 tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5, norm='l2', encoding='latin-1', ngram_range=(1, 2), stop_words='english')
4
----> 5 featur

• I'm submitting a ...
  [/] enhancement

• Summary
  As a result of upgrading the Tensorflow version to 0.15.1, we should refactor all the dataSycn with arraySync. This will greatly improve the overall readability of the code.

Some modifications are perhaps required when testing for problem infeasibility. I suggest that we run this both ways on a set of benchmarks infeasible problems to confirm it will do what we expect.

From GB's comments on the OSQP API edits:

Haven't we discussed that we should relax this inequality, i.e. replace ineq_lhs < -eps with ineq_lhs < eps? See Eqs. (30) and (31) [here](https://s

The following code will result a Model object where model.nr_feature = n-1 even if the number of features in the dataset is n excluding the bias term.

problem.l = l
problem.n = n
problem.x = x
problem.y = y
...
Model model = Linear.train(problem, parameter);

This is because in the above code, bias term is added implicitly (default valu

@HsiehYiChia Thank you for you hard work

After reading your replies on issue #6, it is still unclear how to train our own model.
Can you simplify the training process to us.

cv2.error: /opt/conda/conda-bld/opencv_1491943704081/work/opencv-3.1.0/modules/calib3d/src/calibration.cpp:3314: error: (-215) nimages > 0 in function calibrateCamera

i meet a error, and this is why?what i should do?i made opencv, and seccessfully. i run main.py, this is wrong.

你好，根据我个人理解，app启动时间的测试，默认的黑盒标准一般是从click event触发开始。

请教一下根据report中的各类图表，首先要获取到精确的click event的时间戳t1，然后获取到界面加载完成的t2，这样就可以得到Δt

我这边打开Android的轨迹，这样点击时就会有一个圆点，可以用于辅助识别。

根据你的设计理念，如何根据图表分析得出app启动的时间呢？

谢谢。