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The following do not yet have any pytests:

• boolean_algebra
• digital_image_processing
• divide_and_conquer
• hashes
• linear_algebra #1124
• networking_flow #1126
• traversals
• file_transfer_protocol # This might be difficult
• machine_learning
• neural_network

For ease of review, please only modify one directory per pull request.

It says in the documentation (the last section - "Working with Virtual Environments"):

For Python 3 add the following lines to the top of your .wsgi file:

activate_this = '/path/to/env/bin/activate_this.py'
with open(activate_this) as file_:
    exec(file_.read(), dict(__file__=activate_this))

However `activate_this.p

I could not find anything in the docs about how to handle different frequencies of time series. I have a Dataset A with monthly data that i want to use to predict the values from Dataset B that contains quarterly based data. So the target value e.g. quarter 1 is based on the values from month 1-3.

Dataset A (Features):

| Month | Value1 | Value2 | Value3 |
| ------------- | ------------- |

Brew is listed as the recommended way of installing httpie on macOS. However, it doesn't work with auth plugins.

For example, if you pip3 install requests-hawk and then run http --help hawk will not show as an auth type. If you pip3 install httpie-oauth it will install httpie via pip as a dependency and overwrite the brew installed link in /usr/local/bin/http and now all the plugins wil

tldr; in multi-process environment (Celery) sessions might lead to request/responses being mixed up.

It is unsafe to use Session in a multi-process environment - if the fork happens after Session initialisation the underlying connection pool will be shared across both processes, leading to potentially dangerous and hard to debug issues.

I'm not sure what should happen - whather a code change

SUMMARY

In recent ansible release, the default undefined class in jinja evaluation context is now `AnsibleU

I think it will be readonable to add an option to use the original dataset when training final_estimator. This seems reasonable and has proved to be useful in some Kaggle competitions.

Reference: implementation from mlxtend
http://rasbt.github.io/mlxtend/api_subpackages/mlxtend.classifier/#stackingcvclassifier

use_features_in_secondary : bool (default: False)
If True, the meta-classifier w

This involves, among other things:

• Enabling a Python 3.8 environment in Tox
• Enabling a Python 3.8 job in the Travis CI
• Updating the setup.py file

Context

We would like to add torch::nn::functional::gumbel_softmax to the C++ API, so that C++ users can easily find the equivalent of Python API torch.nn.functional.gumbel_softmax.

Steps

• Add torch::nn::GumbelSoftmaxOptions to torch/csrc/api/include/torch/nn/options/activation.h (add this file if it doesn’t exist), which should include the following parameters (based on

The command certbot register --update-registration has been deprecated for a long time in favor of certbot update_account. Let's remove --update-registration and the code that uses it.

Code Sample, a copy-pastable example if possible

# Your code here
>>> df = pd.DataFrame.from_dict({"date": [1485264372711, 1485265925110, 1540215845888, 1540282121025]})
>>> df["date_dt"] = pd.to_datetime(df["date"], unit='ms', cache=True)
>>> df
            date                 date_dt
0  1485264372711 2017-01-24 13:26:12.711
1  1485265925110 2017-01-24 13:52:05.110
2  1

数轴上放置了一些筹码，每个筹码的位置存在数组 chips 当中。

你可以对 任何筹码 执行下面两种操作之一（不限操作次数，0 次也可以）：

将第 i 个筹码向左或者右移动 2 个单位，代价为 0。
将第 i 个筹码向左或者右移动 1 个单位，代价为 1。
最开始的时候，同一位置上也可能放着两个或者更多的筹码。

返回将所有筹码移动到同一位置（任意位置）上所需要的最小代价。

示例 1：

输入：chips = [1,2,3]
输出：1
解释：第二个筹码移动到位置三的代价是 1，第一个筹码移动到位置三的代价是 0，总代价为 1。
示例 2：

输入：chips = [2,2,2,3,3]
输出：2
解释：第四和第五个筹码移动到位置二的代价都是 1，所以最小总代价为 2。
 

提示：

1 <= chips.length <= 1