How to convince your friends vertical farming is the next big thing

The Urban Vertical Farming Project

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Can resilient, regenerative agriculture refresh the ecological condition of our planet?  It can, and vertical farms can help us.

Why Vertical Farms-

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Easy way to capture Android network packets

tPacketCapture is the easy way to capture Android network packets. It sets up an VPN to do this, so root isn’ t required. It generates standard pcap files that can be analysed on PC with tools like wireshark.

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Note on using OneHotEncoder in scikit-learn to work on categorical features

OneHotEncoder is used to transform categorical feature to a lot of binary features. The fit method takes an argument of array of int. But one thing not clearly stated in the document is that the np.max(int_array) + 1 should be equal to the number of  categories. Otherwise, if you have discrete integers, some very large, you will have a huge memory leak. And get Memory Error.

So the best way is to use LabelEncoder() first to convert discrete integers to a continuous integer set with a smaller max value:

encoder = sklearn.preprocessing.OneHotEncoder()
label_encoder = sklearn.preprocessing.LabelEncoder()
data_label_encoded = label_encoder.fit_transform(data['category_feature'])
data['category_feature'] = data_label_encoded
data_feature_one_hot_encoded = encoder.fit_transform(data[['category_feature']].as_matrix())

Then a sparse matrix containing one hot encoded categorical feature is generated.

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Shadowsocks搭在ramnode seattle 简直酸爽


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Solve Mathematica 10 install error

CRITICAL FAILURE: PrintIntroduction() Error
$ProductTitle not defined.

Copy the installer sh file to a directory with no space.

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Introduction to Backtesting library in the Systematic Investor Toolbox

Systematic Investor

I wrote a simple Backtesting library to evaluate and analyze Trading Strategies. I will use this library to present the performance of trading strategies that I will study in the next series of posts.

It is very easy to write a simple Backtesting routine in R, for example:

The code I implemented in the Systematic Investor Toolbox is a bit longer, but follows the same logic. It provides extra functionality: ability to handle multiple securities, weights or shares backtesting, and customized reporting. Following is a sample code to implement the above strategies using the backtesting library in the Systematic Investor Toolbox:

The bt.prep function merges and aligns all symbols in the data environment. The bt.apply function applies user given function to each symbol in the data environment. The computes the equity curve of strategy specified by data$weight matrix. The data$weight matrix holds weights (signals) to open/close positions. The

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An explanation on ADF test

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