As I said before, I’m working on a text classification project. I use doc2vec to convert text into vectors, then I use LPA to classify the vectors.

LPA is a simple, effective semi-supervised algorithm. It can use the density of unlabeled data to find a hyperplane to split the data.

Here are the main stop of the algorithm:

1. Let $ (x_1,y1)…(x_l,y_l)$ be labeled data, $Y_L = \{y_1…y_l\} $ are the class labels. Let \((x_{l+1},y_{l+u})\) be unlabeled data where \(Y_U = \{y_{l+1}…y_{l+u}\}\) are unobserved, usually \(l \ll u\). Let \(X=\{x_1…x_{l+u}\}\) where \(x_i\in R^D\). The problem is to estimate \(Y_U\) for \(X\) and \(Y_L\).
2. Calculate the similarity of the data points. The most simple metric is Euclidean distance. Use a parameter \(\sigma\) to control the weights.

\[w_{ij}= exp(-\frac{d^2_{ij}}{\sigma^2})=exp(-\frac{\sum^D_{d=1}{(x^d_i-x^d_j})^2}{\sigma^2})\]

These days, I’m working on some text classification works, and I use gensim ’s doc2vec function.

When using gensim, it shows this warning message:

C extension not loaded for Word2Vec, training will be slow.

I search this on Internet and found that gensim has rewrite some part of the code using cython rather than numpy to get better performance. A compiler is required to enable this feature.

I tried to install mingw and add it into the path, but it’s not working.

Here are some shell tools I use, which can boost your productivity. Mordern-unix is a great repo that list lots of modern unix tools.

Prezto

A zsh configuration framework. Provides auto completion, prompt theme and lots of modules to work with other useful tools. I extremely love the agnoster theme.

Fasd

Help you to navigate between folders and launch application.

Here are the official usage example:

Over the years, I have read so many programmers’ blogs, which has helped me a lot. Now I think it’s the time to start my own blog.

I hope this can enforce myself to review what I have learned, and it would even be better if someone can benefit from it.