In this post I am going to share my understanding of the paper: Markov Chain Sampling Methods for Dirichlet Process Mixture Models. In chapter 2, it introduces the basic concept of Dirichlet Process Mixture Models. In (2.1), we have: $latex y_i | \theta_i \sim F(\theta_i) \newline \theta_i | G \sim G \newline G \sim DP(G_0, \alpha)$ …
Continue reading “Understand “Markov Chain Sampling Methods for Dirichlet Process Mixture Models””
The following tables were generated right after a simple linear regression with three independent variables was fit in SAS: The linear regression is Gallons_sold ~ price + line_ad + display. I will mainly illustrate how to read the first table. To give you a background, the number of samples is $latex n=406$ and the number …
State-Action-Reward-State-Action (SARSA) and Q-learning are two forms of reinforcement learning. The difference of the two methods are discussed in: https://studywolf.wordpress.com/2013/07/01/reinforcement-learning-sarsa-vs-q-learning/ http://stackoverflow.com/questions/6848828/reinforcement-learning-differences-between-qlearning-and-sarsatd http://stats.stackexchange.com/questions/184657/difference-between-off-policy-and-on-policy-learning Let’s explain why Q-learning is called off-policy learning and SARSA is called on-policy learning. Suppose at state $latex s_t$, a method takes action $latex a_t$ which results to land in a new state …
This post explains my understanding in a proposed greedy algorithm for the maximum weighted matching problem. The greedy algorithm goes as follows (listed by this paper in Introduction section): It is claimed that the greedy algorithm is a 2 approximation, i.e., greedy result >= 1/2 optimal result. The document where the greedy algorithm is proposed is …
Continue reading “Why the greedy algorithm of maximum weighted matching is a 2-approximation?”
In this post, I am going to explain the code (as much as I can) from theano LSTM tutorial: http://deeplearning.net/tutorial/lstm.html You need to first understand LSTM. Here is an online recommended material: http://colah.github.io/posts/2015-08-Understanding-LSTMs/, in which many beautiful figures are provided to illustrate LSTM step by step. The tutorial aims to predict positive/negative sentiment based on movie reviews …
Twitter Sentiment Analysis: http://thinknook.com/twitter-sentiment-analysis-training-corpus-dataset-2012-09-22/ Topic classification for news (including Reuters, NewsGroup): http://disi.unitn.it/moschitti/corpora.htm Movie reviews: http://www.cs.cornell.edu/People/pabo/movie-review-data/ Other reviews: http://www.text-analytics101.com/2011/07/user-review-datasets_20.html Twitter Evaluation dataset: http://tweenator.com/index.php?page_id=13 Amazon review: https://snap.stanford.edu/data/web-Amazon.html Amazon review (upon request): https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html opinmind: https://inclass.kaggle.com/c/si650winter11/data Large movie reviews: http://ai.stanford.edu/~amaas/data/sentiment/
Use `rsync` with `-n` (dry run) so that it lists diff files but will not execute syncing. rsync -n -avcr –delete local_folder/ username@domain:remote_folder/
Hidden Markov Model You should bear in mind clearly the three questions people usually ask for Hidden Markov Model: 1. what is the probability of an observed sequence? 2. what is the most likely series of states given a specific observed observation? 3. Given a set of observations, what are the values of the state …
I’ve been struggled about where to put test files in a python project for a long time. Ideally, I think it is succinct to create a folder called “test” with all test files in it. However, the test files nested in the test folder need to import modules from parent folder. It is troublesome to import Python module …
Continue reading “Right way to put test codes in a Python project”
In this post, I am going to introduce my favorite way to make cells in Jupyter notebook run in parallel. 1. Initialize cluster using command lines or use Python `popen` (In the example below, I create a cluster with 2 workers): from subprocess import Popen p = Popen([‘ipcluster’, ‘start’, ‘-n’, ‘2’]) 2. Then, programmatically set …