cs401r_w2016:lab9
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cs401r_w2016:lab9 [2016/03/17 21:44] admin |
cs401r_w2016:lab9 [2018/03/26 18:08] sadler [Hints:] |
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| For this lab, you will code two different inference algorithms on the Latent Dirichlet Allocation (LDA) model. | For this lab, you will code two different inference algorithms on the Latent Dirichlet Allocation (LDA) model. | ||
| - | You will use [[http://hatch.cs.byu.edu/courses/stat_ml/files.tar.gz|a dataset of general conference talks]]. Download and untar these files; there is helper code in the ''Hints'' section to help you process them. | + | You will use [[https://www.dropbox.com/s/yr3n9w61ifon04h/files.tar.gz?dl=0|a dataset of general conference talks]]. Download and untar these files; there is helper code in the ''Hints'' section to help you process them. |
| **Part 1: Basic Gibbs Sampler** | **Part 1: Basic Gibbs Sampler** | ||
| Line 120: | Line 120: | ||
| # topic distributions | # topic distributions | ||
| - | topics = np.zeros((V,K)) | + | bs = np.zeros((V,K)) + (1/V) |
| # how should this be initialized? | # how should this be initialized? | ||
| # per-document-topic distributions | # per-document-topic distributions | ||
| - | pdtm = np.zeros((K,D)) | + | pis = np.zeros((K,D)) + (1/K) |
| # how should this be initialized? | # how should this be initialized? | ||
| for iters in range(0,100): | for iters in range(0,100): | ||
| - | p = compute_data_likelihood( docs_i, qs, topics, pdtm ) | + | p = compute_data_likelihood( docs_i, qs, bs, pis) |
| - | print "Iter %d, p=%.2f" % (iters,p) | + | print("Iter %d, p=%.2f" % (iters,p)) |
| - | # resample per-word topic assignments qs | + | # resample per-word topic assignments bs |
| - | # resample per-document topic mixtures pdtm | + | # resample per-document topic mixtures pis |
| # resample topics | # resample topics | ||
cs401r_w2016/lab9.txt · Last modified: 2021/06/30 23:42 (external edit)
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