diff --git a/topic_modeling/gensim_playground.py b/topic_modeling/gensim_playground.py
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+#%%
+
+# pip install -U gensim
+
+from pprint import pprint
+from gensim import models
+from gensim import corpora
+from gensim.parsing.preprocessing import preprocess_documents, preprocess_string
+
+
+# some functions like LsiModel.print_topics() outputs to logging streams
+import logging
+logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
+
+
+
+# concepts: Document, Corpus, Vector, Model
+
+TEXTS = [
+  'this is a document about measuring executive functions using cognitive tests.',
+  'we measured executive functions skills using stop-signal task.',
+  'digit span can be used to measure intelligence and executive functions.'
+]
+
+
+# Steps to be taken:
+# - load corpus
+# - preprocessing (tokenize, lower case documents, remove infrequent words, remove stop words, ...)
+
+
+CORPUS = preprocess_documents(TEXTS)
+# pprint(CORPUS)
+
+# create dictionary
+dictionary = corpora.Dictionary(CORPUS)
+# pprint(dictionary.token2id)
+
+# bag-of-words for a new doc
+# new_doc = "executive function task/test"
+# new_vec = dictionary.doc2bow(preprocess_string(new_doc))
+# pprint(new_vec)    # (id, count)
+
+
+# convert entire corpus to BOW
+BOW_CORPUS = [dictionary.doc2bow(doc) for doc in CORPUS]
+# pprint(BOW_CORPUS)
+
+# now TF-IDF model
+# train the model
+tfidf = models.TfidfModel(BOW_CORPUS)
+
+
+pprint(type(CORPUS))
+# transform a query string to bow
+# query = 'digit_span'
+# query_bow = dictionary.doc2bow(preprocess_string(query))
+# pprint(tfidf[query_bow])
+
+
+# similarity matrix and querying it
+# from gensim.similarities import SparseMatrixSimilarity
+# index = SparseMatrixSimilarity(tfidf[BOW_CORPUS], num_features=13)
+# sims = index[tfidf[query_bow]]
+# sims
+# print(f'Similarities (query: "{query}"):\n-------------')
+# print('doc_index','score', sep='\t')
+# for doc_index, score in sorted(enumerate(sims), key=lambda x: x[1], reverse=True):
+#     print(doc_index, score, sep='\t')
+
+
+## LSI
+# initialize an LSI transformation
+lsi_model = models.LsiModel(tfidf[BOW_CORPUS], id2word=dictionary) 
+
+# create a double wrapper over the original corpus: bow->tfidf->fold-in-lsi
+corpus_lsi = lsi_model[tfidf[BOW_CORPUS]]
+
+# print top two topics
+lsi_model.print_topics(2)
+
+# print doc-topic assignments
+for doc, as_text in zip(corpus_lsi, TEXTS):
+    print(doc, as_text)
+
+
+# TODO: apply RP, LDA, and HDP and compare results