Merged PR from Alexander Von Moll

spark-mooc · Aug 31, 2016 · 688104e · 688104e
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diff --git a/cs110_autograder.dbc b/cs110_autograder.dbc
diff --git a/cs110_autograder_complete.dbc b/cs110_autograder_complete.dbc
diff --git a/cs110_autograder_register.dbc b/cs110_autograder_register.dbc
diff --git a/cs110_lab1_power_plant_ml_pipeline.dbc b/cs110_lab1_power_plant_ml_pipeline.dbc
diff --git a/cs110_lab2_als_prediction.dbc b/cs110_lab2_als_prediction.dbc
diff --git a/cs110_lab3a_word_count_rdd.dbc b/cs110_lab3a_word_count_rdd.dbc
diff --git a/cs110_lab3b_text_analysis_and_entity_resolution.dbc b/cs110_lab3b_text_analysis_and_entity_resolution.dbc
diff --git a/cs110_lab3b_text_analysis_and_entity_resolution.py b/cs110_lab3b_text_analysis_and_entity_resolution.py
@@ -460,7 +460,7 @@ def tf(tokens):
 # MAGIC The steps your function should perform are:
 # MAGIC * Calculate *N*. Think about how you can calculate *N* from the input RDD.
 # MAGIC * Create an RDD (*not a pair RDD*) containing the unique tokens from each document in the input `corpus`. For each document, you should only include a token once, *even if it appears multiple times in that document.*
-# MAGIC * For each of the unique tokens, count how many times it appears in the document and then compute the IDF for that token: *N/n(t)*
+# MAGIC * For each of the unique tokens, count how many documents it appears in and then compute the IDF for that token: *N/n(t)*
 # MAGIC 
 # MAGIC Use your `idfs` to compute the IDF weights for all tokens in `corpusRDD` (the combined small datasets).
 # MAGIC How many unique tokens are there?