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author | Hossein Falaki <falaki@gmail.com> | 2014-01-02 16:22:13 -0800 |
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committer | Hossein Falaki <falaki@gmail.com> | 2014-01-02 16:22:13 -0800 |
commit | 81989e26647ede54e19ef8058846e1bd42c0bfb5 (patch) | |
tree | b7c4d9a8325a44d39d1972a7b40dc703dfe7d317 /docs | |
parent | c189c8362caeaa7a0f46af1c8e0d8d37fd171d7b (diff) | |
download | spark-81989e26647ede54e19ef8058846e1bd42c0bfb5.tar.gz spark-81989e26647ede54e19ef8058846e1bd42c0bfb5.tar.bz2 spark-81989e26647ede54e19ef8058846e1bd42c0bfb5.zip |
Commented the last part of collaborative filtering examples that lead to errors
Diffstat (limited to 'docs')
-rw-r--r-- | docs/mllib-guide.md | 11 |
1 files changed, 6 insertions, 5 deletions
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md index e9d3785427..0bebc41137 100644 --- a/docs/mllib-guide.md +++ b/docs/mllib-guide.md @@ -297,8 +297,9 @@ val numIterations = 20 val model = ALS.train(ratings, 1, 20, 0.01) // Evaluate the model on rating data -val ratesAndPreds = ratings.map{ case Rating(user, item, rate) => (rate, model.predict(user, item))} -val MSE = ratesAndPreds.map{ case(v, p) => math.pow((v - p), 2)}.reduce(_ + _)/ratesAndPreds.count +//val ratesAndPreds = ratings.map{ case Rating(user, item, rate) => (rate, model.predict(user, item))} +//val MSE = ratesAndPreds.map{ case(v, p) => math.pow((v - p), 2)}.reduce(_ + _)/ratesAndPreds.count +//println("Mean Squared Error = " + MSE) {% endhighlight %} If the rating matrix is derived from other source of information (i.e., it is inferred from @@ -406,9 +407,9 @@ ratings = data.map(lambda line: array([float(x) for x in line.split(',')])) model = ALS.train(sc, ratings, 1, 20) # Evaluate the model on training data -ratesAndPreds = ratings.map(lambda p: (p[2], model.predict(int(p[0]), int(p[1])))) -MSE = valuesAndPreds.map(lambda (v, p): (v - p)**2).reduce(lambda x, y: x + y)/valuesAndPreds.count() -print("Mean Squared Error = " + str(MSE)) +#ratesAndPreds = ratings.map(lambda p: (p[2], model.predict(int(p[0]), int(p[1])))) +#MSE = valuesAndPreds.map(lambda (v, p): (v - p)**2).reduce(lambda x, y: x + y)/valuesAndPreds.count() +#print("Mean Squared Error = " + str(MSE)) {% endhighlight %} |