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Diffstat (limited to 'docs/mllib-guide.md')
| -rw-r--r-- | docs/mllib-guide.md | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md index 45ee166688..c977bc4f35 100644 --- a/docs/mllib-guide.md +++ b/docs/mllib-guide.md @@ -330,7 +330,7 @@ from numpy import array # Load and parse the data data = sc.textFile("mllib/data/sample_svm_data.txt") parsedData = data.map(lambda line: array([float(x) for x in line.split(' ')])) -model = LogisticRegressionWithSGD.train(sc, parsedData) +model = LogisticRegressionWithSGD.train(parsedData) # Build the model labelsAndPreds = parsedData.map(lambda point: (int(point.item(0)), @@ -356,7 +356,7 @@ data = sc.textFile("mllib/data/ridge-data/lpsa.data") parsedData = data.map(lambda line: array([float(x) for x in line.replace(',', ' ').split(' ')])) # Build the model -model = LinearRegressionWithSGD.train(sc, parsedData) +model = LinearRegressionWithSGD.train(parsedData) # Evaluate the model on training data valuesAndPreds = parsedData.map(lambda point: (point.item(0), @@ -382,7 +382,7 @@ data = sc.textFile("kmeans_data.txt") parsedData = data.map(lambda line: array([float(x) for x in line.split(' ')])) # Build the model (cluster the data) -clusters = KMeans.train(sc, parsedData, 2, maxIterations=10, +clusters = KMeans.train(parsedData, 2, maxIterations=10, runs=30, initialization_mode="random") # Evaluate clustering by computing Within Set Sum of Squared Errors @@ -411,7 +411,7 @@ data = sc.textFile("mllib/data/als/test.data") ratings = data.map(lambda line: array([float(x) for x in line.split(',')])) # Build the recommendation model using Alternating Least Squares -model = ALS.train(sc, ratings, 1, 20) +model = ALS.train(ratings, 1, 20) # Evaluate the model on training data testdata = ratings.map(lambda p: (int(p[0]), int(p[1]))) @@ -426,5 +426,5 @@ signals), you can use the trainImplicit method to get better results. {% highlight python %} # Build the recommendation model using Alternating Least Squares based on implicit ratings -model = ALS.trainImplicit(sc, ratings, 1, 20) +model = ALS.trainImplicit(ratings, 1, 20) {% endhighlight %} |