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Diffstat (limited to 'docs/mllib-clustering.md')
| -rw-r--r-- | docs/mllib-clustering.md | 11 |
1 files changed, 6 insertions, 5 deletions
diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md index 65ed75b82e..50a8671560 100644 --- a/docs/mllib-clustering.md +++ b/docs/mllib-clustering.md @@ -48,14 +48,15 @@ optimal *k* is usually one where there is an "elbow" in the WSSSE graph. {% highlight scala %} import org.apache.spark.mllib.clustering.KMeans +import org.apache.spark.mllib.linalg.Vectors // Load and parse the data -val data = sc.textFile("kmeans_data.txt") -val parsedData = data.map( _.split(' ').map(_.toDouble)) +val data = sc.textFile("data/kmeans_data.txt") +val parsedData = data.map(s => Vectors.dense(s.split(' ').map(_.toDouble))) // Cluster the data into two classes using KMeans -val numIterations = 20 val numClusters = 2 +val numIterations = 20 val clusters = KMeans.train(parsedData, numClusters, numIterations) // Evaluate clustering by computing Within Set Sum of Squared Errors @@ -85,12 +86,12 @@ from numpy import array from math import sqrt # Load and parse the data -data = sc.textFile("kmeans_data.txt") +data = sc.textFile("data/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(parsedData, 2, maxIterations=10, - runs=30, initialization_mode="random") + runs=10, initialization_mode="random") # Evaluate clustering by computing Within Set Sum of Squared Errors def error(point): |