api/pyspark -> api/python

Author: mengxr

docs/mllib-basics.md

@@ -78,13 +78,13 @@ MLlib recognizes the following types as dense vectors:
 
 and the following as sparse vectors:
 
-* MLlib's [`SparseVector`](api/pyspark/pyspark.mllib.linalg.SparseVector-class.html).
+* MLlib's [`SparseVector`](api/python/pyspark.mllib.linalg.SparseVector-class.html).
 * SciPy's
   [`csc_matrix`](http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csc_matrix.html#scipy.sparse.csc_matrix)
   with a single column
 
 We recommend using NumPy arrays over lists for efficiency, and using the factory methods implemented
-in [`Vectors`](api/pyspark/pyspark.mllib.linalg.Vectors-class.html) to create sparse vectors.
+in [`Vectors`](api/python/pyspark.mllib.linalg.Vectors-class.html) to create sparse vectors.
 
 {% highlight python %}
 import numpy as np
@@ -151,7 +151,7 @@ LabeledPoint neg = new LabeledPoint(1.0, Vectors.sparse(3, new int[] {0, 2}, new
 <div data-lang="python" markdown="1">
 
 A labeled point is represented by
-[`LabeledPoint`](api/pyspark/pyspark.mllib.regression.LabeledPoint-class.html).
+[`LabeledPoint`](api/python/pyspark.mllib.regression.LabeledPoint-class.html).
 
 {% highlight python %}
 from pyspark.mllib.linalg import SparseVector

docs/mllib-guide.md

@@ -107,7 +107,7 @@ create sparse vectors.
 
 We used to represent a labeled feature vector in a NumPy array, where the first entry corresponds to
 the label and the rest are features.  This representation is replaced by class
-[`LabeledPoint`](api/pyspark/pyspark.mllib.regression.LabeledPoint-class.html), which takes both
+[`LabeledPoint`](api/python/pyspark.mllib.regression.LabeledPoint-class.html), which takes both
 dense and sparse feature vectors.
 
 {% highlight python %}

docs/mllib-naive-bayes.md

@@ -102,11 +102,11 @@ double accuracy = 1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Do
 
 <div data-lang="python" markdown="1">
 
-[NaiveBayes](api/pyspark/pyspark.mllib.classification.NaiveBayes-class.html) implements multinomial
+[NaiveBayes](api/python/pyspark.mllib.classification.NaiveBayes-class.html) implements multinomial
 naive Bayes. It takes an RDD of
-[LabeledPoint](api/pyspark/pyspark.mllib.regression.LabeledPoint-class.html) and an optionally
+[LabeledPoint](api/python/pyspark.mllib.regression.LabeledPoint-class.html) and an optionally
 smoothing parameter `lambda` as input, and output a
-[NaiveBayesModel](api/pyspark/pyspark.mllib.classification.NaiveBayesModel-class.html), which can be
+[NaiveBayesModel](api/python/pyspark.mllib.classification.NaiveBayesModel-class.html), which can be
 used for evaluation and prediction.
 
 <!-- TODO: Make Python's example consistent with Scala's and Java's. -->