SPARK-1637: Clean up examples for 1.0

- [x] Move all of them into subpackages of org.apache.spark.examples (right now some are in org.apache.spark.streaming.examples, for instance, and others are in org.apache.spark.examples.mllib)
- [x] Move Python examples into examples/src/main/python
- [x] Update docs to reflect these changes

Author: Sandeep <sandeep@techaddict.me>

This patch had conflicts when merged, resolved by
Committer: Matei Zaharia <matei@databricks.com>

Closes #571 from techaddict/SPARK-1637 and squashes the following commits:

47ef86c [Sandeep] Changes based on Discussions on PR, removing use of RawTextHelper from examples
8ed2d3f [Sandeep] Docs Updated for changes, Change for java examples
5f96121 [Sandeep] Move Python examples into examples/src/main/python
0a8dd77 [Sandeep] Move all Scala Examples to org.apache.spark.examples (some are in org.apache.spark.streaming.examples, for instance, and others are in org.apache.spark.examples.mllib)

1 files changed, 87 insertions, 0 deletions

diff --git a/examples/src/main/python/als.py b/examples/src/main/python/als.py
new file mode 100755
index 0000000000..a77dfb2577
--- /dev/null
+++ b/examples/src/main/python/als.py
@@ -0,0 +1,87 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+This example requires numpy (http://www.numpy.org/)
+"""
+from os.path import realpath
+import sys
+
+import numpy as np
+from numpy.random import rand
+from numpy import matrix
+from pyspark import SparkContext
+
+LAMBDA = 0.01   # regularization
+np.random.seed(42)
+
+def rmse(R, ms, us):
+    diff = R - ms * us.T
+    return np.sqrt(np.sum(np.power(diff, 2)) / M * U)
+
+def update(i, vec, mat, ratings):
+    uu = mat.shape[0]
+    ff = mat.shape[1]
+    XtX = matrix(np.zeros((ff, ff)))
+    Xty = np.zeros((ff, 1))
+
+    for j in range(uu):
+        v = mat[j, :]
+        XtX += v.T * v
+        Xty += v.T * ratings[i, j]
+    XtX += np.eye(ff, ff) * LAMBDA * uu
+    return np.linalg.solve(XtX, Xty)
+
+if __name__ == "__main__":
+    if len(sys.argv) < 2:
+        print >> sys.stderr, "Usage: als <master> <M> <U> <F> <iters> <slices>"
+        exit(-1)
+    sc = SparkContext(sys.argv[1], "PythonALS", pyFiles=[realpath(__file__)])
+    M = int(sys.argv[2]) if len(sys.argv) > 2 else 100
+    U = int(sys.argv[3]) if len(sys.argv) > 3 else 500
+    F = int(sys.argv[4]) if len(sys.argv) > 4 else 10
+    ITERATIONS = int(sys.argv[5]) if len(sys.argv) > 5 else 5
+    slices = int(sys.argv[6]) if len(sys.argv) > 6 else 2
+
+    print "Running ALS with M=%d, U=%d, F=%d, iters=%d, slices=%d\n" % \
+            (M, U, F, ITERATIONS, slices)
+
+    R = matrix(rand(M, F)) * matrix(rand(U, F).T)
+    ms = matrix(rand(M ,F))
+    us = matrix(rand(U, F))
+
+    Rb = sc.broadcast(R)
+    msb = sc.broadcast(ms)
+    usb = sc.broadcast(us)
+
+    for i in range(ITERATIONS):
+        ms = sc.parallelize(range(M), slices) \
+               .map(lambda x: update(x, msb.value[x, :], usb.value, Rb.value)) \
+               .collect()
+        ms = matrix(np.array(ms)[:, :, 0])      # collect() returns a list, so array ends up being
+                                                # a 3-d array, we take the first 2 dims for the matrix
+        msb = sc.broadcast(ms)
+
+        us = sc.parallelize(range(U), slices) \
+               .map(lambda x: update(x, usb.value[x, :], msb.value, Rb.value.T)) \
+               .collect()
+        us = matrix(np.array(us)[:, :, 0])
+        usb = sc.broadcast(us)
+
+        error = rmse(R, ms, us)
+        print "Iteration %d:" % i
+        print "\nRMSE: %5.4f\n" % error