from pyspark.mllib.classification import SVMWithSGD
from pyspark import SparkContext
from pyspark import SparkConf
from convert import *
from reduce_dimension import *
app_name = "WordCount"
spark_master = "spark://Kingdom:7077"
spark_home = "../spark-1.3.1-bin-hadoop2.4"
conf = SparkConf()
conf.setMaster(spark_master)
conf.setSparkHome(spark_home)
conf.setAppName(app_name)
conf.set("spark.executor.memory", "1g")
#conf.set("spark.akka.frameSize", "100")
#conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
#conf.set("spark.kryoserializer.buffer.mb", "64")
#conf.set("spark.executor.extraJavaOptions", "-XX:+UseCompressedOops")
#conf.set("spark.storage.memoryFraction", "0.6")
sc = SparkContext(conf=conf, pyFiles=["main.py", "convert.py", "attributes.py"])
# Return the sorted label, and the weight list
def get_sorted_label(training_data):
rdd = training_data.map(lambda x: int(x.label))
items = sorted(rdd.countByValue().items(), key=lambda x: x[0])
from collections import Counter
from pyspark.mllib.classification import SVMWithSGD
from pyspark import SparkContext
from pyspark import SparkConf
from convert import *
from reduce_dimension import *
app_name = "WordCount"
spark_master = "spark://Kingdom:7077"
spark_home = "../spark-1.3.1-bin-hadoop2.4"
conf = SparkConf()
conf.setMaster(spark_master)
conf.setSparkHome(spark_home)
conf.setAppName(app_name)
conf.set("spark.executor.memory", "1g")
#conf.set("spark.akka.frameSize", "100")
#conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
#conf.set("spark.kryoserializer.buffer.mb", "64")
#conf.set("spark.executor.extraJavaOptions", "-XX:+UseCompressedOops")
#conf.set("spark.storage.memoryFraction", "0.6")
sc = SparkContext(conf=conf,
pyFiles=["main.py", "convert.py", "attributes.py"])
# Return the sorted label, and the weight list
def get_sorted_label(training_data):
rdd = training_data.map(lambda x: int(x.label))
items = sorted(rdd.countByValue().items(), key=lambda x: x[0])