import os
import random
import sys
from pyspark import SparkConf, SparkContext
from pyspark.sql import SQLContext
APP_NAME = "prediction"
stock_cnt = 9
# Load and parse the data
def parsePoint(ss):
line = ss.split(" ")
values = [float(x) for x in line]
return LabeledPoint(values[0], values[1:])
if __name__ == "__main__":
conf = SparkConf().setAppName(APP_NAME)
conf = conf.setMaster("local[*]")
sc = SparkContext(conf=conf)
model_param = sc.textFile("model_param")
pred_data = sc.textFile("pred_input")
# Build the model
model = LinearRegressionWithSGD.setModel(model_param)
# Evaluate the model on training data
pred_res = pred_data.map(lambda fea: model.predict(fea))
pred_res.saveAsTextFile("pred_res")
