本文整理汇总了Python中pyspark.SparkContext.parallelized方法的典型用法代码示例。如果您正苦于以下问题：Python SparkContext.parallelized方法的具体用法？Python SparkContext.parallelized怎么用？Python SparkContext.parallelized使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pyspark.SparkContext的用法示例。
在下文中一共展示了SparkContext.parallelized方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: similar
# 需要导入模块: from pyspark import SparkContext [as 别名]
# 或者: from pyspark.SparkContext import parallelized [as 别名]
words = lines.flatMap(parseContext)
words_swap = words.map(lambda (x, y): (y, x))
wordcount = words.map(lambda s: (s, 1)).reduceByKey(lambda a, b: a + b)
wordcount_page = words_swap.map(lambda s: (s, 1)).reduceByKey(lambda a, b: a + b)
count_page = words.map(lambda (a, b): (a, 1)).reduceByKey(lambda a, b: a + b)
doc_word = words_swap.distinct().map(lambda (a, b): (a, 1)).reduceByKey(lambda a, b: a + b)
app = []
for (((id, title), word), n) in wordcount.collect():
word_page = words.filter(lambda x: (id, title) in x).count()
word_all_page = words.filter(lambda x: word in x).distinct().count()
tf_idf = (n / word_page) * math.log((doc_count / word_all_page))
app.append([(id, title, word, tf_idf)])
##part2 read as RDD
v = sc.parallelized(app)
trans = v.map(lambda (a, b): (a, list(b))).groupByKey() ##apend word as list by id
##key pair similarity(e-distance)
def similar(wf):
fun_result = []
list1 = {}
list2 = {}
for item in v[0][1]:
fun_result.append(item[0])
list1.setdefault(item[0],item[1])
for item in v[1][1]:
if item[0] not in fun_result:
fun_result.append(item[0])
list2.setdefault(item[0],item[1])
result1 = []
result2 = []