K意味着python
我正在尝试使用python实现spark的kmean算法,我正在使用库中的python代码 代码正在运行,但为了绘制图形,我需要操纵sameModel=KMeansModel.loadsc,KMeansModel2中的对象,我不知道如何操作。。我应该在csv文件中加载sc吗?救命啊K意味着python,python,apache-spark,k-means,lib,Python,Apache Spark,K Means,Lib,我正在尝试使用python实现spark的kmean算法,我正在使用库中的python代码 代码正在运行,但为了绘制图形,我需要操纵sameModel=KMeansModel.loadsc,KMeansModel2中的对象,我不知道如何操作。。我应该在csv文件中加载sc吗?救命啊 from __future__ import print_function # $example on$ from numpy import array from math import sqrt
from __future__ import print_function
# $example on$
from numpy import array
from math import sqrt
# $example off$
from pyspark import SparkContext
# $example on$
from pyspark.mllib.clustering import KMeans, KMeansModel
# $example off$
if __name__ == "__main__":
sc = SparkContext(appName="KMeansExample") # SparkContext
# $example on$
# Load and parse the data
data = sc.textFile("kmeans_data2.txt")
parsedData = data.map(lambda line: array([float(x) for x in line.split(' ')]))
# Build the model (cluster the data)
clusters = KMeans.train(parsedData, 2, maxIterations=10, initializationMode="random")
# Evaluate clustering by computing Within Set Sum of Squared Errors
def error(point):
center = clusters.centers[clusters.predict(point)]
return sqrt(sum([x**2 for x in (point - center)]))
WSSSE = parsedData.map(lambda point: error(point)).reduce(lambda x,y:x + y)
print("Within Set Sum of Squared Error = " + str(WSSSE))
# Save and load model
clusters.save(sc, "KMeansModel2")
sameModel = KMeansModel.load(sc, "KMeansModel2")
print (sameModel)
# $example off$
sc.stop()
如果您想使用模式predict,您需要在运行的spark上下文中执行此操作,例如,您可以