用pycharm编写基于sparkml的鸢尾花K-means聚类算法

好的，以下是基于PyCharm编写的基于SparkML的鸢尾花K-means聚类算法的代码示例：

# 导入必要的库
from pyspark.ml.clustering import KMeans
from pyspark.ml.evaluation import ClusteringEvaluator
from pyspark.ml.feature import VectorAssembler
from pyspark.sql import SparkSession

# 创建SparkSession
spark = SparkSession.builder.appName("IrisKMeans").getOrCreate()

# 加载数据集
data = spark.read.format("csv").option("header", "true").load("iris.csv")

# 将特征列转换为向量
assembler = VectorAssembler(inputCols=["sepal_length", "sepal_width", "petal_length", "petal_width"],
                            outputCol="features")
data = assembler.transform(data)

# 训练K-means模型
kmeans = KMeans().setK(3).setSeed(1)
model = kmeans.fit(data)

# 评估模型
predictions = model.transform(data)
evaluator = ClusteringEvaluator()
silhouette = evaluator.evaluate(predictions)
print("Silhouette with squared euclidean distance = " + str(silhouette))

# 显示聚类中心
centers = model.clusterCenters()
print("Cluster Centers: ")
for center in centers:
    print(center)

# 停止SparkSession
spark.stop()

注意：在运行这个代码前，需要先安装pyspark库，并且准备好一个鸢尾花数据集的CSV格式文件。