帮我写一个计算上亿个向量与上千个向量cos距离的pysqark 代码

我可以回答这个问题。以下是一个计算上亿个向量与上千个向量cos距离的pysqark代码的示例：

from pyspark.ml.feature import Normalizer, VectorAssembler
from pyspark.ml.linalg import Vectors
from pyspark.sql.functions import udf
from pyspark.sql.types import DoubleType

# 创建一个包含所有向量的DataFrame
vectors = [(Vectors.dense([1, 2, 3]),), (Vectors.dense([4, 5, 6]),)]
df = spark.createDataFrame(vectors, ["features"])

# 创建一个UDF来计算cos距离
def cos_distance(v1, v2):
    return float(v1.dot(v2) / (v1.norm(2) * v2.norm(2)))

cos_udf = udf(cos_distance, DoubleType())

# 将所有向量归一化
normalizer = Normalizer(inputCol="features", outputCol="norm_features")
normalized_df = normalizer.transform(df)

# 将所有向量转换为稠密向量
assembler = VectorAssembler(inputCols=["norm_features"], outputCol="dense_features")
dense_df = assembler.transform(normalized_df).select("dense_features")

# 计算所有向量与自己的cos距离
self_joined_df = dense_df.crossJoin(dense_df)
self_joined_df = self_joined_df.withColumn("cos_distance", cos_udf(self_joined_df.dense_features, self_joined_df.dense_features))

# 计算所有向量与另一组向量的cos距离
other_vectors = [(Vectors.dense([7, 8, 9]),), (Vectors.dense([10, 11, 12]),)]
other_df = spark.createDataFrame(other_vectors, ["features"])
normalized_other_df = normalizer.transform(other_df)
dense_other_df = assembler.transform(normalized_other_df).select("dense_features")

other_joined_df = dense_df.crossJoin(dense_other_df)
other_joined_df = other_joined_df.withColumn("cos_distance", cos_udf(other_joined_df.dense_features, other_joined_df.dense_features))

# 显示结果
self_joined_df.show()
other_joined_df.show()

请注意，这只是一个示例代码，您需要根据您的数据和计算需求进行修改。