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当使用numpy函数代替数学函数时,为什么pyspark表现不同? 例如pyspark中数学和numpy函数之间的区别
X = sc.parallelize([[DenseVector([4.9, 3.0, 1.4, 0.2]), DenseVector([4.6, 3.1, 1.5, 0.2])],[DenseVector([5.1, 3.5, 1.4, 0.3]), DenseVector([5.7, 3.8, 1.7, 0.3])]])
X_df = sqlcontext.createDataFrame(X, ["x","y"])
udf_foo = udf(lambda x, y: m.exp(-x.squared_distance(y)/2.0), DoubleType())
X_sim = X_df.withColumn("sim", udf_foo(X_df.x, X_df.y))
X_sim.show()
输出
+-----------------+-----------------+------------------+
| x| y| sim|
+-----------------+-----------------+------------------+
|[4.9,3.0,1.4,0.2]|[4.6,3.1,1.5,0.2]|0.9464851479534836|
|[5.1,3.5,1.4,0.3]|[5.7,3.8,1.7,0.3]|0.7633794943368529|
+-----------------+-----------------+------------------+
而代码下面
udf_foonp = udf(lambda x, y: np.exp(-x.squared_distance(y)/2.0), DoubleType())
X_simnp = X_df.withColumn("sim", udf_foonp(X_df.x, X_df.y))
X_simnp.show()
给出错误
expected zero arguments for construction of ClassDict