Sparkã§ã‚³ã‚µã‚¤ãƒ³é¡žä¼¼åº¦ã‚’求ã‚ã‚‹ - Qiita
ãŸã¾ã«ã€ãƒ¬ã‚³ãƒ¼ãƒ‰ãŒæŒã¤å¤‰æ•°é–“ã§ã‚³ã‚µã‚¤ãƒ³é¡žä¼¼åº¦ã‚’計算ã—ãŸã„時ãŒã‚ã‚‹ã®ã§ã€å‚™å¿˜ã¨ã—ã¦æ›¸ã„ã¦ãŠã ãªãŠã€ä»¥ä¸‹ã®ä¾‹ã§æ‰±ã†ãƒ‡ãƒ¼ã‚¿ã¯ã€é©å½“ãªIDã¨ç´ã¥ã‘られãŸãƒ™ã‚¯ãƒˆãƒ«ã‚’ã‚‚ã¤ãƒ‡ãƒ¼ã‚¿ãƒ•ãƒ¬ãƒ¼ãƒ ã¨ã™ã‚‹ã€‚ from pyspark.mllib.linalg import Vectors data = sc.parallelize([ ("a", Vectors.dense([0, 1, 2])), ("b", Vectors.dense([3, 4, 5])), ("c", Vectors.dense([6, 7, 8])) ]).toDF(["id", "features"]) # é©å½“ã«äº¤å·®çµåˆã§çµ„ã¿åˆã‚ã›ãƒ‡ãƒ¼ã‚¿ _data = data.select(col("id").alias("_id"), col("features").alias("_features")) tgt_data = data.cr
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