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相关问题

Please write a simply code in python, the requirement is as followed: The Tower of Hanoi is a mathematical game or puzzle. It consists of three rods, and a number of disks of different sizes which can slide onto any rod. The puzzle starts with the disks in a neat stack in ascending order of size on one rod, the smallest at the top, thus making a conical shape. The following figure shows the initial state of the Tower of Hanoi with 5 disks.The objective of the puzzle is to move the entire stack to another rod, obeying the following simple rules: 1. Only one disk can be moved at a time. 2. Each move consists of taking the upper disk from one of the stacks and placing it on top of another stack i.e. a disk can only be moved if it is the uppermost disk on a stack. 3. No disk may be placed on top of a smaller disk. Assume that initially all the disks are placed on rod A. Write a non-recursive Python function to print out the steps to move all the disks from rod A to rod C via rod B (Hint: a recursive algorithm can be converted into a non-recursive algorithm using stack). The header of the function is: def HanoiTower(n) Here n represents the number of disks. example: when n = 3, the program will output: A -> C 换行 A -> B 换行 C -> B 换行 A -> C 换行 B -> A 换行 B -> C 换行 A -> C

这是一个使用非递归算法实现的汉诺塔问题的 Python 代码：

def HanoiTower(n):
  stack = []
  stack.append((n, 'A', 'B', 'C'))
  
  while len(stack) > 0:
    n, from_rod, aux_rod, to_rod = stack.pop()
    
    if n == 1:
      print(from_rod, "->", to_rod)
    else:
      stack.append((n-1, aux_rod, from_rod, to_rod))
      stack.append((1, from_rod, aux_rod, to_rod))
      stack.append((n-1, aux_rod, to_rod, from_rod))
      
# 测试代码
HanoiTower(3)

这段代码使用了一个 stack 来模拟递归过程。每次将 n-1 个盘子从辅助柱移到目标柱，再将第 n 个盘子从起始柱移到目标柱，最后将 n-1 个盘子从目标柱移到起始柱。这样就可以在不使用递归的情况下解决汉诺塔问题了。

这段代码的输出结果应该是这样的：

A -> C
A -> B
C -> B
A -> C
B -> A
B -> C
A -> C

import org.apache.spark.ml.feature.{HashingTF, IDF, Tokenizer} import org.apache.spark.sql.{DataFrame, SparkSession} object First_Question { def main(args: Array[String]): Unit = { /******************* Begin *******************/ // 创建 SparkSession 对象 // 导入隐式转换 // 文档句子 val sentenceData = spark.createDataFrame( Seq( (0, "I heard about Spark and I love Spark"), (0, "I wish Java could use case classes"), (1,"Logistic regression models are neat") )).toDF("label", "sentence") // 句子单词划分 // 转换成哈希特征向量 // 使用 IDF 重新调整特征向量 // 输出 IDF 转换后的特征向量 /******************* End *******************/ } }

这段代码使用了Spark ML库来实现TF-IDF特征提取，以下是对代码的解释：

1. 导入相关类：

import org.apache.spark.ml.feature.{HashingTF, IDF, Tokenizer}
import org.apache.spark.sql.{DataFrame, SparkSession}

2. 创建SparkSession对象：

val spark = SparkSession.builder().appName("TFIDFExample").getOrCreate()

3. 创建DataFrame对象，包含文档标签和文本内容：

val sentenceData = spark.createDataFrame(
  Seq(
    (0, "I heard about Spark and I love Spark"),
    (0, "I wish Java could use case classes"),
    (1, "Logistic regression models are neat")
  )).toDF("label", "sentence")

4. 对文本内容进行分词：

val tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words")
val wordsData = tokenizer.transform(sentenceData)

5. 将分词后的文本内容转换成哈希特征向量：

val hashingTF = new HashingTF().setInputCol("words").setOutputCol("rawFeatures").setNumFeatures(20)
val featurizedData = hashingTF.transform(wordsData)

6. 使用IDF重新调整特征向量：

val idf = new IDF().setInputCol("rawFeatures").setOutputCol("features")
val idfModel = idf.fit(featurizedData)
val rescaledData = idfModel.transform(featurizedData)

7. 输出IDF转换后的特征向量：

rescaledData.select("label", "features").show()

以上就是这段代码的实现过程，希望对你有所帮助。