文档章节

决策树分类Decision tree classifier

hblt-j
 hblt-j
发布于 2017/08/29 11:28
字数 1690
阅读 24
收藏 0
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.Dataset
import org.apache.spark.sql.Row
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.Column
import org.apache.spark.sql.DataFrameReader
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
import org.apache.spark.sql.Encoder
import org.apache.spark.sql.DataFrameStatFunctions
import org.apache.spark.sql.functions._

import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.DecisionTreeClassificationModel
import org.apache.spark.ml.classification.DecisionTreeClassifier
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
import org.apache.spark.ml.feature.{ VectorAssembler, IndexToString, StringIndexer, VectorIndexer }


val spark = SparkSession.builder().appName("Spark decision tree classifier").config("spark.some.config.option", "some-value").getOrCreate()

// For implicit conversions like converting RDDs to DataFrames
import spark.implicits._

// 这里仅仅是示例数据,完整的数据源,请参考我的博客http://blog.csdn.net/hadoop_spark_storm/article/details/53412598
val dataList: List[(Double, String, Double, Double, String, Double, Double, Double, Double)] = List(
      (0, "male", 37, 10, "no", 3, 18, 7, 4),
      (0, "female", 27, 4, "no", 4, 14, 6, 4),
      (0, "female", 32, 15, "yes", 1, 12, 1, 4),
      (0, "male", 57, 15, "yes", 5, 18, 6, 5),
      (0, "male", 22, 0.75, "no", 2, 17, 6, 3),
      (0, "female", 32, 1.5, "no", 2, 17, 5, 5))


val data = dataList.toDF("affairs", "gender", "age", "yearsmarried", "children", "religiousness", "education", "occupation", "rating") 
data: org.apache.spark.sql.DataFrame = [affairs: double, gender: string ... 7 more fields]

data.printSchema() 
root 
 |-- affairs: double (nullable = false) 
 |-- gender: string (nullable = true) 
 |-- age: double (nullable = false) 
 |-- yearsmarried: double (nullable = false) 
 |-- children: string (nullable = true) 
 |-- religiousness: double (nullable = false) 
 |-- education: double (nullable = false) 
 |-- occupation: double (nullable = false) 
 |-- rating: double (nullable = false) 


data.show(10,truncate=false)
+-------+------+----+------------+--------+-------------+---------+----------+------+
|affairs|gender|age |yearsmarried|children|religiousness|education|occupation|rating|
+-------+------+----+------------+--------+-------------+---------+----------+------+
|0.0    |male  |37.0|10.0        |no      |3.0          |18.0     |7.0       |4.0   |
|0.0    |female|27.0|4.0         |no      |4.0          |14.0     |6.0       |4.0   |
|0.0    |female|32.0|15.0        |yes     |1.0          |12.0     |1.0       |4.0   |
|0.0    |male  |57.0|15.0        |yes     |5.0          |18.0     |6.0       |5.0   |
|0.0    |male  |22.0|0.75        |no      |2.0          |17.0     |6.0       |3.0   |
|0.0    |female|32.0|1.5         |no      |2.0          |17.0     |5.0       |5.0   |
|0.0    |female|22.0|0.75        |no      |2.0          |12.0     |1.0       |3.0   |
|0.0    |male  |57.0|15.0        |yes     |2.0          |14.0     |4.0       |4.0   |
|0.0    |female|32.0|15.0        |yes     |4.0          |16.0     |1.0       |2.0   |
|0.0    |male  |22.0|1.5         |no      |4.0          |14.0     |4.0       |5.0   |
+-------+------+----+------------+--------+-------------+---------+----------+------+
only showing top 10 rows

// 查看数据分布情况
data.describe("affairs", "gender", "age", "yearsmarried", "children", "religiousness", "education", "occupation", "rating").show(10,truncate=false)
+-------+------------------+------+-----------------+-----------------+--------+------------------+-----------------+-----------------+------------------+
|summary|affairs           |gender|age              |yearsmarried     |children|religiousness     |education        |occupation       |rating            |
+-------+------------------+------+-----------------+-----------------+--------+------------------+-----------------+-----------------+------------------+
|count  |601               |601   |601              |601              |601     |601               |601              |601              |601               |
|mean   |1.4559068219633944|null  |32.48752079866888|8.17769550748752 |null    |3.1164725457570714|16.16638935108153|4.194675540765391|3.9317803660565724|
|stddev |3.298757728494681 |null  |9.28876170487667 |5.571303149963791|null    |1.1675094016730692|2.402554565766698|1.819442662708579|1.1031794920503795|
|min    |0.0               |female|17.5             |0.125            |no      |1.0               |9.0              |1.0              |1.0               |
|max    |12.0              |male  |57.0             |15.0             |yes     |5.0               |20.0             |7.0              |5.0               |
+-------+------------------+------+-----------------+-----------------+--------+------------------+-----------------+-----------------+------------------+

data.createOrReplaceTempView("data")

// 字符类型转换成数值
val labelWhere = "case when affairs=0 then 0 else cast(1 as double) end as label"
labelWhere: String = case when affairs=0 then 0 else cast(1 as double) end as label

val genderWhere = "case when gender='female' then 0 else cast(1 as double) end as gender"
genderWhere: String = case when gender='female' then 0 else cast(1 as double) end as gender

val childrenWhere = "case when children='no' then 0 else cast(1 as double) end as children"
childrenWhere: String = case when children='no' then 0 else cast(1 as double) end as children

val dataLabelDF = spark.sql(s"select $labelWhere, $genderWhere,age,yearsmarried,$childrenWhere,religiousness,education,occupation,rating from data")
dataLabelDF: org.apache.spark.sql.DataFrame = [label: double, gender: double ... 7 more fields]

val featuresArray = Array("gender", "age", "yearsmarried", "children", "religiousness", "education", "occupation", "rating")
featuresArray: Array[String] = Array(gender, age, yearsmarried, children, religiousness, education, occupation, rating)

// 字段转换成特征向量
val assembler = new VectorAssembler().setInputCols(featuresArray).setOutputCol("features")
assembler: org.apache.spark.ml.feature.VectorAssembler = vecAssembler_6e2c6bdd631e

val vecDF: DataFrame = assembler.transform(dataLabelDF)
vecDF: org.apache.spark.sql.DataFrame = [label: double, gender: double ... 8 more fields]

vecDF.show(10,truncate=false)
+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+
|label|gender|age |yearsmarried|children|religiousness|education|occupation|rating|features                            |
+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+
|0.0  |1.0   |37.0|10.0        |0.0     |3.0          |18.0     |7.0       |4.0   |[1.0,37.0,10.0,0.0,3.0,18.0,7.0,4.0]|
|0.0  |0.0   |27.0|4.0         |0.0     |4.0          |14.0     |6.0       |4.0   |[0.0,27.0,4.0,0.0,4.0,14.0,6.0,4.0] |
|0.0  |0.0   |32.0|15.0        |1.0     |1.0          |12.0     |1.0       |4.0   |[0.0,32.0,15.0,1.0,1.0,12.0,1.0,4.0]|
|0.0  |1.0   |57.0|15.0        |1.0     |5.0          |18.0     |6.0       |5.0   |[1.0,57.0,15.0,1.0,5.0,18.0,6.0,5.0]|
|0.0  |1.0   |22.0|0.75        |0.0     |2.0          |17.0     |6.0       |3.0   |[1.0,22.0,0.75,0.0,2.0,17.0,6.0,3.0]|
|0.0  |0.0   |32.0|1.5         |0.0     |2.0          |17.0     |5.0       |5.0   |[0.0,32.0,1.5,0.0,2.0,17.0,5.0,5.0] |
|0.0  |0.0   |22.0|0.75        |0.0     |2.0          |12.0     |1.0       |3.0   |[0.0,22.0,0.75,0.0,2.0,12.0,1.0,3.0]|
|0.0  |1.0   |57.0|15.0        |1.0     |2.0          |14.0     |4.0       |4.0   |[1.0,57.0,15.0,1.0,2.0,14.0,4.0,4.0]|
|0.0  |0.0   |32.0|15.0        |1.0     |4.0          |16.0     |1.0       |2.0   |[0.0,32.0,15.0,1.0,4.0,16.0,1.0,2.0]|
|0.0  |1.0   |22.0|1.5         |0.0     |4.0          |14.0     |4.0       |5.0   |[1.0,22.0,1.5,0.0,4.0,14.0,4.0,5.0] |
+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+
only showing top 10 rows


// 索引标签,将元数据添加到标签列中
val labelIndexer = new StringIndexer().setInputCol("label").setOutputCol("indexedLabel").fit(vecDF)
labelIndexer: org.apache.spark.ml.feature.StringIndexerModel = strIdx_d00cad619cd5

labelIndexer.transform(vecDF).show(10,truncate=false)
+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+------------+
|label|gender|age |yearsmarried|children|religiousness|education|occupation|rating|features                            |indexedLabel|
+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+------------+
|0.0  |1.0   |37.0|10.0        |0.0     |3.0          |18.0     |7.0       |4.0   |[1.0,37.0,10.0,0.0,3.0,18.0,7.0,4.0]|0.0         |
|0.0  |0.0   |27.0|4.0         |0.0     |4.0          |14.0     |6.0       |4.0   |[0.0,27.0,4.0,0.0,4.0,14.0,6.0,4.0] |0.0         |
|0.0  |0.0   |32.0|15.0        |1.0     |1.0          |12.0     |1.0       |4.0   |[0.0,32.0,15.0,1.0,1.0,12.0,1.0,4.0]|0.0         |
|0.0  |1.0   |57.0|15.0        |1.0     |5.0          |18.0     |6.0       |5.0   |[1.0,57.0,15.0,1.0,5.0,18.0,6.0,5.0]|0.0         |
|0.0  |1.0   |22.0|0.75        |0.0     |2.0          |17.0     |6.0       |3.0   |[1.0,22.0,0.75,0.0,2.0,17.0,6.0,3.0]|0.0         |
|0.0  |0.0   |32.0|1.5         |0.0     |2.0          |17.0     |5.0       |5.0   |[0.0,32.0,1.5,0.0,2.0,17.0,5.0,5.0] |0.0         |
|0.0  |0.0   |22.0|0.75        |0.0     |2.0          |12.0     |1.0       |3.0   |[0.0,22.0,0.75,0.0,2.0,12.0,1.0,3.0]|0.0         |
|0.0  |1.0   |57.0|15.0        |1.0     |2.0          |14.0     |4.0       |4.0   |[1.0,57.0,15.0,1.0,2.0,14.0,4.0,4.0]|0.0         |
|0.0  |0.0   |32.0|15.0        |1.0     |4.0          |16.0     |1.0       |2.0   |[0.0,32.0,15.0,1.0,4.0,16.0,1.0,2.0]|0.0         |
|0.0  |1.0   |22.0|1.5         |0.0     |4.0          |14.0     |4.0       |5.0   |[1.0,22.0,1.5,0.0,4.0,14.0,4.0,5.0] |0.0         |
+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+------------+
only showing top 10 rows

// 自动识别分类的特征,并对它们进行索引
// 具有大于8个不同的值的特征被视为连续。
val featureIndexer = new VectorIndexer().setInputCol("features").setOutputCol("indexedFeatures").setMaxCategories(8).fit(vecDF)
featureIndexer: org.apache.spark.ml.feature.VectorIndexerModel = vecIdx_8fbcad97fb60

featureIndexer.transform(vecDF).show(10,truncate=false)
+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+----------------------------------+
|label|gender|age |yearsmarried|children|religiousness|education|occupation|rating|features                            |indexedFeatures                   |
+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+----------------------------------+
|0.0  |1.0   |37.0|10.0        |0.0     |3.0          |18.0     |7.0       |4.0   |[1.0,37.0,10.0,0.0,3.0,18.0,7.0,4.0]|[1.0,37.0,6.0,0.0,2.0,5.0,6.0,3.0]|
|0.0  |0.0   |27.0|4.0         |0.0     |4.0          |14.0     |6.0       |4.0   |[0.0,27.0,4.0,0.0,4.0,14.0,6.0,4.0] |[0.0,27.0,4.0,0.0,3.0,2.0,5.0,3.0]|
|0.0  |0.0   |32.0|15.0        |1.0     |1.0          |12.0     |1.0       |4.0   |[0.0,32.0,15.0,1.0,1.0,12.0,1.0,4.0]|[0.0,32.0,7.0,1.0,0.0,1.0,0.0,3.0]|
|0.0  |1.0   |57.0|15.0        |1.0     |5.0          |18.0     |6.0       |5.0   |[1.0,57.0,15.0,1.0,5.0,18.0,6.0,5.0]|[1.0,57.0,7.0,1.0,4.0,5.0,5.0,4.0]|
|0.0  |1.0   |22.0|0.75        |0.0     |2.0          |17.0     |6.0       |3.0   |[1.0,22.0,0.75,0.0,2.0,17.0,6.0,3.0]|[1.0,22.0,2.0,0.0,1.0,4.0,5.0,2.0]|
|0.0  |0.0   |32.0|1.5         |0.0     |2.0          |17.0     |5.0       |5.0   |[0.0,32.0,1.5,0.0,2.0,17.0,5.0,5.0] |[0.0,32.0,3.0,0.0,1.0,4.0,4.0,4.0]|
|0.0  |0.0   |22.0|0.75        |0.0     |2.0          |12.0     |1.0       |3.0   |[0.0,22.0,0.75,0.0,2.0,12.0,1.0,3.0]|[0.0,22.0,2.0,0.0,1.0,1.0,0.0,2.0]|
|0.0  |1.0   |57.0|15.0        |1.0     |2.0          |14.0     |4.0       |4.0   |[1.0,57.0,15.0,1.0,2.0,14.0,4.0,4.0]|[1.0,57.0,7.0,1.0,1.0,2.0,3.0,3.0]|
|0.0  |0.0   |32.0|15.0        |1.0     |4.0          |16.0     |1.0       |2.0   |[0.0,32.0,15.0,1.0,4.0,16.0,1.0,2.0]|[0.0,32.0,7.0,1.0,3.0,3.0,0.0,1.0]|
|0.0  |1.0   |22.0|1.5         |0.0     |4.0          |14.0     |4.0       |5.0   |[1.0,22.0,1.5,0.0,4.0,14.0,4.0,5.0] |[1.0,22.0,3.0,0.0,3.0,2.0,3.0,4.0]|
+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+----------------------------------+
only showing top 10 rows

// 将数据分为训练和测试集(30%进行测试)
val Array(trainingData, testData) = vecDF.randomSplit(Array(0.7, 0.3))
trainingData: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [label: double, gender: double ... 8 more fields]
testData: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [label: double, gender: double ... 8 more fields]

// 训练决策树模型
val dt = new DecisionTreeClassifier()
.setLabelCol("indexedLabel")
.setFeaturesCol("indexedFeatures")
.setImpurity("entropy") // 不纯度
.setMaxBins(100) // 离散化"连续特征"的最大划分数
.setMaxDepth(5) // 树的最大深度
.setMinInfoGain(0.01) //一个节点分裂的最小信息增益,值为[0,1]
.setMinInstancesPerNode(10) //每个节点包含的最小样本数 
.setSeed(123456)

// 将索引标签转换回原始标签
val labelConverter = new IndexToString().setInputCol("prediction").setOutputCol("predictedLabel").setLabels(labelIndexer.labels)
labelConverter: org.apache.spark.ml.feature.IndexToString = idxToStr_2598e79a1d08

// Chain indexers and tree in a Pipeline.
val pipeline = new Pipeline().setStages(Array(labelIndexer, featureIndexer, dt, labelConverter))

// Train model. This also runs the indexers.
val model = pipeline.fit(trainingData)


// 作出预测
val predictions = model.transform(testData)
predictions: org.apache.spark.sql.DataFrame = [label: double, gender: double ... 14 more fields]


// 选择几个示例行展示
predictions.select("predictedLabel", "label", "features").show(10,truncate=false)
+--------------+-----+-------------------------------------+
|predictedLabel|label|features                             |
+--------------+-----+-------------------------------------+
|0.0           |0.0  |[0.0,22.0,0.125,0.0,2.0,14.0,4.0,5.0]|
|0.0           |0.0  |[0.0,22.0,0.125,0.0,2.0,16.0,6.0,3.0]|
|0.0           |0.0  |[0.0,22.0,0.125,0.0,4.0,12.0,4.0,5.0]|
|0.0           |0.0  |[0.0,22.0,0.417,0.0,1.0,17.0,6.0,4.0]|
|0.0           |0.0  |[0.0,22.0,0.75,0.0,2.0,16.0,5.0,5.0] |
|0.0           |0.0  |[0.0,22.0,1.5,0.0,1.0,14.0,1.0,5.0]  |
|0.0           |0.0  |[0.0,22.0,1.5,0.0,2.0,14.0,5.0,4.0]  |
|0.0           |0.0  |[0.0,22.0,1.5,0.0,2.0,16.0,5.0,5.0]  |
|0.0           |0.0  |[0.0,22.0,1.5,0.0,3.0,16.0,6.0,5.0]  |
|0.0           |0.0  |[0.0,22.0,1.5,0.0,4.0,17.0,5.0,5.0]  |
+--------------+-----+-------------------------------------+
only showing top 10 rows


// 选择(预测标签,实际标签),并计算测试误差。
val evaluator = new MulticlassClassificationEvaluator().setLabelCol("indexedLabel").setPredictionCol("prediction").setMetricName("accuracy")

val accuracy = evaluator.evaluate(predictions)
accuracy: Double = 0.7032967032967034

println("Test Error = " + (1.0 - accuracy))
Test Error = 0.29670329670329665


// 这里的stages(2)中的“2”对应pipeline中的“dt”,将model强制转换为DecisionTreeClassificationModel类型
val treeModel = model.stages(2).asInstanceOf[DecisionTreeClassificationModel]
treeModel: org.apache.spark.ml.classification.DecisionTreeClassificationModel = DecisionTreeClassificationModel (uid=dtc_7a8baf97abe7) of depth 5 with 33 nodes

treeModel.getLabelCol
res53: String = indexedLabel

treeModel.getFeaturesCol
res54: String = indexedFeatures

treeModel.featureImportances
res55: org.apache.spark.ml.linalg.Vector = (8,[0,2,3,4,5,6,7],[0.0640344247735859,0.1052957011097811,0.05343872372010684,0.17367191628391196,0.20372870264756315,0.2063093687074741,0.1935211627575769])

treeModel.getPredictionCol
res56: String = prediction

treeModel.getProbabilityCol
res57: String = probability

treeModel.numClasses
res58: Int = 2

treeModel.numFeatures
res59: Int = 8

treeModel.depth
res60: Int = 5

treeModel.numNodes
res61: Int = 33

treeModel.getImpurity
res62: String = entropy

treeModel.getMaxBins
res63: Int = 100

treeModel.getMaxDepth
res64: Int = 5

treeModel.getMaxMemoryInMB
res65: Int = 256

treeModel.getMinInfoGain
res66: Double = 0.01

treeModel.getMinInstancesPerNode
res67: Int = 10

 // 查看决策树
println("Learned classification tree model:\n" + treeModel.toDebugString)
Learned classification tree model:
DecisionTreeClassificationModel (uid=dtc_7a8baf97abe7) of depth 5 with 33 nodes
  If (feature 2 in {0.0,1.0,2.0,3.0})
   If (feature 5 in {3.0,6.0})
    Predict: 0.0
   Else (feature 5 not in {3.0,6.0})
    If (feature 4 in {3.0})
     Predict: 0.0
    Else (feature 4 not in {3.0})
     If (feature 3 in {0.0})
      If (feature 6 in {0.0,4.0,5.0})
       Predict: 0.0
      Else (feature 6 not in {0.0,4.0,5.0})
       Predict: 0.0
     Else (feature 3 not in {0.0})
      Predict: 0.0
  Else (feature 2 not in {0.0,1.0,2.0,3.0})
   If (feature 4 in {0.0,1.0,3.0,4.0})
    If (feature 7 in {0.0,1.0,2.0})
     If (feature 6 in {0.0,1.0,6.0})
      If (feature 4 in {1.0,4.0})
       Predict: 0.0
      Else (feature 4 not in {1.0,4.0})
       Predict: 0.0
     Else (feature 6 not in {0.0,1.0,6.0})
      If (feature 7 in {0.0,2.0})
       Predict: 0.0
      Else (feature 7 not in {0.0,2.0})
       Predict: 1.0
    Else (feature 7 not in {0.0,1.0,2.0})
     If (feature 5 in {0.0,1.0})
      Predict: 0.0
     Else (feature 5 not in {0.0,1.0})
      If (feature 6 in {0.0,1.0,2.0,5.0,6.0})
       Predict: 0.0
      Else (feature 6 not in {0.0,1.0,2.0,5.0,6.0})
       Predict: 0.0
   Else (feature 4 not in {0.0,1.0,3.0,4.0})
    If (feature 5 in {0.0,1.0,2.0,3.0,5.0,6.0})
     If (feature 0 in {0.0})
      If (feature 7 in {3.0})
       Predict: 0.0
      Else (feature 7 not in {3.0})
       Predict: 0.0
     Else (feature 0 not in {0.0})
      If (feature 7 in {0.0,2.0,4.0})
       Predict: 0.0
      Else (feature 7 not in {0.0,2.0,4.0})
       Predict: 1.0
    Else (feature 5 not in {0.0,1.0,2.0,3.0,5.0,6.0})
     Predict: 1.0

本文转载自:http://www.cnblogs.com/wwxbi/p/6114039.html

共有 人打赏支持
hblt-j
粉丝 21
博文 172
码字总数 68547
作品 0
海淀
架构师
私信 提问
机器学习算法 --- Pruning (decision trees) & Random Forest Algorithm

一、Table for Content   在之前的文章中我们介绍了Decision Trees Agorithms,然而这个学习算法有一个很大的弊端,就是很容易出现Overfitting,为了解决此问题人们找到了一种方法,就是对...

码农47
2018/06/26
0
0
MLF(弥勒佛) —— 大数据机器学习框架

让天下没有难做的大数据模型! 功能 下面是弥勒佛框架解决的问题类型,括号中的斜体代表尚未实现以及预计实现的时间 监督式学习:最大熵分类模型(max entropy classifier),决策树模型(d...

oschina
2016/05/03
25
0
机器学习:随机森林学习笔记

前言 随机森林是一个很强大的模型,由一组决策树投票得到最后的结果。要研究清楚随机森林,首先需要研究清楚决策树,然后理解随机森林如何通过多棵树的集成提高模型效果。 本文的目的是将自己...

丹追兵
2017/11/29
0
0
《Effective Detection of Multimedia Protocol Tunneling using Machine Learning》译文(二)

正文之前 紧接上文: 《Effective Detection of Multimedia Protocol Tunneling using Machine Learning》译文(一) 正文 4 Decision Tree-based Classification In this section, we depar......

HustWolf
01/10
0
0
机器学习笔记-Decision Tree

集成学习系列: Blending and Bagging Adaptive Boosting Decision Tree Random Forest Gradient Boosted Decision Tree Decision Tree 上一篇讲解了 算法,这个算法有两个特点:第一个是在第...

robin_Xu_shuai
2017/11/29
0
0

没有更多内容

加载失败,请刷新页面

加载更多

test

//// main.c// Test//// Created by 吕颖 on 2019/1/16.// Copyright © 2019年 carmen. All rights reserved.//#include <stdio.h>#include <stdlib.h>#include <t......

carmen-ly
今天
1
0
Android webview热门组件agentweb:4.0.2无法自适应的问题

Android webview热门组件agentweb:4.0.2无法自适应的问题 //设置自适应屏幕,两者合用mAgentWeb.getAgentWebSettings().getWebSettings().setUseWideViewPort(true); //将图片调整到适合w...

Gemini-Lin
今天
5
0
如何维护一个自己的 golang doc 服务

本文内容是如何维护一个golang 在线的doc 服务。 1 什么是godoc ? godoc 是 golang 官方提供的文档生成工具, 2 为什么要有godoc ? 我们经常遇到一个问题,就是代码和文档不一致,线上代码版...

鼎铭
今天
5
0
js中的对象创建的模式以及继承模式

对象创建模式: 工厂模式 构造函数模式 原型模式 继承模式 原型式继承 寄生式继承 构造函数 原型式和构造函数的组合式(缺点:运行两次超类类函数,积累函数的属性被挂载在原型对象上和实例对...

莫西摩西
昨天
3
0
大数据教程(11.5)仓库工具hive的实现机制

上一篇文章介绍了hadoop联邦集群的搭建过程。至此,hadoop的整个知识系统就差不多结束了。本篇博客开始,博主将分享数据仓库hive工具的原理以及使用。 一、Hive基本概念 (1)什么是Hive Hive...

em_aaron
昨天
8
0

没有更多内容

加载失败,请刷新页面

加载更多

返回顶部
顶部