目錄

　　樸素貝葉斯原理

　　樸素貝葉斯代碼(Spark Python)

　　詳見博文：http://www.cnblogs.com/itmorn/p/7905975.html

返回目錄

　　代碼裏數據：https://pan.baidu.com/s/1jHWKG4I 密碼：acq1

# -*-coding=utf-8 -*-  
from pyspark import SparkConf, SparkContext
sc = SparkContext(‘local‘)

from pyspark.mllib.regression import
 
 LabeledPoint, LinearRegressionWithSGD, LinearRegressionModel

# Load and parse the data 加載和解析數據，將每一個數轉化為浮點數。每一行第一個數作為標記，後面的作為特征
def parsePoint(line):
    values = [float(x) for x in line.replace(‘,‘, ‘ ‘).split(‘ ‘)]
    return LabeledPoint(values[0], values[1:])

data = sc.textFile("data/mllib/ridge-data/lpsa.data
 
")
print data.collect()[0] #-0.4307829,-1.63735562648104 -2.00621178480549 -1.86242597251066 -1.024....-0.864466507337306
parsedData = data.map(parsePoint)
print parsedData.collect()[0] #(-0.4307829,[-1.63735562648,-2.00621178481,-1.86242597251,-1.024....,-0.864466507337])

# Build the model 建立模型
model = LinearRegressionWithSGD.train(parsedData, iterations=1000, step=0.1)

 
# Evaluate the model on training data 評估模型在訓練集上的誤差
valuesAndPreds = parsedData.map(lambda p: (p.label, model.predict(p.features)))
MSE = valuesAndPreds     .map(lambda vp: (vp[0] - vp[1])**2)     .reduce(lambda x, y: x + y) / valuesAndPreds.count()
print("Mean Squared Error = " + str(MSE)) #Mean Squared Error = 6.32693963099

# Save and load model 保存模型和加載模型
model.save(sc, "pythonLinearRegressionWithSGDModel")
sameModel = LinearRegressionModel.load(sc, "pythonLinearRegressionWithSGDModel")
print sameModel.predict(parsedData.collect()[0].features) #-1.86583391312

返回目錄

【Spark MLlib速成寶典】模型篇04樸素貝葉斯【Naive Bayes】（Python版）