【Spark機器學習速成寶典】模型篇01支持向量機【SVM】（Python版）

目錄

　　支持向量機原理

　　支持向量機代碼(Spark Python)

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支持向量機原理

 　　詳見博文：http://www.cnblogs.com/itmorn/p/8011587.html

 

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支持向量機代碼(Spark Python) 

　　

　　代碼里數據：https://pan.baidu.com/s/1jHWKG4I 密碼：acq1

 

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

from pyspark.mllib.classification import SVMWithSGD, SVMModel
from pyspark.mllib.regression import LabeledPoint

# 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，后面的作為特征
def parsePoint(line):
    values = [float(x) for x in line.split(' ')]
    return LabeledPoint(values[0], values[1:])

data = sc.textFile("data/mllib/sample_svm_data.txt")
print data.collect()[0] #1 0 2.52078447201548 0 0 0 2.004684436494304 2.00034729926846.....

parsedData = data.map(parsePoint) 

print parsedData.collect()[0] #(1.0,[0.0,2.52078447202,0.0,0.0,0.0,2.00468....
# Build the model 建立模型
model = SVMWithSGD.train(parsedData, iterations=100)

# Evaluating the model on training data 評估模型在訓練集上的誤差
labelsAndPreds = parsedData.map(lambda p: (p.label, model.predict(p.features)))
trainErr = labelsAndPreds.filter(lambda lp: lp[0] != lp[1]).count() / float(parsedData.count())
print("Training Error = " + str(trainErr))

# Save and load model 保存模型和加載模型
model.save(sc, "pythonSVMWithSGDModel")
sameModel = SVMModel.load(sc, "pythonSVMWithSGDModel")

print sameModel.predict(parsedData.collect()[0].features) #1

 

 

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