<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[彩虹的约定]]></title><description><![CDATA[菜鸟一个]]></description><link>https://blog.youkuaiyun.com/u014372225</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; u014372225]]></copyright><item><title><![CDATA[scala版本的wordCount]]></title><link>https://blog.youkuaiyun.com/u014372225/article/details/76358720</link><guid>https://blog.youkuaiyun.com/u014372225/article/details/76358720</guid><author>u014372225</author><pubDate>Sat, 29 Jul 2017 21:35:49 +0800</pubDate><description><![CDATA[object word_count {

  def main(args: Array[String]) {
    /*
    scala版wordCount
     */
    val arr = Array(2, 3, 5, 4, 5, 2, 4, 3, 5, 2, 4, 4, 2)
    val map = wordCount(arr)
    for ((k, v) map) {]]></description><category></category></item><item><title><![CDATA[快学scala之Array、List、tuple模式匹配]]></title><link>https://blog.youkuaiyun.com/u014372225/article/details/75094879</link><guid>https://blog.youkuaiyun.com/u014372225/article/details/75094879</guid><author>u014372225</author><pubDate>Fri, 14 Jul 2017 00:43:40 +0800</pubDate><description><![CDATA[object operate_match {

  def main(args: Array[String]) {

    // map match
    val map = Map("scala" -> "spark", "java" -> "hadoop")
    map match {
      case m: Map[_, _] => println("--------------]]></description><category></category></item><item><title><![CDATA[快学scala之List操作]]></title><link>https://blog.youkuaiyun.com/u014372225/article/details/75094772</link><guid>https://blog.youkuaiyun.com/u014372225/article/details/75094772</guid><author>u014372225</author><pubDate>Fri, 14 Jul 2017 00:32:04 +0800</pubDate><description><![CDATA[object list_opt {

  def main(args: Array[String]) {
    // init list
    val list1 = List("a", "b", "c")
    val list2 = List(1, 2, 3)
    val list3 = "a" :: "b" :: "c" :: Nil // last element is Nil
]]></description><category></category></item><item><title><![CDATA[快学scala之数组操作]]></title><link>https://blog.youkuaiyun.com/u014372225/article/details/75094666</link><guid>https://blog.youkuaiyun.com/u014372225/article/details/75094666</guid><author>u014372225</author><pubDate>Fri, 14 Jul 2017 00:17:12 +0800</pubDate><description><![CDATA[object Array_opt {

  def main(args: Array[String]) {

    // init an array
    val array = new Array[String](3)
    val arr = Array(1, 2, 3, 4)

    // traversal element
    // method 1
    for (i 0]]></description><category></category></item><item><title><![CDATA[marchine learning 斯皮尔曼排名相关系数]]></title><link>https://blog.youkuaiyun.com/u014372225/article/details/70042466</link><guid>https://blog.youkuaiyun.com/u014372225/article/details/70042466</guid><author>u014372225</author><pubDate>Tue, 11 Apr 2017 01:25:07 +0800</pubDate><description><![CDATA[斯皮尔曼排名相关系数/**斯皮尔曼排名相关系数
 * Created by Administrator on 2017/4/9.
 */
public class SpearmanRankCorrelation {
    /*
       公式1： ρ = 1 - 6∑(xi-yi) / n(n^2 -1)       公式2： ρ = ∑(xi-x*)(yi-y*) / ( ∑(xi-x*)^]]></description><category></category></item><item><title><![CDATA[weka 交叉验证]]></title><link>https://blog.youkuaiyun.com/u014372225/article/details/70042420</link><guid>https://blog.youkuaiyun.com/u014372225/article/details/70042420</guid><author>u014372225</author><pubDate>Tue, 11 Apr 2017 01:19:13 +0800</pubDate><description><![CDATA[调用weka实现交叉验证，并搭载图形界面import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.classifiers.evaluation.ThresholdCurve;
import weka.core]]></description><category></category></item><item><title><![CDATA[marchine learning 之 ManhattanDistance]]></title><link>https://blog.youkuaiyun.com/u014372225/article/details/70042383</link><guid>https://blog.youkuaiyun.com/u014372225/article/details/70042383</guid><author>u014372225</author><pubDate>Tue, 11 Apr 2017 01:12:11 +0800</pubDate><description><![CDATA[/**曼哈顿街区距离
 */
public class ManhattanDistance {    public double manhattanDistance(Instance a,Instance b){
        if (a.noAttributes() != b.noAttributes())
            throw new RuntimeException("两个数据]]></description><category></category></item><item><title><![CDATA[marchine learning 之 皮尔逊相关系数]]></title><link>https://blog.youkuaiyun.com/u014372225/article/details/70042355</link><guid>https://blog.youkuaiyun.com/u014372225/article/details/70042355</guid><author>u014372225</author><pubDate>Tue, 11 Apr 2017 01:10:00 +0800</pubDate><description><![CDATA[/**皮尔逊相关系数
 *
ρ =(∑xy - ∑x∑y/n)/(∑x^2 - (∑x)^2/n)(∑y^2-(∑y)^2/n)^0.5
 */
public class PersonCorrelationCoefficient {    public double personCoefficient(Instance a,Instance b){
        if (a.noAttribute]]></description><category></category></item><item><title><![CDATA[marchine learning 余弦相似度计算]]></title><link>https://blog.youkuaiyun.com/u014372225/article/details/70042261</link><guid>https://blog.youkuaiyun.com/u014372225/article/details/70042261</guid><author>u014372225</author><pubDate>Tue, 11 Apr 2017 01:06:36 +0800</pubDate><description><![CDATA[直接上代码import com.javaPractice.MachineLearning.core.Instance;/**
 * 计算余弦相似度
 */
public class CosineSimilarity {    public double calCosinDistance(Instance ins1, Instance ins2) {
        if (ins1.noAttrib]]></description><category></category></item><item><title><![CDATA[marchine learning 之交叉验证]]></title><link>https://blog.youkuaiyun.com/u014372225/article/details/70042149</link><guid>https://blog.youkuaiyun.com/u014372225/article/details/70042149</guid><author>u014372225</author><pubDate>Tue, 11 Apr 2017 01:02:03 +0800</pubDate><description><![CDATA[评价指标 
public class PerformanceMeasure {
    /**
     * 1、 FN：False Negative,被判定为负样本，但事实上是正样本。
     * 2、 FP：False Positive,被判定为正样本，但事实上是负样本。
     * 3、TN：True Negative,被判定为负样本，事实上也是负样本。
     * 4、TP：True]]></description><category></category></item><item><title><![CDATA[marchine learning 之 correlation]]></title><link>https://blog.youkuaiyun.com/u014372225/article/details/70041927</link><guid>https://blog.youkuaiyun.com/u014372225/article/details/70041927</guid><author>u014372225</author><pubDate>Tue, 11 Apr 2017 00:51:25 +0800</pubDate><description><![CDATA[code ^_^    /**
     * correlation = (xi-∑xi/n)(yi-∑yi/n)/Math.sqrt( (xi-∑xi/n)^2 * (yi-∑yi/n)^2)
     *
     * @param d1
     * @param d2
     * @return 相关系数
     */
    public static final double cor]]></description><category></category></item><item><title><![CDATA[欢迎使用优快云-markdown编辑器]]></title><link>https://blog.youkuaiyun.com/u014372225/article/details/70041833</link><guid>https://blog.youkuaiyun.com/u014372225/article/details/70041833</guid><author>u014372225</author><pubDate>Tue, 11 Apr 2017 00:46:27 +0800</pubDate><description><![CDATA[欢迎使用Markdown编辑器写博客本Markdown编辑器使用StackEdit修改而来，用它写博客，将会带来全新的体验哦：
Markdown和扩展Markdown简洁的语法
代码块高亮
图片链接和图片上传
LaTex数学公式
UML序列图和流程图
离线写博客
导入导出Markdown文件
丰富的快捷键
快捷键
加粗    Ctrl + B 
斜体    Ctrl + I 
引用    Ctrl]]></description><category></category></item><item><title><![CDATA[Java学习-将Vector转化为数组]]></title><link>https://blog.youkuaiyun.com/u014372225/article/details/69267279</link><guid>https://blog.youkuaiyun.com/u014372225/article/details/69267279</guid><author>u014372225</author><pubDate>Wed, 05 Apr 2017 17:51:12 +0800</pubDate><description><![CDATA[将vector集合转化为二维数组public static void main(String[] args) {
        //将集合添加到数组中
        Vector vector = new Vector();
        int[] a1 = {1, 2, 3};
        int[] a2 = {4, 5, 6};
        int[] a3 = {7, 8,]]></description><category></category></item><item><title><![CDATA[java学习-数组遍历]]></title><link>https://blog.youkuaiyun.com/u014372225/article/details/69218986</link><guid>https://blog.youkuaiyun.com/u014372225/article/details/69218986</guid><author>u014372225</author><pubDate>Wed, 05 Apr 2017 00:32:44 +0800</pubDate><description><![CDATA[数组基本操作 
1.按照列遍历二维数组/**
     * 按照列遍历数组
     */
    public static void byCol(int m, int n) {
        for (int i = 0; i < n; i++) {
            for (int j = 0; j < m; j++) {
                System.out.pri]]></description><category></category></item><item><title><![CDATA[spark学习-SQL]]></title><link>https://blog.youkuaiyun.com/u014372225/article/details/68947908</link><guid>https://blog.youkuaiyun.com/u014372225/article/details/68947908</guid><author>u014372225</author><pubDate>Sat, 01 Apr 2017 21:07:49 +0800</pubDate><description><![CDATA[####spark SQL基本操作import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql.SparkSession
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.Row
import org.apache.spa]]></description><category></category></item><item><title><![CDATA[HBase学习-表的增删改插]]></title><link>https://blog.youkuaiyun.com/u014372225/article/details/68947866</link><guid>https://blog.youkuaiyun.com/u014372225/article/details/68947866</guid><author>u014372225</author><pubDate>Sat, 01 Apr 2017 21:01:55 +0800</pubDate><description><![CDATA[import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.HColumnDescriptor;
import org.apache.hadoop.hbase.HTableDescriptor;
import]]></description><category></category></item><item><title><![CDATA[jfree chart 坐标图]]></title><link>https://blog.youkuaiyun.com/u014372225/article/details/68947818</link><guid>https://blog.youkuaiyun.com/u014372225/article/details/68947818</guid><author>u014372225</author><pubDate>Sat, 01 Apr 2017 20:52:37 +0800</pubDate><description><![CDATA[import org.jfree.chart.ChartFactory;
import org.jfree.chart.ChartUtilities;
import org.jfree.chart.JFreeChart;
import org.jfree.chart.plot.PlotOrientation;
import org.jfree.data.xy.XYSeries;
import org]]></description><category></category></item><item><title><![CDATA[jfree chart-时序图]]></title><link>https://blog.youkuaiyun.com/u014372225/article/details/68947798</link><guid>https://blog.youkuaiyun.com/u014372225/article/details/68947798</guid><author>u014372225</author><pubDate>Sat, 01 Apr 2017 20:48:54 +0800</pubDate><description><![CDATA[import org.jfree.chart.ChartFactory;
import org.jfree.chart.ChartUtilities;
import org.jfree.chart.JFreeChart;
import org.jfree.chart.plot.XYPlot;
import org.jfree.chart.renderer.xy.XYItemRenderer;
imp]]></description><category></category></item><item><title><![CDATA[jfree chart -饼状图]]></title><link>https://blog.youkuaiyun.com/u014372225/article/details/68947776</link><guid>https://blog.youkuaiyun.com/u014372225/article/details/68947776</guid><author>u014372225</author><pubDate>Sat, 01 Apr 2017 20:45:00 +0800</pubDate><description><![CDATA[代码如下import org.jfree.chart.ChartFactory;
import org.jfree.chart.ChartUtilities;
import org.jfree.chart.JFreeChart;
import org.jfree.chart.plot.PiePlot;
import org.jfree.data.general.DefaultPieDataset;i]]></description><category></category></item><item><title><![CDATA[jFree Chart学习-柱状图]]></title><link>https://blog.youkuaiyun.com/u014372225/article/details/68947760</link><guid>https://blog.youkuaiyun.com/u014372225/article/details/68947760</guid><author>u014372225</author><pubDate>Sat, 01 Apr 2017 20:40:57 +0800</pubDate><description><![CDATA[前提要添加依赖的jar包，在你个pom.xml文件中添加如下依赖 
1.第一个是jcommon<!-- https://mvnrepository.com/artifact/jfree/jcommon -->
        <dependency>
            <groupId>jfree</groupId>
            <artifactId>jcommon</artif]]></description><category></category></item></channel></rss>