epyc rome

继续进行编程收集情报 （ Programming Collection Intelligence ，PCI），下一个练习是使用距离得分根据相关博客中使用的单词来确定博客列表。

我已经找到Encog作为AI /机器学习算法的框架，为此，我需要一个RSS阅读器和一个HTML解析器。

我最终使用的2个库是：

1. 罗马
2. 汤

对于一般的其他实用程序和收集操作，我使用了：

• 谷歌番石榴

我保持简短的博客列表，包括我关注的一些软件博客，只是为了加快测试速度，不得不对（PCI）中的实现进行一些改动，但仍然获得了理想的结果。

使用的博客：

• http://blog.guykawasaki.com/index.rdf
• http://blog.outer-court.com/rss.xml
• http://flagrantdisregard.com/index.php/feed/
• http://gizmodo.com/index.xml
• http://googleblog.blogspot.com/rss.xml
• http://radar.oreilly.com/index.rdf
• http://www.wired.com/rss/index.xml
• http://feeds.feedburner.com/codinghorror
• http://feeds.feedburner.com/joelonsoftware
• http://martinfowler.com/feed.atom
• http://www.briandupreez.net/feeds/posts/default

对于实现，我只选择了一个主类和一个阅读器类：

package net.briandupreez.pci.data;import com.google.common.base.Predicates;
import com.google.common.collect.Collections2;
import com.sun.syndication.feed.synd.SyndCategoryImpl;
import com.sun.syndication.feed.synd.SyndContent;
import com.sun.syndication.feed.synd.SyndEntryImpl;
import com.sun.syndication.feed.synd.SyndFeed;
import com.sun.syndication.io.SyndFeedInput;
import com.sun.syndication.io.XmlReader;
import org.jsoup.Jsoup;
import org.jsoup.nodes.Document;
import org.jsoup.nodes.Element;
import org.jsoup.select.Elements;import java.net.URL;
import java.util.*;public class FeedReader {@SuppressWarnings("unchecked")public static Set<String> determineAllUniqueWords(final String url, final Set<String> blogWordList) {boolean ok = false;try {URL feedUrl = new URL(url);SyndFeedInput input = new SyndFeedInput();SyndFeed feed = input.build(new XmlReader(feedUrl));final List<SyndEntryImpl> entries = feed.getEntries();for (final SyndEntryImpl entry : entries) {blogWordList.addAll(cleanAndSplitString(entry.getTitle()));blogWordList.addAll(doCategories(entry));blogWordList.addAll(doDescription(entry));blogWordList.addAll(doContent(entry));}ok = true;} catch (Exception ex) {ex.printStackTrace();System.out.println("ERROR: " + url + "\n" + ex.getMessage());}if (!ok) {System.out.println("FeedReader reads and prints any RSS/Atom feed type.");System.out.println("The first parameter must be the URL of the feed to read.");}return blogWordList;}@SuppressWarnings("unchecked")private static List<String> doContent(final SyndEntryImpl entry) {List<String> blogWordList = new ArrayList<>();final List<SyndContent> contents = entry.getContents();if (contents != null) {for (final SyndContent syndContent : contents) {if ("text/html".equals(syndContent.getMode())) {blogWordList.addAll(stripHtmlAndAddText(syndContent));} else {blogWordList.addAll(cleanAndSplitString(syndContent.getValue()));}}}return blogWordList;}private static List<String> doDescription(final SyndEntryImpl entry) {final List<String> blogWordList = new ArrayList<>();final SyndContent description = entry.getDescription();if (description != null) {if ("text/html".equals(description.getType())) {blogWordList.addAll(stripHtmlAndAddText(description));} else {blogWordList.addAll(cleanAndSplitString(description.getValue()));}}return blogWordList;}@SuppressWarnings("unchecked")private static List<String> doCategories(final SyndEntryImpl entry) {final List<String> blogWordList = new ArrayList<>();final List<SyndCategoryImpl> categories = entry.getCategories();for (final SyndCategoryImpl category : categories) {blogWordList.add(category.getName().toLowerCase());}return blogWordList;}private static List<String> stripHtmlAndAddText(final SyndContent description) {String html = description.getValue();Document document = Jsoup.parse(html);Elements elements = document.getAllElements();final List<String> allWords = new ArrayList<>();for (final Element element : elements) {allWords.addAll(cleanAndSplitString(element.text()));}return allWords;}private static List<String> cleanAndSplitString(final String input) {if (input != null) {final String[] dic = input.toLowerCase().replaceAll("\\p{Punct}", "").replaceAll("\\p{Digit}", "").split("\\s+");return Arrays.asList(dic);}return new ArrayList<>();}@SuppressWarnings("unchecked")public static Map<String, Double> countWords(final String url, final Set<String> blogWords) {final Map<String, Double> resultMap = new TreeMap<>();try {URL feedUrl = new URL(url);SyndFeedInput input = new SyndFeedInput();SyndFeed feed = input.build(new XmlReader(feedUrl));final List<SyndEntryImpl> entries = feed.getEntries();final List<String> allBlogWords = new ArrayList<>();for (final SyndEntryImpl entry : entries) {allBlogWords.addAll(cleanAndSplitString(entry.getTitle()));allBlogWords.addAll(doCategories(entry));allBlogWords.addAll(doDescription(entry));allBlogWords.addAll(doContent(entry));}for (final String word : blogWords) {resultMap.put(word, (double) Collections2.filter(allBlogWords, Predicates.equalTo(word)).size());}} catch (Exception ex) {ex.printStackTrace();System.out.println("ERROR: " + url + "\n" + ex.getMessage());}return resultMap;}
}

主要：

package net.briandupreez.pci.data;import com.google.common.base.Predicates;
import com.google.common.collect.Maps;
import com.google.common.io.Resources;
import com.google.common.primitives.Doubles;
import org.encog.ml.MLCluster;
import org.encog.ml.data.MLDataPair;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLData;
import org.encog.ml.data.basic.BasicMLDataPair;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.ml.kmeans.KMeansClustering;import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.util.*;public class FeedReaderMain {public static void main(String[] args) {final FeedReaderMain feedReaderMain = new FeedReaderMain();try {feedReaderMain.run();} catch (IOException e) {e.printStackTrace();}}public void run() throws IOException {final String file = Resources.getResource("short-feedlist.txt").getFile();final Set<String> blogWords = determineWordCompleteList(file);final Map<String, Map<String, Double>> blogWordCount = countWordsPerBlog(file, blogWords);//strip out the outlying wordsstripOutlyingWords(blogWords, blogWordCount);performCusteringAndDisplay(blogWordCount);}private void performCusteringAndDisplay(final Map<String, Map<String, Double>> blogWordCount) {final BasicMLDataSet set = new BasicMLDataSet();final Map<String, List<Double>> inputMap = new HashMap<>();for (final Map.Entry<String, Map<String, Double>> entry : blogWordCount.entrySet()) {final Map<String, Double> mainValues = entry.getValue();final double[] elements = Doubles.toArray(mainValues.values());List<Double> listInput = Doubles.asList(elements);inputMap.put(entry.getKey(), listInput);set.add(new BasicMLData(elements));}final KMeansClustering kmeans = new KMeansClustering(3, set);kmeans.iteration(150);// Display the clusterint i = 1;for (final MLCluster cluster : kmeans.getClusters()) {System.out.println("*** Cluster " + (i++) + " ***");final MLDataSet ds = cluster.createDataSet();final MLDataPair pair = BasicMLDataPair.createPair(ds.getInputSize(), ds.getIdealSize());for (int j = 0; j < ds.getRecordCount(); j++) {ds.getRecord(j, pair);List<Double> listInput = Doubles.asList(pair.getInputArray());System.out.println(Maps.filterValues(inputMap, Predicates.equalTo(listInput)).keySet().toString());}}}private Map<String, Map<String, Double>> countWordsPerBlog(String file, Set<String> blogWords) throws IOException {BufferedReader reader;String line;reader = new BufferedReader(new FileReader(file));final Map<String, Map<String, Double>> blogWordCount = new HashMap<>();while ((line = reader.readLine()) != null) {final Map<String, Double> wordCounts = FeedReader.countWords(line, blogWords);blogWordCount.put(line, wordCounts);}return blogWordCount;}private Set<String> determineWordCompleteList(final String file) throws IOException {FileReader fileReader = new FileReader(file);BufferedReader reader = new BufferedReader(fileReader);String line;Set<String> blogWords = new HashSet<>();while ((line = reader.readLine()) != null) {blogWords = FeedReader.determineAllUniqueWords(line, blogWords);System.out.println("Size: " + blogWords.size());}return blogWords;}private void stripOutlyingWords(final Set<String> blogWords, final Map<String, Map<String, Double>> blogWordCount) {final Iterator<String> wordIter = blogWords.iterator();final double listSize = blogWords.size();while (wordIter.hasNext()) {final String word = wordIter.next();double wordCount = 0;for (final Map<String, Double> values : blogWordCount.values()) {wordCount += values.get(word) != null ? values.get(word) : 0;}double percentage = (wordCount / listSize) * 100;if (percentage < 0.1 || percentage > 20 || word.length() < 3) {wordIter.remove();for (final Map<String, Double> values : blogWordCount.values()) {values.remove(word);}} else {System.out.println("\t keeping: " + word + " Percentage:" + percentage);}}}
}

结果：

*** Cluster 1 ***[http://www.briandupreez.net/feeds/posts/default]*** Cluster 2 ***[http://blog.guykawasaki.com/index.rdf][http://radar.oreilly.com/index.rdf][http://googleblog.blogspot.com/rss.xml][http://blog.outer-court.com/rss.xml][http://gizmodo.com/index.xml][http://flagrantdisregard.com/index.php/feed/][http://www.wired.com/rss/index.xml]*** Cluster 3 ***[http://feeds.feedburner.com/joelonsoftware][http://feeds.feedburner.com/codinghorror][http://martinfowler.com/feed.atom]

翻译自: https://www.javacodegeeks.com/2013/06/blog-categorisation-using-encog-rome-jsoup-and-google-guava.html

epyc rome