2015年8月2日日曜日

共有するGitリポジトリを作成する方法

共有するGitリポジトリを設定する方法をメモしておきます．

$ groupadd group
$ usermod -G group user
$ mkdir /path/to/repository.git
$ git init --bare --shared /path/to/repository.git
$ chown -R user:group /path/to/repository.git
投稿者 時刻: 0 件のコメント:
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2015年6月13日土曜日

GEAのDataNucleus Enhancerが異常終了する問題

GEAのDataNucleus Enhancerが「java.lang.NoClassDefFoundError: org/w3c/dom/ElementTraversal」という例外で異常終了する問題に出くわしました．この問題の解決方法をメモしておきます．

  • 「/WEB-INF/lib」（別のフォルダでもいいかもしれない）に「xml-apis-X.X.X.jar」と「xercesImpl-X.X.X.jar」を配置する（Xは適当な数字）．
  • それら二つのJARファイルを「Java Build Path」の「Libraries」に追加する．

今回は，[1]の質問に対する回答を参考にしました．

[1] AppEngine Error [ java.lang.NoClassDefFoundError: org/w3c/dom/ElementTraversal ], http://stackoverflow.com/questions/10234201/appengine-error-java-lang-noclassdeffounderror-org-w3c-dom-elementtraversal
投稿者 時刻: 0 件のコメント:
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2015年4月22日水曜日

CSSではみ出した部分を「…」にする方法

CSSではみ出した部分を「…」にする方法をメモしておきます． 

.hogehoge {
    width: 4em;
    white-space: nowrap; 
    overflow: hidden;
    text-overflow: ellipsis;
}

<div class="hogehoge">どうしてプログラムのサンプルに使うテキストは「ほげほげ」なんだろう？</div>

「text-overflow: ellipsis;」と記せば，「…」にならなかったので，何でだろうとずっと悩んでいたのですが， このプロパティは，先の例のように他の3つのプロパティといっしょに使わないと効果が発揮されないんですね． ようやく謎が解けた．＼(^O^)／

投稿者 時刻: 0 件のコメント:
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2015年4月19日日曜日

ロジスティック回帰をJavaで実装

文献[1]で紹介されているロジスティック回帰をJavaで実装してみました．

ロジスティック回帰は，分類問題に適用できる機械学習手法の1つです（確率化したパーセプトロンという理解でいいのかな）． 文献で紹介されているとおりあらかじめ用意したデータを分類する関数を求めてみました． グラフにした結果は次の図のとおりです． 青の点が正解，赤の点が不正解，黄色の直線がそれらを分類するために推定された関数，オレンジが正解になる確率が0の領域で，緑が1の領域です． 直線付近は，正解と不正解の境界になっているので，正解になる確率がどんと落ちてます．

ちなみに，これまでグラフはExcelで描いてきたのですが，今回は難しかったので，d3.jsを使ってみました（かなりゴリ押しですけど）． 使ってみて思いましたが，大本の文献で使っているPythonがやっぱり一番簡単そうですね．

[1] 中谷 秀洋: ロジスティック回帰を実装してみよう, 機械学習はじめよう 第20回, http://gihyo.jp/dev/serial/01/machine-learning/0020

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var margin = {top: 20, right: 20, bottom: 30, left: 30},
width = 400 - margin.left - margin.right,
height = 400 - margin.top - margin.bottom;
var x = d3.scale.linear()
.range([0, width]);
var y = d3.scale.linear()
.range([height, 0]);
var color = d3.scale.linear()
.domain([0.0, 1.0])
.range(["orange", "green"]);
var xAxis = d3.svg.axis()
.scale(x)
.orient("bottom")
var yAxis = d3.svg.axis()
.scale(y)
.orient("left");
var svg = d3.select("body")
.append("svg").attr("width", width + margin.left + margin.right).attr("height", height + margin.top + margin.bottom)
.append("g").attr("transform", "translate(" + margin.left + "," + margin.top + ")");
var exampleColor = d3.scale.linear()
.domain([0.0, 1.0])
.range(["red", "blue"]);
var dx = posibilityDistribution[0].length,
dy = posibilityDistribution.length;
x.domain([-4.0, 4.0]);
y.domain([-4.0, 4.0]);
svg.selectAll("g")
.data(posibilityDistribution)
.enter()
.append("g")
.selectAll("rect")
.data(function (d,i,j) {return d;})
.enter()
.append("rect")
.attr("x", function (d, i, j) {return x(-4.0 + i * 0.08);})
.attr("y", function (d, i, j) {return y(-4.0 + j * 0.08);})
.attr("width", function (d, i, j) {return width / dx;})
.attr("height", function (d, i, j) {return height / dy;})
.attr("fill", function(d, i, j) {return color(d);})
.attr("stroke", function(d, i, j) {return color(d);});
svg.selectAll("circle")
.data(trainingSet)
.enter()
.append("circle")
.attr("cx", function (d) {return x(d[0]);})
.attr("cy", function (d) {return y(d[1]);})
.attr("r", "2")
.attr("fill", function(d, i, j) {return exampleColor(d[2]);})
.attr("stroke", "black");
svg.selectAll("line")
.data(weightVector)
.enter()
.append("line")
.attr("x1", function (d) {return x(-d[1] / d[0] * 4.0 - d[2] / d[0]);})
.attr("y1", function (d) {return y(4.0);})
.attr("x2", function (d) {return x(-d[1] / d[0] * -4.0 - d[2] / d[0]);})
.attr("y2", function (d) {return y(-4.0);})
.attr("stroke", "yellow");
svg.append("g").attr("class", "x axis").attr("transform", "translate(0," + height + ")").call(xAxis);
svg.append("g").attr("class", "y axis").call(yAxis);
</script>
</body>
</html>
view raw logistic_regression-result.html hosted with ❤ by GitHub
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import org.apache.commons.math3.linear.ArrayRealVector;
import org.apache.commons.math3.linear.RealVector;
import org.apache.commons.math3.random.GaussianRandomGenerator;
import org.apache.commons.math3.random.JDKRandomGenerator;
public class LogisticRegression {
RealVector weightVector = new ArrayRealVector(3);
void learn(List<Example> trainingSet) {
JDKRandomGenerator randomGenerator = new JDKRandomGenerator();
randomGenerator.setSeed(0);
GaussianRandomGenerator normalizedRandomGenerator = new GaussianRandomGenerator(randomGenerator);
for (int i = 0; i < weightVector.getDimension(); i++) {
this.weightVector.setEntry(i, normalizedRandomGenerator.nextNormalizedDouble());
}
double eta = 0.1;
for (int i = 0; i < 50; i++) {
System.out.println("Loop:" + i);
List<Integer> indexes = LogisticRegression.generateShuffledIntegers(0, trainingSet.size() - 1);
for (int index : indexes) {
Example example = trainingSet.get(index);
double predict = this.predict(example.x, example.y);
System.out.printf("x = %f, y= %f, t= %f, p= %f\n", example.x, example.y, example.t, predict);
System.out.print(this.weightVector);
this.weightVector = this.weightVector.add(this.phiVector(example.x, example.y).mapMultiply(-eta * (predict - example.t)));
System.out.println("->" + this.weightVector);
}
eta *= 0.9;
}
}
double predict(double x, double y) {
return LogisticRegression.sigmoid(this.weightVector.dotProduct(this.phiVector(x, y)));
}
RealVector phiVector(double x, double y) {
RealVector vector = new ArrayRealVector(3);
vector.setEntry(0, x);
vector.setEntry(1, y);
vector.setEntry(2, 1.0);
return vector;
}
static double thresholding(double value) {
return value > 0.0 ? 1.0 : 0.0;
}
static double sigmoid(double value) {
return 1.0 / (1.0 + Math.exp(-value));
}
static List<Integer> generateShuffledIntegers(int lower, int upper) {
List<Integer> integers = new ArrayList<Integer>();
for (int i = lower; i <= upper; i++) {
integers.add(i);
}
Collections.shuffle(integers);
return integers;
}
public static class Example {
double x;
double y;
double t;
Example(double x, double y, double t) {
this.x = x;
this.y = y;
this.t = t;
}
}
}
view raw LogisticRegression.java hosted with ❤ by GitHub
import java.util.ArrayList;
import java.util.List;
import org.apache.commons.math3.random.GaussianRandomGenerator;
import org.apache.commons.math3.random.JDKRandomGenerator;
import org.junit.Test;
public class LogisticRegressionTest {
@Test
public void test() {
List<LogisticRegression.Example> trainingSet = this.generateTrainingSet();
LogisticRegression logisticRegression = new LogisticRegression();
logisticRegression.learn(trainingSet);
System.out.println("[");
if (trainingSet.size() > 0) {
LogisticRegression.Example example = trainingSet.get(0);
System.out.printf("[%f,%f,%f]", example.x, example.y, example.t);
}
for (int i = 1, n = trainingSet.size(); i < n; i++ ) {
LogisticRegression.Example example = trainingSet.get(i);
System.out.printf(",\n[%f,%f,%f]", example.x, example.y, example.t);
}
System.out.println("\n]");
System.out.println("[");
if (logisticRegression.weightVector.getDimension() > 0) {
System.out.print(logisticRegression.weightVector.getEntry(0));
}
for (int i = 1; i < logisticRegression.weightVector.getDimension(); i++) {
System.out.print(",");
System.out.print(logisticRegression.weightVector.getEntry(i));
}
System.out.println("]");
System.out.println("[");
for (int i = 0; i <= 100; i += 1) {
double y = -4.0 + i * 0.08;
double x = -4.0;
double probability = logisticRegression.predict(x, y);
System.out.printf("[%.2f", probability);
for (int j = 1; j <= 100; j++) {
probability = logisticRegression.predict(x, y);
System.out.printf(",%.2f", probability);
x += 0.08;
}
if (i < 100) {
System.out.println("],");
}
else {
System.out.println("]");
}
}
System.out.println("]");
}
double h(double x, double y) {
return 5.0 * x + 3.0 * y - 1.0;
}
List<LogisticRegression.Example> generateTrainingSet() {
final int N = 100;
JDKRandomGenerator randomGenerator = new JDKRandomGenerator();
randomGenerator.setSeed(0);
GaussianRandomGenerator normalizedRandomGenerator = new GaussianRandomGenerator(randomGenerator);
List<LogisticRegression.Example> trainingSet = new ArrayList<LogisticRegression.Example>();
for (int i = 0; i < N; i++) {
double x = normalizedRandomGenerator.nextNormalizedDouble();
double y = normalizedRandomGenerator.nextNormalizedDouble();
double t = LogisticRegression.thresholding(this.h(x, y));
LogisticRegression.Example example = new LogisticRegression.Example(x, y, t);
trainingSet.add(example);
}
return trainingSet;
}
}
view raw LogisticRegressionTest.java hosted with ❤ by GitHub
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2015年4月16日木曜日

パーセプトロンをJavaで実装

文献[1]で紹介されているパーセプトロンをJavaで実装してみました．

パーセプトロンは，分類問題に適用できる機械学習手法の１つです． 文献で紹介されているとおりあらかじめ用意したデータを正解（1）と不正解（-1）に分類する関数を求めてみました． グラフにした結果は次の図のとおりです． そうなるようにデータを作ってあるので，当たり前なのですが， 正解と不正解をうまく仕分ける関数を求めることができました．

[1] 中谷 秀洋: パーセプトロンを実装してみよう, 機械学習はじめよう 第17回, http://gihyo.jp/dev/serial/01/machine-learning/0017

import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import org.apache.commons.math3.linear.ArrayRealVector;
import org.apache.commons.math3.linear.RealVector;
public class Perceptron {
RealVector weightVector = new ArrayRealVector(3);
void learn(List<Example> trainingSet) {
for (int i = 0; i < 1000; i++) {
System.out.println("Loop:" + i);
List<Integer> indexes = Perceptron.generateShuffledIntegers(0, trainingSet.size() - 1);
int numberOfMisses = 0;
for (int index : indexes) {
Example example = trainingSet.get(index);
double predict = this.predict(example.x, example.y);
if (predict != example.t) {
System.out.printf("x = %f, y= %f, t= %f, p= %f\n", example.x, example.y, example.t, predict);
System.out.print(this.weightVector);
this.weightVector = this.weightVector.add(this.phiVector(example.x, example.y).mapMultiply(example.t));
System.out.println("->" + this.weightVector);
numberOfMisses++;
}
}
if (numberOfMisses == 0) {
break;
}
}
}
double predict(double x, double y) {
return Perceptron.sign(this.weightVector.dotProduct(this.phiVector(x, y)));
}
RealVector phiVector(double x, double y) {
RealVector vector = new ArrayRealVector(3);
vector.setEntry(0, x);
vector.setEntry(1, y);
vector.setEntry(2, 1.0);
return vector;
}
static double sign(double value) {
return Math.signum(value) >= 0.0 ? 1.0 : -1.0;
}
static List<Integer> generateShuffledIntegers(int lower, int upper) {
List<Integer> integers = new ArrayList<Integer>();
for (int i = lower; i <= upper; i++) {
integers.add(i);
}
Collections.shuffle(integers);
return integers;
}
public static class Example {
double x;
double y;
double t;
Example(double x, double y, double t) {
this.x = x;
this.y = y;
this.t = t;
}
}
}
view raw Perceptron.java hosted with ❤ by GitHub
import java.util.ArrayList;
import java.util.List;
import org.apache.commons.math3.random.GaussianRandomGenerator;
import org.apache.commons.math3.random.JDKRandomGenerator;
import org.junit.Test;
public class PerceptronTest {
@Test
public void test() {
List<Perceptron.Example> trainingSet = this.generateTrainingSet();
Perceptron perceptron = new Perceptron();
perceptron.learn(trainingSet);
for (Perceptron.Example example : trainingSet) {
System.out.printf("%f\t%f\t%f\n", example.x, example.y, example.t);
}
if (perceptron.weightVector.getDimension() > 0) {
System.out.print(perceptron.weightVector.getEntry(0));
}
for (int i = 1; i < perceptron.weightVector.getDimension(); i++) {
System.out.print("\t");
System.out.print(perceptron.weightVector.getEntry(i));
}
System.out.println();
}
double h(double x, double y) {
return 5.0 * x + 3.0 * y - 1.0;
}
List<Perceptron.Example> generateTrainingSet() {
final int N = 100;
JDKRandomGenerator randomGenerator = new JDKRandomGenerator();
randomGenerator.setSeed(0);
GaussianRandomGenerator normalizedRandomGenerator = new GaussianRandomGenerator(randomGenerator);
List<Perceptron.Example> trainingSet = new ArrayList<Perceptron.Example>();
for (int i = 0; i < N; i++) {
double x = normalizedRandomGenerator.nextNormalizedDouble();
double y = normalizedRandomGenerator.nextNormalizedDouble();
double t = Perceptron.sign(this.h(x, y));
Perceptron.Example example = new Perceptron.Example(x, y, t);
trainingSet.add(example);
}
return trainingSet;
}
}
view raw PerceptronTest.java hosted with ❤ by GitHub
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2015年4月11日土曜日

ベイズ線形回帰をJavaで実装

文献[1]で紹介されているベイズ線形回帰を記事[2]で作成したプログラムを元にしてJavaで実装してみました．

ベイズ線形回帰は，線形回帰を確率化してベイジアンで関数を求める手法です． 文献で紹介されているとおり基底関数をベイズ基底にして関数を求めてみました． 結果は次の図のとおりです． ベイズ線形回帰なので，関数だけでなく，確率密度も求めることができます．

[1] 中谷 秀洋: ベイズ線形回帰を実装してみよう, 機械学習はじめよう 第14回, http://gihyo.jp/dev/serial/01/machine-learning/0014
[2] mizo0409: 線形回帰をJavaで実装, http://mizo0409.blogspot.jp/2014/01/java.html

import java.util.List;
import org.apache.commons.math3.distribution.NormalDistribution;
import org.apache.commons.math3.linear.LUDecomposition;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.RealVector;
public class BasianLinearRegression extends LinearRegression {
private double alpha = 0.1;
private double beta = 9.0;
private RealVector muVector;
private RealMatrix sigmaMatrix;
public void setAlpha(double alpha) {
this.alpha = alpha;
}
public void setBeta(double beta) {
this.beta = beta;
}
public void learn(List<Example> trainingSet) {
RealMatrix I = this.identityMatrix();
RealMatrix PHI = this.phiMatrix(trainingSet);
RealMatrix PHI_T = PHI.transpose();
RealVector t = this.tVector(trainingSet);
this.sigmaMatrix = new LUDecomposition(I.scalarMultiply(this.alpha).add(PHI_T.multiply(PHI).scalarMultiply(this.beta))).getSolver().getInverse();
this.muVector = this.sigmaMatrix.operate(PHI_T.operate(t)).mapMultiply(this.beta);
}
public double calculate(double x) {
return this.muVector.dotProduct(this.phiVector(x));
}
public double calculateDensity(double x, double y) {
RealVector phi = this.phiVector(x);
double mu = this.muVector.dotProduct(phi);
double sigma = Math.sqrt(1.0 / this.beta + phi.dotProduct(this.sigmaMatrix.operate(phi)));
NormalDistribution n = new NormalDistribution(mu, sigma);
return n.density(y);
}
}
view raw BasianLinearRegression.java hosted with ❤ by GitHub
import org.junit.Test;
public class BasianLinearRegressionTest {
@Test
public void test() {
System.out.println("test");
int N = 10;
double s = 0.1;
BasianLinearRegression linearRegression = new BasianLinearRegression();
linearRegression.addBaseFunction(new LinearRegressionTest.PolynominalBasis(0));
for (int i = 0; i <= N; i++) {
linearRegression.addBaseFunction(new LinearRegressionTest.GaussianBasis(i * 1.0 / N, s));
}
linearRegression.learn(LinearRegressionTest.trainingSet);
for (double x = 0.0; x <= 1.0; x += 0.01) {
double y = linearRegression.calculate(x);
System.out.printf("%.2f\t%.2f\n", x, y);
}
for (int i = 0; i <= 50; i += 1) {
double y = -1.5 + i * 0.06;
double x = 0.0;
double density = linearRegression.calculateDensity(x, y);
System.out.printf("%.2f", density);
for (int j = 1; j <= 50; j++) {
density = linearRegression.calculateDensity(x, y);
System.out.printf("\t%.2f", density);
x += 0.02;
}
System.out.println();
}
}
}
view raw BasianLinearRegressionTest.java hosted with ❤ by GitHub
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2015年2月10日火曜日

[読了]良い習慣悪い習慣

「良い習慣悪い習慣」を読みました．

Jeremy Dean: 良い習慣悪い習慣, 東洋経済, 2014-09 [アマゾン, 楽天]

本書についてのメモを以下に記しておきます．

行動を習慣化させるには，どのくらいの時間がかかるか？本書は．この疑問の答えを探すところから始まる．巷によると21日が有力な説であるが，学術的な研究成果によると平均で66日になるらしい．行動によって短くなったり，長くなったりするが，行動を習慣化するにはどうも２ヶ月ぐらいかかるみたいだ．

習慣とは，自動化された行動である．行動の習慣化は，意図的にそうすることもできるし，経験からそうなってしまうこともある．いずれにせよ一旦行動が習慣化されれば，結果の善し悪しにかかわらず，無意識が周囲の状況に基づきその実行をコントロールするようになる．

行動を習慣化するためのポイントは次のとおりである．

  • 達成する価値のある最終目標を持つ．
  • 最終目標までの具体的なプロセスを考える．
  • 同じ状況下で繰り返す．
  • 進捗をモニタリングする．
  • 行動して得られるものが報酬となるようにする．
  • 不満に対処する．
  • 心理的対比（夢想と現実を比較すること）と実行意図（「〜ならば〜する」と定めること）を組み合わせて行動する．
  • 生活に組み込むのが難しい運動などは，既存の生活習慣に付随させる．
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2015年1月18日日曜日

VMware Fusion 7上のWindows 8.1でUSキーボードを使う方法

VMware Fusion 7上のWindows 8.1でUSキーボードを使う方法をメモしておきます．

レジストリエディタ(regedit.exe)を起動した後，次のレジストリエントリにある値を下記の表のとおりに設定します． 

HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Services\i8042prt\Parameters

値の名前 値の種類 値のデータ
LayerDriver JPN REG_SZ kbd101.dll
OverrideKeyboardIdentifier REG_SZ PCAT_101KEY
OverrideKeyboardSubtype DWORD 0
OverrideKeyboardType DWORD 7

[1] コンピューターに USB キーボードを接続したときに Windows で正しいキーボード レイアウトが使用されないことがある, http://support.microsoft.com/kb/927824

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2015年1月11日日曜日

[読了]Hooked ハマるしかけ

「Hooked ハマるしかけ」を読みました．

Nir Eyal, Ryan Hoover: Hooked ハマるしかけ, 翔泳社, 2014-05 [アマゾン, 楽天]

本書についてのメモを以下に記しておきます．

習慣とは，無意識，あるいは，ほとんど意識しないままに行われる行動のことである．ある種のビジネスにとって，プロダクトの習慣化は，成功の必須要素となる．本書で記されているフック・モデルはそのための効果的な枠組みを提供する．

フック・モデルは，トリガー，アクション，リワード，インベストメントという4つのフェーズをスパイラルに回して，プロダクトの習慣化を図ろうとするものである．使うきっかけを与え（トリガー），使っている最中は心地よさを感じさせながら（アクション），好感を持って使い終わるようにし（リワード），そして，次に使う準備をユーザにしてもらうようにすれば（インベストメント），プロダクトは継続して使われる可能性が上がる．

フック・モデルの各フェーズにおいて示されている方針は，心理学等の研究成果に基づいたものなので，それに従ったプロダクトを開発すれば，そのプロダクトに高い習慣化能力を持たせることができるように思える．長期的な利用を狙ったプロダクトの開発にちょうど携わっているので，このプロダクトにフック・モデルを適用させてみたい．

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2015年1月10日土曜日

サーブレットでネイティブライブラリを使う方法

サーブレットでネイティブライブラリをロードする方法についてメモしておきます．

java.lang.Systemクラスのloadメソッドを使うと，Webアプリケーションの範囲外に特別な設定をせずにネイティブライブラリをロードすることができます．例えば，OpenCVのネイティブライブラリが，次のように配置されている場合， 

/opt/local/share/OpenCV/java/libopencv_java2410.dylib
java.lang.Systemクラスのloadメソッドを次のように記述すれば，そのライブラリをロードできます． 
System.load("/opt/local/share/OpenCV/java/libopencv_java2410.dylib");

Javaアプリケーションの場合，ソース内では，java.lang.SystemクラスのloadLibraryメソッドを使い， 

System.loadLibrary("opencv_java2410");
javaコマンドのオプションでネイティブライブラリのディレクトリを指定すればよいのですが， 
java -Djava.library.path=/opt/local/share/OpenCV/java/ ...
これをjava.lang.SystemクラスのsetPropertyメソッドを使って肩代わりさせようとすると，ライブラリのロードに失敗します． どうも「java.library.path」の値は，起動時のものが使われるようで，その後に内容を変更しても使われないみたいです．

そういうわけで，Webアプリケーションで閉じた方法を探していたところ，上記の方法にたどり着きました． 絶対パスが入っているので，環境依存にはなるのですが（必要なら動的に変更できるようにすればいいし）， アプリケーションサーバの設定を触らなくていいので，こちらのほうが自分の好みに合います．

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2015年1月7日水曜日

java.lang.ClassクラスのgetResourceメソッド

java.lang.ClassクラスのgetResourceメソッドについてメモしておきます（裏はとってないので，あくまで自分の推測です）．

java.lang.Class#getResourceがリソースを探索する際の起点は，CLASSPATHに指定されたパスになる．

例えば，「HogeHoge.class」というプログラムと「hogehoge.txt」というファイルが，次のように配置されている場合， 

/tmp/bin/HogeHoge.class
/tmp/etc/hogehoge.txt
「HogeHoge.class.getResource("hogehoge.txt")」が「hogehoge.txt」のURLを返すためには， 
java -cp "/tmp/bin:/tmp/etc" HogeHoge
というように「hogehoge.txt」が置かれたディレクトリのパスをCLASSPATHに指定してプログラムを実行する必要あります． ちなみに，HogeHogeクラスのソースは以下のとおりです．

import java.net.URL;
public class HogeHoge {
    public static void main(String[] args) {
        URL url = HogeHoge.class.getResource("hogehoge.txt");
        System.out.println(url);
    }
}

また，HogeHogeクラスがhogehogeパッケージのクラスの場合，つまり，ソースが次の場合， 

package hogehoge;
import java.net.URL;
public class HogeHoge {
    public static void main(String[] args) {
        URL url = HogeHoge.class.getResource("hogehoge.txt");
        System.out.println(url);
    }
}
ファイル構成は次のようになり， 
/tmp/bin/hogehoge/HogeHoge.class
/tmp/etc/hogehoge/hogehoge.txt
コマンドは次のようになります． 
java -cp "/tmp/bin:/tmp/etc" hogehoge.HogeHoge

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