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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