/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.examples;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.Function2;
import java.io.Serializable;
import java.util.Arrays;
import java.util.Random;
import java.util.regex.Pattern;
/**
* Logistic regression based classification.
*/
public final class JavaHdfsLR {
private static final int D = 10; // Number of dimensions
private static final Random rand = new Random(42);
static class DataPoint implements Serializable {
DataPoint(double[] x, double y) {
this.x = x;
this.y = y;
}
double[] x;
double y;
}
static class ParsePoint implements Function<String, DataPoint> {
private static final Pattern SPACE = Pattern.compile(" ");
@Override
public DataPoint call(String line) {
String[] tok = SPACE.split(line);
double y = Double.parseDouble(tok[0]);
double[] x = new double[D];
for (int i = 0; i < D; i++) {
x[i] = Double.parseDouble(tok[i + 1]);
}
return new DataPoint(x, y);
}
}
static class VectorSum implements Function2<double[], double[], double[]> {
@Override
public double[] call(double[] a, double[] b) {
double[] result = new double[D];
for (int j = 0; j < D; j++) {
result[j] = a[j] + b[j];
}
return result;
}
}
static class ComputeGradient implements Function<DataPoint, double[]> {
private final double[] weights;
ComputeGradient(double[] weights) {
this.weights = weights;
}
@Override
public double[] call(DataPoint p) {
double[] gradient = new double[D];
for (int i = 0; i < D; i++) {
double dot = dot(weights, p.x);
gradient[i] = (1 / (1 + Math.exp(-p.y * dot)) - 1) * p.y * p.x[i];
}
return gradient;
}
}
public static double dot(double[] a, double[] b) {
double x = 0;
for (int i = 0; i < D; i++) {
x += a[i] * b[i];
}
return x;
}
public static void printWeights(double[] a) {
System.out.println(Arrays.toString(a));
}
public static void main(String[] args) {
if (args.length < 3) {
System.err.println("Usage: JavaHdfsLR <master> <file> <iters>");
System.exit(1);
}
JavaSparkContext sc = new JavaSparkContext(args[0], "JavaHdfsLR",
System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaHdfsLR.class));
JavaRDD<String> lines = sc.textFile(args[1]);
JavaRDD<DataPoint> points = lines.map(new ParsePoint()).cache();
int ITERATIONS = Integer.parseInt(args[2]);
// Initialize w to a random value
double[] w = new double[D];
for (int i = 0; i < D; i++) {
w[i] = 2 * rand.nextDouble() - 1;
}
System.out.print("Initial w: ");
printWeights(w);
for (int i = 1; i <= ITERATIONS; i++) {
System.out.println("On iteration " + i);
double[] gradient = points.map(
new ComputeGradient(w)
).reduce(new VectorSum());
for (int j = 0; j < D; j++) {
w[j] -= gradient[j];
}
}
System.out.print("Final w: ");
printWeights(w);
System.exit(0);
}
}