Java tutorial
package com.technobium;/* * 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. */ import com.google.common.base.Charsets; import com.google.common.base.Splitter; import com.google.common.collect.Iterables; import com.google.common.collect.Lists; import com.google.common.io.Closer; import com.google.common.io.Resources; import org.apache.mahout.classifier.sgd.L2; import org.apache.mahout.classifier.sgd.OnlineLogisticRegression; import org.apache.mahout.classifier.sgd.PolymorphicWritable; import org.apache.mahout.common.RandomUtils; import org.apache.mahout.math.DenseVector; import org.apache.mahout.math.Vector; import org.apache.mahout.vectorizer.encoders.Dictionary; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import java.io.DataOutputStream; import java.io.File; import java.io.FileOutputStream; import java.util.Collections; import java.util.List; import java.util.Random; //? //?OnlineLogisticRegression?????OnlineLogisticRegression //Main.java????OnlineLogisticRegression? public final class MultinomialLogisticRegression { private static final Logger logger = LoggerFactory.getLogger(MultinomialLogisticRegression.class); public static void main(String[] args) throws Exception { // this test trains a 3-way classifier on the famous Iris dataset. // a similar exercise can be accomplished in R using this code: // library(nnet) // correct = rep(0,100) // for (j in 1:100) { // i = order(runif(150)) // train = iris[i[1:100],] // test = iris[i[101:150],] // m = multinom(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, train) // correct[j] = mean(predict(m, newdata=test) == test$Species) // } // hist(correct) // // Note that depending on the training/test split, performance can be better or worse. // There is about a 5% chance of getting accuracy < 90% and about 20% chance of getting accuracy // of 100% // // This test uses a deterministic split that is neither outstandingly good nor bad RandomUtils.useTestSeed(); Splitter onComma = Splitter.on(","); // read the data List<String> raw = Resources.readLines(Resources.getResource("iris.csv"), Charsets.UTF_8); // holds features List<Vector> data = Lists.newArrayList(); // holds target variable List<Integer> target = Lists.newArrayList(); // for decoding target values Dictionary dict = new Dictionary(); // for permuting data later List<Integer> order = Lists.newArrayList(); for (String line : raw.subList(1, raw.size())) { // order gets a list of indexes order.add(order.size()); // parse the predictor variables Vector v = new DenseVector(5); v.set(0, 1); int i = 1; Iterable<String> values = onComma.split(line); for (String value : Iterables.limit(values, 4)) { v.set(i++, Double.parseDouble(value)); } data.add(v); // and the target target.add(dict.intern(Iterables.get(values, 4))); } // randomize the order ... original data has each species all together // note that this randomization is deterministic Random random = RandomUtils.getRandom(); Collections.shuffle(order, random); // select training and test data List<Integer> train = order.subList(0, 100); List<Integer> test = order.subList(100, 150); logger.warn("Training set = {}", train); logger.warn("Test set = {}", test); // now train many times and collect information on accuracy each time int[] correct = new int[test.size() + 1]; for (int run = 0; run < 200; run++) { OnlineLogisticRegression lr = new OnlineLogisticRegression(3, 5, new L2(1)); // 30 training passes should converge to > 95% accuracy nearly always but never to 100% for (int pass = 0; pass < 30; pass++) { Collections.shuffle(train, random); for (int k : train) { lr.train(target.get(k), data.get(k)); } } // check the accuracy on held out data int x = 0; int[] count = new int[3]; for (Integer k : test) { Vector vt = lr.classifyFull(data.get(k)); int r = vt.maxValueIndex(); count[r]++; x += r == target.get(k) ? 1 : 0; } correct[x]++; if (run == 199) { Vector v = new DenseVector(5); v.set(0, 1); int i = 1; Iterable<String> values = onComma.split("6.0,2.7,5.1,1.6,versicolor"); for (String value : Iterables.limit(values, 4)) { v.set(i++, Double.parseDouble(value)); } Vector vt = lr.classifyFull(v); for (String value : dict.values()) { System.out.println("target:" + value); } int t = dict.intern(Iterables.get(values, 4)); int r = vt.maxValueIndex(); boolean flag = r == t; lr.close(); Closer closer = Closer.create(); try { FileOutputStream byteArrayOutputStream = closer .register(new FileOutputStream(new File("model.txt"))); DataOutputStream dataOutputStream = closer .register(new DataOutputStream(byteArrayOutputStream)); PolymorphicWritable.write(dataOutputStream, lr); } finally { closer.close(); } } } // verify we never saw worse than 95% correct, for (int i = 0; i < Math.floor(0.95 * test.size()); i++) { System.out.println(String.format("%d trials had unacceptable accuracy of only %.0f%%: ", correct[i], 100.0 * i / test.size())); } // nor perfect System.out.println(String.format("%d trials had unrealistic accuracy of 100%%", correct[test.size() - 1])); } }