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Commit 64f1a90

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Added sample code for toy challenge
1 parent e66c5d5 commit 64f1a90

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15 files changed

+168
-14
lines changed

15 files changed

+168
-14
lines changed

‎code/.gitignore

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.classpath
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.project
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/bin/
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sources.txt

‎code/Dockerfile

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FROM python
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FROM openjdk:8-jdk-alpine3.9
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COPY . .
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# Create a /work directory within the container, copy everything from the
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# build directory and switch there.
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RUN mkdir /work
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COPY . /work
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WORKDIR /work
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RUN pip3 install -r requirements.txt
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# Make sure your build script is executable. Generally, always check that
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# all your scripts are made executable, forgetting this is a common error.
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# Another frequent error is having Windows style line endings in .sh files.
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RUN chmod +x build.sh
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RUN ./build.sh
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ENTRYPOINT [ "python3", "helloworld.py" ]
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# Again, test and train scripts should be executable within the container.
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RUN chmod +x test.sh
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RUN chmod +x train.sh
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# Don't add any CMD or ENTRYPOINT!

‎code/build.sh

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rm -rf ./bin
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mkdir ./bin
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find -name "*.java" > sources.txt
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javac -d ./bin @sources.txt

‎code/data/testing.txt

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name,height
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terry,2.50
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tom,1.80
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jerry,1.05

‎code/data/training.txt

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name,height,weight
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joe,1.80,75
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jack,2.10,99
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jane,1.65,56
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bill,1.70,110
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grace,1.75,60

‎code/helloworld.py

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‎code/model/dummy-model.txt

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58.39999999999997
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-25.119999999999948

‎code/requirements.txt

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This file was deleted.

‎code/src/sample/submission/Tester.java

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package sample.submission;
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import java.io.File;
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import java.io.FileReader;
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import java.io.LineNumberReader;
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import java.io.PrintWriter;
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/*
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* A simple solution, works by doing linear regression with parameters
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* calculated previously by the Trainer.
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*/
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public class Tester {
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public static void main(String[] args) throws Exception {
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// arg0: test file path
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// arg1: output csv path
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String inPath = null;
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String outPath = null;
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String modelPath = null;
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if (args.length != 3) {
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System.out.println("Usage: java sample.submission.Tester <test-path> <output-path> <model-path>");
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System.exit(1);
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}
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else {
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inPath = args[0];
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outPath = args[1];
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modelPath = args[2];
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}
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LineNumberReader lnr = new LineNumberReader(new FileReader(modelPath));
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double a = Double.parseDouble(lnr.readLine());
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double b = Double.parseDouble(lnr.readLine());
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lnr.close();
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PrintWriter out = new PrintWriter(new File(outPath));
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lnr = new LineNumberReader(new FileReader(inPath));
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while (true) {
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String line = lnr.readLine();
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if (line == null) break;
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// name,height
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// joe,1.80
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line = line.trim();
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if (line.isEmpty() || line.contains("name")) continue;
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String[] parts = line.split(",");
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String name = parts[0];
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double h = Double.parseDouble(parts[1]);
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double w = a * h + b;
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out.println(name + "," + w);
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}
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lnr.close();
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out.close();
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}
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}

‎code/src/sample/submission/Trainer.java

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package sample.submission;
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import java.io.File;
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import java.io.FileReader;
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import java.io.LineNumberReader;
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import java.io.PrintWriter;
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import java.util.List;
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import java.util.Vector;
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/*
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* A simple trainer, calculates linear regression parameters from the
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* training data and saves it as 'model'.
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*/
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public class Trainer {
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public static void main(String[] args) throws Exception {
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// arg0: truth file
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// arg1: output 'model' file
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String trainPath = null;
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String outPath = null;
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if (args.length != 2) {
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System.out.println("Usage: java sample.submission.Trainer <truth-path> <model-path>");
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System.exit(1);
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}
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else {
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trainPath = args[0];
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outPath = args[1];
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}
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List<Double> xs = new Vector<>();
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List<Double> ys = new Vector<>();
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File truthCsv = new File(trainPath);
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LineNumberReader lnr = new LineNumberReader(new FileReader(truthCsv));
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while (true) {
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String line = lnr.readLine();
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if (line == null) break;
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// name,height,weight
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// joe,1.80,75
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line = line.trim();
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if (line.isEmpty() || line.contains("name")) continue;
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String[] parts = line.split(",");
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xs.add(Double.parseDouble(parts[1]));
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ys.add(Double.parseDouble(parts[2]));
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}
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lnr.close();
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int n = xs.size();
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double sumX = 0.0, sumY = 0.0;
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for (int i = 0; i < n; i++) {
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double x = xs.get(i);
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double y = ys.get(i);
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sumX += x;
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sumY += y;
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}
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double avgX = sumX / n;
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double avgY = sumY / n;
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double xx = 0.0, xy = 0.0;
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for (int i = 0; i < n; i++) {
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double x = xs.get(i);
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double y = ys.get(i);
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xx += (x - avgX) * (x - avgX);
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xy += (x - avgX) * (y - avgY);
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}
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double a = xy / xx;
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double b = avgY - a * avgX;
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PrintWriter out = new PrintWriter(new File(outPath));
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out.println("" + a);
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out.println("" + b);
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out.close();
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System.out.println("Training finished. Model written to " + outPath);
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}
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}

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