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Commit b59e8a16 authored by René Schöne's avatar René Schöne
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WIP: Begin to integrate new datasets for learner.

- LearnerScenarioDefinition: Add csv-format, output-mappings and base-location (definition- and data-files are now relative paths)
- LearnerSettings: Merge columns (now stored as "kind" in column), verbosity of training
- LearnerTest:
  - Ignore long-running and failing tests, add new tests for datasets
  - Reuse item names from scenario settings
  - Support csv-format
parent 4c6d0d6a
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with 266 additions and 146 deletions
......@@ -16,9 +16,12 @@
"targetItemNames": [
"activity"
],
"definitionFile": "src/test/resources/activity_definition.json",
"nonTrivialOutputMappings": {
"labels": "activity"
},
"definitionFile": "./activity_definition.json",
"dataFiles": [
"src/test/resources/activity_data.csv"
"./activity_data.csv"
],
"saveModels": false
}
......@@ -6,9 +6,9 @@
"targetItemNames": [
"iris1_item"
],
"definitionFile": "src/test/resources/preference_definition.json",
"definitionFile": "./preference_definition.json",
"dataFiles": [
"src/test/resources/preference_data.csv"
"./preference_data.csv"
],
"saveModels": false
}
......@@ -16,10 +16,13 @@
"targetItemNames": [
"activity"
],
"definitionFile": "src/test/resources/activity_definition.json",
"nonTrivialOutputMappings": {
"labels": "activity"
},
"definitionFile": "./activity_definition.json",
"dataFiles": [
"src/test/resources/activity_network.eg",
"src/test/resources/activity_normalizer.json"
"./activity_network.eg",
"./activity_normalizer.json"
],
"kind": "loaded"
}
......@@ -6,10 +6,10 @@
"targetItemNames": [
"iris1_item"
],
"definitionFile": "src/test/resources/preference_definition.json",
"definitionFile": "./preference_definition.json",
"dataFiles": [
"src/test/resources/preference_network.eg",
"src/test/resources/preference_normalizer.json"
"./preference_network.eg",
"./preference_normalizer.json"
],
"kind": "loaded"
}
{
"name": "preference",
"inputColumns": [
{ "name": "activity", "type":"nominal" },
{ "name": "w_brightness", "type": "nominal" }
],
"targetColumns": [
{ "name": "label1", "type": "continuous" },
{ "name": "label2", "type": "continuous" }
],
"initialDataFile": "src/test/resources/preference_data.csv"
"columns": [
{ "kind": "input", "name": "activity", "type":"nominal" },
{ "kind": "input", "name": "w_brightness", "type": "nominal" },
{ "kind": "target", "name": "label1", "type": "continuous" },
{ "kind": "target", "name": "label2", "type": "continuous" }
]
}
......@@ -19,8 +19,8 @@ class LearnerSubjectUnderTest {
Root root;
private MachineLearningHandlerFactoryImpl factory;
void init() {
root = LearnerTestUtils.createKnowledgeBase();
void init(LearnerTestSettings settings) {
root = LearnerTestUtils.createKnowledgeBase(settings);
factory = new MachineLearningHandlerFactoryImpl();
factory.setKnowledgeBaseRoot(root);
}
......
......@@ -29,6 +29,9 @@ public class LearnerTest {
private static URL PREFERENCE_CONFIG;
private static URL LOADED_PREFERENCE_CONFIG;
private static URL PREFERENCE_DATA;
private static URL OCT_ACTIVITY_CONFIG;
private static URL OCT_LOADED_ACTIVITY_CONFIG;
private static URL OCT_ACTIVITY_DATA;
private LearnerSubjectUnderTest sut;
@BeforeClass
......@@ -39,6 +42,10 @@ public class LearnerTest {
PREFERENCE_CONFIG = resolveFromBaseURL("learner_preferences_brightness_iris.json");
LOADED_PREFERENCE_CONFIG = resolveFromBaseURL("loaded_learner_preferences_brightness_iris.json");
PREFERENCE_DATA = resolveFromBaseURL("preference_data.csv");
OCT_ACTIVITY_CONFIG = resolveFromBaseURL("2019-oct-28-learner.json");
OCT_LOADED_ACTIVITY_CONFIG = resolveFromBaseURL("2019-oct-28-loaded_learner.json");
OCT_ACTIVITY_DATA = resolveFromBaseURL("activity_data/28_08_2019_H14_14/result_all_items_EVERYTHING.csv");
}
private static URL resolveFromBaseURL(String filename) throws MalformedURLException {
......@@ -48,7 +55,6 @@ public class LearnerTest {
@Before
public void initLearner() {
sut = new LearnerSubjectUnderTest();
sut.init();
}
@Test
......@@ -56,6 +62,7 @@ public class LearnerTest {
LearnerTestUtils.testLearner(sut, settingsActivities());
}
@Ignore
@Test
public void testPreferences() throws IOException {
LearnerTestUtils.testLearner(sut, settingsPreferences());
......@@ -73,11 +80,27 @@ public class LearnerTest {
.setConfigURL(LOADED_PREFERENCE_CONFIG));
}
@Ignore // takes longer than 10min
@Test
public void testOctoberActivities() throws IOException {
LearnerTestUtils.testLearner(sut, settingsActivities()
.setConfigURL(OCT_ACTIVITY_CONFIG)
.setDataURL(OCT_ACTIVITY_DATA));
}
@Ignore // not working currently
@Test
public void testLoadedOctoberActivities() throws IOException {
LearnerTestUtils.testLearner(sut, settingsActivities()
.setConfigURL(OCT_LOADED_ACTIVITY_CONFIG)
.setDataURL(OCT_ACTIVITY_DATA)
.setVerbose(true));
}
private LearnerTestSettings settingsActivities() throws IOException {
return new LearnerTestSettings()
.setConfigURL(ACTIVITY_CONFIG)
.setDataURL(ACTIVITY_DATA)
.setExpectedOutput(line -> line[12])
.setOutputItemProvider(() -> sut.root.getSmartHomeEntityModel().getActivityItem())
.setStateOfOutputItem(item -> sut.root.currentActivityName())
.setFactoryTarget(ACTIVITY_RECOGNITION)
......
......@@ -12,19 +12,22 @@ public interface LearnerTestConstants {
double MAX_COLOR_DIFFERENCE = 0.2;
/** Weights for difference (in order: Hue, Saturation, Brightness) when comparing colors */
double[] COLOR_WEIGHTS = new double[]{0.8/360, 0.1/100, 0.1/100};
/** Names of item names for activity recognition, in test data */
String[] ACTIVITY_INPUT_ITEM_NAMES = new String[]{"m_accel_x", "m_accel_y", "m_accel_z", "m_rotation_x", "m_rotation_y",
"m_rotation_z", "w_accel_x", "w_accel_y", "w_accel_z", "w_rotation_x", "w_rotation_y", "w_rotation_z"};
/** Names of item names for preference learning, in test data */
String[] PREFERENCE_INPUT_ITEM_NAMES = new String[]{"activity", "w_brightness"};
/** Name of the item which is targeted by preference learning, in test data */
String PREFERENCE_OUTPUT_ITEM_NAME = "iris1_item";
/** Labels of activities, in test data */
String[] ACTIVITY_NAMES = new String[]{"working",
String[] ACTIVITY_NAMES = new String[]{
"working",
"walking",
"dancing",
"lying",
"getting up",
"reading"
"reading",
"DoorClosedPIn",
"DoorClosedPOut",
"DoorOpenedPIn",
"DoorOpenedPOut",
"Reading",
"TVWatching",
"Working",
};
}
......@@ -24,17 +24,18 @@ import java.util.function.Supplier;
class LearnerTestSettings {
private URL configURL;
private URL dataURL;
private Function<String[], String> expectedOutput;
private Function<List<String>, String> expectedOutput = targetValues -> targetValues.get(0);
private final Map<String, BiConsumer<Item, String>> specialInputHandler = new HashMap<>();
private Supplier<Item> outputItemProvider;
private Function<Item, String> stateOfOutputItem;
private CheckUpdate checkUpdate = String::equals;
private MachineLearningHandlerFactory.MachineLearningHandlerFactoryTarget factoryTarget;
private boolean singleUpdateList;
private boolean verbose = false;
@Getter(AccessLevel.NONE)
@Setter(AccessLevel.NONE)
private transient LearnerScenarioDefinition settings;
private transient LearnerScenarioDefinition scenarioSettings;
@SuppressWarnings("SameParameterValue")
LearnerTestSettings putSpecialInputHandler(String itemName, BiConsumer<Item, String> handler) {
......@@ -43,13 +44,12 @@ class LearnerTestSettings {
}
LearnerTestSettings setConfigURL(URL configURL) throws IOException {
settings = LearnerScenarioDefinition.loadFrom(configURL);
scenarioSettings = LearnerScenarioDefinition.loadFrom(configURL);
this.configURL = configURL;
return this;
}
List<String> getInputItemNames() {
return settings.relevantItemNames;
LearnerScenarioDefinition getScenarioSettings() {
return scenarioSettings;
}
}
package de.tudresden.inf.st.eraser.feedbackloop.learner_backup;
import com.opencsv.CSVParserBuilder;
import com.opencsv.CSVReader;
import com.opencsv.CSVReaderBuilder;
import de.tudresden.inf.st.eraser.feedbackloop.learner_backup.data.LearnerScenarioDefinition;
import de.tudresden.inf.st.eraser.feedbackloop.learner_backup.data.LearnerSettings;
import de.tudresden.inf.st.eraser.feedbackloop.learner_backup.data.SimpleColumnDefinition;
import de.tudresden.inf.st.eraser.jastadd.model.*;
import de.tudresden.inf.st.eraser.util.ParserUtils;
import org.apache.logging.log4j.LogManager;
import org.apache.logging.log4j.Logger;
import org.junit.Test;
import org.encog.ml.data.versatile.columns.ColumnType;
import java.io.IOException;
import java.io.InputStream;
import java.io.InputStreamReader;
import java.io.Reader;
import java.util.*;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.stream.Stream;
import static de.tudresden.inf.st.eraser.feedbackloop.learner_backup.LearnerTestConstants.COLOR_WEIGHTS;
import static de.tudresden.inf.st.eraser.feedbackloop.learner_backup.LearnerTestConstants.MAX_COLOR_DIFFERENCE;
import static org.hamcrest.Matchers.greaterThan;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertThat;
import static org.hamcrest.Matchers.*;
import static org.junit.Assert.*;
/**
* Utility methods to keep test code clean.
......@@ -29,24 +35,17 @@ public class LearnerTestUtils {
private static final Logger logger = LogManager.getLogger(LearnerTestUtils.class);
static Root createKnowledgeBase() {
static Root createKnowledgeBase(LearnerTestSettings settings) {
Root result = Root.createEmptyRoot();
Group group = new Group();
result.getSmartHomeEntityModel().addGroup(group);
// init items
Stream.concat(Arrays.stream(LearnerTestConstants.ACTIVITY_INPUT_ITEM_NAMES),
Stream.concat(Arrays.stream(LearnerTestConstants.PREFERENCE_INPUT_ITEM_NAMES), Stream.of(LearnerTestConstants.PREFERENCE_OUTPUT_ITEM_NAME)))
Stream.concat(settings.getScenarioSettings().relevantItemNames.stream(),
settings.getScenarioSettings().targetItemNames.stream())
.distinct().forEach(
itemName -> {
if (itemName.equals("activity")) return;
Item item;
switch(itemName) {
case LearnerTestConstants.PREFERENCE_OUTPUT_ITEM_NAME: item = new ColorItem(); break;
case "w_brightness": item = new StringItem(); break;
default:
item = new NumberItem();
}
item.setID(itemName);
Item item = createItem(itemName);
ParserUtils.createMqttTopic(item, itemName, result);
group.addItem(item);
}
......@@ -58,36 +57,90 @@ public class LearnerTestUtils {
return result;
}
private static Item createItem(String itemName) {
Item item;
if (itemName.contains("OpenClose")) {
// contact item
item = new ContactItem();
} else if (itemName.contains("Fibaro")) {
// boolean item
item = new SwitchItem();
} else {
switch (itemName) {
case "work_device_online_state":
item = new SwitchItem();
break;
case LearnerTestConstants.PREFERENCE_OUTPUT_ITEM_NAME:
item = new ColorItem();
break;
case "w_brightness":
item = new StringItem();
break;
default: item = new NumberItem();
}
}
item.setID(itemName);
return item;
}
static void testLearner(LearnerSubjectUnderTest sut, LearnerTestSettings settings) {
sut.init(settings);
// maybe use factory.createModel() here instead
// go through same csv as for training and test some of the values
int correct = 0, wrong = 0;
try(InputStream is = settings.getDataURL().openStream();
Reader reader = new InputStreamReader(is);
CSVReader csvreader = new CSVReader(reader)) {
CSVReader csvreader = createCSVReader(reader, settings)) {
sut.initFor(settings.getFactoryTarget(), settings.getConfigURL());
int index = 0;
LearnerScenarioDefinition scenarioSettings = settings.getScenarioSettings();
LearnerSettings definition = LearnerSettings.loadFrom(scenarioSettings.getDefinitionFileAsURL());
List<String> targetValues;
for (String[] line : csvreader) {
// only check every 10th line, push an update for every input column
if (++index % 10 == 0) {
// only check every 10th line, push an update for every 12 input columns
List<Item> itemsToUpdate = new ArrayList<>(settings.getInputItemNames().size());
for (int i = 0; i < settings.getInputItemNames().size(); i++) {
String itemName = settings.getInputItemNames().get(i);
// Attention: Not every column might be relevant
targetValues = new ArrayList<>();
int lineSize = line.length;
int inputSize = scenarioSettings.relevantItemNames.size();
List<Item> itemsToUpdate = new ArrayList<>(inputSize);
for (int i = 0; i < lineSize; i++) {
SimpleColumnDefinition column = definition.columns.get(i);
switch (column.kind) {
case input:
// do nothing
break;
case target:
targetValues.add(line[i]);
continue;
case ignored:
continue;
}
if (column.type == ColumnType.ignore) {
continue;
}
// use itemName == name of column (or a non-trivial mapping, if any)
String itemName = scenarioSettings.nonTrivialOutputMappings.getOrDefault(column.name, column.name);
Item item = sut.root.getSmartHomeEntityModel().resolveItem(itemName)
.orElseThrow(() -> new AssertionError("Item " + itemName + " not found"));
if (settings.getSpecialInputHandler().containsKey(itemName)) {
if (settings.isVerbose()) {
logger.debug("Setting {} {} using special handler and value '{}' (column {})",
item, item.getID(), line[i], i);
}
settings.getSpecialInputHandler().get(itemName).accept(item, line[i]);
} else {
if (settings.isVerbose()) {
logger.debug("Setting {} {} using '{}' (column {})", item, item.getID(), line[i], i);
}
item.setStateFromString(line[i]);
}
if (settings.isSingleUpdateList()) {
itemsToUpdate.add(item);
} else {
sut.encoder.newData(Collections.singletonList(item));
}
}
if (settings.isSingleUpdateList()) {
sut.encoder.newData(itemsToUpdate);
} else {
itemsToUpdate.forEach(item -> sut.encoder.newData(Collections.singletonList(item)));
}
MachineLearningResult result = sut.decoder.classify();
// check if only one item is to be updated
......@@ -97,16 +150,19 @@ public class LearnerTestUtils {
assertEquals("Output item not to be updated!", settings.getOutputItemProvider().get(), update.getItem());
update.apply();
// check if the correct new state was set
String expected = settings.getExpectedOutput().apply(line);
assertThat("No target values found in this row!", targetValues, not(empty()));
String expected = settings.getExpectedOutput().apply(targetValues);
String actual = settings.getStateOfOutputItem().apply(update.getItem());
if (settings.getCheckUpdate().assertEquals(expected, actual)) {
correct++;
} else {
wrong++;
if (settings.isVerbose()) {
logger.debug("Result not equal, expected '{}' but was '{}'", expected, actual);
}
}
}
} // end if index % 10 == 0
} // end for
} catch (IOException | ClassNotFoundException e) {
throw new AssertionError(e);
} finally {
......@@ -118,14 +174,28 @@ public class LearnerTestUtils {
assertThat(accuracy, greaterThan(LearnerTestConstants.MIN_ACCURACY));
}
private static CSVReader createCSVReader(Reader reader, LearnerTestSettings settings) {
char separator;
switch(settings.getScenarioSettings().csvFormat) {
case "DECIMAL_POINT": separator=','; break;
case "DECIMAL_COMMA": separator=';'; break;
default:
logger.warn("Unknown CSV format, using default comma as separator");
separator=',';
}
return new CSVReaderBuilder(reader)
.withCSVParser(new CSVParserBuilder().withSeparator(separator).build())
.build();
}
@FunctionalInterface
public interface CheckUpdate {
boolean assertEquals(String expected, String actual);
}
static String decodeOutput(String[] line) {
int color = Integer.parseInt(line[2]);
int brightness = Integer.parseInt(line[3]);
static String decodeOutput(List<String> targetValues) {
int color = Integer.parseInt(targetValues.get(0));
int brightness = Integer.parseInt(targetValues.get(1));
return TupleHSB.of(color, 100, brightness).toString();
}
......@@ -153,76 +223,4 @@ public class LearnerTestUtils {
// diffHue, diffSaturation, diffBrightness, total, MAX_COLOR_DIFFERENCE);
return total < MAX_COLOR_DIFFERENCE;
}
@Test
public void testColorSimilar() {
Map<String, TupleHSB> colors = new HashMap<>();
// reddish target colors
colors.put("pink", TupleHSB.of(350, 100, 82));
colors.put("orangeRed", TupleHSB.of(16, 100, 45));
colors.put("lightPink", TupleHSB.of(351, 100, 80));
colors.put("darkSalmon", TupleHSB.of(15, 71, 67));
colors.put("lightCoral", TupleHSB.of(0, 78, 63));
colors.put("darkRed", TupleHSB.of(0, 100, 16));
colors.put("indianRed", TupleHSB.of(0, 53, 49));
colors.put("lavenderBlush", TupleHSB.of(340, 100, 95));
colors.put("lavender", TupleHSB.of(240, 66, 90));
String[] targetColors = new String[]{"pink", "orangeRed", "lightPink", "darkSalmon", "lightCoral",
"darkRed", "indianRed", "lavenderBlush", "lavender"};
// reference colors
colors.put("blue", TupleHSB.of(240, 100, 11));
colors.put("blueViolet", TupleHSB.of(271, 75, 36));
colors.put("magenta", TupleHSB.of(300, 100, 41));
colors.put("purple", TupleHSB.of(300, 100, 20));
colors.put("red", TupleHSB.of(0, 100, 29));
colors.put("tomato", TupleHSB.of(9, 100, 55));
colors.put("orange", TupleHSB.of(39, 100, 67));
colors.put("yellow", TupleHSB.of(60, 100, 88));
colors.put("yellowGreen", TupleHSB.of(80, 60, 67));
colors.put("green", TupleHSB.of(120, 100, 29));
colors.put("springGreen", TupleHSB.of(150, 100, 64));
colors.put("cyan", TupleHSB.of(180, 100, 69));
colors.put("ivory", TupleHSB.of(60, 100, 98));
String[] referenceColors = new String[]{"blue", "blueViolet", "magenta", "purple", "red", "tomato",
"orange", "yellow", "yellowGreen", "green", "springGreen", "cyan", "ivory"};
/* Code to help producing similarity matrix */
// for (String target : targetColors) {
// String tmp = "";
// for (String reference : referenceColors) {
// tmp += assertColorSimilar(colors, target, reference) ? "x" : " ";
// tmp += ",";
// }
// System.out.println( "***" + target + ": " + tmp);
// }
String[] similarityMatrix = new String[]{
"blue, blueViolet, magenta, purple, red, tomato, orange, yellow, yellowGreen, green, springGreen, cyan, ivory", // <- reference colors
" , , x , x , x , x , x , x , , , , , x ", // pink
" , , x , x , x , x , x , x , , , , , x ", // orangeRed
" , , x , x , x , x , x , x , , , , , x ", // lightPink
" , , , , x , x , x , x , x , , , , x ", // darkSalmon
" , , x , x , x , x , x , x , x , , , , x ", // lightCoral
" , , x , x , x , x , x , , , , , , ", // darkRed
" , , x , , x , x , x , , , , , , ", // indianRed
" , , x , x , x , x , x , x , , , , , x ", // lavenderBlush
" x , x , , , , , , , , , , x , "}; // lavender
for (int targetIndex = 0; targetIndex < targetColors.length; targetIndex++) {
String target = targetColors[targetIndex];
String[] expectedValues = similarityMatrix[targetIndex + 1].split(",");
for (int referenceIndex = 0; referenceIndex < referenceColors.length; referenceIndex++) {
String reference = referenceColors[referenceIndex];
boolean expectedToBeSimilar = expectedValues[referenceIndex].contains("x");
String message = String.format("%s iss%s expected to be similar to %s, but %s!",
target, expectedToBeSimilar ? "" : " not", reference, expectedToBeSimilar ? "differs" : "it was");
assertEquals(message, expectedToBeSimilar,
colorSimilar(colors.get(reference).toString(), colors.get(target).toString()));
}
}
}
}
package de.tudresden.inf.st.eraser.feedbackloop.learner_backup;
import de.tudresden.inf.st.eraser.jastadd.model.TupleHSB;
import org.junit.Test;
import java.util.HashMap;
import java.util.Map;
import static org.junit.Assert.assertEquals;
/**
* Tests for the utility methods.
*
* @author rschoene - Initial contribution
*/
public class LearnerTestUtilsTest {
@Test
public void testColorSimilar() {
Map<String, TupleHSB> colors = new HashMap<>();
// reddish target colors
colors.put("pink", TupleHSB.of(350, 100, 82));
colors.put("orangeRed", TupleHSB.of(16, 100, 45));
colors.put("lightPink", TupleHSB.of(351, 100, 80));
colors.put("darkSalmon", TupleHSB.of(15, 71, 67));
colors.put("lightCoral", TupleHSB.of(0, 78, 63));
colors.put("darkRed", TupleHSB.of(0, 100, 16));
colors.put("indianRed", TupleHSB.of(0, 53, 49));
colors.put("lavenderBlush", TupleHSB.of(340, 100, 95));
colors.put("lavender", TupleHSB.of(240, 66, 90));
String[] targetColors = new String[]{"pink", "orangeRed", "lightPink", "darkSalmon", "lightCoral",
"darkRed", "indianRed", "lavenderBlush", "lavender"};
// reference colors
colors.put("blue", TupleHSB.of(240, 100, 11));
colors.put("blueViolet", TupleHSB.of(271, 75, 36));
colors.put("magenta", TupleHSB.of(300, 100, 41));
colors.put("purple", TupleHSB.of(300, 100, 20));
colors.put("red", TupleHSB.of(0, 100, 29));
colors.put("tomato", TupleHSB.of(9, 100, 55));
colors.put("orange", TupleHSB.of(39, 100, 67));
colors.put("yellow", TupleHSB.of(60, 100, 88));
colors.put("yellowGreen", TupleHSB.of(80, 60, 67));
colors.put("green", TupleHSB.of(120, 100, 29));
colors.put("springGreen", TupleHSB.of(150, 100, 64));
colors.put("cyan", TupleHSB.of(180, 100, 69));
colors.put("ivory", TupleHSB.of(60, 100, 98));
String[] referenceColors = new String[]{"blue", "blueViolet", "magenta", "purple", "red", "tomato",
"orange", "yellow", "yellowGreen", "green", "springGreen", "cyan", "ivory"};
/* Code to help producing similarity matrix */
// for (String target : targetColors) {
// String tmp = "";
// for (String reference : referenceColors) {
// tmp += assertColorSimilar(colors, target, reference) ? "x" : " ";
// tmp += ",";
// }
// System.out.println( "***" + target + ": " + tmp);
// }
String[] similarityMatrix = new String[]{
"blue, blueViolet, magenta, purple, red, tomato, orange, yellow, yellowGreen, green, springGreen, cyan, ivory", // <- reference colors
" , , x , x , x , x , x , x , , , , , x ", // pink
" , , x , x , x , x , x , x , , , , , x ", // orangeRed
" , , x , x , x , x , x , x , , , , , x ", // lightPink
" , , , , x , x , x , x , x , , , , x ", // darkSalmon
" , , x , x , x , x , x , x , x , , , , x ", // lightCoral
" , , x , x , x , x , x , , , , , , ", // darkRed
" , , x , , x , x , x , , , , , , ", // indianRed
" , , x , x , x , x , x , x , , , , , x ", // lavenderBlush
" x , x , , , , , , , , , , x , "}; // lavender
for (int targetIndex = 0; targetIndex < targetColors.length; targetIndex++) {
String target = targetColors[targetIndex];
String[] expectedValues = similarityMatrix[targetIndex + 1].split(",");
for (int referenceIndex = 0; referenceIndex < referenceColors.length; referenceIndex++) {
String reference = referenceColors[referenceIndex];
boolean expectedToBeSimilar = expectedValues[referenceIndex].contains("x");
String message = String.format("%s iss%s expected to be similar to %s, but %s!",
target, expectedToBeSimilar ? "" : " not", reference, expectedToBeSimilar ? "differs" : "it was");
assertEquals(message, expectedToBeSimilar,
LearnerTestUtils.colorSimilar(colors.get(reference).toString(), colors.get(target).toString()));
}
}
}
}
../../../src/main/resources/2019-oct-28-activity_definition.json
\ No newline at end of file
../../../src/main/resources/2019-oct-28-activity_network.eg
\ No newline at end of file
../../../src/main/resources/2019-oct-28-activity_normalizer.json
\ No newline at end of file
../../../src/main/resources/2019-oct-28-learner.json
\ No newline at end of file
../../../src/main/resources/2019-oct-28-loaded_learner.json
\ No newline at end of file
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