Added the K-Means related codes.
This commit is contained in:
Ali Dehghani
parent
94522094c1
commit
d72ed9ad11
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<?xml version="1.0" encoding="UTF-8"?>
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<project xmlns="http://maven.apache.org/POM/4.0.0"
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xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
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xsi:schemaLocation="http://maven.apache.org/POM/4.0.0
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http://maven.apache.org/xsd/maven-4.0.0.xsd">
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<parent>
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<groupId>com.baeldung</groupId>
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<artifactId>parent-modules</artifactId>
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<version>1.0.0-SNAPSHOT</version>
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</parent>
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<modelVersion>4.0.0</modelVersion>
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<artifactId>machine-learning</artifactId>
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<version>0.0.1-SNAPSHOT</version>
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<name>Machine Learning</name>
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<description>Host for all Machine Learning Algorithms</description>
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<dependencies>
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<dependency>
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<groupId>com.squareup.retrofit2</groupId>
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<artifactId>retrofit</artifactId>
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<version>${retrofit.version}</version>
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</dependency>
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<dependency>
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<groupId>com.squareup.retrofit2</groupId>
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<artifactId>converter-jackson</artifactId>
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<version>${retrofit.version}</version>
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</dependency>
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</dependencies>
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<properties>
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<retrofit.version>2.6.0</retrofit.version>
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</properties>
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</project>
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package com.baeldung.ml.kmeans;
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import java.util.Map;
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import java.util.Objects;
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/**
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* Encapsulates all coordinates for a particular cluster centroid.
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*/
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public class Centroid {
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/**
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* The centroid coordinates.
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*/
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private final Map<String, Double> coordinates;
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public Centroid(Map<String, Double> coordinates) {
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this.coordinates = coordinates;
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}
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public Map<String, Double> getCoordinates() {
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return coordinates;
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}
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@Override
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public boolean equals(Object o) {
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if (this == o) return true;
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if (o == null || getClass() != o.getClass()) return false;
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Centroid centroid = (Centroid) o;
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return Objects.equals(getCoordinates(), centroid.getCoordinates());
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}
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@Override
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public int hashCode() {
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return Objects.hash(getCoordinates());
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}
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@Override
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public String toString() {
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return "Centroid " + coordinates;
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}
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}
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package com.baeldung.ml.kmeans;
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import java.util.Map;
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/**
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* Defines a contract to calculate distance between two feature vectors. The less the
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* calculated distance, the more two items are similar to each other.
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*/
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public interface Distance {
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/**
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* Calculates the distance between two feature vectors.
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*
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* @param f1 The first set of features.
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* @param f2 The second set of features.
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* @return Calculated distance.
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* @throws IllegalArgumentException If the given feature vectors are invalid.
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*/
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double calculate(Map<String, Double> f1, Map<String, Double> f2);
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}
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package com.baeldung.ml.kmeans;
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import java.util.List;
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import java.util.Map;
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/**
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* Encapsulates methods to calculates errors between centroid and the cluster members.
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*/
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public class Errors {
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public static double sse(Map<Centroid, List<Record>> clustered, Distance distance) {
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double sum = 0;
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for (Map.Entry<Centroid, List<Record>> entry : clustered.entrySet()) {
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Centroid centroid = entry.getKey();
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for (Record record : entry.getValue()) {
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double d = distance.calculate(centroid.getCoordinates(), record.getFeatures());
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sum += Math.pow(d, 2);
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}
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}
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return sum;
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}
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}
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package com.baeldung.ml.kmeans;
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import java.util.Map;
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/**
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* Calculates the distance between two items using the Euclidean formula.
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*/
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public class EuclideanDistance implements Distance {
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@Override
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public double calculate(Map<String, Double> f1, Map<String, Double> f2) {
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if (f1 == null || f2 == null)
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throw new IllegalArgumentException("Feature vectors can't be null");
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double sum = 0;
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for (String key : f1.keySet()) {
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Double v1 = f1.get(key);
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Double v2 = f2.get(key);
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if (v1 != null && v2 != null) sum += Math.pow(v1 - v2, 2);
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}
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return Math.sqrt(sum);
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}
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}
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package com.baeldung.ml.kmeans;
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import java.util.*;
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import static java.util.stream.Collectors.toList;
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import static java.util.stream.Collectors.toSet;
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/**
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* Encapsulates an implementation of KMeans clustering algorithm.
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*
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* @author Ali Dehghani
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*/
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public class KMeans {
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private KMeans() {
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throw new IllegalAccessError("You shouldn't call this constructor");
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}
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/**
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* Will be used to generate random numbers.
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*/
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private static final Random random = new Random();
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/**
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* Performs the K-Means clustering algorithm on the given dataset.
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*
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* @param records The dataset.
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* @param k Number of Clusters.
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* @param distance To calculate the distance between two items.
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* @param maxIterations Upper bound for the number of iterations.
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* @return K clusters along with their features.
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*/
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public static Map<Centroid, List<Record>> fit(List<Record> records, int k, Distance distance, int maxIterations) {
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applyPreconditions(records, k, distance, maxIterations);
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List<Centroid> centroids = randomCentroids(records, k);
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Map<Centroid, List<Record>> clusters = new HashMap<>();
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Map<Centroid, List<Record>> lastState = new HashMap<>();
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// iterate for a pre-defined number of times
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for (int i = 0; i < maxIterations; i++) {
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boolean isLastIteration = i == maxIterations - 1;
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// in each iteration we should find the nearest centroid for each record
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for (Record record : records) {
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Centroid centroid = nearestCentroid(record, centroids, distance);
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assignToCluster(clusters, record, centroid);
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}
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// if the assignment does not change, then the algorithm terminates
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boolean shouldTerminate = isLastIteration || clusters.equals(lastState);
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lastState = clusters;
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if (shouldTerminate) break;
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// at the end of each iteration we should relocate the centroids
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centroids = relocateCentroids(clusters);
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clusters = new HashMap<>();
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}
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return lastState;
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}
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/**
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* Move all cluster centroids to the average of all assigned features.
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*
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* @param clusters The current cluster configuration.
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* @return Collection of new and relocated centroids.
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*/
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private static List<Centroid> relocateCentroids(Map<Centroid, List<Record>> clusters) {
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return clusters.entrySet().stream()
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.map(e -> average(e.getKey(), e.getValue())).collect(toList());
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}
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/**
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* Moves the given centroid to the average position of all assigned features. If
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* the centroid has no feature in its cluster, then there would be no need for a
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* relocation. Otherwise, for each entry we calculate the average of all records
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* first by summing all the entries and then dividing the final summation value by
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* the number of records.
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*
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* @param centroid The centroid to move.
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* @param records The assigned features.
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* @return The moved centroid.
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*/
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private static Centroid average(Centroid centroid, List<Record> records) {
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// if this cluster is empty, then we shouldn't move the centroid
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if (records == null || records.isEmpty()) return centroid;
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// Since some records don't have all possible attributes, we initialize
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// average coordinates equal to current centroid coordinates
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Map<String, Double> average = centroid.getCoordinates();
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// The average function works correctly if we clear all coordinates corresponding
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// to present record attributes
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records.stream().flatMap(e -> e.getFeatures().keySet().stream())
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.forEach(k -> average.put(k, 0.0));
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for (Record record : records) {
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record.getFeatures().forEach(
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(k, v) -> average.compute(k, (k1, currentValue) -> v + currentValue)
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);
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}
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average.forEach((k, v) -> average.put(k, v / records.size()));
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return new Centroid(average);
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}
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/**
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* Assigns a feature vector to the given centroid. If this is the first assignment for this centroid,
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* first we should create the list.
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*
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* @param clusters The current cluster configuration.
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* @param record The feature vector.
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* @param centroid The centroid.
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*/
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private static void assignToCluster(Map<Centroid, List<Record>> clusters,
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Record record, Centroid centroid) {
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clusters.compute(centroid, (key, list) -> {
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if (list == null) {
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list = new ArrayList<>();
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}
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list.add(record);
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return list;
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});
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}
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/**
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* With the help of the given distance calculator, iterates through centroids and finds the
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* nearest one to the given record.
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*
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* @param record The feature vector to find a centroid for.
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* @param centroids Collection of all centroids.
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* @param distance To calculate the distance between two items.
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* @return The nearest centroid to the given feature vector.
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*/
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private static Centroid nearestCentroid(Record record, List<Centroid> centroids,
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Distance distance) {
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double minimumDistance = Double.MAX_VALUE;
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Centroid nearest = null;
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for (Centroid centroid : centroids) {
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double currentDistance = distance.calculate(record.getFeatures(), centroid.getCoordinates());
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if (currentDistance < minimumDistance) {
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minimumDistance = currentDistance;
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nearest = centroid;
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}
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}
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return nearest;
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}
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/**
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* Generates k random centroids. Before kicking-off the centroid generation process,
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* first we calculate the possible value range for each attribute. Then when
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* we're going to generate the centroids, we generate random coordinates in
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* the [min, max] range for each attribute.
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*
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* @param records The dataset which helps to calculate the [min, max] range for
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* each attribute.
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* @param k Number of clusters.
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* @return Collections of randomly generated centroids.
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*/
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private static List<Centroid> randomCentroids(List<Record> records, int k) {
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List<Centroid> centroids = new ArrayList<>();
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Map<String, Double> maxs = new HashMap<>();
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Map<String, Double> mins = new HashMap<>();
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for (Record record : records) {
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record.getFeatures().forEach((key, value) -> {
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// compares the value with the current max and choose the bigger value between them
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maxs.compute(key, (k1, max) -> max == null || value > max ? value : max);
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// compare the value with the current min and choose the smaller value between them
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mins.compute(key, (k1, min) -> min == null || value < min ? value : min);
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});
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}
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Set<String> attributes = records.stream()
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.flatMap(e -> e.getFeatures().keySet().stream()).collect(toSet());
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for (int i = 0; i < k; i++) {
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Map<String, Double> coordinates = new HashMap<>();
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for (String attribute : attributes) {
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double max = maxs.get(attribute);
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double min = mins.get(attribute);
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coordinates.put(attribute, random.nextDouble() * (max - min) + min);
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}
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centroids.add(new Centroid(coordinates));
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}
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return centroids;
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}
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private static void applyPreconditions(List<Record> records, int k,
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Distance distance, int maxIterations) {
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if (records == null || records.isEmpty())
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throw new IllegalArgumentException("The dataset can't be empty");
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if (k <= 1)
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throw new IllegalArgumentException("It doesn't make sense to have less than or equal to 1 cluster");
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if (distance == null)
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throw new IllegalArgumentException("The distance calculator is required");
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if (maxIterations <= 0)
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throw new IllegalArgumentException("Max iterations should be a positive number");
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}
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}
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package com.baeldung.ml.kmeans;
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import com.fasterxml.jackson.databind.ObjectMapper;
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import okhttp3.OkHttpClient;
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import retrofit2.Retrofit;
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import retrofit2.converter.jackson.JacksonConverterFactory;
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import java.io.IOException;
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import java.util.*;
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import java.util.stream.Collectors;
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import static java.util.stream.Collectors.toSet;
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public class LastFm {
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private static OkHttpClient okHttp = new OkHttpClient.Builder()
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.addInterceptor(new LastFmService.Authenticator("put your API key here"))
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.build();
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private static Retrofit retrofit = new Retrofit.Builder().client(okHttp)
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.addConverterFactory(JacksonConverterFactory.create())
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.baseUrl("http://ws.audioscrobbler.com/")
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.build();
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private static LastFmService lastFm = retrofit.create(LastFmService.class);
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private static ObjectMapper mapper = new ObjectMapper();
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public static void main(String[] args) throws IOException {
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List<String> artists = getTop100Artists();
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Set<String> tags = getTop100Tags();
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List<Record> records = datasetWithTaggedArtists(artists, tags);
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Map<Centroid, List<Record>> clusters = KMeans.fit(records, 7, new EuclideanDistance(), 1000);
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// Print the cluster configuration
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clusters.forEach((key, value) -> {
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System.out.println("------------------------------ CLUSTER -----------------------------------");
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System.out.println(sortedCentroid(key));
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String members = String.join(", ", value.stream().map(Record::getDescription).collect(toSet()));
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System.out.print(members);
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System.out.println();
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System.out.println();
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});
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Map<String, Object> json = convertToD3CompatibleMap(clusters);
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System.out.println(mapper.writeValueAsString(json));
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}
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private static Map<String, Object> convertToD3CompatibleMap(Map<Centroid, List<Record>> clusters) {
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Map<String, Object> json = new HashMap<>();
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json.put("name", "Musicians");
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List<Map<String, Object>> children = new ArrayList<>();
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clusters.forEach((key, value) -> {
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Map<String, Object> child = new HashMap<>();
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child.put("name", dominantGenre(sortedCentroid(key)));
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List<Map<String, String>> nested = new ArrayList<>();
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for (Record record : value) {
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nested.add(Collections.singletonMap("name", record.getDescription()));
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}
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child.put("children", nested);
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children.add(child);
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});
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json.put("children", children);
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return json;
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}
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private static String dominantGenre(Centroid centroid) {
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return centroid.getCoordinates().keySet().stream().limit(2).collect(Collectors.joining(", "));
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}
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private static Centroid sortedCentroid(Centroid key) {
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List<Map.Entry<String, Double>> entries = new ArrayList<>(key.getCoordinates().entrySet());
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entries.sort((e1, e2) -> e2.getValue().compareTo(e1.getValue()));
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Map<String, Double> sorted = new LinkedHashMap<>();
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for (Map.Entry<String, Double> entry : entries) {
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sorted.put(entry.getKey(), entry.getValue());
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}
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return new Centroid(sorted);
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}
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private static List<Record> datasetWithTaggedArtists(List<String> artists,
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Set<String> topTags) throws IOException {
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List<Record> records = new ArrayList<>();
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for (String artist : artists) {
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Map<String, Double> tags = lastFm.topTagsFor(artist).execute().body().all();
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// Only keep popular tags.
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tags.entrySet().removeIf(e -> !topTags.contains(e.getKey()));
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records.add(new Record(artist, tags));
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}
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return records;
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}
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private static Set<String> getTop100Tags() throws IOException {
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return lastFm.topTags().execute().body().all();
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}
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private static List<String> getTop100Artists() throws IOException {
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List<String> artists = new ArrayList<>();
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for (int i = 1; i <= 2; i++) {
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artists.addAll(lastFm.topArtists(i).execute().body().all());
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}
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return artists;
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}
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}
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@ -0,0 +1,101 @@
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package com.baeldung.ml.kmeans;
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import java.io.IOException;
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import java.util.Collections;
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import java.util.HashMap;
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import java.util.List;
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import java.util.Map;
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import java.util.Set;
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import java.util.stream.Collectors;
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import okhttp3.HttpUrl;
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import okhttp3.Interceptor;
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import okhttp3.Request;
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import okhttp3.Response;
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import retrofit2.Call;
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import retrofit2.http.GET;
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import retrofit2.http.Query;
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import static java.util.stream.Collectors.toList;
|
||||
|
||||
public interface LastFmService {
|
||||
|
||||
@GET("/2.0/?method=chart.gettopartists&format=json&limit=50")
|
||||
Call<Artists> topArtists(@Query("page") int page);
|
||||
|
||||
@GET("/2.0/?method=artist.gettoptags&format=json&limit=20&autocorrect=1")
|
||||
Call<Tags> topTagsFor(@Query("artist") String artist);
|
||||
|
||||
@GET("/2.0/?method=chart.gettoptags&format=json&limit=100")
|
||||
Call<TopTags> topTags();
|
||||
|
||||
/**
|
||||
* HTTP interceptor to intercept all HTTP requests and add the API key to them.
|
||||
*/
|
||||
class Authenticator implements Interceptor {
|
||||
|
||||
private final String apiKey;
|
||||
|
||||
Authenticator(String apiKey) {
|
||||
this.apiKey = apiKey;
|
||||
}
|
||||
|
||||
@Override
|
||||
public Response intercept(Chain chain) throws IOException {
|
||||
HttpUrl url = chain.request().url().newBuilder().addQueryParameter("api_key", apiKey).build();
|
||||
Request request = chain.request().newBuilder().url(url).build();
|
||||
|
||||
return chain.proceed(request);
|
||||
}
|
||||
}
|
||||
|
||||
@JsonAutoDetect(fieldVisibility = ANY)
|
||||
class TopTags {
|
||||
|
||||
private Map<String, Object> tags;
|
||||
|
||||
@SuppressWarnings("unchecked")
|
||||
public Set<String> all() {
|
||||
List<Map<String, Object>> topTags = (List<Map<String, Object>>) tags.get("tag");
|
||||
return topTags.stream().map(e -> ((String) e.get("name"))).collect(Collectors.toSet());
|
||||
}
|
||||
}
|
||||
|
||||
@JsonAutoDetect(fieldVisibility = ANY)
|
||||
class Tags {
|
||||
|
||||
@JsonProperty("toptags")
|
||||
private Map<String, Object> topTags;
|
||||
|
||||
@SuppressWarnings("unchecked")
|
||||
public Map<String, Double> all() {
|
||||
try {
|
||||
Map<String, Double> all = new HashMap<>();
|
||||
List<Map<String, Object>> tags = (List<Map<String, Object>>) topTags.get("tag");
|
||||
for (Map<String, Object> tag : tags) {
|
||||
all.put(((String) tag.get("name")), ((Integer) tag.get("count")).doubleValue());
|
||||
}
|
||||
|
||||
return all;
|
||||
} catch (Exception e) {
|
||||
return Collections.emptyMap();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@JsonAutoDetect(fieldVisibility = ANY)
|
||||
class Artists {
|
||||
|
||||
private Map<String, Object> artists;
|
||||
|
||||
@SuppressWarnings("unchecked")
|
||||
public List<String> all() {
|
||||
try {
|
||||
List<Map<String, Object>> artists = (List<Map<String, Object>>) this.artists.get("artist");
|
||||
return artists.stream().map(e -> ((String) e.get("name"))).collect(toList());
|
||||
} catch (Exception e) {
|
||||
return Collections.emptyList();
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
package com.baeldung.ml.kmeans;
|
||||
|
||||
import java.util.Map;
|
||||
import java.util.Objects;
|
||||
|
||||
/**
|
||||
* Encapsulates all feature values for a few attributes. Optionally each record
|
||||
* can be described with the {@link #description} field.
|
||||
*/
|
||||
public class Record {
|
||||
|
||||
/**
|
||||
* The record description. For example, this can be the artist name for the famous musician
|
||||
* example.
|
||||
*/
|
||||
private final String description;
|
||||
|
||||
/**
|
||||
* Encapsulates all attributes and their corresponding values, i.e. features.
|
||||
*/
|
||||
private final Map<String, Double> features;
|
||||
|
||||
public Record(String description, Map<String, Double> features) {
|
||||
this.description = description;
|
||||
this.features = features;
|
||||
}
|
||||
|
||||
public Record(Map<String, Double> features) {
|
||||
this("", features);
|
||||
}
|
||||
|
||||
public String getDescription() {
|
||||
return description;
|
||||
}
|
||||
|
||||
public Map<String, Double> getFeatures() {
|
||||
return features;
|
||||
}
|
||||
|
||||
@Override
|
||||
public String toString() {
|
||||
String prefix = description == null || description.trim().isEmpty() ? "Record" : description;
|
||||
|
||||
return prefix + ": " + features;
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean equals(Object o) {
|
||||
if (this == o) return true;
|
||||
if (o == null || getClass() != o.getClass()) return false;
|
||||
Record record = (Record) o;
|
||||
return Objects.equals(getDescription(), record.getDescription()) &&
|
||||
Objects.equals(getFeatures(), record.getFeatures());
|
||||
}
|
||||
|
||||
@Override
|
||||
public int hashCode() {
|
||||
return Objects.hash(getDescription(), getFeatures());
|
||||
}
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,490 @@
|
|||
{
|
||||
"children": [
|
||||
{
|
||||
"children": [
|
||||
{
|
||||
"name": "Radiohead"
|
||||
},
|
||||
{
|
||||
"name": "Red Hot Chili Peppers"
|
||||
},
|
||||
{
|
||||
"name": "Coldplay"
|
||||
},
|
||||
{
|
||||
"name": "Nirvana"
|
||||
},
|
||||
{
|
||||
"name": "Panic! at the Disco"
|
||||
},
|
||||
{
|
||||
"name": "The Cure"
|
||||
},
|
||||
{
|
||||
"name": "Linkin Park"
|
||||
},
|
||||
{
|
||||
"name": "Radiohead"
|
||||
},
|
||||
{
|
||||
"name": "Red Hot Chili Peppers"
|
||||
},
|
||||
{
|
||||
"name": "Coldplay"
|
||||
},
|
||||
{
|
||||
"name": "Nirvana"
|
||||
},
|
||||
{
|
||||
"name": "Panic! at the Disco"
|
||||
},
|
||||
{
|
||||
"name": "The Cure"
|
||||
},
|
||||
{
|
||||
"name": "Linkin Park"
|
||||
},
|
||||
{
|
||||
"name": "Muse"
|
||||
},
|
||||
{
|
||||
"name": "Maroon 5"
|
||||
},
|
||||
{
|
||||
"name": "Foo Fighters"
|
||||
},
|
||||
{
|
||||
"name": "Paramore"
|
||||
},
|
||||
{
|
||||
"name": "Oasis"
|
||||
},
|
||||
{
|
||||
"name": "Fall Out Boy"
|
||||
},
|
||||
{
|
||||
"name": "OneRepublic"
|
||||
},
|
||||
{
|
||||
"name": "Weezer"
|
||||
},
|
||||
{
|
||||
"name": "System of a Down"
|
||||
},
|
||||
{
|
||||
"name": "The White Stripes"
|
||||
}
|
||||
],
|
||||
"name": "rock, alternative"
|
||||
},
|
||||
{
|
||||
"children": [
|
||||
{
|
||||
"name": "Lil Nas X"
|
||||
},
|
||||
{
|
||||
"name": "Post Malone"
|
||||
},
|
||||
{
|
||||
"name": "Drake"
|
||||
},
|
||||
{
|
||||
"name": "Kanye West"
|
||||
},
|
||||
{
|
||||
"name": "Kendrick Lamar"
|
||||
},
|
||||
{
|
||||
"name": "Tyler, the Creator"
|
||||
},
|
||||
{
|
||||
"name": "Eminem"
|
||||
},
|
||||
{
|
||||
"name": "Childish Gambino"
|
||||
},
|
||||
{
|
||||
"name": "Frank Ocean"
|
||||
},
|
||||
{
|
||||
"name": "Lil Nas X"
|
||||
},
|
||||
{
|
||||
"name": "Post Malone"
|
||||
},
|
||||
{
|
||||
"name": "Drake"
|
||||
},
|
||||
{
|
||||
"name": "Kanye West"
|
||||
},
|
||||
{
|
||||
"name": "Kendrick Lamar"
|
||||
},
|
||||
{
|
||||
"name": "Tyler, the Creator"
|
||||
},
|
||||
{
|
||||
"name": "Eminem"
|
||||
},
|
||||
{
|
||||
"name": "Childish Gambino"
|
||||
},
|
||||
{
|
||||
"name": "Frank Ocean"
|
||||
},
|
||||
{
|
||||
"name": "Lizzo"
|
||||
},
|
||||
{
|
||||
"name": "Travi$ Scott"
|
||||
},
|
||||
{
|
||||
"name": "A$AP Rocky"
|
||||
},
|
||||
{
|
||||
"name": "Nicki Minaj"
|
||||
},
|
||||
{
|
||||
"name": "xxxtentacion"
|
||||
}
|
||||
],
|
||||
"name": "Hip-Hop, rap"
|
||||
},
|
||||
{
|
||||
"children": [
|
||||
{
|
||||
"name": "Arctic Monkeys"
|
||||
},
|
||||
{
|
||||
"name": "Imagine Dragons"
|
||||
},
|
||||
{
|
||||
"name": "The Killers"
|
||||
},
|
||||
{
|
||||
"name": "Gorillaz"
|
||||
},
|
||||
{
|
||||
"name": "The Black Keys"
|
||||
},
|
||||
{
|
||||
"name": "Arctic Monkeys"
|
||||
},
|
||||
{
|
||||
"name": "Imagine Dragons"
|
||||
},
|
||||
{
|
||||
"name": "The Killers"
|
||||
},
|
||||
{
|
||||
"name": "Gorillaz"
|
||||
},
|
||||
{
|
||||
"name": "The Black Keys"
|
||||
},
|
||||
{
|
||||
"name": "Twenty One Pilots"
|
||||
},
|
||||
{
|
||||
"name": "Ellie Goulding"
|
||||
},
|
||||
{
|
||||
"name": "Florence + the Machine"
|
||||
},
|
||||
{
|
||||
"name": "Vampire Weekend"
|
||||
},
|
||||
{
|
||||
"name": "The Smiths"
|
||||
},
|
||||
{
|
||||
"name": "The Strokes"
|
||||
},
|
||||
{
|
||||
"name": "MGMT"
|
||||
},
|
||||
{
|
||||
"name": "Foster the People"
|
||||
},
|
||||
{
|
||||
"name": "Two Door Cinema Club"
|
||||
},
|
||||
{
|
||||
"name": "Cage the Elephant"
|
||||
},
|
||||
{
|
||||
"name": "Arcade Fire"
|
||||
},
|
||||
{
|
||||
"name": "The 1975"
|
||||
}
|
||||
],
|
||||
"name": "indie, alternative"
|
||||
},
|
||||
{
|
||||
"children": [
|
||||
{
|
||||
"name": "Ed Sheeran"
|
||||
},
|
||||
{
|
||||
"name": "Tame Impala"
|
||||
},
|
||||
{
|
||||
"name": "Ed Sheeran"
|
||||
},
|
||||
{
|
||||
"name": "Tame Impala"
|
||||
},
|
||||
{
|
||||
"name": "Green Day"
|
||||
},
|
||||
{
|
||||
"name": "Metallica"
|
||||
},
|
||||
{
|
||||
"name": "blink-182"
|
||||
},
|
||||
{
|
||||
"name": "Bon Iver"
|
||||
},
|
||||
{
|
||||
"name": "The Clash"
|
||||
}
|
||||
],
|
||||
"name": "rock, punk rock"
|
||||
},
|
||||
{
|
||||
"children": [
|
||||
{
|
||||
"name": "Calvin Harris"
|
||||
},
|
||||
{
|
||||
"name": "The Weeknd"
|
||||
},
|
||||
{
|
||||
"name": "The Chainsmokers"
|
||||
},
|
||||
{
|
||||
"name": "Daft Punk"
|
||||
},
|
||||
{
|
||||
"name": "Marshmello"
|
||||
},
|
||||
{
|
||||
"name": "David Guetta"
|
||||
},
|
||||
{
|
||||
"name": "Calvin Harris"
|
||||
},
|
||||
{
|
||||
"name": "The Weeknd"
|
||||
},
|
||||
{
|
||||
"name": "The Chainsmokers"
|
||||
},
|
||||
{
|
||||
"name": "Daft Punk"
|
||||
},
|
||||
{
|
||||
"name": "Marshmello"
|
||||
},
|
||||
{
|
||||
"name": "David Guetta"
|
||||
},
|
||||
{
|
||||
"name": "Avicii"
|
||||
},
|
||||
{
|
||||
"name": "Kygo"
|
||||
},
|
||||
{
|
||||
"name": "Martin Garrix"
|
||||
},
|
||||
{
|
||||
"name": "Major Lazer"
|
||||
},
|
||||
{
|
||||
"name": "Depeche Mode"
|
||||
}
|
||||
],
|
||||
"name": "electronic, dance"
|
||||
},
|
||||
{
|
||||
"children": [
|
||||
{
|
||||
"name": "Queen"
|
||||
},
|
||||
{
|
||||
"name": "The Beatles"
|
||||
},
|
||||
{
|
||||
"name": "David Bowie"
|
||||
},
|
||||
{
|
||||
"name": "Fleetwood Mac"
|
||||
},
|
||||
{
|
||||
"name": "Pink Floyd"
|
||||
},
|
||||
{
|
||||
"name": "The Rolling Stones"
|
||||
},
|
||||
{
|
||||
"name": "Led Zeppelin"
|
||||
},
|
||||
{
|
||||
"name": "Queen"
|
||||
},
|
||||
{
|
||||
"name": "The Beatles"
|
||||
},
|
||||
{
|
||||
"name": "David Bowie"
|
||||
},
|
||||
{
|
||||
"name": "Fleetwood Mac"
|
||||
},
|
||||
{
|
||||
"name": "Pink Floyd"
|
||||
},
|
||||
{
|
||||
"name": "The Rolling Stones"
|
||||
},
|
||||
{
|
||||
"name": "Led Zeppelin"
|
||||
},
|
||||
{
|
||||
"name": "Elton John"
|
||||
}
|
||||
],
|
||||
"name": "classic rock, rock"
|
||||
},
|
||||
{
|
||||
"children": [
|
||||
{
|
||||
"name": "Billie Eilish"
|
||||
},
|
||||
{
|
||||
"name": "Ariana Grande"
|
||||
},
|
||||
{
|
||||
"name": "Taylor Swift"
|
||||
},
|
||||
{
|
||||
"name": "Beyoncé"
|
||||
},
|
||||
{
|
||||
"name": "Shawn Mendes"
|
||||
},
|
||||
{
|
||||
"name": "Rihanna"
|
||||
},
|
||||
{
|
||||
"name": "Lana Del Rey"
|
||||
},
|
||||
{
|
||||
"name": "Katy Perry"
|
||||
},
|
||||
{
|
||||
"name": "Lady Gaga"
|
||||
},
|
||||
{
|
||||
"name": "Miley Cyrus"
|
||||
},
|
||||
{
|
||||
"name": "Mark Ronson"
|
||||
},
|
||||
{
|
||||
"name": "Madonna"
|
||||
},
|
||||
{
|
||||
"name": "Lorde"
|
||||
},
|
||||
{
|
||||
"name": "Khalid"
|
||||
},
|
||||
{
|
||||
"name": "Billie Eilish"
|
||||
},
|
||||
{
|
||||
"name": "Ariana Grande"
|
||||
},
|
||||
{
|
||||
"name": "Taylor Swift"
|
||||
},
|
||||
{
|
||||
"name": "Beyoncé"
|
||||
},
|
||||
{
|
||||
"name": "Shawn Mendes"
|
||||
},
|
||||
{
|
||||
"name": "Rihanna"
|
||||
},
|
||||
{
|
||||
"name": "Lana Del Rey"
|
||||
},
|
||||
{
|
||||
"name": "Katy Perry"
|
||||
},
|
||||
{
|
||||
"name": "Lady Gaga"
|
||||
},
|
||||
{
|
||||
"name": "Miley Cyrus"
|
||||
},
|
||||
{
|
||||
"name": "Mark Ronson"
|
||||
},
|
||||
{
|
||||
"name": "Madonna"
|
||||
},
|
||||
{
|
||||
"name": "Lorde"
|
||||
},
|
||||
{
|
||||
"name": "Khalid"
|
||||
},
|
||||
{
|
||||
"name": "Sia"
|
||||
},
|
||||
{
|
||||
"name": "Sam Smith"
|
||||
},
|
||||
{
|
||||
"name": "Halsey"
|
||||
},
|
||||
{
|
||||
"name": "Michael Jackson"
|
||||
},
|
||||
{
|
||||
"name": "Charli XCX"
|
||||
},
|
||||
{
|
||||
"name": "Britney Spears"
|
||||
},
|
||||
{
|
||||
"name": "Dua Lipa"
|
||||
},
|
||||
{
|
||||
"name": "Jonas Brothers"
|
||||
},
|
||||
{
|
||||
"name": "Bruno Mars"
|
||||
},
|
||||
{
|
||||
"name": "Carly Rae Jepsen"
|
||||
},
|
||||
{
|
||||
"name": "P!nk"
|
||||
},
|
||||
{
|
||||
"name": "Adele"
|
||||
}
|
||||
],
|
||||
"name": "pop, female vocalists"
|
||||
}
|
||||
],
|
||||
"name": "Musicians"
|
||||
}
|
||||
|
|
@ -0,0 +1,54 @@
|
|||
<!DOCTYPE html>
|
||||
<meta charset="utf-8">
|
||||
<style>
|
||||
.node circle {
|
||||
fill: #fff;
|
||||
stroke: steelblue;
|
||||
stroke-width: 1.5px;
|
||||
}
|
||||
.node {
|
||||
font: 10px sans-serif;
|
||||
}
|
||||
.link {
|
||||
fill: none;
|
||||
stroke: #ccc;
|
||||
stroke-width: 1.5px;
|
||||
}
|
||||
</style>
|
||||
<body>
|
||||
<script src="http://d3js.org/d3.v3.min.js"></script>
|
||||
<script>
|
||||
var diameter = 1100;
|
||||
var tree = d3.layout.tree()
|
||||
.size([360, diameter / 2 - 300])
|
||||
.separation(function(a, b) { return (a.parent == b.parent ? 1 : 2) / a.depth; });
|
||||
var diagonal = d3.svg.diagonal.radial()
|
||||
.projection(function(d) { return [d.y, d.x / 180 * Math.PI]; });
|
||||
var svg = d3.select("body").append("svg")
|
||||
.attr("width", diameter)
|
||||
.attr("height", diameter - 150)
|
||||
.append("g")
|
||||
.attr("transform", "translate(" + diameter / 2 + "," + diameter / 2 + ")");
|
||||
d3.json("lastfm.json", function(error, root) {
|
||||
var nodes = tree.nodes(root),
|
||||
links = tree.links(nodes);
|
||||
var link = svg.selectAll(".link")
|
||||
.data(links)
|
||||
.enter().append("path")
|
||||
.attr("class", "link")
|
||||
.attr("d", diagonal);
|
||||
var node = svg.selectAll(".node")
|
||||
.data(nodes)
|
||||
.enter().append("g")
|
||||
.attr("class", "node")
|
||||
.attr("transform", function(d) { return "rotate(" + (d.x - 90) + ")translate(" + d.y + ")"; })
|
||||
node.append("circle")
|
||||
.attr("r", 4.5);
|
||||
node.append("text")
|
||||
.attr("dy", ".31em")
|
||||
.attr("text-anchor", function(d) { return d.x < 180 ? "start" : "end"; })
|
||||
.attr("transform", function(d) { return d.x < 180 ? "translate(8)" : "rotate(180)translate(-8)"; })
|
||||
.text(function(d) { return d.name; });
|
||||
});
|
||||
d3.select(self.frameElement).style("height", diameter - 150 + "px");
|
||||
</script>
|
||||
1
pom.xml
1
pom.xml
|
|
@ -339,6 +339,7 @@
|
|||
<module>algorithms-miscellaneous-1</module>
|
||||
<module>algorithms-miscellaneous-2</module>
|
||||
<module>algorithms-miscellaneous-3</module>
|
||||
<module>machine-learning</module>
|
||||
<module>algorithms-sorting</module>
|
||||
<module>animal-sniffer-mvn-plugin</module>
|
||||
<module>annotations</module>
|
||||
|
|
|
|||
Loading…
Reference in New Issue