├── .gitignore
├── node2vec.jar
├── lib
    ├── argparse4j-0.7.0.jar
    └── argparse4j-0.7.0-javadoc.jar
├── src
    ├── word2vec
    │   ├── domain
    │   │   ├── HiddenNeuron.java
    │   │   ├── Neuron.java
    │   │   └── WordNeuron.java
    │   ├── util
    │   │   ├── MapCount.java
    │   │   └── Haffman.java
    │   ├── Word2VEC.java
    │   └── Model.java
    └── node2vec
    │   ├── Main.java
    │   ├── AliasMethod.java
    │   └── Graph.java
├── graph
    └── karate.edgelist
├── node2vec.iml
├── README.md
└── emb
    └── karate.emb


/.gitignore:
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1 | .idea/
2 | out/
3 | target/
4 | META-INF/
5 | .DS_Store


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/node2vec.jar:
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https://raw.githubusercontent.com/freemso/node2vec-java/HEAD/node2vec.jar


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/lib/argparse4j-0.7.0.jar:
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https://raw.githubusercontent.com/freemso/node2vec-java/HEAD/lib/argparse4j-0.7.0.jar


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/lib/argparse4j-0.7.0-javadoc.jar:
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https://raw.githubusercontent.com/freemso/node2vec-java/HEAD/lib/argparse4j-0.7.0-javadoc.jar


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/src/word2vec/domain/HiddenNeuron.java:
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 1 | package word2vec.domain;
 2 | 
 3 | public class HiddenNeuron extends Neuron{
 4 |     
 5 |     public double[] syn1 ; //hidden->out
 6 |     
 7 |     public HiddenNeuron(int layerSize){
 8 |         syn1 = new double[layerSize] ;
 9 |     }
10 |     
11 | }
12 | 


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/src/word2vec/domain/Neuron.java:
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 1 | package word2vec.domain;
 2 | 
 3 | public abstract class Neuron implements Comparable<Neuron> {
 4 |   public double freq;
 5 |   public Neuron parent;
 6 |   public int code;
 7 |   public int category = -1;
 8 | 
 9 |   @Override
10 |   public int compareTo(Neuron neuron) {
11 |     if (this.category == neuron.category) {
12 |       if (this.freq > neuron.freq) {
13 |         return 1;
14 |       } else {
15 |         return -1;
16 |       }
17 |     } else if (this.category > neuron.category) {
18 |       return 1;
19 |     } else {
20 |       return -1;
21 |     }
22 |   }
23 | }
24 | 


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/graph/karate.edgelist:
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74 | 31 34
75 | 31 33
76 | 32 34
77 | 32 33
78 | 33 34


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/src/word2vec/util/MapCount.java:
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 1 | //
 2 | // Source code recreated from a .class file by IntelliJ IDEA
 3 | // (powered by Fernflower decompiler)
 4 | //
 5 | 
 6 | package word2vec.util;
 7 | 
 8 | import java.util.HashMap;
 9 | 
10 | public class MapCount<T> {
11 |     private HashMap<T, Integer> hm = null;
12 | 
13 |     public MapCount() {
14 |         this.hm = new HashMap();
15 |     }
16 | 
17 |     public void add(T t, int n) {
18 |         Integer integer;
19 |         if((integer = this.hm.get(t)) != null) {
20 |             this.hm.put(t, Integer.valueOf(integer.intValue() + n));
21 |         } else {
22 |             this.hm.put(t, Integer.valueOf(n));
23 |         }
24 | 
25 |     }
26 | 
27 |     public void add(T t) {
28 |         this.add(t, 1);
29 |     }
30 | 
31 |     public int size() {
32 |         return this.hm.size();
33 |     }
34 | 
35 |     public HashMap<T, Integer> get() {
36 |         return this.hm;
37 |     }
38 | 
39 | }
40 | 


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/src/word2vec/util/Haffman.java:
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 1 | package word2vec.util;
 2 | 
 3 | import word2vec.domain.HiddenNeuron;
 4 | import word2vec.domain.Neuron;
 5 | 
 6 | import java.util.Collection;
 7 | import java.util.TreeSet;
 8 | 
 9 | public class Haffman {
10 |   private int layerSize;
11 | 
12 |   public Haffman(int layerSize) {
13 |     this.layerSize = layerSize;
14 |   }
15 | 
16 |   private TreeSet<Neuron> set = new TreeSet<>();
17 | 
18 |   public void make(Collection<Neuron> neurons) {
19 |     set.addAll(neurons);
20 |     while (set.size() > 1) {
21 |       merger();
22 |     }
23 |   }
24 | 
25 |   private void merger() {
26 |     HiddenNeuron hn = new HiddenNeuron(layerSize);
27 |     Neuron min1 = set.pollFirst();
28 |     Neuron min2 = set.pollFirst();
29 |     hn.category = min2.category;
30 |     hn.freq = min1.freq + min2.freq;
31 |     min1.parent = hn;
32 |     min2.parent = hn;
33 |     min1.code = 0;
34 |     min2.code = 1;
35 |     set.add(hn);
36 |   }
37 | 
38 | }
39 | 


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/node2vec.iml:
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 1 | <?xml version="1.0" encoding="UTF-8"?>
 2 | <module org.jetbrains.idea.maven.project.MavenProjectsManager.isMavenModule="true" type="JAVA_MODULE" version="4">
 3 |   <component name="NewModuleRootManager" LANGUAGE_LEVEL="JDK_1_8" inherit-compiler-output="false">
 4 |     <output url="file://$MODULE_DIR$/target/classes" />
 5 |     <output-test url="file://$MODULE_DIR$/target/test-classes" />
 6 |     <content url="file://$MODULE_DIR$">
 7 |       <sourceFolder url="file://$MODULE_DIR$/src" isTestSource="false" />
 8 |       <excludeFolder url="file://$MODULE_DIR$/target" />
 9 |     </content>
10 |     <orderEntry type="inheritedJdk" />
11 |     <orderEntry type="sourceFolder" forTests="false" />
12 |     <orderEntry type="library" name="argparse4j-0.7.0-javadoc" level="project" />
13 |     <orderEntry type="library" name="argparse4j-0.7.0-sources" level="project" />
14 |     <orderEntry type="library" name="argparse4j-0.7.0" level="project" />
15 |     <orderEntry type="library" scope="TEST" name="Maven: junit:junit:4.12" level="project" />
16 |     <orderEntry type="library" scope="TEST" name="Maven: org.hamcrest:hamcrest-core:1.3" level="project" />
17 |   </component>
18 | </module>


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/src/word2vec/domain/WordNeuron.java:
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 1 | package word2vec.domain;
 2 | 
 3 | import java.util.Collections;
 4 | import java.util.LinkedList;
 5 | import java.util.List;
 6 | import java.util.Random;
 7 | 
 8 | public class WordNeuron extends Neuron {
 9 |   public String name;
10 |   public double[] syn0 = null; // input->hidden
11 |   public List<Neuron> neurons = null;// 路径神经元
12 |   public int[] codeArr = null;
13 | 
14 |   public List<Neuron> makeNeurons() {
15 |     if (neurons != null) {
16 |       return neurons;
17 |     }
18 |     Neuron neuron = this;
19 |     neurons = new LinkedList<>();
20 |     while ((neuron = neuron.parent) != null) {
21 |       neurons.add(neuron);
22 |     }
23 |     Collections.reverse(neurons);
24 |     codeArr = new int[neurons.size()];
25 | 
26 |     for (int i = 1; i < neurons.size(); i++) {
27 |       codeArr[i - 1] = neurons.get(i).code;
28 |     }
29 |     codeArr[codeArr.length - 1] = this.code;
30 | 
31 |     return neurons;
32 |   }
33 | 
34 |   public WordNeuron(String name, double freq, int layerSize) {
35 |     this.name = name;
36 |     this.freq = freq;
37 |     this.syn0 = new double[layerSize];
38 |     Random random = new Random();
39 |     for (int i = 0; i < syn0.length; i++) {
40 |       syn0[i] = (random.nextDouble() - 0.5) / layerSize;
41 |     }
42 |   }
43 | 
44 |   /**
45 |    * 用于有监督的创造hoffman tree
46 |    * 
47 |    * @param name
48 |    * @param freq
49 |    * @param layerSize
50 |    */
51 |   public WordNeuron(String name, double freq, int category, int layerSize) {
52 |     this.name = name;
53 |     this.freq = freq;
54 |     this.syn0 = new double[layerSize];
55 |     this.category = category;
56 |     Random random = new Random();
57 |     for (int i = 0; i < syn0.length; i++) {
58 |       syn0[i] = (random.nextDouble() - 0.5) / layerSize;
59 |     }
60 |   }
61 | 
62 | }


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/README.md:
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 1 | # node2vec-java
 2 | 
 3 | This repository is an implementation of *node2vec* using Java.
 4 | 
 5 | The *node2vec* algorithm learns continuous representations for nodes in any (un)directed, (un)weighted graph. Please check the [project page](https://snap.stanford.edu/node2vec/) for more details.
 6 | 
 7 | >[node2vec: Scalable Feature Learning for Networks](http://arxiv.org/abs/1607.00653). A. Grover, J. Leskovec. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016.
 8 | 
 9 | The original oficial Python implementation can be found in [this repository](https://github.com/aditya-grover/node2vec).
10 | 
11 | ### Basic Usage
12 | 
13 | #### Example
14 | To run *node2vec* on Zachary's karate club network, execute the following command from the project home directory:<br/>
15 | 	``java -jar node2vec.jar --input graph/karate.edgelist --output emb/karate.emd``
16 | 
17 | #### Options
18 | You can check out the other options available to use with *node2vec* using:<br/>
19 | 	``java -jar node2vec.jar --help``
20 | 
21 | #### Input
22 | The supported input format is an edgelist:
23 | 
24 | 	node1_id_int node2_id_int <weight_float, optional>
25 | 
26 | The graph is assumed to be undirected and unweighted by default. These options can be changed by setting the appropriate flags.
27 | 
28 | #### Output
29 | The output file has *n+1* lines for a graph with *n* vertices.
30 | The first line has the following format:
31 | 
32 | 	num_of_nodes dim_of_representation
33 | 
34 | The next *n* lines are as follows:
35 | 
36 | 	node_id dim1 dim2 ... dimd
37 | 
38 | where dim1, ... , dimd is the *d*-dimensional representation learned by *node2vec*.
39 | 
40 | ### Library
41 | - [argparse4j](https://github.com/tatsuhiro-t/argparse4j) by [tatsuhiro-t](https://github.com/tatsuhiro-t)
42 | - [AliasMethod.java](http://www.keithschwarz.com/interesting/code/?dir=alias-method) by Keith Schwarz(htiek@cs.stanford.edu).
43 | - [Word2VEC_java](https://github.com/NLPchina/Word2VEC_java) by [NLPchina](https://github.com/NLPchina)
44 | 


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/src/word2vec/Word2VEC.java:
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 1 | package word2vec;
 2 | 
 3 | import java.io.*;
 4 | import java.util.HashMap;
 5 | 
 6 | public class Word2VEC {
 7 | 
 8 |     private static final int MAX_SIZE = 50;
 9 | 
10 | 	private HashMap<String, float[]> wordMap = new HashMap<>();
11 |     private int words, size;
12 |     public void loadGoogleModel(String path) throws IOException {
13 | 		DataInputStream dis = null;
14 | 		BufferedInputStream bis = null;
15 | 		double len = 0;
16 | 		float vector = 0;
17 | 		try {
18 | 			bis = new BufferedInputStream(new FileInputStream(path));
19 | 			dis = new DataInputStream(bis);
20 | 			words = Integer.parseInt(readString(dis));
21 | 			size = Integer.parseInt(readString(dis));
22 | 			String word;
23 | 			float[] vectors;
24 | 			for (int i = 0; i < words; i++) {
25 | 				word = readString(dis);
26 | 				vectors = new float[size];
27 | 				len = 0;
28 | 				for (int j = 0; j < size; j++) {
29 | 					vector = readFloat(dis);
30 | 					len += vector * vector;
31 | 					vectors[j] = vector;
32 | 				}
33 | 				len = Math.sqrt(len);
34 | 
35 | 				for (int j = 0; j < size; j++) {
36 | 					vectors[j] /= len;
37 | 				}
38 | 
39 | 				wordMap.put(word, vectors);
40 | 				dis.read();
41 | 			}
42 | 		} finally {
43 | 			bis.close();
44 | 			dis.close();
45 | 		}
46 | 	}
47 | 
48 | 
49 | 
50 | 	private static float readFloat(InputStream is) throws IOException {
51 | 		byte[] bytes = new byte[4];
52 | 		is.read(bytes);
53 | 		return getFloat(bytes);
54 | 	}
55 | 
56 | 	private static float getFloat(byte[] b) {
57 | 		int accum = 0;
58 | 		accum = accum | (b[0] & 0xff) << 0;
59 | 		accum = accum | (b[1] & 0xff) << 8;
60 | 		accum = accum | (b[2] & 0xff) << 16;
61 | 		accum = accum | (b[3] & 0xff) << 24;
62 | 		return Float.intBitsToFloat(accum);
63 | 	}
64 | 
65 | 	private static String readString(DataInputStream dis) throws IOException {
66 | 		// TODO Auto-generated method stub
67 | 		byte[] bytes = new byte[MAX_SIZE];
68 | 		byte b = dis.readByte();
69 | 		int i = -1;
70 | 		StringBuilder sb = new StringBuilder();
71 | 		while (b != 32 && b != 10) {
72 | 			i++;
73 | 			bytes[i] = b;
74 | 			b = dis.readByte();
75 | 			if (i == 49) {
76 | 				sb.append(new String(bytes));
77 | 				i = -1;
78 | 				bytes = new byte[MAX_SIZE];
79 | 			}
80 | 		}
81 | 		sb.append(new String(bytes, 0, i + 1));
82 | 		return sb.toString();
83 | 	}
84 | 
85 | 	public HashMap<String, float[]> getWordMap() {
86 | 		return wordMap;
87 | 	}
88 | 
89 | 
90 | }
91 | 


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/src/node2vec/Main.java:
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  1 | package node2vec;
  2 | 
  3 | import net.sourceforge.argparse4j.ArgumentParsers;
  4 | import net.sourceforge.argparse4j.impl.Arguments;
  5 | import net.sourceforge.argparse4j.inf.ArgumentParser;
  6 | import net.sourceforge.argparse4j.inf.ArgumentParserException;
  7 | import net.sourceforge.argparse4j.inf.Namespace;
  8 | import word2vec.Model;
  9 | 
 10 | import java.io.File;
 11 | import java.io.FileWriter;
 12 | import java.io.IOException;
 13 | import java.util.List;
 14 | 
 15 | /**
 16 |  * Created by freemso on 17-3-14.
 17 |  */
 18 | public class Main {
 19 |     public static void main(String[] args) {
 20 |         // parse arguments
 21 |         ArgumentParser parser = ArgumentParsers
 22 |                 .newArgumentParser("node2vec")
 23 |                 .defaultHelp(true)
 24 |                 .description("Run node2vec");
 25 |         parser.addArgument("-i", "--input")
 26 |                 .nargs("?")
 27 |                 .setDefault("graph/karate.edgelist")
 28 |                 .help("Input graph edge information path");
 29 |         parser.addArgument("-o", "--output")
 30 |                 .nargs("?")
 31 |                 .setDefault("emb/karate.emb")
 32 |                 .help("Output embedding path");
 33 |         parser.addArgument("--dimensions")
 34 |                 .type(Integer.class)
 35 |                 .setDefault(128)
 36 |                 .help("Number of dimensions. Default is 128");
 37 |         parser.addArgument("--walkLength")
 38 |                 .type(Integer.class)
 39 |                 .setDefault(80)
 40 |                 .help("Length og walk per source. Default is 80");
 41 |         parser.addArgument("--numWalks")
 42 |                 .type(Integer.class)
 43 |                 .setDefault(10)
 44 |                 .help("Number of walks per source. Default is 10");
 45 |         parser.addArgument("--windowSize")
 46 |                 .type(Integer.class)
 47 |                 .setDefault(10)
 48 |                 .help("Context size for optimization. Default is 10");
 49 |         parser.addArgument("--iter")
 50 |                 .type(Integer.class)
 51 |                 .setDefault(1)
 52 |                 .help("Number of epochs in SGD");
 53 |         parser.addArgument("--workers")
 54 |                 .type(Integer.class)
 55 |                 .setDefault(8)
 56 |                 .help("Number of parallel workers. Default is 8");
 57 |         parser.addArgument("-p", "--p")
 58 |                 .type(Double.class)
 59 |                 .setDefault(1.0)
 60 |                 .help("Return hyperparameter. Default is 1");
 61 |         parser.addArgument("-q", "--q")
 62 |                 .type(Double.class)
 63 |                 .setDefault(1.0)
 64 |                 .help("Inout hyperparameter. Default is 1");
 65 |         parser.addArgument("--weighted")
 66 |                 .dest("weighted")
 67 |                 .action(Arguments.storeTrue())
 68 |                 .help("Boolean specifying (un)weighted. Default is unweighted");
 69 |         parser.addArgument("--unweighted")
 70 |                 .dest("weighted")
 71 |                 .action(Arguments.storeFalse());
 72 |         parser.setDefault("weighted", false);
 73 |         parser.addArgument("--directed")
 74 |                 .dest("directed")
 75 |                 .action(Arguments.storeTrue())
 76 |                 .help("node2vec.Graph is (un)directed. Default is undirected");
 77 |         parser.addArgument("--undirected")
 78 |                 .dest("directed")
 79 |                 .action(Arguments.storeFalse());
 80 |         parser.setDefault("directed", false);
 81 | 
 82 |         try {
 83 |             Namespace ns = parser.parseArgs(args);
 84 |             Graph graph = new Graph(ns.get("input"),
 85 |                     ns.getBoolean("directed"),
 86 |                     ns.getDouble("p"),
 87 |                     ns.getDouble("q"));
 88 |             List<List> pathList = graph.
 89 |                     simulateWalks(ns.getInt("numWalks"),
 90 |                     ns.getInt("walkLength"));
 91 | 
 92 |             System.out.println("Learning Embedding...");
 93 | 
 94 |             // convert path list to string
 95 |             String sentList = "";
 96 |             for (List<Graph.Node> path :
 97 |                     pathList) {
 98 |                 String sent = "";
 99 |                 for (Graph.Node node :
100 |                         path) {
101 |                     sent += node.getId() + " ";
102 |                 }
103 |                 sentList += sent + "\n";
104 |             }
105 |             // write to temp file
106 |             String tempPath = System.getProperty("java.io.tmpdir");
107 |             File tempFile = File.createTempFile("pathList", "txt", new File(tempPath));
108 |             FileWriter fw = new FileWriter(tempFile);
109 |             fw.write(sentList);
110 |             fw.flush();
111 |             fw.close();
112 |             // use word2vec to do word embedding
113 |             Model model = new Model(false, ns.getInt("dimensions"), ns.getInt("windowSize"), null, null);
114 |             model.learnFile(tempFile);
115 |             model.storeModel(new File(ns.getString("output")));
116 | 
117 |         } catch (ArgumentParserException e) {
118 |             parser.handleError(e);
119 |         } catch (IOException e) {
120 |             System.err.println("invalid arguments");
121 |         }
122 | 
123 |     }
124 | }
125 | 


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/src/node2vec/AliasMethod.java:
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  1 | package node2vec; /******************************************************************************
  2 |  * File: node2vec.AliasMethod.java
  3 |  * Author: Keith Schwarz (htiek@cs.stanford.edu)
  4 |  *
  5 |  * An implementation of the alias method implemented using Vose's algorithm.
  6 |  * The alias method allows for efficient sampling of random values from a
  7 |  * discrete probability distribution (i.e. rolling a loaded die) in O(1) time
  8 |  * each after O(n) preprocessing time.
  9 |  *
 10 |  * For a complete writeup on the alias method, including the intuition and
 11 |  * important proofs, please see the article "Darts, Dice, and Coins: Smpling
 12 |  * from a Discrete Distribution" at
 13 |  *
 14 |  *                 http://www.keithschwarz.com/darts-dice-coins/
 15 |  */
 16 | import java.util.*;
 17 | 
 18 | public final class AliasMethod {
 19 |     /* The random number generator used to sample from the distribution. */
 20 |     private final Random random;
 21 | 
 22 |     /* The probability and alias tables. */
 23 |     private final int[] alias;
 24 |     private final double[] probability;
 25 | 
 26 |     /**
 27 |      * Constructs a new node2vec.AliasMethod to sample from a discrete distribution and
 28 |      * hand back outcomes based on the probability distribution.
 29 |      * <p>
 30 |      * Given as input a list of probabilities corresponding to outcomes 0, 1,
 31 |      * ..., n - 1, this constructor creates the probability and alias tables
 32 |      * needed to efficiently sample from this distribution.
 33 |      *
 34 |      * @param probabilities The list of probabilities.
 35 |      */
 36 |     public AliasMethod(List<Double> probabilities) {
 37 |         this(probabilities, new Random());
 38 |     }
 39 | 
 40 |     /**
 41 |      * Constructs a new node2vec.AliasMethod to sample from a discrete distribution and
 42 |      * hand back outcomes based on the probability distribution.
 43 |      * <p>
 44 |      * Given as input a list of probabilities corresponding to outcomes 0, 1,
 45 |      * ..., n - 1, along with the random number generator that should be used
 46 |      * as the underlying generator, this constructor creates the probability
 47 |      * and alias tables needed to efficiently sample from this distribution.
 48 |      *
 49 |      * @param probabilities The list of probabilities.
 50 |      * @param random The random number generator
 51 |      */
 52 |     public AliasMethod(List<Double> probabilities, Random random) {
 53 |         /* Begin by doing basic structural checks on the inputs. */
 54 |         if (probabilities == null || random == null)
 55 |             throw new NullPointerException();
 56 |         if (probabilities.size() == 0)
 57 |             throw new IllegalArgumentException("Probability vector must be nonempty.");
 58 | 
 59 |         /* Allocate space for the probability and alias tables. */
 60 |         probability = new double[probabilities.size()];
 61 |         alias = new int[probabilities.size()];
 62 | 
 63 |         /* Store the underlying generator. */
 64 |         this.random = random;
 65 | 
 66 |         /* Compute the average probability and cache it for later use. */
 67 |         final double average = 1.0 / probabilities.size();
 68 | 
 69 |         /* Make a copy of the probabilities list, since we will be making
 70 |          * changes to it.
 71 |          */
 72 |         probabilities = new ArrayList<Double>(probabilities);
 73 | 
 74 |         /* Create two stacks to act as worklists as we populate the tables. */
 75 |         Deque<Integer> small = new ArrayDeque<Integer>();
 76 |         Deque<Integer> large = new ArrayDeque<Integer>();
 77 | 
 78 |         /* Populate the stacks with the input probabilities. */
 79 |         for (int i = 0; i < probabilities.size(); ++i) {
 80 |             /* If the probability is below the average probability, then we add
 81 |              * it to the small list; otherwise we add it to the large list.
 82 |              */
 83 |             if (probabilities.get(i) >= average)
 84 |                 large.add(i);
 85 |             else
 86 |                 small.add(i);
 87 |         }
 88 | 
 89 |         /* As a note: in the mathematical specification of the algorithm, we
 90 |          * will always exhaust the small list before the big list.  However,
 91 |          * due to floating point inaccuracies, this is not necessarily true.
 92 |          * Consequently, this inner loop (which tries to pair small and large
 93 |          * elements) will have to check that both lists aren't empty.
 94 |          */
 95 |         while (!small.isEmpty() && !large.isEmpty()) {
 96 |             /* Get the index of the small and the large probabilities. */
 97 |             int less = small.removeLast();
 98 |             int more = large.removeLast();
 99 | 
100 |             /* These probabilities have not yet been scaled up to be such that
101 |              * 1/n is given weight 1.0.  We do this here instead.
102 |              */
103 |             probability[less] = probabilities.get(less) * probabilities.size();
104 |             alias[less] = more;
105 | 
106 |             /* Decrease the probability of the larger one by the appropriate
107 |              * amount.
108 |              */
109 |             probabilities.set(more,
110 |                     (probabilities.get(more) + probabilities.get(less)) - average);
111 | 
112 |             /* If the new probability is less than the average, add it into the
113 |              * small list; otherwise add it to the large list.
114 |              */
115 |             if (probabilities.get(more) >= 1.0 / probabilities.size())
116 |                 large.add(more);
117 |             else
118 |                 small.add(more);
119 |         }
120 | 
121 |         /* At this point, everything is in one list, which means that the
122 |          * remaining probabilities should all be 1/n.  Based on this, set them
123 |          * appropriately.  Due to numerical issues, we can't be sure which
124 |          * stack will hold the entries, so we empty both.
125 |          */
126 |         while (!small.isEmpty())
127 |             probability[small.removeLast()] = 1.0;
128 |         while (!large.isEmpty())
129 |             probability[large.removeLast()] = 1.0;
130 |     }
131 | 
132 |     /**
133 |      * Samples a value from the underlying distribution.
134 |      *
135 |      * @return A random value sampled from the underlying distribution.
136 |      */
137 |     public int next() {
138 |         /* Generate a fair die roll to determine which column to inspect. */
139 |         int column = random.nextInt(probability.length);
140 | 
141 |         /* Generate a biased coin toss to determine which option to pick. */
142 |         boolean coinToss = random.nextDouble() < probability[column];
143 | 
144 |         /* Based on the outcome, return either the column or its alias. */
145 |         return coinToss? column : alias[column];
146 |     }
147 | }
148 | 


--------------------------------------------------------------------------------
/src/node2vec/Graph.java:
--------------------------------------------------------------------------------
  1 | package node2vec;
  2 | 
  3 | import java.io.BufferedReader;
  4 | import java.io.FileReader;
  5 | import java.io.IOException;
  6 | import java.util.*;
  7 | 
  8 | /**
  9 |  * Created by freemso on 17-3-14.
 10 |  */
 11 | public class Graph {
 12 | 
 13 |     private static final double DEFAULT_WEIGHT = 1;
 14 | 
 15 |     private Set<Node> nodeSet = new HashSet<>();
 16 |     private Set<Edge> edgeSet = new HashSet<>();
 17 | 
 18 |     private boolean directed;
 19 | 
 20 |     private double p, q;
 21 | 
 22 |     private Map<Graph.Node, AliasMethod> aliasNodes = new HashMap<>();
 23 |     private Map<Graph.Edge, AliasMethod> aliasEdges = new HashMap<>();
 24 | 
 25 |     public Graph(String file, boolean directed, double p, double q) throws IOException {
 26 |         this.directed = directed;
 27 |         this.p = p;
 28 |         this.q = q;
 29 | 
 30 |         loadGraphFrom(file);
 31 |         preprocess();
 32 |     }
 33 | 
 34 |     /**
 35 |      * load graph data from file
 36 |      * input format: node1_id_int node2_id_int <weight_float, optional>
 37 |      * @param file path of the input file
 38 |      * @throws IOException file not found or file format not fit
 39 |      */
 40 |     private void loadGraphFrom(String file) throws IOException {
 41 |         // read graph info from file
 42 |         FileReader fr = new FileReader(file);
 43 |         BufferedReader br = new BufferedReader(fr);
 44 |         String lineTxt;
 45 |         while ((lineTxt = br.readLine()) != null) {
 46 |             // parse the line text to get the edge info
 47 |             String[] strList = lineTxt.split(" ");
 48 |             int node1ID = Integer.parseInt(strList[0]);
 49 |             int node2ID = Integer.parseInt(strList[1]);
 50 |             // add the nodes to the graph
 51 |             Node node1 = this.addNode(node1ID);
 52 |             Node node2 = this.addNode(node2ID);
 53 |             // add the edge to the graph
 54 |             if (strList.length > 2) {
 55 |                 double weight = Double.parseDouble((strList[2]));
 56 |                 this.addEdge(node1, node2, weight);
 57 |             } else {
 58 |                 this.addEdge(node1, node2, DEFAULT_WEIGHT);
 59 |             }
 60 |         }
 61 |     }
 62 | 
 63 |     /**
 64 |      * pre-processing of transition probabilities for guiding the random walks
 65 |      */
 66 |     private void preprocess() {
 67 |         for (Node node : nodeSet) {
 68 |             List<Node> neighbors = this.sortedNeighborList(node);
 69 |             List<Double> probs = new ArrayList<>();
 70 |             double weightSum = 0;
 71 |             for (Node neighbor : neighbors) {
 72 |                 // assert has an edge
 73 |                 double weight = this.getEdge(node, neighbor).weight;
 74 |                 probs.add(weight);
 75 |                 weightSum += weight;
 76 |             }
 77 |             double norm = weightSum;
 78 |             probs.forEach(aDouble -> aDouble /= norm);
 79 |             aliasNodes.put(node, new AliasMethod(probs));
 80 |         }
 81 |         for (Edge edge :
 82 |                 edgeSet) {
 83 |             aliasEdges.put(edge, this.computeAliasEdge(edge));
 84 |         }
 85 |     }
 86 | 
 87 |     /**
 88 |      * to compute the alias method for an edge
 89 |      * @param edge the edge to compute
 90 |      * @return the node2vec.AliasMethod object that store distribution information
 91 |      */
 92 |     private AliasMethod computeAliasEdge(Edge edge) {
 93 |         List<Node> neighbors = this.sortedNeighborList(edge.dst);
 94 |         List<Double> probs = new ArrayList<>();
 95 |         double weightSum = 0;
 96 |         for (Node neighbor :
 97 |                 neighbors) {
 98 |             double weight;
 99 |             if (neighbor == edge.src)
100 |                 weight = edge.weight / this.p;
101 |             else if (this.hasEdge(neighbor, edge.src))
102 |                 weight = edge.weight;
103 |             else weight = edge.weight / this.q;
104 |             weightSum += weight;
105 |             probs.add(weight);
106 |         }
107 |         double norm = weightSum;
108 |         probs.forEach(aDouble -> aDouble /= norm);
109 |         return new AliasMethod(probs);
110 |     }
111 | 
112 |     /**
113 |      * random walk in the graph starting from a node
114 |      * @param walkLength the steps of this walk
115 |      * @param startNode the start node of this walk
116 |      * @return the path that we pass, expressed as a Node List
117 |      */
118 |     private List<Node> walk(int walkLength, Node startNode) {
119 |         List<Node> path = new ArrayList<>();
120 |         path.add(startNode);
121 | 
122 |         while (path.size() < walkLength) {
123 |             Node current = path.get(path.size()-1); // the last node on the path
124 |             List<Node> neighbors = this.sortedNeighborList(current);
125 |             if (neighbors.size() > 0) {
126 |                 if (path.size() == 1) {
127 |                     int nextIndex = aliasNodes.get(current).next();
128 |                     path.add(neighbors.get(nextIndex));
129 |                 } else {
130 |                     Node prev = path.get(path.size()-2);
131 |                     int nextIndex = aliasEdges.get(this.getEdge(prev, current)).next();
132 |                     path.add(neighbors.get(nextIndex));
133 |                 }
134 |             } else break;
135 |         }
136 |         return path;
137 |     }
138 | 
139 |     /**
140 |      * simulation of a bunch of walks
141 |      * @param numWalks iteration times
142 |      * @param walkLength steps of every walk
143 |      * @return the list of paths that we've walked
144 |      */
145 |     public List<List> simulateWalks(int numWalks, int walkLength) {
146 |         List<List> pathList = new ArrayList<>();
147 |         System.out.println("Walk iteration:");
148 |         List<Node> nodeList = new ArrayList<>(nodeSet);
149 |         for (int i = 0; i < numWalks; i++) {
150 |             System.out.println(i+1+"/"+numWalks);
151 |             Collections.shuffle(nodeList);
152 |             for (Node node :
153 |                     nodeList) {
154 |                 pathList.add(walk(walkLength, node));
155 |             }
156 |         }
157 |         return pathList;
158 |     }
159 | 
160 |     /**
161 |      * get a node's neighbors in a sorted list
162 |      * the set of the neighbors of node is defined as {x|node-->x}
163 |      * sort the nodes according to its ids
164 |      * @param node the node
165 |      * @return a sorted list of nodes
166 |      */
167 |     private List<Node> sortedNeighborList(Node node) {
168 |         List<Node> neighborList = new ArrayList<>();
169 |         for (Node n : nodeSet) {
170 |             if (this.hasEdge(node, n)) neighborList.add(n); // only node-->n
171 |         }
172 |         neighborList.sort(Comparator.comparingInt(n -> n.id));
173 |         return neighborList;
174 |     }
175 | 
176 |     /**
177 |      * check whether there is an edge between two nodes
178 |      * note that all the edges in the graph are directive
179 |      * @param src node1
180 |      * @param dst node2
181 |      * @return true is there is an edge
182 |      */
183 |     private boolean hasEdge(Node src, Node dst) {
184 |         for (Edge edge : edgeSet) {
185 |             if (edge.equals(new Edge(src, dst))) {
186 |                 return true;
187 |             }
188 |         }
189 |         return false;
190 |     }
191 | 
192 |     /**
193 |      * get the edge between two nodes
194 |      * @param src node1
195 |      * @param dst node2
196 |      * @return the edge, null is not exist such an edge
197 |      */
198 |     private Edge getEdge(Node src, Node dst) {
199 |         for (Edge edge : edgeSet) {
200 |             if (edge.equals(new Edge(src, dst))) {
201 |                 return edge;
202 |             }
203 |         }
204 |         throw new NoSuchElementException();
205 |     }
206 | 
207 |     /**
208 |      * add a new edge to the graph
209 |      * if such an edge already exists, update the weight
210 |      * note that all the edges in the graph are directed
211 |      * if the graph is not directed,
212 |      * we just simply add two directed edges with the opposite directions
213 |      * that connect two nodes
214 |      * @param src first node of the edge
215 |      * @param dst second node of the edge
216 |      * @param weight of the edge
217 |      */
218 |     private void addEdge(Node src, Node dst, double weight) {
219 |         if (directed) {
220 |             Edge edge;
221 |             if (hasEdge(src, dst)) {
222 |                 edge = getEdge(src, dst);
223 |                 edge.weight = weight; // update the weight of the edge
224 |             } else {
225 |                 edge = new Edge(src, dst, weight);
226 |                 edgeSet.add(edge); // add it to edge set
227 |             }
228 |         } else {
229 |             Edge edge1;
230 |             Edge edge2;
231 |             if (hasEdge(src, dst)) {
232 |                 edge1 = getEdge(src, dst);
233 |                 edge2 = getEdge(dst, src);
234 |                 // update the weight of the edges
235 |                 edge1.weight = weight;
236 |                 edge2.weight = weight;
237 |             } else {
238 |                 edge1 = new Edge(src, dst, weight);
239 |                 edge2 = new Edge(dst, src, weight);
240 |                 // add it to edge set
241 |                 edgeSet.add(edge1);
242 |                 edgeSet.add(edge2);
243 |             }
244 |         }
245 |     }
246 | 
247 |     /**
248 |      * add a node with the id to the graph
249 |      * if such a node already exists, return it and do nothing
250 |      * if not, create a new node, add it to the graph and return it
251 |      * @param id the id of the node
252 |      * @return the node found
253 |      */
254 |     private Node addNode(int id) {
255 |         for (Node node : nodeSet) {
256 |             if (node.id == id) {
257 |                 return node;
258 |             }
259 |         }
260 |         // not exists, create a new node with the id
261 |         Node node = new Node(id);
262 |         // add it to the nodeSet
263 |         nodeSet.add(node);
264 |         return node;
265 |     }
266 | 
267 |     class Node {
268 | 
269 |         private int id;
270 | 
271 |         Node(int id) {
272 |             this.id = id;
273 |         }
274 | 
275 |         boolean equals(Node that) {
276 |             return this.id == that.id;
277 |         }
278 | 
279 |         public int getId() {
280 |             return id;
281 |         }
282 |     }
283 | 
284 |     class Edge {
285 | 
286 |         private Node src, dst;
287 |         private double weight;
288 | 
289 |         Edge(Node src, Node dst) {
290 |             if (src == null || dst == null)
291 |                 throw new IllegalArgumentException();
292 |             this.src = src;
293 |             this.dst = dst;
294 |         }
295 | 
296 |         Edge(Node src, Node dst, double weight) {
297 |             if (src == null || dst == null)
298 |                 throw new IllegalArgumentException();
299 |             this.src = src;
300 |             this.dst = dst;
301 |             this.weight = weight;
302 |         }
303 | 
304 |         /**
305 |          * two edges are equal if and only if they start at the same node
306 |          * and end at the same node
307 |          * @param that the node to compare
308 |          * @return true if two are equal
309 |          */
310 |         boolean equals(Edge that) {
311 |             return this.src.equals(that.src)
312 |                     && this.dst.equals(that.dst);
313 |         }
314 |     }
315 | 
316 | }
317 | 
318 | 
319 | 


--------------------------------------------------------------------------------
/src/word2vec/Model.java:
--------------------------------------------------------------------------------
  1 | package word2vec;
  2 | 
  3 | import word2vec.domain.HiddenNeuron;
  4 | import word2vec.domain.Neuron;
  5 | import word2vec.domain.WordNeuron;
  6 | import word2vec.util.Haffman;
  7 | import word2vec.util.MapCount;
  8 | 
  9 | import java.io.*;
 10 | import java.util.ArrayList;
 11 | import java.util.HashMap;
 12 | import java.util.List;
 13 | import java.util.Map;
 14 | import java.util.Map.Entry;
 15 | 
 16 | public class Model {
 17 |     private static final int DEFAULT_LAYER_SIZE = 200;
 18 |     private static final int DEFAULT_WINDOW_SIZE = 5;
 19 |     private static final double DEFAULT_SAMPLE = 1e-3;
 20 |     private static final double DEFAULT_ALPHA = 0.025;
 21 |     
 22 |     private static final int EXP_TABLE_SIZE = 1000;
 23 | 
 24 |     private Map<String, Neuron> word2neuron = new HashMap<>();
 25 | 
 26 |     private int layerSize, windowSize;
 27 |     private double sample, alpha, startingAlpha;
 28 |     private boolean isCBOW;
 29 |     
 30 |     private double[] expTable = new double[EXP_TABLE_SIZE];
 31 |     
 32 |     private int trainWordsCount = 0;
 33 |     
 34 |     private int MAX_EXP = 6;
 35 |     
 36 |     public Model(Boolean isCBOW, Integer layerSize, Integer windowSize, Double alpha, Double sample) {
 37 |         createExpTable();
 38 |         if (isCBOW != null) this.isCBOW = isCBOW;
 39 |         else this.isCBOW = false;
 40 |         if (layerSize != null) this.layerSize = layerSize;
 41 |         else this.layerSize = DEFAULT_LAYER_SIZE;
 42 |         if (windowSize != null) this.windowSize = windowSize;
 43 |         else this.windowSize = DEFAULT_WINDOW_SIZE;
 44 |         if (alpha != null) this.alpha = alpha;
 45 |         else this.alpha = DEFAULT_ALPHA;
 46 |         if (sample != null) this.sample = sample;
 47 |         else this.sample = DEFAULT_SAMPLE;
 48 |     }
 49 |     
 50 |     /**
 51 |     * train model with the file data
 52 |     *
 53 |     * @throws IOException
 54 |     */
 55 |     private void trainModel(File file) throws IOException {
 56 |         BufferedReader br = new BufferedReader(new InputStreamReader(new FileInputStream(file)));
 57 |         long nextRandom = 5;
 58 |         int wordCount = 0;
 59 |         int lastWordCount = 0;
 60 |         int wordCountActual = 0;
 61 |         String lineTxt;
 62 |         while ((lineTxt = br.readLine()) != null) {
 63 |             if (wordCount - lastWordCount > 10000) {
 64 |                 System.out.println("alpha:" + alpha + "\tProgress: "
 65 |                         + (int) (wordCountActual / (double) (trainWordsCount + 1) * 100)
 66 |                         + "%");
 67 |                 wordCountActual += wordCount - lastWordCount;
 68 |                 lastWordCount = wordCount;
 69 |                 alpha = startingAlpha * (1 - wordCountActual / (double) (trainWordsCount + 1));
 70 |                 if (alpha < startingAlpha * 0.0001) {
 71 |                     alpha = startingAlpha * 0.0001;
 72 |                 }
 73 |             }
 74 |             String[] strs = lineTxt.split(" ");
 75 |             wordCount += strs.length;
 76 |             List<WordNeuron> sentence = new ArrayList<WordNeuron>();
 77 |             for (int i = 0; i < strs.length; i++) {
 78 |                 Neuron entry = word2neuron.get(strs[i]);
 79 |                 if (entry == null) {
 80 |                 continue;
 81 |                 }
 82 |                 // The subsampling randomly discards frequent words while keeping the
 83 |                 // ranking same
 84 |                 if (sample > 0) {
 85 |                     double ran = (Math.sqrt(entry.freq / (sample * trainWordsCount)) + 1)
 86 |                                 * (sample * trainWordsCount) / entry.freq;
 87 |                     nextRandom = nextRandom * 25214903917L + 11;
 88 |                     if (ran < (nextRandom & 0xFFFF) / (double) 65536) {
 89 |                         continue;
 90 |                     }
 91 |                 }
 92 |                 sentence.add((WordNeuron) entry);
 93 |             }
 94 |             for (int index = 0; index < sentence.size(); index++) {
 95 |                 nextRandom = nextRandom * 25214903917L + 11;
 96 |                 if (isCBOW) cbowGram(index, sentence, (int) nextRandom % windowSize);
 97 |                 else skipGram(index, sentence, (int) nextRandom % windowSize);
 98 |             }
 99 | 
100 |         }
101 |         System.out.println("Vocab size: " + word2neuron.size());
102 |         System.out.println("Words in train file: " + trainWordsCount);
103 |         System.out.println("success train over!");
104 |     }
105 |     
106 |     /**
107 |     * skip gram train
108 |     *
109 |     */
110 |     private void skipGram(int index, List<WordNeuron> sentence, int b) {
111 |         WordNeuron word = sentence.get(index);
112 |         int a, c;
113 |         for (a = b; a < windowSize * 2 + 1 - b; a++) {
114 |             if (a == windowSize) {
115 |                 continue;
116 |             }
117 |             c = index - windowSize + a;
118 |             if (c < 0 || c >= sentence.size()) {
119 |                 continue;
120 |             }
121 | 
122 |             double[] neu1e = new double[layerSize];// 误差项
123 |             // HIERARCHICAL SOFTMAX
124 |             List<Neuron> neurons = word.neurons;
125 |             WordNeuron we = sentence.get(c);
126 |             for (int i = 0; i < neurons.size(); i++) {
127 |                 HiddenNeuron out = (HiddenNeuron) neurons.get(i);
128 |                 double f = 0;
129 |                 // Propagate hidden -> output
130 |                 for (int j = 0; j < layerSize; j++) {
131 |                     f += we.syn0[j] * out.syn1[j];
132 |                 }
133 |                 if (f <= -MAX_EXP || f >= MAX_EXP) {
134 |                     continue;
135 |                 } else {
136 |                     f = (f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2);
137 |                     f = expTable[(int) f];
138 |                 }
139 |                 // 'g' is the gradient multiplied by the learning rate
140 |                 double g = (1 - word.codeArr[i] - f) * alpha;
141 |                 // Propagate errors output -> hidden
142 |                 for (c = 0; c < layerSize; c++) {
143 |                     neu1e[c] += g * out.syn1[c];
144 |                 }
145 |                 // Model weights hidden -> output
146 |                 for (c = 0; c < layerSize; c++) {
147 |                     out.syn1[c] += g * we.syn0[c];
148 |                 }
149 |             }
150 | 
151 |             // Model weights input -> hidden
152 |             for (int j = 0; j < layerSize; j++) {
153 |                 we.syn0[j] += neu1e[j];
154 |             }
155 |         }
156 |     
157 |     }
158 |     
159 |     /**
160 |     * bag of words
161 |     *
162 |     */
163 |     private void cbowGram(int index, List<WordNeuron> sentence, int b) {
164 |         WordNeuron word = sentence.get(index);
165 |         int a, c;
166 | 
167 |         List<Neuron> neurons = word.neurons;
168 |         double[] neu1e = new double[layerSize];
169 |         double[] neu1 = new double[layerSize];
170 |         WordNeuron last_word;
171 | 
172 |         for (a = b; a < windowSize * 2 + 1 - b; a++)
173 |             if (a != windowSize) {
174 |                 c = index - windowSize + a;
175 |                 if (c < 0) continue;
176 |                 if (c >= sentence.size()) continue;
177 |                 last_word = sentence.get(c);
178 |                 if (last_word == null) continue;
179 |                 for (c = 0; c < layerSize; c++) neu1[c] += last_word.syn0[c];
180 |             }
181 | 
182 |         // HIERARCHICAL SOFTMAX
183 |         for (int d = 0; d < neurons.size(); d++) {
184 |             HiddenNeuron out = (HiddenNeuron) neurons.get(d);
185 |             double f = 0;
186 |             // Propagate hidden -> output
187 |             for (c = 0; c < layerSize; c++) f += neu1[c] * out.syn1[c];
188 |             if (f <= -MAX_EXP) continue;
189 |             else if (f >= MAX_EXP) continue;
190 |             else f = expTable[(int) ((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))];
191 |             // 'g' is the gradient multiplied by the learning rate
192 |             // double g = (1 - word.codeArr[d] - f) * alpha;
193 |             // double g = f*(1-f)*( word.codeArr[i] - f) * alpha;
194 |             double g = f * (1 - f) * (word.codeArr[d] - f) * alpha;
195 |             for (c = 0; c < layerSize; c++) {
196 |                 neu1e[c] += g * out.syn1[c];
197 |             }
198 |             // Model weights hidden -> output
199 |             for (c = 0; c < layerSize; c++) {
200 |                 out.syn1[c] += g * neu1[c];
201 |             }
202 |         }
203 |         for (a = b; a < windowSize * 2 + 1 - b; a++) {
204 |             if (a != windowSize) {
205 |                 c = index - windowSize + a;
206 |                 if (c < 0) continue;
207 |                 if (c >= sentence.size()) continue;
208 |                 last_word = sentence.get(c);
209 |                 if (last_word == null) continue;
210 |                 for (c = 0; c < layerSize; c++) last_word.syn0[c] += neu1e[c];
211 |             }
212 | 
213 |         }
214 |     }
215 |     
216 |     /**
217 |     *
218 |     * count word frequency in a file
219 |     * @param file
220 |     * @throws IOException
221 |     */
222 |     private void countWordFreq(File file) throws IOException {
223 |         MapCount<String> mc = new MapCount<>();
224 |         BufferedReader br = new BufferedReader(new InputStreamReader(new FileInputStream(file)));
225 |         String lineTxt;
226 |         while ((lineTxt = br.readLine()) != null) {
227 |             String[] split = lineTxt.split(" ");
228 |             trainWordsCount += split.length;
229 |             for (String string : split) {
230 |                 mc.add(string);
231 |             }
232 |         }
233 |         for (Entry<String, Integer> element : mc.get().entrySet()) {
234 |             word2neuron.put(element.getKey(), new WordNeuron(element.getKey(),
235 |             (double) element.getValue() / mc.size(), layerSize));
236 |         }
237 |     }
238 |     
239 |     /**
240 |     * Pre-compute the exp() table f(x) = x / (x + 1)
241 |     */
242 |     private void createExpTable() {
243 |         for (int i = 0; i < EXP_TABLE_SIZE; i++) {
244 |             expTable[i] = Math.exp(((i / (double) EXP_TABLE_SIZE * 2 - 1) * MAX_EXP));
245 |             expTable[i] = expTable[i] / (expTable[i] + 1);
246 |         }
247 |     }
248 |     
249 |     /**
250 |     *
251 |     * learn from the data in the file
252 |     */
253 |     public void learnFile(File file) throws IOException {
254 |         countWordFreq(file);
255 |         new Haffman(layerSize).make(word2neuron.values());
256 | 
257 |         for (Neuron neuron : word2neuron.values()) {
258 |             ((WordNeuron) neuron).makeNeurons();
259 |         }
260 | 
261 |         trainModel(file);
262 |     }
263 |     
264 |     public void storeModel(File file) {
265 |         try {
266 |             FileWriter fw = new FileWriter(file);
267 |             fw.write(word2neuron.size()+" "+layerSize+"\n");
268 |             double[] syn0;
269 |             for (Entry<String, Neuron> element : word2neuron.entrySet()) {
270 |                 fw.write(element.getKey()+" ");
271 |                 syn0 = ((WordNeuron) element.getValue()).syn0;
272 |                 for (double d : syn0) {
273 |                     fw.write(((Double) d).floatValue()+" ");
274 |                 }
275 |                 fw.write("\n");
276 |             }
277 |             fw.flush();
278 |             fw.close();
279 |         } catch (IOException e) {
280 |             e.printStackTrace();
281 |         }
282 |     }
283 | }
284 | 


--------------------------------------------------------------------------------
/emb/karate.emb:
--------------------------------------------------------------------------------
 1 | 34 128
 2 | 22 0.1821487 -0.14600317 0.116326064 -0.10979314 0.105511196 0.102590404 0.14767711 0.06910531 -0.10638263 0.099735096 -0.027917862 0.039294045 -0.0071753636 0.06037757 -0.057677213 -0.019134559 -0.03582664 -0.010924784 0.17873587 0.10809856 0.055747878 0.060848434 0.018695476 0.016508283 -0.063683055 0.06899306 0.082780704 -0.030826978 -0.04816471 0.0047709146 0.06363708 -0.032162134 -0.04182954 0.0028047364 0.063349195 0.043027 0.12741727 -0.008102806 -0.0016466641 -0.06405969 0.10115922 -0.03241647 0.036528803 -0.1640987 0.08161492 0.0037793338 0.030930828 0.0055984003 -0.11831737 0.026254252 0.060630977 -0.004279551 -0.03624406 -0.09632961 -0.03572987 -0.14861874 0.02314351 0.048774447 0.043954197 -0.12985384 -0.003383798 0.011586147 -0.06616301 -0.07558104 0.095947385 -0.011789435 -0.120202996 0.08661656 0.121825464 -0.038976487 -0.05697792 -0.03993333 -0.017476127 0.022369806 0.04293568 0.07009442 0.0390006 -0.052700084 0.11259083 -0.04016899 0.064478405 -0.104010046 0.0074903714 -0.07703918 0.1259057 0.027939904 -0.09567071 -0.06925666 0.029572716 -0.038965248 -0.05420588 -0.1258046 0.11898506 0.09050788 -0.052481655 -0.06349785 -0.04859992 0.09840988 0.04429312 -0.1030435 -0.0100439675 0.07017873 -0.094130225 0.11530836 -0.16998728 0.015001894 0.12221362 0.032665916 0.015437954 0.014901171 0.0329289 0.070511 -0.03665882 -0.024702474 0.09679714 0.043778926 0.08514011 -0.043605056 -0.046964396 -0.0283106 0.0128531195 -0.12924019 -0.023189792 0.11238788 0.096063465 0.051087108 -0.072582826 0.12809199 
 3 | 23 0.028375661 0.04795634 0.10599798 0.04046922 0.16443151 0.079451874 -0.05754446 0.049944397 -0.10623677 -0.09537121 0.118634045 -0.14276728 -0.10341239 0.11091766 -0.040854443 -0.025891878 0.061824806 0.035266418 0.06897366 -0.063835196 0.042173937 -0.053931773 0.22115009 0.01986362 -0.039733917 0.11461788 0.069328696 0.045040634 -0.00860743 0.0015088667 0.03869549 -0.13835678 -0.002315054 -0.053963102 0.1489051 -0.080735445 0.102075875 -0.056646895 -0.016122468 -0.026013635 -0.021883387 -0.15846747 -0.069300175 -0.104295254 -0.052402012 -0.0060268724 -0.07230706 -0.04142504 -0.2016928 0.073439956 -0.13861775 0.030878382 -0.13026863 -0.02793336 -0.06964886 0.05471191 0.04766108 -0.06489452 0.07063249 -0.12674396 -0.05244932 0.017628696 -0.05196734 -0.070001125 0.026363172 0.02233561 -0.21235436 0.09905592 -0.033558767 -0.13196716 -0.0037705975 0.0034768407 0.055066556 -0.0066931364 -0.15713312 -0.045633797 0.008313906 0.019732572 0.066875115 0.018622562 -0.10354879 -0.10170343 0.08773783 -0.07883883 -0.021138825 0.07460113 -0.19947542 0.0032242571 0.10113554 -0.010281366 -0.19166705 -0.109787524 0.1503132 0.1300915 0.015804496 -0.10867038 -0.028355205 -0.044126626 -0.020656845 0.047165144 0.08851092 0.053451724 -0.031468127 0.14158396 -0.021540698 0.08859927 -0.04343107 -0.16688865 0.105404295 0.021255404 0.15843153 0.07225651 0.070443116 0.01662086 0.18742022 0.0059783137 0.105789885 -0.027951064 0.04041941 0.14491078 -0.04893504 -0.1958695 -0.056535505 0.05393233 -0.1273838 0.0902796 -0.1814225 0.1923107 
 4 | 24 -0.048994362 0.11846702 0.0743396 0.031634677 0.18724047 0.06544468 -0.09001176 -0.018809948 -0.12544626 -0.124479204 0.08637826 -0.12732741 -0.12228497 0.036238365 0.0716063 0.08820847 0.096138984 0.075112976 -0.020335447 -0.0045082937 0.062943034 -0.0487621 0.19408727 0.03093611 -0.02958481 0.1878024 0.08964418 0.11551742 0.05228678 0.025589805 0.025675988 -0.097252 -0.019627752 -0.06142453 0.10943794 -0.04119083 0.018895295 -0.023383923 0.039869037 -0.02488888 -0.15344484 -0.08286309 -0.105242915 -0.06530535 -0.061994974 0.09153183 -0.10260162 -0.047825597 -0.24874924 0.11418831 -0.12197794 0.07802706 -0.16352177 0.041731223 -0.0010736005 0.05255872 0.022329064 -0.12621571 0.06204006 -0.058587253 -0.055273205 -0.028100746 -0.01920598 -0.013955939 0.052773066 -0.0027463709 -0.1492269 0.093065076 -0.08720536 -0.0835943 -0.107209094 -0.023597792 0.059640493 -0.0023358958 -0.14735182 -0.015164342 0.019601233 0.11291385 0.057855897 -0.023598261 -0.10313679 -0.07852023 0.12824029 -0.056300372 -0.08172824 0.053257886 -0.19869831 0.045333754 0.10657276 -0.029702438 -0.14720726 -0.07572335 0.033390626 0.08051954 0.012770471 -0.051595945 -0.0065196436 -0.15558104 -0.028033879 0.059823178 0.13908589 0.014074686 0.02323012 0.15039419 -0.0040520667 0.077116676 -0.063514866 -0.21417661 0.1329269 0.04554744 0.09912121 0.00417222 -0.017480155 0.045104876 0.1347777 0.03534074 0.041379247 -0.049795024 -0.003665155 0.14331885 -0.17303006 -0.17858794 0.008965891 -0.0072263787 -0.16609786 0.12599082 -0.11641517 0.089804225 
 5 | 25 0.022734346 0.16669345 0.1342203 0.058562193 0.14373356 -0.039631322 -0.0859904 0.13395624 0.002193284 -0.06812119 0.10675106 -0.08327502 -0.03157942 0.13087004 -0.08994663 0.06243978 0.05412838 0.09667864 -0.02669939 0.018557489 -0.041769866 0.008776961 0.17922406 -0.024083933 0.037944205 0.13897848 -0.0022936238 -0.06861979 -0.038195506 0.071683854 0.17705125 -0.042471033 0.0247901 -0.06886569 0.09155166 -0.06682337 -0.013079276 0.0068600774 0.05985017 -0.06771978 -0.25290236 -0.003655058 -0.046168346 0.098010436 -0.091923565 0.09703767 -0.13487165 -0.052628133 -0.23621248 0.17458081 -0.08599201 0.07119102 -0.13160437 -0.059518106 0.17460422 0.09190632 0.06276772 -0.3236851 0.1121994 0.05832523 0.040747207 -0.22385517 -0.058269437 -0.1249894 0.25974542 -0.102110945 -0.14876443 0.18747205 -0.08138787 -0.0735902 -0.14947246 -0.036881205 0.09864841 -0.09155626 -0.08094405 -0.0102731595 0.103235744 0.16557662 0.12713152 0.011814891 0.062269226 -0.06969533 0.03233952 -0.1524944 -0.12450363 0.16154166 -0.039367788 0.04372336 0.068130605 -0.14061427 -0.053953227 -0.051081493 -0.020701107 0.21243018 0.09766622 -0.010896843 -0.117392294 -0.08433346 -0.06682336 0.0043665646 0.042803664 0.065754876 -0.03219353 0.06666533 -0.08528164 -0.07733689 0.024523336 -0.11283239 0.14982346 0.13179491 0.11022328 -0.021127239 -0.073718496 0.09555856 0.042765867 -0.114241675 0.061793208 -9.318305E-4 -0.090526715 0.03475302 -0.18051095 -0.24470867 -0.06764471 -0.030491013 -0.14299193 0.06779758 -0.1800463 0.0260493 
 6 | 26 -0.0068591074 0.13414489 0.14879334 0.0064438423 0.08443095 -0.04518342 -0.05671771 0.064837225 -0.023514058 -0.081924714 0.045116562 -0.13090965 -0.045194834 0.19819777 0.00806405 0.016686631 0.050729547 0.024752261 -0.019718247 0.008246339 0.046463337 0.042153247 0.1577874 0.009552787 -0.019406786 0.17745681 0.028759565 0.011805487 -0.046893355 0.033795066 0.16355433 -0.041845374 0.028410316 -0.0933049 0.11396246 -0.075480044 0.038490202 0.04533059 0.011376723 0.03159977 -0.18117768 0.007163877 -0.031990506 0.013523729 -0.09465614 0.024065554 -0.16074833 -0.0026612724 -0.264788 0.11190502 -0.10817086 0.08627926 -0.11112116 -0.059918076 0.082186595 0.08931916 -0.030172933 -0.292156 0.07991076 -0.040955085 -0.08956138 -0.22656706 -0.036665093 -0.11288254 0.13508075 -0.08158595 -0.11427994 0.22539483 0.007968694 -0.09944911 -0.13213238 -0.06195775 0.016500693 -0.064616755 -0.16600807 0.029806415 0.0469521 0.087129235 0.14617306 0.022646321 2.0051881E-4 -0.11423625 0.028045168 -0.1381094 -0.09756532 0.082388334 -0.057295203 -0.014876247 -0.0070980364 -0.114779584 0.0013614513 -0.05063741 -0.12778798 0.23806764 0.118041635 -0.048236642 -0.10808676 -0.036975432 -0.075119734 -0.024653926 0.0865149 0.045477252 -0.060537215 0.12700999 -0.009925997 0.0030250847 0.011446343 -0.122044586 0.110751696 0.07576046 0.12522732 -0.03721183 -0.05992572 0.039584305 0.063558534 -0.0437275 0.13507764 0.052912287 -0.0940867 0.012536588 -0.12361215 -0.21362294 -0.031651206 -0.038594663 -0.09518999 0.082502544 -0.19015884 0.023395747 
 7 | 27 -0.066345155 0.10038279 0.08755364 -8.9629967E-4 0.15133557 0.027589181 -0.091664135 -0.0021953199 -0.12178096 -0.11609908 0.078149356 -0.21127908 -0.14611489 0.10544191 0.066726476 -0.012804321 0.13321835 -0.015103809 -0.044365074 -0.014858057 0.07071372 -0.04447895 0.24486236 0.033546764 -0.07621639 0.22925787 0.07191055 0.1525348 0.033680826 0.05490346 0.04620381 -0.18493775 -0.011290822 -0.03868687 0.17558177 -0.057149753 0.09600344 0.0070670815 0.016488578 0.006227668 -0.111537516 -0.08115876 -0.091945276 -0.08563715 -0.106338084 0.065427415 -0.16396773 -0.036743697 -0.28383356 0.06743263 -0.10433949 0.047413934 -0.123001985 0.029521259 -0.018279092 0.11614583 0.017716156 -0.13150366 0.10209649 -0.17018442 -0.13192257 -0.096287735 -0.03992033 -0.0050648735 0.01845822 -0.0031260748 -0.1872218 0.17361332 -0.08978067 -0.11513207 -0.028619988 -0.013339595 0.016698405 0.038773347 -0.20485567 -0.03693199 0.04136567 0.07122237 0.07024043 0.019258788 -0.17824721 -0.09345924 0.097863 -0.05481856 -0.046143565 -0.0030946713 -0.16169292 0.02674691 0.06529228 -0.024761679 -0.13844304 -0.08161848 -0.012412548 0.16882746 0.09089227 -0.14978294 -0.04263519 -0.12563775 -0.12981263 0.03840281 0.10685192 0.057978462 -0.030400192 0.18840295 0.07202665 0.09233784 -0.05058634 -0.2018243 0.16206679 0.0038567705 0.10791762 0.026597051 -0.013716318 0.01228094 0.12658207 -0.005512573 0.13902214 -0.052778315 0.024272352 0.17991677 -0.14500791 -0.24442233 -0.032498013 0.031146595 -0.19449265 0.12436769 -0.14203773 0.17275715 
 8 | 28 0.0056517418 0.11344297 0.1531956 0.009543954 0.06515459 -0.025771989 -0.02296741 0.107915424 -0.014943627 -0.072277956 0.09418213 -0.047785334 -5.275279E-4 0.08029638 -0.06738301 0.03789656 0.04893098 0.07855713 0.04941621 -0.034994993 0.024605459 -0.018225517 0.14760318 -0.028846433 0.014588093 0.052865073 0.056990884 -0.049942475 -0.01553949 0.10459857 0.116221115 -0.023787575 -0.02488796 -0.05119349 -0.008931158 -0.14507475 0.060825195 -0.052841894 0.051252797 -0.11556171 -0.10233515 -0.045815222 -0.03668238 0.06800913 -0.021428006 0.02923297 -0.060118876 -0.07754408 -0.21689273 0.06639371 -0.07181323 0.045458462 -0.08689378 -0.046170842 0.09112715 -0.009572329 -0.020187113 -0.23322056 0.07730574 0.051340345 -0.034114197 -0.12421946 -0.07255637 -0.1367145 0.24091855 -0.08911035 -0.099130414 0.08310205 -0.08115728 -0.049245864 -0.1350907 -0.049058743 0.07746352 -0.075297035 -0.11150963 0.03367216 0.08009355 0.14644897 0.104415506 0.031147804 -0.0019173817 -0.100683615 0.09021383 -0.08143787 -0.023434635 0.09378655 -0.05023269 0.014519783 0.09182249 -0.106006846 -0.029280242 -0.08306241 -0.017441282 0.13147296 0.03898693 -0.014609411 -0.09910752 -0.09290528 -0.011394235 -0.02119072 0.054622784 3.872566E-4 6.8187586E-4 0.07334594 -0.085255206 -0.0637122 0.013358551 -0.10289767 0.15239522 0.11114748 0.10890317 -0.018781453 -0.058359798 0.07139973 0.050760496 -0.053505003 0.040577825 0.025827032 0.0055311928 -0.020251704 -0.07078454 -0.23577195 -0.027660217 -0.026196055 -0.039419036 0.06259185 -0.16254863 0.0632596 
 9 | 29 0.07933041 0.019016286 0.08321108 -0.0043732915 0.100601785 0.010669411 0.017745826 0.14975882 0.005835006 -0.040870216 0.07099433 -0.054803528 -0.014169453 0.09786296 -0.091845065 0.02050313 0.016408004 0.02737145 0.04483912 0.02599855 -0.04615165 0.038206104 0.122393034 0.024427287 0.0028330362 0.06645258 0.008815544 -0.077794805 -0.055907227 0.07886094 0.09431202 -0.053578828 -0.0436714 -0.009563467 0.036516078 -0.061893173 0.044314537 -0.032879464 0.068864144 -0.080433086 -0.057214256 -0.0088931685 -0.030684689 0.038263444 -0.016310716 0.044199087 -0.08574285 -0.071447834 -0.16978526 0.066371076 0.024751816 0.02635597 -0.073722154 -0.048613306 0.06480617 0.005248854 0.064247414 -0.12175045 0.10417074 -0.048580248 0.026433839 -0.11250058 -0.06366597 -0.08897656 0.17173052 -0.05448437 -0.15244183 0.07912383 -0.050421398 -0.039785694 -0.055045515 0.022733338 0.02179318 -0.06413518 -0.04794997 -0.017855575 0.0729116 0.08768567 0.056538995 0.0107084075 0.01939235 -0.065128624 0.03414731 -0.079356916 0.02629552 0.06844047 -0.043009017 0.028822694 0.07123606 -0.060399566 -0.07036619 -0.084880374 0.078343675 0.15495437 0.052616812 -0.053984378 -0.077238314 -0.032020174 -0.057463706 -0.029142926 -0.021074377 0.060115166 -0.016007498 0.08180933 -0.09536566 -0.045217518 0.030227344 -0.086849526 0.102435134 0.05736538 0.06488336 0.022487134 -0.021325596 0.051433343 0.046728823 -0.068048365 0.047337536 -0.060300108 -0.033839155 0.019923666 -0.029192528 -0.2024329 -0.041000236 0.036066152 -0.039207675 0.07517499 -0.10255894 0.110060684 
10 | 30 -0.111210205 0.060428083 0.082548805 0.0026796702 0.065212876 0.07491174 -0.07040108 -0.05670935 -0.08356306 -0.1341931 0.045791402 -0.14042732 -0.11043879 0.04980886 0.09124593 -0.0031984348 0.095156394 -0.011924883 0.018790772 -0.08289335 0.07421601 -0.0532975 0.15310757 0.020971073 -0.08007526 0.098727964 0.07737293 0.13080509 0.03213887 0.04339686 0.013090487 -0.10315489 -0.03827624 -0.07925446 0.050307985 -0.10520782 0.12975958 -0.019463982 0.015200942 0.0076411883 0.024414599 -0.08304533 -0.036562517 -0.09073913 -0.050521143 -0.001978272 -0.08633934 -0.04820506 -0.17754166 -0.028326817 -0.10970097 0.030305948 -0.06375164 0.0074724155 -0.12926163 -0.0060752663 -0.060029957 -0.048517834 0.031374957 -0.08677632 -0.14272267 0.03868851 -0.019207716 -0.03128359 -0.054197263 0.024998855 -0.08193532 0.043305792 -0.03252196 -0.10034365 -0.018196972 -0.005948069 -0.018734224 -0.005419117 -0.19457021 -0.016441943 -0.014041165 0.02262273 0.026757756 0.0045453054 -0.19232649 -0.065668546 0.1496478 -0.017979085 -0.0135935 -0.047281705 -0.1489871 0.005191093 0.059833076 0.051046573 -0.075399004 -0.07008021 -0.02132588 0.04218369 0.025946666 -0.12913302 -0.009629836 -0.08449851 -0.0047081504 0.045195866 0.14161865 -0.006438729 0.02622019 0.12831114 0.094420314 0.09193879 -0.1088442 -0.18698458 0.096623205 -0.039132655 0.08441477 0.016519956 0.016621599 -0.020185415 0.12404598 0.07520368 0.06004181 0.008803566 0.07341934 0.07177757 0.010178519 -0.13426548 0.020141011 -0.0155616915 -0.083162434 0.10428444 -0.075344935 0.14696096 
11 | 31 -0.02288271 -0.044855732 0.086582094 0.0050856797 0.16626984 0.21430212 -0.017563203 -0.07848647 -0.13959834 -0.06896653 0.030178104 -0.07608346 -0.1483747 -0.0011144275 0.057655457 -0.0024555107 0.040718276 -0.02924844 0.095032014 -0.038494095 0.09158803 -0.058617476 0.11071275 0.023869772 -0.10024978 0.1273531 0.104189895 0.11808559 0.024317648 -0.046980545 0.016530357 -0.14500982 -0.03452227 -0.06627853 0.14592813 0.020618612 0.14322652 -0.014774259 0.0117755085 -0.0023458102 0.0728758 -0.13178337 0.027831994 -0.2049101 -0.028761348 0.026382335 -0.03439531 -0.059174243 -0.09567416 -0.004999957 -0.12088663 0.035742667 -0.073684685 -0.05193624 -0.19070596 -0.069029495 0.00914858 0.0952188 0.0024238417 -0.11653402 -0.08387249 0.17395712 -0.023887932 -0.0018041228 -0.120071895 0.13571715 -0.12513684 0.060896978 0.06843334 -0.11969713 0.02731605 0.0024045536 0.0018962794 0.039110415 -0.10193346 -0.02494966 -0.060700934 -0.095843606 0.02951898 -0.048883956 -0.16049753 -0.032348752 0.117466666 -0.045884047 0.02198536 -0.039422136 -0.24243836 -0.02162019 0.03592284 0.10457269 -0.14142677 -0.07148089 0.117568284 0.01801379 -0.051138278 -0.15643777 0.026547978 -0.0044994783 0.06354581 0.006779583 0.16417092 0.051266138 -0.015722286 0.0977488 0.028634703 0.1405302 -0.087592475 -0.15531397 0.006077067 -0.09238769 0.06121178 0.11434865 0.04680161 -0.05435353 0.20382199 0.13922048 0.052233204 -0.081526354 0.06736446 0.110819645 -9.52976E-6 -0.049603496 0.005113996 0.056421913 -0.084074944 0.06577062 -0.052995197 0.1920239 
12 | 32 0.06827344 0.03971069 0.084925935 0.024882145 0.12861742 0.04844021 -0.0011114024 0.0923398 -0.06778136 -0.041119583 0.033406682 0.025347255 -0.016678859 0.047712367 -0.08369032 0.04945012 -0.021198919 0.07790715 0.09430294 0.042815186 -0.010785835 0.02921064 0.063104704 -0.019594813 -0.025515368 0.026898142 0.049259976 -0.0802359 -0.05810831 0.104749754 0.15222383 0.015381545 -0.031969097 -0.0018294506 0.030146597 -0.075511105 0.028635204 -0.0024920874 0.06785205 -0.11976456 -0.08124446 -0.03137279 -0.012629611 0.080870636 -0.046019964 0.0524693 -0.06200089 -0.073572315 -0.18261524 0.06200079 0.009066108 0.062287487 -0.043227643 -0.060882594 0.09601064 -0.05617921 0.026783308 -0.19606571 0.05618772 0.053925652 0.0021166345 -0.107372835 -0.09591698 -0.14870876 0.25721014 -0.10911488 -0.1203971 0.053532857 -0.059622034 0.0030696606 -0.13403276 -0.036115922 0.060798343 -0.07511756 -0.042461507 -0.00798287 0.062225405 0.105036154 0.12640585 -0.06446003 0.08419349 -0.07588774 0.06522254 -0.07777449 -0.02800758 0.06644234 -0.04202093 0.028944049 0.086965464 -0.054384287 -0.029120594 -0.12111023 0.03979327 0.105731316 0.050084047 0.026172101 -0.045564137 -0.010889604 -0.03818183 -0.03390182 0.060108285 0.06534077 -0.020590983 0.02503277 -0.15383112 -0.059820477 0.058796 -0.08029837 0.124164656 0.12103363 0.067391075 0.0105144335 -0.03592766 0.048980102 0.051708873 -0.035189148 0.028298642 -0.032165598 -0.05441652 -0.016311033 -0.10438019 -0.18676715 0.010907392 0.006781064 -0.013672366 0.105177246 -0.1254315 0.016396232 
13 | 10 0.053594932 0.03921308 0.11317014 -0.04118246 0.0553294 -0.026486903 0.047425214 0.1443937 0.0126622375 -0.047271512 0.11697948 -0.07838807 -0.0036829158 0.085032344 -0.042798534 0.06785667 0.06274005 0.044078723 0.04050873 -0.033736695 0.019197062 0.0057139164 0.17710039 0.03919881 0.026872871 0.07979278 0.043801468 -0.014251876 0.029117675 0.06470864 -0.01241174 -0.059431158 -0.07827575 -0.041301627 -0.038279355 -0.1182142 0.064254254 -0.12552054 0.054418642 -0.08178906 -0.027927969 -0.037183125 -0.09038477 -0.021023976 0.06590578 0.030297065 -0.022139197 -0.067371495 -0.20593868 0.063553564 -0.021819975 0.0035732696 -0.12991935 0.015535916 -0.0012504798 -0.029487023 -0.009002332 -0.07255454 0.086279914 -0.08090385 -0.002264392 -0.03751555 -0.026557956 -0.04322493 0.12342073 -0.035197355 -0.13756236 0.044620913 -0.07053056 -0.07182605 -0.08440176 0.0317666 0.032016948 -0.039111163 -0.08552104 0.03895429 0.07099735 0.1308069 0.00763379 0.07557981 -0.0684014 -0.10971959 0.08019556 -0.054804463 0.073760815 0.078219876 -0.106131226 0.023457866 0.11666692 -0.09589741 -0.093969405 -0.07905462 0.063311994 0.096874446 -0.015904522 -0.070226744 -0.06727377 -0.12465977 0.0203512 0.0031904348 -0.018906087 -0.043954395 0.031696245 0.16682759 -0.057654697 -0.004433584 -0.018691115 -0.11465333 0.1261636 0.03585358 0.104207754 -0.0069361166 -0.024765272 0.07206541 0.07118574 -0.022812301 0.0053344388 -0.014386722 0.032130867 0.029241903 0.02637228 -0.20899466 -0.04146881 0.029974177 -0.028524483 0.0526106 -0.110312395 0.16369507 
14 | 33 -0.0084953755 -0.046977773 0.050056566 -0.053508516 0.057259936 0.14074235 0.030376274 -0.12263033 -0.13376243 -0.008681679 -0.037288357 0.022230437 -0.116037615 -0.06609054 0.060351048 0.05008256 0.023884537 -0.021337947 0.05487022 0.026041036 0.06158257 -0.046132702 0.0151686985 -0.021483518 -0.07379722 0.112000935 0.09540952 0.11897147 0.01807802 0.022831494 0.00978143 -0.062663354 -0.0704289 -0.030434739 0.04796157 0.008627588 0.102217935 -0.009598371 0.068692505 -0.040116824 0.060419478 -0.007514363 0.031982232 -0.13404913 0.02285234 0.104590714 0.010900472 -0.0717097 -0.09801575 -0.026415398 -0.0043878276 0.031107694 -0.009154884 -0.034291226 -0.10446933 -0.13719839 -0.039495848 0.053205088 6.007097E-4 -0.028630754 -0.08739664 0.09099597 -0.016468856 -0.025375033 -0.02468219 0.020702358 -0.010983574 -9.5506554E-4 0.034697063 0.015027707 -0.032588977 -0.04626291 -0.008778522 0.029404644 -0.013452261 0.013934899 0.01337333 -0.0689896 0.041163128 -0.09052949 -0.081374645 -0.01790648 0.08806657 0.0152378455 0.049691264 -0.08552621 -0.10046687 0.004262634 0.035739742 0.081318386 -0.023825847 -0.06203641 -0.0068013207 -0.046959944 -0.055029914 -0.09641142 0.029292999 -0.024252992 0.05079508 -0.020304777 0.09118141 0.0017555399 -0.013811299 0.046033084 -0.005778883 0.049194705 -0.009030295 -0.05687065 0.039321244 -0.087022 -0.013473726 0.04727826 -0.057890978 -0.03996594 0.05909689 0.08032023 0.004400512 -0.047469757 0.027429735 -0.009964108 -0.016717734 -0.043994267 0.07851973 -0.012583708 -0.022373572 0.074836075 0.022776056 0.09234424 
15 | 11 0.065646894 -0.065108374 -0.15090083 0.071771994 0.079468876 0.05236764 -0.015273843 0.051433012 -0.03645063 0.00892643 -0.11918988 0.07403228 -0.01611983 0.011527265 -0.08064611 -0.04616409 -0.096871145 -0.03091134 0.022328261 0.0841041 -0.17840455 0.10218899 -0.10430696 0.01263396 -0.063074164 -0.08068626 -0.08690173 -0.117064506 -0.14020562 0.100191675 0.12823881 0.047783867 0.011327631 0.108230196 0.0449704 0.05666709 -0.04635859 0.11585655 0.085062675 -0.010133725 -0.016054725 0.06435807 0.057983458 0.1662126 -0.1401176 0.03835713 -0.124367096 -0.025713837 0.049917057 -0.03486573 0.19207679 0.013834722 0.12092178 -0.037713457 0.06920468 0.02098541 0.12816226 -0.03312041 0.008627903 0.010279653 0.06940059 -0.11756816 -0.076772995 -0.061450806 0.085006975 -0.10028046 -0.054077804 -0.04336415 -0.034488957 0.09350591 0.072809406 0.050577763 -0.045934632 -0.049180046 0.063244365 -0.12926377 -0.017668918 -0.011094322 0.012631424 -0.1328643 0.12350787 0.07988706 -0.0739988 0.006727669 -0.036335494 -0.07159528 0.12703638 0.06335538 -0.023112683 0.12184643 0.053053584 -0.059756197 0.058328602 0.02849358 0.14276755 0.030875068 0.0372106 0.15585208 -0.1800324 -0.010622328 -0.04691161 0.16375498 -0.004660627 -0.118819475 -0.05626502 -0.055519614 0.06444184 0.02119895 -0.009954879 0.023750342 -0.08246188 0.0339044 0.054100014 -0.06022897 -0.051999737 -0.06850945 -0.0022578607 -0.114731506 -0.08647937 0.0021687837 -0.025596859 0.012611685 0.015418447 0.061153762 0.02780142 0.098317966 0.052940793 -0.088698395 
16 | 34 -0.03653256 -0.092074156 0.031306483 -0.08093728 0.036695503 0.1627509 0.061735608 -0.10763594 -0.0954292 0.0013925084 -0.074387655 0.010775321 -0.114564754 -0.046638265 0.11246852 0.029867165 0.024667272 -0.09654262 0.052159004 0.018307101 0.074846126 3.701538E-4 -0.00622967 0.01128017 -0.11591834 0.10891056 0.07626241 0.13924618 0.042513154 0.026069075 0.007318024 -0.088955194 -0.097553015 -0.040649842 0.042717814 0.033021938 0.1315891 0.009055623 0.07272016 -0.0076874327 0.112590715 0.016981468 0.06236927 -0.14939715 0.021166611 0.082763605 -0.015810382 -0.06537142 -0.06085272 -0.08693795 0.027757145 0.0078990515 0.028841302 -0.023160908 -0.16265762 -0.16185662 -0.08701093 0.112529725 -0.02008837 -0.0808137 -0.11796748 0.12327517 0.0012996746 0.027677545 -0.103214465 0.059913367 -0.023594497 0.02201191 0.07846696 -0.021837903 -0.0071862056 0.015316979 -0.067190096 0.05308763 -0.034717057 0.013630758 -0.026894324 -0.084633864 0.014318737 -0.07072006 -0.14131415 -0.0055122967 0.08349038 0.006051535 0.08580291 -0.14178877 -0.10702435 -0.009207674 -0.00587199 0.10193774 0.016018093 -0.040125377 -0.06338002 -0.050663695 -0.03288565 -0.15263705 0.036565244 -7.6081866E-4 0.049630128 -0.041202534 0.10072717 0.004322953 0.0040444047 0.0769819 0.061942738 0.07352827 -0.058416955 -0.070574075 0.003400498 -0.13944358 -0.028715566 0.05734107 -0.044339094 -0.069121085 0.067210965 0.13630234 -0.007824334 -0.067668445 0.045199852 0.0038127794 0.063382134 -0.0034103307 0.07330414 0.019776003 -0.0015660072 0.052651495 0.073591374 0.12574556 
17 | 12 0.26636052 -0.18304625 -0.07245026 -0.027564865 0.18398495 0.08045351 0.13675036 0.16548267 -0.05355983 0.087764144 -0.05476184 0.0361746 0.0020501725 0.10399107 -0.09805866 0.015074482 -0.118558146 -0.019216117 0.101177335 0.17716981 -0.101265304 0.17165716 -0.04158134 0.10898498 -0.053144515 0.05526177 -0.033536866 -0.13006544 -0.12195247 -0.023511657 0.051474195 0.010697735 -0.024389159 0.09861902 0.111843206 0.13905242 -0.037301704 0.055117693 0.030522788 0.008470418 -2.28372E-4 0.03374155 -0.02394781 -0.02800669 -7.3858903E-4 0.035348922 -0.06282645 0.035013225 -0.048090305 0.08708734 0.19695185 0.012296378 -0.03372015 -0.041895367 0.02965887 -0.018041665 0.16849479 0.07391487 0.061782062 -0.18356532 0.12877868 -0.07986088 -0.054388084 -0.019101957 0.077718765 -0.04301743 -0.1954452 0.05911631 0.060121354 0.02383969 0.020486651 0.08397071 -0.06664209 -0.015450752 0.114812195 -0.06165368 0.012329189 -0.04022093 0.023587156 -0.08689662 0.17870495 -0.025474235 -0.10442177 -0.072015144 0.07899797 0.041749828 -0.026540069 0.013499901 -4.039554E-4 0.010545122 -0.09150355 -0.11816153 0.2182036 0.13345738 0.053294376 -3.5801797E-5 0.026202848 0.1513983 -0.09541217 -0.0545031 -0.11216132 0.16198193 -0.06395583 0.08041681 -0.19542018 0.027644327 0.1384812 0.02179823 -0.030905103 0.034882054 -0.018788064 0.06744401 0.06416479 -0.025449343 0.042861708 -0.03328274 0.057905275 -0.14857145 -0.16455701 0.058960266 -0.028251283 -0.053918935 -0.03270889 0.16988806 0.044796333 0.09955951 -0.008955221 0.039840557 
18 | 13 0.2216544 -0.17125036 0.09201414 -0.07977124 0.19459417 0.12690705 0.16946018 0.17620838 -0.03967693 0.15711358 -0.030205205 0.116696 0.06453038 0.026314491 -0.09616216 0.0017520528 -0.096757144 0.014899757 0.20032595 0.17937969 0.02935532 0.14319053 -0.028244974 0.046460394 -0.03438225 0.008440967 0.041816477 -0.14935839 -0.039667066 -0.02914929 0.11968434 0.0188353 -0.018379612 0.026917046 0.051127624 0.16276631 0.04946769 0.01637526 -0.007443236 -0.079137065 0.046086174 -0.028071688 0.084835805 -0.10793847 0.0758354 -0.022509163 0.030208498 0.030395644 -0.010505409 0.07925452 0.09256232 -9.967978E-4 -0.048457235 -0.096681684 0.030234968 -0.16084425 0.11315286 0.073131144 0.022917124 -0.0882436 0.14717662 0.026873434 -0.06136605 -0.023905523 0.1300896 0.0457576 -0.13661267 0.10636897 0.16721058 -0.08637165 -0.09152707 0.003974193 0.014421131 0.02722039 0.14178137 0.09559604 -0.0026783668 0.0065576974 0.09786926 -0.034033205 0.15908931 -0.049386784 -0.036437906 -0.13817392 0.098454185 0.102672584 -0.107361615 -0.075184815 0.010515665 -0.083869964 -0.051943295 -0.08629088 0.18730503 0.09407671 -0.072601035 0.01714021 -0.06758013 0.14242037 0.069566645 -0.15068795 -0.02577359 0.11435885 -0.06700281 0.07791065 -0.2565265 -0.0075816577 0.12160533 0.07083214 -0.07078402 0.07669457 -0.005677512 0.08953171 -0.030181121 0.014176579 0.11137845 0.08350044 0.028871924 -0.1297093 -0.102044046 0.005834258 -0.028341103 -0.028416047 -0.074645095 0.1945262 0.123751596 -0.014469846 -0.039902546 0.076100275 
19 | 14 0.009470174 0.0057734502 0.10672589 0.0032503477 0.080312684 0.094534434 0.014596663 0.039967265 -0.031338748 -0.023242798 0.03184757 0.0520416 -0.011580965 -0.039806746 -0.06467869 0.044125155 0.011406385 0.043259997 0.09899704 -0.009748351 0.0043890635 -0.022734364 0.03787115 -0.046346854 -0.020651948 -0.006927889 0.05435791 -0.06378761 -0.015772173 0.06559949 0.10245389 -0.02110787 -0.044768035 -0.029774329 -0.011532096 -0.053989217 0.07676274 -0.034480557 0.06889629 -0.13168012 0.0033725586 -0.038658276 0.05372586 0.024812438 0.011693405 0.055171728 3.5794376E-4 -0.097016245 -0.07403569 0.0011221796 -0.022304175 0.020952001 -0.010340459 -0.07998611 0.01759387 -0.11102401 0.0063513783 -0.079005755 0.02764062 0.08752779 0.009296798 0.029352808 -0.06489931 -0.09880781 0.15415102 -0.01020126 -0.043978233 -0.0017812789 -0.012612008 -0.017540803 -0.075629994 -0.020608637 0.05693845 -0.046693824 -0.005004258 0.010430428 0.028944053 0.04438932 0.05914401 -0.04120147 0.010030508 -0.008845162 0.09974705 -0.049387738 0.022126745 0.0316448 -0.063115954 0.013263241 0.06636572 0.0016774962 -0.01700859 -0.067278415 0.05177277 0.019543564 -0.03280517 -0.036705524 -0.045494128 -0.020216873 0.057016272 -0.04390165 0.06645298 0.025856016 0.013458654 -0.009861034 -0.08893188 -0.06300476 0.0075751003 -0.053488407 0.06887712 0.04131729 0.016815769 0.043478005 -0.046966434 0.027724866 0.05655993 0.011057591 -0.029847264 -0.03714542 0.032311514 -0.041086048 -0.01608266 -0.11049912 0.0016300781 -7.241128E-4 0.003922007 0.028298503 -0.045614026 0.071529254 
20 | 15 -0.031638622 0.04916783 0.16589043 0.0063529243 0.1760791 0.13081005 -0.039982814 0.029822102 -0.11061566 -0.15416934 0.10073998 -0.1613615 -0.1272162 0.12347494 0.05694884 0.039205797 0.094309494 0.011739549 0.070098706 -0.089123316 0.12868051 -0.03940524 0.23715255 0.03446449 -0.09458164 0.19072603 0.11541517 0.10997328 0.03277799 0.019024614 0.060054976 -0.1535575 -0.06706833 -0.110703975 0.13953929 -0.112386465 0.1536716 -0.05566898 0.017685791 -0.029718917 -0.02318752 -0.1419994 -0.066307224 -0.13831529 -0.032005314 0.026329529 -0.09954169 -0.07425937 -0.281504 0.038103305 -0.17913482 0.057877507 -0.1459486 -0.03805848 -0.121867605 1.96998E-4 -0.07205965 -0.09165004 0.06305237 -0.12243945 -0.15475926 0.037304424 -0.03272794 -0.06277777 0.012220612 0.03902435 -0.2248025 0.13727172 -0.013906785 -0.16478209 -0.08420818 0.024921456 0.018803207 -0.010620263 -0.23617296 -0.014902993 -0.020358562 0.042119168 0.08858574 0.0038860042 -0.18489335 -0.13443159 0.16494323 -0.1041126 -0.009447037 0.027189292 -0.27652213 0.0011787598 0.0922485 -0.008182534 -0.16093709 -0.119979456 0.037007768 0.12921439 0.00928569 -0.17024496 -0.01797774 -0.09737567 0.027327633 0.026496438 0.18614708 0.010322878 -0.00476412 0.21847177 0.03022169 0.11555054 -0.10687849 -0.2587971 0.13281691 0.003778172 0.17754336 0.06381544 0.03450488 0.0061254487 0.22208817 0.0927508 0.08332377 -0.017672427 0.060697388 0.12794314 -0.03583486 -0.2230706 -0.0057381624 0.01677847 -0.13223055 0.11410433 -0.17225352 0.21428473 
21 | 16 -0.036150802 -0.0023838594 0.023285618 0.0017463702 0.10642111 0.09038737 -0.05366574 -0.045942243 -0.1183795 -0.08844449 0.049789026 -0.14059642 -0.12805378 0.04783033 0.050380103 -0.039080866 0.07449204 -0.05506847 0.0042529213 -0.05539879 0.07928272 -0.062742524 0.1583919 0.035007894 -0.10010812 0.12922134 0.06077126 0.13910948 0.036201842 -0.0036650524 -0.009916257 -0.16475612 -0.02116405 -0.010621845 0.16468212 -0.031923365 0.10312811 -0.0078037763 -0.028425701 0.021544036 0.03415342 -0.11881166 -0.060748454 -0.121600084 -0.06840882 0.01689293 -0.07285612 -0.025613733 -0.13943349 -0.016760033 -0.090850584 0.0048801014 -0.04582455 0.029201483 -0.13085584 0.056830507 -0.016698318 0.01609913 0.037178908 -0.16498792 -0.12319788 0.059998833 -0.022085926 0.017157419 -0.090862885 0.04430705 -0.16973643 0.068042755 -0.037974045 -0.09042281 0.054111745 0.043229755 -0.0028074482 0.06126964 -0.15416221 -0.07815571 -0.022441529 -0.032967407 0.026515007 -0.0025411176 -0.1718279 -0.05167735 0.0842343 -0.017285943 -0.0021990186 -0.05700477 -0.16762257 0.019557135 0.054327797 0.05596139 -0.14939483 -0.07837607 0.05385981 0.056894407 0.040980183 -0.15472654 0.034857098 -0.052480288 -0.068069056 0.04735729 0.09566898 0.04110807 -0.023737315 0.12996571 0.09869823 0.12309698 -0.06553169 -0.14841878 0.088009275 -0.056230973 0.10131956 0.07245732 0.070751525 -0.027505033 0.13486193 0.047700986 0.09008489 -0.04690533 0.0719783 0.17946549 -0.032086562 -0.11238396 -0.014583429 0.053235188 -0.14789827 0.077799395 -0.07621731 0.18595544 
22 | 17 0.15180287 -0.13916856 -0.34444153 0.1196004 0.050252825 -0.030699752 0.0106007485 0.11080106 0.1207573 0.100314155 -0.16202748 0.07644759 0.04305925 0.07702029 -0.08895205 -0.047970578 -0.21111858 -0.072989486 -0.0838686 0.20204099 -0.3329531 0.2504477 -0.27270305 0.110584244 -0.0026147945 -0.12243026 -0.2590417 -0.20262861 -0.18789111 -0.04445893 0.053646885 0.12727451 0.09065953 0.17092618 0.066059016 0.2563658 -0.2526524 0.2114761 0.025973473 0.17086308 -0.074086905 0.18910079 0.052190334 0.22482103 -0.16487207 -0.008909999 -0.13610284 0.11120813 0.27636114 0.036084373 0.31220388 -0.0025454808 0.122081876 0.008616153 0.121562086 0.1411451 0.26879188 0.09475135 -0.025833702 -0.05594964 0.25798142 -0.17723265 0.004274278 0.07230819 -0.071375936 -0.038161837 0.002109718 -0.016082132 0.042733744 0.12251636 0.18252644 0.15348127 -0.13940467 -0.061540827 0.23502146 -0.19499151 -0.048586637 -0.109352276 -0.10231883 -0.10544906 0.28648123 0.17938407 -0.28887993 0.0068928436 -0.06349133 -0.020975623 0.2657233 0.061994996 -0.17776829 0.11647148 0.08608513 0.05215441 0.10931438 0.056525465 0.20019682 0.13642186 0.102251254 0.28928 -0.22747052 0.017315293 -0.19854285 0.23748589 -0.031595737 -0.19356827 -0.05546854 -0.016720032 0.09460561 0.14519988 -0.20426194 -0.015531001 -0.19546543 0.0064672832 0.1335004 -0.069600634 -0.16109027 -0.12936692 -0.007953315 -0.14564991 -0.25789252 0.016477972 0.010424812 0.251366 -0.0188517 0.09899721 0.04863569 0.0268086 0.17779441 -0.22194968 
23 | 18 0.22224864 -0.19883546 0.10976189 -0.04558409 0.17052576 0.23837975 0.13219702 0.0012427762 -0.17917894 0.12713856 -0.083933756 0.117116734 -0.06171215 0.038819972 -0.10777729 -0.052916244 -0.1227404 -0.007275034 0.26830435 0.12074956 0.016206188 0.056263205 -0.05818672 -0.02541922 -0.10255738 0.041625965 0.08521732 -0.069757506 -0.12850289 -0.032254096 0.15297459 -0.005740253 -0.009083777 -0.014859601 0.14447713 0.09467028 0.15570302 0.048698194 0.011964334 -0.05215051 0.14931522 -0.06639051 0.14435901 -0.20512217 0.026114317 0.0037627572 0.04158061 -0.010480371 -0.034807075 0.015901059 0.043661788 0.023382433 0.012634392 -0.20935401 -0.08935639 -0.22038203 0.07681884 0.09037342 -0.005641705 -0.06737706 0.019457376 0.08661681 -0.10298319 -0.15703925 0.06747971 0.020371102 -0.10799765 0.08880468 0.22634675 -0.023620376 -0.01864467 -0.085936606 -0.006126523 0.001128636 0.101482995 0.02635983 -0.011510161 -0.19375466 0.16767679 -0.14789003 0.12942761 -0.06791244 -0.024875702 -0.09809441 0.08551651 0.019455157 -0.104978494 -0.08902508 -0.016802752 0.07048182 -0.04122786 -0.15081489 0.19661987 0.061645277 -0.06042394 -0.05973976 -0.013237206 0.256706 0.085385144 -0.11551597 0.054940462 0.15929963 -0.14678071 0.0024062076 -0.22727914 0.039148774 0.13627267 0.06676493 -0.060490467 -0.020720929 0.006705503 0.14904875 0.0032021755 -0.094541594 0.15603249 0.08324955 0.10092419 -0.06774967 -0.07658753 -0.07338824 0.022608664 -0.037739217 0.0014449705 0.11857579 0.13093314 0.06765371 -0.07445371 0.09267784 
24 | 19 0.061135154 0.024190526 0.10320703 0.06622575 0.17937887 0.10598847 -0.063101634 0.023497408 -0.12916781 -0.061514154 0.08111085 -0.11243379 -0.09951193 0.12751712 -0.047041673 -0.059023194 0.011450777 0.04686098 0.089110866 -0.034785654 0.04242261 -0.043219093 0.16321738 0.0019783997 -0.04434653 0.11505069 0.061755292 0.035721976 -0.042432733 -0.042024035 0.07607157 -0.098002814 0.03980084 -0.066022724 0.19589628 -0.027108293 0.08273923 0.0022482213 -0.052731417 0.010858768 -0.028942306 -0.16563423 -0.03155108 -0.14055905 -0.07433867 -0.030585438 -0.057841532 0.0044447198 -0.14025362 0.093708165 -0.16245696 0.040120006 -0.117641285 -0.08510028 -0.055605624 0.06403018 0.057388764 -0.048097387 0.0398934 -0.08622383 -0.043570798 0.016823519 -0.04652555 -0.09469994 0.007657961 0.03192228 -0.19430088 0.1447204 0.03967601 -0.12533517 -0.004561217 -0.03537471 0.055229634 -0.0056596976 -0.115745805 -0.043984286 -0.018126097 -0.04651834 0.1204127 -0.019484181 -0.03843542 -0.10422373 0.026216522 -0.102942504 -0.07128734 0.09612375 -0.17980574 -0.03015495 0.044007957 -0.0012039159 -0.17086461 -0.0945952 0.15288155 0.12532617 0.014590073 -0.06912265 -0.00849388 0.049061887 0.0021206457 0.040303823 0.114568956 0.08962225 -0.085184395 0.091165744 -0.054764017 0.10376988 -0.01592027 -0.11391158 0.046403524 0.035376333 0.15104705 0.09140518 0.08399828 -0.01927325 0.19911389 0.027273625 0.1324152 -0.0070682936 -0.0049565947 0.11414474 -0.07690956 -0.12411253 -0.052801836 0.049467653 -0.09340981 0.07528228 -0.18219078 0.110013194 
25 | 1 -0.07248621 -0.021234794 0.02202946 -0.015151738 0.058341585 0.13968052 -0.036493164 -0.089107715 -0.20201214 -0.053466935 -0.09192623 0.02402264 -0.0842383 -0.093344696 -0.004697614 -0.100056544 0.041805618 0.006667141 0.14696628 -0.008874721 -0.035634547 -0.061175954 0.04391359 -0.054516282 -0.10404887 -0.08621663 0.095539734 0.05044701 -0.059143722 0.22484764 0.088221356 -0.0354646 -0.0619217 0.019378714 -0.051323503 -0.10845412 0.20215125 0.009459609 0.104452886 -0.14595556 0.1618932 -0.067406416 0.07231436 -0.02079567 -0.058979694 0.016014814 -0.070051774 -0.112686574 -0.105190106 -0.13943124 0.09389717 -0.0152628925 0.10463608 -0.0405358 -0.068340555 -0.15144868 0.0410153 0.012288071 0.020491645 -0.006022312 -0.102778085 0.047951296 -0.13199276 -0.13823222 0.09715068 -0.08684761 -0.019007977 -0.11997411 -0.061285265 0.03548963 0.027634982 -0.09317917 -0.014786415 0.0044233496 -0.08174992 -0.021094806 0.0172018 0.024201011 0.06677403 -0.12409649 -0.118259326 -0.008989793 0.16173775 0.07060383 0.050866593 -0.14632949 -0.008756196 0.01387642 0.1114677 0.17585234 0.0014011224 -0.14846987 0.06874757 -0.07123666 0.014428133 -0.08013057 -0.02513229 0.039122876 -0.07554531 -0.015580931 0.088949725 0.076369226 0.032167733 -0.031480912 -0.033202678 -0.031105202 0.009627668 -0.07438377 0.11771704 -0.003414737 -0.04480433 0.052675508 -0.026914036 -0.083042435 0.06408962 0.05201783 0.024180535 -0.08128753 0.11886801 -0.030908087 0.034801222 -0.16537209 0.046739712 0.03934109 0.08893966 0.17638324 -0.0028291037 0.09524274 
26 | 2 0.07794314 -0.2047732 -0.00644866 -0.032919332 0.13410848 0.25385076 0.08944962 -0.0289081 -0.1655108 0.046380974 -0.14354797 0.13392293 -0.055512637 -0.07939425 -0.019875161 -0.0643372 -0.0879981 -0.07092592 0.20714277 0.08565737 0.023405064 0.048459556 -0.103797905 -0.01832737 -0.16946453 -0.049933907 0.08147784 -0.024774032 -0.07619654 0.09420099 0.13625981 -0.020178305 -0.07864704 0.06785112 0.074075714 0.06301488 0.14396694 0.076804735 0.06686761 -0.11027193 0.19244285 -0.049456127 0.14096579 -0.066721134 -0.031650644 0.015225723 -0.014055711 -0.07511693 0.013043154 -0.13504535 0.13156928 -3.894904E-4 0.13940565 -0.11550757 -0.086444244 -0.20406325 0.009641506 0.1206333 -0.027753131 -0.019310405 -0.062187415 0.116473384 -0.10533949 -0.08186912 0.0536221 0.004761582 -0.088947386 -0.036667902 0.08512419 0.038092013 0.0171309 0.0018268124 -0.043611847 0.03490295 0.04244053 -0.03723421 -0.0615724 -0.1023193 0.10514764 -0.18284915 0.01467012 0.003403823 0.051598527 -7.117519E-5 0.089522116 -0.13882506 -0.062181253 -0.019473646 0.008635101 0.15830547 0.009922207 -0.12960652 0.100686796 -0.049806293 -0.00744912 -0.06698246 0.05022353 0.18330027 -0.016120818 -0.10552505 0.09526589 0.1361495 -0.045502275 -0.046943154 -0.10967977 0.007889387 0.050855413 -1.2625568E-4 -0.013535254 -0.016999464 -0.05778921 0.13623425 0.020880288 -0.10628211 0.09548798 0.12860703 0.011670505 -0.14150473 0.024336621 -0.014394111 0.021415394 -0.006161799 0.062766895 0.11293911 0.11529383 0.09199068 0.052528974 0.050179888 
27 | 3 -0.03622468 -0.052848402 0.051904265 -0.05282837 0.05304789 0.13999514 0.033100296 -0.058926053 -0.12787178 0.0058981916 -0.02961086 0.12816694 -0.008950633 -0.21185854 -0.036183983 7.477044E-4 0.031049354 0.011466168 0.1332466 -0.014682748 0.05696215 -0.094005905 -0.0035492054 -0.07195217 -0.05888787 -0.09853592 0.11350622 -0.010345372 0.03280949 0.106590696 0.04141547 -0.035830796 -0.06733844 0.054752443 -0.0650221 -0.03825861 0.1360952 -0.055004682 0.06200578 -0.20021339 0.13439852 -0.081006706 0.07855787 0.0050794063 0.042555027 0.05637317 0.061912116 -0.121206954 -0.009566968 -0.10911405 0.04186019 -0.019434689 0.09196815 -0.010918907 -0.042865492 -0.18684551 -4.2601916E-4 0.045922138 -0.008015656 0.06790297 -0.026586011 0.15204555 -0.095099725 -0.048466306 0.11802221 -0.0024961268 0.010413243 -0.15776482 -0.056395315 0.04276973 -0.009806353 -0.040182203 0.06351092 0.045705117 0.02219994 0.020725021 0.007240321 0.037427828 0.014309662 -0.07868645 -0.05266472 0.048312515 0.17093375 0.0513195 0.09047747 -0.09954809 -0.0617373 0.017411286 0.122488745 0.089562126 -0.022879172 -0.09296976 0.08463649 -0.119759284 -0.092162095 -0.042882472 -0.014212305 -0.04808444 0.03547622 -0.06267999 0.059855357 0.00310109 0.05260973 -0.05578547 -0.06048107 -0.06591062 0.030046768 -0.010337952 0.079662144 0.008009062 -0.045532487 0.06477293 -0.04939162 -0.0024914742 0.039150182 0.06213137 -0.075592995 -0.09335992 0.14224452 -0.0036183887 -0.0076109995 -0.07727406 0.03395332 0.04961256 0.038689494 0.025911655 0.029427227 0.11007095 
28 | 4 0.05516301 -0.040758047 0.13607025 -0.05764336 0.11199366 0.12003728 0.03573005 -0.017623797 -0.16363408 0.05678867 -0.02433006 0.10499335 -0.006822736 -0.10723319 -0.08391983 -0.04713075 -0.0044983234 0.04485644 0.18119812 0.07800611 0.096162654 -0.059695054 0.021938432 -0.08910221 -0.046021763 -0.03681658 0.14482196 -0.02801712 -0.016587889 0.11354888 0.14569327 -0.027473304 -0.022476498 0.035523675 0.029069386 -0.011188703 0.13456456 0.0016934865 0.027852897 -0.20782194 0.061478082 -0.09268151 0.090431914 -0.036436956 0.009970049 0.02650632 0.04164747 -0.085676834 -0.099989384 -0.01607713 0.0112988595 0.015963674 0.045166757 -0.08236967 0.07295075 -0.1622336 0.023691962 -0.06721265 0.025510708 0.06349788 -0.03114291 0.029540312 -0.13241173 -0.13009544 0.24299748 -0.0520121 -0.0556666 0.0018897584 -0.0024028786 0.00793247 -0.09729215 -0.13343865 0.09844089 0.039561246 0.023546707 0.06926987 0.055359542 0.042621527 0.17348953 -0.08796665 0.042724386 -0.055102896 0.10774134 -0.030217934 0.039042052 -0.0032916134 -0.06430812 -0.040079575 0.096103325 -0.022789098 -0.028011749 -0.11038682 0.07107138 0.0056553083 -0.06704007 0.009942165 -0.07476346 0.02441465 0.016542336 -0.11695366 0.075789705 0.064034626 -0.06547768 -0.040316314 -0.17830202 -0.07646662 0.119471535 0.028097909 0.08930397 0.0962581 0.009723059 0.06965662 -0.09065112 0.0135996565 0.06942561 0.03954294 0.042516816 -0.06331249 0.042231835 -0.022109516 -0.12322408 -0.15363786 0.0046367757 0.053285588 0.05480813 0.03881904 -0.08505025 0.05737067 
29 | 5 0.17507696 -0.049109653 -0.06944049 0.013443012 0.0907545 -0.02970631 0.05490514 0.08672183 -0.032056324 0.0787892 -0.07926755 0.03588383 0.013089915 0.11981907 -0.06521502 0.009979292 -0.111448064 0.031992182 0.023547268 0.17469364 -0.123126805 0.15305711 -0.082012445 0.05228466 0.0015836974 0.04417592 -0.057595465 -0.11146822 -0.14196502 -0.0033360778 0.088211566 0.097757585 0.046304177 0.05574032 0.063357756 0.09745144 -0.09961686 0.10576199 0.026878454 0.06296812 -0.1028227 0.095484704 -0.007341872 0.047637224 -0.057807036 0.01855344 -0.080502145 0.07364804 -0.0394217 0.11537225 0.15065879 0.050345406 -0.024380388 -0.04532515 0.122767106 0.03133584 0.13976194 -0.0643831 0.029408118 -0.060272407 0.11022837 -0.20246077 -0.035419416 -0.06660137 0.10492657 -0.10750342 -0.0493204 0.08236468 0.059965327 0.06315119 -0.030552536 -0.02887464 -0.045036376 -0.055684198 0.102794535 -0.021905765 0.033579346 -0.017624604 0.06541171 -0.07801037 0.22471188 -0.028608492 -0.14403425 -0.051707167 -0.033213273 0.06214152 0.09342461 -0.004324093 -0.056808013 -0.021215845 0.03263083 -0.058363326 0.058583446 0.1268655 0.09355923 0.09817483 -0.0029892833 0.15518117 -0.109743156 -0.031989843 -0.09191717 0.1327103 -0.07768306 -0.0027452647 -0.16521926 -0.008501881 0.14315994 0.06984347 -0.03715878 0.066881664 -0.04322131 -0.019908832 0.0011307054 -0.023625003 -0.040988162 -0.083794475 0.07411149 -0.037459914 -0.20838407 -0.042921066 -0.0833241 -0.011579472 -8.554749E-4 0.056759212 0.06690518 0.09447547 -0.028037576 -0.12145719 
30 | 6 0.051212825 -0.023487901 -0.13127449 0.061668217 0.13408041 0.08208532 -0.043687128 -0.051479295 -0.12408622 -0.0020505888 -0.1403897 0.03914679 -0.06431522 4.0008518E-4 0.016977152 0.009175205 -0.07398035 0.034732927 0.010020998 0.14056829 -0.14739975 0.08675818 -0.09908959 0.02984167 -0.048071716 0.019728435 -0.0349825 -0.031674724 -0.1272848 0.04922199 0.08702369 0.07326197 0.036997247 0.054370522 0.06919528 0.10503971 -0.0778582 0.14324021 0.081577115 0.033072002 -0.092150316 0.07095471 0.020851854 0.05421071 -0.11803599 0.079770684 -0.13194697 0.014985895 -0.01705686 0.039651774 0.14811663 0.057052605 0.031149983 -0.027012475 0.074591234 0.018993484 0.15605314 -0.029044293 0.0071596424 -0.0015792826 0.059432533 -0.11104004 -0.052054584 -0.05104255 0.043618534 -0.08689696 -0.012429315 -0.012220699 0.0032257382 0.100310214 0.011550466 -0.04001653 -0.058186766 -0.041972425 0.05970696 -0.08331745 -0.015205887 -0.02749791 0.037047263 -0.17953868 0.13413796 0.05424673 -0.04949715 0.015456956 -0.080367446 -0.034453657 0.06427462 0.041290138 -0.033818446 0.12358419 0.022854658 -0.058175158 0.06421186 0.021563075 0.090957366 0.0681193 0.043792617 0.12528329 -0.13385537 0.013529994 0.0147694005 0.15597598 -0.01425957 -0.073508956 -0.090133406 -0.0046942886 0.07735537 -0.013439685 -0.02173798 0.034828585 -0.11145331 -0.0010953636 0.01014418 -0.070106246 -0.01621178 -0.02522673 0.025816372 -0.1034183 -0.14524102 -0.006416381 -0.12545596 5.465885E-5 0.051259656 0.019133188 0.026070658 0.16084385 0.047660217 -0.14558823 
31 | 7 0.060373157 -0.08569849 -0.14555304 0.09063924 0.18656267 0.14332117 -0.03983608 0.028006837 -0.050500397 -0.05692306 -0.07930485 -0.044797692 -0.13088585 0.08165056 -0.022704298 -0.0032495689 -0.069844745 -0.07545785 -0.005523182 0.10121144 -0.16797891 0.11235711 -0.03635069 0.07334448 -0.11831675 0.08244897 -0.08215078 -0.025394317 -0.13390972 0.04050826 0.115984015 -0.060671404 -0.016049296 0.06965047 0.18345235 0.10820416 -0.030291276 0.14017561 0.096311495 0.06361204 -0.043573152 0.054702345 0.03167928 0.061453044 -0.18383688 0.089478515 -0.19355848 -0.040241595 -0.027349908 0.011902865 0.13888882 0.048995625 0.03537206 -0.049041767 -0.021660274 0.058748495 0.14976868 0.007809479 0.042916425 -0.10865237 0.038187087 -0.085564 -0.048271216 -0.017447012 -0.03135983 -0.012619567 -0.16049282 0.0560067 -0.011023135 0.017704714 0.097743474 0.11540488 -0.087094456 -0.04871072 0.010238392 -0.19947416 -0.032464374 -0.081938766 0.0064245877 -0.14680624 0.03673603 0.06119316 -0.05452967 -0.038158 -0.04631555 -0.078794435 0.0012827882 0.08046586 -0.045353565 0.16644487 -0.051575407 -0.07256586 0.10031879 0.10330246 0.16307768 -0.08496992 0.081711695 0.14431709 -0.17987889 0.026979735 0.015462212 0.21419503 -0.035533406 -0.022926513 0.0046643214 0.05922799 -0.004969641 -0.095694914 -0.014509716 -0.060520627 -0.054278456 0.08714872 0.09233709 -0.07554899 0.03370872 -0.027290622 0.05048533 -0.16500963 -0.11651717 0.10409478 -0.04671709 -7.023993E-5 0.026419872 0.070528276 -0.09341853 0.1514202 0.04943157 0.02336931 
32 | 8 0.044713367 -0.03497532 0.27609393 -0.052319445 0.12237357 0.16218005 0.06335016 0.09674113 -0.06878626 0.028531902 0.09860221 0.035377756 0.0032634907 -0.02165641 -0.111334056 -0.051956665 0.043323744 0.03930432 0.23747604 -0.057018906 0.13123353 -0.078240745 0.1545195 -0.07487952 -0.034998294 -0.010642982 0.13887973 -0.056306727 0.032343 0.051659077 0.13622889 -0.10537771 -0.0545897 -0.09824615 0.023238733 -0.080438 0.23931365 -0.10046058 -0.00224755 -0.20609833 0.12709175 -0.1836561 0.09873971 -0.14588101 0.08032739 -0.032624505 0.071582176 -0.100895055 -0.09382009 -0.013487095 -0.13779521 -0.025598494 -0.04284027 -0.14666396 -0.05958234 -0.19759616 -0.03293776 -0.014199945 0.034187056 0.035535827 -0.023334503 0.1605212 -0.09539641 -0.12980057 0.16410291 0.06931605 -0.13983394 0.08259184 0.08451233 -0.16154958 -0.098308675 -0.055759843 0.11290927 0.019316975 -0.04806822 0.096798375 0.024188226 0.035578877 0.14131396 0.03486932 -0.07644557 -0.09666243 0.1522444 -0.12753306 0.08211404 0.08774034 -0.20268188 -0.07788813 0.111334704 -0.063808925 -0.09651001 -0.10166536 0.13414897 0.045849796 -0.12703347 -0.1216064 -0.12105159 -0.0029067637 0.17217565 -0.10239616 0.11898583 0.014801591 -0.034353808 0.07409265 -0.13450406 -0.03460143 -0.0048417444 -0.04498099 0.075867034 0.04927213 0.11997049 0.12668721 -0.04506013 0.030263813 0.202673 0.10510387 0.023673259 -0.031191627 0.13640791 -0.008411058 0.049400017 -0.17995107 -0.08416068 0.08896332 0.044606414 -0.041327696 -0.14510854 0.23879853 
33 | 9 -0.005212816 -0.05359284 0.11297921 -0.044596422 0.049682047 0.11650387 0.04900906 0.0405005 -0.06581046 -0.05318306 0.042243503 -0.00881735 -0.027953375 -0.013708007 -0.009804972 -0.016351538 0.043835167 -0.013106062 0.14226678 -0.06851227 0.09816847 -0.042821147 0.116434775 -0.009889214 -0.06341597 -0.016321091 0.111850776 0.034710363 0.03779499 0.10561193 0.025046365 -0.08357809 -0.11903131 -0.03861969 -0.025263019 -0.12292363 0.17313038 -0.09962294 0.041311514 -0.13397123 0.15904488 -0.11083244 0.008925581 -0.0897467 0.057565194 -0.027066607 0.026173599 -0.10567472 -0.11833486 -0.08968358 -0.031232081 -0.022898054 6.5325544E-4 -0.031115603 -0.106215805 -0.13950144 -0.09184323 0.044937156 0.018664647 -0.034974556 -0.10761409 0.13342124 -0.051298346 -0.0671748 0.05481436 0.010647324 -0.12654257 -0.03244854 -0.014904945 -0.06949156 -0.048710648 0.023304135 0.02083941 0.0129643995 -0.10772175 0.02430052 -0.011636925 0.04883223 0.055319678 -0.0015784915 -0.15165469 -0.0959133 0.15030974 -0.01536746 0.099067025 -0.040094603 -0.15517728 -0.008504329 0.11686002 0.019867204 -0.073955774 -0.09994389 0.0604899 -0.029313432 -0.06730717 -0.11320126 -0.0156609 -0.04199228 0.067159146 -0.030246986 0.08554846 -0.037637208 0.026461232 0.095399894 -0.022262111 0.012007426 -0.062751964 -0.113394246 0.09489105 0.001809431 0.09284046 0.07417505 0.008500977 0.0020965948 0.13153355 0.10420447 -0.015408659 -0.036230687 0.1379387 0.023715649 0.09507743 -0.134283 0.01150387 0.041928843 0.03971726 0.046253722 -0.05342882 0.17570178 
34 | 20 0.04151019 -0.05116444 0.022490272 -0.019586155 0.09204956 0.12103163 0.016890123 -0.07208123 -0.1287832 0.0046484326 -0.04474158 0.0023639228 -0.09950353 -0.01003997 0.013603689 0.0075176945 -0.008111376 -0.006808907 0.07126941 0.059981134 0.0014375042 -0.00878236 -5.464153E-4 -0.008252382 -0.06962736 0.08533082 0.059198156 0.05827944 -0.0401642 0.022426676 0.04762277 -0.043954257 -0.026222497 -0.0058310446 0.08921179 0.031021178 0.07360291 0.03893261 0.043957658 -0.024872791 0.034145832 -0.011750743 0.02423334 -0.09851905 -0.020487063 0.07103058 -0.02572443 -0.03871164 -0.08381899 0.0013432744 0.025424695 0.028506773 -0.009193884 -0.054807693 -0.04721612 -0.084387496 0.027803479 0.021966036 0.01939512 -0.04333052 -0.044656273 0.024219617 -0.044535875 -0.0585952 0.018609751 -0.007540146 -0.052710578 0.03362786 0.042374298 0.019311234 -0.010924668 -0.04818935 -0.017541502 0.0063270004 0.004708606 -0.020807073 0.02359425 -0.07422043 0.06838549 -0.09697608 -0.0063671474 -0.020038988 0.039371558 -0.009618758 0.019186424 -0.041656304 -0.061024934 -0.0050662467 0.012314601 0.07103562 -0.04097061 -0.08118887 0.050957523 0.016536469 -0.007254811 -0.06306093 0.021179724 0.04609436 -7.069764E-4 -0.018073386 0.06040305 0.07052336 -0.04978633 0.023564853 -0.050457735 0.037180554 0.03740268 -0.031979647 0.027774688 -0.03936629 -0.016899465 0.052192207 -0.018592723 -0.043787245 0.05470228 0.031428903 0.050972708 -0.048890177 -0.02695163 7.48382E-5 -0.041524317 -0.06427929 0.041083634 0.014211487 -0.009868171 0.09635462 -0.007475949 0.059973266 
35 | 21 -0.0020232217 0.04302368 0.19965662 0.023443544 0.173959 0.15501517 -0.05777205 -0.028578406 -0.13892487 -0.06335526 0.11553417 -0.12030239 -0.13241126 0.07479139 -0.0025169824 -0.04087119 0.069698706 0.028706728 0.10545689 -0.069805786 0.14203516 -0.108613774 0.20879072 -0.035925716 -0.055162583 0.17150491 0.12735076 0.10753648 0.033508107 -0.048735008 0.073163696 -0.16949867 0.010019271 -0.12209924 0.20573726 -0.043576986 0.15916617 -0.03728708 -0.048742406 -0.03122906 -0.002013534 -0.19571297 0.0025461514 -0.22203456 -0.030183496 -0.0045614745 -0.012884499 -0.039544005 -0.16819254 0.07427586 -0.24783877 0.035875246 -0.13584994 -0.104111284 -0.10492221 -0.009871518 -0.019185256 -0.03400531 0.034677684 -0.066098705 -0.1030017 0.1077003 -0.037090294 -0.073236786 -0.017028721 0.10293229 -0.18171135 0.1811844 0.06758532 -0.17872718 -0.04369805 -0.060567867 0.091304615 0.035281967 -0.1463039 0.01016423 -0.014547759 -0.054335896 0.13987073 0.008918651 -0.1387466 -0.11911164 0.09953852 -0.11670717 -0.046981823 0.084115505 -0.26388508 -0.059481345 0.05695656 -0.02162998 -0.1752781 -0.076017864 0.09807729 0.101283856 -0.05541869 -0.13933602 -0.03322522 -0.02001418 0.09396541 0.011107877 0.18237197 0.037100863 -0.08499879 0.12394953 -0.012045313 0.115681924 -0.06372113 -0.14005646 0.06615614 -0.011259477 0.16963845 0.111512624 0.029027427 -0.008356806 0.24246256 0.090233415 0.12385082 0.006768905 0.06786283 0.117098816 -0.07272069 -0.14244685 -0.047990963 0.028963782 -0.12931451 0.027171016 -0.18933007 0.1952464 
36 | 


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