├── .gitignore ├── LICENSE ├── README.md ├── pom.xml ├── prediction-k3 ├── .gitignore ├── README.md ├── data │ ├── bayes.model │ ├── train-cp-20.txt │ └── train.txt ├── pom.xml └── src │ ├── main │ ├── java │ │ └── org │ │ │ └── yinyayun │ │ │ └── prediction │ │ │ ├── common │ │ │ └── Utils.java │ │ │ └── k3 │ │ │ ├── Model.java │ │ │ ├── bayes │ │ │ ├── BayesTrainModel.java │ │ │ ├── OutPutProbability.java │ │ │ └── ProbabilityDistribution.java │ │ │ ├── maxent │ │ │ └── MaxEntModel.java │ │ │ └── xxx │ │ │ ├── PredictNextNumberAndState.java │ │ │ └── state │ │ │ ├── StateDefineStrategy.java │ │ │ ├── StateDefineStrategyBySum.java │ │ │ ├── StateParserStragegy.java │ │ │ └── StateStructs.java │ └── resources │ │ └── cp.txt │ └── test │ └── java │ └── org │ └── yinyayun │ └── predict │ ├── BayesTrainTest.java │ └── MaxEntTrainTest.java └── prediction-preprocess ├── .gitignore ├── pom.xml └── src └── main └── java └── org └── yinyayun └── prediction └── preprocess ├── DataReader.java ├── GenerateToJson.java ├── GenerateTrainDataSum.java └── common ├── DataSample.java └── DataSet.java /.gitignore: -------------------------------------------------------------------------------- 1 | # Compiled class file 2 | *.class 3 | 4 | # Log file 5 | *.log 6 | 7 | # BlueJ files 8 | *.ctxt 9 | 10 | # Mobile Tools for Java (J2ME) 11 | .mtj.tmp/ 12 | 13 | # Package Files # 14 | *.jar 15 | *.war 16 | *.ear 17 | *.zip 18 | *.tar.gz 19 | *.rar 20 | 21 | # virtual machine crash logs, see http://www.java.com/en/download/help/error_hotspot.xml 22 | hs_err_pid* 23 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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-------------------------------------------------------------------------------- 1 | # -prediction 2 | 预测 3 | 4 | 免责申明：本程序只提供实验测试用，一切商业用途带来的损失不承担。 5 | 6 | # About me 7 | ![](http://img1.ph.126.net/xTPOKnUu-Eao6HSK-e7AXQ==/6632583992235991768.jpg) 8 | -------------------------------------------------------------------------------- /pom.xml: -------------------------------------------------------------------------------- 1 | 3 | 4.0.0 4 | org.yinyayun 5 | prediction 6 | 0.0.1-SNAPSHOT 7 | pom 8 | 9 | prediction-k3 10 | prediction-preprocess 11 | 12 | 13 | 1.8 14 | 15 | 16 | 17 | 18 | org.apache.maven.plugins 19 | maven-compiler-plugin 20 | 3.6.1 21 | 22 | ${java-version} 23 | ${java-version} 24 | 25 | 26 | 27 | org.apache.maven.plugins 28 | maven-jar-plugin 29 | 3.0.2 30 | 31 | ${java-version} 32 | ${java-version} 33 | utf-8 34 | 35 | 36 | 37 | 38 | -------------------------------------------------------------------------------- /prediction-k3/.gitignore: -------------------------------------------------------------------------------- 1 | /target/ 2 | -------------------------------------------------------------------------------- /prediction-k3/README.md: -------------------------------------------------------------------------------- 1 | 对快三组合进行预测 2 | -------------------------------------------------------------------------------- /prediction-k3/data/bayes.model: -------------------------------------------------------------------------------- 1 | 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-------------------------------------------------------------------------------- /prediction-k3/data/train.txt: -------------------------------------------------------------------------------- 1 | Outdoor Sunny Happy 2 | Outdoor Sunny Happy Dry 3 | Outdoor Sunny Happy Humid 4 | Outdoor Sunny Sad Dry 5 | Outdoor Sunny Sad Humid 6 | Outdoor Cloudy Happy Humid 7 | Outdoor Cloudy Happy Humid 8 | Outdoor Cloudy Sad Humid 9 | Outdoor Cloudy Sad Humid 10 | Indoor Rainy Happy Humid 11 | Indoor Rainy Happy Dry 12 | Indoor Rainy Sad Dry 13 | Indoor Rainy Sad Humid 14 | Indoor Cloudy Sad Humid 15 | Indoor Cloudy Sad Humid -------------------------------------------------------------------------------- /prediction-k3/pom.xml: -------------------------------------------------------------------------------- 1 | 3 | 4.0.0 4 | 5 | org.yinyayun 6 | prediction 7 | 0.0.1-SNAPSHOT 8 | 9 | prediction-k3 10 | 11 | 12 | commons-io 13 | commons-io 14 | 2.1 15 | 16 | 17 | org.yinyayun 18 | prediction-preprocess 19 | 0.0.1-SNAPSHOT 20 | 21 | 22 | org.json 23 | json 24 | 20160810 25 | 26 | 27 | -------------------------------------------------------------------------------- /prediction-k3/src/main/java/org/yinyayun/prediction/common/Utils.java: -------------------------------------------------------------------------------- 1 | /** 2 | * 3 | */ 4 | package org.yinyayun.prediction.common; 5 | 6 | /** 7 | * @author yinyayun 8 | * 9 | */ 10 | public class Utils { 11 | public static int sum(int[] array) { 12 | int sum = 0; 13 | for (int i : array) { 14 | sum += i; 15 | } 16 | return sum; 17 | } 18 | } 19 | -------------------------------------------------------------------------------- /prediction-k3/src/main/java/org/yinyayun/prediction/k3/Model.java: -------------------------------------------------------------------------------- 1 | /** 2 | * 3 | */ 4 | package org.yinyayun.prediction.k3; 5 | 6 | import org.yinyayun.prediction.preprocess.common.DataSet; 7 | 8 | /** 9 | * @author yinyayun 10 | * 11 | */ 12 | public interface Model { 13 | public void train(DataSet datas); 14 | } 15 | -------------------------------------------------------------------------------- /prediction-k3/src/main/java/org/yinyayun/prediction/k3/bayes/BayesTrainModel.java: -------------------------------------------------------------------------------- 1 | /** 2 | * 3 | */ 4 | package org.yinyayun.prediction.k3.bayes; 5 | 6 | import java.io.File; 7 | import java.io.IOException; 8 | import java.io.Serializable; 9 | import java.nio.file.Files; 10 | import java.nio.file.StandardOpenOption; 11 | import java.util.ArrayList; 12 | import java.util.Collections; 13 | import java.util.HashMap; 14 | import java.util.List; 15 | import java.util.Map; 16 | 17 | import org.json.JSONArray; 18 | import org.json.JSONObject; 19 | import org.yinyayun.prediction.k3.Model; 20 | import org.yinyayun.prediction.preprocess.common.DataSet; 21 | 22 | /** 23 | * @author yinyayun 贝叶斯模型 24 | */ 25 | public class BayesTrainModel implements Model { 26 | private Map outPutProbabilitys; 27 | private Map> outPutConditionProbabilitys; 28 | 29 | public int[] predict1(int[] inputs) { 30 | List outPutProbabilities = new ArrayList(); 31 | for (int i = 3; i <= 18; i++) { 32 | // 输出为i的概率 33 | float outi = outPutProbabilitys.get(i); 34 | // outi = 1; 35 | Map inputProbabilitys = outPutConditionProbabilitys.get(i); 36 | // 输出是i的条件下出险inputs的概率 37 | float condiction = 1f; 38 | for (int j = 0; j < inputs.length; j++) { 39 | condiction *= inputProbabilitys.get(inputs[j])[j]; 40 | } 41 | outPutProbabilities.add(new OutPutProbability(i, condiction * outi)); 42 | } 43 | Collections.sort(outPutProbabilities); 44 | int[] ret = new int[Math.min(3, outPutProbabilities.size())]; 45 | for (int i = 0; i < ret.length; i++) { 46 | ret[i] = outPutProbabilities.get(i).getOut(); 47 | } 48 | return ret; 49 | } 50 | 51 | @Override 52 | public void train(DataSet datas) { 53 | ProbabilityDistribution probabilityDistribution = new ProbabilityDistribution(datas); 54 | // 输出标签对应的概率分布 55 | this.outPutProbabilitys = probabilityDistribution.outPutProbability(); 56 | // 输出标签在每个分量上的条件概率分布 57 | this.outPutConditionProbabilitys = probabilityDistribution.outPutConditionProbabilitys(); 58 | } 59 | 60 | public void saveMode(String savePath) throws IOException { 61 | Map map = new HashMap(); 62 | map.put("outPutProbabilitys", outPutProbabilitys); 63 | map.put("outPutConditionProbabilitys", outPutConditionProbabilitys); 64 | JSONObject jsonObject = new JSONObject(map); 65 | Files.write(new File(savePath).toPath(), jsonObject.toString().getBytes(), StandardOpenOption.CREATE); 66 | } 67 | 68 | public void loadModel(String savePath) throws IOException { 69 | List lines = Files.readAllLines(new File(savePath).toPath()); 70 | if (lines != null && lines.size() > 0) { 71 | JSONObject json = new JSONObject(lines.get(0)); 72 | JSONObject outPutProbabilitysJson = json.getJSONObject("outPutProbabilitys"); 73 | JSONObject outPutConditionProbabilitysJson = json.getJSONObject("outPutConditionProbabilitys"); 74 | // 75 | this.outPutProbabilitys = new HashMap(); 76 | for (String key : outPutProbabilitysJson.keySet()) { 77 | this.outPutProbabilitys.put(Integer.valueOf(key), (float) outPutProbabilitysJson.getDouble(key)); 78 | } 79 | // 80 | this.outPutConditionProbabilitys = new HashMap>(); 81 | for (String key : outPutConditionProbabilitysJson.keySet()) { 82 | Map map = new HashMap(); 83 | JSONObject jsonObject = outPutConditionProbabilitysJson.getJSONObject(key); 84 | for (String innerKey : jsonObject.keySet()) { 85 | JSONArray array = jsonObject.getJSONArray(innerKey); 86 | float[] probabilitys = new float[array.length()]; 87 | for (int i = 0; i < array.length(); i++) { 88 | probabilitys[i] = (float) array.getDouble(i); 89 | } 90 | map.put(Integer.valueOf(innerKey), probabilitys); 91 | } 92 | this.outPutConditionProbabilitys.put(Integer.valueOf(key), map); 93 | } 94 | } 95 | } 96 | 97 | class Probabilitys implements Serializable { 98 | private static final long serialVersionUID = 1L; 99 | private Map outPutProbabilitys; 100 | private Map> outPutConditionProbability; 101 | 102 | public Probabilitys(Map outPutProbabilitys, 103 | Map> outPutConditionProbability) { 104 | super(); 105 | this.outPutProbabilitys = outPutProbabilitys; 106 | this.outPutConditionProbability = outPutConditionProbability; 107 | } 108 | 109 | public Map getOutPutProbabilitys() { 110 | return outPutProbabilitys; 111 | } 112 | 113 | public void setOutPutProbabilitys(Map outPutProbabilitys) { 114 | this.outPutProbabilitys = outPutProbabilitys; 115 | } 116 | 117 | public Map> getOutPutConditionProbability() { 118 | return outPutConditionProbability; 119 | } 120 | 121 | public void setOutPutConditionProbability(Map> outPutConditionProbability) { 122 | this.outPutConditionProbability = outPutConditionProbability; 123 | } 124 | 125 | } 126 | } 127 | -------------------------------------------------------------------------------- /prediction-k3/src/main/java/org/yinyayun/prediction/k3/bayes/OutPutProbability.java: -------------------------------------------------------------------------------- 1 | /** 2 | * 3 | */ 4 | package org.yinyayun.prediction.k3.bayes; 5 | 6 | /** 7 | * @author yinyayun 8 | * 9 | */ 10 | public class OutPutProbability implements Comparable { 11 | private int out; 12 | private float probability; 13 | 14 | public OutPutProbability(int out, float probability) { 15 | super(); 16 | this.out = out; 17 | this.probability = probability; 18 | } 19 | 20 | /** 21 | * @return the out 22 | */ 23 | public int getOut() { 24 | return out; 25 | } 26 | 27 | /** 28 | * @return the probability 29 | */ 30 | public float getProbability() { 31 | return probability; 32 | } 33 | 34 | @Override 35 | public int compareTo(OutPutProbability o) { 36 | if (o.probability > probability) { 37 | return 1; 38 | } else if (o.probability < probability) { 39 | return -1; 40 | } 41 | return 0; 42 | } 43 | 44 | /* 45 | * (non-Javadoc) 46 | * 47 | * @see java.lang.Object#toString() 48 | */ 49 | @Override 50 | public String toString() { 51 | return "OutPutProbability [out=" + out + ", probability=" + probability + "]"; 52 | } 53 | 54 | } 55 | -------------------------------------------------------------------------------- /prediction-k3/src/main/java/org/yinyayun/prediction/k3/bayes/ProbabilityDistribution.java: -------------------------------------------------------------------------------- 1 | /** 2 | * 3 | */ 4 | package org.yinyayun.prediction.k3.bayes; 5 | 6 | import java.util.HashMap; 7 | import java.util.Map; 8 | import java.util.Map.Entry; 9 | 10 | import org.yinyayun.prediction.preprocess.common.DataSample; 11 | import org.yinyayun.prediction.preprocess.common.DataSet; 12 | 13 | /** 14 | * @author yinyayun 概率分布生成 15 | */ 16 | public class ProbabilityDistribution { 17 | private DataSet dataSet; 18 | 19 | public ProbabilityDistribution(DataSet dataSet) { 20 | this.dataSet = dataSet; 21 | } 22 | 23 | /** 24 | * 输出标签的概率分布 25 | * 26 | */ 27 | public Map outPutProbability() { 28 | Map counts = new HashMap(); 29 | for (DataSample dataMapper : dataSet.getSamples()) { 30 | Integer count = counts.get(dataMapper.getLabel()); 31 | if (count == null) { 32 | count = 0; 33 | } 34 | counts.put(dataMapper.getLabel(), ++count); 35 | } 36 | int dataSize = dataSet.getDataSize(); 37 | Map probabilitys = new HashMap(); 38 | for (Entry entry : counts.entrySet()) { 39 | // 拉普拉斯平滑估计 40 | float probability = (entry.getValue() + 1.f) / (dataSize * 1.0f + dataSet.getLabels()); 41 | probabilitys.put(entry.getKey(), probability); 42 | } 43 | return probabilitys; 44 | } 45 | 46 | /** 47 | * 输入分量的概率分布 48 | * 49 | * @return 50 | */ 51 | public Map inputProbability() { 52 | float dataSize = 0; 53 | Map counts = new HashMap(); 54 | for (DataSample dataMapper : dataSet.getSamples()) { 55 | int[] inputs = dataMapper.getInput(); 56 | for (int input : inputs) { 57 | Integer count = counts.get(input); 58 | if (count == null) { 59 | count = 0; 60 | } 61 | counts.put(input, ++count); 62 | } 63 | dataSize += inputs.length; 64 | } 65 | Map probabilitys = new HashMap(); 66 | for (Entry entry : counts.entrySet()) { 67 | float probability = entry.getValue() / dataSize; 68 | probabilitys.put(entry.getKey(), probability); 69 | } 70 | return probabilitys; 71 | } 72 | 73 | /** 74 | * 在指定输出下，对应特征分量的条件概率 75 | * 76 | * @return 77 | */ 78 | public Map> outPutConditionProbabilitys() { 79 | Map outputCounts = new HashMap(); 80 | Map> outPutConditionCounts = new HashMap>(); 81 | for (DataSample dataMapper : dataSet.getSamples()) { 82 | int out = dataMapper.getLabel(); 83 | // 输出标签计数 84 | outputCounts.compute(out, (k, v) -> v == null ? 0 : ++v); 85 | // 输出标签对应每个输入分量的计数 86 | outPutConditionCounts.putIfAbsent(out, new HashMap()); 87 | Map inputsCounts = outPutConditionCounts.get(out); 88 | int[] inputs = dataMapper.getInput(); 89 | for (int i = 0; i < inputs.length; i++) { 90 | // 每个特征出现在每个位置上的次数 91 | inputsCounts.putIfAbsent(inputs[i], new int[inputs.length]); 92 | int[] inputCount = inputsCounts.get(inputs[i]); 93 | inputCount[i]++; 94 | } 95 | } 96 | Map> outPutConditionProbabilitys = new HashMap>(); 97 | for (Entry> entry : outPutConditionCounts.entrySet()) { 98 | int output = entry.getKey(); 99 | int outputCount = outputCounts.get(output); 100 | Map inputProbabilitys = new HashMap(); 101 | outPutConditionProbabilitys.put(output, inputProbabilitys); 102 | for (Entry inputsEntry : entry.getValue().entrySet()) { 103 | int inputi = inputsEntry.getKey(); 104 | int[] inputiCountOnOut = inputsEntry.getValue(); 105 | float[] inputiCountOnOutPro = new float[inputiCountOnOut.length]; 106 | for (int i = 0; i < inputiCountOnOut.length; i++) { 107 | // 拉普拉斯平滑估计 108 | inputiCountOnOutPro[i] = (inputiCountOnOut[i] + 1.f) / (outputCount * 1.0f + dataSet.getLabels()); 109 | } 110 | inputProbabilitys.put(inputi, inputiCountOnOutPro); 111 | } 112 | } 113 | return outPutConditionProbabilitys; 114 | } 115 | 116 | /** 117 | * 在指定输出下，对应特征分量的条件概率 118 | * 119 | * @return 120 | */ 121 | public Map> outPutConditionProbabilitys2() { 122 | Map outputCounts = new HashMap(); 123 | // label->pos->inputs 124 | Map> outPutConditionCounts = new HashMap>(); 125 | for (DataSample dataMapper : dataSet.getSamples()) { 126 | int out = dataMapper.getLabel(); 127 | // 输出标签计数 128 | outputCounts.compute(out, (k, v) -> v == null ? 0 : ++v); 129 | // 输出标签对应每个输入分量的计数 130 | outPutConditionCounts.putIfAbsent(out, new HashMap()); 131 | Map inputsCounts = outPutConditionCounts.get(out); 132 | int[] inputs = dataMapper.getInput(); 133 | for (int i = 0; i < inputs.length; i++) { 134 | // 每个特征出现在每个位置上的次数 135 | inputsCounts.putIfAbsent(i, new int[dataSet.getLabels()]); 136 | inputsCounts.get(i)[inputs[i] - dataSet.getMinLabelValue()]++; 137 | } 138 | } 139 | Map> outPutConditionProbabilitys = new HashMap>(); 140 | for (Entry> entry : outPutConditionCounts.entrySet()) { 141 | int output = entry.getKey(); 142 | int outputCount = outputCounts.get(output); 143 | // pos->inputcount 144 | Map posInputsProbabilitys = new HashMap(); 145 | outPutConditionProbabilitys.put(output, posInputsProbabilitys); 146 | for (Entry inputsEntry : entry.getValue().entrySet()) { 147 | int pos = inputsEntry.getKey(); 148 | int[] inputCounts = inputsEntry.getValue(); 149 | float[] inputProbabilitysOnCurrentPos = new float[dataSet.getLabels()]; 150 | for (int i = 0; i < inputProbabilitysOnCurrentPos.length; i++) { 151 | inputProbabilitysOnCurrentPos[i] = inputCounts[i] / (outputCount * 1.0f); 152 | } 153 | posInputsProbabilitys.put(pos, inputProbabilitysOnCurrentPos); 154 | } 155 | for (Entry inputProbabilityEntry : posInputsProbabilitys.entrySet()) { 156 | float all = 0f; 157 | for (float f : inputProbabilityEntry.getValue()) 158 | all += f; 159 | System.out.println(all); 160 | } 161 | } 162 | return outPutConditionProbabilitys; 163 | } 164 | } 165 | -------------------------------------------------------------------------------- /prediction-k3/src/main/java/org/yinyayun/prediction/k3/maxent/MaxEntModel.java: -------------------------------------------------------------------------------- 1 | /** 2 | * 3 | */ 4 | package org.yinyayun.prediction.k3.maxent; 5 | 6 | import java.util.ArrayList; 7 | import java.util.Arrays; 8 | import java.util.Collections; 9 | import java.util.HashMap; 10 | import java.util.List; 11 | import java.util.Map; 12 | 13 | import org.yinyayun.prediction.k3.Model; 14 | import org.yinyayun.prediction.k3.bayes.OutPutProbability; 15 | import org.yinyayun.prediction.preprocess.common.DataSample; 16 | import org.yinyayun.prediction.preprocess.common.DataSet; 17 | 18 | /** 19 | * @author yinyayun 20 | * 21 | */ 22 | public class MaxEntModel implements Model { 23 | private List samples; 24 | private Map keyPos = new HashMap(); 25 | private List featureList = new ArrayList(); 26 | private List featureCount = new ArrayList(); 27 | private List labels = new ArrayList(); 28 | double[] weight; 29 | int maxFeatureSize; 30 | 31 | public void train(DataSet dataSet, int maxIt) { 32 | samples = dataSet.getSamples(); 33 | for (DataSample sample : samples) { 34 | int label = sample.getLabel(); 35 | List featurs = new ArrayList(); 36 | for (int input : sample.getInput()) { 37 | featurs.add(input); 38 | Key key = new Key(input, label); 39 | Integer pos = keyPos.get(key); 40 | if (pos == null) { 41 | featureList.add(key); 42 | featureCount.add(1); 43 | keyPos.put(key, featureList.size() - 1); 44 | } else { 45 | featureCount.set(pos, featureCount.get(pos) + 1); 46 | } 47 | } 48 | if (sample.getInput().length > maxFeatureSize) 49 | maxFeatureSize = sample.getInput().length; 50 | if (labels.indexOf(label) == -1) 51 | labels.add(label); 52 | } 53 | int size = featureList.size(); 54 | weight = new double[size]; // 特征权重 55 | double[] empiricalE = new double[size]; // 经验期望 56 | double[] modelE = new double[size]; // 模型期望 57 | for (int i = 0; i < size; ++i) { 58 | empiricalE[i] = (double) featureCount.get(i) / samples.size(); 59 | } 60 | 61 | double[] lastWeight = new double[weight.length]; // 上次迭代的权重 62 | for (int i = 0; i < maxIt; ++i) { 63 | System.out.println("iterator " + i); 64 | modelExpect(modelE); 65 | for (int w = 0; w < weight.length; w++) { 66 | lastWeight[w] = weight[w]; 67 | weight[w] += 1.0 / maxFeatureSize * Math.log(empiricalE[w] / modelE[w]); 68 | } 69 | if (checkConvergence(lastWeight, weight)) 70 | break; 71 | } 72 | } 73 | 74 | /** 75 | * 预测类别 76 | * 77 | * @param fieldList 78 | * @return 79 | */ 80 | public int[] predict(int[] inputs) { 81 | double[] prob = prob(inputs); 82 | List outPutProbabilities = new ArrayList(); 83 | for (int i = 0; i < prob.length; ++i) { 84 | outPutProbabilities.add(new OutPutProbability(labels.get(i), (float) prob[i])); 85 | } 86 | Collections.sort(outPutProbabilities); 87 | int[] ret = new int[Math.min(3, outPutProbabilities.size())]; 88 | for (int i = 0; i < ret.length; i++) { 89 | ret[i] = outPutProbabilities.get(i).getOut(); 90 | } 91 | return ret; 92 | } 93 | 94 | /** 95 | * 检查是否收敛 96 | * 97 | * @param w1 98 | * @param w2 99 | * @return 是否收敛 100 | */ 101 | public boolean checkConvergence(double[] w1, double[] w2) { 102 | for (int i = 0; i < w1.length; ++i) { 103 | if (Math.abs(w1[i] - w2[i]) >= 0.01) // 收敛阀值0.01可自行调整 104 | return false; 105 | } 106 | return true; 107 | } 108 | 109 | /** 110 | * 模型期望 111 | * 112 | * @param modelE 113 | */ 114 | public void modelExpect(double[] modelE) { 115 | Arrays.fill(modelE, 0.0f); 116 | for (DataSample sample : samples) { 117 | int[] inputs = sample.getInput(); 118 | // 计算当前样本X对应所有类别的概率 119 | double[] pro = prob(inputs); 120 | for (int input : inputs) { 121 | for (int i = 0; i < labels.size(); i++) { 122 | Key key = new Key(input, labels.get(i)); 123 | Integer pos = keyPos.get(key); 124 | if (pos != null) 125 | modelE[pos] += pro[i] * (1.0 / samples.size()); 126 | } 127 | } 128 | } 129 | } 130 | 131 | /** 132 | * 计算p(y|x) 133 | * 134 | */ 135 | public double[] prob(int[] inputs) { 136 | double[] p = new double[labels.size()]; 137 | double sum = 0; // 正则化因子，保证概率和为1 138 | for (int i = 0; i < labels.size(); ++i) { 139 | double weightSum = 0; 140 | for (int input : inputs) { 141 | Key key = new Key(input, labels.get(i)); 142 | Integer pos = keyPos.get(key); 143 | if (pos != null) 144 | weightSum += weight[pos]; 145 | } 146 | p[i] = Math.exp(weightSum); 147 | sum += p[i]; 148 | } 149 | for (int i = 0; i < p.length; ++i) { 150 | p[i] /= sum; 151 | } 152 | return p; 153 | } 154 | 155 | @Override 156 | public void train(DataSet datas) { 157 | train(datas, 100); 158 | } 159 | 160 | public class Key { 161 | Integer k; 162 | Integer v; 163 | 164 | public Key(Integer k, Integer v) { 165 | super(); 166 | this.k = k; 167 | this.v = v; 168 | } 169 | 170 | @Override 171 | public String toString() { 172 | return super.toString(); 173 | } 174 | } 175 | } 176 | -------------------------------------------------------------------------------- /prediction-k3/src/main/java/org/yinyayun/prediction/k3/xxx/PredictNextNumberAndState.java: -------------------------------------------------------------------------------- 1 | /** 2 | * Copyright (c) 2017, yayunyin@126.com All Rights Reserved 3 | */ 4 | 5 | package org.yinyayun.prediction.k3.xxx; 6 | 7 | import java.io.File; 8 | import java.io.InputStream; 9 | import java.nio.charset.Charset; 10 | import java.nio.file.Files; 11 | import java.nio.file.StandardCopyOption; 12 | import java.util.ArrayList; 13 | import java.util.Arrays; 14 | import java.util.Collections; 15 | import java.util.Comparator; 16 | import java.util.List; 17 | import java.util.Map; 18 | import java.util.Map.Entry; 19 | 20 | import org.yinyayun.prediction.k3.xxx.state.StateDefineStrategy; 21 | import org.yinyayun.prediction.k3.xxx.state.StateDefineStrategyBySum; 22 | import org.yinyayun.prediction.k3.xxx.state.StateParserStragegy; 23 | import org.yinyayun.prediction.k3.xxx.state.StateStructs; 24 | 25 | /** 26 | * PredictNextNumberAndState 基于隐马进行预测，默认采用号码组的和值表示该组号码的状态 27 | * 28 | * @author yinyayun 29 | */ 30 | public class PredictNextNumberAndState { 31 | private int byHistorySize; 32 | private StateStructs stateStructs; 33 | private StateDefineStrategy stateDefineStrategy = new StateDefineStrategyBySum(); 34 | 35 | public static void main(String[] args) { 36 | int[][] history = new int[][]{{1, 6, 6}, {2, 3, 4}}; 37 | List res = new PredictNextNumberAndState(history.length).predict(history, 5); 38 | System.out.println(String.format("历史为：%s,下一期可能为：", Arrays.deepToString(history))); 39 | for (PredictResult predictResult : res) { 40 | System.out.println(predictResult); 41 | } 42 | } 43 | 44 | /** 45 | * @param byHistorySize 根据历史多少期推算下一期 46 | */ 47 | public PredictNextNumberAndState(int byHistorySize) { 48 | this.byHistorySize = byHistorySize; 49 | StateParserStragegy stateParser = new StateParserStragegy(stateDefineStrategy, byHistorySize); 50 | try (InputStream inputStream = PredictNextNumberAndState.class.getClassLoader().getResourceAsStream("cp.txt")) { 51 | File tmpFile = File.createTempFile("cp-k3", "txt"); 52 | Files.copy(inputStream, tmpFile.toPath(), StandardCopyOption.REPLACE_EXISTING); 53 | List lines = Files.readAllLines(tmpFile.toPath(), Charset.forName("utf-8")); 54 | List numbers = new ArrayList(); 55 | for (String line : lines) { 56 | int[] number = strToArrayAndSort(line.split("\t")[1]); 57 | numbers.add(number); 58 | } 59 | this.stateStructs = stateParser.parser(numbers); 60 | tmpFile.delete(); 61 | } 62 | catch (Exception e) { 63 | throw new RuntimeException("load data error!", e); 64 | } 65 | } 66 | 67 | /** 68 | * @param dataPath 彩票数据地址 69 | * @param byHistorySize 根据历史多少期推算下一期 70 | */ 71 | public PredictNextNumberAndState(String dataPath, int byHistorySize) { 72 | this.byHistorySize = byHistorySize; 73 | StateParserStragegy stateParser = new StateParserStragegy(stateDefineStrategy, byHistorySize); 74 | try { 75 | List lines = Files.readAllLines(new File(dataPath).toPath(), Charset.forName("utf-8")); 76 | List numbers = new ArrayList(); 77 | for (String line : lines) { 78 | int[] number = strToArrayAndSort(line.split("\t")[1]); 79 | numbers.add(number); 80 | } 81 | this.stateStructs = stateParser.parser(numbers); 82 | } 83 | catch (Exception e) { 84 | throw new RuntimeException("load data error!", e); 85 | } 86 | } 87 | 88 | /** 89 | * 给定历史记录 90 | * 91 | * @param historyNumbers 该数据的长度必须和byHistorySize大小一致 92 | * @param topN 93 | * @return 94 | */ 95 | public List predict(int[][] historyNumbers, int topN) { 96 | List res = predict(historyNumbers); 97 | Collections.sort(res, new Comparator() { 98 | 99 | @Override 100 | public int compare(PredictResult o1, PredictResult o2) { 101 | if (o1.probability == o2.probability) { 102 | return 0; 103 | } 104 | else { 105 | return o1.probability > o2.probability ? -1 : 1; 106 | } 107 | } 108 | }); 109 | return res.subList(0, Math.min(topN, res.size())); 110 | } 111 | 112 | public List predict(int[][] historyNumbers) { 113 | if (historyNumbers.length != byHistorySize) { 114 | throw new IllegalArgumentException( 115 | String.format("model train by historysize:%s,you give the numbers to predict is:%s", byHistorySize, 116 | historyNumbers.length)); 117 | } 118 | for (int i = 0; i < historyNumbers.length; i++) { 119 | Arrays.sort(historyNumbers[i]); 120 | } 121 | List res = new ArrayList(); 122 | List historyNumberList = new ArrayList(); 123 | for (int[] historyNumber : historyNumbers) { 124 | historyNumberList.add(historyNumber); 125 | } 126 | String historyState = stateDefineStrategy.buildLastState(historyNumberList); 127 | // 历史状态转移状态可能性组合 128 | Integer historyStateCount = stateStructs.getlastStateCount(historyState); 129 | // 历史上未有该状态 130 | if (historyStateCount == null) { 131 | return res; 132 | } 133 | Map stateCounts = stateStructs.getJumpStates(historyState); 134 | // 后续状态的概率计算 135 | for (Entry entry : stateCounts.entrySet()) { 136 | String predictState = entry.getKey(); 137 | Integer predictStateCount = entry.getValue(); 138 | float predictStateProbaility = predictStateCount / (float) historyStateCount; 139 | String stateJumpString = historyState.concat("_").concat(predictState); 140 | // 该状态下出现某个彩票组合的概率计算 141 | int jumpstateCount = stateStructs.getCountForJumpState(stateJumpString); 142 | Map numberCounts = stateStructs.getNumberCountForState(stateJumpString); 143 | for (Entry e : numberCounts.entrySet()) { 144 | String predictNumber = e.getKey(); 145 | Integer predictNumberCount = e.getValue(); 146 | float predictNumberProbability = predictNumberCount / (float) jumpstateCount; 147 | res.add(new PredictResult(predictState, predictNumber, predictStateProbaility, predictNumberProbability, 148 | predictNumberProbability * predictStateProbaility)); 149 | } 150 | } 151 | return res; 152 | } 153 | 154 | private int[] strToArrayAndSort(String str) { 155 | String[] splits = str.split(","); 156 | int[] array = new int[splits.length]; 157 | for (int i = 0; i < array.length; i++) { 158 | array[i] = Integer.valueOf(splits[i]); 159 | } 160 | Arrays.sort(array); 161 | return array; 162 | } 163 | 164 | public class PredictResult { 165 | public final String state; 166 | public final String number; 167 | public final float stateProbability; 168 | public final float numberProbability; 169 | public final float probability; 170 | 171 | public PredictResult(String state, String number, float stateProbability, float numberProbability, 172 | float probability) { 173 | this.state = state; 174 | this.number = number; 175 | this.stateProbability = stateProbability; 176 | this.numberProbability = numberProbability; 177 | this.probability = probability; 178 | } 179 | 180 | @Override 181 | public String toString() { 182 | return String.format("state:%s,number:%s,probability:%f*%f=%s", state, number, stateProbability, 183 | numberProbability, probability); 184 | } 185 | } 186 | } 187 | -------------------------------------------------------------------------------- /prediction-k3/src/main/java/org/yinyayun/prediction/k3/xxx/state/StateDefineStrategy.java: -------------------------------------------------------------------------------- 1 | /** 2 | * Copyright (c) 2016, yayunyin@126.com All Rights Reserved 3 | */ 4 | package org.yinyayun.prediction.k3.xxx.state; 5 | 6 | import java.util.List; 7 | 8 | /** 9 | * StateDefineStrategy.java 状态定义可能存在多种方案 10 | * 11 | * @author yinyayun 12 | */ 13 | public interface StateDefineStrategy { 14 | 15 | /** 16 | * 生成状态 17 | * 18 | * @param lastNumbers 19 | * @param currentNumbers 20 | */ 21 | public String buildCurrentState(int[] currentNumber); 22 | 23 | public String buildLastState(List lastNumbers); 24 | 25 | public String startState(); 26 | } 27 | -------------------------------------------------------------------------------- /prediction-k3/src/main/java/org/yinyayun/prediction/k3/xxx/state/StateDefineStrategyBySum.java: -------------------------------------------------------------------------------- 1 | /** 2 | * Copyright (c) 2016, yayunyin@126.com All Rights Reserved 3 | */ 4 | package org.yinyayun.prediction.k3.xxx.state; 5 | 6 | import java.util.List; 7 | 8 | /** 9 | * StateDefineStrategyBySum.java 采用求和方式作为状态 10 | * 11 | * @author yinyayun 12 | */ 13 | public class StateDefineStrategyBySum implements StateDefineStrategy { 14 | 15 | @Override 16 | public String startState() { 17 | return "3"; 18 | } 19 | 20 | @Override 21 | public String buildCurrentState(int[] currentNumber) { 22 | int sum = 0; 23 | for (int n : currentNumber) { 24 | sum += n; 25 | } 26 | return String.valueOf(String.format("%d!%d", currentNumber[1], sum)); 27 | } 28 | 29 | @Override 30 | public String buildLastState(List lastNumbers) { 31 | StringBuilder builder = new StringBuilder(); 32 | for (int[] number : lastNumbers) { 33 | String state = buildCurrentState(number); 34 | if (builder.length() > 0) { 35 | builder.append("_"); 36 | } 37 | builder.append(state); 38 | } 39 | return builder.toString(); 40 | } 41 | 42 | } 43 | -------------------------------------------------------------------------------- /prediction-k3/src/main/java/org/yinyayun/prediction/k3/xxx/state/StateParserStragegy.java: -------------------------------------------------------------------------------- 1 | /** 2 | * Copyright (c) 2017, yayunyin@126.com All Rights Reserved 3 | */ 4 | 5 | package org.yinyayun.prediction.k3.xxx.state; 6 | 7 | import java.util.List; 8 | 9 | /** 10 | * StateParserStragegy 11 | * 12 | * @author yinyayun 13 | */ 14 | public class StateParserStragegy { 15 | private int byHistoryNumber; 16 | private StateDefineStrategy stateDefineStrategy; 17 | 18 | public StateParserStragegy(StateDefineStrategy stateDefineStrategy, int byHistoryNumber) { 19 | this.byHistoryNumber = byHistoryNumber; 20 | this.stateDefineStrategy = stateDefineStrategy; 21 | } 22 | 23 | /** 24 | * 假设只关心前一个状态 25 | * 26 | * @param numbers 27 | */ 28 | public StateStructs parser(List numbers) { 29 | if (numbers.size() < byHistoryNumber) { 30 | throw new IllegalArgumentException("历史数据太小..."); 31 | } 32 | StateStructs stateStructs = new StateStructs(numbers.size()); 33 | String lastState = stateDefineStrategy.buildLastState(numbers.subList(0, byHistoryNumber)); 34 | for (int i = byHistoryNumber; i < numbers.size(); i++) { 35 | // 当前的一个状态 36 | String currentState = stateDefineStrategy.buildCurrentState(numbers.get(i)); 37 | // 历史状态计数 38 | stateStructs.addLastStateCounts(lastState); 39 | // 上一个状态到当前状态的转移计数 40 | String stateJumpString = lastState.concat("_").concat(currentState); 41 | stateStructs.addStateJumpCounts(lastState, currentState); 42 | stateStructs.addJumpStateCount(stateJumpString); 43 | // 上一个状态转移到该状态并且是号码组合的计数 44 | stateStructs.addNumberCountForCurrentStates(stateJumpString, numbers.get(i)); 45 | // 46 | lastState = stateDefineStrategy.buildLastState(numbers.subList(i - byHistoryNumber + 1, i + 1)); 47 | } 48 | return stateStructs; 49 | } 50 | } 51 | -------------------------------------------------------------------------------- /prediction-k3/src/main/java/org/yinyayun/prediction/k3/xxx/state/StateStructs.java: -------------------------------------------------------------------------------- 1 | /** 2 | * Copyright (c) 2016, yayunyin@126.com All Rights Reserved 3 | */ 4 | package org.yinyayun.prediction.k3.xxx.state; 5 | 6 | import java.util.HashMap; 7 | import java.util.Map; 8 | 9 | /** 10 | * StateStructs.java 状态对象 11 | * 12 | * @author yinyayun 13 | */ 14 | public class StateStructs { 15 | // 样本数 16 | private int samples; 17 | /** 18 | * 状态转移情况计数 19 | */ 20 | private Map> stateJumpCounts = new HashMap>(); 21 | /** 22 | * 转移状态频率 23 | */ 24 | private Map jumpStateCount = new HashMap(); 25 | /** 26 | * 历史上某个状态出现的次数 27 | */ 28 | private Map lastStateCounts = new HashMap(); 29 | /** 30 | * 某一状态下出现一个号码的次数 31 | */ 32 | private Map> stateForNumberCounts = new HashMap>(); 33 | 34 | public StateStructs(int samples) { 35 | this.samples = samples; 36 | } 37 | 38 | public void addStateJumpCounts(String lastState, String currentState) { 39 | Map stateCounts = stateJumpCounts.get(lastState); 40 | if (stateCounts == null) { 41 | stateCounts = new HashMap(); 42 | stateJumpCounts.put(lastState, stateCounts); 43 | } 44 | Integer count = stateCounts.get(currentState); 45 | if (count == null) { 46 | stateCounts.put(currentState, 1); 47 | } 48 | else { 49 | stateCounts.put(currentState, ++count); 50 | } 51 | } 52 | 53 | public void addNumberCountForCurrentStates(String state, int[] number) { 54 | Map numberCounts = stateForNumberCounts.get(state); 55 | if (numberCounts == null) { 56 | numberCounts = new HashMap(); 57 | stateForNumberCounts.put(state, numberCounts); 58 | } 59 | String numberStr = intArrayToString(number); 60 | Integer count = numberCounts.get(numberStr); 61 | if (count == null) { 62 | numberCounts.put(numberStr, 1); 63 | } 64 | else { 65 | numberCounts.put(numberStr, ++count); 66 | } 67 | } 68 | 69 | public void addLastStateCounts(String state) { 70 | Integer count = lastStateCounts.get(state); 71 | if (count == null) { 72 | lastStateCounts.put(state, 1); 73 | } 74 | else { 75 | lastStateCounts.put(state, ++count); 76 | } 77 | } 78 | 79 | /** 80 | * 根据历史状态获取后面可能的所有状态 81 | * 82 | * @param historyState 83 | * @return 84 | */ 85 | public Map getJumpStates(String historyState) { 86 | return stateJumpCounts.get(historyState); 87 | } 88 | 89 | /** 90 | * 获取某个状态下出现某组号码的次数 91 | * 92 | * @param numerForstateCount 93 | * @return 94 | */ 95 | public Map getNumberCountForState(String state) { 96 | return stateForNumberCounts.get(state); 97 | } 98 | 99 | public void addJumpStateCount(String jumpState) { 100 | Integer count = jumpStateCount.get(jumpState); 101 | if (count == null) { 102 | jumpStateCount.put(jumpState, 1); 103 | } 104 | else { 105 | jumpStateCount.put(jumpState, ++count); 106 | } 107 | } 108 | 109 | /** 110 | * 该转移状态出现的次数 111 | * 112 | * @return 113 | */ 114 | public int getCountForJumpState(String jumpState) { 115 | return jumpStateCount.get(jumpState); 116 | } 117 | 118 | public Integer getlastStateCount(String state) { 119 | return lastStateCounts.get(state); 120 | } 121 | 122 | public int getSamples() { 123 | return samples; 124 | } 125 | 126 | private String intArrayToString(int[] number) { 127 | StringBuilder builder = new StringBuilder(); 128 | for (int n : number) { 129 | builder.append(n); 130 | } 131 | return builder.toString(); 132 | } 133 | } 134 | -------------------------------------------------------------------------------- /prediction-k3/src/test/java/org/yinyayun/predict/BayesTrainTest.java: -------------------------------------------------------------------------------- 1 | /** 2 | * 3 | */ 4 | package org.yinyayun.predict; 5 | 6 | import java.util.Arrays; 7 | 8 | import org.yinyayun.prediction.k3.bayes.BayesTrainModel; 9 | import org.yinyayun.prediction.preprocess.GenerateTrainDataSum; 10 | import org.yinyayun.prediction.preprocess.common.DataSample; 11 | import org.yinyayun.prediction.preprocess.common.DataSet; 12 | 13 | /** 14 | * @author yinyayun 15 | * 16 | */ 17 | public class BayesTrainTest { 18 | public static void main(String[] args) throws Exception { 19 | int pastnumber = 5; 20 | DataSet datas = new GenerateTrainDataSum("data/train-cp-20.txt").generate(pastnumber); 21 | BayesTrainModel model = new BayesTrainModel(); 22 | model.train(datas); 23 | model.saveMode("data/bayes.model"); 24 | model.loadModel("data/bayes.model"); 25 | int[] randoms = { 10, 500, 600, 200, 700, 800 }; 26 | for (int random : randoms) { 27 | DataSample dataMapper = datas.getDataSample(random); 28 | int[] inputs = dataMapper.getInput(); 29 | int out = dataMapper.getLabel(); 30 | int[] y = model.predict1(inputs); 31 | System.out.println("==========="); 32 | System.out.println("输入：" + Arrays.toString(inputs)); 33 | System.out.println("理论输出：" + out + " 实际输出：" + Arrays.toString(y)); 34 | } 35 | } 36 | } 37 | -------------------------------------------------------------------------------- /prediction-k3/src/test/java/org/yinyayun/predict/MaxEntTrainTest.java: -------------------------------------------------------------------------------- 1 | /** 2 | * 3 | */ 4 | package org.yinyayun.predict; 5 | 6 | import java.util.Arrays; 7 | 8 | import org.yinyayun.prediction.k3.maxent.MaxEntModel; 9 | import org.yinyayun.prediction.preprocess.GenerateTrainDataSum; 10 | import org.yinyayun.prediction.preprocess.common.DataSample; 11 | import org.yinyayun.prediction.preprocess.common.DataSet; 12 | 13 | /** 14 | * @author yinyayun 15 | * 16 | */ 17 | public class MaxEntTrainTest { 18 | public static void main(String[] args) throws Exception { 19 | int pastnumber = 8; 20 | DataSet datas = new GenerateTrainDataSum("data/train-cp-20.txt").generate(pastnumber); 21 | MaxEntModel model = new MaxEntModel(); 22 | model.train(datas, 10); 23 | int[] randoms = { 10, 500, 600, 200, 700, 800, 10000, 10001, 10002 }; 24 | for (int random : randoms) { 25 | DataSample dataMapper = datas.getDataSample(random); 26 | int[] inputs = dataMapper.getInput(); 27 | int out = dataMapper.getLabel(); 28 | int[] ret = model.predict(inputs); 29 | System.out.println("==========="); 30 | System.out.println("输入：" + Arrays.toString(inputs)); 31 | System.out.println("理论输出：" + out + " 实际输出：" + Arrays.toString(ret)); 32 | } 33 | } 34 | } 35 | -------------------------------------------------------------------------------- /prediction-preprocess/.gitignore: -------------------------------------------------------------------------------- 1 | /target/ 2 | -------------------------------------------------------------------------------- /prediction-preprocess/pom.xml: -------------------------------------------------------------------------------- 1 | 2 | 4.0.0 3 | 4 | org.yinyayun 5 | prediction 6 | 0.0.1-SNAPSHOT 7 | 8 | prediction-preprocess 9 | -------------------------------------------------------------------------------- /prediction-preprocess/src/main/java/org/yinyayun/prediction/preprocess/DataReader.java: -------------------------------------------------------------------------------- 1 | /** 2 | * 3 | */ 4 | package org.yinyayun.prediction.preprocess; 5 | 6 | import java.io.BufferedReader; 7 | import java.io.FileInputStream; 8 | import java.io.IOException; 9 | import java.io.InputStreamReader; 10 | import java.nio.charset.Charset; 11 | import java.util.ArrayList; 12 | import java.util.List; 13 | import java.util.function.Function; 14 | 15 | /** 16 | * @author yinyayun 17 | * 18 | */ 19 | public class DataReader implements AutoCloseable { 20 | private BufferedReader reader; 21 | private String line; 22 | 23 | public DataReader(String path) { 24 | try { 25 | reader = new BufferedReader(new InputStreamReader(new FileInputStream(path), Charset.forName("utf-8")), 26 | 1024); 27 | } catch (Exception e) { 28 | throw new RuntimeException(e); 29 | } 30 | } 31 | 32 | public boolean hasNext() throws IOException { 33 | this.line = reader.readLine(); 34 | if (this.line != null) { 35 | return true; 36 | } 37 | return false; 38 | } 39 | 40 | public R readData(Function function) { 41 | return function.apply(line); 42 | } 43 | 44 | public int[] readData() { 45 | return readData(this.line); 46 | } 47 | 48 | private int[] readData(String line) { 49 | String[] parts = line.split("##"); 50 | if (parts.length == 0) { 51 | return null; 52 | } 53 | String str = parts[0]; 54 | int[] data = new int[3]; 55 | for (int i = 0; i < str.length(); i++) { 56 | data[i] = str.charAt(i) - '0'; 57 | } 58 | return data; 59 | } 60 | 61 | public List readAllDatas() throws IOException { 62 | List datas = new ArrayList(); 63 | String line = null; 64 | while ((line = reader.readLine()) != null) { 65 | int[] data = readData(line); 66 | if (data != null) { 67 | datas.add(data); 68 | } 69 | } 70 | return datas; 71 | } 72 | 73 | @Override 74 | public void close() throws Exception { 75 | if (reader != null) { 76 | try { 77 | reader.close(); 78 | } catch (Exception e) { 79 | } 80 | } 81 | } 82 | } 83 | -------------------------------------------------------------------------------- /prediction-preprocess/src/main/java/org/yinyayun/prediction/preprocess/GenerateToJson.java: -------------------------------------------------------------------------------- 1 | /** 2 | * 3 | */ 4 | package org.yinyayun.prediction.preprocess; 5 | 6 | /** 7 | * @author yinyayun 8 | * 9 | */ 10 | public class GenerateToJson { 11 | 12 | } 13 | -------------------------------------------------------------------------------- /prediction-preprocess/src/main/java/org/yinyayun/prediction/preprocess/GenerateTrainDataSum.java: -------------------------------------------------------------------------------- 1 | /** 2 | * 3 | */ 4 | package org.yinyayun.prediction.preprocess; 5 | 6 | import java.util.ArrayList; 7 | import java.util.List; 8 | 9 | import org.yinyayun.prediction.preprocess.common.DataSample; 10 | import org.yinyayun.prediction.preprocess.common.DataSet; 11 | 12 | /** 13 | * @author yinyayun 根据历史N期的和值预测下一期的和值 14 | */ 15 | public class GenerateTrainDataSum { 16 | 17 | private String dataPath; 18 | 19 | public static void main(String[] args) throws Exception { 20 | new GenerateTrainDataSum("D:\\GithubRepository\\prediction\\prediction-k3\\data\\train-cp-20.txt").generate(5); 21 | } 22 | 23 | public GenerateTrainDataSum(String dataPath) { 24 | this.dataPath = dataPath; 25 | } 26 | 27 | /** 28 | * 是根据历史多少生成 29 | * 30 | * @param pastNumber 31 | * @throws Exception 32 | */ 33 | public DataSet generate(final int pastNumber) throws Exception { 34 | List datas = new ArrayList(); 35 | try (DataReader dataReader = new DataReader<>(dataPath)) { 36 | List trainDatas = dataReader.readAllDatas(); 37 | for (int i = pastNumber; i < trainDatas.size(); i++) { 38 | int output = sum(trainDatas.get(i)); 39 | int[] inputs = new int[pastNumber]; 40 | for (int j = i - pastNumber; j < i; j++) { 41 | int[] numbers = trainDatas.get(j); 42 | int sum = sum(numbers); 43 | inputs[j - i + pastNumber] = sum; 44 | } 45 | datas.add(new DataSample(inputs, output)); 46 | } 47 | } 48 | return new DataSet(datas, 16, 3); 49 | } 50 | 51 | private int sum(int[] array) { 52 | int sum = 0; 53 | for (int i : array) { 54 | sum += i; 55 | } 56 | return sum; 57 | } 58 | } 59 | -------------------------------------------------------------------------------- /prediction-preprocess/src/main/java/org/yinyayun/prediction/preprocess/common/DataSample.java: -------------------------------------------------------------------------------- 1 | /** 2 | * 3 | */ 4 | package org.yinyayun.prediction.preprocess.common; 5 | 6 | /** 7 | * @author yinyayun 输入与输出的映射 8 | */ 9 | public class DataSample { 10 | private int[] input; 11 | private int label; 12 | 13 | public DataSample(int[] input, int label) { 14 | this.input = input; 15 | this.label = label; 16 | } 17 | 18 | /** 19 | * @return the input 20 | */ 21 | public int[] getInput() { 22 | return input; 23 | } 24 | 25 | /** 26 | * @return the label 27 | */ 28 | public int getLabel() { 29 | return label; 30 | } 31 | 32 | @Override 33 | public String toString() { 34 | StringBuilder builder = new StringBuilder(); 35 | for (int i : input) { 36 | if (builder.length() > 0) { 37 | builder.append(","); 38 | } 39 | builder.append(i); 40 | } 41 | builder.append("##").append(label); 42 | return builder.toString(); 43 | } 44 | 45 | } 46 | -------------------------------------------------------------------------------- /prediction-preprocess/src/main/java/org/yinyayun/prediction/preprocess/common/DataSet.java: -------------------------------------------------------------------------------- 1 | /** 2 | * 3 | */ 4 | package org.yinyayun.prediction.preprocess.common; 5 | 6 | import java.util.List; 7 | 8 | /** 9 | * @author yinyayun 10 | * 11 | */ 12 | public class DataSet { 13 | private List samples; 14 | /** 15 | * 输出标签数量 16 | */ 17 | private int labels; 18 | // label的最小值 19 | private int minLabelValue; 20 | 21 | private int dataSize; 22 | 23 | /** 24 | * @param samples 25 | * @param labels 26 | * @param minLabelValue 27 | */ 28 | public DataSet(List samples, int labels, int minLabelValue) { 29 | super(); 30 | this.samples = samples; 31 | this.labels = labels; 32 | this.minLabelValue = minLabelValue; 33 | this.dataSize = samples.size(); 34 | } 35 | 36 | /** 37 | * @return the samples 38 | */ 39 | public List getSamples() { 40 | return samples; 41 | } 42 | 43 | public DataSample getDataSample(int i) { 44 | return samples.get(i); 45 | } 46 | 47 | /** 48 | * @return the labels 49 | */ 50 | public int getLabels() { 51 | return labels; 52 | } 53 | 54 | /** 55 | * @return the minLabelValue 56 | */ 57 | public int getMinLabelValue() { 58 | return minLabelValue; 59 | } 60 | 61 | /** 62 | * @return the dataSize 63 | */ 64 | public int getDataSize() { 65 | return dataSize; 66 | } 67 | } 68 | --------------------------------------------------------------------------------