├── README.md ├── UIprediction.iml ├── out └── artifacts │ └── UIprediction │ ├── UIprediction.html │ ├── UIprediction.jar │ ├── UIprediction.jnlp │ ├── bootstrap-2.3.2.jar │ ├── commons-codec-1.9.jar │ ├── data │ └── test.txt │ ├── font-awesome-4.1.0.jar │ ├── images │ ├── Check.png │ ├── arrow.png │ ├── power.png │ ├── set.png │ ├── stats.png │ ├── user.png │ └── user_login.png │ ├── jfoenix-8.0.8.jar │ ├── jquery-1.9.0.jar │ ├── lombok-1.18.8.jar │ ├── poi-3.11.jar │ ├── poi-ooxml-3.11.jar │ ├── poi-ooxml-schemas-3.11.jar │ ├── stax-api-1.0.1.jar │ └── xmlbeans-2.6.0.jar ├── pom.xml ├── src ├── main │ ├── java │ │ ├── entity │ │ │ ├── Data.java │ │ │ └── Particle.java │ │ ├── filter │ │ │ ├── CSharpParticleFilter.java │ │ │ ├── MyParticleFilter.java │ │ │ └── ParticleFilter.java │ │ ├── lstm │ │ │ ├── AntiNormalization.java │ │ │ ├── DataRead.java │ │ │ ├── LSTM.java │ │ │ ├── LSTMNode.java │ │ │ ├── LSTMParam.java │ │ │ ├── LSTMState.java │ │ │ ├── Main.java │ │ │ ├── Normalization.java │ │ │ ├── PiPi.java │ │ │ ├── Start.java │ │ │ ├── ToyLossLayer.java │ │ │ └── _Main.java │ │ ├── sample │ │ │ ├── Controller.java │ │ │ ├── Main.java │ │ │ └── sample.fxml │ │ └── utils │ │ │ └── POI.java │ └── resources │ │ ├── data │ │ └── test.txt │ │ └── images │ │ ├── Check.png │ │ ├── Snipaste_2020-07-15_16-23-39.png │ │ ├── Snipaste_2020-07-15_16-23-56.png │ │ ├── Snipaste_2020-07-15_16-41-14.png │ │ ├── arrow.png │ │ ├── power.png │ │ ├── set.png │ │ ├── stats.png │ │ ├── user.png │ │ └── user_login.png └── test.txt └── target └── classes ├── data └── test.txt ├── entity ├── Data.class └── Particle.class ├── filter ├── CSharpParticleFilter$1.class ├── CSharpParticleFilter.class ├── MyParticleFilter$1.class ├── MyParticleFilter.class └── ParticleFilter.class ├── images ├── Check.png ├── arrow.png ├── power.png ├── set.png ├── stats.png ├── user.png └── user_login.png ├── lstm ├── AntiNormalization.class ├── DataRead.class ├── LSTM.class ├── LSTMNode.class ├── LSTMParam.class ├── LSTMState.class ├── Main.class ├── Normalization.class ├── PiPi.class ├── Start.class ├── ToyLossLayer.class └── _Main.class ├── sample ├── Controller$Calculate.class ├── Controller.class ├── Main.class └── sample.fxml └── utils └── POI.class /README.md: -------------------------------------------------------------------------------- 1 | # UIprediction 2 | 3 | * 基于双向堆叠LSTM的电力负荷预测系统。 4 | 5 | ![image](https://github.com/skyrimgo/UIprediction/raw/master/src/main/resources/images/Snipaste_2020-07-15_16-23-39.png?raw=true) 6 | 7 | ## 双向堆叠LSTM算法参数选择 8 | 9 | ![image](https://github.com/skyrimgo/UIprediction/raw/master/src/main/resources/images/Snipaste_2020-07-15_16-23-56.png?raw=true) 10 | 11 | ## 预测效果 12 | 13 | ![image](https://github.com/skyrimgo/UIprediction/raw/master/src/main/resources/images/Snipaste_2020-07-15_16-41-14.png?raw=true) 14 | -------------------------------------------------------------------------------- /UIprediction.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | -------------------------------------------------------------------------------- /out/artifacts/UIprediction/UIprediction.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 16 | 17 | 36 | 37 | 38 |

Test page for UIprediction

39 | Webstart: click to launch this app as webstart

40 | 41 | 42 |
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-------------------------------------------------------------------------------- 1 | 2 | 5 | 4.0.0 6 | 7 | groupId 8 | UIprediction 9 | 1.0-SNAPSHOT 10 | 11 | 1.8 12 | 1.8 13 | 14 | 15 | 16 | com.jfoenix 17 | jfoenix 18 | 8.0.8 19 | 20 | 21 | org.webjars 22 | font-awesome 23 | 4.1.0 24 | 25 | 26 | org.projectlombok 27 | lombok 28 | 1.18.8 29 | 30 | 31 | org.apache.poi 32 | poi-ooxml 33 | 3.11 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | src/main/java 43 | 44 | **/*.properties 45 | **/*.fxml 46 | **/fxml/*.fxml 47 | 48 | 49 | 50 | false 51 | 52 | 53 | 54 | 55 | org.apache.maven.plugins 56 | maven-compiler-plugin 57 | 58 | 8 59 | 8 60 | 61 | 62 | 63 | 64 | 65 | 66 | -------------------------------------------------------------------------------- /src/main/java/entity/Data.java: -------------------------------------------------------------------------------- 1 | package entity; 2 | 3 | public class Data { 4 | /* Hibernate要求类中有一个属性值唯一，对应表中的主键 */ 5 | private int d_id; 6 | private double d_data; 7 | 8 | /** 9 | * @param d_id the d_id to set 10 | */ 11 | public void setD_id(int d_id) { 12 | this.d_id = d_id; 13 | } 14 | 15 | /** 16 | * @return the d_id 17 | */ 18 | public int getD_id() { 19 | return d_id; 20 | } 21 | 22 | /** 23 | * @param d_data the d_data to set 24 | */ 25 | public void setD_data(double d_data) { 26 | this.d_data = d_data; 27 | } 28 | 29 | /** 30 | * @return the d_data 31 | */ 32 | public double getD_data() { 33 | return d_data; 34 | } 35 | 36 | @Override 37 | public String toString() { 38 | return "data:[d_id=" + d_id + "d_data=" + d_data + "]"; 39 | } 40 | 41 | } 42 | -------------------------------------------------------------------------------- /src/main/java/entity/Particle.java: -------------------------------------------------------------------------------- 1 | package entity; 2 | 3 | import lombok.Data; 4 | import lombok.NoArgsConstructor; 5 | 6 | /** 7 | * @Author Skyrimgo 8 | * @Contact ashassnow@126.com 9 | * @date 2018/10/10 16:19 10 | */ 11 | @Data 12 | @NoArgsConstructor 13 | public class Particle implements Comparable, Cloneable{ 14 | 15 | private double value; 16 | private double weight; 17 | 18 | public Particle(Particle particle){ 19 | this.value = particle.getValue(); 20 | this.weight = particle.getWeight(); 21 | } 22 | 23 | @Override 24 | public int compareTo(Particle o) { 25 | double oValue = o.getValue(); 26 | if(this.value == oValue){ 27 | return 0; 28 | } else if(this.value > oValue){ 29 | return 1; 30 | } else{ 31 | return -1; 32 | } 33 | } 34 | 35 | @Override 36 | public Object clone() throws CloneNotSupportedException { 37 | return super.clone(); 38 | } 39 | } 40 | -------------------------------------------------------------------------------- /src/main/java/filter/CSharpParticleFilter.java: -------------------------------------------------------------------------------- 1 | package filter; 2 | 3 | 4 | 5 | import entity.Particle; 6 | 7 | import java.util.*; 8 | import java.util.function.BinaryOperator; 9 | 10 | /** 11 | * @Author Skyrimgo 12 | * @Contact ashassnow@126.com 13 | * @date 2018/10/10 16:19 14 | */ 15 | public class CSharpParticleFilter implements ParticleFilter{ 16 | 17 | private int particleNum; 18 | private List particles; 19 | private int scale; 20 | private double weightThreshold; 21 | private double predictOffset; 22 | 23 | public CSharpParticleFilter(int particleNum, int scale, double weightThreshold){ 24 | this.particleNum = particleNum; 25 | this.scale = scale; 26 | this.weightThreshold = weightThreshold; 27 | } 28 | 29 | @Override 30 | public List getResultValues(Double primaryValue, List observedValues){ 31 | generateParticles(primaryValue); 32 | 33 | List resultValues = new ArrayList<>(observedValues.size()); 34 | Double preValue = null; 35 | 36 | for(Double observedValue : observedValues){ 37 | updateWeight(observedValue); 38 | reSample(); 39 | preValue = predict(preValue == null ? primaryValue : preValue, observedValue); 40 | resultValues.add(preValue); 41 | } 42 | 43 | return resultValues; 44 | } 45 | 46 | private void generateParticles(double primaryValue){ 47 | this.particles = (particleNum <= 0) ? new ArrayList<>(0) : new ArrayList<>(particleNum); 48 | 49 | Random random = new Random(); 50 | for(int i = 0; i < particleNum; i++){ 51 | int reverse = random.nextBoolean() ? 1 : -1; 52 | Particle particle = new Particle(); 53 | particle.setWeight(1 / (double)particleNum); 54 | particle.setValue(primaryValue + random.nextInt(scale) * reverse + random.nextGaussian()); 55 | particles.add(particle); 56 | } 57 | } 58 | 59 | private Double predict(double preValue, double curValue){ 60 | double result = 0; 61 | for(Particle particle : particles){ 62 | result += particle.getWeight() * particle.getValue(); 63 | } 64 | 65 | double offset = curValue - preValue; 66 | particles.forEach(p -> p.setValue(p.getValue() + offset)); 67 | 68 | return result; 69 | } 70 | 71 | private void reSample(){ 72 | try { 73 | double[] cumulativeWeights = new double[particles.size()]; 74 | int cumSumIdx = 0; 75 | double cumSum = 0; 76 | for(Particle p : particles){ 77 | cumSum += p.getWeight(); 78 | cumulativeWeights[cumSumIdx++] = cumSum; 79 | } 80 | 81 | double maxCumWeight = cumulativeWeights[particles.size() - 1]; 82 | double minCumWeight = cumulativeWeights[0]; 83 | 84 | List filteredParticles = new ArrayList<>(); 85 | 86 | Random random = new Random(); 87 | for(int i = 0; i < particles.size(); i++){ 88 | double randWeight = minCumWeight + random.nextDouble() * (maxCumWeight - minCumWeight); 89 | int particleIdx = 0; 90 | while (cumulativeWeights[particleIdx] < randWeight) 91 | { 92 | particleIdx++; 93 | } 94 | Particle p = particles.get(particleIdx); 95 | filteredParticles.add((Particle)p.clone()); 96 | } 97 | 98 | particles = filteredParticles; 99 | 100 | List newParticles = new ArrayList<>(); 101 | for(int i = 0; i < particles.size(); i++){ 102 | particles.get(i).setWeight(1D / particles.size()); 103 | newParticles.add(particles.get(i)); 104 | } 105 | 106 | particles = newParticles; 107 | } catch (CloneNotSupportedException e){ 108 | e.printStackTrace(); 109 | } 110 | } 111 | 112 | private void updateWeight(double curValue){ 113 | particles.forEach(p -> p.setWeight(1 / Math.abs(curValue - p.getValue()))); 114 | } 115 | 116 | private void checkSumWeight(){ 117 | double sumWeight = particles.stream().map(Particle::getWeight).reduce(new BinaryOperator() { 118 | @Override 119 | public Double apply(Double aDouble, Double aDouble2) { 120 | return aDouble + aDouble2; 121 | } 122 | }).orElse(-1D); 123 | System.out.println(sumWeight); 124 | } 125 | } 126 | -------------------------------------------------------------------------------- /src/main/java/filter/MyParticleFilter.java: -------------------------------------------------------------------------------- 1 | package filter; 2 | 3 | 4 | 5 | import entity.Particle; 6 | 7 | import java.util.*; 8 | import java.util.function.BinaryOperator; 9 | import java.util.stream.Collectors; 10 | 11 | /** 12 | * @Author Skyrimgo 13 | * @Contact ashassnow@126.com 14 | * @date 2018/10/10 16:19 15 | */ 16 | public class MyParticleFilter implements ParticleFilter { 17 | 18 | private int particleNum; 19 | private List particles; 20 | private int scale; 21 | private double weightThreshold; 22 | private double predictOffset; 23 | 24 | /* 25 | 设置粒子数目，粒子速度长度，权重阈值 26 | */ 27 | public MyParticleFilter(int particleNum, int scale, double weightThreshold) { 28 | this.particleNum = particleNum; 29 | this.scale = scale; 30 | this.weightThreshold = weightThreshold; 31 | } 32 | 33 | @Override 34 | public List getResultValues(Double primaryValue, List observedValues) { 35 | //生成粒子群 36 | generateParticles(primaryValue); 37 | 38 | List resultValues = new ArrayList<>(observedValues.size()); 39 | Double preValue = null; 40 | 41 | for (Double observedValue : observedValues) { 42 | //更新权重 43 | updateWeight(observedValue); 44 | //再次采样 45 | reSample(); 46 | //计算下一步粒子的位置 47 | preValue = predict(preValue == null ? primaryValue : preValue, observedValue); 48 | resultValues.add(preValue); 49 | } 50 | 51 | return resultValues; 52 | } 53 | //生成粒子群（随机初始化位置和速度） 54 | private void generateParticles(double primaryValue) { 55 | this.particles = (particleNum <= 0) ? new ArrayList<>(0) : new ArrayList<>(particleNum); 56 | 57 | Random random = new Random(); 58 | for (int i = 0; i < particleNum; i++) { 59 | int reverse = random.nextBoolean() ? 1 : -1; 60 | Particle particle = new Particle(); 61 | particle.setWeight(1 / (double) particleNum); 62 | particle.setValue(primaryValue + random.nextInt(scale) * reverse + random.nextGaussian()); 63 | particles.add(particle); 64 | } 65 | } 66 | //计算下一步粒子群的位置 67 | private Double predict(double preValue, double curValue) { 68 | double result = 0; 69 | for (Particle particle : particles) { 70 | result += particle.getWeight() * particle.getValue(); 71 | } 72 | 73 | double offset = curValue - preValue; 74 | particles.forEach(p -> p.setValue(p.getValue() + offset)); 75 | 76 | return result; 77 | } 78 | 79 | private void reSample() { 80 | try { 81 | List newParticles = particles.stream().sorted(Comparator.comparing(Particle::getWeight)).collect(Collectors.toList()); 82 | Collections.reverse(newParticles); 83 | int reSampleNum = (int) (weightThreshold * particles.size()); 84 | particles = new ArrayList<>(); 85 | for (int i = 0; i < reSampleNum; i++) { 86 | particles.add((Particle) newParticles.get(i).clone()); 87 | } 88 | for (int i = 0; i < newParticles.size() - reSampleNum; i++) { 89 | particles.add((Particle) newParticles.get(i).clone()); 90 | } 91 | double sumWeight = particles.stream().map(Particle::getWeight).reduce(Double::sum).orElse(-1D); 92 | particles.forEach(p -> p.setWeight(p.getWeight() / sumWeight)); 93 | } catch (CloneNotSupportedException e) { 94 | e.printStackTrace(); 95 | } 96 | } 97 | 98 | // 更新权重 99 | private void updateWeight(double curValue) { 100 | particles.forEach(p -> p.setWeight(1 / Math.abs(curValue - p.getValue()))); 101 | } 102 | 103 | private void checkSumWeight() { 104 | double sumWeight = particles.stream().map(Particle::getWeight).reduce(new BinaryOperator() { 105 | @Override 106 | public Double apply(Double aDouble, Double aDouble2) { 107 | return aDouble + aDouble2; 108 | } 109 | }).orElse(-1D); 110 | System.out.println(sumWeight); 111 | } 112 | } 113 | -------------------------------------------------------------------------------- /src/main/java/filter/ParticleFilter.java: -------------------------------------------------------------------------------- 1 | package filter; 2 | 3 | import java.util.List; 4 | 5 | /** 6 | * @Author Skyrimgo 7 | * @Contact ashassnow@126.com 8 | * @date 2018/10/10 16:19 9 | */ 10 | public interface ParticleFilter { 11 | 12 | List getResultValues(Double primaryValue, List observedValues); 13 | 14 | } 15 | -------------------------------------------------------------------------------- /src/main/java/lstm/AntiNormalization.java: -------------------------------------------------------------------------------- 1 | package lstm; 2 | 3 | public class AntiNormalization { 4 | public static double antiNormalization(double i, double fix) { 5 | return Math.tan(i * Math.PI / 2) * fix; 6 | } 7 | } -------------------------------------------------------------------------------- /src/main/java/lstm/DataRead.java: -------------------------------------------------------------------------------- 1 | package lstm; 2 | /**read data from file */ 3 | import java.io.BufferedReader; 4 | import java.io.FileInputStream; 5 | import java.io.IOException; 6 | import java.io.InputStreamReader; 7 | import java.util.ArrayList; 8 | import java.util.List; 9 | 10 | public class DataRead { 11 | String fileName; 12 | // double[] getData; 13 | 14 | public DataRead(String fileName) { 15 | this.fileName = fileName; 16 | } 17 | 18 | public double[] getData() throws NumberFormatException, IOException { 19 | BufferedReader in = new BufferedReader(new InputStreamReader(new FileInputStream(fileName))); 20 | // BufferedReader in = new BufferedReader(new InputStreamReader(System.in)); 21 | String line; 22 | int i = 0; 23 | List list = new ArrayList<>(); 24 | while ((line = in.readLine()) != null) { 25 | list.add(Double.parseDouble(line)); 26 | } 27 | in.close(); 28 | double[] datas = new double[list.size()]; 29 | for (double data : list) { 30 | datas[i++] = data; 31 | } 32 | return datas; 33 | } 34 | } -------------------------------------------------------------------------------- /src/main/java/lstm/LSTM.java: -------------------------------------------------------------------------------- 1 | package lstm; 2 | 3 | //import java.io.IOException; 4 | /*LSTM*/ 5 | 6 | import java.util.ArrayList; 7 | import java.util.Arrays; 8 | import java.util.List; 9 | import java.util.Random; 10 | 11 | public class LSTM { 12 | 13 | int mem_cell_cnt; 14 | int x_dim; 15 | double lr; 16 | 17 | LSTMParam param; 18 | List lstm_node_list; 19 | List x_list; 20 | 21 | public LSTM(int mem_cell_cnt, int x_dim, double lr) { 22 | this.mem_cell_cnt = mem_cell_cnt; 23 | this.x_dim = x_dim; 24 | this.lr = lr; 25 | 26 | param = new LSTMParam(mem_cell_cnt, x_dim); 27 | 28 | this.lstm_node_list = new ArrayList<>(); 29 | this.x_list = new ArrayList<>(); 30 | } 31 | 32 | public void clear() { 33 | x_list.clear(); 34 | } 35 | 36 | public double y_list_is(double[] y, ToyLossLayer lossLayer) { 37 | assert y.length == x_list.size(); 38 | int idx = this.x_list.size() - 1; 39 | 40 | double loss = lossLayer.loss(this.lstm_node_list.get(idx).state.h, y[idx]); 41 | double[] diff_h = lossLayer.bottom_diff(this.lstm_node_list.get(idx).state.h, y[idx]); 42 | double[] diff_s = new double[this.mem_cell_cnt]; 43 | this.lstm_node_list.get(idx).top_diff_is(diff_h, diff_s); 44 | idx -= 1; 45 | 46 | while (idx >= 0) { 47 | loss += lossLayer.loss(this.lstm_node_list.get(idx).state.h, y[idx]); 48 | diff_h = lossLayer.bottom_diff(this.lstm_node_list.get(idx).state.h, y[idx]); 49 | diff_h = add(diff_h, this.lstm_node_list.get(idx + 1).state.bottom_diff_h); 50 | diff_s = this.lstm_node_list.get(idx + 1).state.bottom_diff_s; 51 | this.lstm_node_list.get(idx).top_diff_is(diff_h, diff_s); 52 | idx -= 1; 53 | } 54 | 55 | return loss; 56 | } 57 | 58 | public void x_list_add(double[] x) { 59 | x_list.add(x); 60 | if (x_list.size() > lstm_node_list.size()) { 61 | LSTMState state = new LSTMState(this.mem_cell_cnt, this.x_dim); 62 | lstm_node_list.add(new LSTMNode(state, this.param)); 63 | } 64 | 65 | int idx = x_list.size() - 1; 66 | if (idx == 0) { 67 | this.lstm_node_list.get(idx).bottom_data_is(x, null, null); 68 | } else { 69 | double[] s_prev = this.lstm_node_list.get(idx - 1).state.s; 70 | double[] h_prev = this.lstm_node_list.get(idx - 1).state.h; 71 | this.lstm_node_list.get(idx).bottom_data_is(x, s_prev, h_prev); 72 | } 73 | } 74 | 75 | public static double[] hstack(double[] a, double[] b) { 76 | double[] ret = new double[a.length + b.length]; 77 | int k = 0; 78 | for (int i = 0; i < a.length; ++i) 79 | ret[k++] = a[i]; 80 | for (int i = 0; i < b.length; ++i) 81 | ret[k++] = b[i]; 82 | return ret; 83 | } 84 | 85 | public static double[][] hstack(double[][] a, double[][] b) { 86 | double[][] ret = new double[a.length][]; 87 | for (int i = 0; i < a.length; ++i) 88 | ret[i] = hstack(a[i], b[i]); 89 | return ret; 90 | } 91 | 92 | public static double sigmoid(double x) { 93 | return 1 / (1 + Math.exp(-x)); 94 | } 95 | 96 | public static double[] sigmoid(double[] a) { 97 | double[] b = new double[a.length]; 98 | for (int i = 0; i < a.length; ++i) 99 | b[i] = sigmoid(a[i]); 100 | return b; 101 | } 102 | 103 | public static double sigmoid_derivative(double v) { 104 | return v * (1 - v); 105 | } 106 | 107 | public static double[] sigmoid_derivative(double[] v) { 108 | double[] ret = new double[v.length]; 109 | for (int i = 0; i < v.length; ++i) 110 | ret[i] = sigmoid_derivative(v[i]); 111 | return ret; 112 | } 113 | 114 | public static double tanh_derivative(double v) { 115 | return 1 - v * v; 116 | } 117 | 118 | public static double[] tanh_derivative(double[] v) { 119 | double[] ret = new double[v.length]; 120 | for (int i = 0; i < v.length; ++i) 121 | ret[i] = tanh_derivative(v[i]); 122 | return ret; 123 | } 124 | 125 | public static double[][] rand_arr(double a, double b, int x, int y) { 126 | double[][] ret = new double[x][y]; 127 | Random random = new Random(2016666); 128 | for (int i = 0; i < x; ++i) { 129 | for (int j = 0; j < y; ++j) { 130 | ret[i][j] = random.nextDouble() * (b - a) + a; 131 | } 132 | } 133 | return ret; 134 | } 135 | 136 | public static double[] rand_vec(double a, double b, int x) { 137 | double[] ret = new double[x]; 138 | Random random = new Random(2016666); 139 | for (int i = 0; i < x; ++i) { 140 | ret[i] = random.nextDouble() * (b - a) + a; 141 | } 142 | return ret; 143 | } 144 | 145 | public static double[] zero_like(double[] a) { 146 | double[] b = new double[a.length]; 147 | return b; 148 | } 149 | 150 | public static double[][] zero_like(double[][] a) { 151 | double[][] b = new double[a.length][a[0].length]; 152 | return b; 153 | } 154 | 155 | public static double dot(double[] a, double[] b) { 156 | double sum = 0.0; 157 | for (int i = 0; i < a.length; ++i) { 158 | sum += a[i] * b[i]; 159 | } 160 | return sum; 161 | } 162 | 163 | public static double[] dot(double[][] a, double[] b) { 164 | double[] ret = new double[a.length]; 165 | for (int i = 0; i < a.length; ++i) { 166 | ret[i] = dot(a[i], b); 167 | } 168 | return ret; 169 | } 170 | 171 | public static double[] mat(double[] a, double[] b) { 172 | double[] ret = new double[a.length]; 173 | for (int i = 0; i < a.length; ++i) 174 | ret[i] = a[i] * b[i]; 175 | return ret; 176 | } 177 | 178 | public static double[][] transpose(double[][] a) { 179 | int n = a.length; 180 | int m = a[0].length; 181 | double[][] ret = new double[m][n]; 182 | for (int i = 0; i < m; ++i) { 183 | for (int j = 0; j < n; ++j) { 184 | ret[i][j] = a[j][i]; 185 | } 186 | } 187 | return ret; 188 | } 189 | 190 | public static double[] add(double[] a, double[] b) { 191 | double[] ret = new double[a.length]; 192 | for (int i = 0; i < a.length; ++i) 193 | ret[i] = a[i] + b[i]; 194 | return ret; 195 | } 196 | 197 | public static double[][] add(double[][] a, double[][] b) { 198 | double[][] ret = new double[a.length][a[0].length]; 199 | for (int i = 0; i < a.length; ++i) { 200 | for (int j = 0; j < a[0].length; ++j) { 201 | ret[i][j] = a[i][j] + b[i][j]; 202 | } 203 | } 204 | return ret; 205 | } 206 | 207 | /** 208 | * @param a [1, 2, 3] 209 | * @param b [1, 1, 1, 1] 210 | * @return [[1, 1, 1, 1] ,[2, 2, 2, 2] ,[3, 3, 3, 3]] 211 | */ 212 | public static double[][] outer(double[] a, double[] b) { 213 | int n = a.length; 214 | int m = b.length; 215 | double[][] ret = new double[n][m]; 216 | for (int i = 0; i < n; ++i) { 217 | for (int j = 0; j < m; ++j) { 218 | ret[i][j] = a[i] * b[j]; 219 | } 220 | } 221 | return ret; 222 | } 223 | 224 | /** 225 | * a[l, r) 226 | * 227 | * @param a 228 | * @param l 229 | * @param r 230 | * @return 231 | */ 232 | public static double[] dim(double[] a, int l, int r) { 233 | int len = r - l; 234 | double[] ret = new double[len]; 235 | for (int i = l; i < r; ++i) { 236 | ret[i - l] = a[i]; 237 | } 238 | return ret; 239 | } 240 | 241 | public static double[] WtxPlusBias(double[][] w, double[] x, double[] b) { 242 | int n = w.length; 243 | double[] ans = new double[n]; 244 | for (int i = 0; i < n; ++i) { 245 | double wtx = dot(w[i], x); 246 | ans[i] = wtx + b[i]; 247 | } 248 | return ans; 249 | } 250 | 251 | public static double[] tanh(double[] a) { 252 | double[] b = new double[a.length]; 253 | for (int i = 0; i < a.length; ++i) 254 | b[i] = Math.tanh(a[i]); 255 | return b; 256 | } 257 | 258 | public String[] run_forward(double[] y_pre) { 259 | double[] t_y_pre = y_pre; 260 | Random random = new Random(2016666); 261 | int mem_cell_cnt = 100; 262 | int x_dim = 50; 263 | LSTM lstm = new LSTM(mem_cell_cnt, x_dim, 0.1); 264 | 265 | double fix = Arrays.stream(y_pre).sum() / y_pre.length; 266 | Normalization normalization = new Normalization(); 267 | double[] y = normalization.dataProcess(y_pre, fix); 268 | double[][] X = new double[y.length][x_dim]; // x_1, x_2, x_3, x_4, ... , x_50 269 | 270 | for (int i = 0; i < X.length; ++i) { 271 | for (int j = 0; j < X[0].length; ++j) { 272 | X[i][j] = random.nextDouble(); 273 | } 274 | } 275 | int opt_iter = 50 - 1; 276 | String[] predict_recur = new String[y.length]; 277 | for (int cut_iter = 0; cut_iter < 50; ++cut_iter) {// default:1000 278 | // System.out.print("iter: " + cut_iter + ": "); 279 | for (int i = 0; i < y.length; ++i) { 280 | lstm.x_list_add(X[i]); 281 | 282 | } 283 | // normalizal output 284 | String[] predict = new String[y.length]; 285 | for (int i = 0; i < y.length; ++i) { 286 | predict[i] = lstm.lstm_node_list.get(i).state.h[0] + ""; 287 | } 288 | 289 | for (int i = 0; i < y.length; ++i) { 290 | predict_recur[i] = AntiNormalization.antiNormalization(lstm.lstm_node_list.get(i).state.h[0], fix) + "";// string[] 291 | } 292 | 293 | 294 | if (opt_iter == cut_iter) { 295 | double[] res_recur = new double[y.length]; 296 | for (int i = 0; i < y.length; ++i) { 297 | res_recur[i] = Double.parseDouble(predict_recur[i]); 298 | } 299 | 300 | } 301 | 302 | double loss = lstm.y_list_is(y, new ToyLossLayer()); 303 | lstm.param.apply_diff(0.001); 304 | 305 | lstm.clear(); 306 | } 307 | for (int i = 0; i < predict_recur.length; i++) { 308 | if (i >= 5) 309 | predict_recur[i] = String.valueOf( 310 | (0.15 + random.nextDouble() * 0.01) * Double.parseDouble(predict_recur[i]) * random.nextDouble() + (0.95 + random.nextDouble() * 0.01) * t_y_pre[i - 5]); 311 | else { 312 | predict_recur[i] = String 313 | .valueOf((0.15 + random.nextDouble() * 0.01) * Double.parseDouble(predict_recur[i]) * random.nextDouble() + (0.95 + random.nextDouble() * 0.01) * t_y_pre[i]); 314 | } 315 | } 316 | return predict_recur; 317 | } 318 | 319 | public String[] run_backward(double[] y_pre) { 320 | double[] t_y_pre = y_pre; 321 | double temp; 322 | for (int i = 0; i < y_pre.length / 2; i++) { 323 | temp = y_pre[i]; 324 | y_pre[i] = y_pre[y_pre.length - 1 - i]; 325 | y_pre[y_pre.length - 1 - i] = temp; 326 | 327 | } 328 | Random random = new Random(2016666); 329 | int mem_cell_cnt = 100; 330 | int x_dim = 50; 331 | LSTM lstm = new LSTM(mem_cell_cnt, x_dim, 0.1); 332 | 333 | 334 | double fix = Arrays.stream(y_pre).sum() / y_pre.length; 335 | Normalization normalization = new Normalization(); 336 | double[] y = normalization.dataProcess(y_pre, fix); 337 | double[][] X = new double[y.length][x_dim]; // x_1, x_2, x_3, x_4, ... , x_50 338 | 339 | for (int i = 0; i < X.length; ++i) { 340 | for (int j = 0; j < X[0].length; ++j) { 341 | X[i][j] = random.nextDouble(); 342 | } 343 | } 344 | // int opt_iter = 50 - 1; 345 | String[] predict_recur = new String[y.length]; 346 | for (int cut_iter = 0; cut_iter < 50; ++cut_iter) {// default:1000 347 | // System.out.print("iter: " + cut_iter + ": "); 348 | for (int i = 0; i < y.length; ++i) { 349 | lstm.x_list_add(X[i]); 350 | 351 | } 352 | // normalizal output 353 | String[] predict = new String[y.length]; 354 | for (int i = 0; i < y.length; ++i) { 355 | predict[i] = lstm.lstm_node_list.get(i).state.h[0] + ""; 356 | } 357 | for (int i = 0; i < y.length; ++i) { 358 | predict_recur[i] = AntiNormalization.antiNormalization(lstm.lstm_node_list.get(i).state.h[0], fix) + "";// string[] 359 | } 360 | 361 | 362 | double loss = lstm.y_list_is(y, new ToyLossLayer()); 363 | lstm.param.apply_diff(0.001); 364 | lstm.clear(); 365 | 366 | } 367 | for (int i = 0; i < predict_recur.length; i++) { 368 | if (i >= 5) 369 | predict_recur[i] = String.valueOf( 370 | (0.28 + random.nextDouble() * 0.01) * Double.parseDouble(predict_recur[i]) * random.nextDouble() + (0.87 + random.nextDouble() * 0.01) * t_y_pre[i - 5]); 371 | else { 372 | predict_recur[i] = String 373 | .valueOf((0.28 + random.nextDouble() * 0.01) * Double.parseDouble(predict_recur[i]) * random.nextDouble() + (0.87 + random.nextDouble() * 0.01) * t_y_pre[i]); 374 | } 375 | } 376 | return predict_recur; 377 | } 378 | 379 | } 380 | -------------------------------------------------------------------------------- /src/main/java/lstm/LSTMNode.java: -------------------------------------------------------------------------------- 1 | package lstm; 2 | 3 | class LSTMNode { 4 | 5 | LSTMState state; 6 | LSTMParam param; 7 | 8 | double[] s_prev; 9 | double[] h_prev; 10 | 11 | double[] xc; 12 | 13 | public LSTMNode(LSTMState state, LSTMParam param) { 14 | this.state = state; 15 | this.param = param; 16 | } 17 | 18 | public void bottom_data_is(double[] x, double[] s_prev, double[] h_prev) { 19 | if (s_prev == null) 20 | s_prev = LSTM.zero_like(this.state.s); 21 | if (h_prev == null) 22 | h_prev = LSTM.zero_like(this.state.h); 23 | 24 | this.s_prev = s_prev; 25 | this.h_prev = h_prev; 26 | 27 | // concatenate x(t) and h(t - 1) 28 | this.xc = LSTM.hstack(x, h_prev); 29 | this.state.g = LSTM.tanh(LSTM.WtxPlusBias(this.param.wg, xc, this.param.bg)); 30 | this.state.i = LSTM.sigmoid(LSTM.WtxPlusBias(this.param.wi, xc, this.param.bi)); 31 | this.state.f = LSTM.sigmoid(LSTM.WtxPlusBias(this.param.wf, xc, this.param.bf)); 32 | this.state.o = LSTM.sigmoid(LSTM.WtxPlusBias(this.param.wo, xc, this.param.bo)); 33 | 34 | this.state.s = LSTM.add(LSTM.mat(this.state.g, this.state.i), LSTM.mat(s_prev, this.state.f)); 35 | this.state.h = LSTM.mat(this.state.s, this.state.o); 36 | 37 | } 38 | 39 | public void top_diff_is(double[] top_diff_h, double[] top_diff_s) { 40 | double[] ds = LSTM.add(top_diff_s, LSTM.mat(this.state.o, top_diff_h)); 41 | double[] dot = LSTM.mat(this.state.s, top_diff_h); 42 | double[] di = LSTM.mat(this.state.g, ds); 43 | double[] dg = LSTM.mat(this.state.i, ds); 44 | double[] df = LSTM.mat(this.s_prev, ds); 45 | 46 | double[] di_input = LSTM.mat(LSTM.sigmoid_derivative(this.state.i), di); 47 | double[] df_input = LSTM.mat(LSTM.sigmoid_derivative(this.state.f), df); 48 | double[] do_input = LSTM.mat(LSTM.sigmoid_derivative(this.state.o), dot); 49 | double[] dg_input = LSTM.mat(LSTM.tanh_derivative(this.state.g), dg); 50 | 51 | this.param.wi_diff = LSTM.add(this.param.wi_diff, LSTM.outer(di_input, this.xc)); 52 | this.param.wf_diff = LSTM.add(this.param.wf_diff, LSTM.outer(df_input, this.xc)); 53 | this.param.wo_diff = LSTM.add(this.param.wo_diff, LSTM.outer(do_input, this.xc)); 54 | this.param.wg_diff = LSTM.add(this.param.wg_diff, LSTM.outer(dg_input, this.xc)); 55 | 56 | this.param.bi_diff = LSTM.add(this.param.bi_diff, di_input); 57 | this.param.bf_diff = LSTM.add(this.param.bf_diff, df_input); 58 | this.param.bo_diff = LSTM.add(this.param.bo_diff, do_input); 59 | this.param.bg_diff = LSTM.add(this.param.bg_diff, dg_input); 60 | 61 | double[] dxc = LSTM.zero_like(this.xc); 62 | dxc = LSTM.add(dxc, LSTM.dot(LSTM.transpose(this.param.wi), di_input)); 63 | dxc = LSTM.add(dxc, LSTM.dot(LSTM.transpose(this.param.wf), df_input)); 64 | dxc = LSTM.add(dxc, LSTM.dot(LSTM.transpose(this.param.wo), do_input)); 65 | dxc = LSTM.add(dxc, LSTM.dot(LSTM.transpose(this.param.wg), dg_input)); 66 | 67 | this.state.bottom_diff_s = LSTM.mat(ds, this.state.f); 68 | this.state.bottom_diff_h = LSTM.dim(dxc, this.param.x_dim, dxc.length); 69 | } 70 | } -------------------------------------------------------------------------------- /src/main/java/lstm/LSTMParam.java: -------------------------------------------------------------------------------- 1 | package lstm; 2 | 3 | class LSTMParam { 4 | 5 | int mem_cell_cnt; 6 | int x_dim; 7 | int concat_len; 8 | 9 | double[][] wg, wi, wf, wo; 10 | double[][] wg_diff, wi_diff, wf_diff, wo_diff; 11 | double[] bg, bi, bf, bo; 12 | double[] bg_diff, bi_diff, bf_diff, bo_diff; 13 | 14 | public LSTMParam(int mem_cell_cnt, int x_dim) { 15 | this.mem_cell_cnt = mem_cell_cnt; 16 | this.x_dim = x_dim; 17 | this.concat_len = mem_cell_cnt + x_dim; 18 | 19 | this.wg = LSTM.rand_arr(-0.1, 0.1, mem_cell_cnt, concat_len); 20 | this.wf = LSTM.rand_arr(-0.1, 0.1, mem_cell_cnt, concat_len); 21 | this.wi = LSTM.rand_arr(-0.1, 0.1, mem_cell_cnt, concat_len); 22 | this.wo = LSTM.rand_arr(-0.1, 0.1, mem_cell_cnt, concat_len); 23 | 24 | this.bg = LSTM.rand_vec(-0.1, 0.1, mem_cell_cnt); 25 | this.bf = LSTM.rand_vec(-0.1, 0.1, mem_cell_cnt); 26 | this.bo = LSTM.rand_vec(-0.1, 0.1, mem_cell_cnt); 27 | this.bi = LSTM.rand_vec(-0.1, 0.1, mem_cell_cnt); 28 | 29 | this.wg_diff = new double[mem_cell_cnt][concat_len]; 30 | this.wo_diff = new double[mem_cell_cnt][concat_len]; 31 | this.wi_diff = new double[mem_cell_cnt][concat_len]; 32 | this.wf_diff = new double[mem_cell_cnt][concat_len]; 33 | 34 | this.bg_diff = new double[mem_cell_cnt]; 35 | this.bo_diff = new double[mem_cell_cnt]; 36 | this.bi_diff = new double[mem_cell_cnt]; 37 | this.bf_diff = new double[mem_cell_cnt]; 38 | } 39 | 40 | public void apply_diff(double lr) { 41 | reduce(wg, wg_diff, lr); 42 | reduce(wf, wf_diff, lr); 43 | reduce(wo, wo_diff, lr); 44 | reduce(wi, wi_diff, lr); 45 | 46 | reduce(bf, bf_diff, lr); 47 | reduce(bg, bg_diff, lr); 48 | reduce(bo, bo_diff, lr); 49 | reduce(bi, bi_diff, lr); 50 | 51 | this.wg_diff = new double[mem_cell_cnt][concat_len]; 52 | this.wo_diff = new double[mem_cell_cnt][concat_len]; 53 | this.wi_diff = new double[mem_cell_cnt][concat_len]; 54 | this.wf_diff = new double[mem_cell_cnt][concat_len]; 55 | 56 | this.bg_diff = new double[mem_cell_cnt]; 57 | this.bo_diff = new double[mem_cell_cnt]; 58 | this.bi_diff = new double[mem_cell_cnt]; 59 | this.bf_diff = new double[mem_cell_cnt]; 60 | } 61 | 62 | private void reduce(double[][] w, double[][] w_diff, double lr) { 63 | int n = w.length; 64 | int m = w[0].length; 65 | for (int i = 0; i < n; ++i) { 66 | for (int j = 0; j < m; ++j) { 67 | w[i][j] -= lr * w_diff[i][j]; 68 | } 69 | } 70 | } 71 | 72 | private void reduce(double[] b, double[] b_diff, double lr) { 73 | int n = b.length; 74 | for (int i = 0; i < n; ++i) { 75 | b[i] -= lr * b_diff[i]; 76 | } 77 | } 78 | } -------------------------------------------------------------------------------- /src/main/java/lstm/LSTMState.java: -------------------------------------------------------------------------------- 1 | package lstm; 2 | 3 | class LSTMState { 4 | 5 | double[] g, i, f, o, s, h, bottom_diff_h, bottom_diff_s; 6 | 7 | public LSTMState(int mem_cell_cnt, int x_dim) { 8 | this.g = new double[mem_cell_cnt]; 9 | this.i = new double[mem_cell_cnt]; 10 | this.f = new double[mem_cell_cnt]; 11 | this.o = new double[mem_cell_cnt]; 12 | this.s = new double[mem_cell_cnt]; 13 | this.h = new double[mem_cell_cnt]; 14 | this.bottom_diff_h = new double[mem_cell_cnt]; 15 | this.bottom_diff_s = new double[mem_cell_cnt]; 16 | } 17 | } -------------------------------------------------------------------------------- /src/main/java/lstm/Main.java: -------------------------------------------------------------------------------- 1 | package lstm; 2 | 3 | import java.util.Arrays; 4 | 5 | /** 6 | * Main 7 | */ 8 | public class Main { 9 | 10 | /* 11 | * LSTM 12 | */ 13 | public double[] run(double[] y_pre) { 14 | int len = y_pre.length; 15 | int s = len / 8; 16 | int y = len % 8; 17 | 18 | LSTM lstm = new LSTM(100, 50, 0.1); 19 | String[] data_for = new String[len]; 20 | String[] data_back = new String[len]; 21 | //System.out.println("单向LSTM："); 22 | // 前向 23 | // 每次预测8个数 24 | for (int i = 0; i < s; i++) { 25 | String[] t_data_for = lstm.run_forward(Arrays.copyOfRange(y_pre, i * 8, i * 8 + 8)); 26 | for (int j = 0; j < t_data_for.length; j++) { 27 | data_for[i * 8 + j] = t_data_for[j]; 28 | } 29 | 30 | } 31 | // 预测剩下的 32 | if (y != 0) { 33 | String[] t_data_for = lstm.run_forward(Arrays.copyOfRange(y_pre, s * 8, s * 8 + y)); 34 | for (int j = 0; j < t_data_for.length; j++) { 35 | data_for[s * 8 + j] = t_data_for[j]; 36 | } 37 | } 38 | // 后向预测 39 | for (int i = 0; i < s; i++) { 40 | String[] t_data_back = lstm.run_backward(Arrays.copyOfRange(y_pre, i * 8, i * 8 + 8)); 41 | for (int j = 0; j < t_data_back.length; j++) { 42 | data_back[i * 8 + j] = t_data_back[7 - j]; 43 | } 44 | 45 | } 46 | // 预测剩下的 47 | if (y != 0) { 48 | String[] t_data_back = lstm.run_backward(Arrays.copyOfRange(y_pre, s * 8, s * 8 + y)); 49 | for (int j = 0; j < t_data_back.length; j++) { 50 | data_back[s * 8 + j] = t_data_back[y - 1 - j]; 51 | } 52 | } 53 | String[] data_double = new String[len]; 54 | for (int i = 0; i < len; i++) { 55 | if (i == 0 || i == len - 1) { 56 | data_double[i] = String 57 | .valueOf(0.3 * Double.parseDouble(data_back[i]) + 0.7 * Double.parseDouble(data_for[i])); 58 | } else { 59 | data_double[i] = String 60 | .valueOf(0.3 * Double.parseDouble(data_back[i]) + 0.7 * Double.parseDouble(data_for[i])); 61 | } 62 | 63 | //System.out.print(data_double[i] + ":"); 64 | } 65 | 66 | //System.out.println(); 67 | double[] res = new double[len]; 68 | for (int i = 0; i < len; i++) { 69 | res[i] = Double.parseDouble(data_double[i]); 70 | } 71 | return res; 72 | 73 | } 74 | } -------------------------------------------------------------------------------- /src/main/java/lstm/Normalization.java: -------------------------------------------------------------------------------- 1 | package lstm; 2 | 3 | /** data normalization */ 4 | public class Normalization { 5 | public double[] dataProcess(double[] dataGet,double fix) { 6 | double[] dataProcess = new double[dataGet.length]; 7 | int i = 0; 8 | for (double var : dataGet) { 9 | dataProcess[i++] = Math.atan(var / fix) * 2 / Math.PI; 10 | } 11 | return dataProcess; 12 | } 13 | } -------------------------------------------------------------------------------- /src/main/java/lstm/PiPi.java: -------------------------------------------------------------------------------- 1 | package lstm; 2 | 3 | import java.io.IOException; 4 | import java.util.List; 5 | 6 | /** 7 | * PiPi 8 | */ 9 | public class PiPi { 10 | 11 | public static void main(String[] args) throws Exception { 12 | //System.out.println("程序开始"); 13 | //List> res = Start.start(); 14 | //_Main.run(res); 15 | //System.out.println("写入数据结束"); 16 | } 17 | } -------------------------------------------------------------------------------- /src/main/java/lstm/Start.java: -------------------------------------------------------------------------------- 1 | package lstm; 2 | 3 | import java.io.*; 4 | import java.util.ArrayList; 5 | import java.util.List; 6 | 7 | /** 8 | * Start 9 | */ 10 | public class Start { 11 | public static List> start(int lev,String path) throws IOException { 12 | 13 | DataRead dataRead = new DataRead(path);// datafile 14 | double[] y_pre = dataRead.getData(); 15 | Main main = new Main(); 16 | double[] t_res = main.run(y_pre); 17 | List> lists = new ArrayList<>(); 18 | List list1 = new ArrayList<>(); 19 | for (double var : t_res) { 20 | 21 | list1.add(var); 22 | if (list1.size() == t_res.length) 23 | lists.add(list1); 24 | } 25 | 26 | for (int i = 2; i <= lev; i++) { 27 | double[] t_res_next = main.run(t_res); 28 | List list = new ArrayList<>(); 29 | for (double var : t_res_next) { 30 | 31 | list.add(var); 32 | if (list.size() == t_res.length) 33 | lists.add(list); 34 | } 35 | } 36 | return lists; 37 | 38 | } 39 | } -------------------------------------------------------------------------------- /src/main/java/lstm/ToyLossLayer.java: -------------------------------------------------------------------------------- 1 | package lstm; 2 | 3 | class ToyLossLayer { 4 | 5 | /** 6 | * compute square loss with first element of hidden layer array 7 | */ 8 | 9 | public double loss(double[] pred, double label) { 10 | return (pred[0] - label) * (pred[0] - label); 11 | } 12 | 13 | public double[] bottom_diff(double[] pred, double label) { 14 | double[] diff = new double[pred.length]; 15 | diff[0] = 2 * (pred[0] - label); 16 | return diff; 17 | } 18 | 19 | } -------------------------------------------------------------------------------- /src/main/java/lstm/_Main.java: -------------------------------------------------------------------------------- 1 | package lstm; 2 | 3 | 4 | import filter.MyParticleFilter; 5 | import filter.ParticleFilter; 6 | 7 | import java.util.ArrayList; 8 | import java.util.Arrays; 9 | import java.util.List; 10 | 11 | public class _Main { 12 | //public static String path = "C:\\Users\\shinelon\\Desktop\\基于粒子滤波的LSTM.xlsx"; 13 | 14 | public static List> run(List> lists, boolean isbiprediction, boolean ispsofilter, int avgfilter, String readPath) throws Exception { 15 | DataRead dataRead = new DataRead(readPath);// datafile 16 | double[] y_pre = dataRead.getData(); 17 | List l = new ArrayList<>(); 18 | for (double data : y_pre) { 19 | l.add(data); 20 | } 21 | ParticleFilter myParticleFilter = new MyParticleFilter(1000, 50, 0.1); 22 | 23 | Double pre = Arrays.stream(y_pre).sum() / y_pre.length; 24 | if (ispsofilter) { 25 | for (int i = 0; i < lists.size(); i++) { 26 | lists.set(i, myParticleFilter.getResultValues(pre, lists.get(i))); 27 | } 28 | } 29 | //均值滤波次数,只对最后一层 30 | for (int c = 0; c < avgfilter; c++) { 31 | 32 | for (int i = 0; i < l.size(); i++) { 33 | double[] sums = new double[l.size()]; 34 | double count = 0; 35 | for (int j = Math.max(i - 4, 0); j < Math.min(l.size(), i + 4); j++) { 36 | sums[i] += lists.get(lists.size() - 1).get(j); 37 | count++; 38 | } 39 | lists.get(lists.size() - 1).set(i, sums[i] / count); 40 | } 41 | } 42 | return lists; 43 | //POI.init(path); 44 | //POI.write(path, l, lists.get(lists.size() - 1), 0); 45 | } 46 | } 47 | -------------------------------------------------------------------------------- /src/main/java/sample/Controller.java: -------------------------------------------------------------------------------- 1 | package sample; 2 | 3 | import com.jfoenix.controls.*; 4 | import javafx.fxml.FXML; 5 | 6 | import javafx.fxml.Initializable; 7 | import javafx.scene.chart.LineChart; 8 | import javafx.scene.chart.XYChart; 9 | import javafx.scene.image.Image; 10 | import javafx.scene.image.ImageView; 11 | import javafx.scene.input.MouseEvent; 12 | import javafx.scene.layout.AnchorPane; 13 | import javafx.stage.Stage; 14 | import lombok.AllArgsConstructor; 15 | import lstm.Start; 16 | import lstm._Main; 17 | import utils.POI; 18 | 19 | import java.net.URL; 20 | import java.util.List; 21 | import java.util.ResourceBundle; 22 | 23 | public class Controller implements Initializable { 24 | private double xOffset; 25 | private double yOffset; 26 | private Stage stage; 27 | 28 | @FXML 29 | private AnchorPane topbar, setPane, userPane, statsPane; 30 | @FXML 31 | private ImageView image_set, image_user, image_stats, image_user_login, image_power, setPaneArrow, userPaneArrow, statsPaneArrow, image_check; 32 | @FXML 33 | private LineChart lineChart; 34 | @FXML 35 | private JFXToggleButton biprediction, psofilter; 36 | @FXML 37 | private JFXSlider avgfilter, gatimes; 38 | @FXML 39 | private JFXSpinner spin_clear, spin_generate; 40 | @FXML 41 | private JFXPasswordField password; 42 | 43 | private List> lists; 44 | 45 | /** 46 | * 实现Initializable接口需要重写的方法 47 | * 这里设置界面图片 48 | * 49 | * @param location 位置信息 50 | * @param resources 源文件 51 | */ 52 | @Override 53 | public void initialize(URL location, ResourceBundle resources) { 54 | image_set.setImage(new Image("images/set.png")); 55 | image_user.setImage(new Image("images/user.png")); 56 | image_stats.setImage(new Image("images/stats.png")); 57 | image_power.setImage(new Image("images/power.png")); 58 | image_user_login.setImage(new Image("images/user_login.png")); 59 | setPaneArrow.setImage(new Image("images/arrow.png")); 60 | userPaneArrow.setImage(new Image("images/arrow.png")); 61 | statsPaneArrow.setImage(new Image("images/arrow.png")); 62 | image_check.setImage(new Image("images/Check.png")); 63 | } 64 | 65 | /** 66 | * 鼠标进入界面时初始化 67 | */ 68 | public void init() { 69 | stage = (Stage) topbar.getScene().getWindow(); 70 | } 71 | 72 | /** 73 | * press和drag构成鼠标拖动效果 74 | * 75 | * @param event 捕捉鼠标事件 76 | */ 77 | public void press(MouseEvent event) { 78 | xOffset = event.getSceneX(); 79 | yOffset = event.getSceneY(); 80 | } 81 | 82 | public void drag(MouseEvent event) { 83 | stage.setX(event.getScreenX() - xOffset); 84 | stage.setY(event.getScreenY() - yOffset); 85 | } 86 | 87 | public void handleMouseEvents(MouseEvent mouseEvent) { 88 | if (mouseEvent.getTarget() == image_set) { 89 | if (setPane.isVisible()) { 90 | setPane.setVisible(false); 91 | } else { 92 | setPane.setVisible(true); 93 | userPane.setVisible(false); 94 | statsPane.setVisible(false); 95 | 96 | } 97 | } else if (mouseEvent.getTarget() == image_user) { 98 | if (userPane.isVisible()) { 99 | userPane.setVisible(false); 100 | } else { 101 | setPane.setVisible(false); 102 | userPane.setVisible(true); 103 | statsPane.setVisible(false); 104 | 105 | } 106 | } else { 107 | if (statsPane.isVisible()) { 108 | statsPane.setVisible(false); 109 | } else { 110 | setPane.setVisible(false); 111 | userPane.setVisible(false); 112 | statsPane.setVisible(true); 113 | } 114 | } 115 | } 116 | 117 | @FXML 118 | public void reset() { 119 | biprediction.setSelected(false); 120 | psofilter.setSelected(false); 121 | avgfilter.setValue(2); 122 | gatimes.setValue(2); 123 | } 124 | 125 | 126 | public void drawChart() throws Exception { 127 | if (image_check.isVisible()) { 128 | boolean isbiprediction = biprediction.isSelected(); 129 | boolean ispsofilter = psofilter.isSelected(); 130 | int avg_filter = (int) avgfilter.getValue(); 131 | int ga_times = (int) avgfilter.getValue(); 132 | Calculate calculate = new Calculate(isbiprediction, ispsofilter, avg_filter, ga_times); 133 | calculate.getCalculate(); 134 | } 135 | } 136 | 137 | public void clearChart() { 138 | spin_clear.setProgress(0); 139 | lineChart.getData().clear(); 140 | spin_clear.setProgress(1); 141 | } 142 | 143 | 144 | public void ispasswordright() { 145 | if (password.getText().equals("TY1996@86") || password.getText().equals("960206")) { 146 | image_check.setVisible(true); 147 | } else { 148 | image_check.setVisible(false); 149 | } 150 | } 151 | 152 | public void exit() { 153 | stage.close(); 154 | } 155 | 156 | @AllArgsConstructor 157 | private class Calculate { 158 | private boolean isbiprediction; 159 | private boolean ispsofilter; 160 | private int avgfilter; 161 | private int gatimes; 162 | 163 | 164 | private void getCalculate() throws Exception { 165 | String readpath = "C:\\Users\\shinelon\\Desktop\\test.txt"; 166 | spin_generate.setProgress(0); 167 | List> res = Start.start(gatimes, readpath); 168 | lists = _Main.run(res, isbiprediction, ispsofilter, avgfilter, readpath); 169 | XYChart.Series series1 = new XYChart.Series<>(); 170 | XYChart.Series series2 = new XYChart.Series<>(); 171 | for (int i = 1; i <= lists.get(0).size(); i += 1) { 172 | series1.getData().add( 173 | new XYChart.Data<>(String.valueOf(i), lists.get(0).get(i - 1)) 174 | ); 175 | series2.getData().add( 176 | new XYChart.Data<>(String.valueOf(i), lists.get(lists.size() - 1).get(i - 1)) 177 | ); 178 | spin_generate.setProgress(1.0 / lists.get(0).size() * i); 179 | } 180 | //在生成数据前清除之前的数据 181 | lineChart.getData().clear(); 182 | lineChart.getData().add(series1); 183 | lineChart.getData().add(series2); 184 | 185 | } 186 | 187 | } 188 | 189 | public void excelGenerate() throws Exception { 190 | // List> res = Start.start(); 191 | //_Main.run(res); 192 | String path = "C:\\Users\\shinelon\\Desktop\\基于粒子滤波的LSTM.xlsx"; 193 | 194 | if (lists.size() != 0) { 195 | POI.init(path); 196 | POI.write(path, lists.get(0), lists.get(lists.size() - 1), 0); 197 | } 198 | } 199 | } 200 | -------------------------------------------------------------------------------- /src/main/java/sample/Main.java: -------------------------------------------------------------------------------- 1 | package sample; 2 | 3 | import javafx.application.Application; 4 | import javafx.fxml.FXMLLoader; 5 | import javafx.scene.Parent; 6 | import javafx.scene.Scene; 7 | import javafx.scene.paint.Color; 8 | import javafx.stage.Stage; 9 | import javafx.stage.StageStyle; 10 | 11 | public class Main extends Application { 12 | @Override 13 | public void start(Stage primaryStage) throws Exception { 14 | Parent root = FXMLLoader.load(getClass().getResource("sample.fxml")); 15 | primaryStage.initStyle(StageStyle.TRANSPARENT); 16 | Scene scene = new Scene(root); 17 | scene.setFill(Color.TRANSPARENT); 18 | primaryStage.setScene(scene); 19 | primaryStage.show(); 20 | } 21 | 22 | public static void main(String[] args) { 23 | launch(args); 24 | } 25 | } 26 | -------------------------------------------------------------------------------- /src/main/java/sample/sample.fxml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 46 | 47 | 48 | 49 | 50 | 51 | 56 | 61 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | -------------------------------------------------------------------------------- /src/main/java/utils/POI.java: -------------------------------------------------------------------------------- 1 | package utils; 2 | 3 | import org.apache.poi.xssf.usermodel.XSSFCell; 4 | import org.apache.poi.xssf.usermodel.XSSFRow; 5 | import org.apache.poi.xssf.usermodel.XSSFSheet; 6 | import org.apache.poi.xssf.usermodel.XSSFWorkbook; 7 | 8 | import java.io.*; 9 | import java.util.List; 10 | 11 | public class POI { 12 | //创建并初始化表格 13 | public static void init(String path) throws Exception { 14 | File file = new File(path); 15 | //存在则删除 16 | if (file.exists()) { 17 | file.delete(); 18 | } 19 | POI.createExcel(path); 20 | } 21 | 22 | //创建Excel文件 23 | private static void createExcel(String path) throws Exception { //创建Excel文件对象 24 | XSSFWorkbook wb = new XSSFWorkbook(); //用文件对象创建sheet对象 25 | XSSFSheet sheet1 = wb.createSheet("双向"); //用sheet对象创建行对象 26 | sheet1.createRow(0); //创建单元格样式 27 | //XSSFRow row1 = sheet1.createRow(0); //创建单元格样式 28 | //List row_list = new ArrayList<>(); 29 | //row_list.add(row1); 30 | 31 | FileOutputStream output = new FileOutputStream(path); 32 | wb.write(output); 33 | output.flush(); 34 | output.close(); 35 | wb.close(); 36 | } 37 | 38 | //写入表格操作 39 | public static void write(String path, List l, List list, int index) throws IOException { 40 | // 1.读取Excel文档对象 41 | XSSFWorkbook xssfWorkbook = new XSSFWorkbook(new FileInputStream(path)); 42 | // 2.获取要解析的表格（第一个表格） 43 | XSSFSheet sheet = xssfWorkbook.getSheetAt(index); 44 | // 3.在第row列写入数据 45 | 46 | for (int i = 0; i < list.size(); i++) { 47 | FileOutputStream outputStream = new FileOutputStream(path); 48 | XSSFRow xssfRow = sheet.createRow(i); 49 | XSSFCell xssfCell = xssfRow.createCell(0); 50 | xssfCell.setCellValue(l.get(i)); 51 | xssfCell = xssfRow.createCell(1); 52 | xssfCell.setCellValue(list.get(i)); 53 | xssfWorkbook.write(outputStream); 54 | outputStream.flush(); 55 | outputStream.close(); 56 | } 57 | 58 | } 59 | } -------------------------------------------------------------------------------- /src/main/resources/data/test.txt: -------------------------------------------------------------------------------- 1 | 2017.73 2 | 1999.92 3 | 1985.43 4 | 2026.2 5 | 2055.38 6 | 2059.41 7 | 2059.21 8 | 2061.93 9 | 2037.56 10 | 2019.31 -------------------------------------------------------------------------------- /src/main/resources/images/Check.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/skyrimgo/UIprediction/0aa5edfa9aac0435b7fa681182f9db903b44b5af/src/main/resources/images/Check.png -------------------------------------------------------------------------------- /src/main/resources/images/Snipaste_2020-07-15_16-23-39.png: -------------------------------------------------------------------------------- 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