This is an automated email from the ASF dual-hosted git repository. leirui pushed a commit to branch research/area-visualization in repository https://gitbox.apache.org/repos/asf/iotdb.git
commit b74a25f14d2956775b3d830535c4ffffe0f3fede Author: Lei Rui <[email protected]> AuthorDate: Tue Jan 28 21:17:36 2025 +0800 fix --- server/pom.xml | 8 +- server/sample_minmax-jar-with-dependencies.jar | Bin 40768753 -> 40769057 bytes .../org/apache/iotdb/db/query/eBUG/MinMax.java | 28 +- .../java/org/apache/iotdb/db/query/eBUG/Rdp.java | 526 ++++++++++----------- 4 files changed, 283 insertions(+), 279 deletions(-) diff --git a/server/pom.xml b/server/pom.xml index f21af9a30cd..760ce5fe6dd 100644 --- a/server/pom.xml +++ b/server/pom.xml @@ -287,17 +287,17 @@ <plugin> <artifactId>maven-assembly-plugin</artifactId> <configuration> - <finalName>sample_swab</finalName> + <!-- <finalName>sample_swab</finalName>--> <!-- <finalName>sample_rdp</finalName>--> - <!-- <finalName>sample_minmax</finalName>--> + <finalName>sample_minmax</finalName> <!-- <finalName>sample_fsw</finalName>--> <!-- <finalName>sample_eBUG</finalName>--> <!-- <finalName>sample_BUYdiff</finalName>--> <archive> <manifest> - <mainClass>org.apache.iotdb.db.query.eBUG.sample_SWAB</mainClass> + <!-- <mainClass>org.apache.iotdb.db.query.eBUG.sample_SWAB</mainClass>--> <!-- <mainClass>org.apache.iotdb.db.query.eBUG.sample_Rdp</mainClass>--> - <!-- <mainClass>org.apache.iotdb.db.query.eBUG.sample_MinMax</mainClass>--> + <mainClass>org.apache.iotdb.db.query.eBUG.sample_MinMax</mainClass> <!-- <mainClass>org.apache.iotdb.db.query.eBUG.sample_FSW</mainClass>--> <!-- <mainClass>org.apache.iotdb.db.query.eBUG.sample_eBUG</mainClass>--> <!-- <mainClass>org.apache.iotdb.db.query.eBUG.sample_bottomUpYdiff</mainClass>--> diff --git a/server/sample_minmax-jar-with-dependencies.jar b/server/sample_minmax-jar-with-dependencies.jar index 1cf9f21e3f1..f827302b64a 100644 Binary files a/server/sample_minmax-jar-with-dependencies.jar and b/server/sample_minmax-jar-with-dependencies.jar differ diff --git a/server/src/main/java/org/apache/iotdb/db/query/eBUG/MinMax.java b/server/src/main/java/org/apache/iotdb/db/query/eBUG/MinMax.java index 5ee516f0f9f..0afee480b29 100644 --- a/server/src/main/java/org/apache/iotdb/db/query/eBUG/MinMax.java +++ b/server/src/main/java/org/apache/iotdb/db/query/eBUG/MinMax.java @@ -38,12 +38,14 @@ public class MinMax { Point p = points.get(i); while (i >= currentBucketEndIdx) { // record the results of the last bucket - if (minIdx < maxIdx) { - res.add(points.get(minIdx)); - res.add(points.get(maxIdx)); - } else { - res.add(points.get(maxIdx)); - res.add(points.get(minIdx)); + if (minIdx != -1) { + if (minIdx < maxIdx) { + res.add(points.get(minIdx)); + res.add(points.get(maxIdx)); + } else { + res.add(points.get(maxIdx)); + res.add(points.get(minIdx)); + } } if (debug) { System.out.println( @@ -129,12 +131,14 @@ public class MinMax { Point p = points.get(i); while (p.x >= currentBucketEndTime) { // record the results of the last bucket - if (minIdx < maxIdx) { - res.add(points.get(minIdx)); - res.add(points.get(maxIdx)); - } else { - res.add(points.get(maxIdx)); - res.add(points.get(minIdx)); + if (minIdx != -1) { + if (minIdx < maxIdx) { + res.add(points.get(minIdx)); + res.add(points.get(maxIdx)); + } else { + res.add(points.get(maxIdx)); + res.add(points.get(minIdx)); + } } if (debug) { System.out.println( diff --git a/server/src/main/java/org/apache/iotdb/db/query/eBUG/Rdp.java b/server/src/main/java/org/apache/iotdb/db/query/eBUG/Rdp.java index 77b6df5aa3c..45593369ebc 100644 --- a/server/src/main/java/org/apache/iotdb/db/query/eBUG/Rdp.java +++ b/server/src/main/java/org/apache/iotdb/db/query/eBUG/Rdp.java @@ -7,286 +7,286 @@ import java.util.Random; import java.util.Stack; public class Rdp { - // Ramer–Douglas–Peucker algorithm (RDP) - // top-down, for min perpendicular distance - // worst case complexity n2 - // best case complexity nlogn - - public static double point_line_distance(Point point, Point lineStart, Point lineEnd) { - // 计算点到直线的垂直正交距离 - // (x0,y0)到直线（经过(x1,y1)、(x2,y2)点的直线）的正交距离 - double x0 = point.x; - double y0 = point.y; - double x1 = lineStart.x; - double y1 = lineStart.y; - double x2 = lineEnd.x; - double y2 = lineEnd.y; - - double numerator = Math.abs((x2 - x1) * (y1 - y0) - (x1 - x0) * (y2 - y1)); - double denominator = Math.sqrt(Math.pow(x2 - x1, 2) + Math.pow(y2 - y1, 2)); - return numerator / denominator; + // Ramer–Douglas–Peucker algorithm (RDP) + // top-down, for min perpendicular distance + // worst case complexity n2 + // best case complexity nlogn + + public static double point_line_distance(Point point, Point lineStart, Point lineEnd) { + // 计算点到直线的垂直正交距离 + // (x0,y0)到直线（经过(x1,y1)、(x2,y2)点的直线）的正交距离 + double x0 = point.x; + double y0 = point.y; + double x1 = lineStart.x; + double y1 = lineStart.y; + double x2 = lineEnd.x; + double y2 = lineEnd.y; + + double numerator = Math.abs((x2 - x1) * (y1 - y0) - (x1 - x0) * (y2 - y1)); + double denominator = Math.sqrt(Math.pow(x2 - x1, 2) + Math.pow(y2 - y1, 2)); + return numerator / denominator; + } + + // public static List<Point> reducePoints(List<Point> points, double epsilon, Object... kwargs) { + // if (points == null || points.size() < 3) { + // return points; + // } + // + // // 找到距离最远的点 + // double maxDistance = 0; + // int index = 0; + // Point start = points.get(0); + // Point end = points.get(points.size() - 1); + // + // for (int i = 1; i < points.size() - 1; i++) { + // double distance = point_line_distance(points.get(i), start, end); + // if (distance > maxDistance) { + // maxDistance = distance; + // index = i; + // } + // } + // + // List<Point> result = new ArrayList<>(); + // if (maxDistance > epsilon) { + // List<Point> firstPart = reducePoints(points.subList(0, index + 1), epsilon); + // List<Point> secondPart = reducePoints(points.subList(index, points.size()), epsilon); + // + // // 合并结果 + // result.addAll(firstPart.subList(0, firstPart.size() - 1)); // 避免重复添加中间点 + // result.addAll(secondPart); + // } else { + // // 如果最远距离小于阈值，则只保留起点和终点 + // result.add(start); + // result.add(end); + // } + // + // return result; + // } + + /** + * 使用迭代实现 RDP 算法。避免递归深度过大。 + * + * @param points 点集 + * @param epsilon 简化阈值 + * @return 简化后的点集 + */ + public static List<Point> reducePoints(List<Point> points, double epsilon, Object... kwargs) { + if (points.size() <= 2) { + return points; } - // public static List<Point> reducePoints(List<Point> points, double epsilon, Object... kwargs) { - // if (points == null || points.size() < 3) { - // return points; - // } - // - // // 找到距离最远的点 - // double maxDistance = 0; - // int index = 0; - // Point start = points.get(0); - // Point end = points.get(points.size() - 1); - // - // for (int i = 1; i < points.size() - 1; i++) { - // double distance = point_line_distance(points.get(i), start, end); - // if (distance > maxDistance) { - // maxDistance = distance; - // index = i; - // } - // } - // - // List<Point> result = new ArrayList<>(); - // if (maxDistance > epsilon) { - // List<Point> firstPart = reducePoints(points.subList(0, index + 1), epsilon); - // List<Point> secondPart = reducePoints(points.subList(index, points.size()), epsilon); - // - // // 合并结果 - // result.addAll(firstPart.subList(0, firstPart.size() - 1)); // 避免重复添加中间点 - // result.addAll(secondPart); - // } else { - // // 如果最远距离小于阈值，则只保留起点和终点 - // result.add(start); - // result.add(end); - // } - // - // return result; - // } - - /** - * 使用迭代实现 RDP 算法。避免递归深度过大。 - * - * @param points 点集 - * @param epsilon 简化阈值 - * @return 简化后的点集 - */ - public static List<Point> reducePoints(List<Point> points, double epsilon, Object... kwargs) { - if (points.size() <= 2) { - return points; + // 使用栈模拟递归 + Stack<int[]> stack = new Stack<>(); + stack.push(new int[] {0, points.size() - 1}); + + // 用于标记需要保留的点 + boolean[] keep = new boolean[points.size()]; + keep[0] = true; + keep[points.size() - 1] = true; + + int count = 0; // TODO debug + + while (!stack.isEmpty()) { + int[] range = stack.pop(); + int start = range[0]; + int end = range[1]; + + // 找到距离最远的点 + double maxDistance = 0; + int index = start; + Point startPoint = points.get(start); + Point endPoint = points.get(end); + + for (int i = start + 1; i < end; i++) { + count++; + double distance = point_line_distance(points.get(i), startPoint, endPoint); + if (distance > maxDistance) { + maxDistance = distance; + index = i; } - - // 使用栈模拟递归 - Stack<int[]> stack = new Stack<>(); - stack.push(new int[]{0, points.size() - 1}); - - // 用于标记需要保留的点 - boolean[] keep = new boolean[points.size()]; - keep[0] = true; - keep[points.size() - 1] = true; - - int count = 0; // TODO debug - - while (!stack.isEmpty()) { - int[] range = stack.pop(); - int start = range[0]; - int end = range[1]; - - // 找到距离最远的点 - double maxDistance = 0; - int index = start; - Point startPoint = points.get(start); - Point endPoint = points.get(end); - - for (int i = start + 1; i < end; i++) { - count++; - double distance = point_line_distance(points.get(i), startPoint, endPoint); - if (distance > maxDistance) { - maxDistance = distance; - index = i; - } - } - - // 如果最远距离大于阈值，则继续分割 - if (maxDistance > epsilon) { - keep[index] = true; // 保留当前点 - stack.push(new int[]{start, index}); - stack.push(new int[]{index, end}); - } - } - - // 构建简化后的点集 - List<Point> result = new ArrayList<>(); - for (int i = 0; i < points.size(); i++) { - if (keep[i]) { - result.add(points.get(i)); - } - } - - System.out.println("count=" + count); - return result; + } + + // 如果最远距离大于阈值，则继续分割 + if (maxDistance > epsilon) { + keep[index] = true; // 保留当前点 + stack.push(new int[] {start, index}); + stack.push(new int[] {index, end}); + } } - public static Object[] normalizePoints(List<Point> points) { - // 提取 x 和 y 值 - double[] xValues = points.stream().mapToDouble(p -> p.x).toArray(); - double[] yValues = points.stream().mapToDouble(p -> p.y).toArray(); + // 构建简化后的点集 + List<Point> result = new ArrayList<>(); + for (int i = 0; i < points.size(); i++) { + if (keep[i]) { + result.add(points.get(i)); + } + } - // 计算 x 和 y 的最小值和最大值 - double xMin = Double.MAX_VALUE, xMax = Double.MIN_VALUE; - double yMin = Double.MAX_VALUE, yMax = Double.MIN_VALUE; + System.out.println("count=" + count); + return result; + } - for (double x : xValues) { - if (x < xMin) xMin = x; - if (x > xMax) xMax = x; - } - for (double y : yValues) { - if (y < yMin) yMin = y; - if (y > yMax) yMax = y; - } + public static Object[] normalizePoints(List<Point> points) { + // 提取 x 和 y 值 + double[] xValues = points.stream().mapToDouble(p -> p.x).toArray(); + double[] yValues = points.stream().mapToDouble(p -> p.y).toArray(); - // 标准化 - List<Point> normalizedPoints = new ArrayList<>(); - for (Point p : points) { - double xNormalized = (xMax != xMin) ? (p.x - xMin) / (xMax - xMin) : 0; - double yNormalized = (yMax != yMin) ? (p.y - yMin) / (yMax - yMin) : 0; - normalizedPoints.add(new Point(xNormalized, yNormalized)); - } + // 计算 x 和 y 的最小值和最大值 + double xMin = Double.MAX_VALUE, xMax = Double.MIN_VALUE; + double yMin = Double.MAX_VALUE, yMax = Double.MIN_VALUE; - return new Object[]{normalizedPoints, xMin, xMax, yMin, yMax}; + for (double x : xValues) { + if (x < xMin) xMin = x; + if (x > xMax) xMax = x; + } + for (double y : yValues) { + if (y < yMin) yMin = y; + if (y > yMax) yMax = y; } - public static List<Point> denormalizePoints(List<Point> points, double[] originalScale) { - double xMin = originalScale[0]; - double xMax = originalScale[1]; - double yMin = originalScale[2]; - double yMax = originalScale[3]; - - List<Point> denormalizedPoints = new ArrayList<>(); - for (Point p : points) { - double xOriginal = p.x * (xMax - xMin) + xMin; - double yOriginal = p.y * (yMax - yMin) + yMin; - denormalizedPoints.add(new Point(xOriginal, yOriginal)); - } - - return denormalizedPoints; + // 标准化 + List<Point> normalizedPoints = new ArrayList<>(); + for (Point p : points) { + double xNormalized = (xMax != xMin) ? (p.x - xMin) / (xMax - xMin) : 0; + double yNormalized = (yMax != yMin) ? (p.y - yMin) / (yMax - yMin) : 0; + normalizedPoints.add(new Point(xNormalized, yNormalized)); } - public static void main(String[] args) throws IOException { - Random rand = new Random(10); - String input = "D:\\datasets\\regular\\tmp2.csv"; - boolean hasHeader = true; - int timeIdx = 0; - int valueIdx = 1; - int N = 10; - List<Point> points = Tool.readFromFile(input, hasHeader, timeIdx, valueIdx, N); - // Polyline polyline = new Polyline(); - // for (int i = 0; i < N; i += 1) { - // double v = rand.nextInt(1000); - // polyline.addVertex(new Point(i, v)); - // } - // List<Point> points = polyline.getVertices(); - try (FileWriter writer = new FileWriter("raw.csv")) { - // 写入CSV头部 - writer.append("x,y,z\n"); - - // 写入每个点的数据 - for (Point point : points) { - writer.append(point.x + "," + point.y + "," + point.z + "\n"); - } - System.out.println(points.size() + " Data has been written"); - } catch (IOException e) { - System.out.println("Error writing to CSV file: " + e.getMessage()); - } + return new Object[] {normalizedPoints, xMin, xMax, yMin, yMax}; + } - // 标准化 - Object[] normalizedData = normalizePoints(points); - List<Point> normalizedPoints = (List<Point>) normalizedData[0]; - double xMin = (double) normalizedData[1]; - double xMax = (double) normalizedData[2]; - double yMin = (double) normalizedData[3]; - double yMax = (double) normalizedData[4]; - - int m = 7; - double tolerantPts = 0; - double epsilon = Tool.getParam(normalizedPoints, m, Rdp::reducePoints, tolerantPts); -// double epsilon = 0.001; -// double epsilon = 0.004547272052729534; - System.out.println("epsilon=" + epsilon); - - long startTime = System.currentTimeMillis(); - List<Point> sampled_tmp = reducePoints(normalizedPoints, epsilon); - long endTime = System.currentTimeMillis(); - System.out.println("Time taken: " + (endTime - startTime) + "ms"); - - // 还原到原始尺度 - double[] originalScale = {xMin, xMax, yMin, yMax}; - List<Point> sampled = denormalizePoints(sampled_tmp, originalScale); - - // for (Point p : sampled) { - // System.out.println(p); - // } - System.out.println(sampled.size()); - - try (PrintWriter writer = new PrintWriter(new File("output.csv"))) { - // 写入字符串 - for (int i = 0; i < sampled.size(); i++) { - writer.println(sampled.get(i).x + "," + sampled.get(i).y); - } - - } catch (FileNotFoundException e) { - e.printStackTrace(); - } + public static List<Point> denormalizePoints(List<Point> points, double[] originalScale) { + double xMin = originalScale[0]; + double xMax = originalScale[1]; + double yMin = originalScale[2]; + double yMax = originalScale[3]; + + List<Point> denormalizedPoints = new ArrayList<>(); + for (Point p : points) { + double xOriginal = p.x * (xMax - xMin) + xMin; + double yOriginal = p.y * (yMax - yMin) + yMin; + denormalizedPoints.add(new Point(xOriginal, yOriginal)); } - // public static void reducePoints(List<Point> points, double epsilon, int s, int e, - // List<Point> resultList) { - // double dmax = 0; - // int index = 0; - // - // final int start = s; - // final int end = e - 1; - // for (int i = start + 1; i < end; i++) { - // // Point - // final double px = list.get(i)[0]; - // final double py = list.get(i)[1]; - // // Start - // final double vx = list.get(start)[0]; - // final double vy = list.get(start)[1]; - // // End - // final double wx = list.get(end)[0]; - // final double wy = list.get(end)[1]; - // final double d = perpendicularDistance(px, py, vx, vy, wx, wy); - // if (d > dmax) { - // index = i; - // dmax = d; - // } + return denormalizedPoints; + } + + public static void main(String[] args) throws IOException { + Random rand = new Random(10); + String input = "D:\\datasets\\regular\\tmp2.csv"; + boolean hasHeader = true; + int timeIdx = 0; + int valueIdx = 1; + int N = 10; + List<Point> points = Tool.readFromFile(input, hasHeader, timeIdx, valueIdx, N); + // Polyline polyline = new Polyline(); + // for (int i = 0; i < N; i += 1) { + // double v = rand.nextInt(1000); + // polyline.addVertex(new Point(i, v)); // } - // // If max distance is greater than epsilon, recursively simplify - // if (dmax > epsilon) { - // // Recursive call - // douglasPeucker(list, s, index, epsilon, resultList); - // douglasPeucker(list, index, e, epsilon, resultList); - // } else { - // if ((end - start) > 0) { - // resultList.add(list.get(start)); - // resultList.add(list.get(end)); - // } else { - // resultList.add(list.get(start)); - // } + // List<Point> points = polyline.getVertices(); + try (FileWriter writer = new FileWriter("raw.csv")) { + // 写入CSV头部 + writer.append("x,y,z\n"); + + // 写入每个点的数据 + for (Point point : points) { + writer.append(point.x + "," + point.y + "," + point.z + "\n"); + } + System.out.println(points.size() + " Data has been written"); + } catch (IOException e) { + System.out.println("Error writing to CSV file: " + e.getMessage()); + } + + // 标准化 + Object[] normalizedData = normalizePoints(points); + List<Point> normalizedPoints = (List<Point>) normalizedData[0]; + double xMin = (double) normalizedData[1]; + double xMax = (double) normalizedData[2]; + double yMin = (double) normalizedData[3]; + double yMax = (double) normalizedData[4]; + + int m = 7; + double tolerantPts = 0; + double epsilon = Tool.getParam(normalizedPoints, m, Rdp::reducePoints, tolerantPts); + // double epsilon = 0.001; + // double epsilon = 0.004547272052729534; + System.out.println("epsilon=" + epsilon); + + long startTime = System.currentTimeMillis(); + List<Point> sampled_tmp = reducePoints(normalizedPoints, epsilon); + long endTime = System.currentTimeMillis(); + System.out.println("Time taken: " + (endTime - startTime) + "ms"); + + // 还原到原始尺度 + double[] originalScale = {xMin, xMax, yMin, yMax}; + List<Point> sampled = denormalizePoints(sampled_tmp, originalScale); + + // for (Point p : sampled) { + // System.out.println(p); // } - // } - - // /** - // * Given a curve composed of line segments find a similar curve with fewer points. - // * - // * @param list List of Double[] points (x,y) - // * @param epsilon Distance dimension - // * @return Similar curve with fewer points - // */ - // public static final List<Double[]> douglasPeucker(List<Double[]> list, double epsilon) { - // final List<Double[]> resultList = new ArrayList<Double[]>(); - // douglasPeucker(list, 0, list.size(), epsilon, resultList); - // return resultList; - // } + System.out.println(sampled.size()); + + try (PrintWriter writer = new PrintWriter(new File("output.csv"))) { + // 写入字符串 + for (int i = 0; i < sampled.size(); i++) { + writer.println(sampled.get(i).x + "," + sampled.get(i).y); + } + + } catch (FileNotFoundException e) { + e.printStackTrace(); + } + } + + // public static void reducePoints(List<Point> points, double epsilon, int s, int e, + // List<Point> resultList) { + // double dmax = 0; + // int index = 0; + // + // final int start = s; + // final int end = e - 1; + // for (int i = start + 1; i < end; i++) { + // // Point + // final double px = list.get(i)[0]; + // final double py = list.get(i)[1]; + // // Start + // final double vx = list.get(start)[0]; + // final double vy = list.get(start)[1]; + // // End + // final double wx = list.get(end)[0]; + // final double wy = list.get(end)[1]; + // final double d = perpendicularDistance(px, py, vx, vy, wx, wy); + // if (d > dmax) { + // index = i; + // dmax = d; + // } + // } + // // If max distance is greater than epsilon, recursively simplify + // if (dmax > epsilon) { + // // Recursive call + // douglasPeucker(list, s, index, epsilon, resultList); + // douglasPeucker(list, index, e, epsilon, resultList); + // } else { + // if ((end - start) > 0) { + // resultList.add(list.get(start)); + // resultList.add(list.get(end)); + // } else { + // resultList.add(list.get(start)); + // } + // } + // } + + // /** + // * Given a curve composed of line segments find a similar curve with fewer points. + // * + // * @param list List of Double[] points (x,y) + // * @param epsilon Distance dimension + // * @return Similar curve with fewer points + // */ + // public static final List<Double[]> douglasPeucker(List<Double[]> list, double epsilon) { + // final List<Double[]> resultList = new ArrayList<Double[]>(); + // douglasPeucker(list, 0, list.size(), epsilon, resultList); + // return resultList; + // } }
