This is an automated email from the ASF dual-hosted git repository. hui pushed a commit to branch research/encoding-reorder in repository https://gitbox.apache.org/repos/asf/iotdb.git
commit 1afeaa74fb96e9e8c5790414427df3aa23f74248 Author: xjz17 <[email protected]> AuthorDate: Mon Dec 4 20:59:39 2023 +0800 update --- .../apache/iotdb/tsfile/encoding/EncodeTest.java | 4 +- .../tsfile/encoding/KernelDensityEstimation.java | 137 +-------------------- 2 files changed, 6 insertions(+), 135 deletions(-) diff --git a/iotdb-core/tsfile/src/test/java/org/apache/iotdb/tsfile/encoding/EncodeTest.java b/iotdb-core/tsfile/src/test/java/org/apache/iotdb/tsfile/encoding/EncodeTest.java index ef0bf35793e..3de168287c8 100644 --- a/iotdb-core/tsfile/src/test/java/org/apache/iotdb/tsfile/encoding/EncodeTest.java +++ b/iotdb-core/tsfile/src/test/java/org/apache/iotdb/tsfile/encoding/EncodeTest.java @@ -91,8 +91,8 @@ public class EncodeTest { output_path_list.add(output_parent_dir + "/FANYP-Sensors_ratio.csv"); // 12 output_path_list.add(output_parent_dir + "/TRAJET-Transport_ratio.csv"); // 13 - for(int file_i=4;file_i<5;file_i++){ -// for (int file_i = 0; file_i < input_path_list.size(); file_i++) { +// for(int file_i=4;file_i<5;file_i++){ + for (int file_i = 0; file_i < input_path_list.size(); file_i++) { String inputPath = input_path_list.get(file_i); String Output = output_path_list.get(file_i); // String Output = "C:\\Users\\xiaoj\\Desktop\\test_ratio_ts_2diff.csv"; diff --git a/iotdb-core/tsfile/src/test/java/org/apache/iotdb/tsfile/encoding/KernelDensityEstimation.java b/iotdb-core/tsfile/src/test/java/org/apache/iotdb/tsfile/encoding/KernelDensityEstimation.java index 429b95ccf8a..aaf65669061 100644 --- a/iotdb-core/tsfile/src/test/java/org/apache/iotdb/tsfile/encoding/KernelDensityEstimation.java +++ b/iotdb-core/tsfile/src/test/java/org/apache/iotdb/tsfile/encoding/KernelDensityEstimation.java @@ -17,45 +17,9 @@ import java.util.Map; public class KernelDensityEstimation { public static void main(String[] args) { - // 第一个整数（正数） - int int1 = -1; - // 第二个整数（负数） - int int2 = -67890; - // 将两个整数拼接成一个长整型数 - long combined = ((long) int1 << 32) | (int2 & 0xFFFFFFFFL); - - System.out.println("Combined long value: " + combined); - - // 将长整型数分解回两个整数 - int originalInt1 = (int) (combined >> 32); - int originalInt2 = (int) combined; - - System.out.println("Original Int 1: " + originalInt1); - System.out.println("Original Int 2: " + originalInt2); - - // 离散分布数据 -// Map<Integer, Integer> discreteDistribution = new HashMap<>(); -// discreteDistribution.put(2, 10); -// discreteDistribution.put(3, 100); -// discreteDistribution.put(1, 3); -// discreteDistribution.put(4, 12); -// -// // 计算核密度估计 -// double[] kernelDensity = calculateKernelDensity(discreteDistribution); -// -// // 打印核密度估计 -// System.out.println("Kernel Density Estimation:"); -// for (int i = 0; i < kernelDensity.length; i++) { -// System.out.println("x=" + (i + 1) + ": " + kernelDensity[i]); -// } -// -// // 寻找核密度估计的极小值点 -// int[] minIndex = findMinIndex(kernelDensity); -// System.out.println("Minimum point: x=" + (Arrays.toString(minIndex))); } - // 计算核密度估计 static double[] calculateKernelDensity(Map<Integer, Integer> discreteDistribution) { int maxKey = discreteDistribution.keySet().stream().max(Integer::compare).orElse(0); double[] kernelDensity = new double[maxKey]; @@ -71,18 +35,16 @@ public class KernelDensityEstimation { return kernelDensity; } - // 高斯核函数 + private static double gaussianKernel(int x, int dataPoint) { - double bandwidth = 1.0; // 可调整的带宽参数 + double bandwidth = 1.0; return Math.exp(-0.5 * Math.pow((x - dataPoint) / bandwidth, 2)) / (Math.sqrt(2 * Math.PI) * bandwidth); } - // 寻找数组中的最小值索引 static int[] findMinIndex(double[] array) { int[] minIndex = new int[array.length]; int final_min_count = 0; int pre_value = 0; -// double preValue = array[0]; for (int i = 1; i < array.length-1; i++) { if (array[i] < array[i-1] && array[i] < array[i+1]) { @@ -100,100 +62,11 @@ public class KernelDensityEstimation { } } int[] final_minIndex = new int[final_min_count]; -// if(final_min_count>0){ -// final_minIndex[0] = minIndex[0]; -// int pre_value = minIndex[0]; -// for(int mv = 1; mv<final_min_count;mv++){ -// if(minIndex[mv]-pre_value>16){ -// pre_value = minIndex[mv]; -// -// } -// } + System.arraycopy(minIndex, 0, final_minIndex, 0, final_min_count); // } return final_minIndex; } -// public static void main(String[] args) { -// // 数据分布 -// Map<Integer, Integer> data = new HashMap<>(); -// data.put(1, 3); -// data.put(2, 10); -// data.put(3, 100); -// data.put(4, 12); -// if( data.containsKey(10)){ -// System.out.println("contain"); -// } -// if( data.containsKey(1)){ -// System.out.println("contain 1"); -// } -// // 选择带宽 -// double bandwidth = 1.0; -// -// // 计算核密度曲线的极小值点 -// findMinima(data, bandwidth); -// } -// -// static void findMinima(Map<Integer, Integer> data, double bandwidth) { -// // 计算核密度估计 -// Map<Integer, Double> kernelDensityEstimate = calculateKernelDensity(data, bandwidth); -// -// // 计算导数 -// Map<Integer, Double> derivative = calculateDerivative(kernelDensityEstimate); -// -// System.out.println(derivative); -// -// // 打印导数为零的点 -// System.out.println("Minima Points:"); -// for (Map.Entry<Integer, Double> entry : derivative.entrySet()) { -// if (entry.getValue() == 0.0) { -// System.out.println("Point " + entry.getKey()); -// } -// } -// } -// -// private static Map<Integer, Double> calculateKernelDensity(Map<Integer, Integer> data, double bandwidth) { -// // 计算核密度估计 -// Map<Integer, Double> kernelDensityEstimate = new HashMap<>(); -// -// for (Map.Entry<Integer, Integer> entry : data.entrySet()) { -// int point = entry.getKey(); -// double sum = 0.0; -// -// for (Map.Entry<Integer, Integer> dataEntry : data.entrySet()) { -// double x = dataEntry.getKey(); -// double kernel = gaussianKernel(x, point, bandwidth); -// sum += kernel; -// } -// -// kernelDensityEstimate.put(point, sum / (data.size() * bandwidth)); -// } -// -// return kernelDensityEstimate; -// } -// -// private static Map<Integer, Double> calculateDerivative(Map<Integer, Double> function) { -// // 计算导数 -// Map<Integer, Double> derivative = new HashMap<>(); -// -// for (Map.Entry<Integer, Double> entry : function.entrySet()) { -// int point = entry.getKey(); -// -// if (point > 1 && point < 4) { -// double derivativeValue = (function.get(point + 1) - function.get(point - 1)) / 2.0; -// derivative.put(point, derivativeValue); -// } else { -// // 边缘点处理 -// derivative.put(point, 0.0); -// } -// } -// -// return derivative; -// } -// -// private static double gaussianKernel(double x, double xi, double bandwidth) { -// // 高斯核函数 -// return Math.exp(-0.5 * Math.pow((x - xi) / bandwidth, 2)) / Math.sqrt(2 * Math.PI); -// } public static void calculate(int[] data, int block_size) { Map<Integer, Integer> data_map = new HashMap<>(); @@ -217,9 +90,7 @@ public class KernelDensityEstimation { } double[] kernelDensity = calculateKernelDensity(data_map); third_value= findMinIndex(kernelDensity); -// for(int j=0;j<third_value.length;j++){ -// third_value[j] += min_value; -// } + System.out.println("Minimum point: x=" + (Arrays.toString(third_value))); }
