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 bcdf021888a866d0c4768fe977f46d87b4455c45 Merge: b9188de3294 7b10c98093f Author: xjz17 <[email protected]> AuthorDate: Tue Nov 14 23:26:20 2023 +0800 Merge branch 'reorder-icde' of https://github.com/xjz17/iotdb into reorder-icde iotdb-core/tsfile/pom.xml | 5 + .../iotdb/tsfile/encoding/decoder/Decoder.java | 13 + .../tsfile/encoding/decoder/DoubleBUFFDecoder.java | 86 + .../tsfile/encoding/decoder/FloatBUFFDecoder.java | 86 + .../tsfile/encoding/decoder/IntBUFFDecoder.java | 81 + .../tsfile/encoding/decoder/LongBUFFDecoder.java | 81 + .../tsfile/encoding/encoder/DoubleBUFFEncoder.java | 161 + .../tsfile/encoding/encoder/FloatBUFFEncoder.java | 161 + .../tsfile/encoding/encoder/IntBUFFEncoder.java | 143 + .../tsfile/encoding/encoder/LongBUFFEncoder.java | 143 + .../tsfile/encoding/encoder/TSEncodingBuilder.java | 25 + .../tsfile/file/metadata/enums/TSEncoding.java | 5 +- .../apache/iotdb/tsfile/encoding/EncodeTest.java | 847 +- .../tsfile/encoding/KernelDensityEstimation.java | 240 +- .../org/apache/iotdb/tsfile/encoding/PFORTest.java | 2069 ++-- .../iotdb/tsfile/encoding/REGERCompressTest.java | 6852 +++++++------- .../apache/iotdb/tsfile/encoding/REGERTest.java | 6897 +++++++------- .../iotdb/tsfile/encoding/REGERTimeTest.java | 9835 ++++++++++---------- .../iotdb/tsfile/encoding/RegerPDoubleTest.java | 5417 +++++------ .../iotdb/tsfile/encoding/RegerPFloatTest.java | 6735 +++++++------- .../encoding/RegerWithoutReorderingTest.java | 2557 ++--- .../org/apache/iotdb/tsfile/encoding/myPFOR.java | 564 +- 22 files changed, 22511 insertions(+), 20492 deletions(-) diff --cc iotdb-core/tsfile/src/test/java/org/apache/iotdb/tsfile/encoding/KernelDensityEstimation.java index bcd5b0621bf,7b4d69917d7..5fde4ef7aa1 --- 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 @@@ -5,183 -5,168 +5,141 @@@ import java.util.HashMap import java.util.Map; public class KernelDensityEstimation { - public static void main(String[] args) { - // 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]; - - for (int x = 1; x <= maxKey; x++) { - for (Map.Entry<Integer, Integer> entry : discreteDistribution.entrySet()) { - int dataPoint = entry.getKey(); - int weight = entry.getValue(); - kernelDensity[x - 1] += gaussianKernel(x, dataPoint) * weight; - } - } - - 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 - 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]) { - if (final_min_count != 0) { - if (i > pre_value + 32) { - minIndex[final_min_count] = i; - final_min_count++; - pre_value = i; - } - } else { - minIndex[final_min_count] = i; - final_min_count++; - pre_value = i; + public static void main(String[] args) { + // 第一个整数（正数） + int int1 = -12345; + // 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]; - - for (int x = 1; x <= maxKey; x++) { - for (Map.Entry<Integer, Integer> entry : discreteDistribution.entrySet()) { - int dataPoint = entry.getKey(); - int weight = entry.getValue(); - kernelDensity[x - 1] += gaussianKernel(x, dataPoint) * weight; - } - } - - 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 - 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]) { - if(final_min_count != 0){ - if(i>pre_value+32){ - minIndex[final_min_count] = i; - final_min_count ++; - pre_value = i; - } - }else{ - minIndex[final_min_count] = i; - final_min_count ++; - pre_value = i; - } - } - } - 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); - // } + } + } + 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); + // } } -
