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Author: Tianbo-Diao

Python-testing.ipynb

@@ -0,0 +1,103 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Python 代码运行并测试运行时间"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "jupyter": {
+     "is_executing": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "随机数生成结果: (200, 2000)\n",
+      "随机数生成时间: 0.08186912536621094 seconds\n",
+      "矩阵乘法结果: (2000, 2000)\n",
+      "矩阵乘法时间: 0.07513713836669922 seconds\n",
+      "分布式随机数生成时间: 3.210928201675415 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from time import time\n",
+    "\n",
+    "rng = np.random.default_rng()\n",
+    "\n",
+    "\n",
+    "def measure_runtime(func, *args, **kwargs):\n",
+    "    start_time = time()\n",
+    "    result = func(*args, **kwargs)\n",
+    "    runtime = time() - start_time\n",
+    "    return result, runtime\n",
+    "\n",
+    "\n",
+    "def RandomData(n, p):\n",
+    "    mean_vec = np.zeros(p)\n",
+    "    cov_mat = 0.5 ** np.abs(np.arange(1, p + 1).reshape(-1, 1) - np.arange(1, p + 1))\n",
+    "    X = rng.multivariate_normal(mean_vec, cov_mat, n, method=\"cholesky\")\n",
+    "    return X\n",
+    "\n",
+    "\n",
+    "def MatrixMultiplication(p):\n",
+    "    a = rng.normal(size=(p, p))\n",
+    "    b = a.T @ a\n",
+    "    return b\n",
+    "\n",
+    "\n",
+    "def DistributedRandom(n, p, m):\n",
+    "    mean_vec = np.zeros(p)\n",
+    "    cov_mat = 0.5 ** np.abs(np.arange(1, p + 1).reshape(-1, 1) - np.arange(1, p + 1))\n",
+    "    x = [0] * m\n",
+    "    for i in range(m):\n",
+    "        x[i] = rng.multivariate_normal(mean_vec, cov_mat, n, method=\"cholesky\")\n",
+    "    return x\n",
+    "\n",
+    "\n",
+    "# Example usage:\n",
+    "Data_result, Time_data = measure_runtime(RandomData, n=200, p=2000)\n",
+    "print(f\"随机数生成结果: {Data_result.shape}\")\n",
+    "print(f\"随机数生成时间: {Time_data} seconds\")\n",
+    "\n",
+    "Matrix_result, Time_matrix = measure_runtime(MatrixMultiplication, p=2000)\n",
+    "print(f\"矩阵乘法结果: {Matrix_result.shape}\")\n",
+    "print(f\"矩阵乘法时间: {Time_matrix} seconds\")\n",
+    "\n",
+    "\n",
+    "Distributed_result, Distributed_time = measure_runtime(DistributedRandom, n=6000, p=600, m=60)\n",
+    "print(f\"分布式随机数生成时间: {Distributed_time} seconds\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}