From 1ff85cfa8b8c6b906b7b4959e2e2eeba1ba16147 Mon Sep 17 00:00:00 2001 From: FANBOYANG Date: Sat, 5 Nov 2022 15:18:14 +0800 Subject: [PATCH] add cut and split --- README.md | 3 +- cut_split.ipynb | 121 ++++++++++++++++++++++++++++++++++++++++++++++++ random.ipynb | 40 ++++++++++++---- 3 files changed, 153 insertions(+), 11 deletions(-) create mode 100644 cut_split.ipynb diff --git a/README.md b/README.md index d4ede3a..7c9e409 100644 --- a/README.md +++ b/README.md @@ -2,4 +2,5 @@ This repo is a note of numpy leaning ## Content -[random](./random.ipynb) \ No newline at end of file +[random](./random.ipynb) +[cut and split](./cut_split.ipynb) diff --git a/cut_split.ipynb b/cut_split.ipynb new file mode 100644 index 0000000..367dacd --- /dev/null +++ b/cut_split.ipynb @@ -0,0 +1,121 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 数据拼接\n", + "\n", + "1. np.concatenate 是numpy中对array进行拼接的函数\n", + "axis参数为指定按照哪个维度进行拼接" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 2.62434536 0.38824359 0.47182825 -0.07296862]\n", + " [ 1.86540763 -1.3015387 2.74481176 0.2387931 ]\n", + " [ 1.3190391 0.75062962 2.46210794 -1.06014071]\n", + " [ 0.6775828 0.61594565 2.13376944 -0.09989127]\n", + " [ 0.82757179 0.12214158 1.04221375 1.58281521]] \n", + " (5, 4) \n", + "\n", + "[[-0.10061918 2.14472371 1.90159072 1.50249434]\n", + " [ 1.90085595 0.31627214 0.87710977 0.06423057]\n", + " [ 0.73211192 1.53035547 0.30833925 0.60324647]] \n", + " (3, 4) \n", + "\n", + "[[ 0.3128273 0.15479436]\n", + " [ 0.32875387 0.9873354 ]\n", + " [-0.11731035 1.2344157 ]\n", + " [ 2.65980218 1.74204416]\n", + " [ 0.80816445 0.11237104]] \n", + " (5, 2) \n", + "\n", + "[[ 2.62434536 0.38824359 0.47182825 -0.07296862]\n", + " [ 1.86540763 -1.3015387 2.74481176 0.2387931 ]\n", + " [ 1.3190391 0.75062962 2.46210794 -1.06014071]\n", + " [ 0.6775828 0.61594565 2.13376944 -0.09989127]\n", + " [ 0.82757179 0.12214158 1.04221375 1.58281521]\n", + " [-0.10061918 2.14472371 1.90159072 1.50249434]\n", + " [ 1.90085595 0.31627214 0.87710977 0.06423057]\n", + " [ 0.73211192 1.53035547 0.30833925 0.60324647]] \n", + " (8, 4) \n", + "\n", + "[[ 2.62434536 0.38824359 0.47182825 -0.07296862 0.3128273 0.15479436]\n", + " [ 1.86540763 -1.3015387 2.74481176 0.2387931 0.32875387 0.9873354 ]\n", + " [ 1.3190391 0.75062962 2.46210794 -1.06014071 -0.11731035 1.2344157 ]\n", + " [ 0.6775828 0.61594565 2.13376944 -0.09989127 2.65980218 1.74204416]\n", + " [ 0.82757179 0.12214158 1.04221375 1.58281521 0.80816445 0.11237104]] \n", + " (5, 6) \n", + "\n" + ] + } + ], + "source": [ + "rdm = np.random.RandomState(1)\n", + "x1 = rdm.normal(1,1,(5,4))\n", + "x2 = rdm.normal(1,1,(3,4))\n", + "x3 = rdm.normal(1,1,(5,2))\n", + "print(x1,\"\\n\",x1.shape,\"\\n\")\n", + "print(x2,\"\\n\",x2.shape,\"\\n\")\n", + "print(x3,\"\\n\",x3.shape,\"\\n\")\n", + "\n", + "con1 = np.concatenate([x1,x2],axis=0)\n", + "print(con1,\"\\n\",con1.shape,\"\\n\")\n", + "\n", + "con2 = np.concatenate([x1,x3],axis=1)\n", + "print(con2,\"\\n\",con2.shape,\"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.13 ('gym')", + "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": "3.9.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "eb62451c918ef6a4174992a3510c06ea27ba0bc2fc5ee7a9f470b6f52b0b170f" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/random.ipynb b/random.ipynb index c8b9f7b..c99ff9d 100644 --- a/random.ipynb +++ b/random.ipynb @@ -134,12 +134,22 @@ "功能： 生成[0,1)之间的浮点数，通过size参数来指定维数。\n", "说明：\n", "size : int or tuple of ints, optional.\n", - "Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned. " + "Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned. \n", + "\n", + "4. numpy.random.rand()\n", + "均匀分布\n", + "范围 [0, 1)\n", + "\n", + "5. numpy.random.normal(loc=mu, scale=sigma, size)\n", + "正态分布\n", + "mu，均值\n", + "sigma，标准差\n", + "size，数据shape，默认一个值" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -148,17 +158,21 @@ "text": [ "[0 0 0 0] \n", "\n", - "[[ 0 -2 0]\n", - " [ 1 -1 -1]] \n", + "[[ 2 1 0]\n", + " [-2 0 0]] \n", "\n", - "0.0940234518052504 \n", + "0.9888926077885405 \n", "\n", - "[0.58264778] \n", + "[0.32897483] \n", "\n", - "[0.19358132 0.3565561 ] \n", + "[0.96680953 0.76946094] \n", "\n", - "[[0.32759701 0.74016873 0.83999783]\n", - " [0.62572959 0.13477642 0.86937912]] \n", + "[[0.0936316 0.2776172 0.16938396]\n", + " [0.21494328 0.26245945 0.22463559]] \n", + "\n", + "[0.36292738 0.51118156 0.35250669] \n", + "\n", + "[[ 0.07101816 0.1111697 -0.89099377]] \n", "\n" ] } @@ -176,7 +190,13 @@ "z3 = np.random.random(2) #生成1×2的数组\n", "print(z3,\"\\n\")\n", "z4 = np.random.random((2,3)) #生成一个2行3列的数组\n", - "print(z4,\"\\n\")" + "print(z4,\"\\n\")\n", + "\n", + "z5 = np.random.rand(3) # 生成1×3的数组\n", + "print(z5,\"\\n\")\n", + "\n", + "z6 = np.random.normal(0,1,(1,3))\n", + "print(z6,\"\\n\")" ] } ],