add cut and split

README.md

@@ -3,3 +3,4 @@ This repo is a note of numpy leaning
 ## Content
 
 [random](./random.ipynb)
+[cut and split](./cut_split.ipynb)

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
+}

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",
+      "[[ 2  1  0]\n",
-      " [ 1 -1 -1]] \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",
+      "[[0.0936316  0.2776172  0.16938396]\n",
-      " [0.62572959 0.13477642 0.86937912]] \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\")"
    ]
   }
  ],