3. Sampling Methods.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### set seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### set random state which is independent from the global seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.88594794, 0.07791236, 0.97964616, 0.24767146, 0.75288472,\n",
       "       0.52667564, 0.90755375, 0.8840703 , 0.08926896, 0.5173446 ])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs = np.random.RandomState(321)\n",
    "rs.rand(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### generate a random sample from interval [0, 1) in a given shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6964691855978616\n"
     ]
    }
   ],
   "source": [
    "# generate a random scalar\n",
    "print(np.random.rand())      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.28613933 0.22685145 0.55131477]\n"
     ]
    }
   ],
   "source": [
    "# generate a 1-D array\n",
    "print(np.random.rand(3))           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.71946897 0.42310646 0.9807642 ]\n",
      " [0.68482974 0.4809319  0.39211752]\n",
      " [0.34317802 0.72904971 0.43857224]]\n"
     ]
    }
   ],
   "source": [
    "# generate a 2-D array\n",
    "print(np.random.rand(3,3))          "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### generate a sample from the standard normal distribution (mean = 0, var = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.14337247 -0.6191909  -0.76943347]\n",
      " [ 0.57674602  0.12652592 -1.30148897]\n",
      " [ 2.20742744  0.52274247  0.46564476]]\n"
     ]
    }
   ],
   "source": [
    "print(np.random.randn(3,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### generate an array of random integers in a given interval [low, high)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 7 2]\n"
     ]
    }
   ],
   "source": [
    "# np.ranodm.randint(low, high, size, dtype)\n",
    "print(np.random.randint(1, 10, 3, 'i8'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### generate an array of random floating-point numbers in the interval [0.0, 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.65472131 0.37380143 0.23451288 0.98799529 0.76599595 0.77700444\n",
      " 0.02798196 0.17390652 0.15408224 0.07708648]\n",
      "[0.8898657  0.7503787  0.69340324 0.51176338 0.46426806 0.56843069\n",
      " 0.30254945 0.49730879 0.68326291 0.91669867]\n",
      "[0.10892895 0.49549179 0.23283593 0.43686066 0.75154299 0.48089213\n",
      " 0.79772841 0.28270293 0.43341824 0.00975735]\n",
      "[0.34079598 0.68927201 0.86936929 0.26780382 0.45674792 0.26828131\n",
      " 0.8370528  0.27051466 0.53006201 0.17537266]\n"
     ]
    }
   ],
   "source": [
    "# the following methods are the same as np.random.rand()\n",
    "print(np.random.random_sample(10))\n",
    "print(np.random.random(10))\n",
    "print(np.random.ranf(10))\n",
    "print(np.random.sample(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### generate a random sample from a given 1-D array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 1 2 2 1]\n"
     ]
    }
   ],
   "source": [
    "# np.random.choice(iterable_or_int, size, replace=True, p=weights)\n",
    "print(np.random.choice(range(3), 10, replace=True, p=[0.1, 0.8, 0.1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 1 2 2 0 1 1 1 0]\n"
     ]
    }
   ],
   "source": [
    "print(np.random.choice(3, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 2 1 3 2 3 1 2 1 3]\n"
     ]
    }
   ],
   "source": [
    "print(np.random.choice([1,2,3], 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### shuffle an array in place"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array(range(10))\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 8 5 4 0 6 7 9 3]\n"
     ]
    }
   ],
   "source": [
    "np.random.shuffle(arr)\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### generate a permutation of an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The initial array:  [0 1 2 3 4 5 6 7 8 9]\n",
      "A permutation of the array:  [3 6 2 4 5 9 1 8 0 7]\n"
     ]
    }
   ],
   "source": [
    "# similar to np.random.shuffle(), but it returns a copy rather than making changes in place\n",
    "arr = np.array(range(10))\n",
    "print('The initial array: ', arr)\n",
    "print('A permutation of the array: ', np.random.permutation(arr))"
   ]
  }
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