Chapter1_Tensors/Random_Sampling_solution.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import torch\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2017, 9, 27)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import date\n",
    "date.today()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "author = \"kyubyong. https://github.com/Kyubyong/pytorch_exercises\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.2.0_3'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.13.0'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE on notation\n",
    "\n",
    "_x, _y, _z, ...: NumPy 0-d or 1-d arrays<br/>\n",
    "_X, _Y, _Z, ...: NumPy 2-d or higer dimensional arrays<br/>\n",
    "x, y, z, ...: 0-d or 1-d tensors<br/>\n",
    "X, Y, Z, ...: 2-d or higher dimensional tensors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple random data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q1. Create a tensor of shape (3, 2) and populate it with random samples from a **uniform distribution** over [0, 1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 0.2166  0.4383\n",
      " 0.0214  0.9917\n",
      " 0.8378  0.5924\n",
      "[torch.FloatTensor of size 3x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X = torch.rand(3, 2)\n",
    "print(X)\n",
    "\n",
    "# cf\n",
    "# np.random.rand(3, 2) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q2. Create a tensor of shape (3, 2) and populate it with random samples from a **normal distribution** with zero mean and variance of one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 0.9536  0.3673\n",
      "-1.1265  2.2443\n",
      " 0.7806  0.0989\n",
      "[torch.FloatTensor of size 3x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X = torch.randn(3, 2)\n",
    "print(X)\n",
    "\n",
    "# cf.\n",
    "# np.random.randn(3, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q3. Create a tensor of random numbers drawn from the given mean and standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 1.0411\n",
      " 2.0000\n",
      " 3.0156\n",
      "[torch.FloatTensor of size 3]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "means = torch.Tensor([1, 2, 3])\n",
    "std = torch.Tensor([0.1, 0., -0.1])\n",
    "X = torch.normal(means=means, std=std)\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Permutations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q4. Shuffle integers between 0 and 10 (exclusive)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 4\n",
      " 9\n",
      " 1\n",
      " 3\n",
      " 7\n",
      " 0\n",
      " 8\n",
      " 5\n",
      " 6\n",
      " 2\n",
      "[torch.LongTensor of size 10]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "x = torch.randperm(10)\n",
    "print(x)\n",
    "\n",
    "# cf.\n",
    "# np.random.permutation(10)"
   ]
  }
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