{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# Load test module for sanity check\n",
    "from test_utils import test"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "Data Generation\n",
    "==="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_samples, num_features = 10, 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(10)\n",
    "data = np.random.rand(num_samples, num_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "Solution\n",
    "==="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def standardize(x):\n",
    "    \"\"\"Standardize the input data x\n",
    "\n",
    "    Args:\n",
    "        x: numpy array of shape=(num_samples, num_features)\n",
    "\n",
    "    Returns:\n",
    "        standardized data, shape=(num_samples, num_features)\n",
    "\n",
    "    >>> standardize(np.array([[1, 2], [3, 4], [5, 6]]))\n",
    "    array([[-1.22474487, -1.22474487],\n",
    "           [ 0.        ,  0.        ],\n",
    "           [ 1.22474487,  1.22474487]])\n",
    "    \"\"\"\n",
    "    # ***************************************************\n",
    "    # INSERT YOUR CODE HERE\n",
    "    # TODO: standardize input data x\n",
    "    # ***************************************************\n",
    "    raise NotImplementedError\n",
    "    return std_data\n",
    "\n",
    "\n",
    "test(standardize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "std_data = standardize(data)\n",
    "print(std_data, \"\\n\\n\", np.mean(std_data, axis=0), \"\\n\\n\", np.std(std_data, axis=0))"
   ]
  }
 ],
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