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   "source": [
    "## Recitation1-Test2 (35 marks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a matrix **A** of size (3,5) with 0 mean and 5 standard deviation. (**1 mark**)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set all the elements in first row of **A** to 7. (**1 mark**)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set all the elements in second row of **A** to -2. Set the element in third row and second column of **A** to 9. (**2 marks**) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a vector **a** of size 30 with values ranging from 60 to 89. (**2 marks**) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a (5,5) matrix **M** with values ranging from 0 to 24. (**1 mark**) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a (3,3) matrix **N** with 5 on the border and 0 inside. (**1 mark**) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a matrix **P** of size (3,4) with random integer values. (**1 mark**) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subtract the mean of each row of matrix **A**. (**1 mark**) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**listA** =  [1,2,3,4,5,6,7,8,9,10]  \n",
    "\n",
    "If an element of **listA** is smaller than 5, replace it with 0. And if an element of x is bigger than 5, replace it with 1. (**3 marks**) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the square of **listA** elements in one line. (**2 marks**) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Concatenate b1 and b2. (**2 marks**) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "b1 = np.array(['Hello', 'in','first'])\n",
    "b2 = np.array(['Students','the','recitation'])\n",
    "combine_b = "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the element of combine_b (above list) with spaces. (**2 marks**) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a 3-D array **L** with five on the diagonal and zeros elsewhere. (**1 mark**) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a function that can print a dictionary where the keys are numbers between 1 and 5 and the values are square of keys. (**2 marks**) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a dictionary of student **Ali** where the keys are courses and values are total and obtaining marks in each course. Save this dictionary in a text file and numpy file. (**4 marks**) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a class 'calculator' with the following functions to compute: **(9 marks)**\n",
    "- the dot product between two vectors\n",
    "- the cross product between two vectors\n",
    "- the length (norm) of the vector\n",
    "- matrix-vector product of **M** and **v**\n",
    "- the mean squared error between **u**  and **v**\n",
    "- the unit norm of vector **u**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "M = np.array([5,2,4],[2,9,8],[2,1,7]])\n",
    "u = np.array([1,5,2])\n",
    "v = np.array([4,2,3])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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