Session3_Assignment1.ipynb

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   "source": [
    "# Task 1"
   ]
  },
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   "source": [
    "## 1. Write a function to compute 5/0 and use try/except to catch the exceptions."
   ]
  },
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     "text": [
      "Division By Zero\n",
      "Executing Finally Block\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    print(5/0)\n",
    "except ZeroDivisionError:\n",
    "    print(\"Division By Zero\")\n",
    "except Exception as e:\n",
    "    print(\"There is some error: \",e)\n",
    "else:\n",
    "    print(\"try block executed successfully\")\n",
    "finally:\n",
    "    print(\"Executing Finally Block\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Implement a Python program to generate all sentences where subject is in [\"Americans\", \"Indians\"] and verb is in [\"Play\", \"watch\"] and the object is in [\"Baseball\",\"cricket\"]\n",
    "\n",
    "## Hint: Subject,Verb and Object should be declared in the program as shown below.\n",
    "\n",
    "#### subjects=[\"Americans \",\"Indians\"]\n",
    "#### verbs=[\"play\",\"watch\"]\n",
    "#### objects=[\"Baseball\",\"Cricket\"]\n",
    "\n",
    "### Output should come as below:\n",
    "#### Americans play Baseball.\n",
    "#### Americans play Cricket.\n",
    "#### Americans watch Baseball.\n",
    "#### Americans watch Cricket.\n",
    "#### Indians play Baseball.\n",
    "#### Indians play Cricket.\n",
    "#### Indians watch Baseball.\n",
    "#### Indians watch Cricket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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     "name": "stdout",
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     "text": [
      "Americans  play Baseball\n",
      "Americans  play Cricket\n",
      "Americans  watch Baseball\n",
      "Americans  watch Cricket\n",
      "Indians play Baseball\n",
      "Indians play Cricket\n",
      "Indians watch Baseball\n",
      "Indians watch Cricket\n"
     ]
    }
   ],
   "source": [
    "subjects=[\"Americans \",\"Indians\"]\n",
    "verbs=[\"play\",\"watch\"]\n",
    "objects=[\"Baseball\",\"Cricket\"]\n",
    "\n",
    "for s in subjects:\n",
    "    for v in verbs:\n",
    "        for o in objects:\n",
    "            print(s, v, o)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 2:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write a function so that the columns of the output matrix are powers of the input vector.\n",
    "## The order of the powers is determined by the increasing boolean argument. Specifically, when increasing is False, the i-th output column is the input vector raised element-wise to the power of N - i - 1.\n",
    "\n",
    "### HINT: Such a matrix with a geometric progression in each row is named for AlexandreTheophile Vandermonde. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "------------OUTPUT-------------\n",
      "\n",
      "The input array is: [1 2 3 4 5] \n",
      "\n",
      "Number of columns in output matrix should be: 3 \n",
      "\n",
      "Vander matrix of the input array in decreasing order of powers:\n",
      "\n",
      " [[ 1  1  1]\n",
      " [ 4  2  1]\n",
      " [ 9  3  1]\n",
      " [16  4  1]\n",
      " [25  5  1]] \n",
      "\n",
      "Vander matrix of the input array in increasing order of powers:\n",
      "\n",
      " [[ 1  1  1]\n",
      " [ 1  2  4]\n",
      " [ 1  3  9]\n",
      " [ 1  4 16]\n",
      " [ 1  5 25]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "## Iterate over each number in the input vector n times, n being the number of columns in the o/p matrix and output \n",
    "## an intermediate vector. Use diff order formula based on increasing and decreasing condition.\n",
    "## Reshape the intermediate vector using the size of the input vector (rows) and n (columns) to generate the o/p matrix. \n",
    "   \n",
    "def gen_vander_matrix(ipvector, n, increasing=False):\n",
    "    \n",
    "    if not increasing:\n",
    "        op_matx = np.array([x**(n-1-i) for x in ipvector for i in range(n)]).reshape(ipvector.size,n)\n",
    "    elif increasing:\n",
    "        op_matx = np.array([x**i for x in ipvector for i in range(n)]).reshape(ipvector.size,n)\n",
    "    \n",
    "    return op_matx\n",
    "\n",
    "print(\"------------OUTPUT-------------\\n\")\n",
    "\n",
    "inputvector = np.array([1,2,3,4,5])\n",
    "no_col_opmat = 3\n",
    "op_matx_dec_order = gen_vander_matrix(inputvector,no_col_opmat,False)\n",
    "op_matx_inc_order = gen_vander_matrix(inputvector,no_col_opmat,True)\n",
    "\n",
    "print(\"The input array is:\",inputvector,\"\\n\")\n",
    "print(\"Number of columns in output matrix should be:\",no_col_opmat,\"\\n\")\n",
    "print(\"Vander matrix of the input array in decreasing order of powers:\\n\\n\",op_matx_dec_order,\"\\n\")\n",
    "print(\"Vander matrix of the input array in increasing order of powers:\\n\\n\",op_matx_inc_order,\"\\n\")\n"
   ]
  }
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