Sri_Harsh_ME19B031_Assignment_1

Sri_Harsh_ME19B031.ipynb

@@ -0,0 +1,146 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ca90d92b-4342-44c8-a111-616ea99fdd80",
+   "metadata": {},
+   "source": [
+    "#**Q1**#"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "fbc00ecf-2165-41a1-83c7-fadbf79cd145",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8df6c7dd-9f52-47c2-a6a1-eafae631f1ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N=100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0bccee96-d21a-483a-aefa-5de216715977",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_hat=np.random.rand(1,N)\n",
+    "y=np.random.randint(0,2,(1,N))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "79e3f76d-78c4-427e-a456-5a13e92651b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.4501441872060048"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "O = -(1/N)*np.sum(y*np.log2(y_hat)+(1-y)*np.log2(1-y_hat))\n",
+    "O"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e88d1d4-86f1-4919-98d7-9e13619dbdff",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#**Q2**#\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "15178731-8cfd-463b-a2d5-ce6b648cd60e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Input:\n",
+    "    def __init__(self,Numbers,Target):\n",
+    "        self.Numbers=Numbers\n",
+    "        self.Target = Target\n",
+    "    def target_elements(self):\n",
+    "        self.TE = []\n",
+    "        for i in range(len(self.Numbers)):\n",
+    "            for j in range(len(self.Numbers)):\n",
+    "                if self.Numbers[i]+self.Numbers[j] ==self.Target:\n",
+    "                    self.TE.append([[i],[j]])\n",
+    "        #self.target_elements = TE\n",
+    "        self.Output = {}\n",
+    "        for k in range(len(self.TE)):\n",
+    "            self.Output[k+1] = self.TE[k]\n",
+    "        print(\"Output = \",self.Output)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b7d38d90-2cdf-4749-8847-ecde154b74c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Output =  {1: [[0], [3]], 2: [[2], [3]], 3: [[3], [0]], 4: [[3], [2]]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "I=Input([10,20,10,40,50,60,70],50)\n",
+    "I.target_elements()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c22351e-a266-450e-a9ad-812743bc806e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}