Perceptron & Shallow Neuron .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "135ecda2",
   "metadata": {},
   "source": [
    "### Perceptron & Shallow Neuron"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb14407a",
   "metadata": {},
   "source": [
    "### Cristel "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0465d087",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import random \n",
    "from random import *\n",
    "from math import exp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cffd7651",
   "metadata": {},
   "source": [
    "### Problem 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5acf025e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ones</th>\n",
       "      <th>LevelOfViolence</th>\n",
       "      <th>CriticsRating</th>\n",
       "      <th>Watched</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Ones  LevelOfViolence  CriticsRating  Watched\n",
       "0    1.0                1            1.2       -1\n",
       "1    1.0                1            3.5        1\n",
       "2    1.0                1            4.2        1\n",
       "3    1.0                2            3.9        1\n",
       "4    1.0                2            2.8       -1\n",
       "5    1.0                3            3.0       -1\n",
       "6    1.0                5            4.5       -1\n",
       "7    1.0                4            1.8       -1\n",
       "8    1.0                1            2.1       -1\n",
       "9    1.0                3            4.8        1\n",
       "10   1.0                5            4.9        1\n",
       "11   1.0                3            1.5       -1\n",
       "12   1.0                3            2.6       -1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Movie_data = pd.read_csv(\"movieData.csv\")\n",
    "Movie_df = np.ones(Movie_data.shape[0])\n",
    "Movie_data.insert(0,\"Ones\",Movie_df)\n",
    "Movie_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "81a0222c",
   "metadata": {},
   "outputs": [],
   "source": [
    "Watched = Movie_data[Movie_data[\"Watched\"] == 1]\n",
    "Not_Watched = Movie_data[Movie_data[\"Watched\"] == -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f45d88f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,6))\n",
    "plt.scatter(Watched.LevelOfViolence,Watched.CriticsRating ,color='blue')\n",
    "plt.scatter(Not_Watched.LevelOfViolence,Not_Watched.CriticsRating ,color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b26a7461",
   "metadata": {},
   "source": [
    "#### (a) Train a perceptron that will create a linear boundary decision that willhelp the company to make future recommendations to Ms. X. After how many iterations does the algorithm converge?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bdd74778",
   "metadata": {},
   "outputs": [],
   "source": [
    "def perceptron(Data, Labels, lr, Number_Of_Iterations):\n",
    "    \n",
    "    # Data --> Inputs\n",
    "    # Labels --> labels/target\n",
    "    # lr --> Learning Rate\n",
    "    # Number Of Iteration --> Epochs\n",
    "    \n",
    "    A,B = Data.shape\n",
    "    \n",
    "    \n",
    "    # Initializing parapeters(theta) to zeros.\n",
    "    # +1 in n+1 for the bias term.\n",
    "    \n",
    "    Theta = np.zeros((B, 1))\n",
    "    \n",
    "    # Empty list to store how many examples were \n",
    "    # misclassified at every iteration.\n",
    "    \n",
    "    n_miss_list = []\n",
    "    \n",
    "    # Training\n",
    "\n",
    "    for epoch in range(Number_Of_Iterations):\n",
    "        \n",
    "        # variable to store #misclassified.\n",
    "        n_miss = 0\n",
    "        \n",
    "        for i, x in enumerate(Data):\n",
    "            #print(np.dot(Data[i], Theta))\n",
    "            t = np.dot(Data[i], Theta)\n",
    "            k = 1.0 if (t > 0) else -1.0\n",
    "            \n",
    "            if (Labels[i] - k) != 0:\n",
    "                n_miss += 1\n",
    "                \n",
    "                Theta = Theta + (Data[i] * Labels[i]).reshape(3,1)\n",
    "                \n",
    "                \n",
    "                \n",
    "        n_miss_list.append(n_miss)\n",
    "        \n",
    "        plt.plot(n_miss_list)\n",
    "        plt.xlabel('Epoch')\n",
    "        plt.ylabel('Total Loss')\n",
    "        \n",
    "        \n",
    "    \n",
    "        \n",
    "    return Theta, n_miss_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d23b4d3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,6))\n",
    "Weights, Values = perceptron(Movie_data.iloc[:, :-1].values, Movie_data.iloc[:, -1].values, 1, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f9eb07bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-28. ]\n",
      " [ -9. ]\n",
      " [ 15.2]]\n"
     ]
    }
   ],
   "source": [
    "print(Weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cc96f8b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The algorithm becomes stable on epoch 39 as the value becomes 0\n"
     ]
    }
   ],
   "source": [
    "print('The algorithm becomes stable on epoch', Values.index(0) + 1, 'as the value becomes 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "74dcacfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slope is: 0.5921052631578945\n",
      "The intercept is: 1.842105263157894\n"
     ]
    }
   ],
   "source": [
    "Slope = -Weights[1][0] / Weights[2][0]\n",
    "Intercept = -Weights[0][0] / Weights[2][0]\n",
    "print(\"The slope is:\", Slope)\n",
    "print('The intercept is:', Intercept)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "090baa8e",
   "metadata": {},
   "source": [
    "#### (b) Upon creating a model, plot the boundary line along with all the datapoints and axes clearly marked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "203fe45b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xvalue = np.linspace(0,6)\n",
    "abline_values = [(Slope  * i) + Intercept for i in xvalue]\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "colors=['blue','green']\n",
    "label=Movie_data['Watched']\n",
    "plt.scatter(Movie_data['LevelOfViolence'],  Movie_data['CriticsRating'],c=label, cmap=matplotlib.colors.ListedColormap(colors))\n",
    "plt.plot(xvalue, abline_values, 'red')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0944ac0",
   "metadata": {},
   "source": [
    "### Problem 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3101f274",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.889238</td>\n",
       "      <td>-0.334713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.532539</td>\n",
       "      <td>0.898156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.596540</td>\n",
       "      <td>0.870667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.375349</td>\n",
       "      <td>-0.713875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.083465</td>\n",
       "      <td>0.401115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y\n",
       "0 -0.889238 -0.334713\n",
       "1  0.532539  0.898156\n",
       "2  0.596540  0.870667\n",
       "3 -0.375349 -0.713875\n",
       "4  0.083465  0.401115"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Co_df = pd.read_csv(\"siCoData.csv\")\n",
    "Co_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74a3c444",
   "metadata": {},
   "source": [
    "#### (a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "abd38291",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Converting the data into a matrix\n",
    "CoData_df = np.matrix(Co_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "78bc6409",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tanh_derivative(num):\n",
    "    return (1.0 - np.power(np.tanh(num), 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e11bf333",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Activation(Data, epochs):\n",
    "    \n",
    "    X = Data[:, :-1]\n",
    "    Y = Data[:, -1]\n",
    "    \n",
    "    # Generating random weights\n",
    "    W_0_1 = np.random.normal(size=(2, 2))\n",
    "    W_1_2 = np.random.normal(size=(3, 1))\n",
    "    Old_W_1_2 = W_1_2\n",
    "    \n",
    "    yhat = []\n",
    "    listoferror = []\n",
    "    for j in range(epochs):\n",
    "        epocherror = 0\n",
    "        Prediction = []\n",
    "        for i in range(len(X)):\n",
    "            # Forward Pass\n",
    "            X_val = np.insert(X[i], 0, 1) \n",
    "            Hidden_Init = np.dot(X_val, W_0_1) \n",
    "            Temp = np.tanh(Hidden_Init)\n",
    "            Temp = np.insert(Temp, 0, 1) \n",
    "            Hidden_Output = np.dot(Temp, W_1_2) \n",
    "            \n",
    "            Error = (Hidden_Output - Y[i])**2\n",
    "            \n",
    "            # Back Propogation\n",
    "            # Output --> hidden layer\n",
    "            D1 = (2*(Hidden_Output - Y[i])).item(0,0) \n",
    "            D2 = (tanh_derivative(Hidden_Output)).item(0,0) \n",
    "            D3 = Temp.reshape(3,1)\n",
    "            D3[0][0] = 0 \n",
    "            Total_Delta = D1*D2*D3\n",
    "            \n",
    "            W_1_2 = W_1_2 - Total_Delta*0.1\n",
    "            \n",
    "            D3 = Old_W_1_2[1][0]\n",
    "            D4 = tanh_derivative(Hidden_Init.item(0,0))\n",
    "            D5 = X_val\n",
    "            Total_Delta_1 = D1*D2*D3*D4*D5\n",
    "            W_0_1 = W_0_1.T\n",
    "\n",
    "            W_0_1[0] = W_0_1[0] - Total_Delta_1*0.1\n",
    "            \n",
    "            D3 = Old_W_1_2[2][0]\n",
    "            D4 = tanh_derivative(Hidden_Init.item(0,1))\n",
    "            D5 = X_val\n",
    "            Total_Delta_2 = D1*D2*D3*D4*D5\n",
    "            \n",
    "            W_0_1[1] = W_0_1[1] - Total_Delta_2*0.1\n",
    "            \n",
    "            W_0_1 = W_0_1.T\n",
    "            Old_W_1_2 = W_1_2\n",
    "            \n",
    "            epocherror += Error\n",
    "            Prediction.append(Hidden_Output)\n",
    "            \n",
    "        yhat.append(Prediction)\n",
    "        listoferror.append(epocherror/250)\n",
    "            \n",
    "    return yhat, listoferror"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5731ca3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data, err = Activation(CoData_df, 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "786f2668",
   "metadata": {},
   "source": [
    "#### (b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b6fc468",
   "metadata": {},
   "source": [
    "#### Plotting the orignal data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4619a5db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fbcc2b7f970>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Co_df.x, Co_df.y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab55afb2",
   "metadata": {},
   "source": [
    "#### Plotting the predicted values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ec36288b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Co_df.x, Data[np.argmin(err)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b4134ba",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}