lin-reg-from-scratcvh.ipynb

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      "/kaggle/input/coursera-machine-learning-andrew-ng/week_1-ex_1.txt\n",
      "/kaggle/input/coursera-machine-learning-andrew-ng/week_3-ex_2.txt\n",
      "/kaggle/input/coursera-machine-learning-andrew-ng/week_4-ex_2.txt\n",
      "/kaggle/input/coursera-machine-learning-andrew-ng/week_2-ex_1.txt\n"
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    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
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    "df = pd.read_csv('/kaggle/input/coursera-machine-learning-andrew-ng/week_1-ex_1.txt', header=None)"
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   "source": [
    "X0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0c79ac34",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T02:54:28.944129Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta = np.zeros((2))\n",
    "theta.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "06383028",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T02:54:28.967117Z",
     "iopub.status.busy": "2024-07-18T02:54:28.966435Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 97)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We have to combine X and X0 \n",
    "Xcom = np.array([X0,X])\n",
    "Xcom.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "de489d4c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T02:54:28.990319Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2,), (97,))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta.shape, X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c82f2c1",
   "metadata": {
    "papermill": {
     "duration": 0.006264,
     "end_time": "2024-07-18T02:54:29.012339",
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     "start_time": "2024-07-18T02:54:29.006075",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0399233a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T02:54:29.027723Z",
     "iopub.status.busy": "2024-07-18T02:54:29.026911Z",
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   },
   "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>6.1101</td>\n",
       "      <td>17.5920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.5277</td>\n",
       "      <td>9.1302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.5186</td>\n",
       "      <td>13.6620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.0032</td>\n",
       "      <td>11.8540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.8598</td>\n",
       "      <td>6.8233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        X        y\n",
       "0  6.1101  17.5920\n",
       "1  5.5277   9.1302\n",
       "2  8.5186  13.6620\n",
       "3  7.0032  11.8540\n",
       "4  5.8598   6.8233"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b3c51a8d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T02:54:29.056130Z",
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     "status": "completed"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(97,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3786f5a7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T02:54:29.079732Z",
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   "outputs": [],
   "source": [
    "class LinearRegression:\n",
    "    def __init__(self, n_iterations=1000, learning_rate=0.01):\n",
    "        self.n_iterations = n_iterations\n",
    "        self.learning_rate = learning_rate\n",
    "        self.w = None\n",
    "        self.b = None\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        np.random.seed(42)\n",
    "        m, n = X.shape\n",
    "        self.w = np.random.randn(n, 1)\n",
    "        self.b = np.zeros((1,))\n",
    "        \n",
    "        for i in range(self.n_iterations):\n",
    "            y_pred = self.predict(X)\n",
    "            dw = -2/m * np.dot(X.T, (y - y_pred))\n",
    "            db = -2/m * np.sum(y - y_pred)\n",
    "            self.w -= self.learning_rate * dw\n",
    "            self.b -= self.learning_rate * db\n",
    "\n",
    "    def predict(self, X):\n",
    "        return np.dot(X, self.w) + self.b\n",
    "    \n",
    "    def coeff(self):\n",
    "        return self.w,self.b\n",
    "    \n",
    "# Train the Linear Regression model\n",
    "model = LinearRegression(n_iterations=1000, learning_rate=0.01)\n",
    "model.fit(X.reshape(97,1), y.reshape(97,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dad18660",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T02:54:29.128770Z",
     "iopub.status.busy": "2024-07-18T02:54:29.127907Z",
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     "shell.execute_reply": "2024-07-18T02:54:29.157939Z"
    },
    "papermill": {
     "duration": 0.041799,
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class mylinreg:\n",
    "    def __init__(self, iterations=1000, lr = 0.001):\n",
    "        self.iterations = iterations\n",
    "        self.lr = lr\n",
    "        self.w = None\n",
    "        self.b = None\n",
    "    \n",
    "    def fit(self,X,y):\n",
    "#         X = X.reshape(len(X), X.ndim)\n",
    "#         y = y.reshape(len(y), y.ndim)\n",
    "        np.random.seed(42)\n",
    "        m,n = X.shape\n",
    "        self.w = np.random.randn(n,1)\n",
    "        self.b = np.zeros((1,))\n",
    "        \n",
    "        for i in range(self.iterations):\n",
    "            y_pred = self.predict(X)\n",
    "            dw = -2/m * np.dot(X.T, (y - y_pred))\n",
    "            db = -2/m * np.sum(y - y_pred)\n",
    "            self.w -= self.lr * dw\n",
    "            self.b -= self.lr * db\n",
    "\n",
    "    def coeff(self):\n",
    "        return (self.w, self.b)\n",
    "    \n",
    "    def predict(self,X):\n",
    "        return np.dot(X,self.w) + self.b\n",
    "    \n",
    "lin_reg = mylinreg()\n",
    "lin_reg.fit(X.reshape(97,1), y.reshape(97,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "33315beb",
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    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_line = np.linspace(df['X'].min(), df['X'].max(), 97)\n",
    "# X_line.shapey\n",
    "# y_pred = lin_reg.predict(X_line.reshape(97,1))\n",
    "plt.plot(X_line,  lin_reg.predict(X_line.reshape(97,1)), color='red', label='Reg from Scratch')\n",
    "plt.plot(X_line,  model.predict(X_line.reshape(97,1)), color='green', label='Reg from GPT')\n",
    "plt.scatter(X, y, color='blue', label='Data points')\n",
    "\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('y')\n",
    "plt.title('Linear Regression Fit')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d28900e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T02:54:29.535155Z",
     "iopub.status.busy": "2024-07-18T02:54:29.534369Z",
     "iopub.status.idle": "2024-07-18T02:54:29.541778Z",
     "shell.execute_reply": "2024-07-18T02:54:29.540806Z"
    },
    "papermill": {
     "duration": 0.018281,
     "end_time": "2024-07-18T02:54:29.543809",
     "exception": false,
     "start_time": "2024-07-18T02:54:29.525528",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.91801662]]), array([-1.15822659]))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg.coeff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e2bed6e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-18T02:54:29.560640Z",
     "iopub.status.busy": "2024-07-18T02:54:29.560262Z",
     "iopub.status.idle": "2024-07-18T02:54:29.566928Z",
     "shell.execute_reply": "2024-07-18T02:54:29.565983Z"
    },
    "papermill": {
     "duration": 0.017579,
     "end_time": "2024-07-18T02:54:29.568989",
     "exception": false,
     "start_time": "2024-07-18T02:54:29.551410",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[1.18238203]]), array([-3.78975339]))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coeff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "471e7768",
   "metadata": {
    "papermill": {
     "duration": 0.007373,
     "end_time": "2024-07-18T02:54:29.584099",
     "exception": false,
     "start_time": "2024-07-18T02:54:29.576726",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "datasetId": 96201,
     "sourceId": 224801,
     "sourceType": "datasetVersion"
    }
   ],
   "isGpuEnabled": false,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "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": "3.10.13"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 4.923551,
   "end_time": "2024-07-18T02:54:30.011582",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-07-18T02:54:25.088031",
   "version": "2.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}