1950 lines
1.6 MiB
1950 lines
1.6 MiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"# CSC 3105 Project"
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],
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"metadata": {
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"collapsed": false
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},
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"id": "cda961ffb493d00c"
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Load and Clean the Data\n",
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"\n",
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"This code block performs the following operations:\n",
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"\n",
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"1. Imports necessary libraries for data handling and cleaning.\n",
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"2. Defines a function `load_data` to load the data from a given directory into a pandas dataframe.\n",
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"3. Defines a function `clean_data` to clean the loaded data. The cleaning process includes:\n",
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" - Handling missing values by dropping them.\n",
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" - Removing duplicate rows.\n",
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" - Converting the 'NLOS' column to integer data type.\n",
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" - Normalizing the 'Measured range (time of flight)' column.\n",
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" - Creating new features 'FP_SUM' and 'SNR'.\n",
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" - One-hot encoding categorical features.\n",
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" - Performing feature extraction on 'CIR' columns.\n",
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" - Dropping the original 'CIR' columns.\n",
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" - Checking for columns with only one unique value and dropping them.\n",
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"4. Checks if a pickle file with the cleaned data exists. If it does, it loads the data from the file. If it doesn't, it loads and cleans the data using the defined functions.\n",
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"5. Prints the first few rows of the cleaned data and its column headers."
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],
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"metadata": {
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"collapsed": false
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},
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"id": "73fe8802e95a784f"
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},
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{
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"cell_type": "code",
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"outputs": [],
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"source": [
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"import os\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"from sklearn.decomposition import PCA\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn.metrics import accuracy_score, classification_report\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"import pickle\n",
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"from sklearn.model_selection import cross_val_score\n",
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"from sklearn.metrics import roc_curve, auc\n",
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"import tensorflow as tf\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pywt\n",
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"from skimage import restoration\n",
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"from tensorflow.keras.utils import to_categorical\n"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-03-15T14:42:29.201267Z",
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"start_time": "2024-03-15T14:42:29.196875Z"
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}
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},
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"id": "2aa3c6c09e8645d1",
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"execution_count": 40
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},
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{
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"cell_type": "code",
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"outputs": [],
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"source": [
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"# Define the directory where the dataset is located\n",
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"DATASET_DIR = './UWB-LOS-NLOS-Data-Set/dataset'\n",
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"\n",
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"def load_data(dataset_dir):\n",
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" # Load the data\n",
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" # file_paths = [os.path.join(dirpath, file) for dirpath, _, filenames in os.walk(dataset_dir) for file in filenames if 'uwb_dataset_part7.csv' not in file]\n",
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" file_paths = [os.path.join(dirpath, file) for dirpath, _, filenames in os.walk(dataset_dir) for file in filenames]\n",
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" data = pd.concat((pd.read_csv(file_path) for file_path in file_paths))\n",
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" print(f\"Original data shape: {data.shape}\")\n",
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" return data\n"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-03-15T14:42:29.522520Z",
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"start_time": "2024-03-15T14:42:29.518205Z"
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}
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},
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"id": "7bcd7cfc8dd11cbb",
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"execution_count": 41
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},
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{
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"cell_type": "code",
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"outputs": [],
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"source": [
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"def stat_analysis_and_plots(data):\n",
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" # Statistical Analysis\n",
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" print(\"Statistical Analysis:\")\n",
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" print(data.describe())\n",
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"\n",
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" # Plot Boxplot to check for outliers for the first 15 columns\n",
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" print(\"Boxplot of the first 15 columns:\")\n",
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" fig, axs = plt.subplots(15,1,dpi=95, figsize=(7,17))\n",
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" for i, col in enumerate(data.columns[:15]):\n",
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" axs[i].boxplot(data[col], vert=False)\n",
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" axs[i].set_ylabel(col)\n",
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" plt.show()\n"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-03-15T14:42:29.710949Z",
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"start_time": "2024-03-15T14:42:29.706865Z"
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}
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},
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"id": "9e0b1ed6f23a17cf",
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"execution_count": 42
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Channel Impulse Response (CIR) Graphs\n",
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"\n",
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"This code block defines a function `cir_graphs` to plot the Channel Impulse Response (CIR) for Line of Sight (LOS) and Non-Line of Sight (NLOS) data. The CIR is a sequence of values representing the channel response to a single impulse. It is used to characterize the channel in wireless communication systems."
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],
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"metadata": {
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"collapsed": false
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},
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"id": "1dd92fe7b6881ea6"
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},
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{
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"cell_type": "code",
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"outputs": [],
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"source": [
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"def cir_graphs(data):\n",
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" # Separate the data into LOS and NLOS\n",
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" data_los = data[data['NLOS'] == 0]\n",
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" data_nlos = data[data['NLOS'] == 1]\n",
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"\n",
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" # Extract the CIR columns\n",
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" cir_columns = [col for col in data.columns if 'CIR' in col]\n",
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" data_los_cir = data_los[cir_columns]\n",
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" data_nlos_cir = data_nlos[cir_columns]\n",
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"\n",
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" # Calculate the magnitude and time for each CIR column\n",
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" time_los = np.arange(len(data_los_cir.columns))\n",
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" magnitude_los = np.linalg.norm(data_los_cir.values, axis=0)\n",
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"\n",
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" time_nlos = np.arange(len(data_nlos_cir.columns))\n",
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" magnitude_nlos = np.linalg.norm(data_nlos_cir.values, axis=0)\n",
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"\n",
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" # Plot the magnitude vs time for LOS\n",
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" plt.figure(figsize=(20, 10), dpi=300) # Increase figure size and DPI\n",
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" plt.plot(time_los, magnitude_los, linewidth=2) # Increase line width\n",
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" plt.title('Magnitude vs Time for LOS')\n",
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" plt.xlabel('Time (ns)')\n",
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" plt.ylabel('Magnitude')\n",
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" plt.xlim([600, max(time_los)]) # Set x-axis limits\n",
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" plt.show()\n",
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"\n",
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" # Plot the magnitude vs time for NLOS\n",
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" plt.figure(figsize=(20, 10), dpi=300) # Increase figure size and DPI\n",
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" plt.plot(time_nlos, magnitude_nlos, linewidth=2) # Increase line width\n",
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" plt.title('Magnitude vs Time for NLOS')\n",
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" plt.xlabel('Time (ns)')\n",
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" plt.ylabel('Magnitude')\n",
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" plt.xlim([600, max(time_los)]) # Set x-axis limits\n",
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" plt.show()\n",
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" "
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-03-15T14:42:29.803776Z",
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"start_time": "2024-03-15T14:42:29.797272Z"
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}
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},
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"id": "308d64639b199bc7",
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"execution_count": 43
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Signal to Noise Ratio (SNR) Graph\n",
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"\n",
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"This code block defines a function `snr_graph` to plot the Signal to Noise Ratio (SNR) for Line of Sight (LOS) and Non-Line of Sight (NLOS) data. The SNR is calculated as the ratio of the 'CIR_PWR' to the 'STDEV_NOISE' for each data point."
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],
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"metadata": {
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"collapsed": false
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},
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"id": "bfd97fbe797a7067"
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},
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{
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"cell_type": "code",
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"outputs": [],
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"source": [
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"\n",
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"def snr_graph(data):\n",
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" # Separate the data into LOS and NLOS\n",
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" data_los = data[data['NLOS'] == 0]\n",
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" data_nlos = data[data['NLOS'] == 1]\n",
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"\n",
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" # Extract the SNR values\n",
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" snr_los = data_los['SNR']\n",
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" snr_nlos = data_nlos['SNR']\n",
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"\n",
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" # Create a new figure\n",
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" plt.figure(figsize=(10, 5))\n",
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"\n",
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" # Plot SNR for LOS\n",
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" plt.plot(snr_los, label='LOS')\n",
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"\n",
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" # Plot SNR for NLOS\n",
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" plt.plot(snr_nlos, color='red', label='NLOS')\n",
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"\n",
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" # Set title and labels\n",
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" plt.title('SNR for LOS and NLOS')\n",
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" plt.xlabel('Index')\n",
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" plt.ylabel('SNR')\n",
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"\n",
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" # Add a legend\n",
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" plt.legend()\n",
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"\n",
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" # Show the plot\n",
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" plt.show()\n",
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" "
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-03-15T14:42:29.842232Z",
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"start_time": "2024-03-15T14:42:29.837581Z"
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}
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},
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"id": "4afc8d71b3271351",
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"execution_count": 44
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},
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{
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"cell_type": "markdown",
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"source": [
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"## denoise_cir Function\n",
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"\n",
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"The `denoise_cir` function uses the Discrete Wavelet Transform (DWT) to denoise the Channel Impulse Response (CIR) values. The DWT is a linear transformation that operates on a data vector whose length is an integer power of two, transforming it into a numerically different vector of the same length. The DWT of a signal `x` is calculated as follows:\n",
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"\n",
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"1. **Wavelet Decomposition:**\n",
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"\n",
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" The input signal `x` is passed through two complementary filters and emerges as two signals. The filter outputs are decimated by 2 (down-sampled) to get the approximation coefficients (cA) and detail coefficients (cD).\n",
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"\n",
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" The approximation coefficients represent the high-scale, low-frequency component of the signal, while the detail coefficients represent the low-scale, high-frequency component.\n",
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"\n",
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"2. **Thresholding:**\n",
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"\n",
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" The detail coefficients are thresholded to remove noise. The thresholding function `T` applied to the detail coefficients `x` is defined as:\n",
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"$$\n",
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"T(x) = x * I(|x| > \\text{{value}}) \\quad \\text{{for 'hard' thresholding}}\n",
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"$$\n",
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"\n",
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"$$\n",
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"T(x) = \\text{{sign}}(x)(|x| - \\text{{value}})_+ \\quad \\text{{for 'soft' thresholding}}\n",
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"$$\n",
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"\n",
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"where $I$ is the indicator function that is one if the argument is true and zero otherwise, $\\text{{value}}$ is the threshold value, and $(x)_+$ equals $x$ if $x > 0$ and zero otherwise.\n",
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"\n",
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"3. **Wavelet Reconstruction:**\n",
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"\n",
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" The original signal is reconstructed from the approximation and detail coefficients."
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],
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"metadata": {
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"collapsed": false
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},
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"id": "69413268ac5b549d"
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},
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{
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"cell_type": "code",
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"outputs": [],
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"source": [
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"def denoise_cir(cir_values, wavelet='db1', level=1):\n",
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" # Perform wavelet decomposition\n",
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" coeffs = pywt.wavedec(cir_values, wavelet, level=level)\n",
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"\n",
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" # Set the detail coefficients to zero\n",
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" for i in range(1, len(coeffs)):\n",
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" coeffs[i] = pywt.threshold(coeffs[i], value=0.5, mode='soft')\n",
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"\n",
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" # Perform wavelet reconstruction\n",
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" denoised_cir = pywt.waverec(coeffs, wavelet)\n",
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"\n",
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" return denoised_cir\n"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-03-15T14:42:29.917731Z",
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"start_time": "2024-03-15T14:42:29.913912Z"
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}
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},
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"id": "fe3089568e99a58d",
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"execution_count": 45
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},
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{
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"cell_type": "markdown",
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"source": [
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"## deconvolve_cir Function\n",
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"\n",
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"The `deconvolve_cir` function applies the Richardson-Lucy deconvolution algorithm to deconvolve the Channel Impulse Response (CIR) values. \n",
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"\n",
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"In the context of signal processing, deconvolution is the process of reversing the effects of convolution on a signal. Convolution is a mathematical operation that blends two functions together and is often used to describe the effect of a linear time-invariant system on a signal. Deconvolution, therefore, attempts to recover the original signal that was convolved with the system's impulse response to produce the current signal.\n",
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"\n",
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"The Richardson-Lucy algorithm is an iterative method for deconvolution. It is particularly suitable for cases where the impulse response of the system (also known as the Point Spread Function, or PSF) is known, and the noise is Poissonian (such as in astronomical images). \n",
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"\n",
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"The algorithm works by iteratively refining an estimate of the original signal. In each iteration, it performs a prediction step where it convolves the current estimate with the PSF to predict the observed signal, and a correction step where it computes the ratio of the observed signal to the predicted signal, convolves this ratio with the PSF, and multiplies the result with the current estimate to get the next estimate.\n",
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"\n",
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"This process is repeated for a fixed number of iterations, or until the estimate converges to a stable solution. The result is a denoised estimate of the original signal.\n",
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"\n",
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"The mathematical formulas involved in the Richardson-Lucy deconvolution algorithm are as follows:\n",
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"\n",
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"1. **Prediction Step:**\n",
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"\n",
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" The current estimate of the latent image `x` is convolved with the PSF `h` to predict the observed image `y`. This can be represented as:\n",
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"\n",
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" $$\n",
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" y = h \\ast x\n",
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" $$\n",
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"\n",
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" where $\\ast$ denotes the convolution operation.\n",
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"\n",
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"2. **Correction Step:**\n",
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"\n",
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"The ratio of the observed image $y$ to the predicted image $y'$ is computed, then the PSF $h$ is convolved with this ratio and multiplied with the current estimate $x$ to get the next estimate $x'$. This can be represented as:\n",
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"\n",
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"$$\n",
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"x' = x \\cdot (h \\ast \\left(\\frac{y}{y'}\\right))\n",
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"$$\n",
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"\n",
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"where $\\div$ denotes element-wise division and $\\ast$ denotes the convolution operation.\n"
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],
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"metadata": {
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"collapsed": false
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|
},
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"id": "e1edd5ef4f54e752"
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},
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{
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"cell_type": "code",
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"outputs": [],
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"source": [
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"\n",
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"def deconvolve_cir(cir_values, psf=None, iterations=50):\n",
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" # If no point spread function is provided, create a simple one\n",
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" if psf is None:\n",
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" psf = np.ones((5,)) / 5\n",
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"\n",
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" # Perform Richardson-Lucy deconvolution\n",
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" deconvolved_cir = restoration.richardson_lucy(cir_values, psf, num_iter=iterations)\n",
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"\n",
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" return deconvolved_cir"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-03-15T14:42:29.971472Z",
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"start_time": "2024-03-15T14:42:29.967730Z"
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}
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},
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"id": "670e8c2cf19126ea",
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"execution_count": 46
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},
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{
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"cell_type": "code",
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"outputs": [],
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"source": [
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"\n",
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"def clean_data(data):\n",
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" print(\"Starting data cleaning process...\")\n",
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" \n",
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" # print(\"Before Cleaning\")\n",
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" # stat_analysis_and_plots(data)\n",
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"\n",
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" # Calculate total number of missing values in the data\n",
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" total_missing = data.isnull().sum().sum()\n",
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" print(f\"Total number of missing values: {total_missing}\")\n",
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"\n",
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" # Data has no missing values\n",
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" data = data.dropna()\n",
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" print(\"Missing values dropped.\")\n",
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"\n",
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" # Data has no duplicate rows\n",
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" data = data.drop_duplicates()\n",
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" print(\"Duplicate rows dropped.\")\n",
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"\n",
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" # Convert 'NLOS' column to integer data type (0 for LOS, 1 for NLOS)\n",
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" data['NLOS'] = data['NLOS'].astype(int)\n",
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" print(\"'NLOS' column converted to integer data type.\")\n",
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" \n",
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" # Print line where CIR_PWR is 0\n",
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" print(f\"Line where CIR_PWR is 0: {data[data['CIR_PWR'] == 0]}\")\n",
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" \n",
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" # Calculate the expression inside the log10 function\n",
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" expression = (data['CIR_PWR'] * (2**17)) / (data['RXPACC']**2)\n",
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"\n",
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" # If the expression is 0, set 'RX_Level' to 0\n",
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" zero_indices = expression == 0\n",
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" data.loc[zero_indices, 'RX_Level'] = 0\n",
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"\n",
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" # For the rest of the data where the expression is not 0, calculate 'RX_Level'\n",
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" # First, update the 'expression' and 'data' to exclude zero_indices\n",
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" expression = expression.loc[~zero_indices]\n",
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" data = data.loc[~zero_indices]\n",
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"\n",
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" # Now, calculate 'RX_Level' for the rest of the data\n",
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" data['RX_Level'] = 10 * np.log10(expression) - data['PRFR']\n",
|
|
"\n",
|
|
" # Calculate the median of 'RX_Level'\n",
|
|
" median = data['RX_Level'].median()\n",
|
|
"\n",
|
|
" # Create the boolean mask on the same DataFrame 'data'\n",
|
|
" zero_indices = (data['RX_Level'] == 0)\n",
|
|
"\n",
|
|
" # Replace zero values in 'RX_Level' with the median\n",
|
|
" data.loc[zero_indices, 'RX_Level'] = median\n",
|
|
"\n",
|
|
" print(\"New feature 'RX_Level' created.\")\n",
|
|
"\n",
|
|
" # Calculate new feature 'First_Path_Power_Level'\n",
|
|
" data['First_Path_Power_Level'] = (10 * np.log10(\n",
|
|
" (data['FP_AMP1'] ** 2 + data['FP_AMP2'] ** 2 + data['FP_AMP3'] ** 2) / (data['RXPACC'] ** 2))) - 64\n",
|
|
" print(\"New feature 'First_Path_Power_Level' calculated.\")\n",
|
|
" data.drop(['FP_AMP1', 'FP_AMP2', 'FP_AMP3', 'RXPACC', 'PRFR'], axis=1, inplace=True)\n",
|
|
"\n",
|
|
" # Calculate SNR as the ratio of 'CIR_PWR' to 'STDEV_NOISE' for each data point\n",
|
|
" data['SNR'] = data['CIR_PWR'] / data['STDEV_NOISE']\n",
|
|
" print(\"New feature 'SNR' created.\")\n",
|
|
" data.drop(['CIR_PWR', 'STDEV_NOISE'], axis=1, inplace=True)\n",
|
|
"\n",
|
|
" # One-hot encode categorical features\n",
|
|
" categorical_features = ['CH', 'FRAME_LEN', 'PREAM_LEN', 'BITRATE']\n",
|
|
" encoder = LabelEncoder()\n",
|
|
" for feature in categorical_features:\n",
|
|
" data[feature] = encoder.fit_transform(data[feature])\n",
|
|
" print(\"Categorical features one-hot encoded.\")\n",
|
|
"\n",
|
|
" # Extract the 'CIR' columns\n",
|
|
" cir_columns = [col for col in data.columns if 'CIR' in col]\n",
|
|
" cir_data = data[cir_columns] \n",
|
|
" print(\"'CIR' columns extracted.\")\n",
|
|
" \n",
|
|
" # Convert 'CIR' columns to float\n",
|
|
" cir_data = cir_data.astype(float)\n",
|
|
" print(\"'CIR' columns converted to float.\")\n",
|
|
" \n",
|
|
" # Denoise 'CIR' columns\n",
|
|
" # denoised_cir_data = cir_data.apply(denoise_cir)\n",
|
|
" denoised_cir_data = cir_data.apply(deconvolve_cir)\n",
|
|
" print(\"'CIR' columns denoised.\")\n",
|
|
" \n",
|
|
" # Replace original 'CIR' columns with denoised data\n",
|
|
" data[cir_columns] = denoised_cir_data\n",
|
|
" print(\"Original 'CIR' columns replaced with denoised data.\")\n",
|
|
" \n",
|
|
" # Extract the 'CIR' columns\n",
|
|
" # cir_columns = [f\"CIR{i}\" for i in range(1015)]\n",
|
|
" # # cir_columns = [f\"CIR{i}\" for i in range(1016)]\n",
|
|
" # cir_data = data[cir_columns]\n",
|
|
" # print(\"'CIR' columns extracted.\")\n",
|
|
" # \n",
|
|
" # # Perform PCA on the 'CIR' columns\n",
|
|
" # pca = PCA(n_components=0.95)\n",
|
|
" # cir_pca = pca.fit_transform(cir_data)\n",
|
|
" # print(\"PCA performed on 'CIR' columns.\")\n",
|
|
" # \n",
|
|
" # # Create a DataFrame with the principal components\n",
|
|
" # cir_pca_df = pd.DataFrame(cir_pca, columns=[f\"PC{i}\" for i in range(1, pca.n_components_ + 1)])\n",
|
|
" # print(\"DataFrame with principal components created.\")\n",
|
|
" # \n",
|
|
" # # Drop the original 'CIR' columns from the data\n",
|
|
" # data = data.drop(columns=cir_columns)\n",
|
|
" # print(\"Original 'CIR' columns dropped.\")\n",
|
|
"\n",
|
|
" # Add the principal components to the original data\n",
|
|
" # Reset the index of both dataframes\n",
|
|
" # data = data.reset_index(drop=True)\n",
|
|
" # cir_pca_df = cir_pca_df.reset_index(drop=True)\n",
|
|
" # print(\"Indexes of both dataframes reset.\")\n",
|
|
"\n",
|
|
" # Concatenate the dataframes\n",
|
|
" # data = pd.concat([data, cir_pca_df], axis=1)\n",
|
|
" # print(\"Dataframes concatenated.\")\n",
|
|
"\n",
|
|
" # List of columns to check for unique values\n",
|
|
" columns_to_check = ['CH', 'PREAM_LEN', 'BITRATE']\n",
|
|
"\n",
|
|
" # Iterate over the columns\n",
|
|
" for column in columns_to_check:\n",
|
|
" # If the column has only one unique value, drop it\n",
|
|
" if data[column].nunique() == 1:\n",
|
|
" data = data.drop(column, axis=1)\n",
|
|
" print(f\"Column '{column}' dropped due to having only one unique value.\")\n",
|
|
"\n",
|
|
" # Standardize the numerical columns (excluding 'NLOS')\n",
|
|
" # numerical_cols = data.select_dtypes(include=[np.number]).columns\n",
|
|
" # numerical_cols = numerical_cols.drop('NLOS')\n",
|
|
" # scaler = StandardScaler()\n",
|
|
" # data[numerical_cols] = scaler.fit_transform(data[numerical_cols])\n",
|
|
" # print(\"Numerical columns standardized.\")\n",
|
|
"\n",
|
|
" # Print the shape of the cleaned data\n",
|
|
" print(f\"Cleaned data shape: {data.shape}\")\n",
|
|
"\n",
|
|
" # print(\"After Cleaning\")\n",
|
|
" # stat_analysis_and_plots(data)\n",
|
|
" \n",
|
|
" print(\"Data cleaning process completed.\")\n",
|
|
" \n",
|
|
" # Return the cleaned data\n",
|
|
" return data"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:42:30.068935Z",
|
|
"start_time": "2024-03-15T14:42:30.055531Z"
|
|
}
|
|
},
|
|
"id": "685463c2d6065b08",
|
|
"execution_count": 47
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Pickle file not found. Loading and cleaning data...\n",
|
|
"Original data shape: (42000, 1031)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 6000x3000 with 1 Axes>",
|
|
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": "<Figure size 6000x3000 with 1 Axes>",
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"image/png": 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AAAAAAAAAAAAAAACySAEwAAAAAAAAAAAAAAAAAAAAAAAAyCIFwAAAAAAAAAAAAAAAAAAAAAAAACCLFAADAAAAAAAAAAAAAAAAAAAAAACALFIADAAAAAAAAAAAAAAAAAAAAAAAALJIATAAAAAAAAAAAAAAAAAAAAAAAADIIgXAAAAAAAAAAAAAAAAAAAAAAAAAIIsUAAMAAAAAAAAAAAAAAAAAAAAAAIAsUgAMAAAAAAAAAAAAAAAAAAAAAAAAskgBMAAAAAAAAAAAAAAAAAAAAAAAAMgiBcAAAAAAAAAAAAAAAAAAAAAAAAAgixQAAwAAAAAAAAAAAAAAAAAAAAAAgCxSAAwAAAAAAAAAAAAAAAAAAAAAAACySAEwAAAAAAAAAAAAAAAAAAAAAAAAyCIFwAAAAAAAAAAAAAAAAAAAAAAAACCL/n8kxhHOMl49PwAAAABJRU5ErkJggg=="
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Starting data cleaning process...\n",
|
|
"Total number of missing values: 0\n",
|
|
"Missing values dropped.\n",
|
|
"Duplicate rows dropped.\n",
|
|
"'NLOS' column converted to integer data type.\n",
|
|
"Line where CIR_PWR is 0: NLOS RANGE FP_IDX FP_AMP1 FP_AMP2 FP_AMP3 STDEV_NOISE CIR_PWR \\\n",
|
|
"4343 1 7.02 757.0 30.0 214.0 413.0 36.0 0.0 \n",
|
|
"837 1 4.88 739.0 112.0 323.0 227.0 40.0 0.0 \n",
|
|
"1356 1 6.33 747.0 293.0 311.0 187.0 28.0 0.0 \n",
|
|
"\n",
|
|
" MAX_NOISE RXPACC ... CIR1006 CIR1007 CIR1008 CIR1009 CIR1010 \\\n",
|
|
"4343 412.0 192.0 ... 252.0 271.0 190.0 292.0 271.0 \n",
|
|
"837 322.0 128.0 ... 161.0 219.0 295.0 242.0 279.0 \n",
|
|
"1356 310.0 160.0 ... 197.0 84.0 246.0 353.0 196.0 \n",
|
|
"\n",
|
|
" CIR1011 CIR1012 CIR1013 CIR1014 CIR1015 \n",
|
|
"4343 239.0 210.0 260.0 223.0 256.0 \n",
|
|
"837 67.0 153.0 177.0 159.0 0.0 \n",
|
|
"1356 38.0 228.0 42.0 173.0 0.0 \n",
|
|
"\n",
|
|
"[3 rows x 1031 columns]\n",
|
|
"New feature 'RX_Level' created.\n",
|
|
"New feature 'First_Path_Power_Level' calculated.\n",
|
|
"New feature 'SNR' created.\n",
|
|
"Categorical features one-hot encoded.\n",
|
|
"'CIR' columns extracted.\n",
|
|
"'CIR' columns converted to float.\n",
|
|
"'CIR' columns denoised.\n",
|
|
"Original 'CIR' columns replaced with denoised data.\n",
|
|
"Column 'CH' dropped due to having only one unique value.\n",
|
|
"Column 'BITRATE' dropped due to having only one unique value.\n",
|
|
"Cleaned data shape: (41997, 1025)\n",
|
|
"Data cleaning process completed.\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 1000x500 with 1 Axes>",
|
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"image/png": 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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": "<Figure size 6000x3000 with 1 Axes>",
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": "<Figure size 6000x3000 with 1 Axes>",
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|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Data loaded and cleaned successfully.\n",
|
|
"Saving cleaned data to pickle file...\n",
|
|
"Cleaned data saved to pickle file successfully.\n",
|
|
"First few rows of the data:\n",
|
|
" NLOS RANGE FP_IDX MAX_NOISE FRAME_LEN PREAM_LEN CIR0 CIR1 \\\n",
|
|
"0 1 6.18 749.0 3668.0 2 0 0.056048 0.000384 \n",
|
|
"1 1 4.54 741.0 1031.0 2 0 1.000000 1.000000 \n",
|
|
"2 1 4.39 744.0 796.0 0 0 1.000000 1.000000 \n",
|
|
"3 1 1.27 748.0 1529.0 2 0 0.137383 1.000000 \n",
|
|
"4 0 1.16 743.0 2022.0 0 0 1.000000 0.110710 \n",
|
|
"\n",
|
|
" CIR2 CIR3 ... CIR1009 CIR1010 CIR1011 CIR1012 CIR1013 \\\n",
|
|
"0 0.034555 0.000004 ... 0.001883 0.128650 0.008152 0.397881 0.001797 \n",
|
|
"1 1.000000 1.000000 ... 0.121082 1.000000 1.000000 1.000000 1.000000 \n",
|
|
"2 1.000000 1.000000 ... 1.000000 1.000000 1.000000 1.000000 1.000000 \n",
|
|
"3 0.000008 1.000000 ... 1.000000 0.021075 1.000000 0.000044 0.001702 \n",
|
|
"4 0.000015 1.000000 ... 0.000999 0.617535 0.083051 0.268258 1.000000 \n",
|
|
"\n",
|
|
" CIR1014 CIR1015 RX_Level First_Path_Power_Level SNR \n",
|
|
"0 1.756536e-07 1.0 -33.465374 -39.074609 37.700000 \n",
|
|
"1 1.000000e+00 1.0 -34.892422 -53.179491 95.794118 \n",
|
|
"2 1.000000e+00 1.0 -38.436975 -49.812107 55.384615 \n",
|
|
"3 1.095337e-03 1.0 -21.918230 -25.863535 200.859375 \n",
|
|
"4 1.000000e+00 1.0 -21.603535 -23.232193 152.723684 \n",
|
|
"\n",
|
|
"[5 rows x 1025 columns]\n",
|
|
"Column headers:\n",
|
|
"Index(['NLOS', 'RANGE', 'FP_IDX', 'MAX_NOISE', 'FRAME_LEN', 'PREAM_LEN',\n",
|
|
" 'CIR0', 'CIR1', 'CIR2', 'CIR3',\n",
|
|
" ...\n",
|
|
" 'CIR1009', 'CIR1010', 'CIR1011', 'CIR1012', 'CIR1013', 'CIR1014',\n",
|
|
" 'CIR1015', 'RX_Level', 'First_Path_Power_Level', 'SNR'],\n",
|
|
" dtype='object', length=1025)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import pickle\n",
|
|
"\n",
|
|
"# File='data_original.pkl'\n",
|
|
"File = 'data.pkl'\n",
|
|
"\n",
|
|
"# Check if the file exists\n",
|
|
"if os.path.exists(File):\n",
|
|
" # If the file exists, load it\n",
|
|
" print(\"Loading data from pickle file...\")\n",
|
|
" with open(File, 'rb') as f:\n",
|
|
" data = pickle.load(f)\n",
|
|
" print(\"Data loaded successfully.\")\n",
|
|
"else:\n",
|
|
" # If the file doesn't exist, load and clean the data\n",
|
|
" print(\"Pickle file not found. Loading and cleaning data...\")\n",
|
|
" data = load_data(DATASET_DIR)\n",
|
|
" cir_graphs(data)\n",
|
|
" data = clean_data(data)\n",
|
|
" snr_graph(data)\n",
|
|
" cir_graphs(data)\n",
|
|
" print(\"Data loaded and cleaned successfully.\")\n",
|
|
" print(\"Saving cleaned data to pickle file...\")\n",
|
|
" # with open(File, 'wb') as f:\n",
|
|
" # pickle.dump(data, f)\n",
|
|
" print(\"Cleaned data saved to pickle file successfully.\")\n",
|
|
"\n",
|
|
"print(\"First few rows of the data:\")\n",
|
|
"print(data.head())\n",
|
|
"\n",
|
|
"# Print Headers\n",
|
|
"print(\"Column headers:\")\n",
|
|
"print(data.columns)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.406940Z",
|
|
"start_time": "2024-03-15T14:42:30.152556Z"
|
|
}
|
|
},
|
|
"id": "79c2c23691b26753",
|
|
"execution_count": 48
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"MODEL_DIR = './models'\n",
|
|
"\n",
|
|
"\n",
|
|
"def train_and_save_model(classifier, X_train, y_train, file_name):\n",
|
|
" if not os.path.exists(MODEL_DIR):\n",
|
|
" os.makedirs(MODEL_DIR)\n",
|
|
"\n",
|
|
" file_path = os.path.join(MODEL_DIR, file_name)\n",
|
|
"\n",
|
|
" # Check if the file exists\n",
|
|
" if not os.path.exists(file_path):\n",
|
|
" print(f\"Training the model and saving it to {file_path}\")\n",
|
|
" # Train the classifier\n",
|
|
" classifier.fit(X_train, y_train)\n",
|
|
"\n",
|
|
" # Save the trained model as a pickle string.\n",
|
|
" saved_model = pickle.dumps(classifier)\n",
|
|
"\n",
|
|
" # Save the pickled model to a file\n",
|
|
" with open(file_path, 'wb') as file:\n",
|
|
" file.write(saved_model)\n",
|
|
"\n",
|
|
" # Load the pickled model from the file\n",
|
|
" with open(file_path, 'rb') as file:\n",
|
|
" loaded_model = pickle.load(file)\n",
|
|
"\n",
|
|
" return loaded_model"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.413348Z",
|
|
"start_time": "2024-03-15T14:43:01.408298Z"
|
|
}
|
|
},
|
|
"id": "12e16974341e6266",
|
|
"execution_count": 49
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"The selected code is performing data standardization, which is a common preprocessing step in many machine learning workflows. \n",
|
|
"\n",
|
|
"The purpose of standardization is to transform the data such that it has a mean of 0 and a standard deviation of 1. This is done to ensure that all features have the same scale, which is a requirement for many machine learning algorithms.\n",
|
|
"\n",
|
|
"The mathematical formulas used in this process are as follows:\n",
|
|
"\n",
|
|
"1. Calculate the mean (μ) of the data:\n",
|
|
"\n",
|
|
"$$\n",
|
|
"\\mu = \\frac{1}{n} \\sum_{i=1}^{n} x_i\n",
|
|
"$$\n",
|
|
"Where:\n",
|
|
"- $n$ is the number of observations in the data\n",
|
|
"- $x_i$ is the value of the $i$-th observation\n",
|
|
"- $\\sum$ denotes the summation over all observations\n",
|
|
"\n",
|
|
"2. Standardize the data by subtracting the mean from each observation and dividing by the standard deviation:\n",
|
|
"\n",
|
|
"$$\n",
|
|
"\\text{Data}_i = \\frac{x_i - \\mu}{\\sigma}\n",
|
|
"$$\n",
|
|
"Where:\n",
|
|
"- $\\text{Data}_i$ is the standardized value of the $i$-th observation\n",
|
|
"- $\\sigma$ is the standard deviation of the data\n",
|
|
"- $x_i$ is the value of the $i$-th observation\n",
|
|
"- $\\mu$ is the mean of the data\n",
|
|
"\n",
|
|
"The `StandardScaler` class from the `sklearn.preprocessing` module is used to perform this standardization. The `fit_transform` method is used to calculate the mean and standard deviation of the data and then perform the standardization.\n",
|
|
"\n",
|
|
"**Note:** By setting the explained variance to 0.95, we are saying that we want to choose the smallest number of principal components such that 95% of the variance in the original data is retained. This means that the transformed data will retain 95% of the information of the original data, while potentially having fewer dimensions.\n"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"id": "b36814c942066d6"
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"## Data Mining / Machine Learning\n",
|
|
"\n",
|
|
"### I. Supervised Learning\n",
|
|
"- **Decision**: Supervised learning is used due to the labeled dataset.\n",
|
|
"- **Algorithm**: Random Forest Classifier is preferred for its performance in classification tasks.\n",
|
|
"\n",
|
|
"### II. Training/Test Split Ratio\n",
|
|
"- **Decision**: 70:30 split is chosen for training/test dataset.\n",
|
|
"- **Reasoning**: This split ensures sufficient data for training and testing.\n",
|
|
"\n",
|
|
"### III. Performance Metrics\n",
|
|
"- **Classification Accuracy**: Measures the proportion of correctly classified instances.\n",
|
|
"- **Confusion Matrix**: Provides a summary of predicted and actual classes.\n",
|
|
"- **Classification Report**: Provides detailed metrics such as precision, recall, F1-score, and support for each class.\n",
|
|
"\n",
|
|
"The Random Forest Classifier is trained on the training set and evaluated on the test set using accuracy and classification report metrics.\n"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"id": "8fefd253728ea2f0"
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"# Split the data into training and testing sets\n",
|
|
"\n",
|
|
"The next step is to split the data into training and testing sets. This is a common practice in machine learning, where the training set is used to train the model, and the testing set is used to evaluate its performance.\n",
|
|
"\n",
|
|
"We will use the `train_test_split` function from the `sklearn.model_selection` module to split the data into training and testing sets. We will use 70% of the data for training and 30% for testing, which is a common split ratio."
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"id": "7d64d6490fa1c2c2"
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn.model_selection import train_test_split\n",
|
|
"from tensorflow.keras.utils import to_categorical\n",
|
|
"\n",
|
|
"# Assuming 'NLOS' is your target column\n",
|
|
"y = data['NLOS']\n",
|
|
"\n",
|
|
"# Convert labels to categorical one-hot encoding\n",
|
|
"y_categorical = to_categorical(y, num_classes=2)\n",
|
|
"\n",
|
|
"# Now split the data\n",
|
|
"X_train, X_test, y_train, y_test = train_test_split(data, y_categorical, test_size=0.2)\n"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.704441Z",
|
|
"start_time": "2024-03-15T14:43:01.414632Z"
|
|
}
|
|
},
|
|
"id": "54d2a6506b584a03",
|
|
"execution_count": 50
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"# Train a Random Forest Classifier\n",
|
|
"\n",
|
|
"The next step is to train a machine learning model on the training data. We will use the `RandomForestClassifier` class from the `sklearn.ensemble` module to train a random forest classifier.\n",
|
|
"\n",
|
|
"The random forest classifier is an ensemble learning method that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.\n",
|
|
"\n",
|
|
"We will use the `fit` method of the `RandomForestClassifier` object to train the model on the training data."
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"id": "ab55160e30fd6f99"
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"# from sklearn.ensemble import RandomForestClassifier\n",
|
|
"# \n",
|
|
"# # Initialize the classifier with parameters to prevent overfitting\n",
|
|
"# classifier = RandomForestClassifier(n_estimators=200, max_depth=10, min_samples_split=10, min_samples_leaf=5, max_features='sqrt')\n",
|
|
"# \n",
|
|
"# loaded_model = train_and_save_model(classifier, X_train, y_train, 'random_forest_classifier.pkl')\n"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.709441Z",
|
|
"start_time": "2024-03-15T14:43:01.706945Z"
|
|
}
|
|
},
|
|
"id": "dc485f3de9f8936f",
|
|
"execution_count": 51
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"# Evaluate the Model\n",
|
|
"\n",
|
|
"To evaluate the performance of the trained model on the testing data, we will use the `predict` method of the `RandomForestClassifier` object to make predictions on the testing data. We will then use the `accuracy_score` and `classification_report` functions from the `sklearn.metrics` module to calculate the accuracy and generate a classification report.\n",
|
|
"\n",
|
|
"- **Accuracy:** The accuracy score function calculates the proportion of correctly classified instances.\n",
|
|
"\n",
|
|
"- **Precision:** The ratio of correctly predicted positive observations to the total predicted positive observations. It is calculated as:\n",
|
|
"\n",
|
|
" $$\n",
|
|
" \\text{Precision} = \\frac{\\text{True Positives}}{\\text{True Positives} + \\text{False Positives}}\n",
|
|
" $$\n",
|
|
"\n",
|
|
"- **Recall:** The ratio of correctly predicted positive observations to all observations in the actual class. It is calculated as:\n",
|
|
"\n",
|
|
" $$\n",
|
|
" \\text{Recall} = \\frac{\\text{True Positives}}{\\text{True Positives} + \\text{False Negatives}}\n",
|
|
" $$\n",
|
|
"\n",
|
|
"- **F1 Score:** The weighted average of precision and recall. It is calculated as:\n",
|
|
"\n",
|
|
" $$\n",
|
|
" \\text{F1 Score} = 2 \\times \\frac{\\text{Precision} \\times \\text{Recall}}{\\text{Precision} + \\text{Recall}}\n",
|
|
" $$\n",
|
|
"\n",
|
|
"- **Support:** The number of actual occurrences of the class in the dataset.\n",
|
|
"\n",
|
|
"The classification report provides a summary of the precision, recall, F1-score, and support for each class in the testing data, giving insight into how well the model is performing for each class.\n"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"id": "424cc5954c9e81cc"
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"\n",
|
|
"# Make predictions on the test set using the loaded model\n",
|
|
"# y_pred = loaded_model.predict(X_test)\n",
|
|
"# \n",
|
|
"# # Evaluate the loaded model\n",
|
|
"# accuracy = accuracy_score(y_test, y_pred)\n",
|
|
"# classification_rep = classification_report(y_test, y_pred)\n",
|
|
"# cross_val_score = cross_val_score(loaded_model, X_test, y_test, cv=5)\n",
|
|
"# \n",
|
|
"# print(f\"Accuracy: {accuracy}\")\n",
|
|
"# print(f\"Classification Report:\\n{classification_rep}\")\n",
|
|
"# print(f\"Cross Validation Score: {cross_val_score}\")\n"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.715112Z",
|
|
"start_time": "2024-03-15T14:43:01.710507Z"
|
|
}
|
|
},
|
|
"id": "702b4f40dda16736",
|
|
"execution_count": 52
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"# Visualize a Decision Tree from the Random Forest\n"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"id": "41957f9babb74a3"
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"# from sklearn.tree import plot_tree\n",
|
|
"# import matplotlib.pyplot as plt\n",
|
|
"# \n",
|
|
"# # Select one tree from the forest\n",
|
|
"# estimator = loaded_model.estimators_[0]\n",
|
|
"# \n",
|
|
"# plt.figure(figsize=(100, 100))\n",
|
|
"# plot_tree(estimator,\n",
|
|
"# filled=True,\n",
|
|
"# rounded=True,\n",
|
|
"# class_names=['NLOS', 'LOS'],\n",
|
|
"# feature_names=data.columns,\n",
|
|
"# max_depth=5) # Limit the depth of the tree\n",
|
|
"# plt.show()"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.718739Z",
|
|
"start_time": "2024-03-15T14:43:01.716188Z"
|
|
}
|
|
},
|
|
"id": "1f6f826d6234591c",
|
|
"execution_count": 53
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"# Support Vector Machine (SVM)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"id": "eef3be2c3026a909"
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"# import os\n",
|
|
"# from sklearn.svm import SVC\n",
|
|
"# import pickle\n",
|
|
"# \n",
|
|
"# svm = SVC(kernel='linear', random_state=42, verbose=True)\n",
|
|
"# loaded_model = train_and_save_model(svm, X_train, y_train, 'svm_classifier.pkl')\n",
|
|
"# \n",
|
|
"# # Predict the labels for the test set with each model\n",
|
|
"# y_pred_svm = loaded_model.predict(X_test)\n",
|
|
"# \n",
|
|
"# # Calculate the accuracy of each model\n",
|
|
"# accuracy_svm = accuracy_score(y_test, y_pred_svm)\n",
|
|
"# \n",
|
|
"# # Print the accuracy of each model\n",
|
|
"# print(f\"Accuracy of SVM: {accuracy_svm}\")"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.722535Z",
|
|
"start_time": "2024-03-15T14:43:01.719954Z"
|
|
}
|
|
},
|
|
"id": "c970b0c1593d955c",
|
|
"execution_count": 54
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"# Logistic Regression"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"id": "cccaf1db0d5060a8"
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"# from sklearn.linear_model import LogisticRegression\n",
|
|
"# from sklearn.model_selection import cross_val_score\n",
|
|
"# \n",
|
|
"# # Logistic Regression with L2 regularization\n",
|
|
"# log_reg = LogisticRegression(penalty='l2', C=0.1)\n",
|
|
"# \n",
|
|
"# # Use the train_and_save_model function to train and save the model\n",
|
|
"# loaded_model = train_and_save_model(log_reg, X_train, y_train, 'logistic_regression_model.pkl')"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.726792Z",
|
|
"start_time": "2024-03-15T14:43:01.723773Z"
|
|
}
|
|
},
|
|
"id": "ee7506f4aa805faf",
|
|
"execution_count": 55
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"\n",
|
|
"# # Predict on the test set\n",
|
|
"# y_pred_log_reg = loaded_model.predict(X_test)\n",
|
|
"# \n",
|
|
"# # Calculate accuracy\n",
|
|
"# accuracy_log_reg = accuracy_score(y_test, y_pred_log_reg)\n",
|
|
"# print(f\"Accuracy of Logistic Regression: {accuracy_log_reg}\")\n",
|
|
"# \n",
|
|
"# # Perform 5-fold cross validation\n",
|
|
"# scores = cross_val_score(log_reg, X_train, y_train, cv=5)\n",
|
|
"# print(f\"Cross-validated scores: {scores}\")"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.730508Z",
|
|
"start_time": "2024-03-15T14:43:01.727861Z"
|
|
}
|
|
},
|
|
"id": "a44d38efa4b86d93",
|
|
"execution_count": 56
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"# from sklearn.metrics import roc_curve, auc\n",
|
|
"# import matplotlib.pyplot as plt\n",
|
|
"# \n",
|
|
"# # Compute ROC curve and ROC area for each class\n",
|
|
"# fpr, tpr, _ = roc_curve(y_test, y_pred_log_reg)\n",
|
|
"# roc_auc = auc(fpr, tpr)\n",
|
|
"# \n",
|
|
"# plt.figure()\n",
|
|
"# lw = 2\n",
|
|
"# plt.plot(fpr, tpr, color='darkorange',\n",
|
|
"# lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
|
|
"# plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
|
|
"# plt.xlim([0.0, 1.0])\n",
|
|
"# plt.ylim([0.0, 1.05])\n",
|
|
"# plt.xlabel('False Positive Rate')\n",
|
|
"# plt.ylabel('True Positive Rate')\n",
|
|
"# plt.title('Receiver Operating Characteristic')\n",
|
|
"# plt.legend(loc=\"lower right\")\n",
|
|
"# plt.show()"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.735229Z",
|
|
"start_time": "2024-03-15T14:43:01.733038Z"
|
|
}
|
|
},
|
|
"id": "a3646a4965b0707c",
|
|
"execution_count": 57
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"# Gradient Boosting Classifier"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"id": "aeaf5eeffa7ec104"
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"# from sklearn.ensemble import GradientBoostingClassifier\n",
|
|
"# \n",
|
|
"# # Gradient Boosting Classifier\n",
|
|
"# gbc = GradientBoostingClassifier()\n",
|
|
"# \n",
|
|
"# # Use the train_and_save_model function to train and save the model\n",
|
|
"# loaded_model = train_and_save_model(gbc, X_train, y_train, 'gradient_boosting_classifier.pkl')\n",
|
|
"# "
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.738170Z",
|
|
"start_time": "2024-03-15T14:43:01.736166Z"
|
|
}
|
|
},
|
|
"id": "c7ecae5d021ad44f",
|
|
"execution_count": 58
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"# y_pred_gbc = loaded_model.predict(X_test)\n",
|
|
"# accuracy_gbc = accuracy_score(y_test, y_pred_gbc)\n",
|
|
"# print(f\"Accuracy of Gradient Boosting Classifier: {accuracy_gbc}\")\n",
|
|
"# "
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.741225Z",
|
|
"start_time": "2024-03-15T14:43:01.739060Z"
|
|
}
|
|
},
|
|
"id": "4a8a1c3a7289ef7a",
|
|
"execution_count": 59
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"# K-Nearest Neighbors (KNN, K=15)\n",
|
|
"\n",
|
|
"This code block is implementing the K-Nearest Neighbors (KNN) algorithm for classification. The KNN algorithm is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until function evaluation. \n",
|
|
"\n",
|
|
"The KNN algorithm works by finding the distances between a query and all the examples in the data, selecting the specified number examples (K) closest to the query, then votes for the most frequent label (in the case of classification) or averages the labels (in the case of regression). \n",
|
|
"\n",
|
|
"The number of neighbors, K, is set to 15 in this case. This means that the algorithm looks at the 15 nearest neighbors to decide the class of the test instance. \n",
|
|
"\n",
|
|
"The mathematical concept behind KNN is the Euclidean distance. Given two points P1(x1, y1) and P2(x2, y2) in a 2D space, the Euclidean distance between P1 and P2 is calculated as:\n",
|
|
"\n",
|
|
"$$\n",
|
|
"\\text{Distance} = \\sqrt{(x2 - x1)^2 + (y2 - y1)^2}\n",
|
|
"$$\n",
|
|
"In higher dimensional space, the formula is generalized as:\n",
|
|
"$$\n",
|
|
"\\text{Distance} = \\sqrt{\\sum_{i=1}^{n} (x_i - y_i)^2}\n",
|
|
"$$\n",
|
|
"Where:\n",
|
|
"- $n$ is the number of dimensions\n",
|
|
"- $x_i$ and $y_i$ are the $i$-th dimensions of the two points\n"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"id": "25102568a6e5c457"
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"# from sklearn.neighbors import KNeighborsClassifier\n",
|
|
"# \n",
|
|
"# # K-Nearest Neighbors\n",
|
|
"# knn = KNeighborsClassifier(n_neighbors=13)\n",
|
|
"# loaded_model = train_and_save_model(knn, X_train, y_train, 'knn_classifier.pkl')\n"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.744082Z",
|
|
"start_time": "2024-03-15T14:43:01.742124Z"
|
|
}
|
|
},
|
|
"id": "705c62e64bf6d614",
|
|
"execution_count": 60
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"# y_pred_knn = loaded_model.predict(X_test)\n",
|
|
"# accuracy_knn = accuracy_score(y_test, y_pred_knn)\n",
|
|
"# print(f\"Accuracy of K-Nearest Neighbors: {accuracy_knn}\")"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.746879Z",
|
|
"start_time": "2024-03-15T14:43:01.744957Z"
|
|
}
|
|
},
|
|
"id": "cf4df4ef7bbfd74",
|
|
"execution_count": 61
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"# from sklearn.model_selection import GridSearchCV\n",
|
|
"# \n",
|
|
"# # Define the parameter values that should be searched\n",
|
|
"# k_range = list(range(1, 31))\n",
|
|
"# \n",
|
|
"# # Create a parameter grid: map the parameter names to the values that should be searched\n",
|
|
"# param_grid = dict(n_neighbors=k_range)\n",
|
|
"# \n",
|
|
"# # Instantiate the grid\n",
|
|
"# grid = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy')\n",
|
|
"# \n",
|
|
"# # Fit the grid with data\n",
|
|
"# grid.fit(X_train, y_train)\n",
|
|
"# \n",
|
|
"# # View the complete results\n",
|
|
"# grid.cv_results_\n",
|
|
"# \n",
|
|
"# # Examine the best model\n",
|
|
"# print(grid.best_score_)\n",
|
|
"# print(grid.best_params_)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.750321Z",
|
|
"start_time": "2024-03-15T14:43:01.747941Z"
|
|
}
|
|
},
|
|
"id": "faabcf63e34005a9",
|
|
"execution_count": 62
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"# import matplotlib.pyplot as plt\n",
|
|
"# import numpy as np\n",
|
|
"# \n",
|
|
"# # Apply PCA to reduce dimensionality to 2D\n",
|
|
"# pca = PCA(n_components=2)\n",
|
|
"# X_test_2d = pca.fit_transform(X_test)\n",
|
|
"# \n",
|
|
"# # Print the number of features\n",
|
|
"# print(f\"Original number of features: {X_test.shape[1]}, reduced number of features: {X_test_2d.shape[1]}\")\n",
|
|
"# \n",
|
|
"# # Create a scatter plot\n",
|
|
"# plt.figure(figsize=(10, 7))\n",
|
|
"# \n",
|
|
"# # Create a color map\n",
|
|
"# cmap = plt.cm.viridis\n",
|
|
"# \n",
|
|
"# # Plot NLOS points\n",
|
|
"# nlos = plt.scatter(X_test_2d[y_pred_knn == 1, 0], X_test_2d[y_pred_knn == 1, 1], c='blue', label='NLOS')\n",
|
|
"# \n",
|
|
"# # Plot LOS points\n",
|
|
"# los = plt.scatter(X_test_2d[y_pred_knn == 0, 0], X_test_2d[y_pred_knn == 0, 1], c='red', label='LOS')\n",
|
|
"# \n",
|
|
"# # Add labels\n",
|
|
"# plt.xlabel('Principal Component 1')\n",
|
|
"# plt.ylabel('Principal Component 2')\n",
|
|
"# plt.title('2D Scatter Plot for LOS and NLOS')\n",
|
|
"# \n",
|
|
"# # Add a legend\n",
|
|
"# plt.legend(handles=[nlos, los])\n",
|
|
"# \n",
|
|
"# plt.show()"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.753981Z",
|
|
"start_time": "2024-03-15T14:43:01.751404Z"
|
|
}
|
|
},
|
|
"id": "2ed22b3fc59f74e6",
|
|
"execution_count": 63
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"# from sklearn.neighbors import KNeighborsClassifier\n",
|
|
"# from sklearn.metrics import accuracy_score\n",
|
|
"# import matplotlib.pyplot as plt\n",
|
|
"# \n",
|
|
"# # Define the list of numbers of neighbors (from 1-20)\n",
|
|
"# num_neighbors = np.arange(1, 100, 2)\n",
|
|
"# \n",
|
|
"# # Initialize the lists to store the accuracies\n",
|
|
"# train_acc = []\n",
|
|
"# test_acc = []\n",
|
|
"# \n",
|
|
"# # Loop over the different numbers of neighbors\n",
|
|
"# for k in num_neighbors:\n",
|
|
"# # Initialize the KNN classifier\n",
|
|
"# clf = KNeighborsClassifier(n_neighbors=k)\n",
|
|
"# \n",
|
|
"# # Fit the classifier on the training data\n",
|
|
"# clf.fit(X_train, y_train)\n",
|
|
"# \n",
|
|
"# # Make predictions on the training and test data\n",
|
|
"# y_pred_train = clf.predict(X_train)\n",
|
|
"# y_pred_test = clf.predict(X_test)\n",
|
|
"# \n",
|
|
"# # Calculate the accuracies\n",
|
|
"# train_acc.append(accuracy_score(y_train, y_pred_train))\n",
|
|
"# test_acc.append(accuracy_score(y_test, y_pred_test))\n",
|
|
"# \n",
|
|
"# # Plot the accuracies\n",
|
|
"# plt.figure(figsize=(10, 5))\n",
|
|
"# plt.plot(num_neighbors, train_acc, 'ro-', num_neighbors, test_acc, 'bv--')\n",
|
|
"# plt.legend(['Training Accuracy', 'Test Accuracy'])\n",
|
|
"# plt.xlabel('Number of Neighbors')\n",
|
|
"# plt.ylabel('Accuracy')\n",
|
|
"# plt.title('Training and Test Accuracy for Different Numbers of Neighbors in KNN')\n",
|
|
"# plt.grid()\n",
|
|
"# plt.show()"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.757935Z",
|
|
"start_time": "2024-03-15T14:43:01.755073Z"
|
|
}
|
|
},
|
|
"id": "4ac86c268055c1b8",
|
|
"execution_count": 64
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"# Naive Bayes"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"id": "5b9b66f92968957c"
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"# from sklearn.naive_bayes import GaussianNB\n",
|
|
"# \n",
|
|
"# # Naive Bayes\n",
|
|
"# nb = GaussianNB()\n",
|
|
"# loaded_model = train_and_save_model(nb, X_train, y_train, 'naive_bayes_classifier.pkl')"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.761377Z",
|
|
"start_time": "2024-03-15T14:43:01.759027Z"
|
|
}
|
|
},
|
|
"id": "3d984228fb1d3026",
|
|
"execution_count": 65
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"# y_pred_nb = loaded_model.predict(X_test)\n",
|
|
"# accuracy_nb = accuracy_score(y_test, y_pred_nb)\n",
|
|
"# print(f\"Accuracy of Naive Bayes: {accuracy_nb}\")"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.764552Z",
|
|
"start_time": "2024-03-15T14:43:01.762477Z"
|
|
}
|
|
},
|
|
"id": "98cd350871bc3201",
|
|
"execution_count": 66
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"# K-Means Clustering"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"id": "92c8498137a5e32e"
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"# from sklearn.cluster import KMeans\n",
|
|
"# \n",
|
|
"# # K-Means Clustering\n",
|
|
"# kmeans = KMeans(n_clusters=2, max_iter=600)\n",
|
|
"# loaded_model = train_and_save_model(kmeans, X_train, y_train, 'kmeans_clustering.pkl')"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.767920Z",
|
|
"start_time": "2024-03-15T14:43:01.765741Z"
|
|
}
|
|
},
|
|
"id": "305a796294814705",
|
|
"execution_count": 67
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"# y_pred_kmeans = loaded_model.predict(X_test)\n",
|
|
"# accuracy_kmeans = accuracy_score(y_test, y_pred_kmeans)\n",
|
|
"# print(f\"Accuracy of K-Means Clustering: {accuracy_kmeans}\")\n"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.771109Z",
|
|
"start_time": "2024-03-15T14:43:01.769076Z"
|
|
}
|
|
},
|
|
"id": "494bb537046bf5a7",
|
|
"execution_count": 68
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"# labels = loaded_model.labels_\n",
|
|
"# # Print the data table with the cluster labels\n",
|
|
"# print(f\"Data table with cluster labels:\\n{pd.concat([X_test, pd.DataFrame({'Cluster': labels})], axis=1)}\")\n",
|
|
"# \n"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.774323Z",
|
|
"start_time": "2024-03-15T14:43:01.772204Z"
|
|
}
|
|
},
|
|
"id": "62401c8d1a4d61cc",
|
|
"execution_count": 69
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"# from sklearn.cluster import KMeans\n",
|
|
"# from sklearn.metrics import accuracy_score\n",
|
|
"# from sklearn.decomposition import PCA\n",
|
|
"# from mpl_toolkits.mplot3d import Axes3D\n",
|
|
"# import matplotlib.pyplot as plt\n",
|
|
"# \n",
|
|
"# # Define the range of cluster numbers\n",
|
|
"# cluster_range = range(1, 15)\n",
|
|
"# \n",
|
|
"# # For each number of clusters\n",
|
|
"# for n_clusters in cluster_range:\n",
|
|
"# # Create a KMeans model\n",
|
|
"# kmeans = KMeans(n_clusters=n_clusters, max_iter=600)\n",
|
|
"# \n",
|
|
"# # Fit the model to the training data\n",
|
|
"# kmeans.fit(X_train)\n",
|
|
"# \n",
|
|
"# # Make predictions on the test data\n",
|
|
"# y_pred_kmeans = kmeans.predict(X_test)\n",
|
|
"# \n",
|
|
"# # Calculate the accuracy of the model\n",
|
|
"# accuracy_kmeans = accuracy_score(y_test, y_pred_kmeans)\n",
|
|
"# \n",
|
|
"# # Print the number of clusters and the corresponding accuracy\n",
|
|
"# print(f\"Number of clusters: {n_clusters}, Accuracy: {accuracy_kmeans}\")\n",
|
|
"# \n",
|
|
"# # Apply PCA to reduce dimensionality to 3D\n",
|
|
"# pca = PCA(n_components=3)\n",
|
|
"# X_test_3d = pca.fit_transform(X_test)\n",
|
|
"# \n",
|
|
"# # Create a 3D scatter plot\n",
|
|
"# fig = plt.figure(figsize=(10, 7))\n",
|
|
"# ax = fig.add_subplot(111, projection='3d')\n",
|
|
"# \n",
|
|
"# # Create a color map\n",
|
|
"# cmap = plt.cm.get_cmap('viridis', n_clusters) # We use 'viridis' colormap and we specify that we have n_clusters\n",
|
|
"# \n",
|
|
"# # Plot the points with colors according to their cluster assignment\n",
|
|
"# scatter = ax.scatter(X_test_3d[:, 0], X_test_3d[:, 1], X_test_3d[:, 2], c=y_pred_kmeans, cmap=cmap)\n",
|
|
"# \n",
|
|
"# # Add labels\n",
|
|
"# ax.set_xlabel('Principal Component 1')\n",
|
|
"# ax.set_ylabel('Principal Component 2')\n",
|
|
"# ax.set_zlabel('Principal Component 3')\n",
|
|
"# plt.title(f'3D Visualization of {n_clusters} Clusters')\n",
|
|
"# \n",
|
|
"# # Display the plot\n",
|
|
"# plt.show()"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.778002Z",
|
|
"start_time": "2024-03-15T14:43:01.775201Z"
|
|
}
|
|
},
|
|
"id": "f0f5284581e70e6e",
|
|
"execution_count": 70
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"# from sklearn.decomposition import PCA\n",
|
|
"# import matplotlib.pyplot as plt\n",
|
|
"# \n",
|
|
"# # Apply PCA to reduce dimensionality to 2D\n",
|
|
"# pca = PCA(n_components=2)\n",
|
|
"# X_test_2d = pca.fit_transform(X_test)\n",
|
|
"# \n",
|
|
"# # Predict the cluster labels for the data points you're plotting\n",
|
|
"# labels = loaded_model.predict(X_test)\n",
|
|
"# \n",
|
|
"# # Create a scatter plot\n",
|
|
"# plt.figure(figsize=(10, 7))\n",
|
|
"# \n",
|
|
"# # Create a color map\n",
|
|
"# cmap = plt.cm.get_cmap('viridis', 2) # We use 'viridis' colormap and we specify that we have 2 clusters\n",
|
|
"# \n",
|
|
"# # Plot the points with colors according to their cluster assignment\n",
|
|
"# plt.scatter(X_test_2d[:, 0], X_test_2d[:, 1], c=labels, cmap=cmap)\n",
|
|
"# \n",
|
|
"# # Add labels\n",
|
|
"# plt.xlabel('Principal Component 1')\n",
|
|
"# plt.ylabel('Principal Component 2')\n",
|
|
"# plt.title('2D Visualization of Clusters')\n",
|
|
"# \n",
|
|
"# # Display the plot\n",
|
|
"# plt.show()"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:43:01.781217Z",
|
|
"start_time": "2024-03-15T14:43:01.778851Z"
|
|
}
|
|
},
|
|
"id": "82c7ba8cbb2aa17a",
|
|
"execution_count": 71
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},
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{
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"cell_type": "markdown",
|
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"source": [
|
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"# Convolution Neural Network\n",
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"\n",
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"This code block is implementing a Convolutional Neural Network (CNN) for a classification task using TensorFlow. The CNN is a class of deep learning neural networks, most commonly applied to analyzing visual imagery. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on their shared-weights architecture and translation invariance characteristics. Here's a step-by-step breakdown of what the code does: \n",
|
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"1. Data Preparation: The target column 'NLOS' is separated from the rest of the dataset. The target values are then encoded from categorical to numerical values using LabelEncoder. These numerical values are then one-hot encoded to create binary variables for each class. \n",
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"2. Data Reshaping: The input data is reshaped to fit the model. Each data instance is reshaped to a 3D array where the third dimension represents the number of input channels, which is 1 in this case. \n",
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"3. Data Splitting: The data is split into training and testing sets using a 80:20 ratio. \n",
|
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"4. Model Creation: A Sequential model is created using Keras. This model is composed of the following layers: \n",
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"5. Conv1D layers: These are convolutional layers that will convolve the input data with a set of learnable filters, each producing one feature map in the output. The kernel size is set to 3, and the activation function used is ReLU (Rectified Linear Unit). \n",
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"6. MaxPooling1D layers: These layers are used to down-sample the input along its spatial dimensions (height and width). The pool size is set to 2. \n",
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"7. Dense layers: These are fully connected layers. The first Dense layer has 64 units and uses the ReLU activation function. The second Dense layer has a number of units equal to the number of classes and uses the softmax activation function to output a probability distribution over the classes. \n",
|
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"9. Model Compilation: The model is compiled with the Adam optimizer, categorical cross-entropy loss function, and accuracy as the evaluation metric. \n",
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"10. Model Training: The model is trained on the training data for 10 epochs with a batch size of 32. The validation data is set to the testing set. \n",
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"11. Model Evaluation: The model's performance is evaluated on the testing set and the accuracy is printed. \n",
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"\n",
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"12. The mathematical concept behind the Convolutional layer (Conv1D) is the convolution operation, which is a mathematical operation on two functions that produces a third function. In the context of a CNN, the two functions are the input data and the kernel or filter. The convolution operation involves sliding the kernel across the input data and computing the dot product at each position.\n",
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"\n",
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"The mathematical formula for the convolution operation is: $$ (f * g)(t) = \\int_{-\\infty}^{\\infty} f(\\tau)g(t - \\tau) d\\tau $$ Where: \n",
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"$f$ and $g$ are the input data and kernel respectively\n",
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"$t$ is the position of the kernel\n",
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"$\\tau$ is a dummy integration variable\n",
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"In the context of a CNN, the integral is replaced by a sum over the discrete spatial dimensions (height and width) of the input data and kernel."
|
|
],
|
|
"metadata": {
|
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"collapsed": false
|
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},
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"id": "862a9b7ee430a667"
|
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},
|
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{
|
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"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
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"Epoch 1/10\n",
|
|
"1050/1050 [==============================] - 11s 10ms/step - loss: 0.7047 - accuracy: 0.6446 - val_loss: 0.6124 - val_accuracy: 0.6857\n",
|
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"Epoch 2/10\n",
|
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"1050/1050 [==============================] - 10s 10ms/step - loss: 0.5902 - accuracy: 0.6931 - val_loss: 0.5560 - val_accuracy: 0.6894\n",
|
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"Epoch 3/10\n",
|
|
"1050/1050 [==============================] - 10s 9ms/step - loss: 0.4494 - accuracy: 0.7892 - val_loss: 0.3539 - val_accuracy: 0.8371\n",
|
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"Epoch 4/10\n",
|
|
"1050/1050 [==============================] - 10s 9ms/step - loss: 0.2222 - accuracy: 0.9093 - val_loss: 0.0957 - val_accuracy: 0.9645\n",
|
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"Epoch 5/10\n",
|
|
"1050/1050 [==============================] - 10s 10ms/step - loss: 0.0654 - accuracy: 0.9786 - val_loss: 0.0244 - val_accuracy: 0.9951\n",
|
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"Epoch 6/10\n",
|
|
"1050/1050 [==============================] - 10s 10ms/step - loss: 0.0145 - accuracy: 0.9959 - val_loss: 0.0011 - val_accuracy: 1.0000\n",
|
|
"Epoch 7/10\n",
|
|
"1050/1050 [==============================] - 11s 10ms/step - loss: 0.0010 - accuracy: 0.9999 - val_loss: 4.8477e-04 - val_accuracy: 1.0000\n",
|
|
"Epoch 8/10\n",
|
|
"1050/1050 [==============================] - 10s 10ms/step - loss: 0.0248 - accuracy: 0.9932 - val_loss: 0.0011 - val_accuracy: 1.0000\n",
|
|
"Epoch 9/10\n",
|
|
"1050/1050 [==============================] - 10s 9ms/step - loss: 3.7832e-04 - accuracy: 1.0000 - val_loss: 1.7883e-04 - val_accuracy: 1.0000\n",
|
|
"Epoch 10/10\n",
|
|
"1050/1050 [==============================] - 10s 9ms/step - loss: 8.9044e-05 - accuracy: 1.0000 - val_loss: 6.3343e-05 - val_accuracy: 1.0000\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
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"text/plain": "<Figure size 640x480 with 1 Axes>",
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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 1200x600 with 1 Axes>",
|
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"263/263 [==============================] - 1s 3ms/step - loss: 6.3343e-05 - accuracy: 1.0000\n",
|
|
"Test accuracy: 1.0\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from tensorflow.keras.models import Sequential\n",
|
|
"from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import tensorflow as tf\n",
|
|
"\n",
|
|
"# Set random seed for reproducibility\n",
|
|
"tf.random.set_seed(42)\n",
|
|
"\n",
|
|
"# Define the CNN model\n",
|
|
"model = Sequential()\n",
|
|
"\n",
|
|
"# Add a 1D convolutional layer\n",
|
|
"model.add(Conv1D(filters=32, kernel_size=3, activation='relu', input_shape=(X_train.shape[1], 1)))\n",
|
|
"\n",
|
|
"# Add a max pooling layer\n",
|
|
"model.add(MaxPooling1D(pool_size=2))\n",
|
|
"\n",
|
|
"# Add another 1D convolutional layer\n",
|
|
"model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))\n",
|
|
"\n",
|
|
"# Add another max pooling layer\n",
|
|
"model.add(MaxPooling1D(pool_size=2))\n",
|
|
"\n",
|
|
"# Flatten the tensor output from the previous layer\n",
|
|
"model.add(Flatten())\n",
|
|
"\n",
|
|
"# Add a dense layer\n",
|
|
"model.add(Dense(64, activation='relu'))\n",
|
|
"\n",
|
|
"# Add the output layer with softmax activation\n",
|
|
"model.add(Dense(2, activation='softmax')) # Change number of classes to 2 and activation to 'softmax'\n",
|
|
"\n",
|
|
"# Compile the model with categorical_crossentropy loss function\n",
|
|
"model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
|
|
"\n",
|
|
"# Reshape the training and testing data to fit the model input\n",
|
|
"X_train_reshaped = X_train.values.reshape((X_train.shape[0], X_train.shape[1], 1))\n",
|
|
"X_test_reshaped = X_test.values.reshape((X_test.shape[0], X_test.shape[1], 1))\n",
|
|
"\n",
|
|
"# Train the model and store the history\n",
|
|
"history = model.fit(X_train_reshaped, y_train, epochs=10, batch_size=32, validation_data=(X_test_reshaped, y_test))\n",
|
|
"\n",
|
|
"# Plot the training and test accuracy\n",
|
|
"plt.plot(history.history['accuracy'], 'ro-', history.history['val_accuracy'], 'bv--')\n",
|
|
"plt.title('Training and Test Accuracy')\n",
|
|
"plt.xlabel('Epoch')\n",
|
|
"plt.ylabel('Accuracy')\n",
|
|
"plt.legend(['Training Accuracy', 'Test Accuracy'])\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"# Plot the training and validation loss\n",
|
|
"plt.figure(figsize=(12, 6))\n",
|
|
"plt.plot(history.history['loss'], label='Training Loss')\n",
|
|
"plt.plot(history.history['val_loss'], label='Validation Loss')\n",
|
|
"plt.title('Training and Validation Loss Over Time')\n",
|
|
"plt.xlabel('Epoch')\n",
|
|
"plt.ylabel('Loss')\n",
|
|
"plt.legend()\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"# Evaluate the model on the test data\n",
|
|
"test_loss, test_acc = model.evaluate(X_test_reshaped, y_test)\n",
|
|
"print('Test accuracy:', test_acc)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:44:45.445553Z",
|
|
"start_time": "2024-03-15T14:43:01.782334Z"
|
|
}
|
|
},
|
|
"id": "3fe2ed86c3527d2",
|
|
"execution_count": 72
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": "<IPython.core.display.Image object>"
|
|
},
|
|
"execution_count": 73,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Plot the model\n",
|
|
"from tensorflow.keras.utils import plot_model\n",
|
|
"\n",
|
|
"# Generate the plot\n",
|
|
"plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)\n"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:44:45.537756Z",
|
|
"start_time": "2024-03-15T14:44:45.446707Z"
|
|
}
|
|
},
|
|
"id": "81f2d793ada5c410",
|
|
"execution_count": 73
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"# Save the model\n",
|
|
"model.save('CNN.keras')"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:44:45.624570Z",
|
|
"start_time": "2024-03-15T14:44:45.539010Z"
|
|
}
|
|
},
|
|
"id": "6b7329b28452b82a",
|
|
"execution_count": 74
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"# Multi-layer Perceptron (MLP)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"id": "42eff9445377f73c"
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 1/10\n",
|
|
"1050/1050 [==============================] - 2s 1ms/step - loss: 0.8294 - accuracy: 0.6602 - val_loss: 0.6055 - val_accuracy: 0.8129\n",
|
|
"Epoch 2/10\n",
|
|
"1050/1050 [==============================] - 1s 1ms/step - loss: 0.6153 - accuracy: 0.7962 - val_loss: 0.5363 - val_accuracy: 0.8283\n",
|
|
"Epoch 3/10\n",
|
|
"1050/1050 [==============================] - 2s 2ms/step - loss: 0.5471 - accuracy: 0.8205 - val_loss: 0.5016 - val_accuracy: 0.8326\n",
|
|
"Epoch 4/10\n",
|
|
" 244/1050 [=====>........................] - ETA: 6s - loss: 0.5188 - accuracy: 0.8300"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from tensorflow.keras.models import Sequential\n",
|
|
"from tensorflow.keras.layers import Dense, Dropout\n",
|
|
"from tensorflow.keras.callbacks import EarlyStopping\n",
|
|
"from sklearn.model_selection import train_test_split\n",
|
|
"from sklearn.preprocessing import StandardScaler\n",
|
|
"from tensorflow.keras.layers import BatchNormalization\n",
|
|
"from tensorflow.keras import regularizers\n",
|
|
"from tensorflow.keras.optimizers import Adam\n",
|
|
"\n",
|
|
"# Set random seed for reproducibility\n",
|
|
"tf.random.set_seed(42)\n",
|
|
"\n",
|
|
"# Assuming data is your cleaned data and 'NLOS' is your target variable\n",
|
|
"X = data.drop('NLOS', axis=1)\n",
|
|
"y = data['NLOS']\n",
|
|
"\n",
|
|
"# Split the data into training and testing sets\n",
|
|
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
|
|
"\n",
|
|
"# It's a good practice to scale your data to zero mean and unit variance before training a neural network\n",
|
|
"scaler = StandardScaler()\n",
|
|
"X_train = scaler.fit_transform(X_train)\n",
|
|
"X_test = scaler.transform(X_test)\n",
|
|
"\n",
|
|
"# Define the model with adjusted learning rate and regularization strength\n",
|
|
"model = Sequential()\n",
|
|
"model.add(Dense(64, input_dim=X_train.shape[1], activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
|
|
"model.add(BatchNormalization())\n",
|
|
"model.add(Dropout(0.5))\n",
|
|
"model.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
|
|
"model.add(BatchNormalization())\n",
|
|
"model.add(Dropout(0.5))\n",
|
|
"model.add(Dense(16, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
|
|
"model.add(BatchNormalization())\n",
|
|
"model.add(Dropout(0.5))\n",
|
|
"model.add(Dense(1, activation='sigmoid'))\n",
|
|
"\n",
|
|
"# Define early stopping\n",
|
|
"early_stopping = EarlyStopping(monitor='val_loss', patience=50)\n",
|
|
"\n",
|
|
"# Compile the model with a lower learning rate\n",
|
|
"model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0001), metrics=['accuracy'])\n",
|
|
"\n",
|
|
"# Train the model with reduced epochs\n",
|
|
"history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test), callbacks=[early_stopping])\n",
|
|
"\n",
|
|
"# Evaluate the model\n",
|
|
"scores = model.evaluate(X_test, y_test, verbose=0)\n",
|
|
"\n",
|
|
"# Make predictions and generate classification report\n",
|
|
"y_pred = model.predict(X_test)\n",
|
|
"y_pred_classes = (y_pred > 0.5).astype(\"int32\")\n",
|
|
"report = classification_report(y_test, y_pred_classes)\n",
|
|
"\n",
|
|
"# Plot training and test accuracy\n",
|
|
"plt.plot(history.history['accuracy'], 'ro-', history.history['val_accuracy'], 'bv--')\n",
|
|
"plt.title('Training and Test Accuracy')\n",
|
|
"plt.xlabel('Epoch')\n",
|
|
"plt.ylabel('Accuracy')\n",
|
|
"plt.legend(['Training Accuracy', 'Test Accuracy'])\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"# Plot training and validation loss\n",
|
|
"plt.figure(figsize=(12, 6))\n",
|
|
"plt.plot(history.history['loss'], label='Training Loss')\n",
|
|
"plt.plot(history.history['val_loss'], label='Validation Loss')\n",
|
|
"plt.title('Training and Validation Loss Over Time')\n",
|
|
"plt.xlabel('Epoch')\n",
|
|
"plt.ylabel('Loss')\n",
|
|
"plt.legend()\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"# Print the accuracy and the classification report\n",
|
|
"print('Test loss:', scores[0])\n",
|
|
"print('Test accuracy:', scores[1])\n",
|
|
"print('Classification Report: \\n', report)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"is_executing": true,
|
|
"ExecuteTime": {
|
|
"start_time": "2024-03-15T15:52:13.445525Z"
|
|
}
|
|
},
|
|
"id": "c8745832a585d5ec",
|
|
"execution_count": null
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 2
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython2",
|
|
"version": "2.7.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|