1928 lines
1.5 MiB
1928 lines
1.5 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": "2024-03-15T14:26:43.693526Z",
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"start_time": "2024-03-15T14:26:43.690020Z"
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}
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},
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"id": "2aa3c6c09e8645d1",
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"execution_count": 14
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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": "2024-03-15T14:26:45.362150Z",
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"start_time": "2024-03-15T14:26:45.358690Z"
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}
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},
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"id": "7bcd7cfc8dd11cbb",
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"execution_count": 15
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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": "2024-03-15T14:26:46.944782Z",
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"start_time": "2024-03-15T14:26:46.940743Z"
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}
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},
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"id": "9e0b1ed6f23a17cf",
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"execution_count": 16
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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": "2024-03-15T14:26:49.148035Z",
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"start_time": "2024-03-15T14:26:49.143523Z"
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}
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},
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"id": "308d64639b199bc7",
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"execution_count": 17
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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": "2024-03-15T14:26:51.125346Z",
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"start_time": "2024-03-15T14:26:51.122572Z"
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}
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},
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"id": "4afc8d71b3271351",
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"execution_count": 18
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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
|
|
},
|
|
"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": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
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"end_time": "2024-03-15T14:26:53.782293Z",
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"start_time": "2024-03-15T14:26:53.779139Z"
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}
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},
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"id": "fe3089568e99a58d",
|
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"execution_count": 19
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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": {
|
|
"collapsed": false
|
|
},
|
|
"id": "e1edd5ef4f54e752"
|
|
},
|
|
{
|
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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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],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:26:55.854360Z",
|
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"start_time": "2024-03-15T14:26:55.850855Z"
|
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}
|
|
},
|
|
"id": "670e8c2cf19126ea",
|
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"execution_count": 20
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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": "2024-03-15T14:26:57.378062Z",
|
|
"start_time": "2024-03-15T14:26:57.371048Z"
|
|
}
|
|
},
|
|
"id": "685463c2d6065b08",
|
|
"execution_count": 21
|
|
},
|
|
{
|
|
"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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aQ/rj19pm1SBwOTi4oVy/dLBZhzZVrrgLrwAAAAAAAAAAAAslmclNBqakCgEq0VvDUwYm56c61jusZjltFrlsFjlthfmb77vsFjmtFpnNpulwNNNkGJpJN+6bJ9sWmE0mlZXYtaver9bGYFHaT+Tzhq6PxHSqO6LT3YUOZhf7x27bJnch2S2F18NjtxSmDqtcNovcdovcDqvctsll09tYFXDZJkPF3KooWZoByeYrk8vrRGdYr18Z1uuXh2cViGUyST5nIdSgxGGdfo9Nve+mplPzjsmpYRgaT2Y1lsxoLDE5TWY1nihMxxKZW9pgOKxmban23hIqtqXGt2La70hS52hML54f0IvnB+fc6dthNc+64+d8OaxmrSv3aH2FR+vLS7S+wqMqn1NTb/XCJ4Vuuj85nVzgtlvUUlEy53bHU4FtPzzVpx+d6VN36O7tzXBndqt5xuCV2TKZpMc3V+qrDzbpsY0ViqWzujI4rov947o8MK5LA2O6NDA+55Adr8Mqn8smk0kyjML7IW9Ihiank8sMSXnDUDqbn1egwXLitlv0se3VemZPrQ5uKL9ruy/DMDQ8kVLXaKHD8OBYSrl8Xnmj8NrkDUmT06n706/dTe2ETJr8DmAq9OcyqfD9QCbT9P/vwbHkZLBY/K4hM8vNvqagvnSgUZ/cVbNs29nebKqd+MhESqMThenweEqjsbRGxlMamUgplsrJ77appeJGW8WWypIZ/0aOTKT0N8e69a33uu6pHe9MTCZpT0NAhzZV6tDmCu2s8y95O5+xZEbdobh6wonpaU94apooesBtjd+pjVVeba4qUWOZR69eLLTjXKge3QfWlernH2jSU9url1V/iKlgySuD47o6OK6rgxMajaULYWLprGKpnGKprGKprOKZ3B1fn7qAS3saA2qdDBDaXjtzu/grg+P67vEefe9kr4bHU3fcbibVPqcebLkRPNZQujyDV2KprI62h/RW24iOtI3cMZjR67ROB48F3LZbQsgqvU5tqfZqa41PpfcYEmgYhcG9v/lOp54/1z+vsM7mMrf2NgX1wPoyPbWj+rZBfYutczSmbx/t1t8e79bIxOxDQO5Vc5l7MnSsED7WXOZWLm/otcvD+uujXXrt8pCK9RM44Lbp8c2VenpnjR7fUrnk/dQMw9CbV0f0Z2+1640rw0Xfv8Nq1s46v1obA2qd7FNT47+1T+nIRErPn+3Xj07362hHaM7Hmupj+XBLuR7aUKZ9TUE5rEv/naI7FNe/e+GSfnymf1GOZ7OY1FJRoi3VXm2u9qkvktBzZ/vnFKRzLyq8Dn3l/kZ9+f7Gu4b2LZRzvVH9X8+e1Zl7CKcrNrfdoqDbPu/vbR+0pdqrJ7ZU6smtldrTEFzyz4oplwbG9M9/cF7vtc/9/+qUPQ0Bfba1TrsbAqr2OVVeYr/nPjVD40kday/0F3qvPXTXYDe/y6b71xVCx1ZC8Njp7oj+yffOFiUY32E1q9rvLPzWzN/47Z4zjOn5wjpDE6ls0f7e3cxuMWtHnU9PbKnUz+6r/9DfgcV2rjeq3/z+OZ3qjsx7XyUOayHs/YFGlXkcyubzyuUNZfPG9DSby0/fT+fyGowm1TUZKDZ16w0n7nq+3euw6sGWMj2yqUKPbixXU1nxwvoWimEYev3KsP7tTy7p8uC9B5dvrfHpVw6u06d21y7o7yqCxoAV7IP/gV9/97gePdC6Ii8E3k3hxHpWsXRO8VRhOn0SJ51TLJ1VMpOX12lVfdCl+oBbNQHnXdOA83lDJ7vD+tHpfj13tn/OJ24kyWO3aGe9X3sagtOJxMshjfhO8nlD5/vGdHFgTOPJrPKTf7TzhqFszlBu8svRzcvyhiGHzayWihLtqPVrY1XJrJ5jPJ3V65eH9fy5Ab16cVCxe7hYEnDbtKPWr+2T4WM7av1qLHUv6y+ySyWfN3SsI6RnT/bqubP9c7rw5bSZta3Gp131Ae2s8+uRTeVL9iN3NobGkro2HFMyW3hPTV9ImlxvMt16wdgkqcRZSIqfTWKuYRg61hHWX7zdrhfPD06PADJbGypL9MjGcj26qUL3rytd9iEWoVhar10a0vm+MYViKflcNpV5HCr32gvTErvKShwqK7HL67AW5e/QWDKjF84O6Hsne/Ree+ieLmZMhVY8uL5MD7SUaW9jcEVcaJuSzOR0qjuivkhiupGLz2WbnFrlc9rktlsW5O98MpPTj8/063++3aGzvR8+seSyWbSt1qcdtT5trysEE26onN3fgKU2kcrqhXMD+sGpXp3oDMuQ1FzmmRwt0Ds9auC9XpSYrXO9Uf3ZkXb9+HT/LQ2NPmhqdJRC8FhQe5sCK+JzWCoEML5wbkAvnBvQ+52h6ZMrQbdNzeUerSvzTAcwNZd51FzuKXoIWSaX12uXhvSd4z167dLQLScZKr0O7W4IaHe9X7sbAtpVF7gl7GI5648m9OqlIb12aUhvtY3MaYQUqXACZ0NlibbW+LStptDAbXP1zA3bhsdT+v7JXv3t8Z5ZndS4HZvFpG01Pu1vLtUndtZob2NgyX67GIahtqEJvX55WK9dHtKxjlDRRwKs9jnVXO7WunKP1pV71FxWaNjWUOqWw2qRYRS+j//4TL9+fKZPPeH5n1y3mgt/Cw9uLNdHtlZpe61v2fw+HB5P6d3ro3rn+qjevTaq6yOxGbf3u2yTIcR+ba8thI+tK/cU5SLBVMj0c2f69cK5AY3e5sLRVLj07vpA4bOjwa9NVd5l+bdvdCKlYx1hHW0P6VhHSOf7onM6we2wFn7rbfhACFlTmWdWz3toLKlXLg7p5QsDOnJt9K6NAH1Oqx5qKdfBjYVg3pU0ql06m1dfJKHucFzdoYTi6az8Lpt21vsJI5+DeLowcutAtNDBoP+m+anpyEThvE2l16Eav0u1Aadq/C7V+J2qC7hUE3Cp1l8YPbAY5w36owm9dH5QL54f0Hvtobv+Bizz2G8JHmupLNGGihLVBlxFeT+MJTN67dKQXjo/qPfaRxe08Uix1QVc+sMvtWpvY0CvXx7WH7/WpveLODLZQnNYzfrc3nr94sPN2lTFKLIAAAAAAAAAAABzYRiGUtm87Bbzim0HnMzkdL5vbDp47FR3RF2h+F0f57SZVe1zqtLnVLXPqWq/U5Veh/wum9x2q9wOywcCwwpTt92yLNsqLIXBsaTeujqizlBcmVx+Opjggzefyyavw7pg77FkJqfxZFaGYajUc+8dQVeCwbGkXrowqHM9UQ1PpORxWAsDXrps8k8PfmmT33VjIEy/yyaL2aR0Nj8Z0JZRdPI2lswWpokby8eTWaWyeWVyeaWzeaUnp1P3U5PLMrm8Mtm8Am77dPur9RUera8o0fpyj+oCxR9Qb65uHmTyhXMDRe9kvRpZzSbd11yqQ5srdGhzpVoqPDrcNqLvvN+tly8MFrUtn9dpnXOgGGZWXmLXJ3fV6jOtdfI6rYVOwpOBYp2jcXVPdhpeToPlLXc+p1Wf21uvLx1o1Obq4rZPyecL38Pi6awSmZySmZwS6bxi6ULfvInUjcClicn+erF0VhM3LRtLZDQykVIolp5zJ/xav3NyoFSvNky2U5Skb73XqZ+cHZixnf18lHnsemxThR7bXKFHN1YoOI8+A+PJjAaiyUIYaTKjiVRW48msJibvj998P5VROJZRTziusTXyWVReYtcX72vQlw40qj64eO1BDcPQ8HhKVwYndHkyVOzy4LjaBic0XuQQN6nQLn5rjU+tDQHtaQxoT0NQfpdNPzrdp7893nPbfjnztbGyRJ/fV6/P7q1b0j4mmVxeZ3oieuvqqI60jehEV7ioYcxVPsd0iO/WGp+21frUXHajDXk8ndX3T/bpG+903DHUbD48dos+t7deX3uoSRsqF7etYCaX18sXBvWt97r0VtvSDOBa4XXIJGloHv2s70Wt36kvHWjUFw80LPr7OZHO6Xsne/TnRzrUNjSxqMeu8TvV2hjQ5iqf3u8M6UjbyIIE23jsFn18Z41+dm+97l9Xuui/naKJjP7La2368yMdC/a3fTmxWUz65K5a/d2HmrW7ITCnfRiGobFkVol0bjpIfqbzDbFUVr//8hX92ZH2BXkPLRflJQ49s6dWn22tW7L+UdFERr//8hV9893OOfcnvxuzqfD5O3XOsNrnVNXktNrnVF80qaPtozrWEVb7Xfpi3U3AbdOB5uUXPBZLZfUfXrqiv3h79b6nzZOh6H/nQKMe31wx53OKoVhaZ3ujisTT8jqtqvQ6VeVzqsxjv+O/ZSSe1u+9dFl/9V7XggUPL6aGUpce2VihRzaU66GW8mXXR/hsT1T/5vmLevva6Jz3UeF16GsPNukr9zfN6/frnRA0BqxgH/wPXPNLfyx/7Xo1lbnVVFYIcGgq86h5cjSiGn9xOjUupIlUVsfaQ3r72oiOtofUFYprIpWd04UCs0mq8jkLwWNBt+oCLtUHXaoLuuS0WfTKhUH9+Ez/gl3IKfPY9andhZPmu+v9y6KDf380ocNXR/TmlWEdaRtROJ6Z1/7sFrM2V3u1o86nbbV+7agtnMC5OdhnLJnRqxeH9Py5fr1xZXjOwRg38zqt2tMQ0NM7a/SZ1rplGyRkGIY6RuM61R2eHHllMsQtn1fOmExlnQx1m0pnzU8Gu/ldNlV4HarwOlReUphWlDgUdH/4i9614Qk9e6JXz57sLfr72WySPrmrVn//8RZtqfYVdd/zEYmn9e71UR1pG9Xb10Z0bXjuP45q/U5tqPJqY2WJNlWVaEOlVxurSm4ZfSCZyemHp/v0F0c6dKEIScg3c1jN+tTuWv3qo+u1cZl0VjYMQ1cGJ/TKxUG9emlIJ7rC9/zjwW41q9xzI3isxu+a/BwufBY3BF137PSfyeX15pVhfe9kr165MDjv0c3sVrP2NRbCHz+2vbroF9vmyzAMXewf11ttwzp8tfB3727P2Wo2TYaPWadDyKr9Tu1rCurhlvJZB3P0RxP6y3c79e2j3bNO57dbzdpa7dX2yRDIB9aXan1Fyaz2sdAyubwOXx3Wsyf79PKFgXv6G1Tlc0xfkNhS7dW2Gp/WlXvm9MM9k8vrxfMD+osjHfMKUWgodWlvY1D7m4L6me3VqvItn+CxrtG4nj/Xr+fPDcwpybzUY1dTmVvryjxqqSxRa0Mh0MczyxEdrwyO6zvvd+vZk72zCtxYV+7R7nq/dk0GCW2v9S2L7xW5vKFT3WG9emlIr16a3Wicc9FU5r7lYtyWaq8u9I/pO+/36PXLQws2CuvGyhJ98b4GfW5vfVFD/jK5/HRjtUi80EgtkkgrEi8s648k9VbbyJI16DKbpNqAS2aT6Z4ams7H9lqfvnx/o57ZU7foI6WGYoXva+9cK4SLFeMinNtu0dYan7bX+rSxyquWCo82VJbc08i7U6G8z53t10/ODkyHFc2G02bW9lr/ZPiYX/uaggvWCCKby2s8mb1plNzJkXFvmh8aS+r9zvCCX+C0mk1qKnNPN+zZWFVSCFCaHNV1KrjvpQuDevnC4LxHtmgsdU+GjpXrwZbyoodSzkY+XxhRszsUV3c4rq7RqVCxwiiC/dHEHS9ieOwW7aoPTI9gt6dxYcNDk5mcDl8d0fXhCYXjGVV6HdP/XtU+55IGO0YTGQ2PF0atHJ4cvfLm+0NjKfVHE0VtSGWzmFTlc6o24FJDsHCerLG0cH6ssdStMo/9jq9J29CEXjw/oJfOD+h0kUZ3ctstam0MaH9TqQ6sK1VrY+CeA6eHx1N6+UIh7OztayNFD8ZcTBazSevKPYveMGM+tlR79YX9Dfpca92CXKABAAAAAAAAAADAyheKpXW6O6LzfVGNJbPyOqzTnQGrJjsE+lzFGcQVwPJmGIbO9kb1k7MDeuFcvzpGF7Z92EpS43fq0OYKPbapUg9vKLvjYOGhWFo/ONWrvznWvSDhIcBKsa8pqC8daNQnd9XcsW1zJpdXTzih68MTah+J6fpITJ2jMUUTGSXSOSUzNweLrf6Qj3tlNkl7GgJ6fHOlDm2u1PZa3237tyQzObUNTejKZGDVlYFxXRmcIFDyHplN0hNbKvXzDzTp0Y0Vdw2wGEtmdK43qnO9UZ3tHdP14QklMzmZTCaZJJlMkkkmTX2lNplMMpsKyw1D6gknFE3Mr5/kSmExm/T45kp9YX+9nthSWfRg4nzeUDyTUzyVVSxdCBuMpbK60D+mI20jevd6SBMLEN42E6fNrM3VPtUHXXrz8vCChMfdzsMbyvS1B5v15NaqWfXL7g7F9ebVQv/da0MxpbI5mU2F96/FbJqcL7yHLeYb892h+IoagLYYrGaTPra9Wl95oFEPri+b1e9mwzDUE07o0sC4wvG08vlCP918vtBXN2doellust9uKJ7Wsyd7FZlnv+qVpC7g0mdb6/S5vXWz6veXyuZ0qiuid68XBkPvCcdlqNAX1mmz3DJ13HTfYjLpx2f65t13faVqbQzo7z7UrI/vqJHdeufP52wurwv9Y3q/I6zjnWG93xnS4NitfVzsVrM8dovcdqs8jsK0xGGV227Rud6o+qLJhX46y8qmqhJ9bm+9PrOnTtX+2fXLmPq8ONsb1chESg6rWV5noW+ud7KfrtdplddplcNa+O6dzxv6zvFu/e4LlzU6y363K0mpx65P767Vzz/QNB0CvNhevzyk//vZc2vqe3al16Ev7K/XF/c33rVPeDKT0/sdYR1uG9ZbV0d0vu/2/UwtZpMqvQ5V+pyq8jpU5XOqylfob/c/3mqfdf/xlcJsknbVB/TYpgp9andNUYJis7m8TvdE1RdJKJ3Nq8bvVE3ApRq/c8a+x92huH7vpcv6wam+edcwxWkz62f31mt/c1CpTGGAhlQmr1Q2Nz1ow41bTlazSS0VJfrC/oYZ++sSNAasYLcLGrNXNN1xe7vFrPUVHj3YUqYnt1TpwLrSGb+oLoZkJqcTnWG9fa0QFnS6J7pgia5LaX25R59prdNn9tTNOgRmPuLprN5rD+nwlREdvjqsq4vQkdNiNqmlwqMdtX5FEhm9dXVkQVOnSz12feX+Rn31gSZVLnHwSi5v6GL/mI51FH48H20PzynAYCYWs0llHvt0AFk4ntaZInV8vpuPbK3SbzzeotbG4KIc72bxdFbHOsJ6+9qI3m4b1bm+6IKn5lb7nNOd8n96aWhRvkR/ZGuVfv1Qi/Y1Lf5rnMrm9O71kF69OKifXhpST3jhfpTZrWbVBwrBj1MBZENjSf3oTP+Cvs4PtZTplw+u0+ObK5csZXtqRL3DV4f1Vtto0T8j6oMuPdRSpoc3lOvBlrLbhlkYhqFjHWH9xdvtevH8YFH/7t7XHNSvH2rR45srlzTM4lR3RN8/2Vu095TDatb6isKISRsmpy2VhRHypk4e3SwUS+vbR7v0l+92qr/IJ+2sZpM+tbtWf+/R9dpaszQBkG1DE3phMlzsTicm5sNskjZX+7SvKaC9jUHtbQyqqcz9ofdUNJHRj0736TvHe3R6nqE2U6xmk1obC2Gmn9hVs+CBMKFYevoWjqc1OlFIlH/98tCaOqlus5j0M9uq9cX7GnRwQ/k9fUbn84auDU/oeGdYJ7rCujQwrtGJtKKJzKJfuFwJ3HaLntlTqy8faNLOev+sHjsQTepoR0gnu8IaHEtOn5TKTI8matwywujU/GK+h71Oa+GzeeozenJaH3TpTE9EPzrdr+fP9X/owksx7K7365k9dfrU7lpVeB1z2kc4ltbLFwf1yoVBXegfUziWViy9/EdxNJkK3z1MWrjQPLNJOrCuVJ/aXatP7KiZd8hPPm+ofTSm/khS4XhakXghhDAczygSL3wWRyZDCsPxwmdKMX931AVc2tMQUGtjQHsaAtpR559XwGUyk9MbV4b1k7P9euXC4B3fNyWOwv+RjZPBY1OhccUe3TeXN/RW24heODegi/1j02Fiy3EkKo/doobSQujYVAhZfzSpF88PzCvI+l5ZzCbtqPXpvuZS7W8u1X3NQZWV3PgM6Q7F9eL5Ab1wbkDHZxH8XAxeh1XPtNbqsU2VctrMsprNsltNsprNsllumreadbYnqn/yvTOrrsFFqceuZ/bU6vP76rW9dnZ/NwEAAAAAAAAAAAAAkG4MSPzCuX795NzAihqUa77cdouCbrvWV3j08IZyHdpcoc1V3lkHR5zvG9N33u/W90/1rZnwmIVQ5XPos6312lRVojevDOu1y8O8niuIz2nV5/bW69DmCvVHkzdCxYZj6grFF2ww4bWkvMShxzZVaH9zUP3R5GSg2Lg6RmN3HIgUs9NY6taX72/Uz012do/GMzrXNxUqVpgSzjk35SV2fba1Tl/Y36BNVTOHKqSzebWPxHRlcFxXB8d1dWhCw+MpxdI5xdPZyUCxnBKZ5d+GebHVBVz66oNN+uL+htu2ZR5PZvTOtVEdnuwvxvt5bloqPPrK/U362X31tx2oemgsqdM9UZ3tiRSmvdFVG5qyUFobA/rc3np9aleNAu5b38vpbF6neyJ6d3Kg+eOdYaWyy68N+EpR6XXo5x9o0pcONKrC69BYMqMTnZOhYh1hneqO8Hk7RyZToZ/w51rr9dSOankcHx78e2QipTM9EZ3qjupMT0SnuyP33MfJYTXL57LJbNKC9EFazh5cX6avPtikj26rmnWQ6bXhwmDvr1wY1PWRmGKprPwuu4JumwJumwLuqXl74f7kuhfODxQ1lGklOrihXH/nQIM+uq1KDqtF+byhC/1jeqttRG9dHdGxjhCfx7OwrcanZ/bU6lO7a1UbcN3z4zK5vN65Nqrnz/XrxfODd/yOUeqxF4LH/C7VBm5Mz/VG9T/f7lw2/adKHFb996/t1wPry267nqAxYAWbbdDYB3kdVj26qUJPbKnU41sqZ0wlLJZMLq8zPRG93Taqt6+N6nhXWOk19sdtf1NQn2mt09M7599JWip0lB5L3ugYHYlndHlwXIevDutYe3jZ/EFaaDZLIXjllw+uW7ROoMlMTqe7I4VQsY6wTnSG10SwxcMbyvQbhzbowZbZpcTPxDAMxdI5jSUyit50u9Q/riPXRnSyK6xMbu18bTjQXKpfP9SiQ5sr5vQa5/NGIZV2MoH2Tim16WxewxMpvX55SIevjii+AgItimF9uUe/eHCdPr+3Xi773IMd7iSfNxRJZBSKpRSKZTQykdLxzrDeujqiy4OLO7LWpqoSPdRSroc3lGtPQ0CvXhrUX7zdqYv9xQ+HutmWaq9+7bEWfXJXjaxFHh3lTjpHY3r2ZK++f7J30U7Km02Fi16F4LEStZSX6P3OkL5/qm9Rvt88srFcv/poix7eUJzP42wur/FkVuPJrMaSmcItkdV4MqOxZFbD4ym9emlQVwYXv7FLmceu1saAWhuDai7z6KULheCNhTxJYjZJD6wv06d21+qp7dWz/t4Wiad1sjuiU10RdY7GFIoXPhfCsYxCsTQnhO+gLuDSz+1v0Bf2199yMmcildXp7oiOT55YP9kV1lhy9X/vWgg76/z68v2N+vTu2g+dUDcMQ9dHYjrWHtLRyfDc7tDKHRFiapSyxWAxm/TwhnJ9Zk+tPrb99hcrbjY0ntSL5wf1wrl+vXs9tCoDt4vNajbpkY3l+tTuWv3M9mqV3OU1ntIbSeitq8M6fHVEb18bXVYXlC1mkzZWlmhHnV876/zaUefT1hqf3PY7P7d7DRe7Fy6bRS2VHu2qD+jRjeV6aEO5fHcYLXYmlwbG9L0The9hQ+Nr62JaMbVUeLS7IaCL/eML/n39dvY3BfV3DjTqEzurZ3wPflB/NKH/49undLQjtIDVLTyr2aQntlTq8/vqdWhz5ZIPzgAAAAAAAAAAAAAAWF2uDo7r+XMDen5yALcP8jmtKvc6VFHimJ5WTE7LSuxKZfManUhpZCKt0VhKI+OF6ehEWiMTqQVpT2ezmG50BnYVOgMH3XYFPDc6BU91Eg5Obud32247iO98JDM5vXxhUH9zrFtvtY0Udd+rldNm1lPbq/W5vfV6eEO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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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UKbF58+Z03Lt37+jUqVPG4/fff/9E/OGHH5bat+h7mX4wEBHRpUuX6NWrVzretGlTTJ06NePxAEB2ZCN/ySb5CwBQ1BNPPJH4okeHDh2KPa2sqKI5xbBhw6J+/foZr5mt+y9DhgyJhg0bpuPPPvss8ZQRAGD7VJn8JZvkLwBQ9+25556JeNq0aRk/aGX8+PGJeNiwYSX2++KLLxJPo2/YsGEMHjw44z1m6/5L/fr1i11DWWsBALUnGzlMNslhAKBuW7p0aSJu06ZNhca3bds2EZf2u94ZGACgumQrf8km+QsAUFRdrOeSzWuCylA0jJwwYcKERLxt5esPPvggLrroothjjz2idevW0aRJk+jVq1ccccQRcdttt8XChQszWmPatGmJeODAgRXaY9H+ReerrbUAgNqRjfylLIsWLYpJkybF66+/Hh9//HEsWrSoSvPJXwCAoh5++OFEfOaZZ5Z7+DFbOcXmzZsTT3Ot6FoNGzaMvn37ZrQWALD9qEz+Upb58+fHhAkT4o033ogpU6ZU6YCi/AUAdgzdunVLHETcuHFj3H333eWO27hxY9x1112JtnPOOafEvkVzgJ122ikKCgoy3mPRHGTGjBmxZcuWjNby+REA1E3ZyGHKsmTJkvjwww/j9ddfjw8//DDmz58fqVSqwvN8RQ4DAHVbvXrJr8Jt+wDgTGz7Rc+IiFWrVpV4rtcZGACgumQrfymLMzAAQE2ri/VcfOZErlM0jJxQtOhGnz59Ys2aNXHOOefE4MGD45577omPPvooVqxYEevXr4+5c+fGyy+/HKNHj46dd945fvzjHxf7h29R06dPT8Tdu3ev0B6L9p87d25s2LChWL/169fHvHnzqnWtonsHAGpfNvKXknz88cfRp0+f6NKlSwwdOjQOPvjg2H333aNLly7RuXPn+MY3vhH//Oc/KzSn/AUAKGrGjBnx1ltvJdrOPvvscsdV9/2X0nKKWbNmJb7M2rhx42jXrl2NrAUAbB8qm7+U5MUXX4wuXbpEjx49YtiwYXHQQQfFbrvtFh06dIjevXvH2WefHe+8806F5pS/AMCO4+abb058+eOaa66JMWPGlNp/xYoVccoppyQOBp5wwglxwgknlNi/qvdf2rdvH40aNUrHmzZtitmzZ9fIWvIXANh+1HQOU5Ivv/wyBg4cGO3bt4+99torDj744Nhrr72iR48e0a5du/ja174WTzzxRIW/SCuHAYC6rU2bNon4yy+/rND4kvqX9IVNZ2AAgOqSrfylJM7AAADZUhfruWTrmqCyFA0jJxStKl2vXr046KCD4sEHHyx37Pr16+PGG2+MY489NlavXl1qv6L/MO7WrVuF9tixY8fE0+gLCwtj6dKlxfotWbIk8YSzBg0aRIcOHSq0VteuXRNxRW8CAAA1Lxv5S0mWLVtW6hc3Pv/883j88cfjmGOOicGDB8fHH3+c0ZzyFwCgqKJfAhk8eHAMGjSo3HFVvf9SNKco7UlmRdcpOq4ya8lfAGD7Vtn8pSSLFi2KRYsWlfjenDlz4uGHH4799tsvDjvssGIHD0ojfwGAHccBBxwQv/71ryMvLy8iIrZs2RKjRo2KYcOGxU033RTPPvts/POf/4w//vGP8YMf/CD69u0bL7zwQnr8EUccEX/+859Lnb+q918iIrp06VLmnF8pem+mqvd65C8AkLtqOocpyfr160v9guuyZcviueeei9NPPz369+8fr732WsbzymEAoG7r06dPIp4zZ06p50tKMnHixGJtX3zxRbE2Z2AAgOqSrfylJM7AAADZUhfruWTrmqCy6pffBWpWYWFhsWIZF110UXzwwQcREZGXlxfHH398HHvssdGtW7dYu3ZtfPDBB/HII4/EZ599lh7z8ssvx6hRo+Lpp58ucZ01a9Yk4qZNm1Zon3l5edG4cePEXovOWVJbkyZN0ocoMlV0byWtAwDUnmzlL1XxwQcfxD777BNjxoyJU089tcy+8hcAYFupVCr+8Ic/JNpGjRqV0diq3n8p2n/z5s2xcePGaNiwYbWuU9IY+QsAbL+qkr9UxdixY2OvvfaKZ599Ng466KAy+8pfAGDHcsEFF0T//v3joosuiilTpkRExIQJE2LChAmljunTp09cfvnlcd5550W9eqU/BzJbecX69etj69atVVpL/gIA25eazGGqYubMmXHYYYfF7bffHhdffHGZfeUwAFD39enTJ7p06ZI4j/v444/H97///XLHrlmzJlH4dNv28tqcgQEAKitb+UtVOAMDAFRVXaznkq1rgsqqmU9noQJWrlyZqOQYEfH+++9HRETbtm3jtddei+effz6+973vxfHHHx+nn3563HTTTTF9+vQYOXJkYtwzzzxT7EshXyn6l2ejRo0qvNfGjRuXOWc21wEAak+28pdttWvXLkaNGhV//OMf46OPPoply5bF5s2bY/ny5fHvf/87fv3rX8cee+yRGLN+/fr45je/Ga+//nqZc8tfAIBtjR07NvG0sIKCgmI5TGmqmlcUzSlKmrM61ilpLfkLAGy/qpK/bKtbt25xwQUXxJNPPhnTpk2LFStWxObNm2PJkiUxYcKEuOWWW4o9/XXZsmUxYsSI+M9//lPm3PIXANjxHHrooTFhwoS47LLLIj8/v8y+PXr0iMsuuyxGjhxZbrGN2jr/Upm15C8AsP2pqRxmWy1atIjTTjstHnjggZg4cWIsXbo0Nm/eHCtXroxp06bFAw88EAcccEBizNatW+PSSy+Nxx57rMy55TAAsGP42te+lohvuummWLFiRbnjbrzxxli1alWx9po4m+IMDACwrWzkL9tyBgYAyLa6WM9F3kOuUzSMWlfaX2r5+fnxt7/9LQ488MAS32/WrFk88sgjceSRRybaf/nLXxYr4hERsWHDhkRcUFBQ4b0WfaLH+vXra20dAKD2ZCt/+cof//jHWLhwYTz00ENx5plnxqBBg6J169ZRv379aNWqVey+++7xf/7P/4kPP/ww7rvvvkQusWnTphg5cmSxHGVb8hcAYFtjxoxJxMcff3y0bds2o7FVzSuK5hQR7r8AAOWrSv4SEdGyZct4/vnnY+7cufHb3/42TjnllBgwYEC0bNky6tevH23bto2hQ4fG6NGj45NPPolrr7028UXYFStWxDe/+c0y7+/IXwBgx3PfffdF375947bbboutW7eW2XfevHlx4YUXRq9eveLBBx8ss29tnX+pzFryFwDY/tRUDvOVW2+9NRYuXBiPP/54fOc734khQ4ZEmzZton79+tGiRYsYMGBAfOc734k33ngjnnnmmWjVqlV6bCqVinPOOSc+//zzUueXwwDAjuHSSy9NfFazcOHCOPnkk2Pt2rWljnnkkUfi5ptvLvG9mjib4gwMALCtbOQvEc7AAAC1py7Wc5H3kOsUDaPWlVZN8dxzz4199tmnzLH16tWLe++9N/GP0unTp8drr71W7jqbNm2q8F43btxY5pzZXAcAqD3Zyl++cuaZZ2b8j8nzzz8/Hn300WIfJvzmN78pdYz8BQD4ypo1a+KZZ55JtI0aNSrj8VXNK4rmFCXNWR3rlLSW/AUAtk9VzV8iIlq3bh0nnHBC4n5KafLz8+O6666LO+64I9E+adKkYvvYlvwFAHYcmzdvjlNOOSUuuOCCWLRoUUREtGnTJq655poYP358LF++PDZt2hSfffZZPP/88/H1r3898vLyIuK/T3A/55xzYvTo0aXOX1vnXyqzlvwFALYfNZ3DfOXUU0+NZs2aZbSnr3/96/GPf/wj8QT2devWxS9+8YtSx8hhAGDHsNNOO8WVV16ZaBs7dmzsuuuucd9998WcOXNi06ZNsXz58njllVfitNNOi7POOiu2bt0aeXl50bJly8TYkvITZ2AAgOqUjfwlwhkYAKD21MV6LvIecp2iYdS60v5xet5552U0vk+fPnH44Ycn2koqulF0nZKeJlaeolUbS9p7ttYBAGpPtvKXyjrppJPiW9/6VqLtkUceKbW//AUA+MqTTz6ZeGpZx44d45hjjsl4fFXzipKemOH+CwBQlqrmL5V18cUXx8EHH5xoc/8FAIiIuOCCC+Lpp59Ox8OGDYspU6bE9ddfH3vvvXe0atUqGjRoEJ07d44TTjghnnnmmfjLX/6SOBR42223xUMPPVTi/LV1/qUya8lfAGD7UdM5TGX9z//8T1x++eWJtkcffTQKCwtL7C+HAYAdxw033BAnnHBCom3u3LlxwQUXRO/evaNhw4bRpk2bOPzww+PJJ59M97n++utjp512Soxr1apVsfmdgQEAqltN5y+V5QwMAFAd6mI9F3kPuU7RMGpd48aNIz8/P9HWvHnz2GuvvTKeo+g/SCdOnFisT9G/PLf9AkkmUqlUpX7JrFu3LlKpVIXWKro3f/EDQG7JVv5SFT/60Y8S8UcffRRffPFFiX3lLwDAVx5++OFEfOaZZ0b9+vUzHl/V+y9F+9evX7/Ep2hUdZ2SxshfAGD7VNX8pSqK3n8ZO3ZsbNmypcS+8hcA2DGMGzcuHnjggXTcoUOHeOGFF6JTp05ljjvxxBPjN7/5TaJt9OjRGX25tKbyipI+D6vqvR75CwDkpmzkMFVx8cUXJ/KSZcuWlXrORg4DADuO/Pz8ePbZZ+PSSy8t9vu/JA0aNIhf/vKXcfXVV8eaNWsS72VSNMwZGACgqmo6f6kKZ2AAgKqqi/VcsnVNUFmKhpETOnTokIh32mmnqFcv8/88+/fvn4i//PLLctdYsGBBBXYY8cUXXyT+kVuvXr1o165dsX7t2rWLvLy8dLx58+YS91OWhQsXJuKiewcAal828peqGDRoUGKPqVQqPvnkkxL7yl8AgIiIWbNmxRtvvJFoGzVqVIXmqOr9l6I5Rfv27TNap+i4yqwlfwGA7U915C9VceihhybuqaxevToWLVpUYl/5CwDsGO6+++5EfMkll5R6f6OoUaNGRb9+/dLx0qVL45lnninWr6r3XyIiPvvsszLn/ErRvVf1Xo/8BQByUzZymKpo3bp1DB48ONE2ffr0UvvLYQBgx5Gfnx933HFHTJ48Oc4555zo0qVLsT5NmzaNs846K8aPHx9XXXVVRPw3Z9nWTjvtVGycMzAAQE2oyfylKpyBAQCqqi7Wc8nWNUFlKRpGTthll10ScYsWLSo0vmj/5cuXF+tTtDDHvHnzKrRG0f49e/Ys8SkfjRs3jh49elTrWgMGDKjQeACg5mUjf6mqbt26JeLFixeX2E/+AgBERPzhD39IPF1j8ODBMWjQoArNUd33X0rLKfr06RP169dPx+vXry8116nqWgBA7qqO/KUqmjZtGq1bt060lZaTyF8AoO5LpVIxduzYRNsJJ5yQ8fh69erFcccdl2h7/fXXi/Wr6v2XL7/8MjZs2JCOCwoKok+fPiX2zda9HgCg9mQrh6mq7t27J+Ky7qvIYQBgxzNgwIC4//77Y+HChTF//vyYNGlSvPHGGzFjxoxYtmxZjBkzJvbcc8+I+G8esWTJkvTYpk2bFjsTHOEMDABQs2oif6kKZ2AAgKqqi/VcsnVNUFmKhpETBg4cmIg3btxYofHbHmaMiGjSpEmxPkX/op46dWqF1pg2bVqZ89XWWgBA7chG/lJVDRo0SMSbN28uta/8BQB2bKlUKv7whz8k2s4+++wKz5OtnKJBgwbRt2/fSq+1cePGmDVrVkZrAQC5qbryl6rK9P6L/AUA6r7ly5fHypUrE229e/eu0BxF+5f0ZPaiOcDMmTNj06ZNGa9R9P5L3759E1/sKGstnx8BQN2TrRymqpyBAQAy1a1btxg8eHAccMAB0bdv3ygoKEi8//HHHyfiPffcM/Lz84vN4wwMAJAt1ZW/VJUzMABAVdTFei4+cyLXKRpGThg8eHAi/uKLLyo0/ssvv0zEbdu2LdZn1113Tfyjdc6cObFo0aKM13jrrbcS8VdVuktS9L23334743UWLVoUc+bMSccNGjQoVpQEAKh92chfqurzzz9PxO3bty+1r/wFAHZsr732WsyePTsdFxQUxMiRIys8T9GcYsKECbFly5aMx2fr/sukSZMSRV87d+4cHTp0yHg8AFD7qit/qYotW7bE0qVLE201df9F/gIAua+kB8yUVoyrNEW/jLF169ZifTp16hSdOnVKrDtp0qSM18jW/ZctW7bE+PHjM14LAKgd2cphqipbZ2DkMABQ9/3tb39LxMccc0yJ/ZyBAQByRab5S1U4AwMAVFVdrOeSzWuCylA0jJxw3HHHRb16//8/x9mzZ8eyZcsyHl/08GP//v2L9WnevHkcdNBBibaXXnopo/lTqVS8/PLLibYTTjih1P7HH398In755ZcjlUpltNaLL76YiIcPHx7NmjXLaCwAkD3ZyF+qYsGCBTF37txEW/fu3UvtL38BgB3bmDFjEvEJJ5wQbdq0qfA8AwYMSDw9bO3atRnffF+7dm2888476TgvL69YjrKtou9lep+npL5l3ecBAHJTdeUvVfHuu+8mvhxSv379RAGPouQvAFC3lfSAmM8++6xCcyxcuDARl/ZljOOOOy4R11ReUXSdt99+O9auXZvROm+99VasW7cuHffr1y/69euX8T4BgOzIZg5TWRs3bowJEyYk2so6AyOHAQBKs3Xr1njyySfTcX5+fpx99tkl9nUGBgDIBRXJX6rCGRgAoKrqYj2XbF4TVIaiYeSEDh06xP77759oe+aZZzIau2XLlnj22WcTbYccckiJfU888cRE/MADD2S0xquvvpp4Wn3Hjh1jn332KbX/fvvtF+3atUvHs2bNinHjxmW0VtE9jRgxIqNxAEB2ZSt/qayiOUX37t1j5513LrW//AUAdlxr166Np556KtE2atSoSs9X2fsvjz/+eKxZsyYdDx06NLp06VJq/2OPPTbxpPtx48bFrFmzyl0nlUrFww8/nGiTvwDA9qW685fKKprn7LvvvtGkSZNS+8tfAKBuKygoiM6dOyfaxo4dW6E5XnnllUS87RdTt1X0/stDDz2U0eHHmTNnxmuvvZaOGzRoEMcee2yp/bt37x577bVXOl6zZk088cQT5a4T4fMjANheZDOHqazHHnssUcirYcOGxc7sbEsOAwCU5ne/+13Mnz8/HR977LFlnk1xBgYAqG0VzV8qyxkYAKA61MV6Ltm6JqgMRcPIGeeff34ivvXWW2Pjxo3ljvv9738fn3/+eTpu0aJFHHXUUSX2/cY3vhFNmzZNx6+//nq5hxtSqVRcf/31ibazzz476tUr/f8+9erVK/bllOuvv77cA5qvvPJKvPHGG+m4efPmcdppp5U5BgCoPdnIXypj2rRpcfvttyfavva1r5U5Rv4CADuup59+OnFQsVOnTnH00UdXer7vfOc7kZeXl44fe+yxmDZtWpljNmzYEDfddFOi7ZxzzilzTJs2bRI5TiqViuuuu67c/T344IMxZ86cdNyzZ884/PDDyx0HAOSO6s5fKmPcuHHxyCOPJNrKu/8ifwGAuu+www5LxHfddVfiqexlee211+Kdd94pc76vHHXUUdGtW7d0PGfOnHjooYfKXeO6665LfPZz8sknR8uWLcscU/QezU033RQbNmwoc8y0adPi8ccfT8clfQ4FAOSObOUwlfH555/HT37yk0TbkUceWeaXViPkMABAcTNnzoyrr746HTdo0KDYWZWinIEBAGpTZfKXynAGBgCoLnWxnku2rgkqw39R5IwzzjgjBg0alI4/+eSTOP/886OwsLDUMe+9915cfvnlibYLL7yw1AONHTp0iO9///uJtnPPPTc+++yzUte48cYb4/XXX0/HLVu2jNGjR5d5LRERV1xxRTRr1iwdv/baa3HzzTeX2n/hwoVx7rnnJtouvvjiRIVLACC31HT+8uGHH8add96ZeFpqeT788MM4+uijY/Xq1em2xo0bx5VXXlnuWPkLAOyYij6x68wzz0w8/auidtttt8RN802bNsW3v/3tWLVqVYn9U6lUXHLJJfHpp5+m2/r06RPf+c53yl3r+uuvT9w0f+SRR+LPf/5zqf2nTp0al112WaLt6quvjoKCgnLXAgByR3XmLy+99FI89NBDGX8RNiJi7NixcdJJJ8XWrVvTbZ07d47vfe975Y6VvwBA3fbNb34zEU+ePDkuvPDCMj87ioiYMWNGjBw5MtG28847x7777lti/4YNGxYrnnHZZZfF1KlTS13j0UcfjT/+8Y/pOD8/v9gBxZKcd9550aNHj3T8ySefxKWXXlrqQctVq1bFWWedFZs2bUq3jRw5MgYOHFjuWgBA7chGDrNo0aK49tprY/ny5Rnva86cOXH00UfHwoUL0215eXkZfQFVDgMAdd+8efMyPl87bdq0OOyww2Lp0qXpttGjR5f7u94ZGACgOtV0/uIMDABQ2+piPZdsXhNUWApyyMsvv5zKy8tLRUT6z+GHH56aOHFiot+KFStSt99+e6pZs2aJvv369UutWrWqzDWWLl2a6tSpU2Jcz549U88991yqsLAw3W/+/Pmp888/P9EvIlK33HJLxtfzy1/+stj4Cy64ILVw4cJ0n61bt6aeffbZVI8ePRL9unTpklq+fHnGawEAtaMm85dXX301FRGptm3bps4777zU3//+99TixYuL9SssLEx99NFHqR/84Aephg0bFss/7rrrroyvR/4CADuWOXPmFMtlPv744yrP++mnn6aaNGmSmHePPfZIvfrqq4l+06dPT5100knF8o8nnngi47W++93vJsbWq1cvdfXVV6eWLVuW7rNp06bUQw89lGrdunWi7+67757avHlzla8XAMie6s5fHnrooVREpLp27Zq65JJLUmPHjk2tWLGiWL8tW7ak3n333dRZZ52VqlevXrH845lnnsl4TfkLANRtw4cPL3av44ADDki9/PLLxX6PL1myJHXbbbelWrZsWWzMk08+WeY6mzZtSu26666JMW3atEmNGTMmsc7SpUtTP/3pT4vlMBdeeGHG1/Too48W298pp5yS+uSTTxL9XnnlldTuu++e6NesWbPUrFmzMl4LAKgdNZ3DzJ49O50bjBw5MvX0008nzqJs69NPP0395Cc/KXH+Sy65JONrksMAQN12zz33pNq2bZu6+OKLU+PGjUutW7euWJ+PPvooNXr06FRBQUHid/2+++6bWr9+fUbrOAMDAFSXms5fnIEBAMrz5ptvpl566aVif2677bbE7+mOHTuW2O+ll15KTZkypcw16mI9l2xeE1REXipVyiOToJbcfPPNceWVVxZr79SpU3Tr1i3Wrl0bM2fOTDzNKyKibdu28eqrr8agQYPKXeP111+Po446KjZs2JBob9WqVfTu3TtWrFgR8+bNS1THjogYMWJEPPvss5GXl5fRtRQWFsaIESPihRdeSLTn5+dHz549o2XLljF79uxYsWJF4v3GjRvHSy+9FPvvv39G6wAAtaum8pdx48bF8OHDi7V37Ngx2rVrF82bN481a9bEwoULS30S649+9KO47bbbMr4W+QsA7Fh+9rOfxTXXXJOOhwwZEhMnTqyWuR977LEYOXJksSe2t2/fPnr06BFffvllLFiwoNj7P/jBD+Luu+/OeJ1169bFwQcfXGzfBQUF0bt372jYsGHMmjUr1qxZk3i/Xbt28dZbb0W/fv0qeGUAQG2q7vzl4YcfjrPPPrtYe9euXaNNmzbRtGnTWLVqVcybN69YPhERkZeXF7/61a/iBz/4QcZryl8AoG77/PPPY7/99ovZs2cXe69Zs2bRu3fvaNy4cSxdujRmzZpV7N5IROaf70ybNi0OOOCAWLZsWbF1+vbtG+vXr4/Zs2fH5s2bE+8PGzYsxo0bF40bN874ui688MK49957E215eXnRvXv3aN++fcydOzeWLFmSeL9evXrx+OOPxymnnJLxOgBA7ajpHGbOnDnRu3fvYu1t27aNDh06RIsWLWL9+vWxaNGiWLx4cYlznHrqqfHYY49FvXr1Mr4uOQwA1F2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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 \\\n",
|
|
"0 0 3.90 745.0 967.0 2 0 8.362214e-08 \n",
|
|
"1 0 0.66 749.0 1133.0 0 0 9.091998e-01 \n",
|
|
"2 1 7.86 746.0 894.0 0 0 1.000000e+00 \n",
|
|
"3 1 3.48 750.0 1127.0 2 0 1.000000e+00 \n",
|
|
"4 0 1.19 746.0 1744.0 0 0 1.000000e+00 \n",
|
|
"\n",
|
|
" CIR1 CIR2 CIR3 ... CIR1009 CIR1010 \\\n",
|
|
"0 2.674740e-07 0.000002 0.000003 ... 5.155971e-09 1.199803e-08 \n",
|
|
"1 7.974981e-02 1.000000 1.000000 ... 6.605308e-04 1.000000e+00 \n",
|
|
"2 1.000000e+00 1.000000 1.000000 ... 1.000000e+00 1.000000e+00 \n",
|
|
"3 1.000000e+00 1.000000 1.000000 ... 1.000000e+00 1.000000e+00 \n",
|
|
"4 1.000000e+00 1.000000 1.000000 ... 3.663934e-01 1.000000e+00 \n",
|
|
"\n",
|
|
" CIR1011 CIR1012 CIR1013 CIR1014 CIR1015 \\\n",
|
|
"0 1.032906e-07 1.706671e-07 0.000070 3.184346e-10 0.000000e+00 \n",
|
|
"1 1.000000e+00 2.527824e-01 0.113831 2.491042e-02 2.350403e-25 \n",
|
|
"2 1.000000e+00 1.000000e+00 1.000000 1.000000e+00 1.590984e-10 \n",
|
|
"3 1.000000e+00 1.000000e+00 0.082226 1.000000e+00 6.469684e-02 \n",
|
|
"4 1.000000e+00 1.000000e+00 0.071744 1.000000e+00 6.469684e-02 \n",
|
|
"\n",
|
|
" RX_Level First_Path_Power_Level SNR \n",
|
|
"0 -27.806709 -32.018158 185.234375 \n",
|
|
"1 -23.050836 -34.255986 296.375000 \n",
|
|
"2 -28.334789 -43.509715 244.983333 \n",
|
|
"3 -33.611812 -41.706556 115.105263 \n",
|
|
"4 -21.081660 -23.834943 167.352941 \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": "2024-03-15T14:27:18.398378Z",
|
|
"start_time": "2024-03-15T14:27:03.916877Z"
|
|
}
|
|
},
|
|
"id": "79c2c23691b26753",
|
|
"execution_count": 22
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:53.699698Z",
|
|
"start_time": "2024-03-15T07:54:53.695579Z"
|
|
}
|
|
},
|
|
"id": "12e16974341e6266",
|
|
"execution_count": 584
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:54.007660Z",
|
|
"start_time": "2024-03-15T07:54:53.700623Z"
|
|
}
|
|
},
|
|
"id": "54d2a6506b584a03",
|
|
"execution_count": 585
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:54.013837Z",
|
|
"start_time": "2024-03-15T07:54:54.010643Z"
|
|
}
|
|
},
|
|
"id": "dc485f3de9f8936f",
|
|
"execution_count": 586
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:54.019460Z",
|
|
"start_time": "2024-03-15T07:54:54.014701Z"
|
|
}
|
|
},
|
|
"id": "702b4f40dda16736",
|
|
"execution_count": 587
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:54.022688Z",
|
|
"start_time": "2024-03-15T07:54:54.020364Z"
|
|
}
|
|
},
|
|
"id": "1f6f826d6234591c",
|
|
"execution_count": 588
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:54.026234Z",
|
|
"start_time": "2024-03-15T07:54:54.023505Z"
|
|
}
|
|
},
|
|
"id": "c970b0c1593d955c",
|
|
"execution_count": 589
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:54.029772Z",
|
|
"start_time": "2024-03-15T07:54:54.027195Z"
|
|
}
|
|
},
|
|
"id": "ee7506f4aa805faf",
|
|
"execution_count": 590
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:54.033203Z",
|
|
"start_time": "2024-03-15T07:54:54.030697Z"
|
|
}
|
|
},
|
|
"id": "a44d38efa4b86d93",
|
|
"execution_count": 591
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:54.037848Z",
|
|
"start_time": "2024-03-15T07:54:54.035362Z"
|
|
}
|
|
},
|
|
"id": "a3646a4965b0707c",
|
|
"execution_count": 592
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:54.041368Z",
|
|
"start_time": "2024-03-15T07:54:54.038716Z"
|
|
}
|
|
},
|
|
"id": "c7ecae5d021ad44f",
|
|
"execution_count": 593
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:54.044962Z",
|
|
"start_time": "2024-03-15T07:54:54.042238Z"
|
|
}
|
|
},
|
|
"id": "4a8a1c3a7289ef7a",
|
|
"execution_count": 594
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:54.048327Z",
|
|
"start_time": "2024-03-15T07:54:54.046119Z"
|
|
}
|
|
},
|
|
"id": "705c62e64bf6d614",
|
|
"execution_count": 595
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:54.051615Z",
|
|
"start_time": "2024-03-15T07:54:54.049222Z"
|
|
}
|
|
},
|
|
"id": "cf4df4ef7bbfd74",
|
|
"execution_count": 596
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:54.055015Z",
|
|
"start_time": "2024-03-15T07:54:54.052488Z"
|
|
}
|
|
},
|
|
"id": "faabcf63e34005a9",
|
|
"execution_count": 597
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:54.058552Z",
|
|
"start_time": "2024-03-15T07:54:54.056001Z"
|
|
}
|
|
},
|
|
"id": "2ed22b3fc59f74e6",
|
|
"execution_count": 598
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:54.062224Z",
|
|
"start_time": "2024-03-15T07:54:54.059494Z"
|
|
}
|
|
},
|
|
"id": "4ac86c268055c1b8",
|
|
"execution_count": 599
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:54.065912Z",
|
|
"start_time": "2024-03-15T07:54:54.063095Z"
|
|
}
|
|
},
|
|
"id": "3d984228fb1d3026",
|
|
"execution_count": 600
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:54.069486Z",
|
|
"start_time": "2024-03-15T07:54:54.066838Z"
|
|
}
|
|
},
|
|
"id": "98cd350871bc3201",
|
|
"execution_count": 601
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:54.072727Z",
|
|
"start_time": "2024-03-15T07:54:54.070408Z"
|
|
}
|
|
},
|
|
"id": "305a796294814705",
|
|
"execution_count": 602
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:54.076019Z",
|
|
"start_time": "2024-03-15T07:54:54.073645Z"
|
|
}
|
|
},
|
|
"id": "494bb537046bf5a7",
|
|
"execution_count": 603
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:54.079162Z",
|
|
"start_time": "2024-03-15T07:54:54.076932Z"
|
|
}
|
|
},
|
|
"id": "62401c8d1a4d61cc",
|
|
"execution_count": 604
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:54.083050Z",
|
|
"start_time": "2024-03-15T07:54:54.080066Z"
|
|
}
|
|
},
|
|
"id": "f0f5284581e70e6e",
|
|
"execution_count": 605
|
|
},
|
|
{
|
|
"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": "2024-03-15T07:54:54.086722Z",
|
|
"start_time": "2024-03-15T07:54:54.083982Z"
|
|
}
|
|
},
|
|
"id": "82c7ba8cbb2aa17a",
|
|
"execution_count": 606
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"# Convolution Neural Network\n",
|
|
"\n",
|
|
"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",
|
|
"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",
|
|
"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",
|
|
"3. Data Splitting: The data is split into training and testing sets using a 80:20 ratio. \n",
|
|
"4. Model Creation: A Sequential model is created using Keras. This model is composed of the following layers: \n",
|
|
"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",
|
|
"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",
|
|
"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",
|
|
"9. Model Compilation: The model is compiled with the Adam optimizer, categorical cross-entropy loss function, and accuracy as the evaluation metric. \n",
|
|
"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",
|
|
"11. Model Evaluation: The model's performance is evaluated on the testing set and the accuracy is printed. \n",
|
|
"\n",
|
|
"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",
|
|
"\n",
|
|
"The mathematical formula for the convolution operation is: $$ (f * g)(t) = \\int_{-\\infty}^{\\infty} f(\\tau)g(t - \\tau) d\\tau $$ Where: \n",
|
|
"$f$ and $g$ are the input data and kernel respectively\n",
|
|
"$t$ is the position of the kernel\n",
|
|
"$\\tau$ is a dummy integration variable\n",
|
|
"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": {
|
|
"collapsed": false
|
|
},
|
|
"id": "862a9b7ee430a667"
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 1/10\n",
|
|
"1050/1050 [==============================] - 11s 10ms/step - loss: 0.6585 - accuracy: 0.6549 - val_loss: 0.5905 - val_accuracy: 0.6931\n",
|
|
"Epoch 2/10\n",
|
|
"1050/1050 [==============================] - 10s 10ms/step - loss: 0.6054 - accuracy: 0.6798 - val_loss: 0.6396 - val_accuracy: 0.6625\n",
|
|
"Epoch 3/10\n",
|
|
"1050/1050 [==============================] - 10s 10ms/step - loss: 0.5912 - accuracy: 0.6914 - val_loss: 0.6045 - val_accuracy: 0.6818\n",
|
|
"Epoch 4/10\n",
|
|
"1050/1050 [==============================] - 10s 10ms/step - loss: 0.5570 - accuracy: 0.7111 - val_loss: 0.5661 - val_accuracy: 0.6981\n",
|
|
"Epoch 5/10\n",
|
|
"1050/1050 [==============================] - 11s 10ms/step - loss: 0.5180 - accuracy: 0.7379 - val_loss: 0.5362 - val_accuracy: 0.7364\n",
|
|
"Epoch 6/10\n",
|
|
"1050/1050 [==============================] - 11s 10ms/step - loss: 0.4953 - accuracy: 0.7536 - val_loss: 0.5515 - val_accuracy: 0.7302\n",
|
|
"Epoch 7/10\n",
|
|
"1050/1050 [==============================] - 10s 10ms/step - loss: 0.4683 - accuracy: 0.7697 - val_loss: 0.5867 - val_accuracy: 0.7152\n",
|
|
"Epoch 8/10\n",
|
|
"1050/1050 [==============================] - 10s 10ms/step - loss: 0.4411 - accuracy: 0.7861 - val_loss: 0.5814 - val_accuracy: 0.7175\n",
|
|
"Epoch 9/10\n",
|
|
"1050/1050 [==============================] - 10s 10ms/step - loss: 0.3929 - accuracy: 0.8163 - val_loss: 0.6463 - val_accuracy: 0.7149\n",
|
|
"Epoch 10/10\n",
|
|
"1050/1050 [==============================] - 10s 9ms/step - loss: 0.3363 - accuracy: 0.8464 - val_loss: 0.7454 - val_accuracy: 0.7013\n",
|
|
"263/263 [==============================] - 1s 3ms/step - loss: 0.7454 - accuracy: 0.7013\n",
|
|
"Test accuracy: 0.7013095021247864\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from tensorflow.keras.models import Sequential\n",
|
|
"from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense\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\n",
|
|
"model.fit(X_train_reshaped, y_train, epochs=10, batch_size=32, validation_data=(X_test_reshaped, y_test))\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": "2024-03-15T07:56:39.179793Z",
|
|
"start_time": "2024-03-15T07:54:54.087608Z"
|
|
}
|
|
},
|
|
"id": "3fe2ed86c3527d2",
|
|
"execution_count": 607
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": "<IPython.core.display.Image object>"
|
|
},
|
|
"execution_count": 608,
|
|
"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": "2024-03-15T07:56:39.273093Z",
|
|
"start_time": "2024-03-15T07:56:39.180828Z"
|
|
}
|
|
},
|
|
"id": "81f2d793ada5c410",
|
|
"execution_count": 608
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"# Save the model\n",
|
|
"model.save('CNN.keras')"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T07:57:34.813584Z",
|
|
"start_time": "2024-03-15T07:57:34.743558Z"
|
|
}
|
|
},
|
|
"id": "6b7329b28452b82a",
|
|
"execution_count": 610
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:21:00.630109Z",
|
|
"start_time": "2024-03-15T14:21:00.627089Z"
|
|
}
|
|
},
|
|
"id": "e6784b230264758d",
|
|
"execution_count": 12
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:21:01.145895Z",
|
|
"start_time": "2024-03-15T14:21:01.143495Z"
|
|
}
|
|
},
|
|
"id": "3c339a3a9b167026",
|
|
"execution_count": 12
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"# Multi-layer Perceptron (MLP)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"id": "42eff9445377f73c"
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 1/10\n",
|
|
"WARNING:tensorflow:From C:\\Users\\PC\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
|
|
"WARNING:tensorflow:From C:\\Users\\PC\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
|
|
"1050/1050 [==============================] - 2s 989us/step - loss: 0.4350 - accuracy: 0.7986 - val_loss: 0.3971 - val_accuracy: 0.8256\n",
|
|
"Epoch 2/10\n",
|
|
"1050/1050 [==============================] - 1s 871us/step - loss: 0.3188 - accuracy: 0.8687 - val_loss: 0.3908 - val_accuracy: 0.8300\n",
|
|
"Epoch 3/10\n",
|
|
"1050/1050 [==============================] - 1s 876us/step - loss: 0.2563 - accuracy: 0.8975 - val_loss: 0.4429 - val_accuracy: 0.8149\n",
|
|
"Epoch 4/10\n",
|
|
"1050/1050 [==============================] - 1s 859us/step - loss: 0.1882 - accuracy: 0.9283 - val_loss: 0.5142 - val_accuracy: 0.8161\n",
|
|
"Epoch 5/10\n",
|
|
"1050/1050 [==============================] - 1s 857us/step - loss: 0.1279 - accuracy: 0.9531 - val_loss: 0.5977 - val_accuracy: 0.8120\n",
|
|
"Epoch 6/10\n",
|
|
"1050/1050 [==============================] - 1s 867us/step - loss: 0.0862 - accuracy: 0.9697 - val_loss: 0.6960 - val_accuracy: 0.8093\n",
|
|
"Epoch 7/10\n",
|
|
"1050/1050 [==============================] - 1s 857us/step - loss: 0.0637 - accuracy: 0.9783 - val_loss: 0.8073 - val_accuracy: 0.8108\n",
|
|
"Epoch 8/10\n",
|
|
"1050/1050 [==============================] - 1s 858us/step - loss: 0.0535 - accuracy: 0.9822 - val_loss: 0.8436 - val_accuracy: 0.8090\n",
|
|
"Epoch 9/10\n",
|
|
"1050/1050 [==============================] - 1s 897us/step - loss: 0.0457 - accuracy: 0.9854 - val_loss: 0.9384 - val_accuracy: 0.8051\n",
|
|
"Epoch 10/10\n",
|
|
"1050/1050 [==============================] - 1s 862us/step - loss: 0.0348 - accuracy: 0.9899 - val_loss: 0.9996 - val_accuracy: 0.8023\n",
|
|
"263/263 [==============================] - 0s 478us/step\n",
|
|
"Test loss: 0.9995887875556946\n",
|
|
"Test accuracy: 0.8022618889808655\n",
|
|
"Classification Report: \n",
|
|
" precision recall f1-score support\n",
|
|
"\n",
|
|
" 0 0.80 0.81 0.80 4192\n",
|
|
" 1 0.81 0.80 0.80 4208\n",
|
|
"\n",
|
|
" accuracy 0.80 8400\n",
|
|
" macro avg 0.80 0.80 0.80 8400\n",
|
|
"weighted avg 0.80 0.80 0.80 8400\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from tensorflow.keras.models import Sequential\n",
|
|
"from tensorflow.keras.layers import Dense\n",
|
|
"from sklearn.model_selection import train_test_split\n",
|
|
"from sklearn.preprocessing import StandardScaler\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\n",
|
|
"model = Sequential()\n",
|
|
"model.add(Dense(64, input_dim=X_train.shape[1], activation='relu')) # Input layer\n",
|
|
"model.add(Dense(64, activation='relu')) # Hidden layer\n",
|
|
"model.add(Dense(1, activation='sigmoid')) # Output layer\n",
|
|
"\n",
|
|
"# Compile the model\n",
|
|
"model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
|
|
"\n",
|
|
"# Train the model\n",
|
|
"history = model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))\n",
|
|
"\n",
|
|
"# Evaluate the model\n",
|
|
"scores = model.evaluate(X_test, y_test, verbose=0)\n",
|
|
"\n",
|
|
"# Make predictions\n",
|
|
"y_pred = model.predict(X_test)\n",
|
|
"\n",
|
|
"# Convert probabilities to class labels\n",
|
|
"y_pred_classes = (y_pred > 0.5).astype(\"int32\")\n",
|
|
"\n",
|
|
"# Generate classification report\n",
|
|
"report = classification_report(y_test, y_pred_classes)\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,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-03-15T14:34:49.159067Z",
|
|
"start_time": "2024-03-15T14:34:37.294715Z"
|
|
}
|
|
},
|
|
"id": "c8745832a585d5ec",
|
|
"execution_count": 24
|
|
}
|
|
],
|
|
"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"name": "python3"
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"language_info": {
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"codemirror_mode": {
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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