Create data_utils.py

utils/data_utils.py

@@ -0,0 +1,48 @@
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+
+def load_nav_data(file_path):
+    """
+    Load navigation data from a CSV file.
+
+    Args:
+        file_path (str): The path to the CSV file.
+
+    Returns:
+        pandas DataFrame: The loaded navigation data.
+    """
+    data = pd.read_csv(file_path)
+    return data
+
+def preprocess_nav_data(data):
+    """
+    Preprocess navigation data by scaling and normalizing the features.
+
+    Args:
+        data (pandas DataFrame): The navigation data to preprocess.
+
+    Returns:
+        pandas DataFrame: The preprocessed navigation data.
+    """
+    scaler = StandardScaler()
+    data_scaled = scaler.fit_transform(data.drop("target", axis=1))
+    data_preprocessed = pd.DataFrame(data_scaled, columns=data.columns[:-1])
+    data_preprocessed["target"] = data["target"]
+    return data_preprocessed
+
+def generate_synthetic_data(num_samples, num_features):
+    """
+    Generate synthetic data using a Gaussian mixture model.
+
+    Args:
+        num_samples (int): The number of samples to generate.
+        num_features (int): The number of features to generate.
+
+    Returns:
+        pandas DataFrame: The generated synthetic data.
+    """
+    from sklearn.mixture import GaussianMixture
+    gmm = GaussianMixture(n_components=3, covariance_type="full")
+    data = gmm.sample(num_samples)[0]
+    data = pd.DataFrame(data, columns=[f"feature_{i}" for i in range(num_features)])
+    return data