15 """
16 X = data.drop('target', axis=1)
17 y = data['target']
18 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)19 model = RandomForestClassifier(n_estimators=100, random_state=42)
20 model.fit(X_train, y_train)
21 return model
15 """
16 X = data.drop('target', axis=1)
17 y = data['target']
18 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)19 model = RandomForestClassifier(n_estimators=100, random_state=42)
20 model.fit(X_train, y_train)
21 return model
2
3def random_unitary_matrix(n):
4 matrix = np.random.rand(n, n) + 1j * np.random.rand(n, n)
5 q, r = np.linalg.qr(matrix) 6 return q
7
8def random_quantum_state(n):
9 signal_data = np.loadtxt(file_path)
10 return signal_data
11
12 def preprocess_signal(signal_data):13 # Preprocess the signal data (e.g., normalize, remove noise)
14 preprocessed_data =...
15 return preprocessed_data
5
6def process_real_time_data(data_stream):
7 # Load and preprocess the radio signal data in real-time
8 def load_radio_signal(file_path): 9 signal_data = np.loadtxt(file_path)
10 return signal_data
11
An unused variable takes up space in the code, and can lead to confusion, and it should be removed. If this variable is necessary, name the variable _
to indicate that it will be unused, or start the name with unused
or _unused
.
def update():
for i in range(10): # Usused variable `i`
time.sleep(0.01)
display_result()
def update():
for _ in range(10):
time.sleep(0.01)
display_result()