def __init__(self, concept_id=0, seed=None, noise=0, desc=None):
self.cf = concept_id
self.seed = seed
self.difficulty = 0 if desc == None else desc.difficulty
self.n_classes = 2
# self.n_features = self.difficulty + 10
self.n_features = 10
self.n_centroids = self.difficulty * 5 + 15
stream = RandomRBFGenerator(model_random_state=seed,
sample_random_state=seed,
n_centroids=self.n_centroids,
n_classes=self.n_classes,
n_features=self.n_features)
stream.prepare_for_use()
super().__init__(stream)
def test_random_rbf_generator(test_path):
stream = RandomRBFGenerator(model_random_state=99,
sample_random_state=50,
n_classes=4,
n_features=10,
n_centroids=50)
stream.prepare_for_use()
assert stream.n_remaining_samples() == -1
expected_names = [
'att_num_0', 'att_num_1', 'att_num_2', 'att_num_3', 'att_num_4',
'att_num_5', 'att_num_6', 'att_num_7', 'att_num_8', 'att_num_9'
]
assert stream.feature_names == expected_names
expected_target_values = [0, 1, 2, 3]
assert stream.target_values == expected_target_values
assert stream.target_names == ['target_0']
assert stream.n_features == 10
assert stream.n_cat_features == 0
assert stream.n_num_features == 10
assert stream.n_targets == 1
assert stream.get_data_info(
) == 'Random RBF Generator - 1 target(s), 4 classes, 10 features'
assert stream.has_more_samples() is True
assert stream.is_restartable() is True
# Load test data corresponding to first 10 instances
test_file = os.path.join(test_path, 'random_rbf_stream.npz')
data = np.load(test_file)
X_expected = data['X']
y_expected = data['y']
X, y = stream.next_sample()
assert np.alltrue(X[0] == X_expected[0])
assert np.alltrue(y[0] == y_expected[0])
X, y = stream.last_sample()
assert np.alltrue(X[0] == X_expected[0])
assert np.alltrue(y[0] == y_expected[0])
stream.restart()
X, y = stream.next_sample(10)
assert np.alltrue(X == X_expected)
assert np.alltrue(y == y_expected)
assert stream.n_targets == np.array(y).ndim
assert stream.n_features == X.shape[1]
# In[1]: pip install -U git+https://github.com/scikit-multiflow/scikit-multiflow # In[1]: from skmultiflow.data.random_rbf_generator import RandomRBFGenerator # In[2]: RBF = RandomRBFGenerator(n_classes=2, n_features=10) # In[3]: RBF.prepare_for_use() # In[4]: RBF_data = RBF.next_sample(10000) # In[6]: