@@ -0,0 +1,842 @@
+%22%22%22%0ACommon utilities for testing clustering.%0A%0A%22%22%22%0A%0Aimport numpy as np%0A%0A################################################################################%0A# Generate sample data%0A%0Adef generate_clustered_data(seed=0, n_clusters=3, n_features=2,%0A                            n_samples_per_cluster=20, std=.4):%0A    prng = np.random.RandomState(seed)%0A%0A    # the data is voluntary shifted away from zero to check clustering%0A    # algorithm robustness w.r.t. non centered data%0A    means = np.array(%5B%5B 1,  1, 1, 0%5D,%0A                      %5B-1, -1, 0, 1%5D,%0A                      %5B 1, -1, 1, 1%5D,%0A                      %5B -1, 1, 1, 0%5D,%0A                    %5D) + 10%0A%0A    X = np.empty((0, n_features))%0A    for i in range(n_clusters):%0A        X = np.r_%5BX, means%5Bi%5D%5B:n_features%5D%0A                  + std * prng.randn(n_samples_per_cluster, n_features)%5D%0A    return X%0A

FIX : putting back common.py