"""
Common utilities for testing clustering.

"""

import numpy as np

################################################################################
# Generate sample data

def generate_clustered_data(seed=0, n_clusters=3, n_features=2,
                            n_samples_per_cluster=20, std=.4):
    prng = np.random.RandomState(seed)

    # the data is voluntary shifted away from zero to check clustering
    # algorithm robustness w.r.t. non centered data
    means = np.array([[ 1,  1, 1, 0],
                      [-1, -1, 0, 1],
                      [ 1, -1, 1, 1],
                      [ -1, 1, 1, 0],
                    ]) + 10

    X = np.empty((0, n_features))
    for i in range(n_clusters):
        X = np.r_[X, means[i][:n_features]
                  + std * prng.randn(n_samples_per_cluster, n_features)]
    return X

FIX : putting back common.py