def test_compute_centers(self, data_labels):
data, _ = data_labels
ac = cluster.AgglomerativeClustering()
fit = ac.fit(data)
result = compute_centers(fit, data)
assert result.shape == (data.shape[1], len(set(fit.labels_)))
# In[7]:
conn = pg.connect(database='gye_datoss',
user='******',
password='******',
host='52.38.27.79',
port='5432')
df = psql.read_sql("SELECT * FROM trayectoria_gye_hist ", conn)
coords = df.as_matrix(columns=['longitud', 'latitud'])
# In[11]:
connectivity = kneighbors_graph(coords, n_neighbors=10, include_self=False)
n_cluster = 3
result = compute_centers(ward, coords)
st = time.time()
ward = AgglomerativeClustering(n_clusters=n_cluster,
connectivity=connectivity,
linkage='ward').fit(coords)
elapsed_time = time.time() - st
labels = ward.labels_
core_samples_mask = np.zeros_like(n_cluster, dtype=bool)
num_clusters = len(set(labels))
# In[12]:
lats, lons = zip(*result)
#centroids = AgglomerativeClustering.cluster_centers
rep_points = pd.DataFrame({'longitud': lons, 'latitud': lats})
rep_points.to_csv(