def test_affinity_propagation():
"""
Affinity Propagation algorithm
"""
#----------------------------------------------------------------------
# Compute similarities
#----------------------------------------------------------------------
X_norms = np.sum(X*X, axis=1)
S = - X_norms[:,np.newaxis] - X_norms[np.newaxis,:] + 2 * np.dot(X, X.T)
p = 10*np.median(S)
#----------------------------------------------------------------------
# Compute Affinity Propagation
#----------------------------------------------------------------------
cluster_centers_indices, labels = affinity_propagation(S, p)
n_clusters_ = len(cluster_centers_indices)
assert_equal(n_clusters, n_clusters_)
af = AffinityPropagation()
labels = af.fit(S, p).labels
cluster_centers_indices = af.fit(S, p).cluster_centers_indices
n_clusters_ = len(cluster_centers_indices)
assert_equal(np.unique(labels).size, n_clusters_)
assert_equal(n_clusters, n_clusters_)
'HD' : 'Home Depot',
'GSK' : 'GlaxoSmithKline',
'PFE' : 'Pfizer',
'SNY' : 'Sanofi-Aventis',
'NVS' : 'Novartis',
'KMB' : 'Kimberly-Clark',
'R' : 'Ryder',
'GD' : 'General Dynamics',
'RTN' : 'Raytheon',
'CVS' : 'CVS',
'CAT' : 'Caterpillar',
'DD' : 'DuPont de Nemours',
}
symbols, names = np.array(symbol_dict.items()).T
quotes = [finance.quotes_historical_yahoo(symbol, d1, d2, asobject=True)
for symbol in symbols]
#volumes = np.array([q.volume for q in quotes]).astype(np.float)
open = np.array([q.open for q in quotes]).astype(np.float)
close = np.array([q.close for q in quotes]).astype(np.float)
variation = close - open
correlations = np.corrcoef(variation)
_, labels = clustering.affinity_propagation(correlations)
for i in range(labels.max()+1):
print 'Cluster %i: %s' % ((i+1),
', '.join(names[labels==i]))
X = np.empty((0, 2))
for i in range(n_clusters):
X = np.r_[X, means[i] + std * np.random.randn(n_points_per_cluster, 2)]
################################################################################
# Compute similarities
################################################################################
X_norms = np.sum(X*X, axis=1)
S = - X_norms[:,np.newaxis] - X_norms[np.newaxis,:] + 2 * np.dot(X, X.T)
p = 10*np.median(S)
################################################################################
# Compute Affinity Propagation
################################################################################
cluster_centers_indices, labels = affinity_propagation(S, p)
n_clusters_ = len(cluster_centers_indices)
print 'Estimated number of clusters: %d' % n_clusters_
################################################################################
# Plot result
################################################################################
import pylab as pl
from itertools import cycle
pl.close('all')
pl.figure(1)
pl.clf()
