Exemple #1
0
Finds core samples of high density and expands clusters from them.

"""
print __doc__

import numpy as np
from scipy.spatial import distance
from scikits.learn.cluster import DBSCAN
from scikits.learn import metrics
from scikits.learn.datasets.samples_generator import make_blobs


##############################################################################
# Generate sample data
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4)

##############################################################################
# Compute similarities
D = distance.squareform(distance.pdist(X))
S = 1 - (D / np.max(D))

##############################################################################
# Compute DBSCAN
db = DBSCAN().fit(S, eps=0.95, min_samples=10)
core_samples = db.core_sample_indices_
labels = db.labels_

# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
Exemple #2
0
Reference:
K. Funkunaga and L.D. Hosteler, "The Estimation of the Gradient of a
Density Function, with Applications in Pattern Recognition"

"""
print __doc__

import numpy as np
from scikits.learn.cluster import MeanShift, estimate_bandwidth
from scikits.learn.datasets.samples_generator import make_blobs

################################################################################
# Generate sample data
centers = [[1, 1], [-1, -1], [1, -1]]
X, _ = make_blobs(n_samples=750, centers=centers, cluster_std=0.6)

################################################################################
# Compute clustering with MeanShift
bandwidth = estimate_bandwidth(X, quantile=0.3)
ms = MeanShift(bandwidth=bandwidth)
ms.fit(X)
labels = ms.labels_
cluster_centers = ms.cluster_centers_

labels_unique = np.unique(labels)
n_clusters_ = len(labels_unique)

print "number of estimated clusters : %d" % n_clusters_

################################################################################
Exemple #3
0
Reference:
Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
Between Data Points", Science Feb. 2007

"""
print __doc__

import numpy as np
from scikits.learn.cluster import AffinityPropagation
from scikits.learn import metrics
from scikits.learn.datasets.samples_generator import make_blobs

##############################################################################
# Generate sample data
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=300, centers=centers, cluster_std=0.5)

##############################################################################
# Compute similarities
X_norms = np.sum(X**2, 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
af = AffinityPropagation().fit(S, p)
cluster_centers_indices = af.cluster_centers_indices_
labels = af.labels_

n_clusters_ = len(cluster_centers_indices)
Exemple #4
0
Reference:
K. Funkunaga and L.D. Hosteler, "The Estimation of the Gradient of a
Density Function, with Applications in Pattern Recognition"

"""
print __doc__

import numpy as np
from scikits.learn.cluster import MeanShift, estimate_bandwidth
from scikits.learn.datasets.samples_generator import make_blobs

################################################################################
# Generate sample data
centers = [[1, 1], [-1, -1], [1, -1]]
X, _ = make_blobs(n_samples=750, centers=centers, cluster_std=0.6)

################################################################################
# Compute clustering with MeanShift
bandwidth = estimate_bandwidth(X, quantile=0.3)
ms = MeanShift(bandwidth=bandwidth)
ms.fit(X)
labels = ms.labels_
cluster_centers = ms.cluster_centers_

labels_unique = np.unique(labels)
n_clusters_ = len(labels_unique)

print "number of estimated clusters : %d" % n_clusters_

################################################################################