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#/*##########################################################################
#
# The PyMca X-Ray Fluorescence Toolkit
#
# Copyright (c) 2020-2021 European Synchrotron Radiation Facility
#
# This file is part of the PyMca X-ray Fluorescence Toolkit developed at
# the ESRF by the Software group.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
#############################################################################*/
__author__ = "V.A. Sole - ESRF"
__contact__ = "sole@esrf.fr"
__license__ = "MIT"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
import sys
import numpy
import logging
_logger = logging.getLogger(__name__)
try:
from PyMca5.PyMcaMath.mva import _cython_kmeans as _kmeans
KMEANS = "_kmeans"
except:
if _logger.getEffectiveLevel() == logging.DEBUG:
raise
else:
_logger.warning("Cannot load built-in K-means.\n %s" % \
sys.exc_info()[1])
KMEANS = None
try:
from sklearn.cluster import KMeans
KMEANS = "sklearn"
except:
pass
if KMEANS:
_logger.info("kmeans default to <%s>" % KMEANS)
else:
_logger.info("kmeans disabled")
def _labelCythonKMeans(x, k):
labels, means, iterations, converged = _kmeans.kmeans(x, k)
return {"labels": numpy.array(labels, dtype=numpy.int32, copy=False),
"means": means,
"iterations":iterations,
"converged":converged}
def _labelMdp(x, k):
from mdp.nodes import KMeansClassifier
classifier = KMeansClassifier(k)
for i in range(x.shape[0]):
classifier.train(x[i:i+1])
#classifier.train(x)
labels = classifier.label(x)
return {"labels": numpy.array(labels, dtype=numpy.int32, copy=False)}
def _labelScikitLearn(x, k):
from sklearn.cluster import KMeans
km = KMeans(n_clusters=k)
km.fit(x)
labels = km.labels_
#labels = km.predict(x)
converged = len(km.cluster_centers_) == len(labels)
return {"labels": numpy.array(labels, dtype=numpy.int32, copy=False),
"means": km.cluster_centers_,
"iterations":km.n_iter_,
"converged":converged}
def kmeans(x, k, method=None, normalize=True):
"""
x is a 2D array [n_samples, n_features]
k is the desired number of clusters
"""
assert len(x.shape) == 2
# collapse the information to deal with inf and NaNs
raws = x.sum(axis=1, dtype=numpy.float64)
good = numpy.isfinite(raws)
finiteData = numpy.alltrue(good)
data = numpy.ascontiguousarray(x[good])
if normalize:
datamin = data.min(axis=0)
deltas = data.max(axis=0) - datamin
deltas[deltas < 1.0e-200] = 1
data = (data - datamin) / deltas
if method is None:
method = KMEANS
if method == "mdp":
result = _labelMdp(data, k)
elif method == "sklearn":
result = _labelScikitLearn(data, k)
elif method.endswith("kmeans"):
result = _labelCythonKMeans(data, k)
elif "mdp" in sys.modules:
result = _labelMdp(data, k)
else:
raise ValueError("Unknown clustering <%s>" % method)
if not finiteData:
_logger.info("Data contains inf or NaNs")
actualResult = -numpy.ones(raws.shape,dtype=numpy.int32)
actualResult[good] = result["labels"]
result["labels"] = actualResult
return result
def label(*var, **kw):
return kmeans(*var, **kw)["labels"]
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