/usr/share/doc/python-deap/examples/knn.py is in python-deap-doc 0.7.1-1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | # This file is part of DEAP.
#
# DEAP is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as
# published by the Free Software Foundation, either version 3 of
# the License, or (at your option) any later version.
#
# DEAP is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with DEAP. If not, see <http://www.gnu.org/licenses/>.
import numpy
import operator
class KNN(object):
def __init__(self, k):
self.k = k
self.data = None
self.labels = None
self.ndim = 0
def train(self, data, labels):
self.data = numpy.array(data)
self.labels = numpy.array(labels)
self.classes = numpy.unique(self.labels)
self.ndim = len(self.data[0])
def predict(self, data, features=None):
data = numpy.array(data)
if features is None:
features = numpy.ones(self.data.shape[1])
else:
features = numpy.array(features)
if data.ndim == 1:
dist = self.data - data
elif data.ndim == 2:
dist = numpy.zeros((data.shape[0],) + self.data.shape)
for i, d in enumerate(data):
dist[i, :, :] = self.data - d
else:
raise ValueError("Cannot process data with dimensionality > 2")
dist = features * dist
dist = dist * dist
dist = numpy.sum(dist, -1)
dist = numpy.sqrt(dist)
nns = numpy.argsort(dist)
if data.ndim == 1:
classes = {cls : 0 for cls in self.classes}
for n in nns[:self.k]:
classes[self.labels[n]] += 1
labels = sorted(classes.iteritems(), key=operator.itemgetter(1))[-1][0]
elif data.ndim == 2:
labels = list()
for i, d in enumerate(data):
classes = {cls : 0 for cls in self.classes}
for n in nns[i, :self.k]:
classes[self.labels[n]] += 1
labels.append(sorted(classes.iteritems(), key=operator.itemgetter(1))[-1][0])
return labels
if __name__ == "__main__":
trainset = [[1, 0], [1, 1], [1, 2]]
trainlabels = [1, 2, 3]
knn = KNN(1)
knn.train(trainset, trainlabels)
print "Single Data ==========="
print knn.predict([1, 0], [1, 1])
print "Multiple Data ==========="
print knn.predict([[1, 3], [1, 0]], [1, 1])
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