old_contents: # -*- coding: utf-8 -*-
  # Copyright (C) 2008, Luís Pedro Coelho <lpc@cmu.edu>
  # 
  # 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.
  
  from __future__ import division
  from collections import defaultdict
  import numpy as np
  
  class kNN(object):
      '''
      k-Nearest Neighbour Classifier
  
      Naive implementation of a k-nearest neighbour classifier.
  
      C = kNN(k)
  
      Attributes:
      -----------
          * k: the k to use
      '''
      def __init__(self, k=1,):
          self.k = k
  
      def train(self, features, labels, copy_features=False):
          if copy_features:
              self.features = features.copy()
              self.labels = labels.copy()
          else:
              self.features = features
              self.labels = labels
  
      def apply(self,features):
          diff2 = ( (self.features - features)**2 ).sum(1)
          neighbours = diff2.argsort()[:self.k]
          labels = self.labels[neighbours]
          votes = defaultdict(int)
          for L in labels:
              votes[L] += 1
          v,L = max( (v,L) for L,v in votes.items() )
          return L
  
  # vim: set ts=4 sts=4 sw=4 expandtab smartindent:
  
new_contents: # -*- coding: utf-8 -*-
  # Copyright (C) 2008, Luís Pedro Coelho <lpc@cmu.edu>
  # 
  # 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.
  
  from __future__ import division
  from collections import defaultdict
  import numpy as np
  
  class kNN(object):
      '''
      k-Nearest Neighbour Classifier
  
      Naive implementation of a k-nearest neighbour classifier.
  
      C = kNN(k)
  
      Attributes:
      -----------
          * k: the k to use
      '''
      def __init__(self, k=1,):
          self.k = k
  
      def train(self, features, labels, copy_features=False):
          features = np.asanyarray(features)
          labels = np.asanyarray(labels)
          if copy_features:
              self.features = features.copy()
              self.labels = labels.copy()
          else:
              self.features = features
              self.labels = labels
  
      def apply(self,features):
          diff2 = ( (self.features - features)**2 ).sum(1)
          neighbours = diff2.argsort()[:self.k]
          labels = self.labels[neighbours]
          votes = defaultdict(int)
          for L in labels:
              votes[L] += 1
          v,L = max( (v,L) for L,v in votes.items() )
          return L
  
  # vim: set ts=4 sts=4 sw=4 expandtab smartindent:
  
subject: Work for non-ndarray input