Exemplo n.º 1
0
def squared_clustering_errors(inputs, k):
    """finds the total squared error from k-means clustering the inputs"""
    clusterer = KMeans(k)
    clusterer.train(inputs)
    means = clusterer.means
    assignments = map(clusterer.classify, inputs)

    return sum(
        algebra.squared_distance(input, means[cluster])
        for input, cluster in zip(inputs, assignments))
Exemplo n.º 2
0
def squared_clustering_errors(inputs, k):
    """finds the total squared error from k-means clustering the inputs"""
    clusterer = KMeans(k)
    clusterer.train(inputs)
    means = clusterer.means
    assignments = map(clusterer.classify, inputs)

    return sum(algebra.squared_distance(input, means[cluster])
               for input, cluster
               in zip(inputs, assignments))
Exemplo n.º 3
0
 def classify(self, input):
     """return the index of the cluster closest to the input"""
     return min(
         range(self.k),
         key=lambda i: algebra.squared_distance(input, self.means[i]))
Exemplo n.º 4
0
 def classify(self, input):
     """return the index of the cluster closest to the input"""
     return min(range(self.k),
                key=lambda i: algebra.squared_distance(input, self.means[i]))