def squared_clustering_errors(inputs, k):
""" finds total squared error from k-means clustering of inputs """
clusterer = KMeans(k)
clusterer.train(inputs)
means = clusterer.means
assignments = map(clusterer.classify, inputs)
return sum(squared_distance(input, means[cluster])
for input, cluster in zip(inputs, assignments))
def squared_clustering_errors(inputs, k):
""" finds total squared error from k-means clustering of inputs """
clusterer = KMeans(k)
clusterer.train(inputs)
means = clusterer.means
assignments = map(clusterer.classify, inputs)
return sum(
squared_distance(input, means[cluster])
for input, cluster in zip(inputs, assignments))
def helper(cluster_index):
""" calculates the squared distance between input and
cluster vector mean """
return squared_distance(input, self.means[cluster_index])
def helper(cluster_index):
""" calculates the squared distance between input and
cluster vector mean """
return squared_distance(input, self.means[cluster_index])