def __main__():
blognames, words, data = clusters.read_file("blogdata.txt")
clust = clusters.hcluster(data, distance=euclidean_distance)
clusters.draw_dendogram(clust, blognames, jpeg="ex3dendrogram.jpg")
# I think this weights against groupings that have similar word use rates but different word use counts.
def __main__():
entries, words, data = clusters.read_file('entrydata.txt')
clust = clusters.hcluster(data)
clusters.draw_dendogram(clust, entries, jpeg="ex2dend.jpg")
def __main__():
wants, people, data = clusters.read_file('zebo.txt')
clust = clusters.hcluster(data, distance=manhattan_distance)
clusters.draw_dendogram(clust, wants, jpeg="ex4dend.jpg")
def __main__():
wants, people, data = clusters.read_file('zebo.txt')
clust = clusters.hcluster(data, distance=manhattan_distance)
clusters.draw_dendogram(clust, wants, jpeg="ex4dend.jpg")
def __main__():
tag_list = build_tag_list("programming")
tags, urls, data = build_tag_matrix(tag_list)
cluster = hcluster(data)
draw_dendogram(cluster, urls, jpeg="delicious.jpg")
def __main__():
entries, words, data = clusters.read_file('entrydata.txt')
clust = clusters.hcluster(data)
clusters.draw_dendogram(clust, entries, jpeg="ex2dend.jpg")
def __main__():
tag_list = build_tag_list("programming")
tags, urls, data = build_tag_matrix(tag_list)
cluster = hcluster(data)
draw_dendogram(cluster, urls, jpeg="delicious.jpg")
def __main__():
blognames, words, data = clusters.read_file("blogdata.txt")
clust = clusters.hcluster(data, distance=euclidean_distance)
clusters.draw_dendogram(clust, blognames, jpeg="ex3dendrogram.jpg")
# I think this weights against groupings that have similar word use rates but different word use counts.
