def run(self):
worstCluster = self.dataset
while len(self.means) < self.k:
if isinstance(worstCluster, Dataset):
worstDataset = worstCluster
elif isinstance(worstCluster, Mean):
worstDataset = worstCluster.coveredDataset
bisection = Kmeans(dataset=worstDataset,
k=2,
trials=self.trials,
maxRounds=self.trials,
key=self.key)
bisection.run()
bisectionSolution = bisection.getBestSolution()
self.means += bisectionSolution.means
worstCluster = max(self.means,
key=lambda m: m.getMeanSquaredError())
# if the number of means is not enouth remove the worst cluster
# found to bisect it in the next iteration.
if len(self.means) < self.k:
self.means.remove(worstCluster)
self.setMeanSquaredError()
def get_colors_from_image(filepath, number_of_colors):
# print 'processing '+filepath
kmeans = Kmeans(number_of_colors, 6, 5, 200) # default 6,5,200
im = Image.open(filepath)
colors = kmeans.run(im)
im.close()
return colors
def get_color_from_image(filepath):
# print 'processing '+filepath
kmeans = Kmeans(1)
im = Image.open(filepath)
color = kmeans.run(im)[0]
im.close()
return color
def run(self):
worstCluster = self.dataset
while len(self.means) < self.k:
if isinstance(worstCluster, Dataset):
worstDataset = worstCluster
elif isinstance(worstCluster, Mean):
worstDataset = worstCluster.coveredDataset
bisection = Kmeans(dataset=worstDataset, k=2, trials=self.trials,
maxRounds=self.trials, key=self.key)
bisection.run()
bisectionSolution = bisection.getBestSolution()
self.means += bisectionSolution.means
worstCluster = max(self.means, key=lambda m: m.getMeanSquaredError())
# if the number of means is not enouth remove the worst cluster
# found to bisect it in the next iteration.
if len(self.means) < self.k:
self.means.remove(worstCluster)
self.setMeanSquaredError()
def main():
files = []
# 2 - generate database schema
Base.metadata.create_all(engine)
# 3 - create a new session
session = Session()
musics = session.query(Music).all()
musics, distances = Kmeans.split(musics)
session.commit()
return
def run(self):
self.bisecting = [
BisectingKmeans(dataset=self.dataset,
k=i,
trials=self.trials,
maxRounds=self.maxRounds) for i in self.krange
]
self.normal = [
Kmeans(dataset=self.dataset,
k=i,
trials=self.trials,
maxRounds=self.maxRounds) for i in self.krange
]
for i, j in enumerate(self.krange):
self.bisecting[i].run()
self.normal[i].run()
def index(request):
template_name = "index.html"
if request.method == "POST":
if "crawl-btn" in request.POST:
crawler_form = CrawlerForm(request.POST)
if crawler_form.is_valid():
n_docs = crawler_form.cleaned_data["n_docs"]
in_degree = crawler_form.cleaned_data["in_degree"]
out_degree = crawler_form.cleaned_data["out_degree"]
starting_url = crawler_form.cleaned_data["starting_url"]
urls = [x.strip() for x in starting_url.split(",")]
crawler = Scheduler(starting_url=urls, num=n_docs, in_degre=in_degree, out_degree=out_degree)
crawler.crawl()
else:
return render(request, template_name, {"crawler_form": crawler_form})
if "index-btn" in request.POST:
index_form = IndexForm(request.POST)
if index_form.is_valid():
direction = index_form.cleaned_data["direction"]
print(direction)
searcher.index(es, direction)
else:
return render(request, template_name, {"index_form", index_form})
if "cluster-btn" in request.POST:
cluster_form = ClusterForm(request.POST)
if cluster_form.is_valid():
path_to_json = cluster_form.cleaned_data["direction"]
titles = []
json_files = [pos_json for pos_json in os.listdir(path_to_json) if pos_json.endswith(".json")]
for js in json_files:
with open(os.path.join(path_to_json, js)) as json_file:
data = json.load(json_file)
titles.append(data["title"])
json_file.close()
vectorizer = CountVectorizer()
vectors = vectorizer.fit_transform(titles).todense().tolist()
kmeans = Kmeans(vectors)
k_points = []
j_points = []
for i in range(len(vectors)):
k_points.append(i + 1)
j_points.append(kmeans.kmenas(i + 1))
plt.plot(j_points, k_points)
plt.show()
else:
return render(request, template_name, {"cluster_form": cluster_form})
if "page-rank-btn" in request.POST:
page_rank_form = PageRankForm(request.POST)
if page_rank_form.is_valid():
alpha = page_rank_form.cleaned_data["alpha"]
threshold = page_rank_form.cleaned_data["threshold"]
else:
return render(request, template_name, {"page_rank_form": page_rank_form})
if "query-btn" in request.POST:
query_form = QueryForm(request.POST)
if query_form.is_valid():
cluster = query_form.cleaned_data["cluster"]
pagerank = query_form.cleaned_data["pagerank"]
query = query_form.cleaned_data["query"]
pass
else:
return render(request, template_name, {"query_form": query_form})
else:
query_form = QueryForm()
page_rank_form = PageRankForm()
cluster_form = ClusterForm()
crawler_form = CrawlerForm()
index_form = IndexForm()
return render(
request,
template_name,
{
"query_form": query_form,
"page_rank_form": page_rank_form,
"cluster_form": cluster_form,
"crawler_form": crawler_form,
"index_form": index_form,
},
)
