def user_uploaded_dataset(request):
algo_name = request.GET['algo']
data_name = request.GET['data']
# get data path and load data
now = datetime.datetime.now()
if now.month < 10:
mth = str(0)+str(now.month)
else:
mth = str(now.month)
if now.day < 10:
day = str(0)+str(now.day)
else:
day = str(now.day)
path = '/static_media_clustapp/documents/'+str(now.year)+'/'+mth+'/'+day+'/'
full_path = os.path.realpath('.')
file_name = full_path+path+data_name
data_file = scipy.io.loadmat(file_name)
input_data = data_file['X']
correct_label = data_file['y']
# Calling Algorithms
clustered_label = {}
unique_label = {}
if algo_name=="MiniBatchKMeans":
print 'MiniBatchKMeans'
kmean_label = algorithm_manager.call_kmean(2, input_data, True)
clustered_label = kmean_label
elif algo_name=="Spectral Clustering":
print "Spectral Clustering"
spectral_label = algorithm_manager.call_spectral(2, 'arpack', input_data, True)
clustered_label = spectral_label
elif algo_name=="Affinity Propagation":
print "Affinity Propagation"
affinity_label = algorithm_manager.call_affinity(0.9, input_data, True)
clustered_label = affinity_label
# saving the result in the correct directory
output_data = {}
output_data ['y'] = clustered_label
out_file_name = full_path+path+data_name[:-4] + '_result.mat'
data_file = scipy.io.savemat(out_file_name, output_data)
# return the path
return_file_name = path+data_name[:-4] + '_result.mat'
return HttpResponse(return_file_name)
def get_machine_learning_result_given_algorithm_and_dataset(request):
algo_name = request.GET['algo']
data_name = request.GET['data']
print algo_name
print data_name
data_dict = {}
input_data = None
n_samples = 1500
# Generateing Sample Data Algorithm
from sklearn import cluster, datasets
if data_name=="Sample Blob":
blob_dict, X, y = algorithm_manager.generate_blob_sample()
data_dict = blob_dict
input_data = X
elif data_name=="Sample Circle":
circle_dict,X,y = algorithm_manager.generate_circle_sample()
#circle_dict,X,y = datasets.make_blobs(n_samples=n_samples, random_state=8)
data_dict = circle_dict
input_data = X
elif data_name=="Sample Moon":
moon_dict,X,y = algorithm_manager.generate_moon_sample()
data_dict = moon_dict
input_data = X
clustered_label = {}
unique_label = {}
# Calling Algorithms
if algo_name=="MiniBatchKMeans":
print 'MiniBatchKMeans'
kmean_label, unique_label = algorithm_manager.call_kmean(2, input_data, False)
clustered_label = kmean_label
elif algo_name=="Spectral Clustering":
print "Spectral Clustering"
spectral_label, unique_label = algorithm_manager.call_spectral(2, 'arpack', input_data, False)
clustered_label = spectral_label
elif algo_name=="Affinity Propagation":
print "Affinity Propagation"
affinity_label, unique_label = algorithm_manager.call_affinity(0.9, input_data, False)
clustered_label = affinity_label
elif algo_name=="Greedy":
print "Greedy"
greedy_label, unique_label = algorithm_manager.call_greedy(2, input_data, False)
clustered_label = greedy_label
# main return dictionary contain dictionary about everything
return_dict = {}
return_dict['original_data_points'] = data_dict
return_dict['clustered_data_label'] = clustered_label
return_dict['clustered_data_unique_label'] = unique_label
json_return = simplejson.dumps(return_dict)
return HttpResponse(json_return)