def total():
check_dic()
csv_dealer.csv_deal_to_file("./data/training_csv/",no_total=1,to_addr="./data/training_csv_cut")
csv_dealer.csv_deal_to_file('./data/test_csv/',no_total=1,to_addr='./data/test_csv_cut')
csv_dealer.csv_deal_to_file('./data/final_test/',no_total=1,to_addr='./data/final_test_cut')
mat_list = nmf_sklearn.generate_new_mat_list('./data/training_csv')
total_mat = csv_dealer.mat_list_to_total(mat_list)
np.savetxt("./data/total.csv", total_mat, delimiter=',', fmt="%f")
w, h = nmf_sklearn.nmf_sklearn(3,total_mat)
K_Means.k_means(w,250,new_file_addr='./data/class_list.txt')
probability.run()
get_Sit.run()
cal_f1.run()
def apply_algorithm(self, alg_name, training_vectors, test_vectors_clean, test_vectors_anomalous):
"""Applies the specified algorithm onto the feature sets.
"""
if alg_name == 'lof':
result_clean, result_anomalous, result_training = LOF.local_outlier_detection(training_vectors, test_vectors_clean, test_vectors_anomalous)
elif alg_name == 'svm':
result_clean, result_anomalous, result_training = SVM.one_class_svm(training_vectors, test_vectors_clean, test_vectors_anomalous)
elif alg_name == 'dbscan':
result_clean,result_anomalous, result_training = DBSCAN.dbscan(training_vectors,test_vectors_clean,test_vectors_anomalous)
elif alg_name == 'kmeans':
result_clean,result_anomalous, result_training = K_Means.k_means(training_vectors,test_vectors_clean,test_vectors_anomalous)
else:
raise NameError("Invalid Algorithm Name")
return result_clean,result_anomalous, result_training
output.close()
print 'Parameters saved to ' + filenames['params'] + '.pkl'
#########################################################
print 'Initiating K-Means...'
k_results=list()
for i in range(numRounds):
start=datetime.now()
iGenerations=qk_rounds_genNum[i]
numInits=np.int(iGenerations*numOracles*initsPercentage)
k_centroids,k_assignment,k_timings,k_intertia=K_Means.k_means(mixture,numClusters,numInits)
k_results.append([k_centroids,k_assignment,k_timings,k_intertia])
round=(datetime.now() - start).total_seconds()
print float(i+1)*100/numRounds,'%\t','round ', i,':',round,'s - estimated:',(float(numRounds-1)-i)*round,'s / ',(float(numRounds-1)-i)*round/60,'m'
print 'Preparing K-Means data structures...'
k_rounds=dict()
k_rounds['centroids']=list()
k_rounds['assignment']=list()
k_rounds['times']=list()
k_rounds['inertia']=list()
for i in range(numRounds):
k_rounds['centroids'].append(k_results[i][0])
from sklearn import datasets import K_Means, Plot blobs = datasets.make_blobs()[0] centers, clusters, colors, color_labels = K_Means.k_means(blobs, 3) Plot.plot(blobs, centers, colors, color_labels)
pickle.dump(params, output)
output.close()
print 'Parameters saved to ' + filenames['params'] + '.pkl'
#########################################################
print 'Initiating K-Means...'
print "Each round will have ", numInits, " K-Means initializations."
k_results = list()
for i in range(numRounds):
start = datetime.now()
k_centroids, k_assignment, k_timings, k_intertia = K_Means.k_means(
mixture, numClusters, numInits)
k_results.append([k_centroids, k_assignment, k_timings, k_intertia])
round = (datetime.now() - start).total_seconds()
print float(
i + 1
) * 100 / numRounds, '%\t', 'round ', i, ':', round, 's - estimated:', (
float(numRounds - 1) - i) * round, 's / ', (float(numRounds - 1) -
i) * round / 60, 'm'
print 'Preparing K-Means data structures...'
k_rounds = dict()
k_rounds['centroids'] = list()
k_rounds['assignment'] = list()
k_rounds['times'] = list()
