if __name__ == '__main__':
print("PIC")
""" load data """
# dataset = 'dataset/COIL20_32.txt' # K=5 v=1
# dataset = 'dataset/Isolet.txt' # K=25 v=10
# dataset = 'dataset/Jaffe.txt' # K=15 v=10
# dataset = 'dataset/lung.txt' # K=15 v=10
# dataset = 'dataset/mnist.txt' # K=25 v=1
# dataset = 'dataset/TOX.txt' # K=15 v=10
# dataset = 'dataset/USPS.txt' # K=20 v=1
# data = np.loadtxt(dataset)
# fea = data[:, :-1]
# labels = data[:, -1]
# print("dataset = %s data.shape = %s" % (dataset, fea.shape))
fea, labels = loadData.load_coil100() # K=10 v=1
print("------ Normalizing data ------")
# fea = tool.data_Normalized(fea)
Normalizer = MinMaxScaler()
Normalizer.fit(fea)
fea = Normalizer.transform(fea)
print("------ Clustering ------")
start = time.time()
# u = 1
dist = cdist(fea, fea)
dist = dist - np.diag(np.diag(dist))
K = 10
v = 1
if __name__ == '__main__':
print("PK DPC")
# dataset = 'dataset/COIL20_32.txt' # K=10
# dataset = 'dataset/Isolet.txt' # K=5
# dataset = 'dataset/Jaffe.txt' # K=5
# dataset = 'dataset/lung.txt' # K=25
# dataset = 'dataset/mnist.txt' # K=15
# dataset = 'dataset/TOX.txt' # K=10
# dataset = 'dataset/USPS.txt' # K=25
# data = np.loadtxt(dataset)
# fea = data[:, :-1]
# labels = data[:, -1]
# print("dataset = %s data.shape = %s" % (dataset, fea.shape))
fea, labels = loadData.load_coil100()
print("------ Normalizing data ------")
# fea = tool.data_Normalized(fea)
Normalizer = MinMaxScaler()
Normalizer.fit(fea)
fea = Normalizer.transform(fea)
print("------ PCA decomposition ------")
# fea,b,c = tool.PCA.pca(fea, 150)
pca = PCA(n_components=150)
fea = pca.fit_transform(fea)
print("fea.shape =", fea.shape)
K = 5
groupNumber = len(np.unique(labels))
from sklearn.preprocessing import MinMaxScaler
from tool import tool, measure, loadData
import time
if __name__ == '__main__':
print("KROD PIC")
print("------ Loading data ------")
# data_set = 'dataset/COIL20_32.txt' # K=20 u=1
# data_set = 'dataset/mnist.txt' # K=20 u=1
# data_set = 'dataset/lung.txt' # K=10 u=0.1
# data_set = 'dataset/USPS.txt' # K=20 u=1
# data_set = 'dataset/Isolet.txt' # K=25 u=10
# data_set = 'dataset/TOX.txt' # K=20 u=10
# data_set = 'dataset/Jaffe.txt' # K=10 u=0.1
fea, labels = loadData.load_coil100() # K=25 u=1 v=0.1
# data = np.loadtxt(data_set)
# fea = data[:, :-1]
# labels = data[:, -1]
# print("data_set = %s data.shape = %s" % (data_set, fea.shape))
print("------ Normalizing data ------")
# tool.data_Normalized(fea)
Normalizer = MinMaxScaler()
Normalizer.fit(fea)
fea = Normalizer.transform(fea)
# u = 1
# dist = tool.rank_dis_c(fea, u)
dist = tool.rank_order_dis(fea)