def main():
data_name = "CALFW_29v2"
data_path = "D:/DATA/"
if "29v2" in data_name:
X, y_true, N, dim, c_true = Ifuns.load_mat(data_path + "FaceData/" + data_name)
else:
X, y_true, N, dim, c_true = Ifuns.load_mat(data_path + data_name)
print("hh")
Y = Ifuns.kmeans(X, c_true, rep=10, init="random", par=1)
acc = np.array([Ifuns.accuracy(y_true, y) for y in Y])
ari = np.array([Ifuns.ari(y_true, y) for y in Y])
ami = np.array([Ifuns.ami(y_true, y) for y in Y])
nmi = np.array([Ifuns.nmi(y_true, y) for y in Y])
print("{}\t{:.3f}\t{:.3f}\t{:.3f}\t{:.3f}\t{:.3f}\t{:.3f}\t{:.3f}\t{:.3f}".format(data_name,
np.mean(acc), np.std(acc),
np.mean(ari), np.std(ari),
np.mean(ami), np.std(ami),
np.mean(nmi), np.std(nmi)))
import numpy as np
import IDEAL_NPU.funs as Ifuns
from IDEAL_NPU.cluster import SSC
X, y_true, N, dim, c_true = Ifuns.load_USPS()
print("USPS", N, dim, c_true)
obj = SSC(X=X, c_true=c_true)
y = obj.clu(km_rep=10, way="NJW")
acc = Ifuns.accuracy(y_true, y)
print(acc)
# paper: USPS, ACC, 67.52 %
# abandon
import numpy as np
import IDEAL_NPU.funs as Ifuns
from backup import ISR
data_name = "srbct"
X, y_true, N, dim, c_true = Ifuns.load_mat("D:/DATA/" + data_name)
print(data_name, N, dim, c_true)
obj = ISR(X, c_true)
knn_list = [10, 20, 30, 40, 50]
acc = np.zeros(len(knn_list))
for i, knn in enumerate(knn_list):
y = obj.clu(graph_knn=knn, sr_ITER=10)
acc[i] = Ifuns.accuracy(y_true, y)
print(acc)
print(np.mean(acc))
# paper: USPS, ACC, 67.52 %
import numpy as np
import IDEAL_NPU.funs as Ifuns
from IDEAL_NPU.cluster import EEBC, EEBCX
data_name = "mpeg7"
X, y_true, N, dim, c_true = Ifuns.load_mat("D:/DATA/" + data_name)
print(N, dim, c_true)
# EEBC
knn = 20
D = Ifuns.EuDist2(X, X, squared=True)
ind_M = np.argsort(D, axis=1)
NN = ind_M[:, :knn]
NND = Ifuns.matrix_index_take(D, NN)
obj = EEBC(NN.astype(np.int32), NND, c_true)
obj.clu()
y_pred = obj.y_pre
acc = Ifuns.accuracy(y_true=y_true, y_pred=y_pred)
print(acc)
# EEBC-X
obj = EEBCX(X, c_true)
obj.clu()
y_pred = obj.y
acc = Ifuns.accuracy(y_true=y_true, y_pred=y_pred)
print(acc)
import numpy as np
from IDEAL_NPU.cluster import FCDMF
from IDEAL_NPU import funs as Ifuns
X, y_true, N, dim, c_true = Ifuns.load_mat("D:/DATA/face94_256.mat")
X = Ifuns.normalize_fea(X, 0)
print(N, dim, c_true)
obj = FCDMF(X, c_true)
Y = obj.clu(KTIMES=5)
ret = [Ifuns.accuracy(y_true, y) for y in Y]
print(ret)
print(np.mean(ret))