def cal_membership(self, X, y):
if self.membership == 'SVDD':
W = membership.SVDD_membership(X,
y,
g=self.gamma,
C=self.nu,
proj=self.proj)
elif self.membership == 'None':
W = []
return W
def cal_membership(self, X, y):
n_samples = np.shape(X)[0]
if self.membership == 'SVDD':
W = membership.SVDD_membership(X,
y,
g=self.gamma,
C=self.nu,
proj=self.proj)
elif self.membership == 'None':
W = np.ones((n_samples, 1))
elif self.membership == 'OCSVM':
W = membership.OCSVM_membership(X, y, self.gamma)
elif self.membership == 'IFN_SVDD':
W = membership.IFN_membership(X, y, self.gamma, self.C, self.alpha)
return W
def cal_membership(self, X, y):
n_samples = np.shape(X)[0]
if self.membership == 'SVDD':
W = membership.SVDD_membership(X,
y,
g=self.gamma,
C=self.nu,
proj=self.proj)
elif self.membership == 'SVDD_linear':
W = membership.SVDD_linear_kernel(X, y, C=self.nu, proj=self.proj)
elif self.membership == 'None':
W = np.ones((n_samples, 1))
elif self.membership == 'OCSVM':
W = membership.OCSVM_membership(X, y, self.gamma)
elif self.membership == 'IFN_SVDD':
W = membership.IFN_membership(X, y, self.gamma, self.C, self.alpha)
elif self.membership == 'center':
W = membership.class_center_membership(X, y)
elif self.membership == 'FSVM_2':
W = membership.FSVM_2_membership(X, y, self.gamma)
return W
X_pos[k] = X[i]
k = k + 1
y = np.zeros(n_neg + n_pos)
for i in range(n_neg):
y[i] = -1
for i in range(n_neg, n_neg + n_pos):
y[i] = 1
X = np.row_stack((X_neg, X_pos))
return X, y
dataset = ['australian', 'heart', 'sonar']
name = dataset[2]
f1 = np.loadtxt('E:/Study/Bioinformatics/UCI/' + name + '/data.csv',
delimiter=',')
X = f1[:, 0:-1]
y = f1[:, -1]
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=0)
X_train, y_train = split(X_train, y_train)
g = 0.5
nu = 0.1
s = membership.SVDD_membership(X_train, y_train, g=g, C=nu)
print(s)