def my_HSIC(X, Y):
if X.ndim == 1:
temp = X
t1 = temp.reshape(len(temp), 1)
t2 = temp.reshape(1, len(temp))
T_X = np.dot(t1, t2)
else:
T_X = mf.tanimoto(X, X)
if Y.ndim == 1:
temp = Y
t1 = temp.reshape(len(temp), 1)
t2 = temp.reshape(1, len(temp))
T_Y = np.dot(t1, t2)
else:
T_Y = mf.tanimoto(Y, Y)
return np.sum(centering(T_X) * centering(T_Y))
np.set_printoptions(suppress=True)
K1 = np.zeros((m, m))
for i in range(m):
for j in range(m):
K1[i][j] = round(metrics_function.cosine(data[i], data[j]), 6)
print(K1)
with open(
'D:/Study/Bioinformatics/QSP_new/kernel_matrix/KM_train_cosine/KM_cosine_'
+ name + '_train.csv',
'w',
newline='') as csvfile:
writer = csv.writer(csvfile)
for row in K1:
writer.writerow(row)
csvfile.close()
K3 = np.zeros((m, m))
for i in range(m):
for j in range(m):
K3[i][j] = round(metrics_function.tanimoto(data[i], data[j]), 6)
print(K3)
with open(
'D:/Study/Bioinformatics/QSP_new/kernel_matrix/KM_train_tanimoto/KM_tanimoto_'
+ name + '_train.csv',
'w',
newline='') as csvfile:
writer = csv.writer(csvfile)
for row in K3:
writer.writerow(row)
csvfile.close()
K1 = np.zeros((m, p))
for i in range(m):
for j in range(p):
K1[i][j] = round(metrics_function.cosine(X_test[i], X_train[j]), 6)
print(K1)
with open(
'D:/study/Bioinformatics/AMP/kernel_matrix/KM_test_cosine/KM_cosine_'
+ name + '_test.csv',
'w',
newline='') as csvfile:
writer = csv.writer(csvfile)
for row in K1:
writer.writerow(row)
csvfile.close()
K3 = np.zeros((m, p))
for i in range(m):
for j in range(p):
K3[i][j] = round(metrics_function.tanimoto(X_test[i], X_train[j]),
6)
print(K3)
with open(
'D:/study/Bioinformatics/AMP/kernel_matrix/KM_test_tanimoto/KM_tanimoto_'
+ name + '_test.csv',
'w',
newline='') as csvfile:
writer = csv.writer(csvfile)
for row in K3:
writer.writerow(row)
csvfile.close()
metrics_function.cosine(X_test[i], X_train[j]), 6)
print(K2)
with open('D:/study/Bioinformatics/QSP/200p_200n/10_fold/' + name +
'/km_test/KM_cosine_' + name + '_test_' + str(k) + '.csv',
'w',
newline='') as csvfile:
writer = csv.writer(csvfile)
for row in K2:
writer.writerow(row)
csvfile.close()
K3 = np.zeros((p, p))
for i in range(p):
for j in range(p):
K3[i][j] = round(
metrics_function.tanimoto(X_train[i], X_train[j]), 6)
print(K3)
with open('D:/study/Bioinformatics/QSP/200p_200n/10_fold/' + name +
'/km_train/KM_tanimoto_' + name + '_train_' + str(k) +
'.csv',
'w',
newline='') as csvfile:
writer = csv.writer(csvfile)
for row in K3:
writer.writerow(row)
csvfile.close()
K4 = np.zeros((m, p))
for i in range(m):
for j in range(p):
K4[i][j] = round(