def Cal_VIP(model):
t = model.x_scores_
w = model.x_weights_
q = model.y_loadings_
p, h = w.shape
vips = np.zeros((p,))
s = np.diag(np.matmul(np.matmul(np.matmul(t.T, t), q.T), q)).reshape(h, -1)
total_s = np.sum(s)
for i in range(p):
weight = np.array([(w[i, j] / np.linalg.norm(w[:, j])) ** 2 for j in range(h)])
vips[i] = np.sqrt(p * (np.matmul(s.T, weight)) / total_s)
return vips,s
def vip(model):
t = model.x_scores_
w = model.x_weights_
q = model.y_loadings_
p, h = w.shape
vips = np.zeros((p,))
s = np.diag(t.T @ t @ q.T @ q).reshape(h, -1) #@表示矩阵相乘
total_s = np.sum(s)
for i in range(p):
weight = np.array([ (w[i,j] / np.linalg.norm(w[:,j]))**2 for j in range(h) ])
vips[i] = np.sqrt(p*(s.T @ weight)/total_s)
return vips,s
#Where model is a trained instance of a PLSRegression object.
