def pred_classification(self, xdata, y, norm, Nsamp):
pred_all = np.zeros([Nsamp, xdata.shape[0]])
for i in range(Nsamp):
pred_all[i, :] = self.model(xdata)
pred_all = do_inverse_norm(y, pred_all, norm)
pred_mean = np.mean(pred_all, axis=0)
pred_std = np.std(pred_all, axis=0)
return pred_all, pred_mean, pred_std
def pred_classification(self, xdata, y, norm):
pred_all = np.zeros([len(self.models), xdata.shape[0], self.outF])
for i in range(len(self.models)):
pred_all[i, :, :] = self.models[i](xdata)
pred_all = do_inverse_norm(y, pred_all, norm)
# Mean, std as in paper
pred_mean = np.mean(pred_all, axis=0)
pred_std = np.std(pred_all, axis=0)
return pred_all, pred_mean, pred_std
def predict(self, xdata, y, norm, Nsamp):
pred_all = np.zeros([Nsamp, xdata.shape[0], self.outF])
for i in range(Nsamp):
# Predict with dropout using training=True for MC dropout
pred_all[i, :, :] = self.model(xdata, training=True)
pred_all = do_inverse_norm(y, pred_all, norm)
pred_mean = np.mean(pred_all, axis=0)
pred_std = np.std(pred_all, axis=0)
return pred_all, pred_mean, pred_std
def pred_regression(self, xdata, y, norm):
pred_all = np.zeros([len(self.models), xdata.shape[0], self.outF])
for i in range(len(self.models)):
mu = self.models[i](xdata)
pred_all[i, :, 0:self.outF] = mu
pred_all = do_inverse_norm(y, pred_all, norm)
pred_mean = np.mean(pred_all[:, :, 0:self.outF], axis=0)
pred_std = np.sqrt(
np.abs(
np.mean(pred_all[:, :, 0:self.outF]**2, axis=0) -
pred_mean**2))
return pred_all, pred_mean, pred_std
def pred_regression(self, xdata, y, norm, Nsamp):
pred_all = np.zeros([Nsamp, xdata.shape[0], self.outF])
for i in range(Nsamp):
#mu, sigma = self.model(xdata)
#pred_all[i,:,:] = np.array([mu, sigma]).T
pred_all[i, :, 0:self.outF] = self.model(xdata)
# Mean, std as in paper
pred_all = do_inverse_norm(y, pred_all, norm)
pred_mean = np.mean(pred_all, axis=0)
#pred_std = np.sqrt(np.mean(pred_all[:,:,0]**2 + pred_all[:,:,1]+1e-6,axis=0) - pred_mean**2 )
pred_std = np.sqrt(np.mean(pred_all**2, axis=0) - pred_mean**2)
#pred_mean = do_inverse_norm(y, pred_mean, norm)
#pred_std = pred_std*np.std(y)
return pred_all, pred_mean, pred_std
def pred_regression(self, xdata, y, norm):
pred_all = np.zeros([len(self.models),xdata.shape[0], 2*self.outF])
for i in range(len(self.models)):
mu, sigma = self.models[i](xdata)
pred_all[i,:,0:self.outF] = mu
pred_all[i,:,self.outF:] = sigma
# Mean, std as in paper
pred_mean = np.mean(pred_all[:,:,0:self.outF],axis=0)
pred_std = np.sqrt(np.mean(pred_all[:,:,0:self.outF]**2 + pred_all[:,:,self.outF:]+1e-6,axis=0) - pred_mean**2 )
pred_mean = do_inverse_norm(y, pred_mean, norm)
pred_std = pred_std*np.std(y)
return pred_all, pred_mean, pred_std