more_nodes.py 文件源码

def non_linear_inverse(self, y, verbose=False):
        x_lin = self.linear_inverse(y)
        rmse_lin = ((y - self.execute(x_lin)) ** 2).sum(axis=1).mean() ** 0.5
        #        scipy.optimize.leastsq(func, x0, args=(), Dfun=None, full_output=0, col_deriv=0, ftol=1.49012e-08,
        # xtol=1.49012e-08, gtol=0.0, maxfev=0, epsfcn=0.0, factor=100, diag=None)
        x_nl = numpy.zeros_like(x_lin)
        y_dim = y.shape[1]
        x_dim = x_lin.shape[1]
        if y_dim < x_dim:
            num_zeros_filling = x_dim - y_dim
        else:
            num_zeros_filling = 0
        if verbose:
            print("x_dim=", x_dim, "y_dim=", y_dim, "num_zeros_filling=", num_zeros_filling)
        y_long = numpy.zeros(y_dim + num_zeros_filling)

        for i, y_i in enumerate(y):
            y_long[0:y_dim] = y_i
            if verbose:
                print("x_0=", x_lin[i])
                print("y_long=", y_long)
            plsq = scipy.optimize.leastsq(func=f_residual, x0=x_lin[i], args=(self, y_long), full_output=False)
            x_nl_i = plsq[0]
            if verbose:
                print("x_nl_i=", x_nl_i, "plsq[1]=", plsq[1])
            if plsq[1] != 2:
                print("Quitting: plsq[1]=", plsq[1])
                # quit()
            x_nl[i] = x_nl_i
            print("|E_lin(%d)|=" % i, ((y_i - self.execute(x_lin[i].reshape((1, -1)))) ** 2).sum() ** 0.5)
            print("|E_nl(%d)|=" % i, ((y_i - self.execute(x_nl_i.reshape((1, -1)))) ** 2).sum() ** 0.5)
        rmse_nl = ((y - self.execute(x_nl)) ** 2).sum(axis=1).mean() ** 0.5
        print("rmse_lin(all samples)=", rmse_lin, "rmse_nl(all samples)=", rmse_nl)
        return x_nl