self_fun = @(x) (2.*x.*x-3*x+4+sin(x) +exp(x));deri = @(x) (4*x-3+cos(x) +exp(x));deri_1 = @(f,x,h) ((f(x+h)-f(x)) ./h);x = 0:0.1:1;x = x';y = deri(x);h0 = 0.001;y1 = deri_1(self_fun,x,h0);y2 = deri_2(self_fun,x,h0);plot(x,abs(y1-y),'-ro',x,abs(y2-y),'--b*');legend('error of deri 1','error of deri_2')xlabel('x (Step h is set as 0.001)');ylabel('Error of deriviation approximate functions');x0 = 0;d = [(2):(1):(14)]';h = 10.^(-d);yy = deri(x0)*ones(size(d));yy1 = deri_1(self_fun,x0,h);yy2 = deri_2(self_fun,x0,h);figure;plot(d,abs(yy1-yy),'-ro');legend('error of deri. 1');xlabel('Step h (at the point of x=0)');ylabel('Error of deriviation approximate function');此代码第一行明明已经定义了self_fun函数，但在octave运行中还是显示self_fun undefined，能解释并修改代码吗

根据你提供的代码，出现错误的原因是因为 `deri_2` 函数中引用了一个未定义的函数 `deri_1`。因此，你需要先定义 `deri_1` 函数，然后再运行 `deri_2` 函数。

你可以将 `deri_1` 函数定义如下：

deri_1 = @(f,x,h) ((f(x+h)-f(x)) ./h);

然后，你将 `deri_2` 函数修改为以下代码：

deri_2 = @(f,x,h) ((f(x+2*h)-2*f(x+h)+f(x)) ./(h.^2));

这样就可以正常运行代码了。

相关问题

优化这段import numpy as np import matplotlib.pyplot as plt %config InlineBackend.figure_format='retina' def generate_signal(t_vec, A, phi, noise, freq): Omega = 2*np.pi*freq return A * np.sin(Omega*t_vec + phi) + noise * (2*np.random.random def lock_in_measurement(signal, t_vec, ref_freq): Omega = 2*np.pi*ref_freq ref_0 = 2*np.sin(Omega*t_vec) ref_1 = 2*np.cos(Omega*t_vec) # signal_0 = signal * ref_0 signal_1 = signal * ref_1 # X = np.mean(signal_0) Y = np.mean(signal_1) # A = np.sqrt(X**2+Y**2) phi = np.arctan2(Y,X) print("A=", A, "phi=", phi) # t_vec = np.linspace(0, 0.2, 1001) A = 1 phi = np.pi noise = 0.2 ref_freq = 17.77777 # signal = generate_signal(t_vec, A, phi, noise, ref_freq) # lock_in_measurement(signal, t_vec, ref_freq)

你可以优化这段代码的一些地方。首先，你可以将import语放在代码的开，这样可以更清晰地看到你使用了哪些库。其次，你可以将生成信号和锁相测量的函数封装到一个类中，这样可以更好地组织代码。最后，你可以将生成信号和锁相测量的参数作为函数的参数传递进去，而不是在函数内部硬编码。这样可以增加代码的灵活性。以下是优化后的代码示例：

import numpy as np
import matplotlib.pyplot as plt

class SignalGenerator:
    def __init__(self, t_vec, A, phi, noise, freq):
        self.t_vec = t_vec
        self.A = A
        self.phi = phi
        self.noise = noise
        self.freq = freq
        
    def generate_signal(self):
        Omega = 2*np.pi*self.freq
        return self.A * np.sin(Omega*self.t_vec + self.phi) + self.noise * (2*np.random.random)
    
    def lock_in_measurement(self, signal, ref_freq):
        Omega = 2*np.pi*ref_freq
        ref_0 = 2*np.sin(Omega*self.t_vec)
        ref_1 = 2*np.cos(Omega*self.t_vec)
        
        signal_0 = signal * ref_0
        signal_1 = signal * ref_1
        
        X = np.mean(signal_0)
        Y = np.mean(signal_1)
        
        A = np.sqrt(X**2 + Y**2)
        phi = np.arctan2(Y, X)
        
        print("A=", A, "phi=", phi)

# 示例使用
t_vec = np.linspace(0, 0.2, 1001)
A = 1
phi = np.pi
noise = 0.2
ref_freq = 17.77777

generator = SignalGenerator(t_vec, A, phi, noise, ref_freq)
signal = generator.generate_signal()
generator.lock_in_measurement(signal, ref_freq)

这样，你可以更方便地复用代码，并且可以通过修改构造函数的参数来调整生成信号和锁相测量的设置。

class TimeWindowSequence(Sequence): def __init__(self, x, y, batch_size, window_size): self.x = x self.y = y self.batch_size = batch_size self.window_size = window_size self.window_count = int(np.ceil(x.shape[0] / window_size)) def __len__(self): return int(np.ceil(self.x.shape[0] / self.batch_size)) def __getitem__(self, idx): batch_x = np.zeros((self.batch_size, self.window_size, self.x.shape[1])) batch_y = np.zeros((self.batch_size, self.y.shape[1])) for i in range(self.batch_size): j = idx * self.batch_size + i if j >= self.window_count: break window_x = self.x[j*self.window_size:(j+1)*self.window_size, :] window_y = self.y[j*self.window_size:(j+1)*self.window_size, :] batch_x[i, :window_x.shape[0], :] = window_x batch_y[i, :] = window_y[-1, :] return batch_x, batch_y出现

这段代码中似乎缺少了一个缩进，导致 `return` 语句在 `for` 循环内部。可以尝试将 `return` 语句缩进到 `for` 循环外部，如下所示：

class TimeWindowSequence(Sequence):
    def __init__(self, x, y, batch_size, window_size):
        self.x = x
        self.y = y
        self.batch_size = batch_size
        self.window_size = window_size
        self.window_count = int(np.ceil(x.shape[0] / window_size))

    def __len__(self):
        return int(np.ceil(self.x.shape[0] / self.batch_size))

    def __getitem__(self, idx):
        batch_x = np.zeros((self.batch_size, self.window_size, self.x.shape[1]))
        batch_y = np.zeros((self.batch_size, self.y.shape[1]))
        for i in range(self.batch_size):
            j = idx * self.batch_size + i
            if j >= self.window_count:
                break
            window_x = self.x[j*self.window_size:(j+1)*self.window_size, :]
            window_y = self.y[j*self.window_size:(j+1)*self.window_size, :]
            batch_x[i, :window_x.shape[0], :] = window_x
            batch_y[i, :] = window_y[-1, :]
        return batch_x, batch_y

这样应该就可以解决这个问题了。