#使用前面定义的函数进行画图 X0,X1=X_train[:,0],X_train[:,1] xx,yy=make_meshgrid(X0,X1) for clf,title,ax in zip(models,titles,sub.flatten()): plot_contours(ax,clf,xx,yy,cmap=plt.cm.plasma,alpha=0.8) ax.scatter(X0,X1,c=y,cmap=plt.cm.plasma,s=20,edgecolors='k') ax.set_xlim(xx.min(),xx.max()) ax.set_ylim(yy.min(),yy.max()) ax.set_xlabel('Feature 0') ax.set_ylabel('Feature 1') ax.set_xticks(()) ax.set_yticks(()) ax.set_title(title) plt.show() 代码报错TypeError: unsupported operand type(s) for -: 'str' and 'int' 应该如何修改，请给我代码

这个错误通常是由于在`set_xticks()`或`set_yticks()`函数中传入了一个非整型的参数导致的。

你可以将这两个函数中的参数改为整型，或者去掉这两个函数，让Matplotlib自动计算刻度值。

修改后的代码如下：

X0, X1 = X_train[:, 0], X_train[:, 1]
xx, yy = make_meshgrid(X0, X1)

for clf, title, ax in zip(models, titles, sub.flatten()):
    plot_contours(ax, clf, xx, yy, cmap=plt.cm.plasma, alpha=0.8)
    ax.scatter(X0, X1, c=y, cmap=plt.cm.plasma, s=20, edgecolors='k')
    ax.set_xlim(xx.min(), xx.max())
    ax.set_ylim(yy.min(), yy.max())
    ax.set_xlabel('Feature 0')
    ax.set_ylabel('Feature 1')
    ax.set_title(title)
    
plt.show()

在上述代码中，我们去掉了`set_xticks()`和`set_yticks()`函数，并且保证`xx.min()`, `xx.max()`, `yy.min()`和`yy.max()`的返回值为整型。

相关问题

把下方Python函数转化成伪代码def AddRandom(noise_std,x_train,min,max): x_train = pd.DataFrame(x_train) m=x_train.shape[0] n=x_train.shape[1] #生成Mask掩码 Mask = matlab.rand(m,n) for i in range(0,m): for j in range(0,n): if Mask[i,j]<=noise_std: loc_ij = x_train.iloc[i,j] if(loc_ij==0): loc_ij=max Mask[i,j]=np.random.uniform(0,max/loc_ij) #Mask[i,j]=0 else: Mask[i,j]=1; x_train_Random = (x_train*Mask).astype(int) return x_train_Random

以下是将Python函数转化成伪代码的结果：

# 伪代码示例: 添加随机噪声

# 定义函数AddRandom
function AddRandom(noise_std, x_train, min, max):
    # 将x_train转化为DataFrame格式
    x_train = DataFrame(x_train)
    
    # 获取x_train的行列数
    m = x_train.shape[0]
    n = x_train.shape[1]
    
    # 生成Mask掩码
    Mask = rand(m, n)
    
    # 遍历Mask，并根据噪声标准差添加随机噪声
    for i in range(0, m):
        for j in range(0, n):
            if Mask[i, j] <= noise_std:
                loc_ij = x_train.iloc[i, j]
                if(loc_ij == 0):
                    loc_ij = max
                Mask[i, j] = np.random.uniform(0, max/loc_ij)
                # Mask[i, j] = 0
            else:
                Mask[i, j] = 1
    
    # 将x_train与Mask相乘，得到带有随机噪声的x_train_Random
    x_train_Random = (x_train * Mask).astype(int)
    
    # 返回x_train_Random
    return x_train_Random

需要注意的是，这只是一种伪代码的写法，与实际的Python代码可能会有所区别。

把下方函数转化成公式或者算法伪代码def AddRandom(noise_std,x_train,min,max): x_train = pd.DataFrame(x_train) m=x_train.shape[0] n=x_train.shape[1] #生成Mask掩码 Mask = matlab.rand(m,n) for i in range(0,m): for j in range(0,n): if Mask[i,j]<=noise_std: loc_ij = x_train.iloc[i,j] if(loc_ij==0): loc_ij=max Mask[i,j]=np.random.uniform(0,max/loc_ij) #Mask[i,j]=0 else: Mask[i,j]=1; x_train_Random = (x_train*Mask).astype(int) return x_train_Random

以下是将函数转化成算法伪代码：

AddRandom(noise_std, x_train, min, max):
    x_train = pd.DataFrame(x_train)
    m = x_train.shape[0]
    n = x_train.shape[1]
    Mask = matlab.rand(m, n)
    for i = 0 to m-1 do:
        for j = 0 to n-1 do:
            if Mask[i,j] <= noise_std then:
                loc_ij = x_train.iloc[i,j]
                if loc_ij == 0 then:
                    loc_ij = max
                Mask[i,j] = np.random.uniform(0, max/loc_ij)
            else:
                Mask[i,j] = 1
    x_train_Random = (x_train * Mask).astype(int)
    return x_train_Random

需要注意的是，这只是一种伪代码的写法，与实际的算法代码可能会有所区别。