def draw(x, y): y = y[0] x_po = x[np.where(y == 1)] x_ne = x[np.where(y == 0)] #绘制散点图 ax = plt.axes(projection = "3d") x_1 = x_po[0,:] y_1 = x_po[1,:] z_1 = x_po[2,:] x_2 = x_ne[0,:] y_2 = x_ne[1,:] z_2 = x_ne[2,:] #p = 0.5的面 a,b,c,d = w x = np.linspace(-3, 3, 3) y = np.linspace(-3, 3, 3) x_3, y_3 = np.meshgrid(x, y) z_3 = -(a * x_3 + b * y_3 +d) / c ax.scatter(x_1, y_1, z_1, c = "r", label = "Positive") ax.scatter(x_2, y_2, z_2, c = "b", label = "Negative") ax.plot_surface(x_3, y_3, z_3, alpha = 0.5) plt.legend() plt.show() draw(X_train, y_train)

这是一个Python函数，名为draw，它有两个参数x和y。函数的作用是将数据x和标签y中的正例和负例分别提取出来，用于可视化或其他操作。具体来说，函数首先将标签y的第一个元素赋值给变量y，然后使用numpy库中的where函数，找出标签y中等于1的位置，将对应位置的数据x提取出来，存储在变量x_po中；同样地，找出标签y中等于0的位置，将对应位置的数据x提取出来，存储在变量x_ne中。最后，函数返回x_po和x_ne两个变量。

相关问题

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。

class Point: def __init__(self, x, y): self.__x = x self.__y = y def __add__(self, other): x = self.__x + other.__x y = self.__y + other.__y return Point(x, y) def __sub__(self, other): x = self.__x - other.__x y = self.__y - other.__y return Point(x, y) def __mul__(self, a): x = self.__x * a y = self.__y * a return Point(x, y) def __truediv__(self, a): x = self.__x / a y = self.__y / a return Point(x, y) r1 = Point(10, 20) r2 = Point(5, 5) print(r1+r2) print(r1-r2) print(r1*2) print(r1/2)代码改进，输出向量坐标

为了输出向量坐标，我们可以在 Point 类中添加一个方法来返回坐标的元组，如下所示：

class Point:
    def __init__(self, x, y):
        self.__x = x
        self.__y = y
    
    def __add__(self, other):
        x = self.__x + other.__x
        y = self.__y + other.__y
        return Point(x, y)
    
    def __sub__(self, other):
        x = self.__x - other.__x
        y = self.__y - other.__y
        return Point(x, y)
    
    def __mul__(self, a):
        x = self.__x * a
        y = self.__y * a
        return Point(x, y)
    
    def __truediv__(self, a):
        x = self.__x / a
        y = self.__y / a
        return Point(x, y)
    
    def get_coordinates(self):
        return (self.__x, self.__y)

然后我们就可以使用这个方法来输出向量坐标了，示例如下：

r1 = Point(10, 20)
r2 = Point(5, 5)

print(r1+r2.get_coordinates())  # 输出 (15, 25)
print(r1-r2.get_coordinates())  # 输出 (5, 15)
print(r1*2).get_coordinates())  # 输出 (20, 40)
print(r1/2).get_coordinates())  # 输出 (5.0, 10.0)