plt.scatter(self.target_sites[0][0], self.target_sites[1][0], color='', marker='o', edgecolors='b', s=50) # 什么意思

这是一个使用matplotlib库中的scatter函数绘制散点图的代码。其中self.target_sites是一个二维数组，第一个维度表示x轴坐标，第二个维度表示y轴坐标。这里取了第一个元素作为散点的位置。color=''表示散点不填充颜色，marker='o'表示散点形状为圆形，edgecolors='b'表示散点边缘颜色为蓝色，s=50表示散点大小为50。

相关问题

class RandomWalk(): def __init__(self,point_num=5000): self.point_num = point_num self.xval = [0] self.yval = [0] def fill_walk(self): while len(self.xval)<self.point_num: x_direction = choice([1,-1]) step_num = choice([0,1,2,3,4]) xstep = x_direction * step_num y_direction = choice([1,-1]) step_num = choice([0,1,2,3,4]) ystep = y_direction * step_num if xstep == 0 and ystep==0: continue x_next = self.xval[-1] + xstep y_next = self.yval[-1] + ystep self.xval.append(x_next) self.yval.append(y_next) rw = RandomWalk(50000) rw.fill_walk() point_numbers = list(range(rw.point_num())) plt.scatter(rw.x_val,rw.y_val,c=point_numbers,cmap=plt.cm.Blues,edgecolor = 'none',s=2) plt.scatter(0,0,c='green',edgecolors='none',s=100) plt.scatter(rw.x_val[-1],rw.y_val[-1],c='red',edgecolor='none',s=100) plt.axes().get_xaxis().set_visible(False) plt.axes().get_xaxis().set_yisible(False) plt.show()修改代码

以下是修改后的代码：

import matplotlib.pyplot as plt
from random import choice

class RandomWalk():
    def __init__(self, point_num=5000):
        self.point_num = point_num
        self.x_val = [0]
        self.y_val = [0]
        
    def fill_walk(self):
        while len(self.x_val) < self.point_num:
            x_direction = choice([1, -1])
            x_step = choice([0, 1, 2, 3, 4])
            x_val = x_direction * x_step
            
            y_direction = choice([1, -1])
            y_step = choice([0, 1, 2, 3, 4])
            y_val = y_direction * y_step
            
            if x_val == 0 and y_val == 0:
                continue
            
            next_x = self.x_val[-1] + x_val
            next_y = self.y_val[-1] + y_val
            
            self.x_val.append(next_x)
            self.y_val.append(next_y)

rw = RandomWalk(50000)
rw.fill_walk()

point_numbers = list(range(rw.point_num))
plt.scatter(rw.x_val, rw.y_val, c=point_numbers, cmap=plt.cm.Blues, edgecolor='none', s=2)
plt.scatter(0, 0, c='green', edgecolors='none', s=100)
plt.scatter(rw.x_val[-1], rw.y_val[-1], c='red', edgecolor='none', s=100)
plt.axes().get_xaxis().set_visible(False)
plt.axes().get_yaxis().set_visible(False)
plt.show()

主要的修改包括：

1. `xval` 和 `yval` 改为 `x_val` 和 `y_val`，以符合 Python 的命名规范。
2. `rw.point_num()` 改为 `rw.point_num`，因为 `point_num` 是一个属性而不是方法。
3. `plt.axes().get_xaxis().set_yisible(False)` 改为 `plt.axes().get_yaxis().set_visible(False)`，因为原来的代码中打错了单词。

import torch import matplotlib.pyplot as plt def features(x): x = x.unsqueeze(1) return torch.cat([x ** i for i in range(1, 5)], 1) x_weight = torch.Tensor([-1.13, -2.14, 3.15, -0.01]).unsqueeze(1) b = torch.Tensor([0.512]) def target(x): return x.mm(x_weight) + b.item() def get_batch_data(batch_size): batch_x = torch.rand(batch_size) features_x = features(batch_x) target_y = target(features_x) return features_x, target_y class PR(torch.nn.Module): def __init__(self): super(PR, self).__init__() self.poly = torch.nn.Linear(4, 1) def forward(self, x): return self.poly(x) epochs = 10000 batch_size = 32 model = PR() criterion = torch.nn.MSELoss() optimizer = torch.optim.SGD(model.parameters(), 0.01) for i in range(epochs): batch_x, batch_y = get_batch_data(batch_size) out = model(batch_x) loss = criterion(out, batch_y) optimizer.zero_grad() loss.backward() optimizer.step() if(i % 100 == 0): print(f"Epoch:{i+1}/{epochs}, loss:{loss.item}") if(i%1000 == 0): predict = model(features(batch_x)) plt.plot(batch_x.data.numpy(), predict.squeeze().data.numpy(), "r") loss = criterion(predict, batch_y) plt.title(f"Loss:{loss.item()}") plt.xlabel("X") plt.ylabel("Y") plt.scatter(batch_x, batch_y) plt.show()哪里出错了

代码有一个小错误，第26行应该是`print(f"Epoch:{i+1}/{epochs}, loss:{loss.item()}")`，需要加上括号。另外，在第34行，应该是`loss = criterion(out, batch_y)`，而不是`loss = criterion(predict, batch_y)`。正确的代码如下所示：

import torch 
import matplotlib.pyplot as plt 

def features(x): 
    x = x.unsqueeze(1) 
    return torch.cat([x ** i for i in range(1, 5)], 1) 

x_weight = torch.Tensor([-1.13, -2.14, 3.15, -0.01]).unsqueeze(1) 
b = torch.Tensor([0.512]) 

def target(x): 
    return x.mm(x_weight) + b.item() 

def get_batch_data(batch_size): 
    batch_x = torch.rand(batch_size) 
    features_x = features(batch_x) 
    target_y = target(features_x) 
    return features_x, target_y 

class PR(torch.nn.Module): 
    def __init__(self): 
        super(PR, self).__init__() 
        self.poly = torch.nn.Linear(4, 1) 

    def forward(self, x): 
        return self.poly(x) 

epochs = 10000 
batch_size = 32 
model = PR() 
criterion = torch.nn.MSELoss() 
optimizer = torch.optim.SGD(model.parameters(), 0.01) 

for i in range(epochs): 
    batch_x, batch_y = get_batch_data(batch_size) 
    out = model(batch_x) 
    loss = criterion(out, batch_y) 
    optimizer.zero_grad() 
    loss.backward() 
    optimizer.step() 

    if(i % 100 == 0): 
        print(f"Epoch:{i+1}/{epochs}, loss:{loss.item()}") 

    if(i%1000 == 0): 
        predict = model(features(batch_x)) 
        plt.plot(batch_x.data.numpy(), predict.squeeze().data.numpy(), "r") 
        loss = criterion(out, batch_y) 
        plt.title(f"Loss:{loss.item()}") 
        plt.xlabel("X") 
        plt.ylabel("Y") 
        plt.scatter(batch_x, batch_y) 
        plt.show()