format=matplotlib.ticker.FuncFormatter(lambda x,pos:int(x*(max_v-min_v)+min_v))中怎么设置小数位数
时间: 2023-06-01 09:01:31 浏览: 69
可以在lambda函数中使用字符串格式化来设置小数位数。例如,要设置为保留两位小数,可以使用"%.2f"格式,代码如下:
```python
format=matplotlib.ticker.FuncFormatter(lambda x, pos: "{:.2f}".format(x * (max_v - min_v) + min_v))
```
其中,"{:.2f}"表示保留两位小数的格式,将x * (max_v - min_v) + min_v的结果按照这个格式输出即可。
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)
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)
import numpy import numpy as np import matplotlib.pyplot as plt import math import torch from torch import nn from torch.utils.data import DataLoader, Dataset import os os.environ['KMP_DUPLICATE_LIB_OK']='True' dataset = [] for data in np.arange(0, 3, .01): data = math.sin(data * math.pi) dataset.append(data) dataset = np.array(dataset) dataset = dataset.astype('float32') max_value = np.max(dataset) min_value = np.min(dataset) scalar = max_value - min_value print(scalar) dataset = list(map(lambda x: x / scalar, dataset)) def create_dataset(dataset, look_back=3): dataX, dataY = [], [] for i in range(len(dataset) - look_back): a = dataset[i:(i + look_back)] dataX.append(a) dataY.append(dataset[i + look_back]) return np.array(dataX), np.array(dataY) data_X, data_Y = create_dataset(dataset) train_X, train_Y = data_X[:int(0.8 * len(data_X))], data_Y[:int(0.8 * len(data_Y))] test_X, test_Y = data_Y[int(0.8 * len(data_X)):], data_Y[int(0.8 * len(data_Y)):] train_X = train_X.reshape(-1, 1, 3).astype('float32') train_Y = train_Y.reshape(-1, 1, 3).astype('float32') test_X = test_X.reshape(-1, 1, 3).astype('float32') train_X = torch.from_numpy(train_X) train_Y = torch.from_numpy(train_Y) test_X = torch.from_numpy(test_X) class RNN(nn.Module): def __init__(self, input_size, hidden_size, output_size=1, num_layer=2): super(RNN, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layer = num_layer self.rnn = nn.RNN(input_size, hidden_size, batch_first=True) self.linear = nn.Linear(hidden_size, output_size) def forward(self, x): out, h = self.rnn(x) out = self.linear(out[0]) return out net = RNN(3, 20) criterion = nn.MSELoss(reduction='mean') optimizer = torch.optim.Adam(net.parameters(), lr=1e-2) train_loss = [] test_loss = [] for e in range(1000): pred = net(train_X) loss = criterion(pred, train_Y) optimizer.zero_grad() # 反向传播 loss.backward() optimizer.step() if (e + 1) % 100 == 0: print('Epoch:{},loss:{:.10f}'.format(e + 1, loss.data.item())) train_loss.append(loss.item()) plt.plot(train_loss, label='train_loss') plt.legend() plt.show()请适当修改代码,并写出预测值和真实值的代码
以下是适当修改后的代码以及预测值和真实值的代码:
```
import numpy as np
import matplotlib.pyplot as plt
import math
import torch
from torch import nn
from torch.utils.data import DataLoader, Dataset
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
dataset = []
for data in np.arange(0, 3, .01):
data = math.sin(data * math.pi)
dataset.append(data)
dataset = np.array(dataset)
dataset = dataset.astype('float32')
max_value = np.max(dataset)
min_value = np.min(dataset)
scalar = max_value - min_value
print(scalar)
dataset = list(map(lambda x: x / scalar, dataset))
def create_dataset(dataset, look_back=3):
dataX, dataY = [], []
for i in range(len(dataset) - look_back):
a = dataset[i:(i + look_back)]
dataX.append(a)
dataY.append(dataset[i + look_back])
return np.array(dataX), np.array(dataY)
data_X, data_Y = create_dataset(dataset)
train_X, train_Y = data_X[:int(0.8 * len(data_X))], data_Y[:int(0.8 * len(data_Y))]
test_X, test_Y = data_Y[int(0.8 * len(data_X)):], data_Y[int(0.8 * len(data_Y)):]
train_X = train_X.reshape(-1, 1, 3).astype('float32')
train_Y = train_Y.reshape(-1, 1, 1).astype('float32')
test_X = test_X.reshape(-1, 1, 3).astype('float32')
train_X = torch.from_numpy(train_X)
train_Y = torch.from_numpy(train_Y)
test_X = torch.from_numpy(test_X)
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size=1, num_layer=2):
super(RNN, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.num_layer = num_layer
self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)
self.linear = nn.Linear(hidden_size, output_size)
def forward(self, x):
out, h = self.rnn(x)
out = self.linear(out[:, -1, :])
return out
net = RNN(3, 20)
criterion = nn.MSELoss(reduction='mean')
optimizer = torch.optim.Adam(net.parameters(), lr=1e-2)
train_loss = []
test_loss = []
for e in range(1000):
pred = net(train_X)
loss = criterion(pred, train_Y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (e + 1) % 100 == 0:
print('Epoch:{},loss:{:.10f}'.format(e + 1, loss.data.item()))
train_loss.append(loss.item())
plt.plot(train_loss, label='train_loss')
plt.legend()
plt.show()
# 预测值和真实值的代码
net.eval()
pred_test = net(test_X)
pred_test = pred_test.detach().numpy() * scalar
test_Y = test_Y.reshape(-1, 1) * scalar
plt.plot(pred_test, label='predict')
plt.plot(test_Y, label='true')
plt.legend()
plt.show()
```
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