name“np” is not define
时间: 2023-08-18 17:05:14 浏览: 67
这个错误提示意味着在代码中使用了名为"np"的变量,但是该变量没有被定义或导入。通常情况下,"np"是NumPy库的别名,需要在代码中导入NumPy库才能使用它。请确保你的代码中包含了以下导入语句:
```python
import numpy as np
```
如果你已经导入了NumPy库,但仍然出现这个错误,可能是因为你的代码中存在语法错误导致变量未能正确定义。请仔细检查你的代码,查找并修复任何语法错误。
相关问题
name 'image_to_array' is not defined
As an AI language model, I cannot know the entire context of your issue. However, the error message "name 'image_to_array' is not defined" usually occurs when you are trying to call a function or method that is not defined or imported in your code.
To fix this issue, you need to import the required module that contains the 'image_to_array' function or define the function in your code. You can import the required module using the 'import' statement or from the module, import the specific function you want to use.
For example, if you are using the 'image_to_array' function from the 'PIL' module, you can use the following import statement:
```
from PIL import Image
import numpy as np
# Load the image
img = Image.open('image.jpg')
# Convert image to numpy array
img_array = np.array(img)
```
Make sure that the module containing the 'image_to_array' function is installed on your system or virtual environment.
#make predictions preds = [] for i in range(0,103): a = train['close'][len(train)-103+i:].sum() + sum(preds) b = a/103 NameError: name 'rms' is not defined preds.append(b) #calculate rmse import numpy as np preds = [1, 2, 3, 4, 5] preds_np = np.array(preds) sum_preds = preds_np.sum() preds_valid = [] for i in range(0, len(preds) - 102): a = preds[i:i+103].sum() b = a/103 preds_valid.append(b) rms=np.sqrt(np.mean(np.power((np.array(valid['close'])-preds),2))) rms
这段代码中存在两个问题:
1. 在第一部分代码中(make predictions),你使用了未定义的 `train` 变量。如果你需要使用 `train` 变量,需要先定义它。
2. 在第二部分代码中(calculate rmse),你使用了未定义的 `valid` 变量。如果你需要使用 `valid` 变量,需要先定义它。
此外,你还需要将 `rms` 变量的定义放在代码的前面,否则在计算 RMSE 时会报错。
以下是修改后的代码示例:
```python
import numpy as np
# define 'train' and 'valid' variables first
# make predictions
preds = []
for i in range(0,103):
a = train['close'][len(train)-103+i:].sum() + sum(preds)
b = a/103
preds.append(b)
# calculate RMSE
valid = [...] # define 'valid' variable first
preds_np = np.array(preds)
sum_preds = preds_np.sum()
preds_valid = []
for i in range(0, len(preds) - 102):
a = preds[i:i+103].sum()
b = a/103
preds_valid.append(b)
rms = np.sqrt(np.mean(np.power((np.array(valid['close'])-preds_valid),2)))
```
这样就可以避免上述错误了。
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