95% found where number expected
时间: 2024-01-04 12:03:16 浏览: 257
这个错误通常表示你在 Stata 的命令中输入了不正确的语法或参数,导致 Stata 无法正常执行命令。通常情况下,该错误是由于输入命令时少了一个逗号、括号不匹配、变量名拼写错误等常见语法错误引起的。
你需要检查你输入的命令或语法是否正确,确保输入的参数个数、顺序和格式都符合 Stata 的要求。如果你无法自行解决问题,可以在 Stata 中查看帮助文档,或者在网上寻求更多的帮助。
相关问题
Error in query: Invalid number of arguments for function decode. Expected: 2; Found: 4; line 7 pos 12
非常抱歉,我在先前的回答中给出的 `decode` 函数使用了错误的参数数量。根据您提供的 SQL 查询,您想要使用 `decode` 函数对 `area_name` 进行条件判断并进行转换。
以下是修正后的查询语句:
```sql
SELECT
CASE
WHEN area_name = '金华' THEN '金华市'
ELSE area_name
END AS area_name,
SUM(OLT_BUSY_RATE) AS OLT_BUSY_RATE
FROM
spark_odc_data.A_FT_SE_ST_VN_ONU_UP_INDEX_6H
WHERE
p_HOUR >= '2023071100'
AND p_HOUR <= '2023071123'
GROUP BY
CASE
WHEN area_name = '金华' THEN '金华市'
ELSE area_name
END
```
在这个修正后的查询中,我们使用了 `CASE` 表达式来实现对 `area_name` 的条件判断和转换。使用 `CASE` 表达式,当 `area_name` 等于 `'金华'` 时,我们将其转换为 `'金华市'`,否则保持不变。
对于 `GROUP BY` 子句,我们也使用了相同的 `CASE` 表达式来进行分组操作。
请注意,我作为一个AI助手无法直接执行SQL语句或访问特定的数据库。我只能提供查询结果的解释和理解。您需要在适当的环境中运行此SQL查询以获取实际结果。如果您需要更多的帮助或有其他问题,请随时提问。
Input 0 of layer "conv1d" is incompatible with the layer: expected min_ndim=3, found ndim=2. Full shape received: (None, 5)
The error message suggests that the input shape provided to the `Conv1D` layer is incompatible. The `Conv1D` layer expects input tensors with a minimum of 3 dimensions, but the received input tensor has only 2 dimensions.
To fix this issue, you need to reshape your input tensor to have a shape of (batch_size, length, channels). In the case of `Conv1D`, the length dimension corresponds to the temporal dimension of the data, and the channels dimension represents the number of input channels.
Here's an example of how you can reshape your input data to make it compatible with the `Conv1D` layer:
```python
from tensorflow.keras.layers import Conv1D
from tensorflow.keras.models import Sequential
model = Sequential()
model.add(Conv1D(16, kernel_size=3, input_shape=(5, 1))) # Assuming input has 5 time steps and 1 channel
# Reshape your input data to match the required shape
input_data = input_data.reshape((-1, 5, 1))
# Now you can pass your reshaped input data to the model for further processing
output = model.predict(input_data)
```
In this example, we assume that your input data has 5 time steps and 1 channel. We reshape the input data using the `reshape` function to have a shape of (-1, 5, 1), where -1 signifies that the batch size can be variable. This reshaped data can then be passed to the `Conv1D` layer without any shape compatibility issues.