这里有一个包含四个输入的数据集。所以这里我想用LSTM模型预测一个输出。所以对于y值,我每一小时使用append value创建一个值。你知道吗
X = 1
n_out = 1
x,y=list(),list()
start =0
for _ in range(len(df)):
in_end = start+X
out_end= in_end + n_out
if out_end < len(df):
x_input = df[start:in_end]
x.append(x_input)
y.append(df[in_end:out_end,0])
start +=1
在这里,附加值y的值显示如下。你知道吗
2018-06-08 06:15:00 141.0
2018-06-08 07:15:00 0
2018-06-08 08:15:00 0
2018-06-08 09:15:00 0
2018-06-08 10:15:00 0
2018-06-08 11:15:00 0
2018-06-08 12:15:00 0
2018-06-08 13:15:00 0
2018-06-08 14:15:00 0
2018-06-08 15:15:00 0
2018-06-08 16:15:00 0
2018-06-08 17:15:00 0
2018-06-08 18:15:00 0
2018-06-08 19:15:00 0
2018-06-08 20:15:00 0
2018-06-08 21:15:00 0
2018-06-08 22:15:00 0
在这之后我做到了定标器.fit转换为Y值。然后它给了我这个错误。你知道吗
这是我的密码:
y = y.values.astype(int)
scaler_y = preprocessing.MinMaxScaler(feature_range =(0, 1))
y = np.array(y).reshape([-1, 1])
y = scaler_y.fit_transform(y)
错误:
第一个错误:
ttributeError Traceback (most recent call last)
<ipython-input-240-6ac9211db656> in <module>()
----> 1 y = y.values.astype(int)
AttributeError: 'list' object has no attribute 'values'
ValueError Traceback (most recent call last)
<ipython-input-184-ad4efba4dd31> in <module>()
1 scaler_y = preprocessing.MinMaxScaler(feature_range =(0, 1))
2 y = np.array(y).reshape([-1, 1])
----> 3 y = scaler_y.fit_transform(y)
~\Anaconda3\lib\site-packages\sklearn\base.py in fit_transform(self, X, y, **fit_params)
515 if y is None:
516 # fit method of arity 1 (unsupervised transformation)
--> 517 return self.fit(X, **fit_params).transform(X)
518 else:
519 # fit method of arity 2 (supervised transformation)
~\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py in fit(self, X, y)
306 # Reset internal state before fitting
307 self._reset()
--> 308 return self.partial_fit(X, y)
309
310 def partial_fit(self, X, y=None):
~\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py in partial_fit(self, X, y)
332
333 X = check_array(X, copy=self.copy, warn_on_dtype=True,
--> 334 estimator=self, dtype=FLOAT_DTYPES)
335
336 data_min = np.min(X, axis=0)
~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
431 force_all_finite)
432 else:
--> 433 array = np.array(array, dtype=dtype, order=order, copy=copy)
434
435 if ensure_2d:
ValueError: could not convert string to float: "{'level': [141.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
有人能帮我解决这个问题吗?你知道吗
我找到了答案。谢谢你的回复。你知道吗
我的代码是:
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