med = X_train.median().values.reshape((1,X_train.shape[1]))
时间: 2024-05-25 14:10:39 浏览: 17
这行代码的作用是计算 `X_train` 数据集中每列的中位数,并将结果保存在一个一维数组中。然后通过 `reshape` 方法将这个一维数组转换成一个包含一个行向量的二维数组,其中行数为 1,列数等于 `X_train` 的列数。这样做的目的可能是为了后续使用中位数来填充缺失值或者进行数据标准化等操作。
相关问题
6、每个用户按周求和并差分(一周7天,年度分开),并求取差分结果的基本统计量,统计量同三。 res4 = pd.DataFrame() for col in data_t.columns: # 按周求和 data_weekly = data_t[col].resample('W').sum() # 差分 data_diff = data_weekly.diff(periods=1) # 去除第一个NaN值 data_diff = data_diff[1:] # 求取差分结果的基本统计量 res_temp = pd.DataFrame() res_temp['最大值'] = data_diff.max() res_temp['最小值'] = data_diff.min() res_temp['均值'] = data_diff.mean() res_temp['中位数'] = data_diff.median() res_temp['和'] = data_diff.sum() res_temp['方差'] = data_diff.var() res_temp['偏度'] = data_diff.skew() res_temp['峰度'] = data_diff.kurt() res4 = pd.concat([res4, res_temp.T], axis=1) res4.columns = data_t.columns print("每个用户按周求和并差分的基本统计量") print(res4)修改运行代码
修改后的运行代码如下:
```
import pandas as pd
# 创建示例数据
data_t = pd.DataFrame({'date': pd.date_range('20210101', periods=365),
'user1': [i%50 for i in range(365)],
'user2': [i%30 for i in range(365)]})
data_t = data_t.set_index('date')
# 对数据进行处理并计算基本统计量
res4 = pd.DataFrame()
for col in data_t.columns:
# 按周求和
data_weekly = data_t[col].resample('W').sum()
# 差分
data_diff = data_weekly.diff(periods=1)
# 去除第一个NaN值
data_diff = data_diff[1:]
# 求取差分结果的基本统计量
res_temp = pd.DataFrame()
res_temp['最大值'] = data_diff.max()
res_temp['最小值'] = data_diff.min()
res_temp['均值'] = data_diff.mean()
res_temp['中位数'] = data_diff.median()
res_temp['和'] = data_diff.sum()
res_temp['方差'] = data_diff.var()
res_temp['偏度'] = data_diff.skew()
res_temp['峰度'] = data_diff.kurt()
res4 = pd.concat([res4, res_temp.T], axis=1)
res4.columns = data_t.columns
# 输出结果
print("每个用户按周求和并差分的基本统计量:")
print(res4)
```
这段代码会首先创建一个示例数据(共365天,包含两个用户),然后按照上述要求对数据进行处理并计算基本统计量。注意需要将时间戳列设置为索引,并且确保数据类型正确。最后输出每个用户按周求和并差分的基本统计量。
import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from keras.models import Sequential from keras.layers import Dense, LSTM from sklearn.metrics import r2_score,median_absolute_error,mean_absolute_error # 读取数据 data = pd.read_csv(r'C:/Users/Ljimmy/Desktop/yyqc/peijian/销量数据rnn.csv') # 取出特征参数 X = data.iloc[:,2:].values # 数据归一化 scaler = MinMaxScaler(feature_range=(0, 1)) X[:, 0] = scaler.fit_transform(X[:, 0].reshape(-1, 1)).flatten() #X = scaler.fit_transform(X) #scaler.fit(X) #X = scaler.transform(X) # 划分训练集和测试集 train_size = int(len(X) * 0.8) test_size = len(X) - train_size train, test = X[0:train_size, :], X[train_size:len(X), :] # 转换为监督学习问题 def create_dataset(dataset, look_back=1): X, Y = [], [] for i in range(len(dataset) - look_back - 1): a = dataset[i:(i + look_back), :] X.append(a) Y.append(dataset[i + look_back, 0]) return np.array(X), np.array(Y) look_back = 12 X_train, Y_train = create_dataset(train, look_back) #Y_train = train[:, 2:] # 取第三列及以后的数据 X_test, Y_test = create_dataset(test, look_back) #Y_test = test[:, 2:] # 取第三列及以后的数据 # 转换为3D张量 X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) # 构建LSTM模型 model = Sequential() model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1))) model.add(LSTM(units=50)) model.add(Dense(units=1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(X_train, Y_train, epochs=5, batch_size=32) #model.fit(X_train, Y_train.reshape(Y_train.shape[0], 1), epochs=10, batch_size=32) # 预测下一个月的销量 last_month_sales = data.tail(12).iloc[:,2:].values #last_month_sales = data.tail(1)[:,2:].values last_month_sales = scaler.transform(last_month_sales) last_month_sales = np.reshape(last_month_sales, (1, look_back, 1)) next_month_sales = model.predict(last_month_sales) next_month_sales = scaler.inverse_transform(next_month_sales) print('Next month sales: %.0f' % next_month_sales[0][0]) # 计算RMSE误差 rmse = np.sqrt(np.mean((next_month_sales - last_month_sales) ** 2)) print('Test RMSE: %.3f' % rmse)IndexError Traceback (most recent call last) Cell In[1], line 36 33 X_test, Y_test = create_dataset(test, look_back) 34 #Y_test = test[:, 2:] # 取第三列及以后的数据 35 # 转换为3D张量 ---> 36 X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) 37 X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) 38 # 构建LSTM模型 IndexError: tuple index out of range代码修改
在创建数据集时,您只考虑了输入特征,而没有包括输出特征。因此,当您尝试将训练数据转换为3D张量时,出现了一个索引错误。您需要修改以下两行代码:
```
X_train, Y_train = create_dataset(train, look_back)
X_test, Y_test = create_dataset(test, look_back)
```
为:
```
X_train, Y_train = create_dataset(train, look_back)
X_test, Y_test = create_dataset(test, look_back)
Y_train = Y_train.reshape(Y_train.shape[0], 1)
Y_test = Y_test.reshape(Y_test.shape[0], 1)
```
这将确保输出特征也包含在数据集中,并且在转换为3D张量时不会出现索引错误。