0.1.93
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		| @@ -1,7 +1,7 @@ | |||||||
| [metadata] | [metadata] | ||||||
| # replace with your username: | # replace with your username: | ||||||
| name = guan | name = guan | ||||||
| version = 0.1.92 | version = 0.1.93 | ||||||
| author = guanjihuan | author = guanjihuan | ||||||
| author_email = guanjihuan@163.com | author_email = guanjihuan@163.com | ||||||
| description = An open source python package | description = An open source python package | ||||||
|   | |||||||
| @@ -1,6 +1,6 @@ | |||||||
| Metadata-Version: 2.1 | Metadata-Version: 2.1 | ||||||
| Name: guan | Name: guan | ||||||
| Version: 0.1.92 | Version: 0.1.93 | ||||||
| Summary: An open source python package | Summary: An open source python package | ||||||
| Home-page: https://py.guanjihuan.com | Home-page: https://py.guanjihuan.com | ||||||
| Author: guanjihuan | Author: guanjihuan | ||||||
|   | |||||||
| @@ -191,4 +191,21 @@ def load_train_data(x_train, y_train, batch_size=32): | |||||||
|     from torch.utils.data import DataLoader, TensorDataset |     from torch.utils.data import DataLoader, TensorDataset | ||||||
|     train_dataset = TensorDataset(x_train, y_train) |     train_dataset = TensorDataset(x_train, y_train) | ||||||
|     train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) |     train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) | ||||||
|     return train_loader |     return train_loader | ||||||
|  |  | ||||||
|  | # 数据的主成分分析PCA | ||||||
|  | def pca_of_data(data, n_components=None, standard=1): | ||||||
|  |     from sklearn.decomposition import PCA | ||||||
|  |     if standard==1: | ||||||
|  |         from sklearn.preprocessing import StandardScaler | ||||||
|  |         scaler = StandardScaler() | ||||||
|  |         data_scaled = scaler.fit_transform(data) | ||||||
|  |     else: | ||||||
|  |         data_scaled = data | ||||||
|  |     if n_components==None: | ||||||
|  |         pca = PCA() | ||||||
|  |     else: | ||||||
|  |         pca = PCA(n_components=n_components) | ||||||
|  |     data_transformed = pca.fit_transform(data_scaled) | ||||||
|  |     explained_variance_ratio = pca.explained_variance_ratio_ | ||||||
|  |     return data_transformed, explained_variance_ratio | ||||||
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