0.1.93
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		| @@ -1,6 +1,6 @@ | ||||
| Metadata-Version: 2.1 | ||||
| Name: guan | ||||
| Version: 0.1.92 | ||||
| Version: 0.1.93 | ||||
| Summary: An open source python package | ||||
| Home-page: https://py.guanjihuan.com | ||||
| Author: guanjihuan | ||||
|   | ||||
| @@ -191,4 +191,21 @@ def load_train_data(x_train, y_train, batch_size=32): | ||||
|     from torch.utils.data import DataLoader, TensorDataset | ||||
|     train_dataset = TensorDataset(x_train, y_train) | ||||
|     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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