0.1.100

PyPI/setup.cfg

@@ -1,7 +1,7 @@
 [metadata]
 # replace with your username:
 name = guan
-version = 0.1.99
+version = 0.1.100
 author = guanjihuan
 author_email = guanjihuan@163.com
 description = An open source python package

PyPI/src/guan.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: guan
-Version: 0.1.99
+Version: 0.1.100
 Summary: An open source python package
 Home-page: https://py.guanjihuan.com
 Author: guanjihuan

PyPI/src/guan/machine_learning.py

@@ -245,7 +245,7 @@ def load_train_data(x_train, y_train, batch_size=32):
     train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
     return train_loader
 
-# 从pickle文件中读取输入数据和输出数据，用于训练或预测
+# 从pickle文件（多个小文件）中读取输入数据和输出数据，用于训练或预测
 def load_input_data_and_output_data_as_torch_tensors_with_pickle(index_range=[1, 2, 3], directory='./', input_filename='input_index=', output_filename='output_index=', type=None):
     import guan
     import numpy as np
@@ -268,6 +268,23 @@ def load_input_data_and_output_data_as_torch_tensors_with_pickle(index_range=[1,
     output_data = torch.from_numpy(output_data)
     return input_data, output_data
 
+# 从pickle文件（一个数组文件）中读取输入数据和输出数据，用于训练或预测
+def load_input_data_and_output_data_as_torch_tensors_with_pickle_from_array_file(input_filename='input_file', output_filename='output_file', type=None):
+    import guan
+    import numpy as np
+    import torch
+    input_data = guan.load_data(filename=input_filename)
+    output_data = guan.load_data(filename=output_filename)
+    if type == None:
+        input_data = np.array(input_data)
+        output_data= np.array(output_data)
+    else:
+        input_data = np.array(input_data).astype(type)
+        output_data= np.array(output_data).astype(type)
+    input_data = torch.from_numpy(input_data)
+    output_data = torch.from_numpy(output_data)
+    return input_data, output_data
+
 # 数据的主成分分析PCA
 def pca_of_data(data, n_components=None, standard=1):
     from sklearn.decomposition import PCA