From 194b90a0d8493628fb1fc3708c6e30868b51dae0 Mon Sep 17 00:00:00 2001
From: guanjihuan <guanjihuan@163.com>
Date: Wed, 10 Apr 2024 22:40:42 +0800
Subject: [PATCH] 0.1.96

---
 PyPI/setup.cfg                    |  2 +-
 PyPI/src/guan.egg-info/PKG-INFO   |  2 +-
 PyPI/src/guan/machine_learning.py | 23 +++++++++++++++++++++++
 3 files changed, 25 insertions(+), 2 deletions(-)

diff --git a/PyPI/setup.cfg b/PyPI/setup.cfg
index 6d82f6c..9400c42 100644
--- a/PyPI/setup.cfg
+++ b/PyPI/setup.cfg
@@ -1,7 +1,7 @@
 [metadata]
 # replace with your username:
 name = guan
-version = 0.1.95
+version = 0.1.96
 author = guanjihuan
 author_email = guanjihuan@163.com
 description = An open source python package
diff --git a/PyPI/src/guan.egg-info/PKG-INFO b/PyPI/src/guan.egg-info/PKG-INFO
index 0d8191d..657e5d1 100644
--- a/PyPI/src/guan.egg-info/PKG-INFO
+++ b/PyPI/src/guan.egg-info/PKG-INFO
@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: guan
-Version: 0.1.95
+Version: 0.1.96
 Summary: An open source python package
 Home-page: https://py.guanjihuan.com
 Author: guanjihuan
diff --git a/PyPI/src/guan/machine_learning.py b/PyPI/src/guan/machine_learning.py
index 508c03b..4fbabad 100644
--- a/PyPI/src/guan/machine_learning.py
+++ b/PyPI/src/guan/machine_learning.py
@@ -221,6 +221,29 @@ 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
+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
+    import torch
+    input_data = []
+    for index in index_range:
+        input = guan.load_data(filename=directory+input_filename+str(index))
+        input_data.append(input)
+    output_data = []
+    for index in index_range:
+        output = guan.load_data(filename=directory+output_filename+str(index))
+        output_data.append(output)
+    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