From aac69dd965e6fd55e1c4f0f8a5e0da24a04380bc Mon Sep 17 00:00:00 2001 From: guanjihuan Date: Mon, 13 May 2024 21:36:59 +0800 Subject: [PATCH] 0.1.100 --- PyPI/setup.cfg | 2 +- PyPI/src/guan.egg-info/PKG-INFO | 2 +- PyPI/src/guan/machine_learning.py | 19 ++++++++++++++++++- 3 files changed, 20 insertions(+), 3 deletions(-) diff --git a/PyPI/setup.cfg b/PyPI/setup.cfg index fa4fa14..37f10f0 100644 --- a/PyPI/setup.cfg +++ b/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 diff --git a/PyPI/src/guan.egg-info/PKG-INFO b/PyPI/src/guan.egg-info/PKG-INFO index 0b46252..e610764 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: 0.1.99 +Version: 0.1.100 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 ac698d0..cc8a7fa 100644 --- a/PyPI/src/guan/machine_learning.py +++ b/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