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0.1.100

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guanjihuan 2024-05-13 21:36:59 +08:00
parent e4b96d78dd
commit aac69dd965
3 changed files with 20 additions and 3 deletions
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2
PyPI/setup.cfg
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@ -1,7 +1,7 @@
[metadata] [metadata]
# replace with your username: # replace with your username:
name = guan name = guan
version = 0.1.99 version = 0.1.100
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

2
PyPI/src/guan.egg-info/PKG-INFO
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@ -1,6 +1,6 @@
Metadata-Version: 2.1 Metadata-Version: 2.1
Name: guan Name: guan
Version: 0.1.99 Version: 0.1.100
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

19
PyPI/src/guan/machine_learning.py
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@ -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) train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
return train_loader 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): 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 guan
import numpy as np 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) output_data = torch.from_numpy(output_data)
return input_data, 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 # 数据的主成分分析PCA
def pca_of_data(data, n_components=None, standard=1): def pca_of_data(data, n_components=None, standard=1):
from sklearn.decomposition import PCA from sklearn.decomposition import PCA