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0.1.51

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guanjihuan
2023-11-27 21:03:46 +08:00
parent f73c48d199
commit 7483f4484b
8 changed files with 137 additions and 16 deletions
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2
PyPI/setup.cfg
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@@ -1,7 +1,7 @@
[metadata]
# replace with your username:
name = guan
version = 0.1.50
version = 0.1.51
author = guanjihuan
author_email = guanjihuan@163.com
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
Name: guan
Version: 0.1.50
Version: 0.1.51
Summary: An open source python package
Home-page: https://py.guanjihuan.com
Author: guanjihuan

1
PyPI/src/guan.egg-info/SOURCES.txt
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@@ -10,6 +10,7 @@ src/guan/band_structures_and_wave_functions.py
src/guan/basic_functions.py
src/guan/data_processing.py
src/guan/density_of_states.py
src/guan/machine_learning.py
src/guan/others.py
src/guan/quantum_transport.py
src/guan/topological_invariant.py

1
PyPI/src/guan/__init__.py
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@@ -17,5 +17,6 @@ from .Green_functions import *
from .density_of_states import *
from .quantum_transport import *
from .topological_invariant import *
from .machine_learning import *
from .data_processing import *
from .others import *

5
PyPI/src/guan/basic_functions.py
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@@ -4,7 +4,10 @@ import guan
# 测试
@guan.function_decorator
def test():
print('\nSuccess in the installation of Guan package!\n')
import guan
current_version = guan.get_current_version('guan')
print(f'\nSuccess in the installation of Guan package! The installed version is guan-{current_version}.\n')
guan.notification_of_upgrade(timeout=5)
# 泡利矩阵
@guan.function_decorator

115
PyPI/src/guan/machine_learning.py Normal file
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@@ -0,0 +1,115 @@
# Module: machine_learning
import guan
# 全连接神经网络模型（包含一个隐藏层）
@guan.function_decorator
def fully_connected_neural_network_with_one_hidden_layer(input_size=1, hidden_size=10, output_size=1, activation='relu'):
import torch
class model_class(torch.nn.Module):
def __init__(self):
super().__init__()
self.hidden_layer = torch.nn.Linear(input_size, hidden_size)
self.output_layer = torch.nn.Linear(hidden_size, output_size)
def forward(self, x):
if activation == 'relu':
hidden_output = torch.nn.functional.relu(self.hidden_layer(x))
elif activation == 'leaky_relu':
hidden_output = torch.nn.functional.leaky_relu(self.hidden_layer(x))
elif activation == 'sigmoid':
hidden_output = torch.nn.functional.sigmoid(self.hidden_layer(x))
elif activation == 'tanh':
hidden_output = torch.nn.functional.tanh(self.hidden_layer(x))
else:
hidden_output = self.hidden_layer(x)
output = self.output_layer(hidden_output)
return output
model = model_class()
return model
# 全连接神经网络模型（包含两个隐藏层）
@guan.function_decorator
def fully_connected_neural_network_with_two_hidden_layers(input_size=1, hidden_size_1=10, hidden_size_2=10, output_size=1, activation_1='relu', activation_2='relu'):
import torch
class model_class(torch.nn.Module):
def __init__(self):
super().__init__()
self.hidden_layer_1 = torch.nn.Linear(input_size, hidden_size_1)
self.hidden_layer_2 = torch.nn.Linear(hidden_size_1, hidden_size_2)
self.output_layer = torch.nn.Linear(hidden_size_2, output_size)
def forward(self, x):
if activation_1 == 'relu':
hidden_output_1 = torch.nn.functional.relu(self.hidden_layer_1(x))
elif activation_1 == 'leaky_relu':
hidden_output_1 = torch.nn.functional.leaky_relu(self.hidden_layer_1(x))
elif activation_1 == 'sigmoid':
hidden_output_1 = torch.nn.functional.sigmoid(self.hidden_layer_1(x))
elif activation_1 == 'tanh':
hidden_output_1 = torch.nn.functional.tanh(self.hidden_layer_1(x))
else:
hidden_output_1 = self.hidden_layer_1(x)
if activation_2 == 'relu':
hidden_output_2 = torch.nn.functional.relu(self.hidden_layer_2(hidden_output_1))
elif activation_2 == 'leaky_relu':
hidden_output_2 = torch.nn.functional.leaky_relu(self.hidden_layer_2(hidden_output_1))
elif activation_2 == 'sigmoid':
hidden_output_2 = torch.nn.functional.sigmoid(self.hidden_layer_2(hidden_output_1))
elif activation_2 == 'tanh':
hidden_output_2 = torch.nn.functional.tanh(self.hidden_layer_2(hidden_output_1))
else:
hidden_output_2 = self.hidden_layer_2(hidden_output_1)
output = self.output_layer(hidden_output_2)
return output
model = model_class()
return model
# 全连接神经网络模型（包含三个隐藏层）
@guan.function_decorator
def fully_connected_neural_network_with_three_hidden_layers(input_size=1, hidden_size_1=10, hidden_size_2=10, hidden_size_3=10, output_size=1, activation_1='relu', activation_2='relu', activation_3='relu'):
import torch
class model_class(torch.nn.Module):
def __init__(self):
super().__init__()
self.hidden_layer_1 = torch.nn.Linear(input_size, hidden_size_1)
self.hidden_layer_2 = torch.nn.Linear(hidden_size_1, hidden_size_2)
self.hidden_layer_3 = torch.nn.Linear(hidden_size_2, hidden_size_3)
self.output_layer = torch.nn.Linear(hidden_size_3, output_size)
def forward(self, x):
if activation_1 == 'relu':
hidden_output_1 = torch.nn.functional.relu(self.hidden_layer_1(x))
elif activation_1 == 'leaky_relu':
hidden_output_1 = torch.nn.functional.leaky_relu(self.hidden_layer_1(x))
elif activation_1 == 'sigmoid':
hidden_output_1 = torch.nn.functional.sigmoid(self.hidden_layer_1(x))
elif activation_1 == 'tanh':
hidden_output_1 = torch.nn.functional.tanh(self.hidden_layer_1(x))
else:
hidden_output_1 = self.hidden_layer_1(x)
if activation_2 == 'relu':
hidden_output_2 = torch.nn.functional.relu(self.hidden_layer_2(hidden_output_1))
elif activation_2 == 'leaky_relu':
hidden_output_2 = torch.nn.functional.leaky_relu(self.hidden_layer_2(hidden_output_1))
elif activation_2 == 'sigmoid':
hidden_output_2 = torch.nn.functional.sigmoid(self.hidden_layer_2(hidden_output_1))
elif activation_2 == 'tanh':
hidden_output_2 = torch.nn.functional.tanh(self.hidden_layer_2(hidden_output_1))
else:
hidden_output_2 = self.hidden_layer_2(hidden_output_1)
if activation_3 == 'relu':
hidden_output_3 = torch.nn.functional.relu(self.hidden_layer_3(hidden_output_2))
elif activation_3 == 'leaky_relu':
hidden_output_3 = torch.nn.functional.leaky_relu(self.hidden_layer_3(hidden_output_2))
elif activation_3 == 'sigmoid':
hidden_output_3 = torch.nn.functional.sigmoid(self.hidden_layer_3(hidden_output_2))
elif activation_3 == 'tanh':
hidden_output_3 = torch.nn.functional.tanh(self.hidden_layer_3(hidden_output_2))
else:
hidden_output_3 = self.hidden_layer_3(hidden_output_2)
output = self.output_layer(hidden_output_3)
return output
model = model_class()
return model

26
PyPI/src/guan/others.py
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@@ -1065,16 +1065,16 @@ def get_current_version(package_name='guan'):
return None
# Guan软件包升级提示
def notification_of_upgrade():
import random
rand_number = random.randint(1, 10)
if rand_number == 5:
try:
latest_version = get_latest_version(package_name='guan', timeout=2)
current_version = get_current_version('guan')
if latest_version != None and current_version != None:
if latest_version != current_version:
print('提示：您当前使用的版本是 guan-'+current_version+'，目前已经有最新版本 guan-'+latest_version+'。您可以通过以下命令对软件包进行升级：pip install --upgrade guan')
except:
pass
notification_of_upgrade()
def notification_of_upgrade(timeout=2):
try:
latest_version = get_latest_version(package_name='guan', timeout=timeout)
current_version = get_current_version('guan')
if latest_version != None and current_version != None:
if latest_version != current_version:
print('提示：您当前使用的版本是 guan-'+current_version+'，目前已经有最新版本 guan-'+latest_version+'。您可以通过以下命令对软件包进行升级：pip install --upgrade guan')
except:
pass
import random
rand_number = random.randint(1, 10)
if rand_number == 5:
notification_of_upgrade(timeout=2)