diff --git a/PyPI/setup.cfg b/PyPI/setup.cfg index f424707..11b822d 100644 --- a/PyPI/setup.cfg +++ b/PyPI/setup.cfg @@ -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 diff --git a/PyPI/src/guan.egg-info/PKG-INFO b/PyPI/src/guan.egg-info/PKG-INFO index 50d3de9..10bfe32 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: 0.1.50 +Version: 0.1.51 Summary: An open source python package Home-page: https://py.guanjihuan.com Author: guanjihuan diff --git a/PyPI/src/guan.egg-info/SOURCES.txt b/PyPI/src/guan.egg-info/SOURCES.txt index 7c3d4ec..3f3f35a 100644 --- a/PyPI/src/guan.egg-info/SOURCES.txt +++ b/PyPI/src/guan.egg-info/SOURCES.txt @@ -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 diff --git a/PyPI/src/guan/__init__.py b/PyPI/src/guan/__init__.py index ff57729..0a64aad 100644 --- a/PyPI/src/guan/__init__.py +++ b/PyPI/src/guan/__init__.py @@ -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 * \ No newline at end of file diff --git a/PyPI/src/guan/basic_functions.py b/PyPI/src/guan/basic_functions.py index e2e4cc6..f23af45 100644 --- a/PyPI/src/guan/basic_functions.py +++ b/PyPI/src/guan/basic_functions.py @@ -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 diff --git a/PyPI/src/guan/machine_learning.py b/PyPI/src/guan/machine_learning.py new file mode 100644 index 0000000..d2ec692 --- /dev/null +++ b/PyPI/src/guan/machine_learning.py @@ -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 \ No newline at end of file diff --git a/PyPI/src/guan/others.py b/PyPI/src/guan/others.py index 5c56d02..c354154 100644 --- a/PyPI/src/guan/others.py +++ b/PyPI/src/guan/others.py @@ -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() \ No newline at end of file +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) \ No newline at end of file diff --git a/README.md b/README.md index 3467d24..45a9b60 100755 --- a/README.md +++ b/README.md @@ -22,6 +22,7 @@ import guan + density of states + quantum transport + topological invariant ++ machine learning + data processing + others