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0.28.2
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‎.gitignore‎

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@@ -338,3 +338,8 @@ notebooks/model_architecture3.png
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notebooks/model_architecture4
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notebooks/model_architecture4.png
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notebooks/spot_00_TEST_experiment.pickle
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notebooks/00_spotPython_tests.aux
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notebooks/00_spotPython_tests.pdf
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notebooks/00_spotPython_tests.tex
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notebooks/00_spotPython_tests_files/libs/bootstrap/bootstrap-973236bd072d72a04ee9cd82dcc9cb29.min.css
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notebooks/00_spotPython_tests_files/libs/quarto-html/quarto-syntax-highlighting-549806ee2085284f45b00abea8c6df48.css

‎pyproject.toml‎

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@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
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[project]
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name = "spotpython"
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version = "0.28.1"
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version = "0.28.2"
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authors = [
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{ name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
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]

‎src/spotpython/hyperdict/light_hyper_dict.json‎

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{
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"ManyToManyLSTMRegressor": {
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"lstm_units": {
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"type": "int",
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"default": 3,
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"transform": "transform_power_2_int",
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"lower": 8,
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"upper": 10
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},
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"fc_units": {
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"type": "int",
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"default": 8,
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"transform": "transform_power_2_int",
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"lower": 3,
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"upper": 10
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},
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"epochs": {
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"type": "int",
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"default": 10,
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"transform": "transform_power_2_int",
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"lower": 7,
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"upper": 12
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},
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"batch_size": {
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"type": "int",
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"default": 10,
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"transform": "transform_power_2_int",
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"lower": 4,
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"upper": 12
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},
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"act_fn": {
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"levels": [
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"Sigmoid",
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"Tanh",
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"ReLU",
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"LeakyReLU",
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"ELU",
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"Swish"
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],
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"type": "factor",
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"default": "ReLU",
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"transform": "None",
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"class_name": "spotpython.torch.activation",
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"core_model_parameter_type": "instance()",
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"lower": 0,
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"upper": 5
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},
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"optimizer": {
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"levels": [
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"Adadelta",
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"Adagrad",
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"Adam",
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"AdamW",
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"SparseAdam",
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"Adamax",
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"ASGD",
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"NAdam",
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"RAdam",
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"RMSprop",
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"Rprop",
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"SGD"
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],
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"type": "factor",
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"default": "SGD",
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"transform": "None",
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"class_name": "torch.optim",
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"core_model_parameter_type": "str",
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"lower": 0,
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"upper": 11
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},
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"dropout_prob": {
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"type": "float",
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"default": 0.01,
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"transform": "None",
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"lower": 0.0,
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"upper": 0.25
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},
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"lr_mult": {
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"type": "float",
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"default": 1.0,
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"transform": "None",
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"lower": 0.1,
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"upper": 10.0
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},
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"patience": {
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"type": "int",
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"default": 2,
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"transform": "transform_power_2_int",
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"lower": 2,
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"upper": 6
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}
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},
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"ManyToManyGRURegressor": {
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"gru_units": {
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"type": "int",
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"default": 3,
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"transform": "transform_power_2_int",
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"lower": 8,
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"upper": 10
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},
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"fc_units": {
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"type": "int",
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"default": 8,
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"transform": "transform_power_2_int",
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"lower": 3,
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"upper": 10
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},
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"epochs": {
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"type": "int",
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"default": 10,
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"transform": "transform_power_2_int",
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"lower": 7,
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"upper": 12
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},
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"batch_size": {
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"type": "int",
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"default": 10,
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"transform": "transform_power_2_int",
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"lower": 4,
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"upper": 12
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},
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"act_fn": {
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"levels": [
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"Sigmoid",
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"Tanh",
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"ReLU",
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"LeakyReLU",
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"ELU",
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"Swish"
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],
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"type": "factor",
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"default": "ReLU",
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"transform": "None",
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"class_name": "spotpython.torch.activation",
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"core_model_parameter_type": "instance()",
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"lower": 0,
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"upper": 5
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},
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"optimizer": {
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"levels": [
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"Adadelta",
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"Adagrad",
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"Adam",
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"AdamW",
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"SparseAdam",
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"Adamax",
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"ASGD",
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"NAdam",
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"RAdam",
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"RMSprop",
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"Rprop",
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"SGD"
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],
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"type": "factor",
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"default": "SGD",
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"transform": "None",
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"class_name": "torch.optim",
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"core_model_parameter_type": "str",
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"lower": 0,
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"upper": 11
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},
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"dropout_prob": {
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"type": "float",
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"default": 0.01,
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"transform": "None",
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"lower": 0.0,
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"upper": 0.25
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},
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"lr_mult": {
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"type": "float",
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"default": 1.0,
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"transform": "None",
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"lower": 0.1,
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"upper": 10.0
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},
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"patience": {
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"type": "int",
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"default": 2,
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"transform": "transform_power_2_int",
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"lower": 2,
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"upper": 6
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}
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},
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"ManyToManyRNNRegressor": {
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"rnn_units": {
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"type": "int",

‎src/spotpython/light/regression/nn_many_to_many_gru_regressor.py‎

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self.output_layer = nn.Linear(fc_units, output_size)
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self.activation_fct = activation_fct
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def forward(self, x, lengths):
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def forward(self, x, lengths) -> torch.Tensor:
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"""Forward pass of the ManyToManyGRU model.
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Args:

‎src/spotpython/light/regression/nn_many_to_many_lstm_regressor.py‎

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self.output_layer = nn.Linear(fc_units, output_size)
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self.activation_fct = activation_fct
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def forward(self, x, lengths):
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def forward(self, x, lengths) -> torch.Tensor:
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"""Forward pass of the ManyToManyLSTM model.
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Args:

‎src/spotpython/spot/spot.py‎

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8. Show progress if needed
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Args:
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X_start (numpy.ndarray, optional): initial design. Defaults to None.
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The initial design must have shape (n, k), where n is the number of points and k is the number of dimensions.
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X_start (numpy.ndarray, optional):
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initial design. The initial design must have shape (n, k), where n is the number of points and k is the number of dimensions. Defaults to None.
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Returns:
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Spot: The `Spot` instance configured and updated based on the optimization process.
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Args:
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X_start (numpy.ndarray, optional): initial design. Defaults to None.
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Must be of shape (n, k), where n is the number of points and k is the number of dimensions.
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X_start (numpy.ndarray, optional):
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initial design. Must be of shape (n, k), where n is the number
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of points and k is the number of dimensions. Defaults to None.
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Attributes:
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self.X (numpy.ndarray): initial design

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