%global _empty_manifest_terminate_build 0 Name: python-rctorchprivate Version: 0.9819998 Release: 1 Summary: A Python 3 toolset for creating and optimizing Echo State Networks. This library is an extension and expansion of the previous library written by Reinier Maat: https://github.com/1Reinier/Reservoir License: Harvard URL: https://github.com/blindedjoy/RcTorch-private Source0: https://mirrors.nju.edu.cn/pypi/web/packages/bf/70/4da4e6d44e451dd964b55b1b4b5e7d2dab919609d4168920ab5dc4090970/rctorchprivate-0.9819998.tar.gz BuildArch: noarch %description A Pytorch toolset for creating and optimizing Echo State Networks. >License: 2020-2021 MIT >Authors: Hayden Joy, Marios Mattheakis Contains: - A ESN Reservoir architecture class "rc.py" - Bayesian Optimization (BO) class "rc_bayes.py" with optimized routines for Echo State Nets through `Botorch` (GPU optimized), can train multiple RCs in parellel durring BO - an implimentation of the TURBO-1 algorithm as outlined in this paper: https://github.com/uber-research/TuRBO - Capable of solving differential equations (the population equation, the bernoulli equation, a simple harmonic oscillator and a nonlinear oscillator) Reference to prior instantiation: This library is an extension and expansion of a previous library written by Reinier Maat: https://github.com/1Reinier/Reservoir 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1-7. IEEE, 2018 https://arxiv.org/abs/1903.05071 ## For example usage please see the notebooks folder. # Installation ## Using pip Like most standard libraries, `rctorch` is hosted on [PyPI](https://pypi.org/project/RcTorch/). To install the latest stable relesase, ```bash pip install -U rctorch # '-U' means update to latest version ``` ## Example Usages ### Imports ```python from rctorch import * import torch ``` ### Load data RcTorch has several built in datasets. Among these is the forced pendulum dataset. Here we demonstrate ```python fp_data = rctorch.data.load("forced_pendulum", train_proportion = 0.2) force_train, force_test = fp_data["force"] target_train, input_test = fp_data["target"] #Alternatively you can use sklearn's train_test_split. ``` ### Hyper-parameters ```python #declare the hyper-parameters >>> hps = {'connectivity': 0.4, 'spectral_radius': 1.13, 'n_nodes': 202, 'regularization': 1.69, 'leaking_rate': 0.0098085, 'bias': 0.49} ``` ### Setting up your very own EchoStateNetwork ```python my_rc = RcNetwork(**hps, random_state = 210, feedback = True) #fitting the data: my_rc.fit(y = target_train) #making our prediction score, prediction = my_rc.test(y = target_test) my_rc.combined_plot() ``` ![](https://raw.githubusercontent.com/blindedjoy/RcTorch-private/blob/master/resources/pure_prediction1.jpg) Feedback allows the network to feed in the prediction at the previous timestep as an input. This helps the RC to make longer and more stable predictions in many situations. ### Bayesian Optimization Unlike most other reservoir neural network packages ours offers the automatically tune hyper-parameters. ```python #any hyper parameter can have 'log_' in front of it's name. RcTorch will interpret this properly. bounds_dict = {"log_connectivity" : (-2.5, -0.1), "spectral_radius" : (0.1, 3), "n_nodes" : (300,302), "log_regularization" : (-3, 1), "leaking_rate" : (0, 0.2), "bias": (-1,1), } rc_specs = {"feedback" : True, "reservoir_weight_dist" : "uniform", "output_activation" : "tanh", "random_seed" : 209} rc_bo = RcBayesOpt(bounds = bounds_dict, scoring_method = "nmse", n_jobs = 1, cv_samples = 3, initial_samples= 25, **rc_specs ) ``` %package -n python3-rctorchprivate Summary: A Python 3 toolset for creating and optimizing Echo State Networks. This library is an extension and expansion of the previous library written by Reinier Maat: https://github.com/1Reinier/Reservoir Provides: python-rctorchprivate BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-rctorchprivate A Pytorch toolset for creating and optimizing Echo State Networks. >License: 2020-2021 MIT >Authors: Hayden Joy, Marios Mattheakis Contains: - A ESN Reservoir architecture class "rc.py" - Bayesian Optimization (BO) class "rc_bayes.py" with optimized routines for Echo State Nets through `Botorch` (GPU optimized), can train multiple RCs in parellel durring BO - an implimentation of the TURBO-1 algorithm as outlined in this paper: https://github.com/uber-research/TuRBO - Capable of solving differential equations (the population equation, the bernoulli equation, a simple harmonic oscillator and a nonlinear oscillator) Reference to prior instantiation: This library is an extension and expansion of a previous library written by Reinier Maat: https://github.com/1Reinier/Reservoir 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1-7. IEEE, 2018 https://arxiv.org/abs/1903.05071 ## For example usage please see the notebooks folder. # Installation ## Using pip Like most standard libraries, `rctorch` is hosted on [PyPI](https://pypi.org/project/RcTorch/). To install the latest stable relesase, ```bash pip install -U rctorch # '-U' means update to latest version ``` ## Example Usages ### Imports ```python from rctorch import * import torch ``` ### Load data RcTorch has several built in datasets. Among these is the forced pendulum dataset. Here we demonstrate ```python fp_data = rctorch.data.load("forced_pendulum", train_proportion = 0.2) force_train, force_test = fp_data["force"] target_train, input_test = fp_data["target"] #Alternatively you can use sklearn's train_test_split. ``` ### Hyper-parameters ```python #declare the hyper-parameters >>> hps = {'connectivity': 0.4, 'spectral_radius': 1.13, 'n_nodes': 202, 'regularization': 1.69, 'leaking_rate': 0.0098085, 'bias': 0.49} ``` ### Setting up your very own EchoStateNetwork ```python my_rc = RcNetwork(**hps, random_state = 210, feedback = True) #fitting the data: my_rc.fit(y = target_train) #making our prediction score, prediction = my_rc.test(y = target_test) my_rc.combined_plot() ``` ![](https://raw.githubusercontent.com/blindedjoy/RcTorch-private/blob/master/resources/pure_prediction1.jpg) Feedback allows the network to feed in the prediction at the previous timestep as an input. This helps the RC to make longer and more stable predictions in many situations. ### Bayesian Optimization Unlike most other reservoir neural network packages ours offers the automatically tune hyper-parameters. ```python #any hyper parameter can have 'log_' in front of it's name. RcTorch will interpret this properly. bounds_dict = {"log_connectivity" : (-2.5, -0.1), "spectral_radius" : (0.1, 3), "n_nodes" : (300,302), "log_regularization" : (-3, 1), "leaking_rate" : (0, 0.2), "bias": (-1,1), } rc_specs = {"feedback" : True, "reservoir_weight_dist" : "uniform", "output_activation" : "tanh", "random_seed" : 209} rc_bo = RcBayesOpt(bounds = bounds_dict, scoring_method = "nmse", n_jobs = 1, cv_samples = 3, initial_samples= 25, **rc_specs ) ``` %package help Summary: Development documents and examples for rctorchprivate Provides: python3-rctorchprivate-doc %description help A Pytorch toolset for creating and optimizing Echo State Networks. >License: 2020-2021 MIT >Authors: Hayden Joy, Marios Mattheakis Contains: - A ESN Reservoir architecture class "rc.py" - Bayesian Optimization (BO) class "rc_bayes.py" with optimized routines for Echo State Nets through `Botorch` (GPU optimized), can train multiple RCs in parellel durring BO - an implimentation of the TURBO-1 algorithm as outlined in this paper: https://github.com/uber-research/TuRBO - Capable of solving differential equations (the population equation, the bernoulli equation, a simple harmonic oscillator and a nonlinear oscillator) Reference to prior instantiation: This library is an extension and expansion of a previous library written by Reinier Maat: https://github.com/1Reinier/Reservoir 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1-7. IEEE, 2018 https://arxiv.org/abs/1903.05071 ## For example usage please see the notebooks folder. # Installation ## Using pip Like most standard libraries, `rctorch` is hosted on [PyPI](https://pypi.org/project/RcTorch/). To install the latest stable relesase, ```bash pip install -U rctorch # '-U' means update to latest version ``` ## Example Usages ### Imports ```python from rctorch import * import torch ``` ### Load data RcTorch has several built in datasets. Among these is the forced pendulum dataset. Here we demonstrate ```python fp_data = rctorch.data.load("forced_pendulum", train_proportion = 0.2) force_train, force_test = fp_data["force"] target_train, input_test = fp_data["target"] #Alternatively you can use sklearn's train_test_split. ``` ### Hyper-parameters ```python #declare the hyper-parameters >>> hps = {'connectivity': 0.4, 'spectral_radius': 1.13, 'n_nodes': 202, 'regularization': 1.69, 'leaking_rate': 0.0098085, 'bias': 0.49} ``` ### Setting up your very own EchoStateNetwork ```python my_rc = RcNetwork(**hps, random_state = 210, feedback = True) #fitting the data: my_rc.fit(y = target_train) #making our prediction score, prediction = my_rc.test(y = target_test) my_rc.combined_plot() ``` ![](https://raw.githubusercontent.com/blindedjoy/RcTorch-private/blob/master/resources/pure_prediction1.jpg) Feedback allows the network to feed in the prediction at the previous timestep as an input. This helps the RC to make longer and more stable predictions in many situations. ### Bayesian Optimization Unlike most other reservoir neural network packages ours offers the automatically tune hyper-parameters. ```python #any hyper parameter can have 'log_' in front of it's name. RcTorch will interpret this properly. bounds_dict = {"log_connectivity" : (-2.5, -0.1), "spectral_radius" : (0.1, 3), "n_nodes" : (300,302), "log_regularization" : (-3, 1), "leaking_rate" : (0, 0.2), "bias": (-1,1), } rc_specs = {"feedback" : True, "reservoir_weight_dist" : "uniform", "output_activation" : "tanh", "random_seed" : 209} rc_bo = RcBayesOpt(bounds = bounds_dict, scoring_method = "nmse", n_jobs = 1, cv_samples = 3, initial_samples= 25, **rc_specs ) ``` %prep %autosetup -n rctorchprivate-0.9819998 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-rctorchprivate -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon May 15 2023 Python_Bot - 0.9819998-1 - Package Spec generated