path: root/python-rctorchprivate.spec

%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.aliyun.com/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
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.9819998-1
- Package Spec generated