1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
|
%global _empty_manifest_terminate_build 0
Name: python-inference-tools
Version: 0.11.0
Release: 1
Summary: A collection of python tools for Bayesian data analysis
License: MIT
URL: https://github.com/C-bowman/inference-tools
Source0: https://mirrors.aliyun.com/pypi/web/packages/f4/34/4666a4890a09786c4be51e2c6ce631942cd0db760666e14425214bea6fee/inference-tools-0.11.0.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-scipy
Requires: python3-matplotlib
Requires: python3-importlib-metadata
Requires: python3-sphinx
Requires: python3-sphinx-rtd-theme
Requires: python3-pytest
Requires: python3-pytest-cov
Requires: python3-pyqt5
Requires: python3-hypothesis
Requires: python3-freezegun
%description
# inference-tools
[](https://inference-tools.readthedocs.io/en/stable/?badge=stable)
[](https://github.com/C-bowman/inference-tools/blob/master/LICENSE)
[](https://pypi.org/project/inference-tools/)

[](https://zenodo.org/badge/latestdoi/149741362)
This package provides a set of Python-based tools for Bayesian data analysis
which are simple to use, allowing them to applied quickly and easily.
Inference-tools is not a framework for Bayesian modelling (e.g. like [PyMC](https://docs.pymc.io/)),
but instead provides tools to sample from user-defined models using MCMC, and to analyse and visualise
the sampling results.
## Features
- Implementations of MCMC algorithms like Gibbs sampling and Hamiltonian Monte-Carlo for
sampling from user-defined posterior distributions.
- Density estimation and plotting tools for analysing and visualising inference results.
- Gaussian-process regression and optimisation.
| | | |
|:-------------------------:|:-------------------------:|:-------------------------:|
| [Gibbs Sampling](https://github.com/C-bowman/inference-tools/blob/master/demos/gibbs_sampling_demo.ipynb) <img width="1604" alt="1" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/gallery_images/gallery_gibbs_sampling.png"> | [Hamiltonian Monte-Carlo](https://github.com/C-bowman/inference-tools/blob/master/demos/hamiltonian_mcmc_demo.ipynb) <img width="1604" alt="2" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/gallery_images/gallery_hmc.png"> | [Density estimation](https://github.com/C-bowman/inference-tools/blob/master/demos/density_estimation_demo.ipynb) <img width="1604" alt="3" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/gallery_images/gallery_density_estimation.png"> |
| Matrix plotting <img width="1604" alt="4" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/getting_started_images/matrix_plot_example.png"> | Highest-density intervals <img width="1604" alt="5" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/gallery_images/gallery_hdi.png"> | [GP regression](https://github.com/C-bowman/inference-tools/blob/master/demos/gp_regression_demo.ipynb) <img width="1604" alt="6" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/gallery_images/gallery_gpr.png"> |
## Installation
inference-tools is available from [PyPI](https://pypi.org/project/inference-tools/),
so can be easily installed using [pip](https://pip.pypa.io/en/stable/) as follows:
```bash
pip install inference-tools
```
## Documentation
Full documentation is available at [inference-tools.readthedocs.io](https://inference-tools.readthedocs.io/en/stable/).
%package -n python3-inference-tools
Summary: A collection of python tools for Bayesian data analysis
Provides: python-inference-tools
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-inference-tools
# inference-tools
[](https://inference-tools.readthedocs.io/en/stable/?badge=stable)
[](https://github.com/C-bowman/inference-tools/blob/master/LICENSE)
[](https://pypi.org/project/inference-tools/)

[](https://zenodo.org/badge/latestdoi/149741362)
This package provides a set of Python-based tools for Bayesian data analysis
which are simple to use, allowing them to applied quickly and easily.
Inference-tools is not a framework for Bayesian modelling (e.g. like [PyMC](https://docs.pymc.io/)),
but instead provides tools to sample from user-defined models using MCMC, and to analyse and visualise
the sampling results.
## Features
- Implementations of MCMC algorithms like Gibbs sampling and Hamiltonian Monte-Carlo for
sampling from user-defined posterior distributions.
- Density estimation and plotting tools for analysing and visualising inference results.
- Gaussian-process regression and optimisation.
| | | |
|:-------------------------:|:-------------------------:|:-------------------------:|
| [Gibbs Sampling](https://github.com/C-bowman/inference-tools/blob/master/demos/gibbs_sampling_demo.ipynb) <img width="1604" alt="1" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/gallery_images/gallery_gibbs_sampling.png"> | [Hamiltonian Monte-Carlo](https://github.com/C-bowman/inference-tools/blob/master/demos/hamiltonian_mcmc_demo.ipynb) <img width="1604" alt="2" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/gallery_images/gallery_hmc.png"> | [Density estimation](https://github.com/C-bowman/inference-tools/blob/master/demos/density_estimation_demo.ipynb) <img width="1604" alt="3" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/gallery_images/gallery_density_estimation.png"> |
| Matrix plotting <img width="1604" alt="4" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/getting_started_images/matrix_plot_example.png"> | Highest-density intervals <img width="1604" alt="5" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/gallery_images/gallery_hdi.png"> | [GP regression](https://github.com/C-bowman/inference-tools/blob/master/demos/gp_regression_demo.ipynb) <img width="1604" alt="6" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/gallery_images/gallery_gpr.png"> |
## Installation
inference-tools is available from [PyPI](https://pypi.org/project/inference-tools/),
so can be easily installed using [pip](https://pip.pypa.io/en/stable/) as follows:
```bash
pip install inference-tools
```
## Documentation
Full documentation is available at [inference-tools.readthedocs.io](https://inference-tools.readthedocs.io/en/stable/).
%package help
Summary: Development documents and examples for inference-tools
Provides: python3-inference-tools-doc
%description help
# inference-tools
[](https://inference-tools.readthedocs.io/en/stable/?badge=stable)
[](https://github.com/C-bowman/inference-tools/blob/master/LICENSE)
[](https://pypi.org/project/inference-tools/)

[](https://zenodo.org/badge/latestdoi/149741362)
This package provides a set of Python-based tools for Bayesian data analysis
which are simple to use, allowing them to applied quickly and easily.
Inference-tools is not a framework for Bayesian modelling (e.g. like [PyMC](https://docs.pymc.io/)),
but instead provides tools to sample from user-defined models using MCMC, and to analyse and visualise
the sampling results.
## Features
- Implementations of MCMC algorithms like Gibbs sampling and Hamiltonian Monte-Carlo for
sampling from user-defined posterior distributions.
- Density estimation and plotting tools for analysing and visualising inference results.
- Gaussian-process regression and optimisation.
| | | |
|:-------------------------:|:-------------------------:|:-------------------------:|
| [Gibbs Sampling](https://github.com/C-bowman/inference-tools/blob/master/demos/gibbs_sampling_demo.ipynb) <img width="1604" alt="1" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/gallery_images/gallery_gibbs_sampling.png"> | [Hamiltonian Monte-Carlo](https://github.com/C-bowman/inference-tools/blob/master/demos/hamiltonian_mcmc_demo.ipynb) <img width="1604" alt="2" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/gallery_images/gallery_hmc.png"> | [Density estimation](https://github.com/C-bowman/inference-tools/blob/master/demos/density_estimation_demo.ipynb) <img width="1604" alt="3" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/gallery_images/gallery_density_estimation.png"> |
| Matrix plotting <img width="1604" alt="4" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/getting_started_images/matrix_plot_example.png"> | Highest-density intervals <img width="1604" alt="5" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/gallery_images/gallery_hdi.png"> | [GP regression](https://github.com/C-bowman/inference-tools/blob/master/demos/gp_regression_demo.ipynb) <img width="1604" alt="6" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/gallery_images/gallery_gpr.png"> |
## Installation
inference-tools is available from [PyPI](https://pypi.org/project/inference-tools/),
so can be easily installed using [pip](https://pip.pypa.io/en/stable/) as follows:
```bash
pip install inference-tools
```
## Documentation
Full documentation is available at [inference-tools.readthedocs.io](https://inference-tools.readthedocs.io/en/stable/).
%prep
%autosetup -n inference-tools-0.11.0
%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-inference-tools -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.11.0-1
- Package Spec generated
|