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
|
%global _empty_manifest_terminate_build 0
Name: python-neurolab
Version: 0.3.5
Release: 1
Summary: Simple and powerfull neural network library for python
License: LGPL-3
URL: http://neurolab.googlecode.com
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/46/fd/47a9a39158b461b6b862d64c0ad7f679b08ed6d316744299f0db89066342/neurolab-0.3.5.tar.gz
BuildArch: noarch
%description
Neurolab is a simple and powerful Neural Network Library for Python.
Contains based neural networks, train algorithms and flexible framework
to create and explore other neural network types.
:Features:
- Pure python + numpy
- API like Neural Network Toolbox (NNT) from MATLAB
- Interface to use train algorithms form scipy.optimize
- Flexible network configurations and learning algorithms. You may change: train, error, initialization and activation functions
- Unlimited number of neural layers and number of neurons in layers
- Variety of supported types of Artificial Neural Network and learning algorithms
:Example:
>>> import numpy as np
>>> import neurolab as nl
>>> # Create train samples
>>> input = np.random.uniform(-0.5, 0.5, (10, 2))
>>> target = (input[:, 0] + input[:, 1]).reshape(10, 1)
>>> # Create network with 2 inputs, 5 neurons in input layer and 1 in output layer
>>> net = nl.net.newff([[-0.5, 0.5], [-0.5, 0.5]], [5, 1])
>>> # Train process
>>> err = net.train(input, target, show=15)
Epoch: 15; Error: 0.150308402918;
Epoch: 30; Error: 0.072265865089;
Epoch: 45; Error: 0.016931355131;
The goal of learning is reached
>>> # Test
>>> net.sim([[0.2, 0.1]]) # 0.2 + 0.1
array([[ 0.28757596]])
:Links:
- `Home Page <http://code.google.com/p/neurolab/>`_
- `PyPI Page <http://pypi.python.org/pypi/neurolab>`_
- `Documentation <http://packages.python.org/neurolab/>`_
- `Examples <http://packages.python.org/neurolab/example.html>`_
%package -n python3-neurolab
Summary: Simple and powerfull neural network library for python
Provides: python-neurolab
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-neurolab
Neurolab is a simple and powerful Neural Network Library for Python.
Contains based neural networks, train algorithms and flexible framework
to create and explore other neural network types.
:Features:
- Pure python + numpy
- API like Neural Network Toolbox (NNT) from MATLAB
- Interface to use train algorithms form scipy.optimize
- Flexible network configurations and learning algorithms. You may change: train, error, initialization and activation functions
- Unlimited number of neural layers and number of neurons in layers
- Variety of supported types of Artificial Neural Network and learning algorithms
:Example:
>>> import numpy as np
>>> import neurolab as nl
>>> # Create train samples
>>> input = np.random.uniform(-0.5, 0.5, (10, 2))
>>> target = (input[:, 0] + input[:, 1]).reshape(10, 1)
>>> # Create network with 2 inputs, 5 neurons in input layer and 1 in output layer
>>> net = nl.net.newff([[-0.5, 0.5], [-0.5, 0.5]], [5, 1])
>>> # Train process
>>> err = net.train(input, target, show=15)
Epoch: 15; Error: 0.150308402918;
Epoch: 30; Error: 0.072265865089;
Epoch: 45; Error: 0.016931355131;
The goal of learning is reached
>>> # Test
>>> net.sim([[0.2, 0.1]]) # 0.2 + 0.1
array([[ 0.28757596]])
:Links:
- `Home Page <http://code.google.com/p/neurolab/>`_
- `PyPI Page <http://pypi.python.org/pypi/neurolab>`_
- `Documentation <http://packages.python.org/neurolab/>`_
- `Examples <http://packages.python.org/neurolab/example.html>`_
%package help
Summary: Development documents and examples for neurolab
Provides: python3-neurolab-doc
%description help
Neurolab is a simple and powerful Neural Network Library for Python.
Contains based neural networks, train algorithms and flexible framework
to create and explore other neural network types.
:Features:
- Pure python + numpy
- API like Neural Network Toolbox (NNT) from MATLAB
- Interface to use train algorithms form scipy.optimize
- Flexible network configurations and learning algorithms. You may change: train, error, initialization and activation functions
- Unlimited number of neural layers and number of neurons in layers
- Variety of supported types of Artificial Neural Network and learning algorithms
:Example:
>>> import numpy as np
>>> import neurolab as nl
>>> # Create train samples
>>> input = np.random.uniform(-0.5, 0.5, (10, 2))
>>> target = (input[:, 0] + input[:, 1]).reshape(10, 1)
>>> # Create network with 2 inputs, 5 neurons in input layer and 1 in output layer
>>> net = nl.net.newff([[-0.5, 0.5], [-0.5, 0.5]], [5, 1])
>>> # Train process
>>> err = net.train(input, target, show=15)
Epoch: 15; Error: 0.150308402918;
Epoch: 30; Error: 0.072265865089;
Epoch: 45; Error: 0.016931355131;
The goal of learning is reached
>>> # Test
>>> net.sim([[0.2, 0.1]]) # 0.2 + 0.1
array([[ 0.28757596]])
:Links:
- `Home Page <http://code.google.com/p/neurolab/>`_
- `PyPI Page <http://pypi.python.org/pypi/neurolab>`_
- `Documentation <http://packages.python.org/neurolab/>`_
- `Examples <http://packages.python.org/neurolab/example.html>`_
%prep
%autosetup -n neurolab-0.3.5
%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-neurolab -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.5-1
- Package Spec generated
|
