%global _empty_manifest_terminate_build 0 Name: python-growpy Version: 0.0.12 Release: 1 Summary: Trainable growth curves. License: BSD License URL: https://github.com/galenseilis/GrowPy Source0: https://mirrors.aliyun.com/pypi/web/packages/5c/50/085673a7e591f04bb0e9e3cb8773a32d18f59d99947bdf40afd96e821f77/growpy-0.0.12.tar.gz BuildArch: noarch %description # GrowPy ## Project Description For questions and comments contact the developer directly at: . ## Installation GrowPy is available through [PyPi](https://pypi.org/project/growpy/), and can be installed via `pip` using ``` pip install growpy ``` or ``` pip3 install growpy ``` ## Example Usage ```python from growpy import models import tensorflow as tf import matplotlib.pyplot as plt # Constuct/Import data x = tf.abs(tf.random.uniform((100000,1), 0, 10)) y = 500 / (1 + (500-50)/50 * tf.exp(-0.5 * x)) # Construct model model = models.GeneralizedLogistic() optimizer = tf.keras.optimizers.Nadam() loss = tf.keras.losses.MeanSquaredError() model.compile(optimizer=optimizer, loss=loss) # Train model history = model.fit(x, y, epochs=100, batch_size=1000) # Inspect results print(model.weights) fig, axes = plt.subplots(2, 1) axes[0].scatter(x,y, alpha=0.5, s=1) axes[0].scatter(x, model(x), alpha=0.5, s=1) axes[0].set_ylabel('y') axes[0].set_xlabel('x') axes[1].plot(history.history['loss']) axes[1].set_ylabel('MSE') axes[1].set_xlabel('Epoch') plt.show() ``` ## License BSD 3-Clause License Copyright (c) 2021, Galen Seilis All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. %package -n python3-growpy Summary: Trainable growth curves. Provides: python-growpy BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-growpy # GrowPy ## Project Description For questions and comments contact the developer directly at: . ## Installation GrowPy is available through [PyPi](https://pypi.org/project/growpy/), and can be installed via `pip` using ``` pip install growpy ``` or ``` pip3 install growpy ``` ## Example Usage ```python from growpy import models import tensorflow as tf import matplotlib.pyplot as plt # Constuct/Import data x = tf.abs(tf.random.uniform((100000,1), 0, 10)) y = 500 / (1 + (500-50)/50 * tf.exp(-0.5 * x)) # Construct model model = models.GeneralizedLogistic() optimizer = tf.keras.optimizers.Nadam() loss = tf.keras.losses.MeanSquaredError() model.compile(optimizer=optimizer, loss=loss) # Train model history = model.fit(x, y, epochs=100, batch_size=1000) # Inspect results print(model.weights) fig, axes = plt.subplots(2, 1) axes[0].scatter(x,y, alpha=0.5, s=1) axes[0].scatter(x, model(x), alpha=0.5, s=1) axes[0].set_ylabel('y') axes[0].set_xlabel('x') axes[1].plot(history.history['loss']) axes[1].set_ylabel('MSE') axes[1].set_xlabel('Epoch') plt.show() ``` ## License BSD 3-Clause License Copyright (c) 2021, Galen Seilis All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. %package help Summary: Development documents and examples for growpy Provides: python3-growpy-doc %description help # GrowPy ## Project Description For questions and comments contact the developer directly at: . ## Installation GrowPy is available through [PyPi](https://pypi.org/project/growpy/), and can be installed via `pip` using ``` pip install growpy ``` or ``` pip3 install growpy ``` ## Example Usage ```python from growpy import models import tensorflow as tf import matplotlib.pyplot as plt # Constuct/Import data x = tf.abs(tf.random.uniform((100000,1), 0, 10)) y = 500 / (1 + (500-50)/50 * tf.exp(-0.5 * x)) # Construct model model = models.GeneralizedLogistic() optimizer = tf.keras.optimizers.Nadam() loss = tf.keras.losses.MeanSquaredError() model.compile(optimizer=optimizer, loss=loss) # Train model history = model.fit(x, y, epochs=100, batch_size=1000) # Inspect results print(model.weights) fig, axes = plt.subplots(2, 1) axes[0].scatter(x,y, alpha=0.5, s=1) axes[0].scatter(x, model(x), alpha=0.5, s=1) axes[0].set_ylabel('y') axes[0].set_xlabel('x') axes[1].plot(history.history['loss']) axes[1].set_ylabel('MSE') axes[1].set_xlabel('Epoch') plt.show() ``` ## License BSD 3-Clause License Copyright (c) 2021, Galen Seilis All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. %prep %autosetup -n growpy-0.0.12 %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-growpy -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Thu Jun 08 2023 Python_Bot - 0.0.12-1 - Package Spec generated