One of the famous Python libraries for fuzzy logic is scikit-fuzzy. Several fuzzy logic algorithms have already been implemented on this library. Since scikit-fuzzy is an open source library, you can review the source code at https://github.com/scikit-fuzzy/scikit-fuzzy.
Before you install this library, you should already have installed NumPy and SciPy libraries. You can install scikit-fuzzy using pip, by typing the following command:
$ sudo pip install scikit -fuzzy
As another option, you can install the scikit-fuzzy library from source code.
Type these commands:
$ git clone https://github.com/scikit-fuzzy/scikit-fuzzy $ cd scikit-fuzzy/ $ sudo python setup.py install
After completing the installation, you can use scikit-fuzzy. To test how to work with scikit-fuzzy, we will build a fuzzy membership for temperature using the fuzz.trimf() function. You can write the following scripts:
import matplotlib matplotlib.use('Agg') import numpy as np import skfuzzy as fuzz import matplotlib.pyplot as plt # Generate universe variables x_temp = np.arange(0, 11, 1) # Generate fuzzy membership functions temp_lo = fuzz.trimf(x_temp, [0, 0, 5]) temp_md = fuzz.trimf(x_temp, [0, 5, 10]) temp_hi = fuzz.trimf(x_temp, [5, 10, 10]) # Visualize these universes and membership functions fig, ax = plt.subplots() ax.plot(x_temp, temp_lo, 'b--', linewidth=1.5, label='Cold') ax.plot(x_temp, temp_md, 'g-', linewidth=1.5, label='Warm') ax.plot(x_temp, temp_hi, 'r:', linewidth=1.5, label='Hot') ax.set_title('Temperature') ax.legend() ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.get_xaxis().tick_bottom() ax.get_yaxis().tick_left() ax.set_ylabel('Fuzzy membership') plt.tight_layout() print('saving...') plt.grid(True) fig.savefig('fuzzy_membership.png', dpi=100) print('done')
This program will generate a fuzzy_membership.png file. A sample of this file is depicted as follows:
