plot_tnorms.py

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import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
 
import sys
import os
 
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../core')))
 
import tnorms as tn
 
# Create a grid of values
n = 200  # Replace with your desired length (must be even)
arr = np.concatenate([np.zeros(n // 2), np.ones(n // 2)])
 
similarity_vals = np.linspace(0, 1, n)
b_vals = arr
AVals, simVals = np.meshgrid(similarity_vals, b_vals)
 
# Vectorized computation of the implicator
# TNVals = np.minimum(AVals, simVals)
TNVals = np.multiply(AVals, simVals)
 
 
# Plotting
fig = plt.figure(figsize=(10, 7))
ax = fig.add_subplot(111, projection='3d')
 
# Plot surface with colormap
# surf = ax.plot_surface(A, B, Z, cmap='hot', edgecolor='none')
# Plot the points
ax.scatter(AVals, simVals, TNVals, c='blue', marker='.')
 
# Add color bar
# fig.colorbar(surf, ax=ax, shrink=0.5, aspect=5)
 
# Labels and title
ax.set_title("product t-norm")
ax.set_ylabel("A(y)")
ax.set_xlabel("similarity (x,y)")
ax.set_zlabel("T(similarity (x,y), A(y))")
 
plt.tight_layout()
plt.show()