Delete generate_visualization.py

generate_visualization.py

@@ -1,54 +0,0 @@
-#!/usr/bin/env python3
-"""
-Generate English visualization for Chronos o1 1.5B results
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-np.random.seed(42)
-
-train_size = 8
-test_size = 4
-
-K_train = np.random.rand(train_size, train_size)
-K_train = (K_train + K_train.T) / 2
-np.fill_diagonal(K_train, 1.0)
-
-true_labels = [1, 0, 1, 0]
-predictions = [1, 0, 1, 1]
-
-fig, axes = plt.subplots(1, 3, figsize=(15, 4))
-
-models = ['Classical\n(Baseline)', 'Quantum\n(Hybrid)']
-accuracies = [1.0, 0.75]
-colors = ['blue', 'red']
-
-axes[0].bar(models, accuracies, color=colors, alpha=0.7)
-axes[0].set_ylabel('Accuracy')
-axes[0].set_ylim([0, 1])
-axes[0].set_title('Model Comparison')
-axes[0].grid(True, alpha=0.3)
-
-im = axes[1].imshow(K_train, cmap='hot', aspect='auto')
-axes[1].set_title('Quantum Kernel Matrix')
-axes[1].set_xlabel('Sample j')
-axes[1].set_ylabel('Sample i')
-plt.colorbar(im, ax=axes[1])
-
-x_pos = np.arange(len(true_labels))
-axes[2].scatter(x_pos, true_labels, c='blue', s=200, alpha=0.5,
-                marker='o', label='True')
-axes[2].scatter(x_pos, predictions, c='red', s=100,
-                marker='x', label='Predicted')
-axes[2].set_title('Predictions (Quantum Hybrid)')
-axes[2].set_xlabel('Test Sample')
-axes[2].set_ylabel('Class')
-axes[2].set_yticks([0, 1])
-axes[2].set_yticklabels(['Negative', 'Positive'])
-axes[2].legend()
-axes[2].grid(True, alpha=0.3)
-
-plt.tight_layout()
-plt.savefig('chronos_o1_results.png', dpi=150, bbox_inches='tight')
-print("Visualization saved: chronos_o1_results.png")