50b6fbdc27
Analysis shows no significant correlation between conceptual depth and hop distance to letter nodes (r=0.281, p=0.113). Includes Python scripts, visualizations, and LaTeX paper. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
"""Compute correlation statistics for the paper (hop-based)."""
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import json
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import numpy as np
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from scipy import stats
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def main():
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with open(r'C:\Users\alexa\copycat\slipnet_analysis\slipnet.json', 'r') as f:
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data = json.load(f)
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# Extract data points (excluding letter nodes themselves)
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names = []
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depths = []
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hops = []
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is_unreachable = []
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for node in data['nodes']:
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name = node['name']
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depth = node['conceptualDepth']
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path_info = node.get('minPathToLetter', {})
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hop_count = path_info.get('hops')
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nearest = path_info.get('nearestLetter')
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# Skip letter nodes (hops 0)
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if hop_count is not None and hop_count > 0:
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names.append(name)
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depths.append(depth)
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hops.append(hop_count)
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is_unreachable.append(nearest is None)
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# Convert to numpy arrays
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depths = np.array(depths)
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hops = np.array(hops)
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# Compute correlation
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correlation, p_value = stats.pearsonr(depths, hops)
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spearman_corr, spearman_p = stats.spearmanr(depths, hops)
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# Linear regression
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z = np.polyfit(depths, hops, 1)
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# R-squared
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y_pred = np.polyval(z, depths)
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ss_res = np.sum((hops - y_pred) ** 2)
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ss_tot = np.sum((hops - np.mean(hops)) ** 2)
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r_squared = 1 - (ss_res / ss_tot)
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num_unreachable = sum(is_unreachable)
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print(f"Number of nodes analyzed: {len(names)}")
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print(f"Total nodes: {data['nodeCount']}")
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print(f"Letter nodes (excluded): 26")
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print(f"Unreachable nodes (hops = 2*max): {num_unreachable}")
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print()
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print(f"Pearson correlation: r = {correlation:.4f}")
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print(f"Pearson p-value: p = {p_value:.6f}")
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print(f"Spearman correlation: rho = {spearman_corr:.4f}")
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print(f"Spearman p-value: p = {spearman_p:.6f}")
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print(f"R-squared: {r_squared:.4f}")
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print(f"Linear regression: hops = {z[0]:.4f} * depth + {z[1]:.4f}")
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print()
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print(f"Depth range: {min(depths):.1f} - {max(depths):.1f}")
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print(f"Hops range: {min(hops)} - {max(hops)}")
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print(f"Mean depth: {np.mean(depths):.2f}")
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print(f"Mean hops: {np.mean(hops):.2f}")
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print(f"Std depth: {np.std(depths):.2f}")
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print(f"Std hops: {np.std(hops):.2f}")
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print()
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# Distribution of hops
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print("Distribution of hops:")
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for h in sorted(set(hops)):
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count = sum(1 for x in hops if x == h)
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nodes_at_h = [n for n, hp in zip(names, hops) if hp == h]
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print(f" {h} hops: {count} nodes")
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print()
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print("Data points (sorted by hops, then depth):")
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print(f"{'Node':<30} {'Depth':<10} {'Hops':<10} {'Reachable':<10}")
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print("-" * 60)
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for name, depth, hop, unreachable in sorted(zip(names, depths, hops, is_unreachable), key=lambda x: (x[2], x[1])):
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status = "No" if unreachable else "Yes"
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print(f"{name:<30} {depth:<10.1f} {hop:<10} {status:<10}")
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if __name__ == '__main__':
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main()
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