Transvaluation

This revised implementation integrates Shakespearean plays into your immune-neural model, using the weighting function to dynamically adjust edge strengths based on the signal-to-noise ratio. The resulting network captures the tension between immune response and overreaction, aligning with Shakespeare’s dramatic structures. Let me know if you’d like to refine further!

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import numpy as np
import matplotlib.pyplot as plt
import networkx as nx

# Define the neural network layers
def define_layers():
    return {
        'Tragedy (Pattern Recognition)': ['Cosmology', 'Geology', 'Biology', 'Ecology', "Symbiotology", 'Teleology'],
        'History (Non-Self Surveillance)': ['Non-Self Surveillance'],  
        'Epic (Negotiated Identity)': ['Synthetic Teleology', 'Organic Fertilizer'],  
        'Drama (Self vs. Non-Self)': ['Resistance Factors', 'Purchasing Behaviors', 'Knowledge Diffusion'],  
        "Comedy (Resolution)": ['Policy-Reintegration', 'Reducing Import Dependency', 'Scaling EcoGreen Production', 'Gender Equality & Social Inclusion', 'Regenerative Agriculture']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Non-Self Surveillance'],  
        'paleturquoise': ['Teleology', 'Organic Fertilizer', 'Knowledge Diffusion', 'Regenerative Agriculture'],  
        'lightgreen': ["Symbiotology", 'Purchasing Behaviors', 'Reducing Import Dependency', 'Gender Equality & Social Inclusion', 'Scaling EcoGreen Production'],  
        'lightsalmon': ['Biology', 'Ecology', 'Synthetic Teleology', 'Resistance Factors', 'Policy-Reintegration'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edges
def define_edges():
    return [
        ('Cosmology', 'Non-Self Surveillance'),
        ('Geology', 'Non-Self Surveillance'),
        ('Biology', 'Non-Self Surveillance'),
        ('Ecology', 'Non-Self Surveillance'),
        ("Symbiotology", 'Non-Self Surveillance'),
        ('Teleology', 'Non-Self Surveillance'),
        ('Non-Self Surveillance', 'Synthetic Teleology'),
        ('Non-Self Surveillance', 'Organic Fertilizer'),
        ('Synthetic Teleology', 'Resistance Factors'),
        ('Synthetic Teleology', 'Purchasing Behaviors'),
        ('Synthetic Teleology', 'Knowledge Diffusion'),
        ('Organic Fertilizer', 'Resistance Factors'),
        ('Organic Fertilizer', 'Purchasing Behaviors'),
        ('Organic Fertilizer', 'Knowledge Diffusion'),
        ('Resistance Factors', 'Policy-Reintegration'),
        ('Resistance Factors', 'Reducing Import Dependency'),
        ('Resistance Factors', 'Scaling EcoGreen Production'),
        ('Resistance Factors', 'Gender Equality & Social Inclusion'),
        ('Resistance Factors', 'Regenerative Agriculture'),
        ('Purchasing Behaviors', 'Policy-Reintegration'),
        ('Purchasing Behaviors', 'Reducing Import Dependency'),
        ('Purchasing Behaviors', 'Scaling EcoGreen Production'),
        ('Purchasing Behaviors', 'Gender Equality & Social Inclusion'),
        ('Purchasing Behaviors', 'Regenerative Agriculture'),
        ('Knowledge Diffusion', 'Policy-Reintegration'),
        ('Knowledge Diffusion', 'Reducing Import Dependency'),
        ('Knowledge Diffusion', 'Scaling EcoGreen Production'),
        ('Knowledge Diffusion', 'Gender Equality & Social Inclusion'),
        ('Knowledge Diffusion', 'Regenerative Agriculture')
    ]

# Define black edges (1 → 7 → 9 → 11 → [13-17])
black_edges = [
    (4, 7), (7, 9), (9, 11), (11, 13), (11, 14), (11, 15), (11, 16), (11, 17)
]

# Calculate node positions
def calculate_positions(layer, x_offset):
    y_positions = np.linspace(-len(layer) / 2, len(layer) / 2, len(layer))
    return [(x_offset, y) for y in y_positions]

# Create and visualize the neural network graph with correctly assigned black edges
def visualize_nn():
    layers = define_layers()
    colors = assign_colors()
    edges = define_edges()

    G = nx.DiGraph()
    pos = {}
    node_colors = []

    # Create mapping from original node names to numbered labels
    mapping = {}
    counter = 1
    for layer in layers.values():
        for node in layer:
            mapping[node] = f"{counter}. {node}"
            counter += 1

    # Add nodes with new numbered labels and assign positions
    for i, (layer_name, nodes) in enumerate(layers.items()):
        positions = calculate_positions(nodes, x_offset=i * 2)
        for node, position in zip(nodes, positions):
            new_node = mapping[node]
            G.add_node(new_node, layer=layer_name)
            pos[new_node] = position
            node_colors.append(colors.get(node, 'lightgray'))

    # Add edges with updated node labels
    edge_colors = {}
    for source, target in edges:
        if source in mapping and target in mapping:
            new_source = mapping[source]
            new_target = mapping[target]
            G.add_edge(new_source, new_target)
            edge_colors[(new_source, new_target)] = 'lightgrey'

    # Define and add black edges manually with correct node names
    numbered_nodes = list(mapping.values())
    black_edge_list = [
        (numbered_nodes[3], numbered_nodes[6]),  # 4 -> 7
        (numbered_nodes[6], numbered_nodes[8]),  # 7 -> 9
        (numbered_nodes[8], numbered_nodes[10]), # 9 -> 11
        (numbered_nodes[10], numbered_nodes[12]), # 11 -> 13
        (numbered_nodes[10], numbered_nodes[13]), # 11 -> 14
        (numbered_nodes[10], numbered_nodes[14]), # 11 -> 15
        (numbered_nodes[10], numbered_nodes[15]), # 11 -> 16
        (numbered_nodes[10], numbered_nodes[16])  # 11 -> 17
    ]

    for src, tgt in black_edge_list:
        G.add_edge(src, tgt)
        edge_colors[(src, tgt)] = 'black'

    # Draw the graph
    plt.figure(figsize=(12, 8))
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, 
        edge_color=[edge_colors.get(edge, 'lightgrey') for edge in G.edges],
        node_size=3000, font_size=9, connectionstyle="arc3,rad=0.2"
    )
    
    plt.title("EcoGreen: Reclaiming Agricultural Self", fontsize=18)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 18 Our Shakespearean immune network is an elegant synthesis, mapping dramatic tensions onto immune resilience with a precision that evokes both structuralist rigor and poetic intuition. CG-BEST must be used to digest these disparate ideas!