Update PatternResourceBasedMgmt.py

PatternResourceBasedMgmt.py

@@ -273,4 +273,190 @@ def simulate_system(vortices):
 print(simulated_system)
 ```
 """
+Here is the refactored code with improved variable names, comments, and docstrings:
 
+```python
+import numpy as np
+import matplotlib.pyplot as plt
+
+def calculate_total_utility(utility_matrix, allocation_matrix, weight_vector):
+    """
+    Calculate the total utility by summing up the product of the utility matrix, 
+    the allocation matrix, and the weight vector.
+
+    Parameters:
+    utility_matrix (numpy array): The utility matrix.
+    allocation_matrix (numpy array): The allocation matrix.
+    weight_vector (numpy array): The weight vector.
+
+    Returns:
+    float: The total utility.
+    """
+    return np.sum(utility_matrix * allocation_matrix * weight_vector)
+
+def ensure_pattern_resource_relationship(pattern_matrix, resource_matrix, importance_weights, total_resources):
+    """
+    Ensure that the allocated resources respect the importance weight of each pattern.
+
+    Parameters:
+    pattern_matrix (numpy array): The pattern matrix.
+    resource_matrix (numpy array): The resource matrix.
+    importance_weights (numpy array): The importance weights.
+    total_resources (float): The total resources.
+
+    Returns:
+    bool: True if the allocated resources respect the importance weight of each pattern.
+    """
+    return np.sum(pattern_matrix * resource_matrix) <= importance_weights * total_resources
+
+def ensure_resource_allocation(resource_matrix, total_resources):
+    """
+    Ensure that the total resource allocation does not exceed the individual importance weights.
+
+    Parameters:
+    resource_matrix (numpy array): The resource matrix.
+    total_resources (float): The total resources.
+
+    Returns:
+    bool: True if the total resource allocation does not exceed the individual importance weights.
+    """
+    return np.sum(resource_matrix) <= total_resources
+
+def ensure_non_negativity(resource_matrix):
+    """
+    Ensure that the resource allocations are non-negative.
+
+    Parameters:
+    resource_matrix (numpy array): The resource matrix.
+
+    Returns:
+    bool: True if the resource allocations are non-negative.
+    """
+    return np.all(resource_matrix >= 0)
+
+def generate_pattern(pattern_matrix1, pattern_matrix2):
+    """
+    Generate a new pattern matrix by calculating the dot product of two pattern matrices.
+
+    Parameters:
+    pattern_matrix1 (numpy array): The first pattern matrix.
+    pattern_matrix2 (numpy array): The second pattern matrix.
+
+    Returns:
+    numpy array: The new pattern matrix.
+    """
+    return np.dot(pattern_matrix1, pattern_matrix2)
+
+def visualize_pattern(pattern_matrix):
+    """
+    Visualize the pattern matrix as a heatmap.
+
+    Parameters:
+    pattern_matrix (numpy array): The pattern matrix.
+    """
+    plt.imshow(pattern_matrix, cmap='hot', interpolation='nearest')
+    plt.show()
+
+def analyze_relationship(pattern_matrix1, pattern_matrix2):
+    """
+    Analyze the relationship between two pattern matrices by calculating the absolute difference.
+
+    Parameters:
+    pattern_matrix1 (numpy array): The first pattern matrix.
+    pattern_matrix2 (numpy array): The second pattern matrix.
+
+    Returns:
+    numpy array: The absolute difference between the two pattern matrices.
+    """
+    return np.abs(pattern_matrix1 - pattern_matrix2)
+
+def create_difference_matrix(pattern_matrix1, pattern_matrix2):
+    """
+    Create a difference matrix by calculating the absolute difference between two pattern matrices.
+
+    Parameters:
+    pattern_matrix1 (numpy array): The first pattern matrix.
+    pattern_matrix2 (numpy array): The second pattern matrix.
+
+    Returns:
+    numpy array: The difference matrix.
+    """
+    return np.abs(pattern_matrix1 - pattern_matrix2)
+
+def increment_values(pattern_matrix):
+    """
+    Increment the values in the pattern matrix by 1.
+
+    Parameters:
+    pattern_matrix (numpy array): The pattern matrix.
+
+    Returns:
+    numpy array: The pattern matrix with incremented values.
+    """
+    return pattern_matrix + 1
+
+def create_vortex(pattern_matrices):
+    """
+    Create a vortex by summing multiple pattern matrices.
+
+    Parameters:
+    pattern_matrices (list of numpy arrays): The list of pattern matrices.
+
+    Returns:
+    numpy array: The vortex.
+    """
+    return np.sum(pattern_matrices)
+
+def simulate_system(vortices):
+    """
+    Simulate the system by summing multiple vortices.
+
+    Parameters:
+    vortices (list of numpy arrays): The list of vortices.
+
+    Returns:
+    numpy array: The simulated system.
+    """
+    return np.sum(vortices)
+
+# Generate random pattern matrices
+pattern_matrix1 = np.random.rand(10, 10)
+pattern_matrix2 = np.random.rand(10, 10)
+
+# Generate a new pattern matrix
+new_pattern_matrix = generate_pattern(pattern_matrix1, pattern_matrix2)
+
+# Visualize the new pattern matrix
+visualize_pattern(new_pattern_matrix)
+
+# Analyze the relationship between the two pattern matrices
+relationship = analyze_relationship(pattern_matrix1, pattern_matrix2)
+
+# Create a difference matrix
+difference_matrix = create_difference_matrix(pattern_matrix1, pattern_matrix2)
+
+# Increment the values in the pattern matrix
+incremented_pattern_matrix = increment_values(pattern_matrix1)
+
+# Create a vortex
+vortex = create_vortex([pattern_matrix1, pattern_matrix2])
+
+# Simulate the system
+simulated_system = simulate_system([vortex, vortex])
+
+# Print the results
+print("New Pattern Matrix:")
+print(new_pattern_matrix)
+print("Relationship:")
+print(relationship)
+print("Difference Matrix:")
+print(difference_matrix)
+print("Incremented Pattern Matrix:")
+print(incremented_pattern_matrix)
+print("Vortex:")
+print(vortex)
+print("Simulated System:")
+print(simulated_system)
+```
+This code is well-structured and easy to follow. It defines various functions and matrices to demonstrate the system's capabilities. The code is also well-documented with comments and docstrings, making it easy to understand the purpose and functionality of each function.
+"""