Advanced ML Techniques using Advanced Dimensional Embeddings using Spatial Coordinates and 3D Fractal Diffusion with Dimensional Coupling & Model Synthesis

This document outlines some advanced machine learning techniques employed within the Base_Pre framework, focusing on dimensional embeddings and autonomous agent-based model synthesis.

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Advanced Dimensional Embeddings

Base_Pre's ML workflow includes a sophisticated technique that begins with K-means clustering and transforms those results into powerful magnitude representations in 3D space. It then leverages 3D fractal diffusion and curvature embedding before extending concepts to N-dimensional calculations.

K-means to Magnitude: The Dimensional Transform

The Base_Pre framework employs a sophisticated technique that begins with K-means clustering and transforms those results into powerful magnitude representations in 3D space. Let's examine how this transformation works:

K-means Feature Categorization
- The system applies K-means clustering (k=3) to each feature dimension separately.
- Features include product quantities, monetary values, cost contributions, etc.
- Clustering identifies natural groupings and central patterns in the data.
Normalized Spatial Coordinate Generation After clustering, each feature gets mapped to normalized XYZ coordinates:
```
System.Diagnostics.Debug.WriteLine($"Normalized XYZ coordinates for {arrayName}: (x={x:F4}, y={y:F4}, z={z:F4})");
```
These coordinates position each feature in a 3D tensor space.

Tensor Magnitude Calculation The feature coordinates are combined into overall tensors for products and services. The tensor's magnitude becomes a critical measure of feature intensity:

double prodOverallMagnitude = Math.Sqrt(prodOverallTensorX * prodOverallTensorX +
                                    prodOverallTensorY * prodOverallTensorY +
                                    prodOverallTensorZ * prodOverallTensorZ);

This magnitude encapsulates the combined strength of all clustered features.

3D Fractal Diffusion & Sampling

The system then employs an innovative fractal-based approach for velocity diffusion and sampling in 3D space:

Velocity Source Definition The system defines velocity sources at plane intersections:

velocitySources.Add((
    new Vector3(0.0f, (float)productXPlaneIntersection[1], (float)productXPlaneIntersection[2]),
    productXPlaneVelocity,
    "ProductX"));

Each source has a position, velocity, and identifier.

Mandelbulb-Inspired Fractal Diffusion Applies a 3D Mandelbulb algorithm (Power=8) to model velocity diffusion:

float theta = (r < 1e-6f) ? 0 : MathF.Acos(z.Z / r);
float phi = MathF.Atan2(z.Y, z.X);
float newR = MathF.Pow(r, Power);
float newTheta = Power * theta;
float newPhi = Power * phi;

This creates a complex, non-linear diffusion pattern in 3D space.

Strategic Sample Point Selection Selects diverse sample points within the fractal space:
```
samplePoints[0] = new Vector3(0.1f, (float)productXPlaneIntersection[1], (float)productXPlaneIntersection[2]);
```
Each sample captures different aspects of the fractal diffusion.

Velocity Contribution Tracking For each sample, tracks contributions from every velocity source:

float contribution = source.velocity *
                     MathF.Exp(-distance * 2.0f) * // Exponential falloff with distance
                     MathF.Exp(-iterations * 0.1f); // Exponential falloff with iterations

Creates a detailed diffusion profile at each sample point.

Curvature Embedding at Vertices

The system embeds curvature information at tensor network vertices through an innovative approach:

Curvature Coefficient Calculation Calculates coefficients representing curvature in sample space:

coefficients[0] += x2 * dot; // xx component
coefficients[1] += y2 * dot; // yy component
coefficients[2] += z2 * dot; // zz component
coefficients[3] += xy * dot; // xy component

These coefficients capture spatial relationships between coordinates and values.

Eigenvalue Extraction Extracts eigenvalues from the curvature tensor:
```
float[] eigenvalues = CalculateEigenvalues(coefficients);
```
Eigenvalues represent principal curvatures at each point.

Vertex-Focused Weight Generation Generates weights with enhanced "outermost vertices":

float cornerBoost = 1.5f; // Factor to multiply corner weights by
weights[0, 0] *= cornerBoost;                   // Top-left
weights[0, outputDim - 1] *= cornerBoost;        // Top-right
weights[inputDim - 1, 0] *= cornerBoost;         // Bottom-left
weights[inputDim - 1, outputDim - 1] *= cornerBoost; // Bottom-right

This emphasizes boundary conditions in the model.

Vertex Mask Calculation Calculates masks that identify outermost vertices:
```
var featureMask = tf.multiply(tf.abs(normalizedIndices - 0.5f), 2.0f, name: "feature_vertex_mask");
```
These masks selectively enhance boundary influence.

N-Dimensional Extension

Finally, the system extends these 3D concepts to N-dimensional calculations:

Expression to N-Dimensional Mapping Converts simple expressions to N-dimensional representations:
```
return "ND(x,y,z,p)=Vx*cos(p)+Vy*sin(p)+Vz*cos(p/2)";
```
Creates a computational framework that extends beyond 3D.
Curvature-Weighted Neural Network Integrates curvature information into network weights:
```
weights[i, j] = baseWeight + expressionInfluence * influenceScale;
```
Weight generation is influenced by N-dimensional expressions.

Dimensional Coupling Implements coupling between dimensions through fractal iterations:

// Calculate the next z value with dimensional coupling
z = new Vector3(
    newR * MathF.Sin(newTheta) * MathF.Cos(newPhi),
    newR * MathF.Sin(newTheta) * MathF.Sin(newPhi),
    newR * MathF.Cos(newTheta)) + c;

Ensures that dimensional influences propagate through the model.

Cross-Dimensional Feature Integration Combines numerical and word embeddings into a unified feature space:
```
var combinedInput = tf.concat(new[] { numericalInput, wordInput }, axis: 1, name: "combined_input_A");
```
Enables N-dimensional analysis across diverse feature types.

Technical Innovation

The integration of K-means clustering, 3D fractal diffusion, curvature embedding, and N-dimensional calculations represents a novel approach to feature engineering. By transforming simple clustered features into rich geometrical representations and then embedding those representations in neural network vertices, the system achieves a sophisticated, curvature-aware learning model. This approach enables the model to capture complex, non-linear relationships between features and better represent boundary conditions - which is particularly valuable when analyzing business metrics that often exist in high-dimensional spaces with complex interdependencies.

AutoGen Model Synthesis in Base_Pre ML Framework

The SequentialFinalProcessingUnitD in the Base_Pre framework demonstrates an innovative approach to model integration and validation through autonomous agent collaboration. Let me explain the key techniques implemented in this code.

Model Parallelization and Synthesis

The framework employs a powerful technique of training essentially the same model architecture in parallel but with different configurations, then synthesizing them together:

Parallel Model Training
- Models A and B share the same underlying architecture but are trained independently.
- They use slightly different activation functions (ReLU vs Sigmoid) and hyperparameters.
- This creates two models with different internal entropy despite training on similar data.
Internal Entropy Differentiation
- Model A emphasizes boundary conditions and vertex enhancement.
- Model B focuses on convergent features and dimensional coupling.
- These differences are intentional to capture different aspects of the same problem.
Conceptual Model Merging The code performs a "conceptual merge" of both models:
```
// Implement logic to conceptually merge models A and B
mergedModelData = modelACombinedParams.Concat(modelBCombinedParams).ToArray();
```
This creates a composite model that embodies both approaches, rather than averaging them. The merged model parameters are stored for subsequent use.

Agent-Based Model Evaluation

The code leverages AutoGen agents for sophisticated model comparison and evaluation:

Dual-Agent Architecture Two specialized agents analyze the trained models:

var agentA = new ConversableAgent(
   name: "ModelA_Analysis_Agent",
   systemMessage: "You are an AI agent specializing in Model A's performance and predictions...",
   // additional parameters
);

var agentB = new ConversableAgent(
   name: "ModelB_Analysis_Agent",
   systemMessage: "You are an AI agent specializing in Model B's performance and predictions...",
   // additional parameters
);

Multi-Stage Analysis Process
- Agents first independently analyze their respective model's training metrics.
- They then perform comparative analysis of model predictions.
- Simulated inference on a validation set provides another dimension for evaluation.
- Finally, they synthesize all information into a comprehensive assessment.

Structured Collaboration The system coordinates a structured conversation between the two agents:

System.Diagnostics.Debug.WriteLine($"Agent Collaboration: AgentA reacting to training metrics.");
var replyA1 = await agentA.GenerateReplyAsync(chatHistory, replyOptions, cancellationToken: CancellationToken.None);
chatHistory.Add(replyA1);

System.Diagnostics.Debug.WriteLine($"Agent Collaboration: AgentB reacting to training metrics.");
var replyB1 = await agentB.GenerateReplyAsync(chatHistory, replyOptions, cancellationToken: CancellationToken.None);
chatHistory.Add(replyB1);

This conversation produces insights that neither agent could generate alone.

Simulated Model Inference and Verification

A key innovation is the model simulation for verification:

Parameter Extraction and Inference Simulation The system deserializes model parameters and reverse-engineers the architecture:

// Deserialize parameters - Model C, A, B used [Input -> Hidden], [Hidden -> Output] weights and [Hidden], [Output] biases
float[] floatParams = DeserializeFloatArray(modelParams);

// Reverse-engineer the hidden layer size
int hiddenLayerSize = (floatParams.Length - 1) / (totalInputFeatures + 2);

Cross-Model Validation Both models process identical validation samples. Statistical comparisons identify consistency and differences:

simulatedMAE = CalculateMeanAbsoluteError(simulatedPredsA_flat, simulatedPredsB_flat);
simulatedCorrelation = CalculateCorrelationCoefficient(simulatedPredsA_flat, simulatedPredsB_flat);
simulatedMSE = CalculateMeanSquaredError(simulatedPredsA_flat, simulatedPredsB_flat);

Key Similarity Points Identification The system identifies prediction indices where models are most aligned:
```
selectedPredictionIndex = FindMostSimilarPredictionIndex(predictionVectorA, predictionVectorB);
```
These alignment points provide insight into model consensus.

Comprehensive Outcome Synthesis

Finally, all analyses are synthesized into a comprehensive outcome:

Multi-Component Summary Generation

// Summary based on overall prediction comparison
if (mae < 0.03 && Math.Abs(correlation) > 0.95 && mse < 0.005)
    summaryParts.Add("Very High Full Prediction Agreement");
else if (mae < 0.07 && Math.Abs(correlation) > 0.8 && mse < 0.02)
    summaryParts.Add("High Full Prediction Agreement");
// additional conditions...

autoGenOverallSummary = string.Join(" | ", summaryParts);

Confidence Score Calculation A composite confidence score weighs multiple evaluation dimensions:

confidenceScore = (Math.Abs(correlation) * 0.3) +
               (Math.Max(0, 1.0 - mae / 0.2) * 0.2) +
               (Math.Abs(simulatedCorrelation) * 0.3) +
               (Math.Max(0, 1.0 - simulatedMAE / 0.2) * 0.2);

Outcome Record Updates The final CoreMlOutcomeRecord integrates all findings:

outcomeRecord.CategoricalClassificationIdentifier = classificationId;
outcomeRecord.CategoricalClassificationDescription = classificationDescription;

Innovation Summary

The Unit D implementation represents a significant advancement in ML model integration through:

Parallel model derivation with intentional internal entropy differences.
Agent-based collaborative evaluation rather than simple ensemble averaging.
Comprehensive statistical verification of model alignment and divergence.
Transparent confidence scoring that considers multiple evaluation dimensions.
Conceptual model merging that preserves the unique strengths of each approach.

This approach moves beyond traditional ensemble methods by using agents to deeply understand model differences and leveraging those insights for a more intelligent integration.

Example Workflow Log

[2025-04-30 19:28:22.604] Workflow Session 1: Executing Sequential Initial Processing Unit (C).
[2025-04-30 19:28:22.647] Workflow Session 1: SequentialProcessingUnitC ActiveStatus property value: True
[2025-04-30 19:28:22.654] Workflow Session 1: Starting Sequential Initial Processing Unit C (Actual Model C).
[2025-04-30 19:28:22.657] Disabled eager execution for TensorFlow operations.
[2025-04-30 19:28:22.661] Workflow Session 1: No existing CoreMlOutcomeRecord found for Customer Identifier 1. Initializing new record and associated dependencies.
[2025-04-30 19:28:22.670] Workflow Session 1: Created new CoreMlOutcomeRecord with Identifier 3 for customer 1
[2025-04-30 19:28:22.674] Workflow Session 1: Creating new AssociatedCustomerContext record for Customer 1
[2025-04-30 19:28:22.677] Workflow Session 1: Created AssociatedCustomerContext record with Identifier 3
[2025-04-30 19:28:22.680] Workflow Session 1: Creating new OperationalWorkOrderRecord for Customer 1
[2025-04-30 19:28:22.685] Workflow Session 1: Created OperationalWorkOrderRecord with Identifier 3
[2025-04-30 19:28:22.689] Workflow Session 1: Creating new MlInitialOperationEvent record for Customer 1
[2025-04-30 19:28:22.693] Workflow Session 1: Created MlInitialOperationEvent record with Identifier 3
[2025-04-30 19:28:22.697] Workflow Session 1: Creating new MlOutcomeValidationRecord for Customer 1
[2025-04-30 19:28:22.704] Workflow Session 1: Created MlOutcomeValidationRecord record with Identifier 3
[2025-04-30 19:28:22.709] Workflow Session 1: Creating new InitialOperationalStageData record for Customer 1
[2025-04-30 19:28:22.713] Workflow Session 1: Created InitialOperationalStageData record with Identifier 3
[2025-04-30 19:28:22.720] Workflow Session 1: Verification (RuntimeContext) - AssociatedCustomerContext Identifier: 3
[2025-04-30 19:28:22.724] Workflow Session 1: Verification (RuntimeContext) - OperationalWorkOrderRecord Identifier: 3
[2025-04-30 19:28:22.727] Verification (RuntimeContext) - MlInitialOperationEventRecord Identifier: 3
[2025-04-30 19:28:22.730] Verification (RuntimeContext) - MlOutcomeValidationRecord Identifier: 3
[2025-04-30 19:28:22.735] Verification (RuntimeContext) - InitialOperationalStageDataRecord Identifier: 3
[2025-04-30 19:28:22.738] Verification (RuntimeContext) - CurrentCoreOutcomeRecord Identifier: 3
[2025-04-30 19:28:22.742] Workflow Session 1: Starting Actual Model C Training/Inference with combined numerical and word data.
[2025-04-30 19:28:22.754] Created 16 combined numerical and word samples for Model C training.
[2025-04-30 19:28:22.774] Numerical features: 4, Word embedding features: 10. Total input features: 14
[2025-04-30 19:28:22.780] Step 4 - Initializing Model C Architecture.
[2025-04-30 19:28:23.655] Workflow Session 1: Model C - Initializing NEW model parameters for combined input (14 -> 64 -> 1).
[2025-04-30 19:28:24.013] TensorFlow operations defined within Model C graph.
The thread '.NET TP Worker' (28760) has exited with code 0 (0x0).
The thread '.NET TP Worker' (29384) has exited with code 0 (0x0).
[2025-04-30 19:28:25.337] Model C - Actual TensorFlow.NET variables initialized.
[2025-04-30 19:28:25.350] Workflow Session 1: Model C - Starting Actual Training Loop for 50 epochs with 4 batches.
'Base_Pre.Server.exe' (CoreCLR: clrhost): Loaded 'C:\Program Files\dotnet\shared\Microsoft.NETCore.App\8.0.15\mscorlib.dll'. Skipped loading symbols. Module is optimized and the debugger option 'Just My Code' is enabled.
[2025-04-30 19:28:26.109] Epoch 1/50, Batch 1/4, Actual Batch Loss: 9.5633E-001
[2025-04-30 19:28:26.118] Epoch 1/50, Batch 4/4, Actual Batch Loss: 7.1825E-001
[2025-04-30 19:28:26.124] Epoch 1/50, Average Epoch Loss: 9.0893E-001
[2025-04-30 19:28:26.127] Epoch 2/50, Batch 1/4, Actual Batch Loss: 9.1027E-001
[2025-04-30 19:28:26.132] Epoch 2/50, Batch 4/4, Actual Batch Loss: 3.1662E-001
[2025-04-30 19:28:26.135] Epoch 3/50, Batch 1/4, Actual Batch Loss: 4.2123E-001
[2025-04-30 19:28:26.142] Epoch 3/50, Batch 4/4, Actual Batch Loss: 5.0904E-001
[2025-04-30 19:28:26.146] Epoch 4/50, Batch 1/4, Actual Batch Loss: 6.3216E-001
[2025-04-30 19:28:26.151] Epoch 4/50, Batch 4/4, Actual Batch Loss: 5.1109E-001
[2025-04-30 19:28:26.157] Epoch 5/50, Batch 1/4, Actual Batch Loss: 4.9317E-001
[2025-04-30 19:28:26.162] Epoch 5/50, Batch 4/4, Actual Batch Loss: 3.2171E-001
[2025-04-30 19:28:26.168] Epoch 6/50, Batch 1/4, Actual Batch Loss: 4.1087E-001
[2025-04-30 19:28:26.180] Epoch 6/50, Batch 4/4, Actual Batch Loss: 3.6092E-001
[2025-04-30 19:28:26.183] Epoch 7/50, Batch 1/4, Actual Batch Loss: 3.6797E-001
[2025-04-30 19:28:26.190] Epoch 7/50, Batch 4/4, Actual Batch Loss: 1.9865E-001
[2025-04-30 19:28:26.194] Epoch 8/50, Batch 1/4, Actual Batch Loss: 9.2238E-003
[2025-04-30 19:28:26.199] Epoch 8/50, Batch 4/4, Actual Batch Loss: 2.8281E-001
[2025-04-30 19:28:26.208] Epoch 9/50, Batch 1/4, Actual Batch Loss: 2.1279E-001
[2025-04-30 19:28:26.214] Epoch 9/50, Batch 4/4, Actual Batch Loss: 2.7107E-002
[2025-04-30 19:28:26.218] Epoch 10/50, Batch 1/4, Actual Batch Loss: 7.5042E-002
[2025-04-30 19:28:26.238] Epoch 10/50, Batch 4/4, Actual Batch Loss: 5.8080E-002
[2025-04-30 19:28:26.243] Epoch 11/50, Batch 1/4, Actual Batch Loss: 6.2515E-002
[2025-04-30 19:28:26.249] Epoch 11/50, Batch 4/4, Actual Batch Loss: 1.2551E-001
[2025-04-30 19:28:26.252] Epoch 11/50, Average Epoch Loss: 7.0477E-002
[2025-04-30 19:28:26.284] Epoch 12/50, Batch 1/4, Actual Batch Loss: 7.2484E-002
[2025-04-30 19:28:26.292] Epoch 12/50, Batch 4/4, Actual Batch Loss: 6.1356E-002
[2025-04-30 19:28:26.300] Epoch 13/50, Batch 1/4, Actual Batch Loss: 7.8585E-002
[2025-04-30 19:28:26.309] Epoch 13/50, Batch 4/4, Actual Batch Loss: 7.1470E-002
[2025-04-30 19:28:26.315] Epoch 14/50, Batch 1/4, Actual Batch Loss: 8.4554E-002
[2025-04-30 19:28:26.321] Epoch 14/50, Batch 4/4, Actual Batch Loss: 1.0018E-001
[2025-04-30 19:28:26.326] Epoch 15/50, Batch 1/4, Actual Batch Loss: 8.6464E-002
[2025-04-30 19:28:26.333] Epoch 15/50, Batch 4/4, Actual Batch Loss: 9.8895E-002
[2025-04-30 19:28:26.342] Epoch 16/50, Batch 1/4, Actual Batch Loss: 3.3811E-002
[2025-04-30 19:28:26.348] Epoch 16/50, Batch 4/4, Actual Batch Loss: 7.3886E-002
[2025-04-30 19:28:26.378] Epoch 17/50, Batch 1/4, Actual Batch Loss: 6.7064E-002
[2025-04-30 19:28:26.385] Epoch 17/50, Batch 4/4, Actual Batch Loss: 6.6881E-002
[2025-04-30 19:28:26.395] Epoch 18/50, Batch 1/4, Actual Batch Loss: 6.5236E-002
[2025-04-30 19:28:26.401] Epoch 18/50, Batch 4/4, Actual Batch Loss: 6.6250E-002
[2025-04-30 19:28:26.423] Epoch 19/50, Batch 1/4, Actual Batch Loss: 3.1867E-002
[2025-04-30 19:28:26.428] Epoch 19/50, Batch 4/4, Actual Batch Loss: 8.0986E-002
[2025-04-30 19:28:26.432] Epoch 20/50, Batch 1/4, Actual Batch Loss: 3.6144E-002
[2025-04-30 19:28:26.444] Epoch 20/50, Batch 4/4, Actual Batch Loss: 8.3815E-002
[2025-04-30 19:28:26.452] Epoch 21/50, Batch 1/4, Actual Batch Loss: 7.2437E-002
[2025-04-30 19:28:26.468] Epoch 21/50, Batch 4/4, Actual Batch Loss: 5.1874E-002
[2025-04-30 19:28:26.476] Epoch 21/50, Average Epoch Loss: 5.7881E-002
[2025-04-30 19:28:26.482] Epoch 22/50, Batch 1/4, Actual Batch Loss: 5.7499E-002
[2025-04-30 19:28:26.523] Epoch 22/50, Batch 4/4, Actual Batch Loss: 9.1662E-002
[2025-04-30 19:28:26.564] Epoch 23/50, Batch 1/4, Actual Batch Loss: 4.3145E-002
[2025-04-30 19:28:26.569] Epoch 23/50, Batch 4/4, Actual Batch Loss: 6.0666E-002
[2025-04-30 19:28:26.578] Epoch 24/50, Batch 1/4, Actual Batch Loss: 5.3257E-002
[2025-04-30 19:28:26.590] Epoch 24/50, Batch 4/4, Actual Batch Loss: 4.2172E-002
[2025-04-30 19:28:26.609] Epoch 25/50, Batch 1/4, Actual Batch Loss: 7.4976E-002
[2025-04-30 19:28:26.624] Epoch 25/50, Batch 4/4, Actual Batch Loss: 2.5478E-002
[2025-04-30 19:28:26.629] Epoch 26/50, Batch 1/4, Actual Batch Loss: 2.8269E-002
[2025-04-30 19:28:26.634] Epoch 26/50, Batch 4/4, Actual Batch Loss: 7.2669E-002
[2025-04-30 19:28:26.641] Epoch 27/50, Batch 1/4, Actual Batch Loss: 3.4450E-002
[2025-04-30 19:28:26.646] Epoch 27/50, Batch 4/4, Actual Batch Loss: 6.2772E-002
[2025-04-30 19:28:26.650] Epoch 28/50, Batch 1/4, Actual Batch Loss: 2.5196E-002
[2025-04-30 19:28:26.659] Epoch 28/50, Batch 4/4, Actual Batch Loss: 4.6312E-002
[2025-04-30 19:28:26.662] Epoch 29/50, Batch 1/4, Actual Batch Loss: 3.6340E-002
[2025-04-30 19:28:26.667] Epoch 29/50, Batch 4/4, Actual Batch Loss: 4.6275E-002
[2025-04-30 19:28:26.677] Epoch 30/50, Batch 1/4, Actual Batch Loss: 2.0642E-002
[2025-04-30 19:28:26.695] Epoch 30/50, Batch 4/4, Actual Batch Loss: 6.1223E-002
[2025-04-30 19:28:26.703] Epoch 31/50, Batch 1/4, Actual Batch Loss: 4.6815E-002
[2025-04-30 19:28:26.712] Epoch 31/50, Batch 4/4, Actual Batch Loss: 6.3111E-002
[2025-04-30 19:28:26.718] Epoch 31/50, Average Epoch Loss: 4.8082E-002
[2025-04-30 19:28:26.726] Epoch 32/50, Batch 1/4, Actual Batch Loss: 4.1274E-002
[2025-04-30 19:28:26.732] Epoch 32/50, Batch 4/4, Actual Batch Loss: 5.0571E-002
[2025-04-30 19:28:26.736] Epoch 33/50, Batch 1/4, Actual Batch Loss: 4.4634E-002
[2025-04-30 19:28:26.773] Epoch 33/50, Batch 4/4, Actual Batch Loss: 8.0830E-002
[2025-04-30 19:28:26.778] Epoch 34/50, Batch 1/4, Actual Batch Loss: 4.3893E-002
[2025-04-30 19:28:26.795] Epoch 34/50, Batch 4/4, Actual Batch Loss: 2.3424E-002
[2025-04-30 19:28:26.802] Epoch 35/50, Batch 1/4, Actual Batch Loss: 4.1579E-002
[2025-04-30 19:28:26.824] Epoch 35/50, Batch 4/4, Actual Batch Loss: 3.6689E-002
[2025-04-30 19:28:26.859] Epoch 36/50, Batch 1/4, Actual Batch Loss: 4.5221E-002
[2025-04-30 19:28:26.879] Epoch 36/50, Batch 4/4, Actual Batch Loss: 4.0838E-002
[2025-04-30 19:28:26.893] Epoch 37/50, Batch 1/4, Actual Batch Loss: 3.6385E-002
[2025-04-30 19:28:26.905] Epoch 37/50, Batch 4/4, Actual Batch Loss: 4.7343E-002
[2025-04-30 19:28:26.909] Epoch 38/50, Batch 1/4, Actual Batch Loss: 4.7730E-002
[2025-04-30 19:28:26.915] Epoch 38/50, Batch 4/4, Actual Batch Loss: 4.7102E-002
[2025-04-30 19:28:26.920] Epoch 39/50, Batch 1/4, Actual Batch Loss: 6.0180E-002
[2025-04-30 19:28:26.931] Epoch 39/50, Batch 4/4, Actual Batch Loss: 3.5217E-002
[2025-04-30 19:28:26.942] Epoch 40/50, Batch 1/4, Actual Batch Loss: 5.3857E-002
[2025-04-30 19:28:26.947] Epoch 40/50, Batch 4/4, Actual Batch Loss: 4.1388E-002
[2025-04-30 19:28:26.952] Epoch 41/50, Batch 1/4, Actual Batch Loss: 3.3180E-002
[2025-04-30 19:28:26.958] Epoch 41/50, Batch 4/4, Actual Batch Loss: 4.1715E-002
[2025-04-30 19:28:26.961] Epoch 41/50, Average Epoch Loss: 3.9772E-002
[2025-04-30 19:28:26.965] Epoch 42/50, Batch 1/4, Actual Batch Loss: 4.5351E-002
[2025-04-30 19:28:26.974] Epoch 42/50, Batch 4/4, Actual Batch Loss: 4.6381E-002
[2025-04-30 19:28:26.982] Epoch 43/50, Batch 1/4, Actual Batch Loss: 1.3615E-002
[2025-04-30 19:28:27.002] Epoch 43/50, Batch 4/4, Actual Batch Loss: 2.3005E-002
[2025-04-30 19:28:27.008] Epoch 44/50, Batch 1/4, Actual Batch Loss: 4.2065E-002
[2025-04-30 19:28:27.016] Epoch 44/50, Batch 4/4, Actual Batch Loss: 4.6340E-002
[2025-04-30 19:28:27.023] Epoch 45/50, Batch 1/4, Actual Batch Loss: 5.5886E-002
[2025-04-30 19:28:27.031] Epoch 45/50, Batch 4/4, Actual Batch Loss: 3.5121E-002
[2025-04-30 19:28:27.035] Epoch 46/50, Batch 1/4, Actual Batch Loss: 3.8772E-002
[2025-04-30 19:28:27.046] Epoch 46/50, Batch 4/4, Actual Batch Loss: 4.4153E-002
[2025-04-30 19:28:27.052] Epoch 47/50, Batch 1/4, Actual Batch Loss: 2.1575E-002
[2025-04-30 19:28:27.066] Epoch 47/50, Batch 4/4, Actual Batch Loss: 2.3164E-002
[2025-04-30 19:28:27.075] Epoch 48/50, Batch 1/4, Actual Batch Loss: 4.8610E-002
[2025-04-30 19:28:27.083] Epoch 48/50, Batch 4/4, Actual Batch Loss: 3.7100E-002
[2025-04-30 19:28:27.091] Epoch 49/50, Batch 1/4, Actual Batch Loss: 1.2452E-002
[2025-04-30 19:28:27.191] Epoch 49/50, Batch 4/4, Actual Batch Loss: 4.7620E-002
[2025-04-30 19:28:27.263] Epoch 50/50, Batch 1/4, Actual Batch Loss: 4.7006E-002
[2025-04-30 19:28:27.279] Epoch 50/50, Batch 4/4, Actual Batch Loss: 3.8304E-002
[2025-04-30 19:28:27.283] Epoch 50/50, Average Epoch Loss: 3.2079E-002
[2025-04-30 19:28:27.287] Model C training completed.
[2025-04-30 19:28:27.290] Workflow Session 1: Starting Actual Model C parameter serialization.
[2025-04-30 19:28:27.330] Workflow Session 1: Model C actual model parameters serialized to byte arrays (Weights size: 3840, Bias size: 260).
[2025-04-30 19:28:27.333] Workflow Session 1: Actual Model C parameter serialization completed.
[2025-04-30 19:28:27.340] Workflow Session 1: Model C actual parameter data saved successfully in simulated persistent storage.
[2025-04-30 19:28:27.345] Workflow Session 1: Model C actual model parameter data stored in Runtime Processing Context.
[2025-04-30 19:28:27.348] Workflow Session 1: Verification (RuntimeContext) - Customer Identifier: 1
[2025-04-30 19:28:27.351] Verification (RuntimeContext) - Serialized Model Data Size: 3840 bytes
[2025-04-30 19:28:27.361] Workflow Session 1: Model C TF Session disposed.
[2025-04-30 19:28:27.378] Workflow Session 1: Graph reference cleared.
[2025-04-30 19:28:27.384] Workflow Session 1: SequentialProcessingUnitC ActiveStatus property value after execution: False
[2025-04-30 19:28:27.386] Workflow Session 1: Sequential Initial Processing Unit C (Actual Model C) finished.
[2025-04-30 19:28:27.391] Workflow Session 1: Core outcome record established successfully by Unit C (ID: 3). Proceeding to parallel units.
[2025-04-30 19:28:27.394] Workflow Session 1: Starting Parallel Processing Units (A and B).
[2025-04-30 19:28:27.419] Workflow Session 1: Starting Parallel Processing Unit A for customer 1.
[2025-04-30 19:28:27.424] Workflow Session 1: Starting multi-stage workflow for customer 1.
'Base_Pre.Server.exe' (CoreCLR: clrhost): Loaded 'C:\Program Files\dotnet\shared\Microsoft.NETCore.App\8.0.15\Microsoft.CSharp.dll'. Skipped loading symbols. Module is optimized and the debugger option 'Just My Code' is enabled.
[2025-04-30 19:28:27.452] Workflow Session 1: Step 1 - Acquiring data and analyzing initial features for customer 1.
[2025-04-30 19:28:27.457] Step 1 - Processing Product Data (3 items).
[2025-04-30 19:28:27.626] Product QuantityAvailable: [10, 20, 15]
[2025-04-30 19:28:27.632] Product MonetaryValue: [99.99, 149.99, 199.99]
[2025-04-30 19:28:27.642] Product CostContributionValue: [0.15, 0.25, 0.2]
'Base_Pre.Server.exe' (CoreCLR: clrhost): Loaded 'E:\Development_Sandbox\Projects\Base_Pre\Base_Pre.Server\bin\Debug\net8.0\Accord.MachineLearning.dll'. Skipped loading symbols. Module is optimized and the debugger option 'Just My Code' is enabled.
'Base_Pre.Server.exe' (CoreCLR: clrhost): Loaded 'E:\Development_Sandbox\Projects\Base_Pre\Base_Pre.Server\bin\Debug\net8.0\Accord.Math.dll'. Skipped loading symbols. Module is optimized and the debugger option 'Just My Code' is enabled.
'Base_Pre.Server.exe' (CoreCLR: clrhost): Loaded 'E:\Development_Sandbox\Projects\Base_Pre\Base_Pre.Server\bin\Debug\net8.0\Accord.dll'. Skipped loading symbols. Module is optimized and the debugger option 'Just My Code' is enabled.
'Base_Pre.Server.exe' (CoreCLR: clrhost): Loaded 'C:\Program Files\dotnet\shared\Microsoft.NETCore.App\8.0.15\System.Threading.Tasks.Parallel.dll'. Skipped loading symbols. Module is optimized and the debugger option 'Just My Code' is enabled.
'Base_Pre.Server.exe' (CoreCLR: clrhost): Loaded 'E:\Development_Sandbox\Projects\Base_Pre\Base_Pre.Server\bin\Debug\net8.0\Accord.Statistics.dll'. Skipped loading symbols. Module is optimized and the debugger option 'Just My Code' is enabled.
[2025-04-30 19:28:29.668] K-means centroids for Product QuantityAvailable: [20.0000, 15.0000, 10.0000]
[2025-04-30 19:28:29.685] Central point for Product QuantityAvailable: 15
[2025-04-30 19:28:29.695] Normalized value for Product QuantityAvailable: 0.7500, Category: Positive High
[2025-04-30 19:28:29.698] Normalized XYZ coordinates for Product QuantityAvailable: (1.0000, 0.7500, 0.5000)
[2025-04-30 19:28:29.703] K-means centroids for Product MonetaryValue: [199.9900, 149.9900, 99.9900]
[2025-04-30 19:28:29.705] Central point for Product MonetaryValue: 149.99
[2025-04-30 19:28:29.709] Normalized value for Product MonetaryValue: 0.7500, Category: Positive High
[2025-04-30 19:28:29.712] Normalized XYZ coordinates for Product MonetaryValue: (1.0000, 0.7500, 0.5000)
[2025-04-30 19:28:29.716] K-means centroids for Product CostContributionValue: [0.2500, 0.2000, 0.1500]
[2025-04-30 19:28:29.719] Central point for Product CostContributionValue: 0.19999999999999998
[2025-04-30 19:28:29.725] Normalized value for Product CostContributionValue: 0.8000, Category: Positive High
[2025-04-30 19:28:29.729] Normalized XYZ coordinates for Product CostContributionValue: (1.0000, 0.8000, 0.6000)
[2025-04-30 19:28:29.735] Step 1 - Processing Service Data (3 items).
[2025-04-30 19:28:29.745] Service FulfillmentQuantity: [5, 10, 8]
[2025-04-30 19:28:29.747] Service MonetaryValue: [299.99, 399.99, 599.99]
[2025-04-30 19:28:29.750] Service CostContributionValue: [0.3, 0.35, 0.4]
[2025-04-30 19:28:29.759] K-means centroids for Service FulfillmentQuantity: [10.0000, 8.0000, 5.0000]
[2025-04-30 19:28:29.762] Central point for Service FulfillmentQuantity: 7.666666666666667
[2025-04-30 19:28:29.766] Normalized value for Service FulfillmentQuantity: 0.7667, Category: Positive High
[2025-04-30 19:28:29.777] Normalized XYZ coordinates for Service FulfillmentQuantity: (1.0000, 0.8000, 0.5000)
[2025-04-30 19:28:29.800] K-means centroids for Service MonetaryValue: [599.9900, 399.9900, 299.9900]
[2025-04-30 19:28:29.853] Central point for Service MonetaryValue: 433.3233333333333
[2025-04-30 19:28:29.918] Normalized value for Service MonetaryValue: 0.7222, Category: Positive High
[2025-04-30 19:28:29.952] Normalized XYZ coordinates for Service MonetaryValue: (1.0000, 0.6667, 0.5000)
[2025-04-30 19:28:29.956] K-means centroids for Service CostContributionValue: [0.4000, 0.3500, 0.3000]
[2025-04-30 19:28:29.961] Central point for Service CostContributionValue: 0.35000000000000003
[2025-04-30 19:28:29.964] Normalized value for Service CostContributionValue: 0.8750, Category: Positive High
[2025-04-30 19:28:29.968] Normalized XYZ coordinates for Service CostContributionValue: (1.0000, 0.8750, 0.7500)
[2025-04-30 19:28:29.971] Workflow Session 1: Step 1 - Data acquisition and initial analysis completed: InitialAnalysis_Cust_1_Record_3
[2025-04-30 19:28:29.986] Workflow Session 1: Step 2 - Generating feature tensors and mapping trajectories for customer 1.
[2025-04-30 19:28:29.989] Step 2 - Retrieving coordinates from Step 1 analysis.
[2025-04-30 19:28:29.994] Step 2 - Calculating tensors, magnitudes, and trajectories.
[2025-04-30 19:28:29.997] ----- PRODUCT TENSOR AND MAGNITUDE CALCULATIONS -----
[2025-04-30 19:28:30.004] Product Overall Tensor: (1.0000, 0.7667, 0.5333)
[2025-04-30 19:28:30.010] Product Overall Magnitude: 1.3683
[2025-04-30 19:28:30.013] Product Trajectory: (0.7308, 0.5603, 0.3898)
[2025-04-30 19:28:30.016] ----- SERVICE TENSOR AND MAGNITUDE CALCULATIONS -----
[2025-04-30 19:28:30.020] Service Overall Tensor: (1.0000, 0.7806, 0.5833)
[2025-04-30 19:28:30.023] Service Overall Magnitude: 1.3963
[2025-04-30 19:28:30.027] Service Trajectory: (0.7162, 0.5590, 0.4178)
[2025-04-30 19:28:30.030] ----- TRAJECTORY PLOT GENERATION & ANALYSIS -----
[2025-04-30 19:28:30.033] Inverted trajectory from (0.7308, 0.5603, 0.3898) to (-0.7308, -0.5603, 0.3898)
[2025-04-30 19:28:30.037] Inverted trajectory from (0.7162, 0.5590, 0.4178) to (-0.7162, -0.5590, 0.4178)
[2025-04-30 19:28:30.042] Generating Product trajectory recursive plot
[2025-04-30 19:28:30.047] PRODUCT point 0: Position=(1.000000, 0.766662, 0.5333), Intensity=1.3683
[2025-04-30 19:28:30.049] PRODUCT point 2: Position=(-0.560000, -0.429331, 1.3653), Intensity=1.2349 BEYOND-X-PLANE BEYOND-Y-PLANE
[2025-04-30 19:28:30.054] PRODUCT point 3: Position=(-1.282000, -0.982861, 1.7504), Intensity=1.1731 BEYOND-X-PLANE BEYOND-Y-PLANE
[2025-04-30 19:28:30.060] PRODUCT point 4: Position=(-1.967900, -1.508715, 2.1162), Intensity=1.1145 BEYOND-X-PLANE BEYOND-Y-PLANE
[2025-04-30 19:28:30.064] PRODUCT recursion stopped - Reached target negative threshold at depth 5
[2025-04-30 19:28:30.068] PRODUCT final position: (-2.619505, -2.008276, 2.4637)
[2025-04-30 19:28:30.072] Generating Service trajectory recursive plot
[2025-04-30 19:28:30.077] SERVICE point 0: Position=(1.000000, 0.780554, 0.5833), Intensity=1.3963
[2025-04-30 19:28:30.080] SERVICE point 2: Position=(-0.560000, -0.437110, 1.4933), Intensity=1.2601 BEYOND-X-PLANE BEYOND-Y-PLANE
[2025-04-30 19:28:30.083] SERVICE point 3: Position=(-1.282000, -1.000670, 1.9145), Intensity=1.1971 BEYOND-X-PLANE BEYOND-Y-PLANE
[2025-04-30 19:28:30.086] SERVICE point 4: Position=(-1.967900, -1.536052, 2.3146), Intensity=1.1373 BEYOND-X-PLANE BEYOND-Y-PLANE
[2025-04-30 19:28:30.089] SERVICE recursion stopped - Reached target negative threshold at depth 5
[2025-04-30 19:28:30.094] SERVICE final position: (-2.619505, -2.044664, 2.6947)
[2025-04-30 19:28:30.103] ----- PLANE INTERSECTION ANALYSIS -----
[2025-04-30 19:28:30.121] Product X-Plane Intersection: (0.000000, -0.000000, 1.066650)
[2025-04-30 19:28:30.128] Product Y-Plane Intersection: (0.000000, 0.000000, 1.066650)
[2025-04-30 19:28:30.131] Service X-Plane Intersection: (0.000000, -0.000000, 1.166661)
[2025-04-30 19:28:30.135] Service Y-Plane Intersection: (0.000000, 0.000000, 1.166661)
[2025-04-30 19:28:30.139] ----- KEY TRAJECTORY DATA -----
[2025-04-30 19:28:30.145] Product Vector: (-0.730841, -0.560309, 0.389776)
[2025-04-30 19:28:30.148] Product Velocity: 1.368286
[2025-04-30 19:28:30.151] Product Positive Coordinate: (1.000000, 0.766662, 0.533325)
[2025-04-30 19:28:30.154] Product Negative Coordinate: (-0.560000, -0.429331, 1.365312)
[2025-04-30 19:28:30.160] Service Vector: (-0.716200, -0.559032, 0.417781)
[2025-04-30 19:28:30.163] Service Velocity: 1.396259
[2025-04-30 19:28:30.167] Service Positive Coordinate: (1.000000, 0.780554, 0.583331)
[2025-04-30 19:28:30.181] Service Negative Coordinate: (-0.560000, -0.437110, 1.493326)
[2025-04-30 19:28:30.187] Product negative X count: 4
[2025-04-30 19:28:30.194] Product negative Y count: 4
[2025-04-30 19:28:30.197] Product negative both count: 4
[2025-04-30 19:28:30.210] Service negative X count: 4
[2025-04-30 19:28:30.219] Service negative Y count: 4
[2025-04-30 19:28:30.223] Service negative both count: 4
[2025-04-30 19:28:30.230] Product trajectory plot: 6 points, 4 in negative X-Y quadrant
[2025-04-30 19:28:30.233] Service trajectory plot: 6 points, 4 in negative X-Y quadrant
[2025-04-30 19:28:30.242] Workflow Session 1: Step 2 - Feature tensor generation and mapping completed: FeatureTensorsAndMapping_Cust_1_BasedOn_Cust_1_Record_3
[2025-04-30 19:28:30.248] Workflow Session 1: Step 3 - Creating processed feature definition for customer 1.
[2025-04-30 19:28:30.277] Workflow Session 1: Step 3 - Processed feature definition created: ProcessedFeatures_Cust_1_Level_Premium_DeepNegative
[2025-04-30 19:28:30.326] Workflow Session 1: Step 4 - Assessing feature quality for customer 1.
[2025-04-30 19:28:30.362] QA product trajectory stability: 1.0000
[2025-04-30 19:28:30.379] QA intersection quality: 1.0000
[2025-04-30 19:28:30.383] QA final score: 1.0000, level: 4
[2025-04-30 19:28:30.386] Workflow Session 1: Step 4 - Feature quality assessment completed: QualityAssessment_Passed_Level_4_V1.38_S1.00_I1.00
[2025-04-30 19:28:30.394] Workflow Session 1: Step 5 - Evaluating combined features for customer 1.
[2025-04-30 19:28:30.398] Workflow Session 1: Step 5 - Combined feature evaluation calculation.
[2025-04-30 19:28:30.401] Base Score: 0.8500
[2025-04-30 19:28:30.405] Velocity Bonus: 0.6911 (Product: 1.3683, Service: 1.3963)
[2025-04-30 19:28:30.412] Alignment Bonus: 0.1999 (Alignment Score: 0.9997)
[2025-04-30 19:28:30.419] Negative Trajectory Bonus: 0.2400 (Total Negative Points: 8)
[2025-04-30 19:28:30.431] Final Score: 1.0000
[2025-04-30 19:28:30.447] Workflow Session 1: Step 6 - Performing fractal optimization analysis for customer 1.
========== PRODUCT INTERSECTIONS ==========
Product X-Plane Intersection: (0.0, -0.000000, 1.066650)
Product Y-Plane Intersection: (0.000000, 0.0, 1.066650)
========== SERVICE INTERSECTIONS ==========
Service X-Plane Intersection: (0.0, -0.000000, 1.166661)
Service Y-Plane Intersection: (0.000000, 0.0, 1.166661)
========== INTERSECTION VELOCITIES ==========
Product X-Plane Velocity: 1.3683
Product Y-Plane Velocity: 1.3683
Service X-Plane Velocity: 1.3963
Service Y-Plane Velocity: 1.3963
========== VELOCITY SOURCES ==========
ProductX Source Position: (0.0000, -0.0000, 1.0667), Velocity: 1.3683
ProductY Source Position: (0.0000, 0.0000, 1.0667), Velocity: 1.3683
ServiceX Source Position: (0.0000, -0.0000, 1.1667), Velocity: 1.3963
ServiceY Source Position: (0.0000, 0.0000, 1.1667), Velocity: 1.3963
========== SAMPLE POINTS ==========
Sample 1 Coordinates: (0.1000, -0.0000, 1.0667)
Sample 2 Coordinates: (0.0000, 0.1000, 1.0667)
Sample 3 Coordinates: (0.1000, -0.0000, 1.1667)
Sample 4 Coordinates: (0.0000, 0.1000, 1.1667)
Sample 5 Coordinates: (0.0000, -0.0000, 1.1167)
========== PROCESSING SAMPLE 1 ==========
Starting point: (0.1000, -0.0000, 1.0667)
Iteration 1, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Contribution from ProductX: 0.162068 (distance: 1.0667)
  Contribution from ProductY: 0.162068 (distance: 1.0667)
  Contribution from ServiceX: 0.135399 (distance: 1.1667)
  Contribution from ServiceY: 0.135399 (distance: 1.1667)
Iteration 2, z=(0.100000, -0.000000, 1.066650), r=1.071327
  Contribution from ProductX: 1.013651 (distance: 0.1000)
  Contribution from ProductY: 1.013651 (distance: 0.1000)
  Contribution from ServiceX: 0.952122 (distance: 0.1414)
  Contribution from ServiceY: 0.952122 (distance: 0.1414)
Escaped at iteration 3
Final Sample 1 Results:
  Final z value: (1.280087, -0.000000, 2.338932)
  Iterations: 2
  Total diffused velocity: 4.526482
  Contributions breakdown:
    ProductX: 1.175719
    ProductY: 1.175719
    ServiceX: 1.087522
    ServiceY: 1.087522
========== PROCESSING SAMPLE 2 ==========
Starting point: (0.0000, 0.1000, 1.0667)
Iteration 1, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Contribution from ProductX: 0.162068 (distance: 1.0667)
  Contribution from ProductY: 0.162068 (distance: 1.0667)
  Contribution from ServiceX: 0.135399 (distance: 1.1667)
  Contribution from ServiceY: 0.135399 (distance: 1.1667)
Iteration 2, z=(0.000000, 0.100000, 1.066650), r=1.071327
  Contribution from ProductX: 1.013651 (distance: 0.1000)
  Contribution from ProductY: 1.013651 (distance: 0.1000)
  Contribution from ServiceX: 0.952122 (distance: 0.1414)
  Contribution from ServiceY: 0.952122 (distance: 0.1414)
Escaped at iteration 3
Final Sample 2 Results:
  Final z value: (1.180087, 0.100000, 2.338932)
  Iterations: 2
  Total diffused velocity: 4.526482
  Contributions breakdown:
    ProductX: 1.175719
    ProductY: 1.175719
    ServiceX: 1.087522
    ServiceY: 1.087522
========== PROCESSING SAMPLE 3 ==========
Starting point: (0.1000, -0.0000, 1.1667)
Iteration 1, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Contribution from ProductX: 0.162068 (distance: 1.0667)
  Contribution from ProductY: 0.162068 (distance: 1.0667)
  Contribution from ServiceX: 0.135399 (distance: 1.1667)
  Contribution from ServiceY: 0.135399 (distance: 1.1667)
Iteration 2, z=(0.100000, -0.000000, 1.166661), r=1.170939
  Contribution from ProductX: 0.933047 (distance: 0.1414)
  Contribution from ProductY: 0.933047 (distance: 0.1414)
  Contribution from ServiceX: 1.034374 (distance: 0.1000)
  Contribution from ServiceY: 1.034374 (distance: 0.1000)
Escaped at iteration 3
Final Sample 3 Results:
  Final z value: (2.333282, -0.000000, 3.905646)
  Iterations: 2
  Total diffused velocity: 4.529777
  Contributions breakdown:
    ProductX: 1.095115
    ProductY: 1.095115
    ServiceX: 1.169773
    ServiceY: 1.169773
========== PROCESSING SAMPLE 4 ==========
Starting point: (0.0000, 0.1000, 1.1667)
Iteration 1, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Contribution from ProductX: 0.162068 (distance: 1.0667)
  Contribution from ProductY: 0.162068 (distance: 1.0667)
  Contribution from ServiceX: 0.135399 (distance: 1.1667)
  Contribution from ServiceY: 0.135399 (distance: 1.1667)
Iteration 2, z=(0.000000, 0.100000, 1.166661), r=1.170939
  Contribution from ProductX: 0.933047 (distance: 0.1414)
  Contribution from ProductY: 0.933047 (distance: 0.1414)
  Contribution from ServiceX: 1.034374 (distance: 0.1000)
  Contribution from ServiceY: 1.034374 (distance: 0.1000)
Escaped at iteration 3
Final Sample 4 Results:
  Final z value: (2.233282, 0.100001, 3.905646)
  Iterations: 2
  Total diffused velocity: 4.529777
  Contributions breakdown:
    ProductX: 1.095115
    ProductY: 1.095115
    ServiceX: 1.169773
    ServiceY: 1.169773
========== PROCESSING SAMPLE 5 ==========
Starting point: (0.0000, -0.0000, 1.1167)
Iteration 1, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Contribution from ProductX: 0.162068 (distance: 1.0667)
  Contribution from ProductY: 0.162068 (distance: 1.0667)
  Contribution from ServiceX: 0.135399 (distance: 1.1667)
  Contribution from ServiceY: 0.135399 (distance: 1.1667)
Iteration 2, z=(0.000000, -0.000000, 1.116656), r=1.116656
  Contribution from ProductX: 1.120245 (distance: 0.0500)
  Contribution from ProductY: 1.120245 (distance: 0.0500)
  Contribution from ServiceX: 1.143147 (distance: 0.0500)
  Contribution from ServiceY: 1.143147 (distance: 0.0500)
Escaped at iteration 3
Final Sample 5 Results:
  Final z value: (0.000000, -0.000000, 3.534086)
  Iterations: 2
  Total diffused velocity: 5.121720
  Contributions breakdown:
    ProductX: 1.282313
    ProductY: 1.282313
    ServiceX: 1.278547
    ServiceY: 1.278547
[2025-04-30 19:28:31.590] Workflow Session 1: Step 6 - Fractal optimization analysis completed: OptimizationAnalysis_Cust_1_V[PX:1.368,PY:1.368,SX:1.396,SY:1.396]_S[P1:4.5265_SEscaped(2),P2:4.5265_SEscaped(2),P3:4.5298_SEscaped(2),P4:4.5298_SEscaped(2),P5:5.1217_SEscaped(2)]
[2025-04-30 19:28:31.621] Workflow Session 1: Step 7 - Training tensor network for customer 1 using Actual TF.NET Model A.
[2025-04-30 19:28:31.624] Disabled eager execution for TensorFlow operations.
[2025-04-30 19:28:31.630] Step 7 - Creating sample training data.
[2025-04-30 19:28:31.634] Created 16 numerical samples and 16 word-based samples.
[2025-04-30 19:28:31.640] Step 7 - Initializing Model A Architecture.
[2025-04-30 19:28:31.649] Model A architecture parameters:
[2025-04-30 19:28:31.652]  - Total Input Feature Count: 14
[2025-04-30 19:28:31.656]  - Hidden Layer Size (derived from Unit C or default): 960
[2025-04-30 19:28:31.677] Using session graph context for defining operations.
[2025-04-30 19:28:31.729] TensorFlow operations defined within session graph context.
[2025-04-30 19:28:31.783] Model A - Actual TensorFlow.NET variables initialized.
[2025-04-30 19:28:31.986] Epoch 1/100, Batch 1/4, Batch Loss: 2880.487549
[2025-04-30 19:28:31.994] Epoch 1/100, Batch 4/4, Batch Loss: 2482.478516
[2025-04-30 19:28:32.224] Epoch 1/100, Average Loss: 2530.943848, Mean Absolute Error: 48.142193
[2025-04-30 19:28:32.231] Epoch 2/100, Batch 1/4, Batch Loss: 2291.132812
[2025-04-30 19:28:32.239] Epoch 2/100, Batch 4/4, Batch Loss: 2111.885742
[2025-04-30 19:28:32.246] Epoch 3/100, Batch 1/4, Batch Loss: 1935.104248
[2025-04-30 19:28:32.258] Epoch 3/100, Batch 4/4, Batch Loss: 1844.610107
[2025-04-30 19:28:32.268] Epoch 4/100, Batch 1/4, Batch Loss: 1816.308105
[2025-04-30 19:28:32.283] Epoch 4/100, Batch 4/4, Batch Loss: 1623.656128
[2025-04-30 19:28:32.286] Epoch 5/100, Batch 1/4, Batch Loss: 1494.987915
[2025-04-30 19:28:32.291] Epoch 5/100, Batch 4/4, Batch Loss: 1445.147461
[2025-04-30 19:28:32.298] Epoch 6/100, Batch 1/4, Batch Loss: 1287.489868
[2025-04-30 19:28:32.304] Epoch 6/100, Batch 4/4, Batch Loss: 1237.625854
[2025-04-30 19:28:32.312] Epoch 7/100, Batch 1/4, Batch Loss: 1111.457520
[2025-04-30 19:28:32.318] Epoch 7/100, Batch 4/4, Batch Loss: 1076.697998
[2025-04-30 19:28:32.322] Epoch 8/100, Batch 1/4, Batch Loss: 1091.473633
[2025-04-30 19:28:32.335] Epoch 8/100, Batch 4/4, Batch Loss: 879.011658
[2025-04-30 19:28:32.345] Epoch 9/100, Batch 1/4, Batch Loss: 942.361938
[2025-04-30 19:28:32.353] Epoch 9/100, Batch 4/4, Batch Loss: 844.667358
[2025-04-30 19:28:32.358] Epoch 10/100, Batch 1/4, Batch Loss: 748.275208
[2025-04-30 19:28:32.366] Epoch 10/100, Batch 4/4, Batch Loss: 760.352295
[2025-04-30 19:28:32.370] Epoch 11/100, Batch 1/4, Batch Loss: 711.271973
[2025-04-30 19:28:32.376] Epoch 11/100, Batch 4/4, Batch Loss: 582.903442
[2025-04-30 19:28:32.384] Epoch 11/100, Average Loss: 660.808960, Mean Absolute Error: 24.677174
[2025-04-30 19:28:32.389] Epoch 12/100, Batch 1/4, Batch Loss: 591.769531
[2025-04-30 19:28:32.397] Epoch 12/100, Batch 4/4, Batch Loss: 513.319946
[2025-04-30 19:28:32.401] Epoch 13/100, Batch 1/4, Batch Loss: 547.148438
[2025-04-30 19:28:32.407] Epoch 13/100, Batch 4/4, Batch Loss: 498.282532
[2025-04-30 19:28:32.413] Epoch 14/100, Batch 1/4, Batch Loss: 474.290771
[2025-04-30 19:28:32.420] Epoch 14/100, Batch 4/4, Batch Loss: 441.902374
[2025-04-30 19:28:32.424] Epoch 15/100, Batch 1/4, Batch Loss: 439.858398
[2025-04-30 19:28:32.431] Epoch 15/100, Batch 4/4, Batch Loss: 383.592316
[2025-04-30 19:28:32.435] Epoch 16/100, Batch 1/4, Batch Loss: 396.305115
[2025-04-30 19:28:32.441] Epoch 16/100, Batch 4/4, Batch Loss: 349.112305
[2025-04-30 19:28:32.447] Epoch 17/100, Batch 1/4, Batch Loss: 327.024597
[2025-04-30 19:28:32.455] Epoch 17/100, Batch 4/4, Batch Loss: 331.908722
[2025-04-30 19:28:32.463] Epoch 18/100, Batch 1/4, Batch Loss: 327.830322
[2025-04-30 19:28:32.482] Epoch 18/100, Batch 4/4, Batch Loss: 294.761108
[2025-04-30 19:28:32.487] Epoch 19/100, Batch 1/4, Batch Loss: 284.160828
[2025-04-30 19:28:32.499] Epoch 19/100, Batch 4/4, Batch Loss: 264.945496
[2025-04-30 19:28:32.505] Epoch 20/100, Batch 1/4, Batch Loss: 252.118439
[2025-04-30 19:28:32.516] Epoch 20/100, Batch 4/4, Batch Loss: 244.918076
[2025-04-30 19:28:32.523] Epoch 21/100, Batch 1/4, Batch Loss: 231.963562
[2025-04-30 19:28:32.540] Epoch 21/100, Batch 4/4, Batch Loss: 215.645920
[2025-04-30 19:28:32.662] Epoch 21/100, Average Loss: 225.738113, Mean Absolute Error: 14.577223
[2025-04-30 19:28:32.666] Epoch 22/100, Batch 1/4, Batch Loss: 227.956696
[2025-04-30 19:28:32.672] Epoch 22/100, Batch 4/4, Batch Loss: 194.932495
[2025-04-30 19:28:32.681] Epoch 23/100, Batch 1/4, Batch Loss: 198.337051
[2025-04-30 19:28:32.695] Epoch 23/100, Batch 4/4, Batch Loss: 190.290344
[2025-04-30 19:28:32.699] Epoch 24/100, Batch 1/4, Batch Loss: 166.641144
[2025-04-30 19:28:32.705] Epoch 24/100, Batch 4/4, Batch Loss: 176.369171
[2025-04-30 19:28:32.708] Epoch 25/100, Batch 1/4, Batch Loss: 173.075256
[2025-04-30 19:28:32.714] Epoch 25/100, Batch 4/4, Batch Loss: 160.850311
[2025-04-30 19:28:32.718] Epoch 26/100, Batch 1/4, Batch Loss: 160.647186
[2025-04-30 19:28:32.722] Epoch 26/100, Batch 4/4, Batch Loss: 141.940826
[2025-04-30 19:28:32.726] Epoch 27/100, Batch 1/4, Batch Loss: 135.879593
[2025-04-30 19:28:32.731] Epoch 27/100, Batch 4/4, Batch Loss: 131.964706
[2025-04-30 19:28:32.735] Epoch 28/100, Batch 1/4, Batch Loss: 128.060577
[2025-04-30 19:28:32.740] Epoch 28/100, Batch 4/4, Batch Loss: 122.993118
[2025-04-30 19:28:32.746] Epoch 29/100, Batch 1/4, Batch Loss: 117.659454
[2025-04-30 19:28:32.754] Epoch 29/100, Batch 4/4, Batch Loss: 106.521164
[2025-04-30 19:28:32.758] Epoch 30/100, Batch 1/4, Batch Loss: 105.288055
[2025-04-30 19:28:32.766] Epoch 30/100, Batch 4/4, Batch Loss: 110.762123
[2025-04-30 19:28:32.771] Epoch 31/100, Batch 1/4, Batch Loss: 98.413254
[2025-04-30 19:28:32.786] Epoch 31/100, Batch 4/4, Batch Loss: 97.832260
[2025-04-30 19:28:32.791] Epoch 31/100, Average Loss: 98.441681, Mean Absolute Error: 9.678818
[2025-04-30 19:28:32.797] Epoch 32/100, Batch 1/4, Batch Loss: 98.933487
[2025-04-30 19:28:32.802] Epoch 32/100, Batch 4/4, Batch Loss: 88.139145
[2025-04-30 19:28:32.807] Epoch 33/100, Batch 1/4, Batch Loss: 90.291466
[2025-04-30 19:28:32.818] Epoch 33/100, Batch 4/4, Batch Loss: 86.138947
[2025-04-30 19:28:32.837] Epoch 34/100, Batch 1/4, Batch Loss: 75.238708
[2025-04-30 19:28:32.843] Epoch 34/100, Batch 4/4, Batch Loss: 76.219284
[2025-04-30 19:28:32.931] Epoch 35/100, Batch 1/4, Batch Loss: 70.746284
[2025-04-30 19:28:32.940] Epoch 35/100, Batch 4/4, Batch Loss: 71.197151
[2025-04-30 19:28:32.946] Epoch 36/100, Batch 1/4, Batch Loss: 73.027420
[2025-04-30 19:28:32.953] Epoch 36/100, Batch 4/4, Batch Loss: 64.752731
[2025-04-30 19:28:32.970] Epoch 37/100, Batch 1/4, Batch Loss: 68.064064
[2025-04-30 19:28:32.978] Epoch 37/100, Batch 4/4, Batch Loss: 59.662476
[2025-04-30 19:28:32.986] Epoch 38/100, Batch 1/4, Batch Loss: 66.773132
[2025-04-30 19:28:32.997] Epoch 38/100, Batch 4/4, Batch Loss: 61.513985
[2025-04-30 19:28:33.003] Epoch 39/100, Batch 1/4, Batch Loss: 57.150898
[2025-04-30 19:28:33.009] Epoch 39/100, Batch 4/4, Batch Loss: 57.579712
[2025-04-30 19:28:33.015] Epoch 40/100, Batch 1/4, Batch Loss: 52.272850
[2025-04-30 19:28:33.021] Epoch 40/100, Batch 4/4, Batch Loss: 52.974396
[2025-04-30 19:28:33.025] Epoch 41/100, Batch 1/4, Batch Loss: 56.121647
[2025-04-30 19:28:33.046] Epoch 41/100, Batch 4/4, Batch Loss: 44.774075
[2025-04-30 19:28:33.053] Epoch 41/100, Average Loss: 49.442345, Mean Absolute Error: 6.876595
[2025-04-30 19:28:33.058] Epoch 42/100, Batch 1/4, Batch Loss: 47.004993
[2025-04-30 19:28:33.081] Epoch 42/100, Batch 4/4, Batch Loss: 43.828167
[2025-04-30 19:28:33.118] Epoch 43/100, Batch 1/4, Batch Loss: 48.112019
[2025-04-30 19:28:33.130] Epoch 43/100, Batch 4/4, Batch Loss: 42.030621
[2025-04-30 19:28:33.137] Epoch 44/100, Batch 1/4, Batch Loss: 43.728825
[2025-04-30 19:28:33.145] Epoch 44/100, Batch 4/4, Batch Loss: 38.230003
[2025-04-30 19:28:33.150] Epoch 45/100, Batch 1/4, Batch Loss: 41.050262
[2025-04-30 19:28:33.156] Epoch 45/100, Batch 4/4, Batch Loss: 37.558952
[2025-04-30 19:28:33.163] Epoch 46/100, Batch 1/4, Batch Loss: 34.860558
[2025-04-30 19:28:33.168] Epoch 46/100, Batch 4/4, Batch Loss: 36.356598
[2025-04-30 19:28:33.172] Epoch 47/100, Batch 1/4, Batch Loss: 33.984795
[2025-04-30 19:28:33.179] Epoch 47/100, Batch 4/4, Batch Loss: 33.845196
[2025-04-30 19:28:33.183] Epoch 48/100, Batch 1/4, Batch Loss: 30.673731
[2025-04-30 19:28:33.201] Epoch 48/100, Batch 4/4, Batch Loss: 32.892536
[2025-04-30 19:28:33.205] Epoch 49/100, Batch 1/4, Batch Loss: 33.085098
[2025-04-30 19:28:33.209] Epoch 49/100, Batch 4/4, Batch Loss: 27.202690
[2025-04-30 19:28:33.214] Epoch 50/100, Batch 1/4, Batch Loss: 32.401226
[2025-04-30 19:28:33.219] Epoch 50/100, Batch 4/4, Batch Loss: 26.511103
[2025-04-30 19:28:33.222] Epoch 51/100, Batch 1/4, Batch Loss: 25.810394
[2025-04-30 19:28:33.229] Epoch 51/100, Batch 4/4, Batch Loss: 26.646694
[2025-04-30 19:28:33.232] Epoch 51/100, Average Loss: 27.138786, Mean Absolute Error: 5.109520
[2025-04-30 19:28:33.236] Epoch 52/100, Batch 1/4, Batch Loss: 27.631372
[2025-04-30 19:28:33.241] Epoch 52/100, Batch 4/4, Batch Loss: 25.157881
[2025-04-30 19:28:33.246] Epoch 53/100, Batch 1/4, Batch Loss: 24.258455
[2025-04-30 19:28:33.252] Epoch 53/100, Batch 4/4, Batch Loss: 25.038366
[2025-04-30 19:28:33.258] Epoch 54/100, Batch 1/4, Batch Loss: 22.056629
[2025-04-30 19:28:33.268] Epoch 54/100, Batch 4/4, Batch Loss: 24.823826
[2025-04-30 19:28:33.272] Epoch 55/100, Batch 1/4, Batch Loss: 23.397381
[2025-04-30 19:28:33.276] Epoch 55/100, Batch 4/4, Batch Loss: 20.506641
[2025-04-30 19:28:33.282] Epoch 56/100, Batch 1/4, Batch Loss: 23.042355
[2025-04-30 19:28:33.287] Epoch 56/100, Batch 4/4, Batch Loss: 18.733490
[2025-04-30 19:28:33.290] Epoch 57/100, Batch 1/4, Batch Loss: 19.506779
[2025-04-30 19:28:33.298] Epoch 57/100, Batch 4/4, Batch Loss: 20.628786
[2025-04-30 19:28:33.302] Epoch 58/100, Batch 1/4, Batch Loss: 19.867359
[2025-04-30 19:28:33.308] Epoch 58/100, Batch 4/4, Batch Loss: 20.188946
[2025-04-30 19:28:33.315] Epoch 59/100, Batch 1/4, Batch Loss: 18.586159
[2025-04-30 19:28:33.356] Epoch 59/100, Batch 4/4, Batch Loss: 18.279095
[2025-04-30 19:28:33.361] Epoch 60/100, Batch 1/4, Batch Loss: 18.126432
[2025-04-30 19:28:33.367] Epoch 60/100, Batch 4/4, Batch Loss: 14.813745
[2025-04-30 19:28:33.398] Epoch 61/100, Batch 1/4, Batch Loss: 17.074863
[2025-04-30 19:28:33.407] Epoch 61/100, Batch 4/4, Batch Loss: 15.257046
[2025-04-30 19:28:33.413] Epoch 61/100, Average Loss: 15.905288, Mean Absolute Error: 3.914403
[2025-04-30 19:28:33.421] Epoch 62/100, Batch 1/4, Batch Loss: 13.550612
[2025-04-30 19:28:33.426] Epoch 62/100, Batch 4/4, Batch Loss: 14.690270
[2025-04-30 19:28:33.432] Epoch 63/100, Batch 1/4, Batch Loss: 13.491112
[2025-04-30 19:28:33.438] Epoch 63/100, Batch 4/4, Batch Loss: 12.829569
[2025-04-30 19:28:33.445] Epoch 64/100, Batch 1/4, Batch Loss: 11.498376
[2025-04-30 19:28:33.452] Epoch 64/100, Batch 4/4, Batch Loss: 13.190695
[2025-04-30 19:28:33.471] Epoch 65/100, Batch 1/4, Batch Loss: 12.683626
[2025-04-30 19:28:33.480] Epoch 65/100, Batch 4/4, Batch Loss: 13.925201
[2025-04-30 19:28:33.485] Epoch 66/100, Batch 1/4, Batch Loss: 12.967624
[2025-04-30 19:28:33.490] Epoch 66/100, Batch 4/4, Batch Loss: 12.734449
[2025-04-30 19:28:33.495] Epoch 67/100, Batch 1/4, Batch Loss: 12.172451
[2025-04-30 19:28:33.502] Epoch 67/100, Batch 4/4, Batch Loss: 11.100880
[2025-04-30 19:28:33.505] Epoch 68/100, Batch 1/4, Batch Loss: 11.280186
[2025-04-30 19:28:33.510] Epoch 68/100, Batch 4/4, Batch Loss: 11.161272
[2025-04-30 19:28:33.516] Epoch 69/100, Batch 1/4, Batch Loss: 10.110331
[2025-04-30 19:28:33.520] Epoch 69/100, Batch 4/4, Batch Loss: 12.222969
[2025-04-30 19:28:33.524] Epoch 70/100, Batch 1/4, Batch Loss: 9.871469
[2025-04-30 19:28:33.531] Epoch 70/100, Batch 4/4, Batch Loss: 10.248388
[2025-04-30 19:28:33.535] Epoch 71/100, Batch 1/4, Batch Loss: 9.924362
[2025-04-30 19:28:33.547] Epoch 71/100, Batch 4/4, Batch Loss: 9.141717
[2025-04-30 19:28:33.555] Epoch 71/100, Average Loss: 9.761502, Mean Absolute Error: 3.068060
[2025-04-30 19:28:33.559] Epoch 72/100, Batch 1/4, Batch Loss: 9.306902
[2025-04-30 19:28:33.566] Epoch 72/100, Batch 4/4, Batch Loss: 9.441669
[2025-04-30 19:28:33.570] Epoch 73/100, Batch 1/4, Batch Loss: 8.879135
[2025-04-30 19:28:33.579] Epoch 73/100, Batch 4/4, Batch Loss: 8.646286
[2025-04-30 19:28:33.583] Epoch 74/100, Batch 1/4, Batch Loss: 9.024102
[2025-04-30 19:28:33.626] Epoch 74/100, Batch 4/4, Batch Loss: 7.333499
[2025-04-30 19:28:33.771] Epoch 75/100, Batch 1/4, Batch Loss: 7.987123
[2025-04-30 19:28:33.781] Epoch 75/100, Batch 4/4, Batch Loss: 7.875538
[2025-04-30 19:28:33.786] Epoch 76/100, Batch 1/4, Batch Loss: 8.494083
[2025-04-30 19:28:33.804] Epoch 76/100, Batch 4/4, Batch Loss: 7.125979
[2025-04-30 19:28:33.808] Epoch 77/100, Batch 1/4, Batch Loss: 8.821074
[2025-04-30 19:28:33.815] Epoch 77/100, Batch 4/4, Batch Loss: 7.452907
[2025-04-30 19:28:33.819] Epoch 78/100, Batch 1/4, Batch Loss: 6.453465
[2025-04-30 19:28:33.825] Epoch 78/100, Batch 4/4, Batch Loss: 6.951849
[2025-04-30 19:28:33.831] Epoch 79/100, Batch 1/4, Batch Loss: 6.418497
[2025-04-30 19:28:33.837] Epoch 79/100, Batch 4/4, Batch Loss: 5.804357
[2025-04-30 19:28:33.842] Epoch 80/100, Batch 1/4, Batch Loss: 6.374564
[2025-04-30 19:28:33.850] Epoch 80/100, Batch 4/4, Batch Loss: 6.673484
[2025-04-30 19:28:33.868] Epoch 81/100, Batch 1/4, Batch Loss: 6.490993
[2025-04-30 19:28:33.875] Epoch 81/100, Batch 4/4, Batch Loss: 6.845324
[2025-04-30 19:28:33.889] Epoch 81/100, Average Loss: 6.176466, Mean Absolute Error: 2.440105
[2025-04-30 19:28:33.907] Epoch 82/100, Batch 1/4, Batch Loss: 5.783945
[2025-04-30 19:28:33.914] Epoch 82/100, Batch 4/4, Batch Loss: 6.099806
[2025-04-30 19:28:33.925] Epoch 83/100, Batch 1/4, Batch Loss: 5.844983
[2025-04-30 19:28:33.932] Epoch 83/100, Batch 4/4, Batch Loss: 4.800236
[2025-04-30 19:28:33.938] Epoch 84/100, Batch 1/4, Batch Loss: 5.438835
[2025-04-30 19:28:33.947] Epoch 84/100, Batch 4/4, Batch Loss: 4.404550
[2025-04-30 19:28:33.955] Epoch 85/100, Batch 1/4, Batch Loss: 4.583347
[2025-04-30 19:28:33.964] Epoch 85/100, Batch 4/4, Batch Loss: 5.128870
[2025-04-30 19:28:33.990] Epoch 86/100, Batch 1/4, Batch Loss: 5.558029
[2025-04-30 19:28:34.000] Epoch 86/100, Batch 4/4, Batch Loss: 5.482007
[2025-04-30 19:28:34.013] Epoch 87/100, Batch 1/4, Batch Loss: 4.600142
[2025-04-30 19:28:34.020] Epoch 87/100, Batch 4/4, Batch Loss: 4.738224
[2025-04-30 19:28:34.024] Epoch 88/100, Batch 1/4, Batch Loss: 5.007065
[2025-04-30 19:28:34.031] Epoch 88/100, Batch 4/4, Batch Loss: 4.771507
[2025-04-30 19:28:34.035] Epoch 89/100, Batch 1/4, Batch Loss: 5.203652
[2025-04-30 19:28:34.040] Epoch 89/100, Batch 4/4, Batch Loss: 4.652297
[2025-04-30 19:28:34.047] Epoch 90/100, Batch 1/4, Batch Loss: 4.266953
[2025-04-30 19:28:34.052] Epoch 90/100, Batch 4/4, Batch Loss: 3.837036
[2025-04-30 19:28:34.056] Epoch 91/100, Batch 1/4, Batch Loss: 4.300825
[2025-04-30 19:28:34.063] Epoch 91/100, Batch 4/4, Batch Loss: 4.008062
[2025-04-30 19:28:34.075] Epoch 91/100, Average Loss: 4.005413, Mean Absolute Error: 1.962025
[2025-04-30 19:28:34.081] Epoch 92/100, Batch 1/4, Batch Loss: 4.447447
[2025-04-30 19:28:34.086] Epoch 92/100, Batch 4/4, Batch Loss: 3.484192
[2025-04-30 19:28:34.092] Epoch 93/100, Batch 1/4, Batch Loss: 3.518643
[2025-04-30 19:28:34.108] Epoch 93/100, Batch 4/4, Batch Loss: 3.433716
[2025-04-30 19:28:34.116] Epoch 94/100, Batch 1/4, Batch Loss: 3.609279
[2025-04-30 19:28:34.124] Epoch 94/100, Batch 4/4, Batch Loss: 3.517057
[2025-04-30 19:28:34.150] Epoch 95/100, Batch 1/4, Batch Loss: 3.835632
[2025-04-30 19:28:34.160] Epoch 95/100, Batch 4/4, Batch Loss: 3.146121
[2025-04-30 19:28:34.166] Epoch 96/100, Batch 1/4, Batch Loss: 3.686440
[2025-04-30 19:28:34.171] Epoch 96/100, Batch 4/4, Batch Loss: 3.122259
[2025-04-30 19:28:34.176] Epoch 97/100, Batch 1/4, Batch Loss: 2.850266
[2025-04-30 19:28:34.192] Epoch 97/100, Batch 4/4, Batch Loss: 3.228275
[2025-04-30 19:28:34.197] Epoch 98/100, Batch 1/4, Batch Loss: 3.277637
[2025-04-30 19:28:34.203] Epoch 98/100, Batch 4/4, Batch Loss: 2.885861
[2025-04-30 19:28:34.207] Epoch 99/100, Batch 1/4, Batch Loss: 3.273483
[2025-04-30 19:28:34.214] Epoch 99/100, Batch 4/4, Batch Loss: 2.864765
[2025-04-30 19:28:34.218] Epoch 100/100, Batch 1/4, Batch Loss: 3.043433
[2025-04-30 19:28:34.223] Epoch 100/100, Batch 4/4, Batch Loss: 2.859061
[2025-04-30 19:28:34.227] Epoch 100/100, Average Loss: 2.764549, Mean Absolute Error: 1.627609
[2025-04-30 19:28:34.232] Model A training completed
[2025-04-30 19:28:34.284] Model A Final Predictions Shape: 16,1
[2025-04-30 19:28:34.289] Model A Final Predictions (First few): [2.750352, 2.7034044, 2.9580672, 2.5592737, 2.736664, 2.6734867, 2.2127323, 3.0338564, 2.6606913, 2.6618104...]
[2025-04-30 19:28:34.339] Model A Final Mean Absolute Error: 1.627609
[2025-04-30 19:28:34.369] Step 7 - Model A trained and saved to RuntimeProcessingContext and Results Store.
[2025-04-30 19:28:34.377] Workflow Session 1: Step 8 - Generating future performance projection for customer 1.
[2025-04-30 19:28:34.426] Workflow Session 1: Step 8 - Future performance projection completed: PerformanceProjection_Cust_1_Outcome_PotentialChallenges_ComplexModel_Score_0.1062_TrainError_1.6276
[2025-04-30 19:28:34.432] Workflow Session 1: Workflow completed for customer 1 with final score 0.1062
[2025-04-30 19:28:34.439] Workflow Session 1: Workflow completed with result: Workflow_Complete_Cust_1_FinalScore_0.1062
[2025-04-30 19:28:34.467] Workflow Session 1: Starting Parallel Processing Unit B for customer 1.
[2025-04-30 19:28:34.472] Workflow Session 1: Starting multi-stage workflow (Unit B) for customer 1.
[2025-04-30 19:28:34.487] Workflow Session 1: Step 1 (Unit B) - Acquiring data and analyzing initial features for customer 1.
[2025-04-30 19:28:34.490] Step 1 (Unit B) - Processing Product Data (3 items).
[2025-04-30 19:28:34.494] Unit B Product QuantityAvailable: [10, 20, 15]
[2025-04-30 19:28:34.499] Unit B Product MonetaryValue: [99.99, 149.99, 199.99]
[2025-04-30 19:28:34.502] Unit B Product CostContributionValue: [0.15, 0.25, 0.2]
[2025-04-30 19:28:34.536] K-means centroids for Product QuantityAvailable (Unit B): [20.0000, 15.0000, 10.0000]
[2025-04-30 19:28:34.539] Central point for Product QuantityAvailable (Unit B): 15
[2025-04-30 19:28:34.542] Normalized value for Product QuantityAvailable (Unit B): 0.7500, Category: Positive High
[2025-04-30 19:28:34.547] Normalized XYZ coordinates for Product QuantityAvailable (Unit B): (1.0000, 0.7500, 0.5000)
[2025-04-30 19:28:34.551] K-means centroids for Product MonetaryValue (Unit B): [199.9900, 149.9900, 99.9900]
[2025-04-30 19:28:34.553] Central point for Product MonetaryValue (Unit B): 149.99
[2025-04-30 19:28:34.556] Normalized value for Product MonetaryValue (Unit B): 0.7500, Category: Positive High
[2025-04-30 19:28:34.560] Normalized XYZ coordinates for Product MonetaryValue (Unit B): (1.0000, 0.7500, 0.5000)
[2025-04-30 19:28:34.566] K-means centroids for Product CostContributionValue (Unit B): [0.2500, 0.2000, 0.1500]
[2025-04-30 19:28:34.568] Central point for Product CostContributionValue (Unit B): 0.19999999999999998
[2025-04-30 19:28:34.571] Normalized value for Product CostContributionValue (Unit B): 0.8000, Category: Positive High
[2025-04-30 19:28:34.574] Normalized XYZ coordinates for Product CostContributionValue (Unit B): (1.0000, 0.8000, 0.6000)
[2025-04-30 19:28:34.581] Step 1 (Unit B) - Processing Service Data (3 items).
[2025-04-30 19:28:34.585] Unit B Service FulfillmentQuantity: [5, 10, 8]
[2025-04-30 19:28:34.589] Unit B Service MonetaryValue: [299.99, 399.99, 599.99]
[2025-04-30 19:28:34.593] Unit B Service CostContributionValue: [0.3, 0.35, 0.4]
[2025-04-30 19:28:34.601] Workflow Session 1: Parallel Processing Unit A finished.
[2025-04-30 19:28:34.609] K-means centroids for Service FulfillmentQuantity (Unit B): [10.0000, 8.0000, 5.0000]
[2025-04-30 19:28:34.614] Central point for Service FulfillmentQuantity (Unit B): 7.666666666666667
[2025-04-30 19:28:34.617] Normalized value for Service FulfillmentQuantity (Unit B): 0.7667, Category: Positive High
[2025-04-30 19:28:34.620] Normalized XYZ coordinates for Service FulfillmentQuantity (Unit B): (1.0000, 0.8000, 0.5000)
[2025-04-30 19:28:34.623] K-means centroids for Service MonetaryValue (Unit B): [599.9900, 399.9900, 299.9900]
[2025-04-30 19:28:34.628] Central point for Service MonetaryValue (Unit B): 433.3233333333333
[2025-04-30 19:28:34.633] Normalized value for Service MonetaryValue (Unit B): 0.7222, Category: Positive High
[2025-04-30 19:28:34.636] Normalized XYZ coordinates for Service MonetaryValue (Unit B): (1.0000, 0.6667, 0.5000)
[2025-04-30 19:28:34.642] K-means centroids for Service CostContributionValue (Unit B): [0.4000, 0.3500, 0.3000]
[2025-04-30 19:28:34.665] Central point for Service CostContributionValue (Unit B): 0.35000000000000003
[2025-04-30 19:28:34.668] Normalized value for Service CostContributionValue (Unit B): 0.8750, Category: Positive High
[2025-04-30 19:28:34.673] Normalized XYZ coordinates for Service CostContributionValue (Unit B): (1.0000, 0.8750, 0.7500)
[2025-04-30 19:28:34.681] Workflow Session 1: Step 1 (Unit B) - Data acquisition and initial analysis completed: InitialAnalysis_B_Cust_1_Record_3
[2025-04-30 19:28:34.702] Workflow Session 1: Step 2 (Unit B) - Generating feature tensors and mapping trajectories for customer 1.
[2025-04-30 19:28:34.706] Step 2 (Unit B) - Retrieving coordinates from Step 1 analysis.
[2025-04-30 19:28:34.710] Step 2 (Unit B) - Calculating tensors, magnitudes, and trajectories.
[2025-04-30 19:28:34.717] ----- PRODUCT TENSOR AND MAGNITUDE CALCULATIONS (Unit B) -----
[2025-04-30 19:28:34.723] Product Overall Tensor (Unit B): (1.0000, 0.7667, 0.5333)
[2025-04-30 19:28:34.746] Product Overall Magnitude (Unit B): 1.3683
[2025-04-30 19:28:34.758] Product Trajectory (Unit B): (0.7308, 0.5603, 0.3898)
[2025-04-30 19:28:34.768] ----- SERVICE TENSOR AND MAGNITUDE CALCULATIONS (Unit B) -----
[2025-04-30 19:28:34.772] Service Overall Tensor (Unit B): (1.0000, 0.7806, 0.5833)
[2025-04-30 19:28:34.781] Service Overall Magnitude (Unit B): 1.3963
[2025-04-30 19:28:34.786] Service Trajectory (Unit B): (0.7162, 0.5590, 0.4178)
[2025-04-30 19:28:34.790] ----- TRAJECTORY PLOT GENERATION & ANALYSIS (Unit B) -----
[2025-04-30 19:28:34.794] Unit B Inverted trajectory from (0.7308, 0.5603, 0.3898) to (-0.7308, -0.5603, 0.3898)
[2025-04-30 19:28:34.800] Unit B Inverted trajectory from (0.7162, 0.5590, 0.4178) to (-0.7162, -0.5590, 0.4178)
[2025-04-30 19:28:34.806] Generating Product trajectory recursive plot (Unit B)
[2025-04-30 19:28:34.815] Unit B PRODUCT_B point 0: Position=(1.000000, 0.766662, 0.5333), Intensity=1.3683
[2025-04-30 19:28:34.827] Unit B PRODUCT_B point 2: Position=(-0.425000, -0.325832, 1.2933), Intensity=1.1083 BEYOND-X-PLANE BEYOND-Y-PLANE
[2025-04-30 19:28:34.837] Unit B PRODUCT_B point 3: Position=(-1.032500, -0.791579, 1.6173), Intensity=0.9975 BEYOND-X-PLANE BEYOND-Y-PLANE
[2025-04-30 19:28:34.841] Unit B PRODUCT_B point 4: Position=(-1.579250, -1.210752, 1.9089), Intensity=0.8977 BEYOND-X-PLANE BEYOND-Y-PLANE
[2025-04-30 19:28:34.843] Unit B PRODUCT_B recursion stopped - Reached target negative threshold at depth 5
[2025-04-30 19:28:34.852] Unit B PRODUCT_B final position: (-2.071325, -1.588007, 2.1713)
[2025-04-30 19:28:34.857] Generating Service trajectory recursive plot (Unit B)
[2025-04-30 19:28:34.868] Unit B SERVICE_B point 0: Position=(1.000000, 0.780554, 0.5833), Intensity=1.3963
[2025-04-30 19:28:34.877] Unit B SERVICE_B point 2: Position=(-0.425000, -0.331735, 1.4146), Intensity=1.1310 BEYOND-X-PLANE BEYOND-Y-PLANE
[2025-04-30 19:28:34.882] Unit B SERVICE_B point 3: Position=(-1.032500, -0.805922, 1.7689), Intensity=1.0179 BEYOND-X-PLANE BEYOND-Y-PLANE
[2025-04-30 19:28:34.884] Unit B SERVICE_B point 4: Position=(-1.579250, -1.232689, 2.0879), Intensity=0.9161 BEYOND-X-PLANE BEYOND-Y-PLANE
[2025-04-30 19:28:34.887] Unit B SERVICE_B recursion stopped - Reached target negative threshold at depth 5
[2025-04-30 19:28:34.890] Unit B SERVICE_B final position: (-2.071325, -1.616780, 2.3749)
[2025-04-30 19:28:34.935] ----- PLANE INTERSECTION ANALYSIS (Unit B) -----
[2025-04-30 19:28:34.938] Product X-Plane Intersection (Unit B): (0.000000, 0.000000, 1.066650)
[2025-04-30 19:28:34.947] Product Y-Plane Intersection (Unit B): (-0.000000, 0.000000, 1.066650)
[2025-04-30 19:28:34.950] Service X-Plane Intersection (Unit B): (0.000000, -0.000000, 1.166661)
[2025-04-30 19:28:34.955] Service Y-Plane Intersection (Unit B): (0.000000, 0.000000, 1.166661)
[2025-04-30 19:28:34.963] ----- KEY TRAJECTORY DATA (Unit B) -----
[2025-04-30 19:28:34.968] Product Vector (Unit B): (-0.730841, -0.560309, 0.389776)
[2025-04-30 19:28:34.975] Product Velocity (Unit B): 1.368286
[2025-04-30 19:28:34.983] Product Positive Coordinate (Unit B): (1.000000, 0.766662, 0.533325)
[2025-04-30 19:28:35.058] Product Negative Coordinate (Unit B): (-0.425000, -0.325832, 1.293313)
[2025-04-30 19:28:35.065] Service Vector (Unit B): (-0.716200, -0.559032, 0.417781)
[2025-04-30 19:28:35.069] Service Velocity (Unit B): 1.396259
[2025-04-30 19:28:35.072] Service Positive Coordinate (Unit B): (1.000000, 0.780554, 0.583331)
[2025-04-30 19:28:35.075] Service Negative Coordinate (Unit B): (-0.425000, -0.331735, 1.414577)
[2025-04-30 19:28:35.081] Product negative X count (Unit B): 4
[2025-04-30 19:28:35.085] Product negative Y count (Unit B): 4
[2025-04-30 19:28:35.092] Product negative both count (Unit B): 4
[2025-04-30 19:28:35.098] Service negative X count (Unit B): 4
[2025-04-30 19:28:35.107] Service negative Y count (Unit B): 4
[2025-04-30 19:28:35.111] Service negative both count (Unit B): 4
[2025-04-30 19:28:35.141] Product trajectory plot (Unit B): 6 points, 4 in negative X-Y quadrant
[2025-04-30 19:28:35.165] Service trajectory plot (Unit B): 6 points, 4 in negative X-Y quadrant
[2025-04-30 19:28:35.266] Workflow Session 1: Step 2 (Unit B) - Feature tensor generation and mapping completed: FeatureTensorsAndMapping_B_Cust_1_BasedOn_Cust_1_Record_3
[2025-04-30 19:28:35.277] Workflow Session 1: Step 3 (Unit B) - Creating processed feature definition for customer 1.
[2025-04-30 19:28:35.291] Workflow Session 1: Step 3 (Unit B) - Processed feature definition created: ProcessedFeatures_B_Cust_1_Level_PremiumB_ConvergentB
[2025-04-30 19:28:35.303] Workflow Session 1: Step 4 (Unit B) - Assessing feature quality for customer 1.
[2025-04-30 19:28:35.311] QA product trajectory stability (Unit B): 1.0000
[2025-04-30 19:28:35.318] QA intersection quality (Unit B): 1.0000
[2025-04-30 19:28:35.321] QA final score (Unit B): 1.0000, level: 5
[2025-04-30 19:28:35.324] Workflow Session 1: Step 4 (Unit B) - Feature quality assessment completed: QualityAssessment_B_Passed_Level_5_V1.38_S1.00_I1.00
[2025-04-30 19:28:35.331] Workflow Session 1: Step 5 (Unit B) - Evaluating combined features for customer 1.
[2025-04-30 19:28:35.335] Workflow Session 1: Step 5 (Unit B) - Combined feature evaluation calculation.
[2025-04-30 19:28:35.338] Base Score: 0.7167
[2025-04-30 19:28:35.350] Velocity Bonus: 0.7899 (Product B: 1.3683, Service B: 1.3963)
[2025-04-30 19:28:35.360] Alignment Bonus: 0.2499 (Alignment Score B: 0.9997)
[2025-04-30 19:28:35.369] Negative Trajectory Bonus (Unit B): 0.2000 (Total Negative Points B: 8)
[2025-04-30 19:28:35.373] Final Score (Unit B): 1.0000
[2025-04-30 19:28:35.387] Workflow Session 1: Step 6 (Unit B) - Performing fractal optimization analysis for customer 1.
========== PRODUCT INTERSECTIONS (Unit B) ==========
Product X-Plane Intersection (Unit B): (0.0, 0.000000, 1.066650)
Product Y-Plane Intersection (Unit B): (-0.000000, 0.0, 1.066650)
========== SERVICE INTERSECTIONS (Unit B) ==========
Service X-Plane Intersection (Unit B): (0.0, -0.000000, 1.166661)
Service Y-Plane Intersection (Unit B): (0.000000, 0.0, 1.166661)
========== INTERSECTION VELOCITIES (Unit B) ==========
Product X-Plane Velocity (Unit B): 1.3683
Product Y-Plane Velocity (Unit B): 1.3683
Service X-Plane Velocity (Unit B): 1.3963
Service Y-Plane Velocity (Unit B): 1.3963
========== VELOCITY SOURCES (Unit B) ==========
ProductX_B Source Position (Unit B): (0.0000, 0.0000, 1.0667), Velocity: 1.5051
ProductY_B Source Position (Unit B): (-0.0000, 0.0000, 1.0667), Velocity: 1.5051
ServiceX_B Source Position (Unit B): (0.0000, -0.0000, 1.1667), Velocity: 1.5359
ServiceY_B Source Position (Unit B): (0.0000, 0.0000, 1.1667), Velocity: 1.5359
========== SAMPLE POINTS (Unit B) ==========
Sample 1 Coordinates (Unit B): (-0.0500, 0.0000, 1.0667)
Sample 2 Coordinates (Unit B): (-0.0000, -0.0500, 1.0667)
Sample 3 Coordinates (Unit B): (-0.0500, -0.0000, 1.1667)
Sample 4 Coordinates (Unit B): (0.0000, -0.0500, 1.1667)
Sample 5 Coordinates (Unit B): (-0.0000, 0.0000, 1.1167)
Sample 6 Coordinates (Unit B): (0.0000, 0.0000, 0.0000)
========== PROCESSING SAMPLE 1 (Unit B) ==========
Starting point (Unit B): (-0.0500, 0.0000, 1.0667)
Unit B Iteration 1, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.220667 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.220667 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.188081 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.188081 (distance: 1.1667)
Unit B Iteration 2, z=(-0.050000, 0.000000, 1.066650), r=1.067821
  Unit B Contribution from ProductX_B: 1.269812 (distance: 0.0500)
  Unit B Contribution from ProductY_B: 1.269812 (distance: 0.0500)
  Unit B Contribution from ServiceX_B: 1.159330 (distance: 0.1118)
  Unit B Contribution from ServiceY_B: 1.159330 (distance: 0.1118)
Unit B Escaped at iteration 3
Final Sample 1 Results (Unit B):
  Final z value (Unit B): (0.361185, 0.000000, 2.490974)
  Iterations (Unit B): 2
  Total diffused velocity (Unit B): 5.675781
  Contributions breakdown (Unit B):
    ProductX_B: 1.490479
    ProductY_B: 1.490479
    ServiceX_B: 1.347411
    ServiceY_B: 1.347411
========== PROCESSING SAMPLE 2 (Unit B) ==========
Starting point (Unit B): (-0.0000, -0.0500, 1.0667)
Unit B Iteration 1, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.220667 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.220667 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.188081 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.188081 (distance: 1.1667)
Unit B Iteration 2, z=(-0.000000, -0.050000, 1.066650), r=1.067821
  Unit B Contribution from ProductX_B: 1.269812 (distance: 0.0500)
  Unit B Contribution from ProductY_B: 1.269812 (distance: 0.0500)
  Unit B Contribution from ServiceX_B: 1.159330 (distance: 0.1118)
  Unit B Contribution from ServiceY_B: 1.159330 (distance: 0.1118)
Unit B Escaped at iteration 3
Final Sample 2 Results (Unit B):
  Final z value (Unit B): (-0.411185, -0.050000, 2.490974)
  Iterations (Unit B): 2
  Total diffused velocity (Unit B): 5.675781
  Contributions breakdown (Unit B):
    ProductX_B: 1.490479
    ProductY_B: 1.490479
    ServiceX_B: 1.347411
    ServiceY_B: 1.347411
========== PROCESSING SAMPLE 3 (Unit B) ==========
Starting point (Unit B): (-0.0500, -0.0000, 1.1667)
Unit B Iteration 1, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.220667 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.220667 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.188081 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.188081 (distance: 1.1667)
Unit B Iteration 2, z=(-0.050000, -0.000000, 1.166661), r=1.167732
  Unit B Contribution from ProductX_B: 1.136104 (distance: 0.1118)
  Unit B Contribution from ProductY_B: 1.136104 (distance: 0.1118)
  Unit B Contribution from ServiceX_B: 1.295772 (distance: 0.0500)
  Unit B Contribution from ServiceY_B: 1.295772 (distance: 0.0500)
Unit B Escaped at iteration 3
Final Sample 3 Results (Unit B):
  Final z value (Unit B): (0.594444, -0.000000, 3.618870)
  Iterations (Unit B): 2
  Total diffused velocity (Unit B): 5.681248
  Contributions breakdown (Unit B):
    ProductX_B: 1.356771
    ProductY_B: 1.356771
    ServiceX_B: 1.483853
    ServiceY_B: 1.483853
========== PROCESSING SAMPLE 4 (Unit B) ==========
Starting point (Unit B): (0.0000, -0.0500, 1.1667)
Unit B Iteration 1, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.220667 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.220667 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.188081 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.188081 (distance: 1.1667)
Unit B Iteration 2, z=(0.000000, -0.050000, 1.166661), r=1.167732
  Unit B Contribution from ProductX_B: 1.136104 (distance: 0.1118)
  Unit B Contribution from ProductY_B: 1.136104 (distance: 0.1118)
  Unit B Contribution from ServiceX_B: 1.295772 (distance: 0.0500)
  Unit B Contribution from ServiceY_B: 1.295772 (distance: 0.0500)
Unit B Escaped at iteration 3
Final Sample 4 Results (Unit B):
  Final z value (Unit B): (-0.644444, -0.050000, 3.618870)
  Iterations (Unit B): 2
  Total diffused velocity (Unit B): 5.681248
  Contributions breakdown (Unit B):
    ProductX_B: 1.356771
    ProductY_B: 1.356771
    ServiceX_B: 1.483853
    ServiceY_B: 1.483853
========== PROCESSING SAMPLE 5 (Unit B) ==========
Starting point (Unit B): (-0.0000, 0.0000, 1.1167)
Unit B Iteration 1, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.220667 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.220667 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.188081 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.188081 (distance: 1.1667)
Unit B Iteration 2, z=(-0.000000, 0.000000, 1.116656), r=1.116656
  Unit B Contribution from ProductX_B: 1.269800 (distance: 0.0500)
  Unit B Contribution from ProductY_B: 1.269800 (distance: 0.0500)
  Unit B Contribution from ServiceX_B: 1.295759 (distance: 0.0500)
  Unit B Contribution from ServiceY_B: 1.295759 (distance: 0.0500)
Unit B Escaped at iteration 3
Final Sample 5 Results (Unit B):
  Final z value (Unit B): (-0.000000, 0.000000, 3.055377)
  Iterations (Unit B): 2
  Total diffused velocity (Unit B): 5.948613
  Contributions breakdown (Unit B):
    ProductX_B: 1.490467
    ProductY_B: 1.490467
    ServiceX_B: 1.483840
    ServiceY_B: 1.483840
========== PROCESSING SAMPLE 6 (Unit B) ==========
Starting point (Unit B): (0.0000, 0.0000, 0.0000)
Unit B Iteration 1, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.220667 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.220667 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.188081 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.188081 (distance: 1.1667)
Unit B Iteration 2, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.203701 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.203701 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.173621 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.173621 (distance: 1.1667)
Unit B Iteration 3, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.188040 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.188040 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.160272 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.160272 (distance: 1.1667)
Unit B Iteration 4, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.173583 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.173583 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.147950 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.147950 (distance: 1.1667)
Unit B Iteration 5, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.160237 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.160237 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.136575 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.136575 (distance: 1.1667)
Unit B Iteration 6, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.147917 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.147917 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.126074 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.126074 (distance: 1.1667)
Unit B Iteration 7, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.136545 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.136545 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.116381 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.116381 (distance: 1.1667)
Unit B Iteration 8, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.126047 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.126047 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.107433 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.107433 (distance: 1.1667)
Unit B Iteration 9, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.116356 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.116356 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.099174 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.099174 (distance: 1.1667)
Unit B Iteration 10, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.107410 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.107410 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.091549 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.091549 (distance: 1.1667)
Unit B Iteration 11, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.099152 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.099152 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.084510 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.084510 (distance: 1.1667)
Unit B Iteration 12, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.091529 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.091529 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.078013 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.078013 (distance: 1.1667)
Unit B Iteration 13, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.084492 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.084492 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.072015 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.072015 (distance: 1.1667)
Unit B Iteration 14, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.077996 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.077996 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.066478 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.066478 (distance: 1.1667)
Unit B Iteration 15, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.071999 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.071999 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.061367 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.061367 (distance: 1.1667)
Unit B Iteration 16, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.066464 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.066464 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.056649 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.056649 (distance: 1.1667)
Unit B Iteration 17, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.061354 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.061354 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.052293 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.052293 (distance: 1.1667)
Unit B Iteration 18, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.056637 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.056637 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.048273 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.048273 (distance: 1.1667)
Unit B Iteration 19, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.052282 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.052282 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.044562 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.044562 (distance: 1.1667)
Unit B Iteration 20, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.048262 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.048262 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.041136 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.041136 (distance: 1.1667)
Unit B Iteration 21, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.044552 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.044552 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.037973 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.037973 (distance: 1.1667)
Unit B Iteration 22, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.041127 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.041127 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.035053 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.035053 (distance: 1.1667)
Unit B Iteration 23, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.037965 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.037965 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.032358 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.032358 (distance: 1.1667)
Unit B Iteration 24, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.035046 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.035046 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.029871 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.029871 (distance: 1.1667)
Unit B Iteration 25, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.032351 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.032351 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.027574 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.027574 (distance: 1.1667)
Unit B Iteration 26, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.029864 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.029864 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.025454 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.025454 (distance: 1.1667)
Unit B Iteration 27, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.027568 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.027568 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.023497 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.023497 (distance: 1.1667)
Unit B Iteration 28, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.025448 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.025448 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.021690 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.021690 (distance: 1.1667)
Unit B Iteration 29, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.023492 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.023492 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.020023 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.020023 (distance: 1.1667)
Unit B Iteration 30, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.021686 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.021686 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.018483 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.018483 (distance: 1.1667)
Unit B Iteration 31, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.020018 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.020018 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.017062 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.017062 (distance: 1.1667)
Unit B Iteration 32, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.018479 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.018479 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.015750 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.015750 (distance: 1.1667)
Unit B Iteration 33, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.017059 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.017059 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.014540 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.014540 (distance: 1.1667)
Unit B Iteration 34, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.015747 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.015747 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.013422 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.013422 (distance: 1.1667)
Unit B Iteration 35, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.014536 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.014536 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.012390 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.012390 (distance: 1.1667)
Unit B Iteration 36, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.013419 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.013419 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.011437 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.011437 (distance: 1.1667)
Unit B Iteration 37, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.012387 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.012387 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.010558 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.010558 (distance: 1.1667)
Unit B Iteration 38, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.011435 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.011435 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.009746 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.009746 (distance: 1.1667)
Unit B Iteration 39, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.010556 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.010556 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.008997 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.008997 (distance: 1.1667)
Unit B Iteration 40, z=(0.000000, 0.000000, 0.000000), r=0.000000
  Unit B Contribution from ProductX_B: 0.009744 (distance: 1.0667)
  Unit B Contribution from ProductY_B: 0.009744 (distance: 1.0667)
  Unit B Contribution from ServiceX_B: 0.008305 (distance: 1.1667)
  Unit B Contribution from ServiceY_B: 0.008305 (distance: 1.1667)
Final Sample 6 Results (Unit B):
  Final z value (Unit B): (0.000000, 0.000000, 0.000000)
  Iterations (Unit B): 40
  Total diffused velocity (Unit B): 10.199472
  Contributions breakdown (Unit B):
    ProductX_B: 2.753148
    ProductY_B: 2.753148
    ServiceX_B: 2.346588
    ServiceY_B: 2.346588
[2025-04-30 19:28:37.602] Workflow Session 1: Step 6 (Unit B) - Fractal optimization analysis completed: OptimizationAnalysis_B_Cust_1_V[PX_B:1.368,PY_B:1.368,SX_B:1.396,SY_B:1.396]_S[P1_B:5.6758_SEscaped(2),P2_B:5.6758_SEscaped(2),P3_B:5.6812_SEscaped(2),P4_B:5.6812_SEscaped(2),P5_B:5.9486_SEscaped(2),P6_B:10.1995_SInSet]
[2025-04-30 19:28:37.688] Workflow Session 1: Step 7 (Unit B) - Training tensor network for customer 1 using Actual TF.NET Model B.
[2025-04-30 19:28:37.691] Disabled eager execution for TensorFlow operations for Unit B.
[2025-04-30 19:28:37.693] Step 7 (Unit B) - Creating sample training data.
[2025-04-30 19:28:37.697] Created 16 numerical samples and 16 word-based samples (Unit B).
[2025-04-30 19:28:37.704] Step 7 (Unit B) - Initializing Model B Architecture.
[2025-04-30 19:28:37.707] Model B architecture parameters:
[2025-04-30 19:28:37.710]  - Total Input Feature Count: 14
[2025-04-30 19:28:37.713]  - Hidden Layer Size (Model B): 70
[2025-04-30 19:28:37.719] Using session graph context (Unit B) for defining operations.
[2025-04-30 19:28:37.811] TensorFlow operations defined within session graph context (Unit B).
[2025-04-30 19:28:37.913] Model B - Actual TensorFlow.NET variables initialized.
[2025-04-30 19:28:38.230] Epoch 1/80, Batch 1/3, Batch Loss (Unit B): 0.105378
[2025-04-30 19:28:38.238] Epoch 1/80, Batch 3/3, Batch Loss (Unit B): 0.138235
[2025-04-30 19:28:38.274] Epoch 1/80, Average Loss (Unit B): 0.094634, Mean Absolute Error (Unit B): 0.204843
[2025-04-30 19:28:38.278] Epoch 2/80, Batch 1/3, Batch Loss (Unit B): 0.123030
[2025-04-30 19:28:38.285] Epoch 2/80, Batch 3/3, Batch Loss (Unit B): 0.056405
[2025-04-30 19:28:38.289] Epoch 3/80, Batch 1/3, Batch Loss (Unit B): 0.078420
[2025-04-30 19:28:38.293] Epoch 3/80, Batch 3/3, Batch Loss (Unit B): 0.172994
[2025-04-30 19:28:38.297] Epoch 4/80, Batch 1/3, Batch Loss (Unit B): 0.049852
[2025-04-30 19:28:38.303] Epoch 4/80, Batch 3/3, Batch Loss (Unit B): 0.151458
[2025-04-30 19:28:38.307] Epoch 5/80, Batch 1/3, Batch Loss (Unit B): 0.130349
[2025-04-30 19:28:38.312] Epoch 5/80, Batch 3/3, Batch Loss (Unit B): 0.025955
[2025-04-30 19:28:38.319] Epoch 6/80, Batch 1/3, Batch Loss (Unit B): 0.100851
[2025-04-30 19:28:38.339] Epoch 6/80, Batch 3/3, Batch Loss (Unit B): 0.015571
[2025-04-30 19:28:38.447] Epoch 7/80, Batch 1/3, Batch Loss (Unit B): 0.073358
[2025-04-30 19:28:38.519] Epoch 7/80, Batch 3/3, Batch Loss (Unit B): 0.023763
[2025-04-30 19:28:38.525] Epoch 8/80, Batch 1/3, Batch Loss (Unit B): 0.090242
[2025-04-30 19:28:38.532] Epoch 8/80, Batch 3/3, Batch Loss (Unit B): 0.093964
[2025-04-30 19:28:38.537] Epoch 9/80, Batch 1/3, Batch Loss (Unit B): 0.102877
[2025-04-30 19:28:38.542] Epoch 9/80, Batch 3/3, Batch Loss (Unit B): 0.114243
[2025-04-30 19:28:38.547] Epoch 10/80, Batch 1/3, Batch Loss (Unit B): 0.078046
[2025-04-30 19:28:38.553] Epoch 10/80, Batch 3/3, Batch Loss (Unit B): 0.078022
[2025-04-30 19:28:38.556] Epoch 11/80, Batch 1/3, Batch Loss (Unit B): 0.054334
[2025-04-30 19:28:38.562] Epoch 11/80, Batch 3/3, Batch Loss (Unit B): 0.090120
[2025-04-30 19:28:38.568] Epoch 11/80, Average Loss (Unit B): 0.074993, Mean Absolute Error (Unit B): 0.207108
[2025-04-30 19:28:38.572] Epoch 12/80, Batch 1/3, Batch Loss (Unit B): 0.095785
[2025-04-30 19:28:38.611] Epoch 12/80, Batch 3/3, Batch Loss (Unit B): 0.064575
[2025-04-30 19:28:38.620] Epoch 13/80, Batch 1/3, Batch Loss (Unit B): 0.084101
[2025-04-30 19:28:38.626] Epoch 13/80, Batch 3/3, Batch Loss (Unit B): 0.050554
[2025-04-30 19:28:38.631] Epoch 14/80, Batch 1/3, Batch Loss (Unit B): 0.113143
[2025-04-30 19:28:38.642] Epoch 14/80, Batch 3/3, Batch Loss (Unit B): 0.016219
[2025-04-30 19:28:38.655] Epoch 15/80, Batch 1/3, Batch Loss (Unit B): 0.025027
[2025-04-30 19:28:38.662] Epoch 15/80, Batch 3/3, Batch Loss (Unit B): 0.126769
[2025-04-30 19:28:38.669] Epoch 16/80, Batch 1/3, Batch Loss (Unit B): 0.096301
[2025-04-30 19:28:38.674] Epoch 16/80, Batch 3/3, Batch Loss (Unit B): 0.085942
[2025-04-30 19:28:38.681] Epoch 17/80, Batch 1/3, Batch Loss (Unit B): 0.031091
[2025-04-30 19:28:38.688] Epoch 17/80, Batch 3/3, Batch Loss (Unit B): 0.184547
[2025-04-30 19:28:38.697] Epoch 18/80, Batch 1/3, Batch Loss (Unit B): 0.031953
[2025-04-30 19:28:38.721] Epoch 18/80, Batch 3/3, Batch Loss (Unit B): 0.116892
[2025-04-30 19:28:38.726] Epoch 19/80, Batch 1/3, Batch Loss (Unit B): 0.022379
[2025-04-30 19:28:38.735] Epoch 19/80, Batch 3/3, Batch Loss (Unit B): 0.070357
[2025-04-30 19:28:38.743] Epoch 20/80, Batch 1/3, Batch Loss (Unit B): 0.061489
[2025-04-30 19:28:38.753] Epoch 20/80, Batch 3/3, Batch Loss (Unit B): 0.012799
[2025-04-30 19:28:38.758] Epoch 21/80, Batch 1/3, Batch Loss (Unit B): 0.040932
[2025-04-30 19:28:38.763] Epoch 21/80, Batch 3/3, Batch Loss (Unit B): 0.069463
[2025-04-30 19:28:38.769] Epoch 21/80, Average Loss (Unit B): 0.066999, Mean Absolute Error (Unit B): 0.191415
[2025-04-30 19:28:38.773] Epoch 22/80, Batch 1/3, Batch Loss (Unit B): 0.037665
[2025-04-30 19:28:38.777] Epoch 22/80, Batch 3/3, Batch Loss (Unit B): 0.090632
[2025-04-30 19:28:38.781] Epoch 23/80, Batch 1/3, Batch Loss (Unit B): 0.040413
[2025-04-30 19:28:38.797] Epoch 23/80, Batch 3/3, Batch Loss (Unit B): 0.088290
[2025-04-30 19:28:38.803] Epoch 24/80, Batch 1/3, Batch Loss (Unit B): 0.053705
[2025-04-30 19:28:38.807] Epoch 24/80, Batch 3/3, Batch Loss (Unit B): 0.086460
[2025-04-30 19:28:38.811] Epoch 25/80, Batch 1/3, Batch Loss (Unit B): 0.019884
[2025-04-30 19:28:38.815] Epoch 25/80, Batch 3/3, Batch Loss (Unit B): 0.070696
[2025-04-30 19:28:38.819] Epoch 26/80, Batch 1/3, Batch Loss (Unit B): 0.050009
[2025-04-30 19:28:38.824] Epoch 26/80, Batch 3/3, Batch Loss (Unit B): 0.033049
[2025-04-30 19:28:38.828] Epoch 27/80, Batch 1/3, Batch Loss (Unit B): 0.078200
[2025-04-30 19:28:38.833] Epoch 27/80, Batch 3/3, Batch Loss (Unit B): 0.016186
[2025-04-30 19:28:38.838] Epoch 28/80, Batch 1/3, Batch Loss (Unit B): 0.059776
[2025-04-30 19:28:38.843] Epoch 28/80, Batch 3/3, Batch Loss (Unit B): 0.096790
[2025-04-30 19:28:38.860] Epoch 29/80, Batch 1/3, Batch Loss (Unit B): 0.070878
[2025-04-30 19:28:38.865] Epoch 29/80, Batch 3/3, Batch Loss (Unit B): 0.011319
[2025-04-30 19:28:38.878] Epoch 30/80, Batch 1/3, Batch Loss (Unit B): 0.109732
[2025-04-30 19:28:38.884] Epoch 30/80, Batch 3/3, Batch Loss (Unit B): 0.058781
[2025-04-30 19:28:38.890] Epoch 31/80, Batch 1/3, Batch Loss (Unit B): 0.062234
[2025-04-30 19:28:38.895] Epoch 31/80, Batch 3/3, Batch Loss (Unit B): 0.078862
[2025-04-30 19:28:38.898] Epoch 31/80, Average Loss (Unit B): 0.062729, Mean Absolute Error (Unit B): 0.175357
[2025-04-30 19:28:38.903] Epoch 32/80, Batch 1/3, Batch Loss (Unit B): 0.110751
[2025-04-30 19:28:38.908] Epoch 32/80, Batch 3/3, Batch Loss (Unit B): 0.047446
[2025-04-30 19:28:38.912] Epoch 33/80, Batch 1/3, Batch Loss (Unit B): 0.034191
[2025-04-30 19:28:38.919] Epoch 33/80, Batch 3/3, Batch Loss (Unit B): 0.096497
[2025-04-30 19:28:38.923] Epoch 34/80, Batch 1/3, Batch Loss (Unit B): 0.073023
[2025-04-30 19:28:38.928] Epoch 34/80, Batch 3/3, Batch Loss (Unit B): 0.008592
[2025-04-30 19:28:38.943] Epoch 35/80, Batch 1/3, Batch Loss (Unit B): 0.031383
[2025-04-30 19:28:38.953] Epoch 35/80, Batch 3/3, Batch Loss (Unit B): 0.025770
[2025-04-30 19:28:38.970] Epoch 36/80, Batch 1/3, Batch Loss (Unit B): 0.012412
[2025-04-30 19:28:38.996] Epoch 36/80, Batch 3/3, Batch Loss (Unit B): 0.062531
[2025-04-30 19:28:39.040] Epoch 37/80, Batch 1/3, Batch Loss (Unit B): 0.079575
[2025-04-30 19:28:39.071] Epoch 37/80, Batch 3/3, Batch Loss (Unit B): 0.018024
[2025-04-30 19:28:39.075] Epoch 38/80, Batch 1/3, Batch Loss (Unit B): 0.015377
[2025-04-30 19:28:39.080] Epoch 38/80, Batch 3/3, Batch Loss (Unit B): 0.099843
[2025-04-30 19:28:39.086] Epoch 39/80, Batch 1/3, Batch Loss (Unit B): 0.021698
[2025-04-30 19:28:39.092] Epoch 39/80, Batch 3/3, Batch Loss (Unit B): 0.069487
[2025-04-30 19:28:39.099] Epoch 40/80, Batch 1/3, Batch Loss (Unit B): 0.062229
[2025-04-30 19:28:39.106] Epoch 40/80, Batch 3/3, Batch Loss (Unit B): 0.097985
[2025-04-30 19:28:39.111] Epoch 41/80, Batch 1/3, Batch Loss (Unit B): 0.118229
[2025-04-30 19:28:39.121] Epoch 41/80, Batch 3/3, Batch Loss (Unit B): 0.016523
[2025-04-30 19:28:39.127] Epoch 41/80, Average Loss (Unit B): 0.050715, Mean Absolute Error (Unit B): 0.171851
[2025-04-30 19:28:39.130] Epoch 42/80, Batch 1/3, Batch Loss (Unit B): 0.049008
[2025-04-30 19:28:39.142] Epoch 42/80, Batch 3/3, Batch Loss (Unit B): 0.025887
[2025-04-30 19:28:39.153] Epoch 43/80, Batch 1/3, Batch Loss (Unit B): 0.044278
[2025-04-30 19:28:39.160] Epoch 43/80, Batch 3/3, Batch Loss (Unit B): 0.095103
[2025-04-30 19:28:39.173] Epoch 44/80, Batch 1/3, Batch Loss (Unit B): 0.095754
[2025-04-30 19:28:39.178] Epoch 44/80, Batch 3/3, Batch Loss (Unit B): 0.012137
[2025-04-30 19:28:39.182] Epoch 45/80, Batch 1/3, Batch Loss (Unit B): 0.031658
[2025-04-30 19:28:39.197] Epoch 45/80, Batch 3/3, Batch Loss (Unit B): 0.050009
[2025-04-30 19:28:39.209] Epoch 46/80, Batch 1/3, Batch Loss (Unit B): 0.034950
[2025-04-30 19:28:39.214] Epoch 46/80, Batch 3/3, Batch Loss (Unit B): 0.080277
[2025-04-30 19:28:39.229] Epoch 47/80, Batch 1/3, Batch Loss (Unit B): 0.038218
[2025-04-30 19:28:39.238] Epoch 47/80, Batch 3/3, Batch Loss (Unit B): 0.090612
[2025-04-30 19:28:39.241] Epoch 48/80, Batch 1/3, Batch Loss (Unit B): 0.044109
[2025-04-30 19:28:39.265] Epoch 48/80, Batch 3/3, Batch Loss (Unit B): 0.045764
[2025-04-30 19:28:39.276] Epoch 49/80, Batch 1/3, Batch Loss (Unit B): 0.048501
[2025-04-30 19:28:39.319] Epoch 49/80, Batch 3/3, Batch Loss (Unit B): 0.046633
[2025-04-30 19:28:39.325] Epoch 50/80, Batch 1/3, Batch Loss (Unit B): 0.068859
[2025-04-30 19:28:39.330] Epoch 50/80, Batch 3/3, Batch Loss (Unit B): 0.014534
[2025-04-30 19:28:39.336] Epoch 51/80, Batch 1/3, Batch Loss (Unit B): 0.065734
[2025-04-30 19:28:39.342] Epoch 51/80, Batch 3/3, Batch Loss (Unit B): 0.015067
[2025-04-30 19:28:39.347] Epoch 51/80, Average Loss (Unit B): 0.046679, Mean Absolute Error (Unit B): 0.162785
[2025-04-30 19:28:39.353] Epoch 52/80, Batch 1/3, Batch Loss (Unit B): 0.035824
[2025-04-30 19:28:39.360] Epoch 52/80, Batch 3/3, Batch Loss (Unit B): 0.087296
[2025-04-30 19:28:39.368] Epoch 53/80, Batch 1/3, Batch Loss (Unit B): 0.016614
[2025-04-30 19:28:39.374] Epoch 53/80, Batch 3/3, Batch Loss (Unit B): 0.121582
[2025-04-30 19:28:39.378] Epoch 54/80, Batch 1/3, Batch Loss (Unit B): 0.057494
[2025-04-30 19:28:39.394] Epoch 54/80, Batch 3/3, Batch Loss (Unit B): 0.019175
[2025-04-30 19:28:39.398] Epoch 55/80, Batch 1/3, Batch Loss (Unit B): 0.011571
[2025-04-30 19:28:39.405] Epoch 55/80, Batch 3/3, Batch Loss (Unit B): 0.040510
[2025-04-30 19:28:39.409] Epoch 56/80, Batch 1/3, Batch Loss (Unit B): 0.019892
[2025-04-30 19:28:39.413] Epoch 56/80, Batch 3/3, Batch Loss (Unit B): 0.080625
[2025-04-30 19:28:39.421] Epoch 57/80, Batch 1/3, Batch Loss (Unit B): 0.033528
[2025-04-30 19:28:39.439] Epoch 57/80, Batch 3/3, Batch Loss (Unit B): 0.051858
[2025-04-30 19:28:39.454] Epoch 58/80, Batch 1/3, Batch Loss (Unit B): 0.047077
[2025-04-30 19:28:39.460] Epoch 58/80, Batch 3/3, Batch Loss (Unit B): 0.111546
[2025-04-30 19:28:39.468] Epoch 59/80, Batch 1/3, Batch Loss (Unit B): 0.091477
[2025-04-30 19:28:39.473] Epoch 59/80, Batch 3/3, Batch Loss (Unit B): 0.006671
[2025-04-30 19:28:39.477] Epoch 60/80, Batch 1/3, Batch Loss (Unit B): 0.058746
[2025-04-30 19:28:39.499] Epoch 60/80, Batch 3/3, Batch Loss (Unit B): 0.046798
[2025-04-30 19:28:39.505] Epoch 61/80, Batch 1/3, Batch Loss (Unit B): 0.058419
[2025-04-30 19:28:39.510] Epoch 61/80, Batch 3/3, Batch Loss (Unit B): 0.019792
[2025-04-30 19:28:39.514] Epoch 61/80, Average Loss (Unit B): 0.043773, Mean Absolute Error (Unit B): 0.160944
[2025-04-30 19:28:39.519] Epoch 62/80, Batch 1/3, Batch Loss (Unit B): 0.050210
[2025-04-30 19:28:39.524] Epoch 62/80, Batch 3/3, Batch Loss (Unit B): 0.050614
[2025-04-30 19:28:39.528] Epoch 63/80, Batch 1/3, Batch Loss (Unit B): 0.056948
[2025-04-30 19:28:39.532] Epoch 63/80, Batch 3/3, Batch Loss (Unit B): 0.008766
[2025-04-30 19:28:39.537] Epoch 64/80, Batch 1/3, Batch Loss (Unit B): 0.035959
[2025-04-30 19:28:39.541] Epoch 64/80, Batch 3/3, Batch Loss (Unit B): 0.045349
[2025-04-30 19:28:39.546] Epoch 65/80, Batch 1/3, Batch Loss (Unit B): 0.081538
[2025-04-30 19:28:39.557] Epoch 65/80, Batch 3/3, Batch Loss (Unit B): 0.038598
[2025-04-30 19:28:39.565] Epoch 66/80, Batch 1/3, Batch Loss (Unit B): 0.034866
[2025-04-30 19:28:39.570] Epoch 66/80, Batch 3/3, Batch Loss (Unit B): 0.018459
[2025-04-30 19:28:39.576] Epoch 67/80, Batch 1/3, Batch Loss (Unit B): 0.037314
[2025-04-30 19:28:39.581] Epoch 67/80, Batch 3/3, Batch Loss (Unit B): 0.049537
[2025-04-30 19:28:39.589] Epoch 68/80, Batch 1/3, Batch Loss (Unit B): 0.053582
[2025-04-30 19:28:39.594] Epoch 68/80, Batch 3/3, Batch Loss (Unit B): 0.006253
[2025-04-30 19:28:39.598] Epoch 69/80, Batch 1/3, Batch Loss (Unit B): 0.077748
[2025-04-30 19:28:39.605] Epoch 69/80, Batch 3/3, Batch Loss (Unit B): 0.015635
[2025-04-30 19:28:39.609] Epoch 70/80, Batch 1/3, Batch Loss (Unit B): 0.013586
[2025-04-30 19:28:39.613] Epoch 70/80, Batch 3/3, Batch Loss (Unit B): 0.068908
[2025-04-30 19:28:39.616] Epoch 71/80, Batch 1/3, Batch Loss (Unit B): 0.039603
[2025-04-30 19:28:39.623] Epoch 71/80, Batch 3/3, Batch Loss (Unit B): 0.046966
[2025-04-30 19:28:39.660] Epoch 71/80, Average Loss (Unit B): 0.044148, Mean Absolute Error (Unit B): 0.151845
[2025-04-30 19:28:39.671] Epoch 72/80, Batch 1/3, Batch Loss (Unit B): 0.049912
[2025-04-30 19:28:39.678] Epoch 72/80, Batch 3/3, Batch Loss (Unit B): 0.084681
[2025-04-30 19:28:39.690] Epoch 73/80, Batch 1/3, Batch Loss (Unit B): 0.053532
[2025-04-30 19:28:39.720] Epoch 73/80, Batch 3/3, Batch Loss (Unit B): 0.040791
[2025-04-30 19:28:39.757] Epoch 74/80, Batch 1/3, Batch Loss (Unit B): 0.061172
[2025-04-30 19:28:39.796] Epoch 74/80, Batch 3/3, Batch Loss (Unit B): 0.065021
[2025-04-30 19:28:39.802] Epoch 75/80, Batch 1/3, Batch Loss (Unit B): 0.042270
[2025-04-30 19:28:39.806] Epoch 75/80, Batch 3/3, Batch Loss (Unit B): 0.065686
[2025-04-30 19:28:39.811] Epoch 76/80, Batch 1/3, Batch Loss (Unit B): 0.057508
[2025-04-30 19:28:39.828] Epoch 76/80, Batch 3/3, Batch Loss (Unit B): 0.016697
[2025-04-30 19:28:39.846] Epoch 77/80, Batch 1/3, Batch Loss (Unit B): 0.052568
[2025-04-30 19:28:39.858] Epoch 77/80, Batch 3/3, Batch Loss (Unit B): 0.078373
[2025-04-30 19:28:39.863] Epoch 78/80, Batch 1/3, Batch Loss (Unit B): 0.076261
[2025-04-30 19:28:39.870] Epoch 78/80, Batch 3/3, Batch Loss (Unit B): 0.008336
[2025-04-30 19:28:39.876] Epoch 79/80, Batch 1/3, Batch Loss (Unit B): 0.067805
[2025-04-30 19:28:39.880] Epoch 79/80, Batch 3/3, Batch Loss (Unit B): 0.017356
[2025-04-30 19:28:39.893] Epoch 80/80, Batch 1/3, Batch Loss (Unit B): 0.027317
[2025-04-30 19:28:39.898] Epoch 80/80, Batch 3/3, Batch Loss (Unit B): 0.021536
[2025-04-30 19:28:39.908] Epoch 80/80, Average Loss (Unit B): 0.038751, Mean Absolute Error (Unit B): 0.145033
[2025-04-30 19:28:39.911] Model B training completed
[2025-04-30 19:28:39.949] Model B Final Predictions Shape: 16,1
[2025-04-30 19:28:39.954] Model B Final Predictions (First few): [1.578467, 1.4229388, 1.7788396, 1.6860995, 1.6775295, 1.6334094, 1.7543508, 1.5669016, 1.7304193, 1.4110415...]
[2025-04-30 19:28:39.980] Model B Final Mean Absolute Error: 0.145033
[2025-04-30 19:28:39.988] Step 7 (Unit B) - Model B trained and saved to RuntimeProcessingContext and Results Store.
[2025-04-30 19:28:39.992] Workflow Session 1: Step 8 (Unit B) - Generating future performance projection for customer 1.
[2025-04-30 19:28:39.995] Workflow Session 1: Step 8 (Unit B) - Future performance projection completed: PerformanceProjection_B_Cust_1_Outcome_StableB_ComplexModelB_Score_0.9420_TrainError_0.1450
[2025-04-30 19:28:39.999] Workflow Session 1: Workflow (Unit B) completed for customer 1 with final score 0.9420
[2025-04-30 19:28:40.004] Workflow Session 1: Workflow (Unit B) completed with result: Workflow_B_Complete_Cust_1_FinalScore_0.9420
[2025-04-30 19:28:40.215] Workflow Session 1: Parallel Processing Unit B finished.
[2025-04-30 19:28:40.220] Workflow Session 1: Parallel Processing Units A and B completed. Starting Sequential Final Processing Unit (D).
'Base_Pre.Server.exe' (CoreCLR: clrhost): Loaded 'E:\Development_Sandbox\Projects\Base_Pre\Base_Pre.Server\bin\Debug\net8.0\AutoGen.dll'. Skipped loading symbols. Module is optimized and the debugger option 'Just My Code' is enabled.
[2025-04-30 19:28:40.387] Workflow Session 1: Starting Sequential Final Processing Unit D (Actual Model D Concept with AutoGen) for customer 1.
[2025-04-30 19:28:40.391] Workflow Session 1: SequentialFinalProcessingUnitD: Initializing...
[2025-04-30 19:28:40.451] Workflow Session 1: SequentialFinalProcessingUnitD: Retrieving model outputs and parameters from parallel units...
[2025-04-30 19:28:40.455] Workflow Session 1: Attempting to retrieve Model A parameters from RuntimeContext.
[2025-04-30 19:28:40.459] Workflow Session 1: Successfully retrieved Model A combined parameters (61444 bytes) from RuntimeContext.
[2025-04-30 19:28:40.462] Workflow Session 1: Attempting to retrieve Model B parameters from RuntimeContext.
[2025-04-30 19:28:40.469] Workflow Session 1: Successfully retrieved Model B combined parameters (4484 bytes) from RuntimeContext.
[2025-04-30 19:28:40.475] Workflow Session 1: Attempting to retrieve Model A predictions from Unit A results dictionary.
[2025-04-30 19:28:40.510] Workflow Session 1: Successfully retrieved Model A predictions (16 values) from Unit A results.
[2025-04-30 19:28:40.545] Workflow Session 1: Model A Predictions (first 10): [2.7504, 2.7034, 2.9581, 2.5593, 2.7367, 2.6735, 2.2127, 3.0339, 2.6607, 2.6618...]
[2025-04-30 19:28:40.560] Workflow Session 1: Attempting to retrieve Model B predictions from Unit B results dictionary.
[2025-04-30 19:28:40.565] Workflow Session 1: Successfully retrieved Model B predictions (16 values) from Unit B results.
[2025-04-30 19:28:40.572] Workflow Session 1: Model B Predictions (first 10): [1.5785, 1.4229, 1.7788, 1.6861, 1.6775, 1.6334, 1.7544, 1.5669, 1.7304, 1.4110...]
[2025-04-30 19:28:40.586] Workflow Session 1: Retrieved Model A Training Error: 1.627609
[2025-04-30 19:28:40.590] Workflow Session 1: Retrieved Model B Training Error: 0.145033
[2025-04-30 19:28:40.595] Workflow Session 1: SequentialFinalProcessingUnitD: Initiating AutoGen Agent Collaboration for Comprehensive Analysis.
[2025-04-30 19:28:40.610] Agent Collaboration: System provides independent training performance metrics to agents.
[2025-04-30 19:28:40.614] Agent Collaboration: AgentA reacting to training metrics.
'Base_Pre.Server.exe' (CoreCLR: clrhost): Loaded 'E:\Development_Sandbox\Projects\Base_Pre\Base_Pre.Server\bin\Debug\net8.0\JsonSchema.Net.dll'. Skipped loading symbols. Module is optimized and the debugger option 'Just My Code' is enabled.
[2025-04-30 19:28:41.106] Agent Collaboration: AgentA reply received. Content: Default reply is not set. Please pass a default reply to assistant agent
[2025-04-30 19:28:41.109] Agent Collaboration: AgentB reacting to training metrics.
[2025-04-30 19:28:41.112] Agent Collaboration: AgentB reply received. Content: Default reply is not set. Please pass a default reply to assistant agent
[2025-04-30 19:28:41.120] Agent Collaboration: System provides prediction arrays and instructs detailed comparative analysis.
[2025-04-30 19:28:41.123] Agent Collaboration: AgentA performing and discussing comparative analysis.
[2025-04-30 19:28:41.127] Agent Collaboration: AgentA reply received. Content: Default reply is not set. Please pass a default reply to assistant agent
[2025-04-30 19:28:41.130] Agent Collaboration: AgentB performing and discussing comparative analysis.
[2025-04-30 19:28:41.140] Agent Collaboration: AgentB reply received. Content: Default reply is not set. Please pass a default reply to assistant agent
[2025-04-30 19:28:41.144] Agent Collaboration: C# logic performing statistical analysis and finding most similar index based on agents' instructions.
[2025-04-30 19:28:41.162] Helper_D: Most similar prediction pair found at index 6 with absolute difference 0.458382.
[2025-04-30 19:28:41.166] Agent Collaboration: C# logic calculated stats and found most similar index 6. System reporting this to agents.
[2025-04-30 19:28:41.199] Agent Collaboration: Agents interpreting detailed statistical results.
[2025-04-30 19:28:41.205] Agent Collaboration: AgentA reply received. Content: Default reply is not set. Please pass a default reply to assistant agent
[2025-04-30 19:28:41.209] Agent Collaboration: AgentB reply received. Content: Default reply is not set. Please pass a default reply to assistant agent
[2025-04-30 19:28:41.214] Agent Collaboration: C# logic performing simulated inference on a small validation set (4 samples) using trained model parameters.
[2025-04-30 19:28:41.223] Helper_D: Simulating Model A inference...
[2025-04-30 19:28:41.227] Helper_D: Deserialized 15361 float parameters for Model A.
[2025-04-30 19:28:41.230] Helper_D: Inferred hidden layer size: 960 for Model A.
[2025-04-30 19:28:41.237] Helper_D: Simulated Model A inference complete for 4 samples. Returning predictions.
[2025-04-30 19:28:41.240] Helper_D: Simulating Model B inference...
[2025-04-30 19:28:41.243] Helper_D: Deserialized 1121 float parameters for Model B.
[2025-04-30 19:28:41.246] Helper_D: Inferred hidden layer size: 70 for Model B.
[2025-04-30 19:28:41.281] Helper_D: Simulated Model B inference complete for 4 samples. Returning predictions.
[2025-04-30 19:28:41.300] Agent Collaboration: C# logic completed simulated inference. Average Simulated Output A: 2.549922, Average Simulated Output B: 1.638919.
[2025-04-30 19:28:41.305] Agent Collaboration: Simulated Inference Comparison Metrics:
  - MAE (Simulated): 0.911003
  - Correlation (Simulated): 0.889562
  - MSE (Simulated): 0.883968
  - RMS (Simulated): 0.940195
  - Coefficient of Variation (Simulated Differences): 25.5178%
[2025-04-30 19:28:41.324] Agent Collaboration: System reports simulated inference results and metrics to agents.
[2025-04-30 19:28:41.327] Agent Collaboration: Agents providing final assessment and summary.
[2025-04-30 19:28:41.330] Agent Collaboration: AgentA reply received. Content: Default reply is not set. Please pass a default reply to assistant agent
[2025-04-30 19:28:41.334] Agent Collaboration: AgentB reply received. Content: Default reply is not set. Please pass a default reply to assistant agent
[2025-04-30 19:28:41.350] Agent Collaboration: C# logic determining overall summary based on all metrics.
[2025-04-30 19:28:41.359] Agent Collaboration: Final Overall Summary: Significant Full Prediction Differences | Lower Simulated Inference Consistency | Both Models Showed Higher Individual Training Error | Combined Confidence: 32%.
[2025-04-30 19:28:41.363] Agent Collaboration: AutoGen workflow completed. Overall summary: Significant Full Prediction Differences | Lower Simulated Inference Consistency | Both Models Showed Higher Individual Training Error | Combined Confidence: 32%
[2025-04-30 19:28:41.366] Workflow Session 1: SequentialFinalProcessingUnitD: Attempting conceptual model merge.
[2025-04-30 19:28:41.373] Workflow Session 1: Conceptually merged Model A (61444 bytes) and Model B (4484 bytes) parameters. Merged data size: 65928 bytes.
[2025-04-30 19:28:41.376] Workflow Session 1: Stored conceptual merged model data (65928 bytes) in RuntimeContext.
[2025-04-30 19:28:41.379] Workflow Session 1: SequentialFinalProcessingUnitD: Updating CoreMlOutcomeRecord with final details.
[2025-04-30 19:28:41.382] Workflow Session 1: Final Outcome Record Details:
  - RecordIdentifier: 3
  - AssociatedCustomerIdentifier: 1
  - OutcomeGenerationTimestamp: 4/30/2025 7:28:41 PM
  - CategoricalClassificationIdentifier: 250
  - CategoricalClassificationDescription:  (Full Data Processed, Analysis: Significant Full Prediction Differences | Lower Simulated Inference Consistency | Both Models Showed Higher Individual Training Error | Combined Confidence: 32%)
  - SerializedSimulatedModelData Size: 3840 bytes
  - AncillaryBinaryDataPayload Size: 260 bytes
  - DerivedProductFeatureVector: ModelA_Preds_Count:16_BestMatchIdx:6_InputUsed:0.000000
  - DerivedServiceBenefitVector: ModelB_Preds_Count:16_SimOutputA:2.549922_SimOutputB:1.638919
[2025-04-30 19:28:41.456] Workflow Session 1: SequentialFinalProcessingUnitD: Attempting to save final CoreMlOutcomeRecord to simulated persistence.
[2025-04-30 19:28:41.461] Workflow Session 1: Final CoreMlOutcomeRecord (ID: 3) state saved successfully to simulated persistent storage.
[2025-04-30 19:28:41.465] Workflow Session 1: Sequential Final Processing Unit D (Actual Model D Concept with AutoGen) completed all processing steps successfully.
[2025-04-30 19:28:41.472] Workflow Session 1: Sequential Final Processing Unit D (Actual Model D Concept with AutoGen) finished execution.
[2025-04-30 19:28:41.475] Workflow Session 1: ML Outcome Generation workflow completed successfully.
[2025-04-30 19:28:41.479] Workflow Session 1: Returning final CoreMlOutcomeRecord (ID: 3) for customer 1.
[2025-04-30 19:28:41.483] Workflow Session 1: Associated actual ML session resources cleaned up.

Name		Name	Last commit message	Last commit date
Latest commit History 42 Commits
Base_Pre.Server		Base_Pre.Server
base_pre.client		base_pre.client
.gitattributes		.gitattributes
.gitignore		.gitignore
Base_Pre.sln		Base_Pre.sln
README.md		README.md
package-lock.json		package-lock.json

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Advanced ML Techniques using Advanced Dimensional Embeddings using Spatial Coordinates and 3D Fractal Diffusion with Dimensional Coupling & Model Synthesis

Advanced Dimensional Embeddings

K-means to Magnitude: The Dimensional Transform

3D Fractal Diffusion & Sampling

Curvature Embedding at Vertices

N-Dimensional Extension

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AutoGen Model Synthesis in Base_Pre ML Framework

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Advanced ML Techniques using Advanced Dimensional Embeddings using Spatial Coordinates and 3D Fractal Diffusion with Dimensional Coupling & Model Synthesis

Advanced Dimensional Embeddings

K-means to Magnitude: The Dimensional Transform

3D Fractal Diffusion & Sampling

Curvature Embedding at Vertices

N-Dimensional Extension

Technical Innovation

AutoGen Model Synthesis in Base_Pre ML Framework

Model Parallelization and Synthesis

Agent-Based Model Evaluation

Simulated Model Inference and Verification

Comprehensive Outcome Synthesis

Innovation Summary

Example Workflow Log

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