Create csumlm.py

csumlm.py

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+# CognoSphere Unified Multimodal Language Model (CSUMLM)
+
+import tensorflow as tf
+import numpy as np
+import os
+import random
+
+# Data Processing
+class DataProcessor:
+    def __init__(self, data_dir):
+        self.data_dir = data_dir
+        self.text_data = []
+        self.image_data = []
+        self.audio_data = []
+        self.load_data()
+
+    def load_data(self):
+        # Load text data
+        text_files = os.listdir(os.path.join(self.data_dir, 'text'))
+        for file in text_files:
+            with open(os.path.join(self.data_dir, 'text', file), 'r') as f:
+                self.text_data.extend(f.readlines())
+
+        # Load image data
+        image_files = os.listdir(os.path.join(self.data_dir, 'images'))
+        for file in image_files:
+            self.image_data.append(os.path.join(self.data_dir, 'images', file))
+
+        # Load audio data
+        audio_files = os.listdir(os.path.join(self.data_dir, 'audio'))
+        for file in audio_files:
+            self.audio_data.append(os.path.join(self.data_dir, 'audio', file))
+
+    def get_batch(self, batch_size):
+        # Randomly sample data from each modality
+        text_batch = random.sample(self.text_data, batch_size)
+        image_batch = random.sample(self.image_data, batch_size)
+        audio_batch = random.sample(self.audio_data, batch_size)
+
+        return text_batch, image_batch, audio_batch
+
+# Hybrid Learning Engine
+class HybridLearningEngine:
+    def __init__(self, data_processor):
+        self.data_processor = data_processor
+        self.model = self.build_model()
+
+    def build_model(self):
+        # Define the model architecture
+        # Combine transfer learning, deep learning, self-supervised learning, meta-learning,
+        # deep meta-learning, reinforcement learning, and cross-domain analogy extraction
+        # ...
+
+        return model
+
+    def train(self, epochs, batch_size):
+        for epoch in range(epochs):
+            text_batch, image_batch, audio_batch = self.data_processor.get_batch(batch_size)
+
+            # Train the model on the batch
+            # ...
+
+# Advanced Attention Mechanism
+class AttentionMechanism:
+    def __init__(self):
+        self.traditional_attention = TraditionalAttention()
+        self.self_attention = SelfAttention()
+        self.linear_attention = LinearAttention()
+
+    def apply_attention(self, inputs):
+        # Combine traditional attention, self-attention, and linear attention
+        # ...
+
+        return attended_inputs
+
+# Hierarchical Belief Desire Intent Tree/Chain of Thought Structure
+class BeliefDesireIntentTree:
+    def __init__(self):
+        self.root = None
+
+    def build_tree(self, inputs):
+        # Construct the Belief Desire Intent Tree/Chain of Thought Structure
+        # ...
+
+        return self.root
+
+# Modular Python Architecture
+class CSUMLM:
+    def __init__(self, data_dir):
+        self.data_processor = DataProcessor(data_dir)
+        self.learning_engine = HybridLearningEngine(self.data_processor)
+        self.attention_mechanism = AttentionMechanism()
+        self.belief_desire_intent_tree = BeliefDesireIntentTree()
+
+    def train(self, epochs, batch_size):
+        self.learning_engine.train(epochs, batch_size)
+
+    def process_input(self, inputs):
+        # Preprocess inputs
+        # ...
+
+        # Apply attention mechanism
+        attended_inputs = self.attention_mechanism.apply_attention(inputs)
+
+        # Build Belief Desire Intent Tree/Chain of Thought Structure
+        belief_desire_intent_tree = self.belief_desire_intent_tree.build_tree(attended_inputs)
+
+        # Generate output based on the tree
+        # ...
+
+        return output
+
+# Real-time Learning Mechanisms
+class RealtimeLearningMechanism:
+    def __init__(self, model):
+        self.model = model
+
+    def update_model(self, new_data):
+        # Update the model with new data
+        # ...
+
+# Dynamic Knowledge Base
+class DynamicKnowledgeBase:
+    def __init__(self):
+        self.knowledge_base = {}
+
+    def update_knowledge_base(self, new_knowledge):
+        # Update the knowledge base with new linguistic and multimodal patterns
+        # ...
+
+# Explainability and Transparency
+class Explainer:
+    def __init__(self, model):
+        self.model = model
+
+    def explain_prediction(self, inputs):
+        # Generate explanations for model predictions and responses
+        # ...
+
+        return explanation
+
+# Internal Retrieval Augmented Generation Enhanced Logic (I-RAGEL)
+class IRAGEL:
+    def __init__(self, model, knowledge_base):
+        self.model = model
+        self.knowledge_base = knowledge_base
+
+    def retrieve_or_generate(self, inputs):
+        # Retrieve or generate additional linguistic and multimodal data
+        # ...
+
+        return augmented_inputs
+
+    def reflect_and_improve(self, inputs, outputs):
+        # Reflect on generated logic and improve decision-making processes
+        # ...
+
+        return improved_outputs
+
+    def self_train(self, inputs, outputs):
+        # Implement self-training for continuous performance enhancement
+        # ...
+
+# Main CSUMLM Class
+class CSUMLM:
+    def __init__(self, data_dir):
+        self.data_processor = DataProcessor(data_dir)
+        self.learning_engine = HybridLearningEngine(self.data_processor)
+        self.attention_mechanism = AttentionMechanism()
+        self.belief_desire_intent_tree = BeliefDesireIntentTree()
+        self.realtime_learning_mechanism = RealtimeLearningMechanism(self.learning_engine.model)
+        self.knowledge_base = DynamicKnowledgeBase()
+        self.explainer = Explainer(self.learning_engine.model)
+        self.iragel = IRAGEL(self.learning_engine.model, self.knowledge_base)
+
+    def train(self, epochs, batch_size):
+        self.learning_engine.train(epochs, batch_size)
+
+    def process_input(self, inputs):
+        # Preprocess inputs
+        # ...
+
+        # Apply attention mechanism
+        attended_inputs = self.attention_mechanism.apply_attention(inputs)
+
+        # Build Belief Desire Intent Tree/Chain of Thought Structure
+        belief_desire_intent_tree = self.belief_desire_intent_tree.build_tree(attended_inputs)
+
+        # Retrieve or generate additional data
+        augmented_inputs = self.iragel.retrieve_or_generate(attended_inputs)
+
+        # Generate output based on the tree and augmented inputs
+        outputs = self.learning_engine.model(augmented_inputs, belief_desire_intent_tree)
+
+        # Reflect and improve outputs
+        improved_outputs = self.iragel.reflect_and_improve(augmented_inputs, outputs)
+
+        # Explain predictions
+        explanation = self.explainer.explain_prediction(improved_outputs)
+
+        # Update knowledge base and model
+        self.knowledge_base.update_knowledge_base(new_knowledge)
+        self.realtime_learning_mechanism.update_model(new_data)
+
+        # Self-train the model
+        self.iragel.self_train(augmented_inputs, improved_outputs)
+
+        return improved_outputs, explanation