Merge pull request #138 from VishwamAI/consciousness-simulation-enhan…

…cements

Enhance Consciousness Simulation

NeuroFlex/cognitive_architectures/adaptive_learning_rate_scheduler.py

@@ -0,0 +1,21 @@
+class AdaptiveLearningRateScheduler:
+    def __init__(self, initial_lr=0.001, patience=10, factor=0.5):
+        self.lr = initial_lr
+        self.patience = patience
+        self.factor = factor
+        self.best_performance = float('-inf')
+        self.wait = 0
+
+    def step(self, performance):
+        if performance > self.best_performance:
+            self.best_performance = performance
+            self.wait = 0
+        else:
+            self.wait += 1
+            if self.wait >= self.patience:
+                self.lr *= self.factor
+                self.wait = 0
+        return self.lr
+
+def create_adaptive_learning_rate_scheduler(initial_lr=0.001, patience=10, factor=0.5):
+    return AdaptiveLearningRateScheduler(initial_lr, patience, factor)

NeuroFlex/cognitive_architectures/advanced_metacognition.py

@@ -0,0 +1,12 @@
+import jax.numpy as jnp
+import flax.linen as nn
+
+class AdvancedMetacognition(nn.Module):
+    @nn.compact
+    def __call__(self, x):
+        uncertainty = nn.Dense(1)(x)
+        confidence = nn.Dense(1)(x)
+        return jnp.concatenate([uncertainty, confidence], axis=-1)
+
+def create_advanced_metacognition():
+    return AdvancedMetacognition()

NeuroFlex/cognitive_architectures/advanced_self_healing.py

@@ -0,0 +1,26 @@
+import jax.numpy as jnp
+import jax
+
+class AdvancedSelfHealing:
+    @staticmethod
+    def diagnose(model):
+        issues = []
+        # Implement more sophisticated diagnostic checks
+        # For example, check for NaN values in model parameters
+        for param in jax.tree_leaves(model.params):
+            if jnp.isnan(param).any():
+                issues.append("NaN values detected in model parameters")
+                break
+        return issues
+
+    @staticmethod
+    def heal(model, issues):
+        for issue in issues:
+            if issue == "NaN values detected in model parameters":
+                # Replace NaN values with small random values
+                def replace_nan(param):
+                    return jnp.where(jnp.isnan(param), jax.random.uniform(jax.random.PRNGKey(0), param.shape, minval=-0.1, maxval=0.1), param)
+                model.params = jax.tree_map(replace_nan, model.params)
+
+def create_advanced_self_healing():
+    return AdvancedSelfHealing()

NeuroFlex/cognitive_architectures/advanced_working_memory.py

@@ -0,0 +1,96 @@
+import jax.numpy as jnp
+import flax.linen as nn
+from flax.linen import LSTMCell
+import logging
+import sys
+import inspect
+
+logging.basicConfig(level=logging.DEBUG, stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s')
+
+class CustomLSTMCell(nn.Module):
+    gate_fn: callable = nn.sigmoid
+    activation_fn: callable = nn.tanh
+    kernel_init: callable = nn.initializers.lecun_normal()
+    recurrent_kernel_init: callable = nn.initializers.orthogonal()
+    bias_init: callable = nn.initializers.zeros
+
+    @nn.compact
+    def __call__(self, carry, inputs):
+        logging.debug(f"CustomLSTMCell input - inputs shape: {inputs.shape}, carry type: {type(carry)}")
+        if not (isinstance(carry, tuple) and len(carry) == 2):
+            logging.error(f"Invalid carry format in CustomLSTMCell: {carry}")
+            raise ValueError("Carry must be a tuple of (c, h)")
+
+        c, h = carry
+        logging.debug(f"CustomLSTMCell carry - c shape: {c.shape}, h shape: {h.shape}")
+
+        lstm = LSTMCell(
+            gate_fn=self.gate_fn,
+            activation_fn=self.activation_fn,
+            kernel_init=self.kernel_init,
+            recurrent_kernel_init=self.recurrent_kernel_init,
+            bias_init=self.bias_init
+        )
+        new_carry, y = lstm(carry, inputs)
+        new_c, new_h = new_carry
+        logging.debug(f"CustomLSTMCell output - new_carry type: {type(new_carry)}, new_c shape: {new_c.shape}, new_h shape: {new_h.shape}")
+        logging.debug(f"CustomLSTMCell output - y shape: {y.shape}")
+        return new_carry, y
+
+class AdvancedWorkingMemory(nn.Module):
+    memory_size: int
+
+    def initialize_state(self, batch_size):
+        logging.debug(f"Initializing state with batch_size: {batch_size}")
+        initial_state = (jnp.zeros((batch_size, self.memory_size)), jnp.zeros((batch_size, self.memory_size)))
+        logging.debug(f"Initial state shapes: {initial_state[0].shape}, {initial_state[1].shape}")
+        return initial_state
+
+    @nn.compact
+    def __call__(self, x, state=None):
+        try:
+            logging.debug(f"Entering AdvancedWorkingMemory.__call__ - caller: {inspect.stack()[1].function}")
+            logging.debug(f"AdvancedWorkingMemory input shape: {x.shape}, type: {type(x)}")
+            logging.debug(f"Input stats: min={jnp.min(x)}, max={jnp.max(x)}, mean={jnp.mean(x)}")
+
+            batch_size = x.shape[0]
+            if state is None:
+                state = self.initialize_state(batch_size)
+            logging.debug(f"State shape: {state[0].shape}, {state[1].shape}, type: {type(state)}")
+
+            if not (isinstance(state, tuple) and len(state) == 2):
+                logging.error(f"Invalid state format: {state}")
+                raise ValueError("State must be a tuple of (c, h)")
+
+            c, h = state
+            logging.debug(f"State components - c shape: {c.shape}, h shape: {h.shape}")
+            logging.debug(f"c stats: min={jnp.min(c)}, max={jnp.max(c)}, mean={jnp.mean(c)}")
+            logging.debug(f"h stats: min={jnp.min(h)}, max={jnp.max(h)}, mean={jnp.mean(h)}")
+
+            lstm = CustomLSTMCell()
+            try:
+                new_state, y = lstm((c, h), x)
+                logging.debug(f"LSTM output - new_state type: {type(new_state)}, y shape: {y.shape}")
+                if isinstance(new_state, tuple) and len(new_state) == 2:
+                    new_c, new_h = new_state
+                    logging.debug(f"new_c shape: {new_c.shape}, new_h shape: {new_h.shape}")
+                    logging.debug(f"new_c stats: min={jnp.min(new_c)}, max={jnp.max(new_c)}, mean={jnp.mean(new_c)}")
+                    logging.debug(f"new_h stats: min={jnp.min(new_h)}, max={jnp.max(new_h)}, mean={jnp.mean(new_h)}")
+                else:
+                    logging.error(f"Invalid new_state format: {new_state}")
+                    raise ValueError("new_state must be a tuple of (new_c, new_h)")
+                logging.debug(f"y stats: min={jnp.min(y)}, max={jnp.max(y)}, mean={jnp.mean(y)}")
+            except Exception as e:
+                logging.error(f"Error in LSTM cell: {str(e)}")
+                raise
+
+            logging.debug(f"Returning new_state type: {type(new_state)}, y type: {type(y)}")
+            logging.debug(f"new_state shapes: {new_state[0].shape}, {new_state[1].shape}")
+            logging.debug(f"y shape: {y.shape}")
+            return new_state, y
+        except Exception as e:
+            logging.error(f"Unexpected error in AdvancedWorkingMemory.__call__: {str(e)}")
+            raise
+
+def create_advanced_working_memory(memory_size):
+    return AdvancedWorkingMemory(memory_size=memory_size)