Delete sci

sci/__init__.py

@@ -1,45 +0,0 @@
-"""
-SCI: Surgical Cognitive Interpreter
-Metacognitive control for signal dynamics.
-
-This package is structured to keep the top-level import lightweight:
-- `import sci` does NOT import torch or heavy submodules immediately.
-- Actual components are imported lazily when accessed.
-
-Author: Vishal Joshua Meesala
-"""
-
-from importlib import import_module
-from typing import Any
-
-__all__ = [
-    "SCIController",
-    "compute_sp",
-    "Interpreter",
-    "Decomposition",
-    "ReliabilityWeighting",
-]
-
-
-def __getattr__(name: str) -> Any:
-    """
-    Lazy attribute access so that:
-
-        import sci
-        sci.SCIController
-
-    does not import torch until the attribute is actually used.
-    """
-    if name == "SCIController":
-        return import_module("sci.controller").SCIController
-    if name == "compute_sp":
-        return import_module("sci.sp").compute_sp
-    if name == "Interpreter":
-        return import_module("sci.interpreter").Interpreter
-    if name == "Decomposition":
-        return import_module("sci.decomposition").Decomposition
-    if name == "ReliabilityWeighting":
-        return import_module("sci.reliability").ReliabilityWeighting
-
-    raise AttributeError(f"module 'sci' has no attribute {name!r}")
-

sci/config.py

@@ -1,22 +0,0 @@
-"""Placeholder config module for SCI.
-
-This file can be extended to expose default configuration
-objects or helper loaders for YAML config files in `configs/`.
-"""
-
-from pathlib import Path
-
-DEFAULTS = {
-    "feature_dim": 128,
-    "num_markers": 8,
-    "num_classes": 10,
-}
-
-def load_yaml(path: str):
-    try:
-        import yaml
-    except Exception:
-        raise RuntimeError("PyYAML is required to load config files")
-    p = Path(path)
-    with p.open("r", encoding="utf-8") as f:
-        return yaml.safe_load(f)

sci/controller.py

@@ -1,75 +0,0 @@
-import torch
-from torch import nn
-
-
-class SCIController(nn.Module):
-    """
-    Minimal SCI closed-loop controller.
-
-    It monitors a scalar interpretive state SP, compares it
-    to a target SP*, and performs a projected gradient-style
-    update on the interpreter parameters Θ based on ΔSP.
-
-    This is a simplified, minimal prototype to show the core idea.
-    """
-
-    def __init__(
-        self,
-        interpreter: nn.Module,
-        sp_target: float = 0.90,
-        eta: float = 0.01,
-        gamma: float = 0.10,
-        trust_region: float = 0.1,
-    ):
-        super().__init__()
-        self.interpreter = interpreter
-        self.sp_target = sp_target
-        self.eta = eta
-        self.gamma = gamma
-        self.trust_region = trust_region
-
-    @torch.no_grad()
-    def _project(self, theta: torch.Tensor, theta_old: torch.Tensor) -> torch.Tensor:
-        """Simple trust-region projection on parameter vector."""
-        delta = theta - theta_old
-        norm = delta.norm()
-        if norm > self.trust_region:
-            return theta_old + self.trust_region * delta / (norm + 1e-9)
-        return theta
-
-    def forward(self, x: torch.Tensor):
-        """
-        Run a single SCI control step.
-
-        Args:
-            x: input features (batch_size, feature_dim)
-
-        Returns:
-            pred: raw predictions (logits)
-            sp: scalar SP estimate (float tensor)
-            d_sp: SP* - SP
-            interpreter: the (possibly) updated interpreter module
-        """
-        sp, pred = self.interpreter.compute(x)
-        d_sp = self.sp_target - sp
-
-        # No-op zone: if |ΔSP| is small, do not update
-        if torch.abs(d_sp) < self.gamma:
-            return pred, sp, d_sp, self.interpreter
-
-        # Collect old parameters as a flat vector
-        theta_old = self.interpreter.parameters_vector().detach()
-
-        # Compute gradient of SP wrt parameters
-        grad = self.interpreter.grad_sp(x)
-
-        # Basic controller update: Θ_new = Θ_old + η * ΔSP * ∇Θ SP
-        theta_new = theta_old + self.eta * d_sp * grad
-
-        # Trust-region projection
-        theta_new = self._project(theta_new, theta_old)
-
-        # Push updated parameters back into the interpreter
-        self.interpreter.update_parameters(theta_new)
-
-        return pred, sp, d_sp, self.interpreter

sci/decomposition.py

@@ -1,20 +0,0 @@
-import torch
-
-
-class Decomposition:
-    """
-    Placeholder semantic decomposition Π.
-
-    In the full SCI framework, this would include:
-    - Rhythmic features (FFT/STFT, wavelets, etc.)
-    - Trend features (detrending, SSA, etc.)
-    - Spatial / cross-modal features
-    Here we expose a simple identity mapping for now.
-    """
-
-    def __init__(self):
-        pass
-
-    def __call__(self, x: torch.Tensor) -> torch.Tensor:
-        # TODO: replace with real decomposition (e.g., STFT/wavelets)
-        return x

sci/interpreter.py

@@ -1,73 +0,0 @@
-import torch
-from torch import nn
-from .sp import compute_sp
-
-
-class Interpreter(nn.Module):
-    """
-    A lightweight SCI interpreter.
-
-    - Encodes input features into a hidden representation
-    - Emits marker logits (for SP)
-    - Emits task logits (for classification)
-
-    This is a minimal prototype; in practice you would replace
-    the feature encoder with a CNN/Transformer/etc.
-    """
-
-    def __init__(self, feature_dim: int = 128, num_markers: int = 8, num_classes: int = 10):
-        super().__init__()
-        self.encoder = nn.Linear(feature_dim, feature_dim)
-        self.marker_head = nn.Linear(feature_dim, num_markers)
-        self.classifier = nn.Linear(feature_dim, num_classes)
-
-    def encode(self, x: torch.Tensor) -> torch.Tensor:
-        h = torch.relu(self.encoder(x))
-        return h
-
-    def compute(self, x: torch.Tensor):
-        """
-        Compute SP and predictions for a batch of inputs.
-
-        Args:
-            x: tensor of shape (batch_size, feature_dim)
-
-        Returns:
-            sp_mean: scalar SP value (mean over batch)
-            logits: tensor of shape (batch_size, num_classes)
-        """
-        h = self.encode(x)
-        marker_logits = self.marker_head(h)
-        sp = compute_sp(marker_logits)  # (batch_size,)
-        logits = self.classifier(h)
-        return sp.mean(), logits
-
-    def grad_sp(self, x: torch.Tensor) -> torch.Tensor:
-        """
-        Compute gradient of SP wrt parameters as a flat vector.
-        NOTE: This assumes gradients have been zeroed before calling.
-        """
-        self.zero_grad()
-        sp, _ = self.compute(x)
-        sp.backward()
-        grads = []
-        for p in self.parameters():
-            if p.grad is not None:
-                grads.append(p.grad.view(-1))
-        if not grads:
-            return torch.zeros(0)
-        return torch.cat(grads).detach()
-
-    @torch.no_grad()
-    def parameters_vector(self) -> torch.Tensor:
-        """Flatten all parameters into a single vector."""
-        return torch.cat([p.data.view(-1) for p in self.parameters()])
-
-    @torch.no_grad()
-    def update_parameters(self, new_theta: torch.Tensor) -> None:
-        """Load a flat parameter vector back into the module parameters."""
-        offset = 0
-        for p in self.parameters():
-            n = p.numel()
-            p.data.copy_(new_theta[offset : offset + n].view_as(p))
-            offset += n

sci/reliability.py

@@ -1,18 +0,0 @@
-import torch
-
-
-class ReliabilityWeighting:
-    """
-    Placeholder reliability weighting.
-
-    In the full SCI framework this would:
-    - Estimate SNR, persistence, coherence for each feature
-    - Convert them to reliability scores z_f
-    - Normalize via a softmax to obtain weights w_f
-
-    For now, we return the input unchanged.
-    """
-
-    def __call__(self, features: torch.Tensor) -> torch.Tensor:
-        # TODO: implement reliability-based weighting
-        return features

sci/sp.py

@@ -1,24 +0,0 @@
-import torch
-import torch.nn.functional as F
-
-
-def compute_sp(marker_logits: torch.Tensor) -> torch.Tensor:
-    """
-    Compute an entropy-based Surgical Precision (SP) score.
-
-    SP = 1 - H(q) / log(K), where:
-    - q = softmax(marker_logits)
-    - H(q) is Shannon entropy over markers
-    - K is the number of markers
-
-    Args:
-        marker_logits: tensor of shape (..., K)
-
-    Returns:
-        SP: tensor of shape (...,) with values in [0, 1].
-    """
-    q = F.softmax(marker_logits, dim=-1)
-    k = q.shape[-1]
-    entropy = -torch.sum(q * torch.log(q + 1e-9), dim=-1)
-    sp = 1.0 - entropy / torch.log(torch.tensor(float(k), device=marker_logits.device))
-    return sp

sci/utils.py

@@ -1,7 +0,0 @@
-import torch
-from torch import nn
-
-
-def flatten_params(model: nn.Module) -> torch.Tensor:
-    """Flatten all parameters of a model into a single vector."""
-    return torch.cat([p.data.view(-1) for p in model.parameters()])