Delete advanced_inference.py

advanced_inference.py

@@ -1,73 +0,0 @@
-"""
-Hamiltonian Monte Carlo (NUTS) for complex pattern discovery.
-Uses Pyro with NUTS and provides posterior summaries.
-"""
-import logging
-import pyro
-import pyro.distributions as dist
-from pyro.infer import MCMC, NUTS
-import torch
-import numpy as np
-import pandas as pd
-from typing import Dict, Any, Optional
-
-logger = logging.getLogger(__name__)
-
-class HMCAnalyzer:
-    """Runs HMC on a simple regression model to demonstrate advanced inference."""
-
-    def __init__(self):
-        self.mcmc = None
-        self.trace = None
-
-    def _model(self, x, y=None):
-        # Linear regression with unknown noise
-        alpha = pyro.sample("alpha", dist.Normal(0, 10))
-        beta = pyro.sample("beta", dist.Normal(0, 1))
-        sigma = pyro.sample("sigma", dist.HalfNormal(1))
-        mu = alpha + beta * x
-        with pyro.plate("data", len(x)):
-            pyro.sample("obs", dist.Normal(mu, sigma), obs=y)
-
-    def run_inference(self, data: Optional[pd.DataFrame] = None, num_samples: int = 500, warmup: int = 200):
-        """
-        Run HMC on synthetic or provided data.
-        If no data, generate synthetic trend data.
-        """
-        if data is None:
-            # Create synthetic data: a linear trend with noise
-            x = torch.linspace(0, 10, 50)
-            true_alpha, true_beta, true_sigma = 2.0, -0.3, 0.5
-            y = true_alpha + true_beta * x + torch.randn(50) * true_sigma
-        else:
-            # Assume data has columns 'x' and 'y'
-            x = torch.tensor(data['x'].values, dtype=torch.float32)
-            y = torch.tensor(data['y'].values, dtype=torch.float32)
-
-        nuts_kernel = NUTS(self._model)
-        self.mcmc = MCMC(nuts_kernel, num_samples=num_samples, warmup_steps=warmup)
-        self.mcmc.run(x, y)
-
-        self.trace = self.mcmc.get_samples()
-        return self._summary()
-
-    def _summary(self) -> Dict[str, Any]:
-        """Return summary statistics of posterior samples."""
-        if self.trace is None:
-            return {}
-        summary = {}
-        for key in ['alpha', 'beta', 'sigma']:
-            samples = self.trace[key].numpy()
-            summary[key] = {
-                'mean': float(samples.mean()),
-                'std': float(samples.std()),
-                'hpd_5': float(np.percentile(samples, 5)),
-                'hpd_95': float(np.percentile(samples, 95))
-            }
-        return summary
-
-    def get_trace_data(self) -> Dict[str, np.ndarray]:
-        """Return posterior samples for plotting."""
-        if self.trace is None:
-            return {}
-        return {k: v.numpy() for k, v in self.trace.items()}