feat: adds mcmc chain that returns number of repeats

pdme/model/model.py

@@ -99,3 +99,65 @@ class DipoleModel:
 			else:
 				chain.append((numpy.squeeze(current_cost).item(), current))
 		return chain
+
+	def get_repeat_counting_mcmc_chain(
+		self,
+		seed,
+		cost_function,
+		chain_length,
+		threshold_cost: float,
+		stdevs: pdme.subspace_simulation.MCMCStandardDeviation,
+		initial_cost: Optional[float] = None,
+		rng_arg: Optional[numpy.random.Generator] = None,
+	) -> Tuple[int, List[Tuple[float, numpy.ndarray]]]:
+		"""
+		performs constrained markov chain monte carlo starting on seed parameter.
+		The cost function given is used as a constrained to condition the chain;
+		a new state is only accepted if cost_function(state) < cost_function(previous_state).
+		The stdevs passed in are the stdevs we're expected to use.
+
+		Because we're using this for subspace simulation where our proposal function is not too important, we're in good shape.
+		Note that for our adaptive stdevs to work, there's an unwritten contract that we sort each dipole in the state by frequency (increasing).
+
+		The seed is a list of dipoles, and each chain state is a list of dipoles as well.
+
+		initial_cost is a performance guy that lets you pre-populate the initial cost used to define the condition.
+		Probably premature optimisation.
+
+		Chain has type of [ (cost: float, state: dipole_ndarray ) ] format,
+		returning (repeat_count, chain) to keep track of number of repeats
+		"""
+		_logger.debug(
+			f"Starting Markov Chain Monte Carlo with seed: {seed} for chain length {chain_length} and provided stdevs {stdevs}"
+		)
+		chain: List[Tuple[float, numpy.ndarray]] = []
+		if initial_cost is None:
+			current_cost = cost_function(numpy.array([seed]))
+		else:
+			current_cost = initial_cost
+		current = seed
+		repeat_event_count = 0
+		for _ in range(chain_length):
+			dips = []
+			for dipole_index, dipole in enumerate(current):
+				_logger.debug(dipole_index)
+				_logger.debug(dipole)
+				stdev = stdevs[dipole_index]
+				tentative_dip = self.markov_chain_monte_carlo_proposal(
+					dipole, stdev, rng_arg
+				)
+
+				dips.append(tentative_dip)
+			dips_array = pdme.subspace_simulation.sort_array_of_dipoles_by_frequency(
+				dips
+			)
+			tentative_cost = cost_function(numpy.array([dips_array]))[0]
+			if tentative_cost < threshold_cost:
+				chain.append((numpy.squeeze(tentative_cost).item(), dips_array))
+				current = dips_array
+				current_cost = tentative_cost
+			else:
+				# repeating a sample, increase count
+				repeat_event_count += 1
+				chain.append((numpy.squeeze(current_cost).item(), current))
+		return (repeat_event_count, chain)