feat: adds a filter that works with cost functions

deepdog/direct_monte_carlo/cost_function_filter.py

@@ -0,0 +1,19 @@
+from deepdog.direct_monte_carlo.direct_mc import DirectMonteCarloFilter
+from typing import Sequence, Callable
+import numpy
+
+class CostFunctionTargetFilter(DirectMonteCarloFilter):
+	def __init__(self, cost_function: Callable[[numpy.ndarray], numpy.ndarray], target_cost: float):
+		"""
+		Filters dipoles by cost, only leaving dipoles with cost below target_cost
+		"""
+		self.cost_function = cost_function
+		self.target_cost = target_cost
+
+	def filter_samples(self, samples: numpy.ndarray) -> numpy.ndarray:
+		current_sample = samples
+		
+		costs = self.cost_function(current_sample)
+
+		current_sample = current_sample[costs < self.target_cost]
+		return current_sample

tests/direct_monte_carlo/test_cost_function_filter.py

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+import deepdog.direct_monte_carlo.cost_function_filter
+import numpy
+
+def test_px_cost_function_filter_example():
+
+	dipoles_1 = [
+		[1, 2, 3, 4, 5, 6, 7],
+		[2, 3, 2, 5, 4, 7, 6],
+	]
+
+	dipoles_2 = [
+		[15, 9, 8, 7, 6, 5, 3],
+		[30, 4, 4, 7, 3, 1, 4],
+	]
+
+	dipoleses = numpy.array([dipoles_1, dipoles_2])
+
+	def cost_function(dipoleses: numpy.ndarray) -> numpy.ndarray:
+		return dipoleses[:, :, 0].max(axis=-1)
+
+	
+	expected_costs = numpy.array([2, 30])
+
+	numpy.testing.assert_array_equal(cost_function(dipoleses), expected_costs)
+
+	filter = deepdog.direct_monte_carlo.cost_function_filter.CostFunctionTargetFilter(cost_function, 5)
+
+	actual_filtered = filter.filter_samples(dipoleses)
+	expected_filtered = numpy.array([dipoles_1])
+	assert actual_filtered.size != 0
+	numpy.testing.assert_array_equal(actual_filtered, expected_filtered)
+
+
+
+	filter_stricter = deepdog.direct_monte_carlo.cost_function_filter.CostFunctionTargetFilter(cost_function, 0.5)
+
+	actual_filtered_stricter = filter_stricter.filter_samples(dipoleses)
+	assert actual_filtered_stricter.size == 0