Reimplementation of Continuous Space

benchmarks/configurations.py

@@ -90,14 +90,19 @@
             "seeds": 25,
             "replications": 3,
             "steps": 20,
-            "parameters": {"population": 200, "width": 100, "height": 100, "vision": 5},
+            "parameters": {
+                "population_size": 200,
+                "width": 100,
+                "height": 100,
+                "vision": 5,
+            },
         },
         "large": {
             "seeds": 10,
             "replications": 3,
             "steps": 10,
             "parameters": {
-                "population": 400,
+                "population_size": 400,
                 "width": 150,
                 "height": 150,
                 "vision": 15,

mesa/examples/basic/boid_flockers/agents.py

@@ -6,10 +6,10 @@
 
 import numpy as np
 
-from mesa import Agent
+from mesa.experimental.continuous_space import ContinuousSpaceAgent
 
 
-class Boid(Agent):
+class Boid(ContinuousSpaceAgent):
     """A Boid-style flocker agent.
 
     The agent follows three behaviors to flock:
@@ -26,10 +26,12 @@ class Boid(Agent):
     def __init__(
         self,
         model,
-        speed,
-        direction,
-        vision,
-        separation,
+        space,
+        position=(0, 0),
+        speed=1,
+        direction=(1, 1),
+        vision=1,
+        separation=1,
         cohere=0.03,
         separate=0.015,
         match=0.05,
@@ -46,7 +48,8 @@ def __init__(
             separate: Relative importance of avoiding close neighbors (default: 0.015)
             match: Relative importance of matching neighbors' directions (default: 0.05)
         """
-        super().__init__(model)
+        super().__init__(space, model)
+        self.position = position
         self.speed = speed
         self.direction = direction
         self.vision = vision
@@ -58,47 +61,31 @@ def __init__(
 
     def step(self):
         """Get the Boid's neighbors, compute the new vector, and move accordingly."""
-        neighbors = self.model.space.get_neighbors(self.pos, self.vision, True)
+        neighbors, distances = self.get_neighbors_in_radius(radius=self.vision)
         self.neighbors = [n for n in neighbors if n is not self]
 
         # If no neighbors, maintain current direction
-        if not self.neighbors:
-            new_pos = self.pos + self.direction * self.speed
-            self.model.space.move_agent(self, new_pos)
+        if not neighbors:
+            self.position += self.direction * self.speed
             return
 
-        # Initialize vectors for the three flocking behaviors
-        cohere = np.zeros(2)  # Cohesion vector
-        match_vector = np.zeros(2)  # Alignment vector
-        separation_vector = np.zeros(2)  # Separation vector
+        delta = self.space.calculate_difference_vector(self.position, agents=neighbors)
 
-        # Calculate the contribution of each neighbor to the three behaviors
-        for neighbor in self.neighbors:
-            heading = self.model.space.get_heading(self.pos, neighbor.pos)
-            distance = self.model.space.get_distance(self.pos, neighbor.pos)
-
-            # Cohesion - steer towards the average position of neighbors
-            cohere += heading
-
-            # Separation - avoid getting too close
-            if distance < self.separation:
-                separation_vector -= heading
-
-            # Alignment - match neighbors' flying direction
-            match_vector += neighbor.direction
-
-        # Weight each behavior by its factor and normalize by number of neighbors
-        n = len(self.neighbors)
-        cohere = cohere * self.cohere_factor
-        separation_vector = separation_vector * self.separate_factor
-        match_vector = match_vector * self.match_factor
+        cohere_vector = delta.sum(axis=0) * self.cohere_factor
+        separation_vector = (
+            -1 * delta[distances < self.separation].sum(axis=0) * self.separate_factor
+        )
+        match_vector = (
+            np.asarray([n.direction for n in neighbors]).sum(axis=0) * self.match_factor
+        )
 
         # Update direction based on the three behaviors
-        self.direction += (cohere + separation_vector + match_vector) / n
+        self.direction += (cohere_vector + separation_vector + match_vector) / len(
+            neighbors
+        )
 
         # Normalize direction vector
         self.direction /= np.linalg.norm(self.direction)
 
         # Move boid
-        new_pos = self.pos + self.direction * self.speed
-        self.model.space.move_agent(self, new_pos)
+        self.position += self.direction * self.speed

mesa/examples/basic/boid_flockers/app.py

@@ -1,3 +1,8 @@
+import os
+import sys
+
+sys.path.insert(0, os.path.abspath("../../../.."))
+
 from mesa.examples.basic.boid_flockers.model import BoidFlockers
 from mesa.visualization import Slider, SolaraViz, make_space_component
 
@@ -17,7 +22,7 @@ def boid_draw(agent):
         "value": 42,
         "label": "Random Seed",
     },
-    "population": Slider(
+    "population_size": Slider(
         label="Number of boids",
         value=100,
         min=10,

mesa/examples/basic/boid_flockers/model.py

@@ -5,19 +5,25 @@
 Uses numpy arrays to represent vectors.
 """
 
+import os
+import sys
+
+sys.path.insert(0, os.path.abspath("../../../.."))
+
+
 import numpy as np
 
 from mesa import Model
 from mesa.examples.basic.boid_flockers.agents import Boid
-from mesa.space import ContinuousSpace
+from mesa.experimental.continuous_space import ContinuousSpace
 
 
 class BoidFlockers(Model):
     """Flocker model class. Handles agent creation, placement and scheduling."""
 
     def __init__(
         self,
-        population=100,
+        population_size=100,
         width=100,
         height=100,
         speed=1,
@@ -31,7 +37,7 @@
         """Create a new Boids Flocking model.
 
         Args:
-            population: Number of Boids in the simulation (default: 100)
+            population_size: Number of Boids in the simulation (default: 100)
             width: Width of the space (default: 100)
             height: Height of the space (default: 100)
             speed: How fast the Boids move (default: 1)
@@ -44,48 +50,35 @@
         """
         super().__init__(seed=seed)
 
-        # Model Parameters
-        self.population = population
-        self.vision = vision
-        self.speed = speed
-        self.separation = separation
-
         # Set up the space
-        self.space = ContinuousSpace(width, height, torus=True)
-
-        # Store flocking weights
-        self.factors = {"cohere": cohere, "separate": separate, "match": match}
+        self.space = ContinuousSpace(
+            [[0, width], [0, height]],
+            torus=True,
+            random=self.random,
+            n_agents=population_size,
+        )
 
         # Create and place the Boid agents
-        self.make_agents()
+        position = self.rng.random(size=(population_size, 2)) * self.space.size
+        direction = self.rng.uniform(-1, 1, size=(population_size, 2))
+        Boid.create_agents(
+            self,
+            population_size,
+            self.space,
+            position=position,
+            direction=direction,
+            cohere=cohere,
+            separate=separate,
+            match=match,
+            speed=speed,
+            vision=vision,
+            separation=separation,
+        )
 
         # For tracking statistics
         self.average_heading = None
         self.update_average_heading()
 
-    def make_agents(self):
-        """Create and place all Boid agents randomly in the space."""
-        for _ in range(self.population):
-            # Random position
-            x = self.random.random() * self.space.x_max
-            y = self.random.random() * self.space.y_max
-            pos = np.array((x, y))
-
-            # Random initial direction
-            direction = np.random.random(2) * 2 - 1  # Random vector between -1 and 1
-            direction /= np.linalg.norm(direction)  # Normalize
-
-            # Create and place the Boid
-            boid = Boid(
-                model=self,
-                speed=self.speed,
-                direction=direction,
-                vision=self.vision,
-                separation=self.separation,
-                **self.factors,
-            )
-            self.space.place_agent(boid, pos)
-
     def update_average_heading(self):
         """Calculate the average heading (direction) of all Boids."""
         if not self.agents:
@@ -103,3 +96,10 @@
         """
         self.agents.shuffle_do("step")
         self.update_average_heading()
+
+
+if __name__ == "__main__":
+    model = BoidFlockers(population_size=200, width=100, height=100, vision=5, seed=42)
+
+    for _i in range(20):
+        model.step()

mesa/experimental/__init__.py

@@ -15,6 +15,6 @@
     - Features graduate from experimental status once their APIs are stabilized
 """
 
-from mesa.experimental import cell_space, devs, mesa_signals
+from mesa.experimental import cell_space, continuous_space, devs, mesa_signals
 
-__all__ = ["cell_space", "devs", "mesa_signals"]
+__all__ = ["cell_space", "continuous_space", "devs", "mesa_signals"]

mesa/experimental/continuous_space/__init__.py

@@ -0,0 +1,8 @@
+"""Continuous space support."""
+
+from mesa.experimental.continuous_space.continuous_space import ContinuousSpace
+from mesa.experimental.continuous_space.continuous_space_agents import (
+    ContinuousSpaceAgent,
+)
+
+__all__ = ["ContinuousSpace", "ContinuousSpaceAgent"]