fix(opytimizer): Passes pre-commit through files.

examples/optimizers/science/create_sma.py

@@ -5,4 +5,4 @@
 params = {"z": 0.03}
 
 # Creates a WCA optimizer
-o = SMA(params=params)
+o = SMA(params=params)

examples/optimizers/swarm/create_waoa.py

@@ -1,4 +1,4 @@
 from opytimizer.optimizers.swarm import WAOA
 
 # Creates a WAOA optimizer
-o = WAOA()
+o = WAOA()

opytimizer/optimizers/science/sma.py

@@ -6,8 +6,8 @@
 import numpy as np
 
 import opytimizer.math.random as r
-import opytimizer.utils.exception as e
 import opytimizer.utils.constant as c
+import opytimizer.utils.exception as e
 from opytimizer.core import Optimizer
 from opytimizer.core.agent import Agent
 from opytimizer.core.function import Function
@@ -47,7 +47,7 @@ def __init__(self, params: Optional[Dict[str, Any]] = None) -> None:
         self.build(params)
 
         logger.info("Class overrided.")
-    
+
     @property
     def z(self) -> float:
         """Probability threshold."""
@@ -62,7 +62,7 @@ def z(self, z: float) -> None:
             raise e.ValueError("`z` should be >= 0")
 
         self._z = z
-    
+
     @property
     def weight(self) -> np.ndarray:
         """Array of weights."""
@@ -82,10 +82,8 @@ def compile(self, space: Space) -> None:
             space: A Space object containing meta-information.
         """
 
-        self.weight = np.zeros(
-            (space.n_agents, space.n_variables, space.n_dimensions)
-        )
-
+        self.weight = np.zeros((space.n_agents, space.n_variables, space.n_dimensions))
+
     def _update_weight(self, agents: List[Agent]):
         """Updates the weight of slime mould (eq. 2.5).
 
@@ -99,16 +97,19 @@ def _update_weight(self, agents: List[Agent]):
         n_agents = len(agents)
 
         for i in range(n_agents):
-
-            r1 = r.generate_uniform_random_number(0, 1,
-                (agents[i].n_variables, agents[i].n_dimensions))
-
-            if i <= int(n_agents/2):
-                self.weight[i] = 1 + r1 * \
-                    np.log10((best - agents[i].fit)/((best - worst)+c.EPSILON) + 1)
+
+            r1 = r.generate_uniform_random_number(
+                0, 1, (agents[i].n_variables, agents[i].n_dimensions)
+            )
+
+            if i <= int(n_agents / 2):
+                self.weight[i] = 1 + r1 * np.log10(
+                    (best - agents[i].fit) / ((best - worst) + c.EPSILON) + 1
+                )
             else:
-                self.weight[i] = 1 - r1 * \
-                    np.log10((best - agents[i].fit)/((best - worst)+c.EPSILON) + 1)
+                self.weight[i] = 1 - r1 * np.log10(
+                    (best - agents[i].fit) / ((best - worst) + c.EPSILON) + 1
+                )
 
     def update(self, space: Space, iteration: int, n_iterations: int) -> None:
         """Wraps Slime Mould Algorithm over all agents and variables.
@@ -123,13 +124,13 @@ def update(self, space: Space, iteration: int, n_iterations: int) -> None:
 
         self._update_weight(space.agents)
 
-        a = np.arctanh(-((iteration+1)/(n_iterations+1)) + 1)
-        b = 1 - (iteration+1)/(n_iterations+1)
-        
+        a = np.arctanh(-((iteration + 1) / (n_iterations + 1)) + 1)
+        b = 1 - (iteration + 1) / (n_iterations + 1)
+
         for i, agent in enumerate(space.agents):
-            
+
             r2 = r.generate_uniform_random_number()
-            
+
             if r2 < self.z:
                 agent.fill_with_uniform()
             else:
@@ -144,7 +145,9 @@ def update(self, space: Space, iteration: int, n_iterations: int) -> None:
                     l = r.generate_integer_random_number(
                         0, len(space.agents), exclude_value=k
                     )
-                    agent.position = space.agents[0].position + vb * \
-                        (self.weight[i] * (space.agents[k].position - space.agents[l].position))
+                    agent.position = space.agents[0].position + vb * (
+                        self.weight[i]
+                        * (space.agents[k].position - space.agents[l].position)
+                    )
                 else:
-                    agent.position *= vc
+                    agent.position *= vc

opytimizer/optimizers/swarm/waoa.py

@@ -8,16 +8,17 @@
 
 import opytimizer.math.random as r
 import opytimizer.utils.logging as l
-from opytimizer.core.optimizer import Optimizer
 from opytimizer.core.function import Function
+from opytimizer.core.optimizer import Optimizer
 from opytimizer.core.space import Space
 
 logger = l.get_logger(__name__)
 
+
 class WAOA(Optimizer):
     """A WAOA class, inherited from Optimizer.
 
-    This is the designed class to dife WAOA-related 
+    This is the designed class to dife WAOA-related
     variables and methods.
 
     References:
@@ -33,13 +34,13 @@ def __init__(self, params: Optional[Dict[str, Any]] = None) -> None:
             params (str): Contains key-value parameters to the meta-heuristics.
         """
 
-        logger.info('Overriding class: Optimizer -> SSA')
-        
+        logger.info("Overriding class: Optimizer -> SSA")
+
         super(WAOA, self).__init__()
 
         self.build(params)
 
-        logger.info('Class overrided.')
+        logger.info("Class overrided.")
 
     def update(self, space: Space, function: Function, iteration: int) -> None:
         """Wraps Walrus Optimization Algorithm over all agents and variables.
@@ -55,11 +56,13 @@ def update(self, space: Space, function: Function, iteration: int) -> None:
             a = copy.deepcopy(agent)
 
             for j in range(space.n_variables):
-                
+
                 r1 = r.generate_integer_random_number(1, 2)
                 r2 = r.generate_uniform_random_number()
 
-                a.position[j] = agent.position[j] + r2 * (space.best_agent.position[j] - r1 * agent.position[j])
+                a.position[j] = agent.position[j] + r2 * (
+                    space.best_agent.position[j] - r1 * agent.position[j]
+                )
 
             a.clip_by_bound()
 
@@ -69,24 +72,28 @@ def update(self, space: Space, function: Function, iteration: int) -> None:
                 agent.fit = copy.deepcopy(a.fit)
 
             k = r.generate_integer_random_number(0, space.n_agents, i)
-            
-            if (space.agents[k].fit < agent.fit):
+
+            if space.agents[k].fit < agent.fit:
 
                 for j in range(space.n_variables):
-                
+
                     r1 = r.generate_integer_random_number(1, 2)
                     r2 = r.generate_uniform_random_number()
 
-                    a.position[j] = agent.position[j] + r2 * (space.agents[k].position[j] - r1 * agent.position[j])
-
+                    a.position[j] = agent.position[j] + r2 * (
+                        space.agents[k].position[j] - r1 * agent.position[j]
+                    )
+
             else:
 
                 for j in range(space.n_variables):
-                
+
                     r2 = r.generate_uniform_random_number()
 
-                    a.position[j] = agent.position[j] + r2 * (agent.position[j] - space.agents[k].position[j])
-
+                    a.position[j] = agent.position[j] + r2 * (
+                        agent.position[j] - space.agents[k].position[j]
+                    )
+
             a.clip_by_bound()
 
             a.fit = function(a.position)
@@ -98,11 +105,14 @@ def update(self, space: Space, function: Function, iteration: int) -> None:
 
                 r2 = r.generate_uniform_random_number()
 
-                a.position[j] = agent.position[j] + ((agent.ub[j]/(iteration+1)) - r2 * (agent.lb[j]/(iteration+1)))
+                a.position[j] = agent.position[j] + (
+                    (agent.ub[j] / (iteration + 1))
+                    - r2 * (agent.lb[j] / (iteration + 1))
+                )
 
             a.clip_by_bound()
 
             a.fit = function(a.position)
             if a.fit < agent.fit:
                 agent.position = copy.deepcopy(a.position)
-                agent.fit = copy.deepcopy(a.fit)
+                agent.fit = copy.deepcopy(a.fit)

tests/opytimizer/optimizers/science/test_sma.py

@@ -3,28 +3,31 @@
 from opytimizer.optimizers.science import sma
 from opytimizer.spaces import search
 
+
 def test_sma_params():
     params = {"z": 0.03}
 
     new_sma = sma.SMA(params=params)
 
     assert new_sma.z == 0.03
 
+
 def test_sma_params_setter():
     new_sma = sma.SMA()
 
     try:
         new_sma.z = "a"
     except:
         new_sma.z = 0.05
-    
+
     try:
         new_sma.z = -1
     except:
         new_sma.z = 0.05
-    
+
     assert new_sma.z == 0.05
 
+
 def test_sma_compile():
     search_space = search.SearchSpace(
         n_agents=2, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]
@@ -40,6 +43,7 @@ def test_sma_compile():
 
     assert new_sma.weight == np.array([1])
 
+
 def test_sma_update_weight():
     def square(x):
         return np.sum(x**2)
@@ -53,6 +57,7 @@ def square(x):
 
     new_sma._update_weight(search_space.agents)
 
+
 def test_sma_update():
     search_space = search.SearchSpace(
         n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]
@@ -61,4 +66,4 @@ def test_sma_update():
     new_sma = sma.SMA()
     new_sma.compile(search_space)
 
-    new_sma.update(search_space, 1, 10)
+    new_sma.update(search_space, 1, 10)