docs test fix

src/thefittest/utils/crossovers.py

@@ -542,7 +542,7 @@ def empty_crossoverGP(
     >>>
     >>> # Example
     >>> X = np.array([[0.3, 0.7], [0.3, 1.1], [3.5, 11.0]], dtype=np.float64)
-    >>> functional_set_names = ("add", "mul", "neg", "inv")
+    >>> functional_set_names = ("add", "mul")
     >>> max_tree_level = 5
     >>>
     >>> # Initialize Universal Set for Symbolic Regression
@@ -605,7 +605,7 @@ def standard_crossover(
     >>> # Example
     >>>
     >>> X = np.array([[0.3, 0.7], [0.3, 1.1], [3.5, 11.0]], dtype=np.float64)
-    >>> functional_set_names = ("add", "mul", "neg", "inv")
+    >>> functional_set_names = ("add", "mul")
     >>> max_tree_level = 5
     >>>
     >>> # Initialize Universal Set for Symbolic Regression
@@ -685,7 +685,7 @@ def one_point_crossoverGP(
     >>> # Example
     >>>
     >>> X = np.array([[0.3, 0.7], [0.3, 1.1], [3.5, 11.0]], dtype=np.float64)
-    >>> functional_set_names = ("add", "mul", "neg", "inv")
+    >>> functional_set_names = ("add", "mul")
     >>> max_tree_level = 5
     >>>
     >>> # Initialize Universal Set for Symbolic Regression
@@ -763,7 +763,7 @@ def uniform_crossoverGP(
     >>> # Example
     >>>
     >>> X = np.array([[0.3, 0.7], [0.3, 1.1], [3.5, 11.0]], dtype=np.float64)
-    >>> functional_set_names = ("add", "mul", "neg", "inv")
+    >>> functional_set_names = ("add", "mul")
     >>> max_tree_level = 5
     >>>
     >>> # Initialize Universal Set for Symbolic Regression
@@ -849,7 +849,7 @@ def uniform_proportional_crossover_GP(
     >>> # Example
     >>>
     >>> X = np.array([[0.3, 0.7], [0.3, 1.1], [3.5, 11.0]], dtype=np.float64)
-    >>> functional_set_names = ("add", "mul", "neg", "inv")
+    >>> functional_set_names = ("add", "mul")
     >>> max_tree_level = 5
     >>>
     >>> # Initialize Universal Set for Symbolic Regression
@@ -936,7 +936,7 @@ def uniform_rank_crossover_GP(
     >>> # Example
     >>>
     >>> X = np.array([[0.3, 0.7], [0.3, 1.1], [3.5, 11.0]], dtype=np.float64)
-    >>> functional_set_names = ("add", "mul", "neg", "inv")
+    >>> functional_set_names = ("add", "mul")
     >>> max_tree_level = 5
     >>>
     >>> # Initialize Universal Set for Symbolic Regression
@@ -1024,7 +1024,7 @@ def uniform_tournament_crossover_GP(
     >>> # Example
     >>>
     >>> X = np.array([[0.3, 0.7], [0.3, 1.1], [3.5, 11.0]], dtype=np.float64)
-    >>> functional_set_names = ("add", "mul", "neg", "inv")
+    >>> functional_set_names = ("add", "mul")
     >>> max_tree_level = 5
     >>>
     >>> # Initialize Universal Set for Symbolic Regression

src/thefittest/utils/mutations.py

@@ -433,7 +433,6 @@ def current_to_rand_1(
     --------
     >>> from thefittest.utils.mutations import current_to_rand_1
     >>> import numpy as np
-    >>>
     >>> # Example
     >>> current_individual = np.array([0.5, 0.3, 0.8], dtype=np.float64)
     >>> best_individual = np.array([0.6, 0.4, 0.7], dtype=np.float64)
@@ -494,7 +493,6 @@ def current_to_pbest_1_archive(
     --------
     >>> from thefittest.utils.mutations import current_to_pbest_1_archive
     >>> import numpy as np
-    >>>
     >>> # Example
     >>> current_individual = np.array([0.5, 0.3, 0.8], dtype=np.float64)
     >>> population = np.array([[0.2, 0.1, 0.5], [0.8, 0.6, 0.9], [0.3, 0.7, 0.4], [0.9, 0.5, 0.2]], dtype=np.float64)
@@ -556,7 +554,6 @@ def current_to_pbest_1_archive_p_min(
     --------
     >>> from thefittest.utils.mutations import current_to_pbest_1_archive_p_min
     >>> import numpy as np
-    >>>
     >>> # Example
     >>> current_individual = np.array([0.5, 0.3, 0.8], dtype=np.float64)
     >>> population = np.array([[0.2, 0.1, 0.5], [0.8, 0.6, 0.9], [0.3, 0.7, 0.4], [0.9, 0.5, 0.2]], dtype=np.float64)
@@ -615,19 +612,15 @@ def point_mutation(tree: Tree, uniset: UniversalSet, proba: float, max_level: in
     >>> from thefittest.base import Tree
     >>> from thefittest.base import init_symbolic_regression_uniset
     >>> from thefittest.utils.mutations import point_mutation
-    >>>
     >>> # Example Parameters
     >>> X = np.array([[0.3, 0.7], [0.3, 1.1], [3.5, 11.0]], dtype=np.float64)
-    >>> functional_set_names = ("add", "mul", "neg", "inv")
+    >>> functional_set_names = ("add", "mul")
     >>> max_tree_level = 4
     >>> mutation_probability = 0.5
-    >>>
     >>> # Initialize Universal Set for Symbolic Regression
     >>> universal_set = init_symbolic_regression_uniset(X, functional_set_names)
-    >>>
     >>> # Generate a Random Tree
     >>> tree = Tree.random_tree(universal_set, max_tree_level)
-    >>>
     >>> # Perform Point Mutation
     >>> mutated_tree = point_mutation(tree, universal_set, mutation_probability, max_tree_level)
     >>> print("Original Tree:", tree)
@@ -680,19 +673,15 @@ def growing_mutation(tree: Tree, uniset: UniversalSet, proba: float, max_level:
     >>> from thefittest.base import Tree
     >>> from thefittest.base import init_symbolic_regression_uniset
     >>> from thefittest.utils.mutations import growing_mutation
-    >>>
     >>> # Example Parameters
     >>> X = np.array([[0.3, 0.7], [0.3, 1.1], [3.5, 11.0]], dtype=np.float64)
-    >>> functional_set_names = ("add", "mul", "neg", "inv")
+    >>> functional_set_names = ("add", "mul")
     >>> max_tree_level = 4
     >>> mutation_probability = 0.5
-    >>>
     >>> # Initialize Universal Set for Symbolic Regression
     >>> universal_set = init_symbolic_regression_uniset(X, functional_set_names)
-    >>>
     >>> # Generate a Random Tree
     >>> tree = Tree.random_tree(universal_set, max_tree_level)
-    >>>
     >>> # Perform Growing Mutation
     >>> mutated_tree = growing_mutation(tree, universal_set, mutation_probability, max_tree_level)
     >>> print("Original Tree:", tree)
@@ -739,19 +728,15 @@ def swap_mutation(tree: Tree, uniset: UniversalSet, proba: float, max_leve: int)
     >>> from thefittest.base import Tree
     >>> from thefittest.base import init_symbolic_regression_uniset
     >>> from thefittest.utils.mutations import swap_mutation
-    >>>
     >>> # Example Parameters
     >>> X = np.array([[0.3, 0.7], [0.3, 1.1], [3.5, 11.0]], dtype=np.float64)
-    >>> functional_set_names = ("add", "mul", "neg", "inv")
+    >>> functional_set_names = ("add", "mul")
     >>> max_tree_level = 4
     >>> mutation_probability = 0.5
-    >>>
     >>> # Initialize Universal Set for Symbolic Regression
     >>> universal_set = init_symbolic_regression_uniset(X, functional_set_names)
-    >>>
     >>> # Generate a Random Tree
     >>> tree = Tree.random_tree(universal_set, max_tree_level)
-    >>>
     >>> # Perform Swap Mutation
     >>> mutated_tree = swap_mutation(tree, universal_set, mutation_probability, max_tree_level)
     >>> print("Original Tree:", tree)
@@ -806,19 +791,15 @@ def shrink_mutation(tree: Tree, uniset: UniversalSet, proba: float, max_level: i
     >>> from thefittest.base import Tree
     >>> from thefittest.base import init_symbolic_regression_uniset
     >>> from thefittest.utils.mutations import shrink_mutation
-    >>>
     >>> # Example Parameters
     >>> X = np.array([[0.3, 0.7], [0.3, 1.1], [3.5, 11.0]], dtype=np.float64)
-    >>> functional_set_names = ("add", "mul", "neg", "inv")
+    >>> functional_set_names = ("add", "mul")
     >>> max_tree_level = 4
     >>> mutation_probability = 0.5
-    >>>
     >>> # Initialize Universal Set for Symbolic Regression
     >>> universal_set = init_symbolic_regression_uniset(X, functional_set_names)
-    >>>
     >>> # Generate a Random Tree
     >>> tree = Tree.random_tree(universal_set, max_tree_level)
-    >>>
     >>> # Perform Shrink Mutation
     >>> mutated_tree = shrink_mutation(tree, universal_set, mutation_probability, max_tree_level)
     >>> print("Original Tree:", tree)

src/thefittest/utils/random.py

@@ -36,11 +36,10 @@ def sattolo_shuffle(arr):
     --------
     >>> from numba import jit
     >>> import numpy as np
-    >>>
+    >>> from thefittest.utils.random import sattolo_shuffle
     >>> # Example of using Sattolo's shuffle
-    >>> arr = np.array([1, 2, 3, 4, 5])
-    >>> shuffled_arr = sattolo_shuffle(arr)
-    >>> print("Shuffled Array:", shuffled_arr)
+    >>> arr = np.array([1, 2, 3, 4, 5], dtype = np.int64)
+    >>> print("Shuffled Array:", sattolo_shuffle(arr))
     Shuffled Array: ...
 
     Notes
@@ -76,11 +75,10 @@ def sattolo_shuffle_2d(arr):
     --------
     >>> from numba import jit
     >>> import numpy as np
-    >>>
+    >>> from thefittest.utils.random import sattolo_shuffle_2d
     >>> # Example of using Sattolo's shuffle for a 2D array
     >>> arr_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
-    >>> shuffled_arr_2d = sattolo_shuffle_2d(arr_2d)
-    >>> print("Shuffled 2D Array:", shuffled_arr_2d)
+    >>> print("Shuffled 2D Array:", sattolo_shuffle_2d(arr_2d))
     Shuffled 2D Array: ...
 
     Notes
@@ -349,9 +347,9 @@ def randint(low, high, size):
     --------
     >>> from numba import jit
     >>> import numpy as np
-    >>>
+    >>> from thefittest.utils.random import randint
     >>> # Example of generating random integers
-    >>> result = numba_randint(low=1, high=10, size=5)
+    >>> result = randint(low=1, high=10, size=5)
     >>> print("Random Integers:", result)
     Random Integers: ...
 

src/thefittest/utils/transformations.py

@@ -41,7 +41,7 @@ def minmax_scale(data: NDArray[Union[np.int64, np.float64]]) -> NDArray[np.float
     >>> print("Original Data:", example_data)
     Original Data: [ 2  5 10  8  3]
     >>> print("Scaled Data:", scaled_data)
-    Scaled Data: [0.   0.375 1.   0.75 0.125]
+    Scaled Data: [0.    0.375 1.    0.75  0.125]
     """
     data_copy = data.copy()
     max_value = data_copy.max()