Merge branch 'master' into fix-some-ARG002-per-file-ignores

Author: MaximSmolskiy

.pre-commit-config.yaml

@@ -11,12 +11,12 @@ repos:
       - id: requirements-txt-fixer
 
   - repo: https://github.com/MarcoGorelli/auto-walrus
-    rev: 0.3.3
+    rev: 0.3.4
     hooks:
       - id: auto-walrus
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.1
+    rev: v0.4.2
     hooks:
       - id: ruff
       - id: ruff-format
@@ -47,7 +47,7 @@ repos:
       - id: validate-pyproject
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.9.0
+    rev: v1.10.0
     hooks:
       - id: mypy
         args:

DIRECTORY.md

@@ -773,6 +773,7 @@
   * [Inverse Of Matrix](matrix/inverse_of_matrix.py)
   * [Largest Square Area In Matrix](matrix/largest_square_area_in_matrix.py)
   * [Matrix Class](matrix/matrix_class.py)
+  * [Matrix Equalization](matrix/matrix_equalization.py)
   * [Matrix Multiplication Recursion](matrix/matrix_multiplication_recursion.py)
   * [Matrix Operation](matrix/matrix_operation.py)
   * [Max Area Of Island](matrix/max_area_of_island.py)

data_structures/arrays/sudoku_solver.py

@@ -150,7 +150,7 @@ def time_solve(grid):
             display(grid_values(grid))
             if values:
                 display(values)
-            print("(%.5f seconds)\n" % t)
+            print(f"({t:.5f} seconds)\n")
         return (t, solved(values))
 
     times, results = zip(*[time_solve(grid) for grid in grids])
@@ -217,4 +217,4 @@ def shuffled(seq):
         start = time.monotonic()
         solve(puzzle)
         t = time.monotonic() - start
-        print("Solved: %.5f sec" % t)
+        print(f"Solved: {t:.5f} sec")

machine_learning/linear_discriminant_analysis.py

@@ -256,7 +256,7 @@ def valid_input(
     input_type: Callable[[object], num],  # Usually float or int
     input_msg: str,
     err_msg: str,
-    condition: Callable[[num], bool] = lambda x: True,
+    condition: Callable[[num], bool] = lambda _: True,
     default: str | None = None,
 ) -> num:
     """

matrix/matrix_equalization.py

@@ -0,0 +1,55 @@
+from sys import maxsize
+
+
+def array_equalization(vector: list[int], step_size: int) -> int:
+    """
+    This algorithm equalizes all elements of the input vector
+    to a common value, by making the minimal number of
+    "updates" under the constraint of a step size (step_size).
+
+    >>> array_equalization([1, 1, 6, 2, 4, 6, 5, 1, 7, 2, 2, 1, 7, 2, 2], 4)
+    4
+    >>> array_equalization([22, 81, 88, 71, 22, 81, 632, 81, 81, 22, 92], 2)
+    5
+    >>> array_equalization([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 5)
+    0
+    >>> array_equalization([22, 22, 22, 33, 33, 33], 2)
+    2
+    >>> array_equalization([1, 2, 3], 0)
+    Traceback (most recent call last):
+    ValueError: Step size must be positive and non-zero.
+    >>> array_equalization([1, 2, 3], -1)
+    Traceback (most recent call last):
+    ValueError: Step size must be positive and non-zero.
+    >>> array_equalization([1, 2, 3], 0.5)
+    Traceback (most recent call last):
+    ValueError: Step size must be an integer.
+    >>> array_equalization([1, 2, 3], maxsize)
+    1
+    """
+    if step_size <= 0:
+        raise ValueError("Step size must be positive and non-zero.")
+    if not isinstance(step_size, int):
+        raise ValueError("Step size must be an integer.")
+
+    unique_elements = set(vector)
+    min_updates = maxsize
+
+    for element in unique_elements:
+        elem_index = 0
+        updates = 0
+        while elem_index < len(vector):
+            if vector[elem_index] != element:
+                updates += 1
+                elem_index += step_size
+            else:
+                elem_index += 1
+        min_updates = min(min_updates, updates)
+
+    return min_updates
+
+
+if __name__ == "__main__":
+    from doctest import testmod
+
+    testmod()

neural_network/input_data.py

@@ -156,7 +156,8 @@ def __init__(
         self._rng = np.random.default_rng(seed1 if seed is None else seed2)
         dtype = dtypes.as_dtype(dtype).base_dtype
         if dtype not in (dtypes.uint8, dtypes.float32):
-            raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype)
+            msg = f"Invalid image dtype {dtype!r}, expected uint8 or float32"
+            raise TypeError(msg)
         if fake_data:
             self._num_examples = 10000
             self.one_hot = one_hot

pyproject.toml

@@ -83,7 +83,6 @@ max-complexity = 17  # default: 10
 "graphs/minimum_spanning_tree_prims.py" = ["SIM114"]
 "hashes/enigma_machine.py" = ["BLE001"]
 "machine_learning/decision_tree.py" = ["SIM114"]
-"machine_learning/linear_discriminant_analysis.py" = ["ARG005"]
 "machine_learning/sequential_minimum_optimization.py" = ["SIM115"]
 "matrix/sherman_morrison.py" = ["SIM103", "SIM114"]
 "other/l*u_cache.py" = ["RUF012"]