pre-commit: Upgrade psf/black for stable style 2023 (#8110)

* pre-commit: Upgrade psf/black for stable style 2023

Updating https://github.com/psf/black ... updating 22.12.0 -> 23.1.0 for their `2023 stable style`.
* https://github.com/psf/black/blob/main/CHANGES.md#2310

> This is the first [psf/black] release of 2023, and following our stability policy, it comes with a number of improvements to our stable style…

Also, add https://github.com/tox-dev/pyproject-fmt and https://github.com/abravalheri/validate-pyproject to pre-commit.

I only modified `.pre-commit-config.yaml` and all other files were modified by pre-commit.ci and psf/black.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

machine_learning/k_means_clust.py

@@ -74,7 +74,6 @@ def centroid_pairwise_dist(x, centroids):
 
 
 def assign_clusters(data, centroids):
-
     # Compute distances between each data point and the set of centroids:
     # Fill in the blank (RHS only)
     distances_from_centroids = centroid_pairwise_dist(data, centroids)
@@ -100,10 +99,8 @@ def revise_centroids(data, k, cluster_assignment):
 
 
 def compute_heterogeneity(data, k, centroids, cluster_assignment):
-
     heterogeneity = 0.0
     for i in range(k):
-
         # Select all data points that belong to cluster i. Fill in the blank (RHS only)
         member_data_points = data[cluster_assignment == i, :]
 

machine_learning/self_organizing_map.py

@@ -49,7 +49,6 @@ def main() -> None:
 
     for _ in range(epochs):
         for j in range(len(training_samples)):
-
             # training sample
             sample = training_samples[j]
 

machine_learning/sequential_minimum_optimization.py

@@ -82,7 +82,6 @@ class SmoSVM:
         k = self._k
         state = None
         while True:
-
             # 1: Find alpha1, alpha2
             try:
                 i1, i2 = self.choose_alpha.send(state)
@@ -146,7 +145,6 @@ class SmoSVM:
 
     # Predict test samples
     def predict(self, test_samples, classify=True):
-
         if test_samples.shape[1] > self.samples.shape[1]:
             raise ValueError(
                 "Test samples' feature length does not equal to that of train samples"

machine_learning/xgboost_classifier.py

@@ -41,7 +41,6 @@ def xgboost(features: np.ndarray, target: np.ndarray) -> XGBClassifier:
 
 
 def main() -> None:
-
     """
     >>> main()