[pre-commit.ci] pre-commit autoupdate (#9543)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.0.291 → v0.0.292](https://github.com/astral-sh/ruff-pre-commit/compare/v0.0.291...v0.0.292)
- [github.com/codespell-project/codespell: v2.2.5 → v2.2.6](https://github.com/codespell-project/codespell/compare/v2.2.5...v2.2.6)
- [github.com/tox-dev/pyproject-fmt: 1.1.0 → 1.2.0](https://github.com/tox-dev/pyproject-fmt/compare/1.1.0...1.2.0)

* updating DIRECTORY.md

* Fix typos in test_min_spanning_tree_prim.py

* Fix typos

* codespell --ignore-words-list=manuel

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
Co-authored-by: Tianyi Zheng <tianyizheng02@gmail.com>
Co-authored-by: Christian Clauss <cclauss@me.com>

neural_network/convolution_neural_network.py

@@ -2,7 +2,7 @@
      - - - - - -- - - - - - - - - - - - - - - - - - - - - - -
     Name - - CNN - Convolution Neural Network For Photo Recognizing
     Goal - - Recognize Handing Writing Word Photo
-    Detail：Total 5 layers neural network
+    Detail: Total 5 layers neural network
             * Convolution layer
             * Pooling layer
             * Input layer layer of BP
@@ -24,7 +24,7 @@ class CNN:
         self, conv1_get, size_p1, bp_num1, bp_num2, bp_num3, rate_w=0.2, rate_t=0.2
     ):
         """
-        :param conv1_get: [a,c,d]，size, number, step of convolution kernel
+        :param conv1_get: [a,c,d], size, number, step of convolution kernel
         :param size_p1: pooling size
         :param bp_num1: units number of flatten layer
         :param bp_num2: units number of hidden layer
@@ -71,7 +71,7 @@ class CNN:
         with open(save_path, "wb") as f:
             pickle.dump(model_dic, f)
 
-        print(f"Model saved： {save_path}")
+        print(f"Model saved: {save_path}")
 
     @classmethod
     def read_model(cls, model_path):
@@ -210,7 +210,7 @@ class CNN:
     def train(
         self, patterns, datas_train, datas_teach, n_repeat, error_accuracy, draw_e=bool
     ):
-        # model traning
+        # model training
         print("----------------------Start Training-------------------------")
         print((" - - Shape: Train_Data  ", np.shape(datas_train)))
         print((" - - Shape: Teach_Data  ", np.shape(datas_teach)))

neural_network/gan.py_tf

@@ -158,7 +158,7 @@ if __name__ == "__main__":
     # G_b2 = np.random.normal(size=(784),scale=(1. / np.sqrt(784 / 2.)))      *0.002
     G_b7 = np.zeros(784)
 
-    # 3. For Adam Optimzier
+    # 3. For Adam Optimizer
     v1, m1 = 0, 0
     v2, m2 = 0, 0
     v3, m3 = 0, 0