Reenable files when TensorFlow supports the current Python (#11318)

* Remove python_version < '3.12' for tensorflow

* Reenable dynamic_programming/k_means_clustering_tensorflow.py

* updating DIRECTORY.md

* Try to fix ruff

* Try to fix ruff

* Try to fix ruff

* Try to fix ruff

* Try to fix ruff

* Reenable machine_learning/lstm/lstm_prediction.py

* updating DIRECTORY.md

* Try to fix ruff

* Reenable computer_vision/cnn_classification.py

* updating DIRECTORY.md

* Reenable neural_network/input_data.py

* updating DIRECTORY.md

* Try to fix ruff

* Try to fix ruff

* Try to fix mypy

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

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

* Try to fix ruff

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

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

---------

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

DIRECTORY.md

@@ -134,6 +134,7 @@
   * [Run Length Encoding](compression/run_length_encoding.py)
 
 ## Computer Vision
+  * [Cnn Classification](computer_vision/cnn_classification.py)
   * [Flip Augmentation](computer_vision/flip_augmentation.py)
   * [Haralick Descriptors](computer_vision/haralick_descriptors.py)
   * [Harris Corner](computer_vision/harris_corner.py)
@@ -344,6 +345,7 @@
   * [Floyd Warshall](dynamic_programming/floyd_warshall.py)
   * [Integer Partition](dynamic_programming/integer_partition.py)
   * [Iterating Through Submasks](dynamic_programming/iterating_through_submasks.py)
+  * [K Means Clustering Tensorflow](dynamic_programming/k_means_clustering_tensorflow.py)
   * [Knapsack](dynamic_programming/knapsack.py)
   * [Largest Divisible Subset](dynamic_programming/largest_divisible_subset.py)
   * [Longest Common Subsequence](dynamic_programming/longest_common_subsequence.py)
@@ -571,6 +573,8 @@
     * [Local Weighted Learning](machine_learning/local_weighted_learning/local_weighted_learning.py)
   * [Logistic Regression](machine_learning/logistic_regression.py)
   * [Loss Functions](machine_learning/loss_functions.py)
+  * Lstm
+    * [Lstm Prediction](machine_learning/lstm/lstm_prediction.py)
   * [Mfcc](machine_learning/mfcc.py)
   * [Multilayer Perceptron Classifier](machine_learning/multilayer_perceptron_classifier.py)
   * [Polynomial Regression](machine_learning/polynomial_regression.py)
@@ -801,6 +805,7 @@
     * [Swish](neural_network/activation_functions/swish.py)
   * [Back Propagation Neural Network](neural_network/back_propagation_neural_network.py)
   * [Convolution Neural Network](neural_network/convolution_neural_network.py)
+  * [Input Data](neural_network/input_data.py)
   * [Simple Neural Network](neural_network/simple_neural_network.py)
 
 ## Other

ciphers/rsa_cipher.py

@@ -76,11 +76,9 @@ def encrypt_and_write_to_file(
     key_size, n, e = read_key_file(key_filename)
     if key_size < block_size * 8:
         sys.exit(
-            "ERROR: Block size is {} bits and key size is {} bits. The RSA cipher "
+            f"ERROR: Block size is {block_size * 8} bits and key size is {key_size} "
-            "requires the block size to be equal to or greater than the key size. "
+            "bits. The RSA cipher requires the block size to be equal to or greater "
-            "Either decrease the block size or use different keys.".format(
+            "than the key size. Either decrease the block size or use different keys."
-                block_size * 8, key_size
-            )
         )
 
     encrypted_blocks = [str(i) for i in encrypt_message(message, (n, e), block_size)]
@@ -102,11 +100,10 @@ def read_from_file_and_decrypt(message_filename: str, key_filename: str) -> str:
 
     if key_size < block_size * 8:
         sys.exit(
-            "ERROR: Block size is {} bits and key size is {} bits. The RSA cipher "
+            f"ERROR: Block size is {block_size * 8} bits and key size is {key_size} "
-            "requires the block size to be equal to or greater than the key size. "
+            "bits. The RSA cipher requires the block size to be equal to or greater "
-            "Did you specify the correct key file and encrypted file?".format(
+            "than the key size. Did you specify the correct key file and encrypted "
-                block_size * 8, key_size
+            "file?"
-            )
         )
 
     encrypted_blocks = []

machine_learning/lstm/lstm_prediction.py

@@ -17,11 +17,11 @@ if __name__ == "__main__":
     make sure you set the price column on line number 21.  Here we
     use a dataset which have the price on 3rd column.
     """
-    df = pd.read_csv("sample_data.csv", header=None)
+    sample_data = pd.read_csv("sample_data.csv", header=None)
-    len_data = df.shape[:1][0]
+    len_data = sample_data.shape[:1][0]
     # If you're using some other dataset input the target column
-    actual_data = df.iloc[:, 1:2]
+    actual_data = sample_data.iloc[:, 1:2]
-    actual_data = actual_data.values.reshape(len_data, 1)
+    actual_data = actual_data.to_numpy().reshape(len_data, 1)
     actual_data = MinMaxScaler().fit_transform(actual_data)
     look_back = 10
     forward_days = 5

neural_network/input_data.py

@@ -18,9 +18,9 @@ This module and all its submodules are deprecated.
 """
 
 
-import collections
 import gzip
 import os
+import typing
 import urllib
 
 import numpy
@@ -28,7 +28,12 @@ from tensorflow.python.framework import dtypes, random_seed
 from tensorflow.python.platform import gfile
 from tensorflow.python.util.deprecation import deprecated
 
-_Datasets = collections.namedtuple("_Datasets", ["train", "validation", "test"])
+
+class _Datasets(typing.NamedTuple):
+    train: "_DataSet"
+    validation: "_DataSet"
+    test: "_DataSet"
+
 
 # CVDF mirror of http://yann.lecun.com/exdb/mnist/
 DEFAULT_SOURCE_URL = "https://storage.googleapis.com/cvdf-datasets/mnist/"

other/lfu_cache.py

@@ -24,8 +24,9 @@ class DoubleLinkedListNode(Generic[T, U]):
         self.prev: DoubleLinkedListNode[T, U] | None = None
 
     def __repr__(self) -> str:
-        return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
+        return (
-            self.key, self.val, self.freq, self.next is not None, self.prev is not None
+            f"Node: key: {self.key}, val: {self.val}, freq: {self.freq}, "
+            f"has next: {self.next is not None}, has prev: {self.prev is not None}"
         )
 
 

requirements.txt

@@ -17,7 +17,7 @@ rich
 scikit-learn
 statsmodels
 sympy
-tensorflow ; python_version < '3.12'
+tensorflow
 tweepy
 # yulewalker  # uncomment once audio_filters/equal_loudness_filter.py is fixed
 typing_extensions