Reenable files when TensorFlow supports the current Python (#8602)

* Remove python_version < "3.11" for tensorflow

* Reenable neural_network/input_data.py_tf

* updating DIRECTORY.md

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

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* Try to fix ruff

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

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

* Try to fix ruff

* Try to fix ruff

* Try to fix ruff

* Try to fix pre-commit

* Try to fix

* Fix

* Fix

* Reenable dynamic_programming/k_means_clustering_tensorflow.py_tf

* updating DIRECTORY.md

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

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

* Try to fix ruff

---------

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

DIRECTORY.md

@@ -309,6 +309,7 @@
   * [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)
   * [Longest Common Subsequence](dynamic_programming/longest_common_subsequence.py)
   * [Longest Common Substring](dynamic_programming/longest_common_substring.py)
@@ -685,6 +686,7 @@
   * [2 Hidden Layers Neural Network](neural_network/2_hidden_layers_neural_network.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)
   * [Perceptron](neural_network/perceptron.py)
   * [Simple Neural Network](neural_network/simple_neural_network.py)
 

dynamic_programming/k_means_clustering_tensorflow.py

@@ -1,9 +1,10 @@
-import tensorflow as tf
 from random import shuffle
+
+import tensorflow as tf
 from numpy import array
 
 
-def TFKMeansCluster(vectors, noofclusters):
+def tf_k_means_cluster(vectors, noofclusters):
     """
     K-Means Clustering using TensorFlow.
     'vectors' should be a n*k 2-D NumPy array, where n is the number
@@ -30,7 +31,6 @@ def TFKMeansCluster(vectors, noofclusters):
     graph = tf.Graph()
 
     with graph.as_default():
-
         # SESSION OF COMPUTATION
 
         sess = tf.Session()
@@ -95,8 +95,7 @@ def TFKMeansCluster(vectors, noofclusters):
         # iterations. To keep things simple, we will only do a set number of
         # iterations, instead of using a Stopping Criterion.
         noofiterations = 100
-        for iteration_n in range(noofiterations):
-
+        for _ in range(noofiterations):
             ##EXPECTATION STEP
             ##Based on the centroid locations till last iteration, compute
             ##the _expected_ centroid assignments.

neural_network/input_data.py

@@ -21,13 +21,10 @@ This module and all its submodules are deprecated.
 import collections
 import gzip
 import os
+import urllib
 
 import numpy
-from six.moves import urllib
-from six.moves import xrange  # pylint: disable=redefined-builtin
-
-from tensorflow.python.framework import dtypes
-from tensorflow.python.framework import random_seed
+from tensorflow.python.framework import dtypes, random_seed
 from tensorflow.python.platform import gfile
 from tensorflow.python.util.deprecation import deprecated
 
@@ -206,8 +203,8 @@ class _DataSet:
             else:
                 fake_label = 0
             return (
-                [fake_image for _ in xrange(batch_size)],
-                [fake_label for _ in xrange(batch_size)],
+                [fake_image for _ in range(batch_size)],
+                [fake_label for _ in range(batch_size)],
             )
         start = self._index_in_epoch
         # Shuffle for the first epoch
@@ -262,7 +259,7 @@ def _maybe_download(filename, work_directory, source_url):
         gfile.MakeDirs(work_directory)
     filepath = os.path.join(work_directory, filename)
     if not gfile.Exists(filepath):
-        urllib.request.urlretrieve(source_url, filepath)
+        urllib.request.urlretrieve(source_url, filepath)  # noqa: S310
         with gfile.GFile(filepath) as f:
             size = f.size()
         print("Successfully downloaded", filename, size, "bytes.")
@@ -328,7 +325,8 @@ def read_data_sets(
 
     if not 0 <= validation_size <= len(train_images):
         raise ValueError(
-            f"Validation size should be between 0 and {len(train_images)}. Received: {validation_size}."
+            f"Validation size should be between 0 and {len(train_images)}. "
+            f"Received: {validation_size}."
         )
 
     validation_images = train_images[:validation_size]
@@ -336,7 +334,7 @@ def read_data_sets(
     train_images = train_images[validation_size:]
     train_labels = train_labels[validation_size:]
 
-    options = dict(dtype=dtype, reshape=reshape, seed=seed)
+    options = {"dtype": dtype, "reshape": reshape, "seed": seed}
 
     train = _DataSet(train_images, train_labels, **options)
     validation = _DataSet(validation_images, validation_labels, **options)

requirements.txt

@@ -15,7 +15,7 @@ scikit-fuzzy
 scikit-learn
 statsmodels
 sympy
-tensorflow; python_version < "3.11"
+tensorflow
 texttable
 tweepy
 xgboost