Reenable files when TensorFlow supports the current Python

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_tf → 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_tf → neural_network/input_data.py

@@ -21,13 +21,10 @@
 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
 
@@ -46,16 +43,16 @@ def _read32(bytestream):
 def _extract_images(f):
  """Extract the images into a 4D uint8 numpy array [index, y, x, depth].
 
- Args:
- f: A file object that can be passed into a gzip reader.
+Args:
+f: A file object that can be passed into a gzip reader.
 
- Returns:
- data: A 4D uint8 numpy array [index, y, x, depth].
+Returns:
+data: A 4D uint8 numpy array [index, y, x, depth].
 
- Raises:
- ValueError: If the bytestream does not start with 2051.
+Raises:
+ValueError: If the bytestream does not start with 2051.
 
- """
+"""
  print("Extracting", f.name)
  with gzip.GzipFile(fileobj=f) as bytestream:
  magic = _read32(bytestream)
@@ -86,17 +83,17 @@ def _dense_to_one_hot(labels_dense, num_classes):
 def _extract_labels(f, one_hot=False, num_classes=10):
  """Extract the labels into a 1D uint8 numpy array [index].
 
- Args:
- f: A file object that can be passed into a gzip reader.
- one_hot: Does one hot encoding for the result.
- num_classes: Number of classes for the one hot encoding.
+Args:
+f: A file object that can be passed into a gzip reader.
+one_hot: Does one hot encoding for the result.
+num_classes: Number of classes for the one hot encoding.
 
- Returns:
- labels: a 1D uint8 numpy array.
+Returns:
+labels: a 1D uint8 numpy array.
 
- Raises:
- ValueError: If the bystream doesn't start with 2049.
- """
+Raises:
+ValueError: If the bystream doesn't start with 2049.
+"""
  print("Extracting", f.name)
  with gzip.GzipFile(fileobj=f) as bytestream:
  magic = _read32(bytestream)
@@ -115,8 +112,8 @@ def _extract_labels(f, one_hot=False, num_classes=10):
 class _DataSet:
  """Container class for a _DataSet (deprecated).
 
- THIS CLASS IS DEPRECATED.
- """
+THIS CLASS IS DEPRECATED.
+"""
 
  @deprecated(
  None,
@@ -135,21 +132,21 @@ def __init__(
  ):
  """Construct a _DataSet.
 
- one_hot arg is used only if fake_data is true. `dtype` can be either
- `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
- `[0, 1]`. Seed arg provides for convenient deterministic testing.
-
- Args:
- images: The images
- labels: The labels
- fake_data: Ignore inages and labels, use fake data.
- one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
- False).
- dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
- range [0,255]. float32 output has range [0,1].
- reshape: Bool. If True returned images are returned flattened to vectors.
- seed: The random seed to use.
- """
+one_hot arg is used only if fake_data is true. `dtype` can be either
+`uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
+`[0, 1]`. Seed arg provides for convenient deterministic testing.
+
+Args:
+images: The images
+labels: The labels
+fake_data: Ignore inages and labels, use fake data.
+one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
+False).
+dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
+range [0,255]. float32 output has range [0,1].
+reshape: Bool. If True returned images are returned flattened to vectors.
+seed: The random seed to use.
+"""
  seed1, seed2 = random_seed.get_seed(seed)
  # If op level seed is not set, use whatever graph level seed is returned
  numpy.random.seed(seed1 if seed is None else seed2)
@@ -206,8 +203,8 @@ def next_batch(self, batch_size, fake_data=False, shuffle=True):
  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
@@ -250,19 +247,19 @@ def next_batch(self, batch_size, fake_data=False, shuffle=True):
 def _maybe_download(filename, work_directory, source_url):
  """Download the data from source url, unless it's already here.
 
- Args:
- filename: string, name of the file in the directory.
- work_directory: string, path to working directory.
- source_url: url to download from if file doesn't exist.
+Args:
+filename: string, name of the file in the directory.
+work_directory: string, path to working directory.
+source_url: url to download from if file doesn't exist.
 
- Returns:
- Path to resulting file.
- """
+Returns:
+Path to resulting file.
+"""
  if not gfile.Exists(work_directory):
  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.")
@@ -328,15 +325,16 @@ def fake():
 
  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]
  validation_labels = train_labels[:validation_size]
  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

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_tf → 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_tf → neural_network/input_data.py

@@ -21,13 +21,10 @@
 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
 
@@ -46,16 +43,16 @@ def _read32(bytestream):
 def _extract_images(f):
  """Extract the images into a 4D uint8 numpy array [index, y, x, depth].
 
- Args:
- f: A file object that can be passed into a gzip reader.
+Args:
+f: A file object that can be passed into a gzip reader.
 
- Returns:
- data: A 4D uint8 numpy array [index, y, x, depth].
+Returns:
+data: A 4D uint8 numpy array [index, y, x, depth].
 
- Raises:
- ValueError: If the bytestream does not start with 2051.
+Raises:
+ValueError: If the bytestream does not start with 2051.
 
- """
+"""
  print("Extracting", f.name)
  with gzip.GzipFile(fileobj=f) as bytestream:
  magic = _read32(bytestream)
@@ -86,17 +83,17 @@ def _dense_to_one_hot(labels_dense, num_classes):
 def _extract_labels(f, one_hot=False, num_classes=10):
  """Extract the labels into a 1D uint8 numpy array [index].
 
- Args:
- f: A file object that can be passed into a gzip reader.
- one_hot: Does one hot encoding for the result.
- num_classes: Number of classes for the one hot encoding.
+Args:
+f: A file object that can be passed into a gzip reader.
+one_hot: Does one hot encoding for the result.
+num_classes: Number of classes for the one hot encoding.
 
- Returns:
- labels: a 1D uint8 numpy array.
+Returns:
+labels: a 1D uint8 numpy array.
 
- Raises:
- ValueError: If the bystream doesn't start with 2049.
- """
+Raises:
+ValueError: If the bystream doesn't start with 2049.
+"""
  print("Extracting", f.name)
  with gzip.GzipFile(fileobj=f) as bytestream:
  magic = _read32(bytestream)
@@ -115,8 +112,8 @@ def _extract_labels(f, one_hot=False, num_classes=10):
 class _DataSet:
  """Container class for a _DataSet (deprecated).
 
- THIS CLASS IS DEPRECATED.
- """
+THIS CLASS IS DEPRECATED.
+"""
 
  @deprecated(
  None,
@@ -135,21 +132,21 @@ def __init__(
  ):
  """Construct a _DataSet.
 
- one_hot arg is used only if fake_data is true. `dtype` can be either
- `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
- `[0, 1]`. Seed arg provides for convenient deterministic testing.
-
- Args:
- images: The images
- labels: The labels
- fake_data: Ignore inages and labels, use fake data.
- one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
- False).
- dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
- range [0,255]. float32 output has range [0,1].
- reshape: Bool. If True returned images are returned flattened to vectors.
- seed: The random seed to use.
- """
+one_hot arg is used only if fake_data is true. `dtype` can be either
+`uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
+`[0, 1]`. Seed arg provides for convenient deterministic testing.
+
+Args:
+images: The images
+labels: The labels
+fake_data: Ignore inages and labels, use fake data.
+one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
+False).
+dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
+range [0,255]. float32 output has range [0,1].
+reshape: Bool. If True returned images are returned flattened to vectors.
+seed: The random seed to use.
+"""
  seed1, seed2 = random_seed.get_seed(seed)
  # If op level seed is not set, use whatever graph level seed is returned
  numpy.random.seed(seed1 if seed is None else seed2)
@@ -206,8 +203,8 @@ def next_batch(self, batch_size, fake_data=False, shuffle=True):
  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
@@ -250,19 +247,19 @@ def next_batch(self, batch_size, fake_data=False, shuffle=True):
 def _maybe_download(filename, work_directory, source_url):
  """Download the data from source url, unless it's already here.
 
- Args:
- filename: string, name of the file in the directory.
- work_directory: string, path to working directory.
- source_url: url to download from if file doesn't exist.
+Args:
+filename: string, name of the file in the directory.
+work_directory: string, path to working directory.
+source_url: url to download from if file doesn't exist.
 
- Returns:
- Path to resulting file.
- """
+Returns:
+Path to resulting file.
+"""
  if not gfile.Exists(work_directory):
  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.")
@@ -328,15 +325,16 @@ def fake():
 
  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]
  validation_labels = train_labels[:validation_size]
  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

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_tf → 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_tf → neural_network/input_data.py

@@ -21,13 +21,10 @@
 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
 
@@ -46,16 +43,16 @@ def _read32(bytestream):
 def _extract_images(f):
  """Extract the images into a 4D uint8 numpy array [index, y, x, depth].
 
- Args:
- f: A file object that can be passed into a gzip reader.
+Args:
+f: A file object that can be passed into a gzip reader.
 
- Returns:
- data: A 4D uint8 numpy array [index, y, x, depth].
+Returns:
+data: A 4D uint8 numpy array [index, y, x, depth].
 
- Raises:
- ValueError: If the bytestream does not start with 2051.
+Raises:
+ValueError: If the bytestream does not start with 2051.
 
- """
+"""
  print("Extracting", f.name)
  with gzip.GzipFile(fileobj=f) as bytestream:
  magic = _read32(bytestream)
@@ -86,17 +83,17 @@ def _dense_to_one_hot(labels_dense, num_classes):
 def _extract_labels(f, one_hot=False, num_classes=10):
  """Extract the labels into a 1D uint8 numpy array [index].
 
- Args:
- f: A file object that can be passed into a gzip reader.
- one_hot: Does one hot encoding for the result.
- num_classes: Number of classes for the one hot encoding.
+Args:
+f: A file object that can be passed into a gzip reader.
+one_hot: Does one hot encoding for the result.
+num_classes: Number of classes for the one hot encoding.
 
- Returns:
- labels: a 1D uint8 numpy array.
+Returns:
+labels: a 1D uint8 numpy array.
 
- Raises:
- ValueError: If the bystream doesn't start with 2049.
- """
+Raises:
+ValueError: If the bystream doesn't start with 2049.
+"""
  print("Extracting", f.name)
  with gzip.GzipFile(fileobj=f) as bytestream:
  magic = _read32(bytestream)
@@ -115,8 +112,8 @@ def _extract_labels(f, one_hot=False, num_classes=10):
 class _DataSet:
  """Container class for a _DataSet (deprecated).
 
- THIS CLASS IS DEPRECATED.
- """
+THIS CLASS IS DEPRECATED.
+"""
 
  @deprecated(
  None,
@@ -135,21 +132,21 @@ def __init__(
  ):
  """Construct a _DataSet.
 
- one_hot arg is used only if fake_data is true. `dtype` can be either
- `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
- `[0, 1]`. Seed arg provides for convenient deterministic testing.
-
- Args:
- images: The images
- labels: The labels
- fake_data: Ignore inages and labels, use fake data.
- one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
- False).
- dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
- range [0,255]. float32 output has range [0,1].
- reshape: Bool. If True returned images are returned flattened to vectors.
- seed: The random seed to use.
- """
+one_hot arg is used only if fake_data is true. `dtype` can be either
+`uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
+`[0, 1]`. Seed arg provides for convenient deterministic testing.
+
+Args:
+images: The images
+labels: The labels
+fake_data: Ignore inages and labels, use fake data.
+one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
+False).
+dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
+range [0,255]. float32 output has range [0,1].
+reshape: Bool. If True returned images are returned flattened to vectors.
+seed: The random seed to use.
+"""
  seed1, seed2 = random_seed.get_seed(seed)
  # If op level seed is not set, use whatever graph level seed is returned
  numpy.random.seed(seed1 if seed is None else seed2)
@@ -206,8 +203,8 @@ def next_batch(self, batch_size, fake_data=False, shuffle=True):
  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
@@ -250,19 +247,19 @@ def next_batch(self, batch_size, fake_data=False, shuffle=True):
 def _maybe_download(filename, work_directory, source_url):
  """Download the data from source url, unless it's already here.
 
- Args:
- filename: string, name of the file in the directory.
- work_directory: string, path to working directory.
- source_url: url to download from if file doesn't exist.
+Args:
+filename: string, name of the file in the directory.
+work_directory: string, path to working directory.
+source_url: url to download from if file doesn't exist.
 
- Returns:
- Path to resulting file.
- """
+Returns:
+Path to resulting file.
+"""
  if not gfile.Exists(work_directory):
  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.")
@@ -328,15 +325,16 @@ def fake():
 
  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]
  validation_labels = train_labels[:validation_size]
  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

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_tf → 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_tf → neural_network/input_data.py

@@ -21,13 +21,10 @@
 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
 
@@ -46,16 +43,16 @@ def _read32(bytestream):
 def _extract_images(f):
  """Extract the images into a 4D uint8 numpy array [index, y, x, depth].
 
- Args:
- f: A file object that can be passed into a gzip reader.
+Args:
+f: A file object that can be passed into a gzip reader.
 
- Returns:
- data: A 4D uint8 numpy array [index, y, x, depth].
+Returns:
+data: A 4D uint8 numpy array [index, y, x, depth].
 
- Raises:
- ValueError: If the bytestream does not start with 2051.
+Raises:
+ValueError: If the bytestream does not start with 2051.
 
- """
+"""
  print("Extracting", f.name)
  with gzip.GzipFile(fileobj=f) as bytestream:
  magic = _read32(bytestream)
@@ -86,17 +83,17 @@ def _dense_to_one_hot(labels_dense, num_classes):
 def _extract_labels(f, one_hot=False, num_classes=10):
  """Extract the labels into a 1D uint8 numpy array [index].
 
- Args:
- f: A file object that can be passed into a gzip reader.
- one_hot: Does one hot encoding for the result.
- num_classes: Number of classes for the one hot encoding.
+Args:
+f: A file object that can be passed into a gzip reader.
+one_hot: Does one hot encoding for the result.
+num_classes: Number of classes for the one hot encoding.
 
- Returns:
- labels: a 1D uint8 numpy array.
+Returns:
+labels: a 1D uint8 numpy array.
 
- Raises:
- ValueError: If the bystream doesn't start with 2049.
- """
+Raises:
+ValueError: If the bystream doesn't start with 2049.
+"""
  print("Extracting", f.name)
  with gzip.GzipFile(fileobj=f) as bytestream:
  magic = _read32(bytestream)
@@ -115,8 +112,8 @@ def _extract_labels(f, one_hot=False, num_classes=10):
 class _DataSet:
  """Container class for a _DataSet (deprecated).
 
- THIS CLASS IS DEPRECATED.
- """
+THIS CLASS IS DEPRECATED.
+"""
 
  @deprecated(
  None,
@@ -135,21 +132,21 @@ def __init__(
  ):
  """Construct a _DataSet.
 
- one_hot arg is used only if fake_data is true. `dtype` can be either
- `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
- `[0, 1]`. Seed arg provides for convenient deterministic testing.
-
- Args:
- images: The images
- labels: The labels
- fake_data: Ignore inages and labels, use fake data.
- one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
- False).
- dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
- range [0,255]. float32 output has range [0,1].
- reshape: Bool. If True returned images are returned flattened to vectors.
- seed: The random seed to use.
- """
+one_hot arg is used only if fake_data is true. `dtype` can be either
+`uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
+`[0, 1]`. Seed arg provides for convenient deterministic testing.
+
+Args:
+images: The images
+labels: The labels
+fake_data: Ignore inages and labels, use fake data.
+one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
+False).
+dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
+range [0,255]. float32 output has range [0,1].
+reshape: Bool. If True returned images are returned flattened to vectors.
+seed: The random seed to use.
+"""
  seed1, seed2 = random_seed.get_seed(seed)
  # If op level seed is not set, use whatever graph level seed is returned
  numpy.random.seed(seed1 if seed is None else seed2)
@@ -206,8 +203,8 @@ def next_batch(self, batch_size, fake_data=False, shuffle=True):
  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
@@ -250,19 +247,19 @@ def next_batch(self, batch_size, fake_data=False, shuffle=True):
 def _maybe_download(filename, work_directory, source_url):
  """Download the data from source url, unless it's already here.
 
- Args:
- filename: string, name of the file in the directory.
- work_directory: string, path to working directory.
- source_url: url to download from if file doesn't exist.
+Args:
+filename: string, name of the file in the directory.
+work_directory: string, path to working directory.
+source_url: url to download from if file doesn't exist.
 
- Returns:
- Path to resulting file.
- """
+Returns:
+Path to resulting file.
+"""
  if not gfile.Exists(work_directory):
  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.")
@@ -328,15 +325,16 @@ def fake():
 
  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]
  validation_labels = train_labels[:validation_size]
  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

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_tf → 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_tf → neural_network/input_data.py

@@ -21,13 +21,10 @@
 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
 
@@ -46,16 +43,16 @@ def _read32(bytestream):
 def _extract_images(f):
  """Extract the images into a 4D uint8 numpy array [index, y, x, depth].
 
- Args:
- f: A file object that can be passed into a gzip reader.
+Args:
+f: A file object that can be passed into a gzip reader.
 
- Returns:
- data: A 4D uint8 numpy array [index, y, x, depth].
+Returns:
+data: A 4D uint8 numpy array [index, y, x, depth].
 
- Raises:
- ValueError: If the bytestream does not start with 2051.
+Raises:
+ValueError: If the bytestream does not start with 2051.
 
- """
+"""
  print("Extracting", f.name)
  with gzip.GzipFile(fileobj=f) as bytestream:
  magic = _read32(bytestream)
@@ -86,17 +83,17 @@ def _dense_to_one_hot(labels_dense, num_classes):
 def _extract_labels(f, one_hot=False, num_classes=10):
  """Extract the labels into a 1D uint8 numpy array [index].
 
- Args:
- f: A file object that can be passed into a gzip reader.
- one_hot: Does one hot encoding for the result.
- num_classes: Number of classes for the one hot encoding.
+Args:
+f: A file object that can be passed into a gzip reader.
+one_hot: Does one hot encoding for the result.
+num_classes: Number of classes for the one hot encoding.
 
- Returns:
- labels: a 1D uint8 numpy array.
+Returns:
+labels: a 1D uint8 numpy array.
 
- Raises:
- ValueError: If the bystream doesn't start with 2049.
- """
+Raises:
+ValueError: If the bystream doesn't start with 2049.
+"""
  print("Extracting", f.name)
  with gzip.GzipFile(fileobj=f) as bytestream:
  magic = _read32(bytestream)
@@ -115,8 +112,8 @@ def _extract_labels(f, one_hot=False, num_classes=10):
 class _DataSet:
  """Container class for a _DataSet (deprecated).
 
- THIS CLASS IS DEPRECATED.
- """
+THIS CLASS IS DEPRECATED.
+"""
 
  @deprecated(
  None,
@@ -135,21 +132,21 @@ def __init__(
  ):
  """Construct a _DataSet.
 
- one_hot arg is used only if fake_data is true. `dtype` can be either
- `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
- `[0, 1]`. Seed arg provides for convenient deterministic testing.
-
- Args:
- images: The images
- labels: The labels
- fake_data: Ignore inages and labels, use fake data.
- one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
- False).
- dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
- range [0,255]. float32 output has range [0,1].
- reshape: Bool. If True returned images are returned flattened to vectors.
- seed: The random seed to use.
- """
+one_hot arg is used only if fake_data is true. `dtype` can be either
+`uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
+`[0, 1]`. Seed arg provides for convenient deterministic testing.
+
+Args:
+images: The images
+labels: The labels
+fake_data: Ignore inages and labels, use fake data.
+one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
+False).
+dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
+range [0,255]. float32 output has range [0,1].
+reshape: Bool. If True returned images are returned flattened to vectors.
+seed: The random seed to use.
+"""
  seed1, seed2 = random_seed.get_seed(seed)
  # If op level seed is not set, use whatever graph level seed is returned
  numpy.random.seed(seed1 if seed is None else seed2)
@@ -206,8 +203,8 @@ def next_batch(self, batch_size, fake_data=False, shuffle=True):
  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
@@ -250,19 +247,19 @@ def next_batch(self, batch_size, fake_data=False, shuffle=True):
 def _maybe_download(filename, work_directory, source_url):
  """Download the data from source url, unless it's already here.
 
- Args:
- filename: string, name of the file in the directory.
- work_directory: string, path to working directory.
- source_url: url to download from if file doesn't exist.
+Args:
+filename: string, name of the file in the directory.
+work_directory: string, path to working directory.
+source_url: url to download from if file doesn't exist.
 
- Returns:
- Path to resulting file.
- """
+Returns:
+Path to resulting file.
+"""
  if not gfile.Exists(work_directory):
  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.")
@@ -328,15 +325,16 @@ def fake():
 
  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]
  validation_labels = train_labels[:validation_size]
  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

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_tf → 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_tf → neural_network/input_data.py

@@ -21,13 +21,10 @@
 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
 
@@ -46,16 +43,16 @@ def _read32(bytestream):
 def _extract_images(f):
  """Extract the images into a 4D uint8 numpy array [index, y, x, depth].
 
- Args:
- f: A file object that can be passed into a gzip reader.
+Args:
+f: A file object that can be passed into a gzip reader.
 
- Returns:
- data: A 4D uint8 numpy array [index, y, x, depth].
+Returns:
+data: A 4D uint8 numpy array [index, y, x, depth].
 
- Raises:
- ValueError: If the bytestream does not start with 2051.
+Raises:
+ValueError: If the bytestream does not start with 2051.
 
- """
+"""
  print("Extracting", f.name)
  with gzip.GzipFile(fileobj=f) as bytestream:
  magic = _read32(bytestream)
@@ -86,17 +83,17 @@ def _dense_to_one_hot(labels_dense, num_classes):
 def _extract_labels(f, one_hot=False, num_classes=10):
  """Extract the labels into a 1D uint8 numpy array [index].
 
- Args:
- f: A file object that can be passed into a gzip reader.
- one_hot: Does one hot encoding for the result.
- num_classes: Number of classes for the one hot encoding.
+Args:
+f: A file object that can be passed into a gzip reader.
+one_hot: Does one hot encoding for the result.
+num_classes: Number of classes for the one hot encoding.
 
- Returns:
- labels: a 1D uint8 numpy array.
+Returns:
+labels: a 1D uint8 numpy array.
 
- Raises:
- ValueError: If the bystream doesn't start with 2049.
- """
+Raises:
+ValueError: If the bystream doesn't start with 2049.
+"""
  print("Extracting", f.name)
  with gzip.GzipFile(fileobj=f) as bytestream:
  magic = _read32(bytestream)
@@ -115,8 +112,8 @@ def _extract_labels(f, one_hot=False, num_classes=10):
 class _DataSet:
  """Container class for a _DataSet (deprecated).
 
- THIS CLASS IS DEPRECATED.
- """
+THIS CLASS IS DEPRECATED.
+"""
 
  @deprecated(
  None,
@@ -135,21 +132,21 @@ def __init__(
  ):
  """Construct a _DataSet.
 
- one_hot arg is used only if fake_data is true. `dtype` can be either
- `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
- `[0, 1]`. Seed arg provides for convenient deterministic testing.
-
- Args:
- images: The images
- labels: The labels
- fake_data: Ignore inages and labels, use fake data.
- one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
- False).
- dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
- range [0,255]. float32 output has range [0,1].
- reshape: Bool. If True returned images are returned flattened to vectors.
- seed: The random seed to use.
- """
+one_hot arg is used only if fake_data is true. `dtype` can be either
+`uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
+`[0, 1]`. Seed arg provides for convenient deterministic testing.
+
+Args:
+images: The images
+labels: The labels
+fake_data: Ignore inages and labels, use fake data.
+one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
+False).
+dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
+range [0,255]. float32 output has range [0,1].
+reshape: Bool. If True returned images are returned flattened to vectors.
+seed: The random seed to use.
+"""
  seed1, seed2 = random_seed.get_seed(seed)
  # If op level seed is not set, use whatever graph level seed is returned
  numpy.random.seed(seed1 if seed is None else seed2)
@@ -206,8 +203,8 @@ def next_batch(self, batch_size, fake_data=False, shuffle=True):
  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
@@ -250,19 +247,19 @@ def next_batch(self, batch_size, fake_data=False, shuffle=True):
 def _maybe_download(filename, work_directory, source_url):
  """Download the data from source url, unless it's already here.
 
- Args:
- filename: string, name of the file in the directory.
- work_directory: string, path to working directory.
- source_url: url to download from if file doesn't exist.
+Args:
+filename: string, name of the file in the directory.
+work_directory: string, path to working directory.
+source_url: url to download from if file doesn't exist.
 
- Returns:
- Path to resulting file.
- """
+Returns:
+Path to resulting file.
+"""
  if not gfile.Exists(work_directory):
  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.")
@@ -328,15 +325,16 @@ def fake():
 
  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]
  validation_labels = train_labels[:validation_size]
  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

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_tf → 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_tf → neural_network/input_data.py

@@ -21,13 +21,10 @@
 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
 
@@ -46,16 +43,16 @@ def _read32(bytestream):
 def _extract_images(f):
  """Extract the images into a 4D uint8 numpy array [index, y, x, depth].
 
- Args:
- f: A file object that can be passed into a gzip reader.
+Args:
+f: A file object that can be passed into a gzip reader.
 
- Returns:
- data: A 4D uint8 numpy array [index, y, x, depth].
+Returns:
+data: A 4D uint8 numpy array [index, y, x, depth].
 
- Raises:
- ValueError: If the bytestream does not start with 2051.
+Raises:
+ValueError: If the bytestream does not start with 2051.
 
- """
+"""
  print("Extracting", f.name)
  with gzip.GzipFile(fileobj=f) as bytestream:
  magic = _read32(bytestream)
@@ -86,17 +83,17 @@ def _dense_to_one_hot(labels_dense, num_classes):
 def _extract_labels(f, one_hot=False, num_classes=10):
  """Extract the labels into a 1D uint8 numpy array [index].
 
- Args:
- f: A file object that can be passed into a gzip reader.
- one_hot: Does one hot encoding for the result.
- num_classes: Number of classes for the one hot encoding.
+Args:
+f: A file object that can be passed into a gzip reader.
+one_hot: Does one hot encoding for the result.
+num_classes: Number of classes for the one hot encoding.
 
- Returns:
- labels: a 1D uint8 numpy array.
+Returns:
+labels: a 1D uint8 numpy array.
 
- Raises:
- ValueError: If the bystream doesn't start with 2049.
- """
+Raises:
+ValueError: If the bystream doesn't start with 2049.
+"""
  print("Extracting", f.name)
  with gzip.GzipFile(fileobj=f) as bytestream:
  magic = _read32(bytestream)
@@ -115,8 +112,8 @@ def _extract_labels(f, one_hot=False, num_classes=10):
 class _DataSet:
  """Container class for a _DataSet (deprecated).
 
- THIS CLASS IS DEPRECATED.
- """
+THIS CLASS IS DEPRECATED.
+"""
 
  @deprecated(
  None,
@@ -135,21 +132,21 @@ def __init__(
  ):
  """Construct a _DataSet.
 
- one_hot arg is used only if fake_data is true. `dtype` can be either
- `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
- `[0, 1]`. Seed arg provides for convenient deterministic testing.
-
- Args:
- images: The images
- labels: The labels
- fake_data: Ignore inages and labels, use fake data.
- one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
- False).
- dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
- range [0,255]. float32 output has range [0,1].
- reshape: Bool. If True returned images are returned flattened to vectors.
- seed: The random seed to use.
- """
+one_hot arg is used only if fake_data is true. `dtype` can be either
+`uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
+`[0, 1]`. Seed arg provides for convenient deterministic testing.
+
+Args:
+images: The images
+labels: The labels
+fake_data: Ignore inages and labels, use fake data.
+one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
+False).
+dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
+range [0,255]. float32 output has range [0,1].
+reshape: Bool. If True returned images are returned flattened to vectors.
+seed: The random seed to use.
+"""
  seed1, seed2 = random_seed.get_seed(seed)
  # If op level seed is not set, use whatever graph level seed is returned
  numpy.random.seed(seed1 if seed is None else seed2)
@@ -206,8 +203,8 @@ def next_batch(self, batch_size, fake_data=False, shuffle=True):
  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
@@ -250,19 +247,19 @@ def next_batch(self, batch_size, fake_data=False, shuffle=True):
 def _maybe_download(filename, work_directory, source_url):
  """Download the data from source url, unless it's already here.
 
- Args:
- filename: string, name of the file in the directory.
- work_directory: string, path to working directory.
- source_url: url to download from if file doesn't exist.
+Args:
+filename: string, name of the file in the directory.
+work_directory: string, path to working directory.
+source_url: url to download from if file doesn't exist.
 
- Returns:
- Path to resulting file.
- """
+Returns:
+Path to resulting file.
+"""
  if not gfile.Exists(work_directory):
  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.")
@@ -328,15 +325,16 @@ def fake():
 
  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]
  validation_labels = train_labels[:validation_size]
  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

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_tf → 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_tf → neural_network/input_data.py

@@ -21,13 +21,10 @@
 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
 
@@ -46,16 +43,16 @@ def _read32(bytestream):
 def _extract_images(f):
  """Extract the images into a 4D uint8 numpy array [index, y, x, depth].
 
- Args:
- f: A file object that can be passed into a gzip reader.
+Args:
+f: A file object that can be passed into a gzip reader.
 
- Returns:
- data: A 4D uint8 numpy array [index, y, x, depth].
+Returns:
+data: A 4D uint8 numpy array [index, y, x, depth].
 
- Raises:
- ValueError: If the bytestream does not start with 2051.
+Raises:
+ValueError: If the bytestream does not start with 2051.
 
- """
+"""
  print("Extracting", f.name)
  with gzip.GzipFile(fileobj=f) as bytestream:
  magic = _read32(bytestream)
@@ -86,17 +83,17 @@ def _dense_to_one_hot(labels_dense, num_classes):
 def _extract_labels(f, one_hot=False, num_classes=10):
  """Extract the labels into a 1D uint8 numpy array [index].
 
- Args:
- f: A file object that can be passed into a gzip reader.
- one_hot: Does one hot encoding for the result.
- num_classes: Number of classes for the one hot encoding.
+Args:
+f: A file object that can be passed into a gzip reader.
+one_hot: Does one hot encoding for the result.
+num_classes: Number of classes for the one hot encoding.
 
- Returns:
- labels: a 1D uint8 numpy array.
+Returns:
+labels: a 1D uint8 numpy array.
 
- Raises:
- ValueError: If the bystream doesn't start with 2049.
- """
+Raises:
+ValueError: If the bystream doesn't start with 2049.
+"""
  print("Extracting", f.name)
  with gzip.GzipFile(fileobj=f) as bytestream:
  magic = _read32(bytestream)
@@ -115,8 +112,8 @@ def _extract_labels(f, one_hot=False, num_classes=10):
 class _DataSet:
  """Container class for a _DataSet (deprecated).
 
- THIS CLASS IS DEPRECATED.
- """
+THIS CLASS IS DEPRECATED.
+"""
 
  @deprecated(
  None,
@@ -135,21 +132,21 @@ def __init__(
  ):
  """Construct a _DataSet.
 
- one_hot arg is used only if fake_data is true. `dtype` can be either
- `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
- `[0, 1]`. Seed arg provides for convenient deterministic testing.
-
- Args:
- images: The images
- labels: The labels
- fake_data: Ignore inages and labels, use fake data.
- one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
- False).
- dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
- range [0,255]. float32 output has range [0,1].
- reshape: Bool. If True returned images are returned flattened to vectors.
- seed: The random seed to use.
- """
+one_hot arg is used only if fake_data is true. `dtype` can be either
+`uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
+`[0, 1]`. Seed arg provides for convenient deterministic testing.
+
+Args:
+images: The images
+labels: The labels
+fake_data: Ignore inages and labels, use fake data.
+one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
+False).
+dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
+range [0,255]. float32 output has range [0,1].
+reshape: Bool. If True returned images are returned flattened to vectors.
+seed: The random seed to use.
+"""
  seed1, seed2 = random_seed.get_seed(seed)
  # If op level seed is not set, use whatever graph level seed is returned
  numpy.random.seed(seed1 if seed is None else seed2)
@@ -206,8 +203,8 @@ def next_batch(self, batch_size, fake_data=False, shuffle=True):
  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
@@ -250,19 +247,19 @@ def next_batch(self, batch_size, fake_data=False, shuffle=True):
 def _maybe_download(filename, work_directory, source_url):
  """Download the data from source url, unless it's already here.
 
- Args:
- filename: string, name of the file in the directory.
- work_directory: string, path to working directory.
- source_url: url to download from if file doesn't exist.
+Args:
+filename: string, name of the file in the directory.
+work_directory: string, path to working directory.
+source_url: url to download from if file doesn't exist.
 
- Returns:
- Path to resulting file.
- """
+Returns:
+Path to resulting file.
+"""
  if not gfile.Exists(work_directory):
  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.")
@@ -328,15 +325,16 @@ def fake():
 
  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]
  validation_labels = train_labels[:validation_size]
  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