Enable ruff ICN001 rule (#11329)

* Enable ruff ICN001 rule

* [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>

ciphers/hill_cipher.py

@@ -38,7 +38,7 @@ https://www.youtube.com/watch?v=4RhLNDqcjpA
 
 import string
 
-import numpy
+import numpy as np
 
 from maths.greatest_common_divisor import greatest_common_divisor
 
@@ -49,11 +49,11 @@ class HillCipher:
     # i.e. a total of 36 characters
 
     # take x and return x % len(key_string)
-    modulus = numpy.vectorize(lambda x: x % 36)
+    modulus = np.vectorize(lambda x: x % 36)
 
-    to_int = numpy.vectorize(round)
+    to_int = np.vectorize(round)
 
-    def __init__(self, encrypt_key: numpy.ndarray) -> None:
+    def __init__(self, encrypt_key: np.ndarray) -> None:
         """
         encrypt_key is an NxN numpy array
         """
@@ -63,7 +63,7 @@ class HillCipher:
 
     def replace_letters(self, letter: str) -> int:
         """
-        >>> hill_cipher = HillCipher(numpy.array([[2, 5], [1, 6]]))
+        >>> hill_cipher = HillCipher(np.array([[2, 5], [1, 6]]))
         >>> hill_cipher.replace_letters('T')
         19
         >>> hill_cipher.replace_letters('0')
@@ -73,7 +73,7 @@ class HillCipher:
 
     def replace_digits(self, num: int) -> str:
         """
-        >>> hill_cipher = HillCipher(numpy.array([[2, 5], [1, 6]]))
+        >>> hill_cipher = HillCipher(np.array([[2, 5], [1, 6]]))
         >>> hill_cipher.replace_digits(19)
         'T'
         >>> hill_cipher.replace_digits(26)
@@ -83,10 +83,10 @@ class HillCipher:
 
     def check_determinant(self) -> None:
         """
-        >>> hill_cipher = HillCipher(numpy.array([[2, 5], [1, 6]]))
+        >>> hill_cipher = HillCipher(np.array([[2, 5], [1, 6]]))
         >>> hill_cipher.check_determinant()
         """
-        det = round(numpy.linalg.det(self.encrypt_key))
+        det = round(np.linalg.det(self.encrypt_key))
 
         if det < 0:
             det = det % len(self.key_string)
@@ -101,7 +101,7 @@ class HillCipher:
 
     def process_text(self, text: str) -> str:
         """
-        >>> hill_cipher = HillCipher(numpy.array([[2, 5], [1, 6]]))
+        >>> hill_cipher = HillCipher(np.array([[2, 5], [1, 6]]))
         >>> hill_cipher.process_text('Testing Hill Cipher')
         'TESTINGHILLCIPHERR'
         >>> hill_cipher.process_text('hello')
@@ -117,7 +117,7 @@ class HillCipher:
 
     def encrypt(self, text: str) -> str:
         """
-        >>> hill_cipher = HillCipher(numpy.array([[2, 5], [1, 6]]))
+        >>> hill_cipher = HillCipher(np.array([[2, 5], [1, 6]]))
         >>> hill_cipher.encrypt('testing hill cipher')
         'WHXYJOLM9C6XT085LL'
         >>> hill_cipher.encrypt('hello')
@@ -129,7 +129,7 @@ class HillCipher:
         for i in range(0, len(text) - self.break_key + 1, self.break_key):
             batch = text[i : i + self.break_key]
             vec = [self.replace_letters(char) for char in batch]
-            batch_vec = numpy.array([vec]).T
+            batch_vec = np.array([vec]).T
             batch_encrypted = self.modulus(self.encrypt_key.dot(batch_vec)).T.tolist()[
                 0
             ]
@@ -140,14 +140,14 @@ class HillCipher:
 
         return encrypted
 
-    def make_decrypt_key(self) -> numpy.ndarray:
+    def make_decrypt_key(self) -> np.ndarray:
         """
-        >>> hill_cipher = HillCipher(numpy.array([[2, 5], [1, 6]]))
+        >>> hill_cipher = HillCipher(np.array([[2, 5], [1, 6]]))
         >>> hill_cipher.make_decrypt_key()
         array([[ 6, 25],
                [ 5, 26]])
         """
-        det = round(numpy.linalg.det(self.encrypt_key))
+        det = round(np.linalg.det(self.encrypt_key))
 
         if det < 0:
             det = det % len(self.key_string)
@@ -158,16 +158,14 @@ class HillCipher:
                 break
 
         inv_key = (
-            det_inv
+            det_inv * np.linalg.det(self.encrypt_key) * np.linalg.inv(self.encrypt_key)
-            * numpy.linalg.det(self.encrypt_key)
-            * numpy.linalg.inv(self.encrypt_key)
         )
 
         return self.to_int(self.modulus(inv_key))
 
     def decrypt(self, text: str) -> str:
         """
-        >>> hill_cipher = HillCipher(numpy.array([[2, 5], [1, 6]]))
+        >>> hill_cipher = HillCipher(np.array([[2, 5], [1, 6]]))
         >>> hill_cipher.decrypt('WHXYJOLM9C6XT085LL')
         'TESTINGHILLCIPHERR'
         >>> hill_cipher.decrypt('85FF00')
@@ -180,7 +178,7 @@ class HillCipher:
         for i in range(0, len(text) - self.break_key + 1, self.break_key):
             batch = text[i : i + self.break_key]
             vec = [self.replace_letters(char) for char in batch]
-            batch_vec = numpy.array([vec]).T
+            batch_vec = np.array([vec]).T
             batch_decrypted = self.modulus(decrypt_key.dot(batch_vec)).T.tolist()[0]
             decrypted_batch = "".join(
                 self.replace_digits(num) for num in batch_decrypted
@@ -199,7 +197,7 @@ def main() -> None:
         row = [int(x) for x in input().split()]
         hill_matrix.append(row)
 
-    hc = HillCipher(numpy.array(hill_matrix))
+    hc = HillCipher(np.array(hill_matrix))
 
     print("Would you like to encrypt or decrypt some text? (1 or 2)")
     option = input("\n1. Encrypt\n2. Decrypt\n")

fractals/julia_sets.py

@@ -25,8 +25,8 @@ import warnings
 from collections.abc import Callable
 from typing import Any
 
-import numpy
+import matplotlib.pyplot as plt
-from matplotlib import pyplot
+import numpy as np
 
 c_cauliflower = 0.25 + 0.0j
 c_polynomial_1 = -0.4 + 0.6j
@@ -37,22 +37,20 @@ window_size = 2.0
 nb_pixels = 666
 
 
-def eval_exponential(c_parameter: complex, z_values: numpy.ndarray) -> numpy.ndarray:
+def eval_exponential(c_parameter: complex, z_values: np.ndarray) -> np.ndarray:
     """
     Evaluate $e^z + c$.
     >>> eval_exponential(0, 0)
     1.0
-    >>> abs(eval_exponential(1, numpy.pi*1.j)) < 1e-15
+    >>> abs(eval_exponential(1, np.pi*1.j)) < 1e-15
     True
     >>> abs(eval_exponential(1.j, 0)-1-1.j) < 1e-15
     True
     """
-    return numpy.exp(z_values) + c_parameter
+    return np.exp(z_values) + c_parameter
 
 
-def eval_quadratic_polynomial(
+def eval_quadratic_polynomial(c_parameter: complex, z_values: np.ndarray) -> np.ndarray:
-    c_parameter: complex, z_values: numpy.ndarray
-) -> numpy.ndarray:
     """
     >>> eval_quadratic_polynomial(0, 2)
     4
@@ -66,7 +64,7 @@ def eval_quadratic_polynomial(
     return z_values * z_values + c_parameter
 
 
-def prepare_grid(window_size: float, nb_pixels: int) -> numpy.ndarray:
+def prepare_grid(window_size: float, nb_pixels: int) -> np.ndarray:
     """
     Create a grid of complex values of size nb_pixels*nb_pixels with real and
      imaginary parts ranging from -window_size to window_size (inclusive).
@@ -77,20 +75,20 @@ def prepare_grid(window_size: float, nb_pixels: int) -> numpy.ndarray:
            [ 0.-1.j,  0.+0.j,  0.+1.j],
            [ 1.-1.j,  1.+0.j,  1.+1.j]])
     """
-    x = numpy.linspace(-window_size, window_size, nb_pixels)
+    x = np.linspace(-window_size, window_size, nb_pixels)
     x = x.reshape((nb_pixels, 1))
-    y = numpy.linspace(-window_size, window_size, nb_pixels)
+    y = np.linspace(-window_size, window_size, nb_pixels)
     y = y.reshape((1, nb_pixels))
     return x + 1.0j * y
 
 
 def iterate_function(
-    eval_function: Callable[[Any, numpy.ndarray], numpy.ndarray],
+    eval_function: Callable[[Any, np.ndarray], np.ndarray],
     function_params: Any,
     nb_iterations: int,
-    z_0: numpy.ndarray,
+    z_0: np.ndarray,
     infinity: float | None = None,
-) -> numpy.ndarray:
+) -> np.ndarray:
     """
     Iterate the function "eval_function" exactly nb_iterations times.
     The first argument of the function is a parameter which is contained in
@@ -98,22 +96,22 @@ def iterate_function(
     values to iterate from.
     This function returns the final iterates.
 
-    >>> iterate_function(eval_quadratic_polynomial, 0, 3, numpy.array([0,1,2])).shape
+    >>> iterate_function(eval_quadratic_polynomial, 0, 3, np.array([0,1,2])).shape
     (3,)
-    >>> numpy.round(iterate_function(eval_quadratic_polynomial,
+    >>> np.round(iterate_function(eval_quadratic_polynomial,
     ... 0,
     ... 3,
-    ... numpy.array([0,1,2]))[0])
+    ... np.array([0,1,2]))[0])
     0j
-    >>> numpy.round(iterate_function(eval_quadratic_polynomial,
+    >>> np.round(iterate_function(eval_quadratic_polynomial,
     ... 0,
     ... 3,
-    ... numpy.array([0,1,2]))[1])
+    ... np.array([0,1,2]))[1])
     (1+0j)
-    >>> numpy.round(iterate_function(eval_quadratic_polynomial,
+    >>> np.round(iterate_function(eval_quadratic_polynomial,
     ... 0,
     ... 3,
-    ... numpy.array([0,1,2]))[2])
+    ... np.array([0,1,2]))[2])
     (256+0j)
     """
 
@@ -121,8 +119,8 @@ def iterate_function(
     for _ in range(nb_iterations):
         z_n = eval_function(function_params, z_n)
         if infinity is not None:
-            numpy.nan_to_num(z_n, copy=False, nan=infinity)
+            np.nan_to_num(z_n, copy=False, nan=infinity)
-            z_n[abs(z_n) == numpy.inf] = infinity
+            z_n[abs(z_n) == np.inf] = infinity
     return z_n
 
 
@@ -130,21 +128,21 @@ def show_results(
     function_label: str,
     function_params: Any,
     escape_radius: float,
-    z_final: numpy.ndarray,
+    z_final: np.ndarray,
 ) -> None:
     """
     Plots of whether the absolute value of z_final is greater than
     the value of escape_radius. Adds the function_label and function_params to
     the title.
 
-    >>> show_results('80', 0, 1, numpy.array([[0,1,.5],[.4,2,1.1],[.2,1,1.3]]))
+    >>> show_results('80', 0, 1, np.array([[0,1,.5],[.4,2,1.1],[.2,1,1.3]]))
     """
 
     abs_z_final = (abs(z_final)).transpose()
     abs_z_final[:, :] = abs_z_final[::-1, :]
-    pyplot.matshow(abs_z_final < escape_radius)
+    plt.matshow(abs_z_final < escape_radius)
-    pyplot.title(f"Julia set of ${function_label}$, $c={function_params}$")
+    plt.title(f"Julia set of ${function_label}$, $c={function_params}$")
-    pyplot.show()
+    plt.show()
 
 
 def ignore_overflow_warnings() -> None:

fractals/koch_snowflake.py

@@ -22,25 +22,25 @@ Requirements (pip):
 
 from __future__ import annotations
 
-import matplotlib.pyplot as plt  # type: ignore
+import matplotlib.pyplot as plt
-import numpy
+import numpy as np
 
 # initial triangle of Koch snowflake
-VECTOR_1 = numpy.array([0, 0])
+VECTOR_1 = np.array([0, 0])
-VECTOR_2 = numpy.array([0.5, 0.8660254])
+VECTOR_2 = np.array([0.5, 0.8660254])
-VECTOR_3 = numpy.array([1, 0])
+VECTOR_3 = np.array([1, 0])
 INITIAL_VECTORS = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
 
 # uncomment for simple Koch curve instead of Koch snowflake
 # INITIAL_VECTORS = [VECTOR_1, VECTOR_3]
 
 
-def iterate(initial_vectors: list[numpy.ndarray], steps: int) -> list[numpy.ndarray]:
+def iterate(initial_vectors: list[np.ndarray], steps: int) -> list[np.ndarray]:
     """
     Go through the number of iterations determined by the argument "steps".
     Be careful with high values (above 5) since the time to calculate increases
     exponentially.
-    >>> iterate([numpy.array([0, 0]), numpy.array([1, 0])], 1)
+    >>> iterate([np.array([0, 0]), np.array([1, 0])], 1)
     [array([0, 0]), array([0.33333333, 0.        ]), array([0.5       , \
 0.28867513]), array([0.66666667, 0.        ]), array([1, 0])]
     """
@@ -50,13 +50,13 @@ def iterate(initial_vectors: list[numpy.ndarray], steps: int) -> list[numpy.ndar
     return vectors
 
 
-def iteration_step(vectors: list[numpy.ndarray]) -> list[numpy.ndarray]:
+def iteration_step(vectors: list[np.ndarray]) -> list[np.ndarray]:
     """
     Loops through each pair of adjacent vectors. Each line between two adjacent
     vectors is divided into 4 segments by adding 3 additional vectors in-between
     the original two vectors. The vector in the middle is constructed through a
     60 degree rotation so it is bent outwards.
-    >>> iteration_step([numpy.array([0, 0]), numpy.array([1, 0])])
+    >>> iteration_step([np.array([0, 0]), np.array([1, 0])])
     [array([0, 0]), array([0.33333333, 0.        ]), array([0.5       , \
 0.28867513]), array([0.66666667, 0.        ]), array([1, 0])]
     """
@@ -74,22 +74,22 @@ def iteration_step(vectors: list[numpy.ndarray]) -> list[numpy.ndarray]:
     return new_vectors
 
 
-def rotate(vector: numpy.ndarray, angle_in_degrees: float) -> numpy.ndarray:
+def rotate(vector: np.ndarray, angle_in_degrees: float) -> np.ndarray:
     """
     Standard rotation of a 2D vector with a rotation matrix
     (see https://en.wikipedia.org/wiki/Rotation_matrix )
-    >>> rotate(numpy.array([1, 0]), 60)
+    >>> rotate(np.array([1, 0]), 60)
     array([0.5      , 0.8660254])
-    >>> rotate(numpy.array([1, 0]), 90)
+    >>> rotate(np.array([1, 0]), 90)
     array([6.123234e-17, 1.000000e+00])
     """
-    theta = numpy.radians(angle_in_degrees)
+    theta = np.radians(angle_in_degrees)
-    c, s = numpy.cos(theta), numpy.sin(theta)
+    c, s = np.cos(theta), np.sin(theta)
-    rotation_matrix = numpy.array(((c, -s), (s, c)))
+    rotation_matrix = np.array(((c, -s), (s, c)))
-    return numpy.dot(rotation_matrix, vector)
+    return np.dot(rotation_matrix, vector)
 
 
-def plot(vectors: list[numpy.ndarray]) -> None:
+def plot(vectors: list[np.ndarray]) -> None:
     """
     Utility function to plot the vectors using matplotlib.pyplot
     No doctest was implemented since this function does not have a return value

graphics/bezier_curve.py

@@ -78,7 +78,7 @@ class BezierCurve:
             step_size: defines the step(s) at which to evaluate the Bezier curve.
             The smaller the step size, the finer the curve produced.
         """
-        from matplotlib import pyplot as plt  # type: ignore
+        from matplotlib import pyplot as plt
 
         to_plot_x: list[float] = []  # x coordinates of points to plot
         to_plot_y: list[float] = []  # y coordinates of points to plot

machine_learning/gradient_descent.py

@@ -3,7 +3,7 @@ Implementation of gradient descent algorithm for minimizing cost of a linear hyp
 function.
 """
 
-import numpy
+import numpy as np
 
 # List of input, output pairs
 train_data = (
@@ -116,7 +116,7 @@ def run_gradient_descent():
             temp_parameter_vector[i] = (
                 parameter_vector[i] - LEARNING_RATE * cost_derivative
             )
-        if numpy.allclose(
+        if np.allclose(
             parameter_vector,
             temp_parameter_vector,
             atol=absolute_error_limit,

neural_network/input_data.py

@@ -22,7 +22,7 @@ import os
 import typing
 import urllib
 
-import numpy
+import numpy as np
 from tensorflow.python.framework import dtypes, random_seed
 from tensorflow.python.platform import gfile
 from tensorflow.python.util.deprecation import deprecated
@@ -39,8 +39,8 @@ DEFAULT_SOURCE_URL = "https://storage.googleapis.com/cvdf-datasets/mnist/"
 
 
 def _read32(bytestream):
-    dt = numpy.dtype(numpy.uint32).newbyteorder(">")
+    dt = np.dtype(np.uint32).newbyteorder(">")
-    return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
+    return np.frombuffer(bytestream.read(4), dtype=dt)[0]
 
 
 @deprecated(None, "Please use tf.data to implement this functionality.")
@@ -68,7 +68,7 @@ def _extract_images(f):
         rows = _read32(bytestream)
         cols = _read32(bytestream)
         buf = bytestream.read(rows * cols * num_images)
-        data = numpy.frombuffer(buf, dtype=numpy.uint8)
+        data = np.frombuffer(buf, dtype=np.uint8)
         data = data.reshape(num_images, rows, cols, 1)
         return data
 
@@ -77,8 +77,8 @@ def _extract_images(f):
 def _dense_to_one_hot(labels_dense, num_classes):
     """Convert class labels from scalars to one-hot vectors."""
     num_labels = labels_dense.shape[0]
-    index_offset = numpy.arange(num_labels) * num_classes
+    index_offset = np.arange(num_labels) * num_classes
-    labels_one_hot = numpy.zeros((num_labels, num_classes))
+    labels_one_hot = np.zeros((num_labels, num_classes))
     labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
     return labels_one_hot
 
@@ -107,7 +107,7 @@ def _extract_labels(f, one_hot=False, num_classes=10):
             )
         num_items = _read32(bytestream)
         buf = bytestream.read(num_items)
-        labels = numpy.frombuffer(buf, dtype=numpy.uint8)
+        labels = np.frombuffer(buf, dtype=np.uint8)
         if one_hot:
             return _dense_to_one_hot(labels, num_classes)
         return labels
@@ -153,7 +153,7 @@ class _DataSet:
         """
         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)
+        np.random.seed(seed1 if seed is None else seed2)
         dtype = dtypes.as_dtype(dtype).base_dtype
         if dtype not in (dtypes.uint8, dtypes.float32):
             raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype)
@@ -175,8 +175,8 @@ class _DataSet:
                 )
             if dtype == dtypes.float32:
                 # Convert from [0, 255] -> [0.0, 1.0].
-                images = images.astype(numpy.float32)
+                images = images.astype(np.float32)
-                images = numpy.multiply(images, 1.0 / 255.0)
+                images = np.multiply(images, 1.0 / 255.0)
         self._images = images
         self._labels = labels
         self._epochs_completed = 0
@@ -210,8 +210,8 @@ class _DataSet:
         start = self._index_in_epoch
         # Shuffle for the first epoch
         if self._epochs_completed == 0 and start == 0 and shuffle:
-            perm0 = numpy.arange(self._num_examples)
+            perm0 = np.arange(self._num_examples)
-            numpy.random.shuffle(perm0)
+            np.random.shuffle(perm0)
             self._images = self.images[perm0]
             self._labels = self.labels[perm0]
         # Go to the next epoch
@@ -224,8 +224,8 @@ class _DataSet:
             labels_rest_part = self._labels[start : self._num_examples]
             # Shuffle the data
             if shuffle:
-                perm = numpy.arange(self._num_examples)
+                perm = np.arange(self._num_examples)
-                numpy.random.shuffle(perm)
+                np.random.shuffle(perm)
                 self._images = self.images[perm]
                 self._labels = self.labels[perm]
             # Start next epoch
@@ -235,8 +235,8 @@ class _DataSet:
             images_new_part = self._images[start:end]
             labels_new_part = self._labels[start:end]
             return (
-                numpy.concatenate((images_rest_part, images_new_part), axis=0),
+                np.concatenate((images_rest_part, images_new_part), axis=0),
-                numpy.concatenate((labels_rest_part, labels_new_part), axis=0),
+                np.concatenate((labels_rest_part, labels_new_part), axis=0),
             )
         else:
             self._index_in_epoch += batch_size

neural_network/two_hidden_layers_neural_network.py

@@ -5,11 +5,11 @@ References:
     - https://en.wikipedia.org/wiki/Feedforward_neural_network (Feedforward)
 """
 
-import numpy
+import numpy as np
 
 
 class TwoHiddenLayerNeuralNetwork:
-    def __init__(self, input_array: numpy.ndarray, output_array: numpy.ndarray) -> None:
+    def __init__(self, input_array: np.ndarray, output_array: np.ndarray) -> None:
         """
         This function initializes the TwoHiddenLayerNeuralNetwork class with random
         weights for every layer and initializes predicted output with zeroes.
@@ -28,30 +28,28 @@ class TwoHiddenLayerNeuralNetwork:
         # Random initial weights are assigned.
         # self.input_array.shape[1] is used to represent number of nodes in input layer.
         # First hidden layer consists of 4 nodes.
-        self.input_layer_and_first_hidden_layer_weights = numpy.random.rand(
+        self.input_layer_and_first_hidden_layer_weights = np.random.rand(
             self.input_array.shape[1], 4
         )
 
         # Random initial values for the first hidden layer.
         # First hidden layer has 4 nodes.
         # Second hidden layer has 3 nodes.
-        self.first_hidden_layer_and_second_hidden_layer_weights = numpy.random.rand(
+        self.first_hidden_layer_and_second_hidden_layer_weights = np.random.rand(4, 3)
-            4, 3
-        )
 
         # Random initial values for the second hidden layer.
         # Second hidden layer has 3 nodes.
         # Output layer has 1 node.
-        self.second_hidden_layer_and_output_layer_weights = numpy.random.rand(3, 1)
+        self.second_hidden_layer_and_output_layer_weights = np.random.rand(3, 1)
 
         # Real output values provided.
         self.output_array = output_array
 
         # Predicted output values by the neural network.
         # Predicted_output array initially consists of zeroes.
-        self.predicted_output = numpy.zeros(output_array.shape)
+        self.predicted_output = np.zeros(output_array.shape)
 
-    def feedforward(self) -> numpy.ndarray:
+    def feedforward(self) -> np.ndarray:
         """
         The information moves in only one direction i.e. forward from the input nodes,
         through the two hidden nodes and to the output nodes.
@@ -60,24 +58,24 @@ class TwoHiddenLayerNeuralNetwork:
         Return layer_between_second_hidden_layer_and_output
             (i.e the last layer of the neural network).
 
-        >>> input_val = numpy.array(([0, 0, 0], [0, 0, 0], [0, 0, 0]), dtype=float)
+        >>> input_val = np.array(([0, 0, 0], [0, 0, 0], [0, 0, 0]), dtype=float)
-        >>> output_val = numpy.array(([0], [0], [0]), dtype=float)
+        >>> output_val = np.array(([0], [0], [0]), dtype=float)
         >>> nn = TwoHiddenLayerNeuralNetwork(input_val, output_val)
         >>> res = nn.feedforward()
-        >>> array_sum = numpy.sum(res)
+        >>> array_sum = np.sum(res)
-        >>> numpy.isnan(array_sum)
+        >>> np.isnan(array_sum)
         False
         """
         # Layer_between_input_and_first_hidden_layer is the layer connecting the
         # input nodes with the first hidden layer nodes.
         self.layer_between_input_and_first_hidden_layer = sigmoid(
-            numpy.dot(self.input_array, self.input_layer_and_first_hidden_layer_weights)
+            np.dot(self.input_array, self.input_layer_and_first_hidden_layer_weights)
         )
 
         # layer_between_first_hidden_layer_and_second_hidden_layer is the layer
         # connecting the first hidden set of nodes with the second hidden set of nodes.
         self.layer_between_first_hidden_layer_and_second_hidden_layer = sigmoid(
-            numpy.dot(
+            np.dot(
                 self.layer_between_input_and_first_hidden_layer,
                 self.first_hidden_layer_and_second_hidden_layer_weights,
             )
@@ -86,7 +84,7 @@ class TwoHiddenLayerNeuralNetwork:
         # layer_between_second_hidden_layer_and_output is the layer connecting
         # second hidden layer with the output node.
         self.layer_between_second_hidden_layer_and_output = sigmoid(
-            numpy.dot(
+            np.dot(
                 self.layer_between_first_hidden_layer_and_second_hidden_layer,
                 self.second_hidden_layer_and_output_layer_weights,
             )
@@ -100,8 +98,8 @@ class TwoHiddenLayerNeuralNetwork:
         error rate obtained in the previous epoch (i.e., iteration).
         Updation is done using derivative of sogmoid activation function.
 
-        >>> input_val = numpy.array(([0, 0, 0], [0, 0, 0], [0, 0, 0]), dtype=float)
+        >>> input_val = np.array(([0, 0, 0], [0, 0, 0], [0, 0, 0]), dtype=float)
-        >>> output_val = numpy.array(([0], [0], [0]), dtype=float)
+        >>> output_val = np.array(([0], [0], [0]), dtype=float)
         >>> nn = TwoHiddenLayerNeuralNetwork(input_val, output_val)
         >>> res = nn.feedforward()
         >>> nn.back_propagation()
@@ -110,15 +108,15 @@ class TwoHiddenLayerNeuralNetwork:
         False
         """
 
-        updated_second_hidden_layer_and_output_layer_weights = numpy.dot(
+        updated_second_hidden_layer_and_output_layer_weights = np.dot(
             self.layer_between_first_hidden_layer_and_second_hidden_layer.T,
             2
             * (self.output_array - self.predicted_output)
             * sigmoid_derivative(self.predicted_output),
         )
-        updated_first_hidden_layer_and_second_hidden_layer_weights = numpy.dot(
+        updated_first_hidden_layer_and_second_hidden_layer_weights = np.dot(
             self.layer_between_input_and_first_hidden_layer.T,
-            numpy.dot(
+            np.dot(
                 2
                 * (self.output_array - self.predicted_output)
                 * sigmoid_derivative(self.predicted_output),
@@ -128,10 +126,10 @@ class TwoHiddenLayerNeuralNetwork:
                 self.layer_between_first_hidden_layer_and_second_hidden_layer
             ),
         )
-        updated_input_layer_and_first_hidden_layer_weights = numpy.dot(
+        updated_input_layer_and_first_hidden_layer_weights = np.dot(
             self.input_array.T,
-            numpy.dot(
+            np.dot(
-                numpy.dot(
+                np.dot(
                     2
                     * (self.output_array - self.predicted_output)
                     * sigmoid_derivative(self.predicted_output),
@@ -155,7 +153,7 @@ class TwoHiddenLayerNeuralNetwork:
             updated_second_hidden_layer_and_output_layer_weights
         )
 
-    def train(self, output: numpy.ndarray, iterations: int, give_loss: bool) -> None:
+    def train(self, output: np.ndarray, iterations: int, give_loss: bool) -> None:
         """
         Performs the feedforwarding and back propagation process for the
         given number of iterations.
@@ -166,8 +164,8 @@ class TwoHiddenLayerNeuralNetwork:
         give_loss : boolean value, If True then prints loss for each iteration,
                     If False then nothing is printed
 
-        >>> input_val = numpy.array(([0, 0, 0], [0, 1, 0], [0, 0, 1]), dtype=float)
+        >>> input_val = np.array(([0, 0, 0], [0, 1, 0], [0, 0, 1]), dtype=float)
-        >>> output_val = numpy.array(([0], [1], [1]), dtype=float)
+        >>> output_val = np.array(([0], [1], [1]), dtype=float)
         >>> nn = TwoHiddenLayerNeuralNetwork(input_val, output_val)
         >>> first_iteration_weights = nn.feedforward()
         >>> nn.back_propagation()
@@ -179,10 +177,10 @@ class TwoHiddenLayerNeuralNetwork:
             self.output = self.feedforward()
             self.back_propagation()
             if give_loss:
-                loss = numpy.mean(numpy.square(output - self.feedforward()))
+                loss = np.mean(np.square(output - self.feedforward()))
                 print(f"Iteration {iteration} Loss: {loss}")
 
-    def predict(self, input_arr: numpy.ndarray) -> int:
+    def predict(self, input_arr: np.ndarray) -> int:
         """
         Predict's the output for the given input values using
         the trained neural network.
@@ -192,8 +190,8 @@ class TwoHiddenLayerNeuralNetwork:
         than the threshold value else returns 0,
         as the real output values are in binary.
 
-        >>> input_val = numpy.array(([0, 0, 0], [0, 1, 0], [0, 0, 1]), dtype=float)
+        >>> input_val = np.array(([0, 0, 0], [0, 1, 0], [0, 0, 1]), dtype=float)
-        >>> output_val = numpy.array(([0], [1], [1]), dtype=float)
+        >>> output_val = np.array(([0], [1], [1]), dtype=float)
         >>> nn = TwoHiddenLayerNeuralNetwork(input_val, output_val)
         >>> nn.train(output_val, 1000, False)
         >>> nn.predict([0, 1, 0]) in (0, 1)
@@ -204,18 +202,18 @@ class TwoHiddenLayerNeuralNetwork:
         self.array = input_arr
 
         self.layer_between_input_and_first_hidden_layer = sigmoid(
-            numpy.dot(self.array, self.input_layer_and_first_hidden_layer_weights)
+            np.dot(self.array, self.input_layer_and_first_hidden_layer_weights)
         )
 
         self.layer_between_first_hidden_layer_and_second_hidden_layer = sigmoid(
-            numpy.dot(
+            np.dot(
                 self.layer_between_input_and_first_hidden_layer,
                 self.first_hidden_layer_and_second_hidden_layer_weights,
             )
         )
 
         self.layer_between_second_hidden_layer_and_output = sigmoid(
-            numpy.dot(
+            np.dot(
                 self.layer_between_first_hidden_layer_and_second_hidden_layer,
                 self.second_hidden_layer_and_output_layer_weights,
             )
@@ -224,26 +222,26 @@ class TwoHiddenLayerNeuralNetwork:
         return int((self.layer_between_second_hidden_layer_and_output > 0.6)[0])
 
 
-def sigmoid(value: numpy.ndarray) -> numpy.ndarray:
+def sigmoid(value: np.ndarray) -> np.ndarray:
     """
     Applies sigmoid activation function.
 
     return normalized values
 
-    >>> sigmoid(numpy.array(([1, 0, 2], [1, 0, 0]), dtype=numpy.float64))
+    >>> sigmoid(np.array(([1, 0, 2], [1, 0, 0]), dtype=np.float64))
     array([[0.73105858, 0.5       , 0.88079708],
            [0.73105858, 0.5       , 0.5       ]])
     """
-    return 1 / (1 + numpy.exp(-value))
+    return 1 / (1 + np.exp(-value))
 
 
-def sigmoid_derivative(value: numpy.ndarray) -> numpy.ndarray:
+def sigmoid_derivative(value: np.ndarray) -> np.ndarray:
     """
     Provides the derivative value of the sigmoid function.
 
     returns derivative of the sigmoid value
 
-    >>> sigmoid_derivative(numpy.array(([1, 0, 2], [1, 0, 0]), dtype=numpy.float64))
+    >>> sigmoid_derivative(np.array(([1, 0, 2], [1, 0, 0]), dtype=np.float64))
     array([[ 0.,  0., -2.],
            [ 0.,  0.,  0.]])
     """
@@ -264,7 +262,7 @@ def example() -> int:
     True
     """
     # Input values.
-    test_input = numpy.array(
+    test_input = np.array(
         (
             [0, 0, 0],
             [0, 0, 1],
@@ -275,11 +273,11 @@ def example() -> int:
             [1, 1, 0],
             [1, 1, 1],
         ),
-        dtype=numpy.float64,
+        dtype=np.float64,
     )
 
     # True output values for the given input values.
-    output = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]), dtype=numpy.float64)
+    output = np.array(([0], [1], [1], [0], [1], [0], [0], [1]), dtype=np.float64)
 
     # Calling neural network class.
     neural_network = TwoHiddenLayerNeuralNetwork(
@@ -290,7 +288,7 @@ def example() -> int:
     # Set give_loss to True if you want to see loss in every iteration.
     neural_network.train(output=output, iterations=10, give_loss=False)
 
-    return neural_network.predict(numpy.array(([1, 1, 1]), dtype=numpy.float64))
+    return neural_network.predict(np.array(([1, 1, 1]), dtype=np.float64))
 
 
 if __name__ == "__main__":

pyproject.toml

@@ -6,7 +6,6 @@ lint.ignore = [    # `ruff rule S101` for a description of that rule
   "EM101",    # Exception must not use a string literal, assign to variable first
   "EXE001",   # Shebang is present but file is not executable" -- FIX ME
   "G004",     # Logging statement uses f-string
-  "ICN001",   # `matplotlib.pyplot` should be imported as `plt` -- FIX ME
   "INP001",   # File `x/y/z.py` is part of an implicit namespace package. Add an `__init__.py`. -- FIX ME
   "NPY002",   # Replace legacy `np.random.choice` call with `np.random.Generator` -- FIX ME
   "PGH003",   # Use specific rule codes when ignoring type issues -- FIX ME