Merge branch 'master' of github.com:tianyizheng02/Python

Author: tianyizheng02

Diff for: .pre-commit-config.yaml

@@ -16,12 +16,12 @@ repos:
     -   id: auto-walrus
 
   - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: v0.0.259
+    rev: v0.0.267
     hooks:
       - id: ruff
 
   - repo: https://github.com/psf/black
-    rev: 23.1.0
+    rev: 23.3.0
     hooks:
       - id: black
 
@@ -33,7 +33,7 @@ repos:
           - tomli
 
   - repo: https://github.com/tox-dev/pyproject-fmt
-    rev: "0.9.2"
+    rev: "0.11.2"
     hooks:
       - id: pyproject-fmt
 
@@ -46,12 +46,12 @@ repos:
         pass_filenames: false
 
   - repo: https://github.com/abravalheri/validate-pyproject
-    rev: v0.12.1
+    rev: v0.12.2
     hooks:
       - id: validate-pyproject
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.1.1
+    rev: v1.3.0
     hooks:
       - id: mypy
         args:

Diff for: DIRECTORY.md

@@ -195,6 +195,7 @@
     * [Alternate Disjoint Set](data_structures/disjoint_set/alternate_disjoint_set.py)
     * [Disjoint Set](data_structures/disjoint_set/disjoint_set.py)
   * Hashing
+    * [Bloom Filter](data_structures/hashing/bloom_filter.py)
     * [Double Hash](data_structures/hashing/double_hash.py)
     * [Hash Map](data_structures/hashing/hash_map.py)
     * [Hash Table](data_structures/hashing/hash_table.py)
@@ -232,6 +233,7 @@
     * [Double Ended Queue](data_structures/queue/double_ended_queue.py)
     * [Linked Queue](data_structures/queue/linked_queue.py)
     * [Priority Queue Using List](data_structures/queue/priority_queue_using_list.py)
+    * [Queue By Two Stacks](data_structures/queue/queue_by_two_stacks.py)
     * [Queue On List](data_structures/queue/queue_on_list.py)
     * [Queue On Pseudo Stack](data_structures/queue/queue_on_pseudo_stack.py)
   * Stacks
@@ -292,7 +294,6 @@
   * [Mergesort](divide_and_conquer/mergesort.py)
   * [Peak](divide_and_conquer/peak.py)
   * [Power](divide_and_conquer/power.py)
-  * [Strassen Matrix Multiplication](divide_and_conquer/strassen_matrix_multiplication.py)
 
 ## Dynamic Programming
   * [Abbreviation](dynamic_programming/abbreviation.py)
@@ -309,6 +310,7 @@
   * [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)
@@ -324,6 +326,7 @@
   * [Minimum Coin Change](dynamic_programming/minimum_coin_change.py)
   * [Minimum Cost Path](dynamic_programming/minimum_cost_path.py)
   * [Minimum Partition](dynamic_programming/minimum_partition.py)
+  * [Minimum Size Subarray Sum](dynamic_programming/minimum_size_subarray_sum.py)
   * [Minimum Squares To Represent A Number](dynamic_programming/minimum_squares_to_represent_a_number.py)
   * [Minimum Steps To One](dynamic_programming/minimum_steps_to_one.py)
   * [Minimum Tickets Cost](dynamic_programming/minimum_tickets_cost.py)
@@ -336,6 +339,7 @@
   * [Word Break](dynamic_programming/word_break.py)
 
 ## Electronics
+  * [Apparent Power](electronics/apparent_power.py)
   * [Builtin Voltage](electronics/builtin_voltage.py)
   * [Carrier Concentration](electronics/carrier_concentration.py)
   * [Circular Convolution](electronics/circular_convolution.py)
@@ -345,6 +349,7 @@
   * [Electrical Impedance](electronics/electrical_impedance.py)
   * [Ind Reactance](electronics/ind_reactance.py)
   * [Ohms Law](electronics/ohms_law.py)
+  * [Real And Reactive Power](electronics/real_and_reactive_power.py)
   * [Resistor Equivalence](electronics/resistor_equivalence.py)
   * [Resonant Frequency](electronics/resonant_frequency.py)
 
@@ -357,6 +362,7 @@
 ## Financial
   * [Equated Monthly Installments](financial/equated_monthly_installments.py)
   * [Interest](financial/interest.py)
+  * [Present Value](financial/present_value.py)
   * [Price Plus Tax](financial/price_plus_tax.py)
 
 ## Fractals
@@ -442,6 +448,7 @@
 ## Greedy Methods
   * [Fractional Knapsack](greedy_methods/fractional_knapsack.py)
   * [Fractional Knapsack 2](greedy_methods/fractional_knapsack_2.py)
+  * [Minimum Waiting Time](greedy_methods/minimum_waiting_time.py)
   * [Optimal Merge Pattern](greedy_methods/optimal_merge_pattern.py)
 
 ## Hashes
@@ -480,6 +487,7 @@
   * [Astar](machine_learning/astar.py)
   * [Data Transformations](machine_learning/data_transformations.py)
   * [Decision Tree](machine_learning/decision_tree.py)
+  * [Dimensionality Reduction](machine_learning/dimensionality_reduction.py)
   * Forecasting
     * [Run](machine_learning/forecasting/run.py)
   * [Gradient Descent](machine_learning/gradient_descent.py)
@@ -601,6 +609,7 @@
   * [Perfect Number](maths/perfect_number.py)
   * [Perfect Square](maths/perfect_square.py)
   * [Persistence](maths/persistence.py)
+  * [Pi Generator](maths/pi_generator.py)
   * [Pi Monte Carlo Estimation](maths/pi_monte_carlo_estimation.py)
   * [Points Are Collinear 3D](maths/points_are_collinear_3d.py)
   * [Pollard Rho](maths/pollard_rho.py)
@@ -622,6 +631,7 @@
   * [Radians](maths/radians.py)
   * [Radix2 Fft](maths/radix2_fft.py)
   * [Relu](maths/relu.py)
+  * [Remove Digit](maths/remove_digit.py)
   * [Runge Kutta](maths/runge_kutta.py)
   * [Segmented Sieve](maths/segmented_sieve.py)
   * Series
@@ -647,6 +657,7 @@
   * [Sum Of Harmonic Series](maths/sum_of_harmonic_series.py)
   * [Sumset](maths/sumset.py)
   * [Sylvester Sequence](maths/sylvester_sequence.py)
+  * [Tanh](maths/tanh.py)
   * [Test Prime Check](maths/test_prime_check.py)
   * [Trapezoidal Rule](maths/trapezoidal_rule.py)
   * [Triplet Sum](maths/triplet_sum.py)
@@ -683,8 +694,11 @@
 
 ## Neural Network
   * [2 Hidden Layers Neural Network](neural_network/2_hidden_layers_neural_network.py)
+  * Activation Functions
+    * [Exponential Linear Unit](neural_network/activation_functions/exponential_linear_unit.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)
 
@@ -704,6 +718,7 @@
   * [Lru Cache](other/lru_cache.py)
   * [Magicdiamondpattern](other/magicdiamondpattern.py)
   * [Maximum Subarray](other/maximum_subarray.py)
+  * [Maximum Subsequence](other/maximum_subsequence.py)
   * [Nested Brackets](other/nested_brackets.py)
   * [Password](other/password.py)
   * [Quine](other/quine.py)
@@ -920,6 +935,8 @@
     * [Sol1](project_euler/problem_077/sol1.py)
   * Problem 078
     * [Sol1](project_euler/problem_078/sol1.py)
+  * Problem 079
+    * [Sol1](project_euler/problem_079/sol1.py)
   * Problem 080
     * [Sol1](project_euler/problem_080/sol1.py)
   * Problem 081
@@ -1031,6 +1048,7 @@
   * [Q Fourier Transform](quantum/q_fourier_transform.py)
   * [Q Full Adder](quantum/q_full_adder.py)
   * [Quantum Entanglement](quantum/quantum_entanglement.py)
+  * [Quantum Random](quantum/quantum_random.py)
   * [Quantum Teleportation](quantum/quantum_teleportation.py)
   * [Ripple Adder Classic](quantum/ripple_adder_classic.py)
   * [Single Qubit Measure](quantum/single_qubit_measure.py)
@@ -1064,6 +1082,7 @@
 
 ## Sorts
   * [Bead Sort](sorts/bead_sort.py)
+  * [Binary Insertion Sort](sorts/binary_insertion_sort.py)
   * [Bitonic Sort](sorts/bitonic_sort.py)
   * [Bogo Sort](sorts/bogo_sort.py)
   * [Bubble Sort](sorts/bubble_sort.py)
@@ -1154,7 +1173,9 @@
   * [Reverse Words](strings/reverse_words.py)
   * [Snake Case To Camel Pascal Case](strings/snake_case_to_camel_pascal_case.py)
   * [Split](strings/split.py)
+  * [String Switch Case](strings/string_switch_case.py)
   * [Text Justification](strings/text_justification.py)
+  * [Top K Frequent Words](strings/top_k_frequent_words.py)
   * [Upper](strings/upper.py)
   * [Wave](strings/wave.py)
   * [Wildcard Pattern Matching](strings/wildcard_pattern_matching.py)

Diff for: boolean_algebra/and_gate.py

@@ -43,6 +43,8 @@ def test_and_gate() -> None:
 
 
 if __name__ == "__main__":
+    test_and_gate()
+    print(and_gate(1, 0))
     print(and_gate(0, 0))
     print(and_gate(0, 1))
     print(and_gate(1, 1))

Diff for: cellular_automata/game_of_life.py

@@ -34,7 +34,7 @@
 from matplotlib import pyplot as plt
 from matplotlib.colors import ListedColormap
 
-usage_doc = "Usage of script: script_nama <size_of_canvas:int>"
+usage_doc = "Usage of script: script_name <size_of_canvas:int>"
 
 choice = [0] * 100 + [1] * 10
 random.shuffle(choice)

Diff for: ciphers/rsa_cipher.py

@@ -76,10 +76,11 @@ def encrypt_and_write_to_file(
     key_size, n, e = read_key_file(key_filename)
     if key_size < block_size * 8:
         sys.exit(
-            "ERROR: Block size is %s bits and key size is %s bits. The RSA cipher "
+            "ERROR: Block size is {} bits and key size is {} bits. The RSA cipher "
             "requires the block size to be equal to or greater than the key size. "
-            "Either decrease the block size or use different keys."
-            % (block_size * 8, key_size)
+            "Either decrease the block size or use different keys.".format(
+                block_size * 8, key_size
+            )
         )
 
     encrypted_blocks = [str(i) for i in encrypt_message(message, (n, e), block_size)]
@@ -101,10 +102,11 @@ def read_from_file_and_decrypt(message_filename: str, key_filename: str) -> str:
 
     if key_size < block_size * 8:
         sys.exit(
-            "ERROR: Block size is %s bits and key size is %s bits. The RSA cipher "
+            "ERROR: Block size is {} bits and key size is {} bits. The RSA cipher "
             "requires the block size to be equal to or greater than the key size. "
-            "Did you specify the correct key file and encrypted file?"
-            % (block_size * 8, key_size)
+            "Did you specify the correct key file and encrypted file?".format(
+                block_size * 8, key_size
+            )
         )
 
     encrypted_blocks = []

Diff for: conversions/prefix_conversions_string.py

@@ -96,7 +96,7 @@ def add_si_prefix(value: float) -> str:
     for name_prefix, value_prefix in prefixes.items():
         numerical_part = value / (10**value_prefix)
         if numerical_part > 1:
-            return f"{str(numerical_part)} {name_prefix}"
+            return f"{numerical_part!s} {name_prefix}"
     return str(value)
 
 
@@ -111,7 +111,7 @@ def add_binary_prefix(value: float) -> str:
     for prefix in BinaryUnit:
         numerical_part = value / (2**prefix.value)
         if numerical_part > 1:
-            return f"{str(numerical_part)} {prefix.name}"
+            return f"{numerical_part!s} {prefix.name}"
     return str(value)
 
 

Diff for: conversions/rgb_hsv_conversion.py

@@ -121,8 +121,8 @@ def rgb_to_hsv(red: int, green: int, blue: int) -> list[float]:
     float_red = red / 255
     float_green = green / 255
     float_blue = blue / 255
-    value = max(max(float_red, float_green), float_blue)
-    chroma = value - min(min(float_red, float_green), float_blue)
+    value = max(float_red, float_green, float_blue)
+    chroma = value - min(float_red, float_green, float_blue)
     saturation = 0 if value == 0 else chroma / value
 
     if chroma == 0:

Diff for: data_structures/hashing/bloom_filter.py

@@ -0,0 +1,105 @@
+"""
+See https://en.wikipedia.org/wiki/Bloom_filter
+
+The use of this data structure is to test membership in a set.
+Compared to Python's built-in set() it is more space-efficient.
+In the following example, only 8 bits of memory will be used:
+>>> bloom = Bloom(size=8)
+
+Initially, the filter contains all zeros:
+>>> bloom.bitstring
+'00000000'
+
+When an element is added, two bits are set to 1
+since there are 2 hash functions in this implementation:
+>>> "Titanic" in bloom
+False
+>>> bloom.add("Titanic")
+>>> bloom.bitstring
+'01100000'
+>>> "Titanic" in bloom
+True
+
+However, sometimes only one bit is added
+because both hash functions return the same value
+>>> bloom.add("Avatar")
+>>> "Avatar" in bloom
+True
+>>> bloom.format_hash("Avatar")
+'00000100'
+>>> bloom.bitstring
+'01100100'
+
+Not added elements should return False ...
+>>> not_present_films = ("The Godfather", "Interstellar", "Parasite", "Pulp Fiction")
+>>> {
+...   film: bloom.format_hash(film) for film in not_present_films
+... } # doctest: +NORMALIZE_WHITESPACE
+{'The Godfather': '00000101',
+ 'Interstellar': '00000011',
+ 'Parasite': '00010010',
+ 'Pulp Fiction': '10000100'}
+>>> any(film in bloom for film in not_present_films)
+False
+
+but sometimes there are false positives:
+>>> "Ratatouille" in bloom
+True
+>>> bloom.format_hash("Ratatouille")
+'01100000'
+
+The probability increases with the number of elements added.
+The probability decreases with the number of bits in the bitarray.
+>>> bloom.estimated_error_rate
+0.140625
+>>> bloom.add("The Godfather")
+>>> bloom.estimated_error_rate
+0.25
+>>> bloom.bitstring
+'01100101'
+"""
+from hashlib import md5, sha256
+
+HASH_FUNCTIONS = (sha256, md5)
+
+
+class Bloom:
+    def __init__(self, size: int = 8) -> None:
+        self.bitarray = 0b0
+        self.size = size
+
+    def add(self, value: str) -> None:
+        h = self.hash_(value)
+        self.bitarray |= h
+
+    def exists(self, value: str) -> bool:
+        h = self.hash_(value)
+        return (h & self.bitarray) == h
+
+    def __contains__(self, other: str) -> bool:
+        return self.exists(other)
+
+    def format_bin(self, bitarray: int) -> str:
+        res = bin(bitarray)[2:]
+        return res.zfill(self.size)
+
+    @property
+    def bitstring(self) -> str:
+        return self.format_bin(self.bitarray)
+
+    def hash_(self, value: str) -> int:
+        res = 0b0
+        for func in HASH_FUNCTIONS:
+            position = (
+                int.from_bytes(func(value.encode()).digest(), "little") % self.size
+            )
+            res |= 2**position
+        return res
+
+    def format_hash(self, value: str) -> str:
+        return self.format_bin(self.hash_(value))
+
+    @property
+    def estimated_error_rate(self) -> float:
+        n_ones = bin(self.bitarray).count("1")
+        return (n_ones / self.size) ** len(HASH_FUNCTIONS)