Added Fast Inverse Square Root

DIRECTORY.md

@@ -34,6 +34,7 @@
   * [Bitwise Addition Recursive](bit_manipulation/bitwise_addition_recursive.py)
   * [Count 1S Brian Kernighan Method](bit_manipulation/count_1s_brian_kernighan_method.py)
   * [Count Number Of One Bits](bit_manipulation/count_number_of_one_bits.py)
+  * [Excess 3 Code](bit_manipulation/excess_3_code.py)
   * [Gray Code Sequence](bit_manipulation/gray_code_sequence.py)
   * [Highest Set Bit](bit_manipulation/highest_set_bit.py)
   * [Index Of Rightmost Set Bit](bit_manipulation/index_of_rightmost_set_bit.py)
@@ -170,7 +171,10 @@
   * Arrays
     * [Equilibrium Index In Array](data_structures/arrays/equilibrium_index_in_array.py)
     * [Find Triplets With 0 Sum](data_structures/arrays/find_triplets_with_0_sum.py)
+    * [Index 2D Array In 1D](data_structures/arrays/index_2d_array_in_1d.py)
+    * [Kth Largest Element](data_structures/arrays/kth_largest_element.py)
     * [Median Two Array](data_structures/arrays/median_two_array.py)
+    * [Monotonic Array](data_structures/arrays/monotonic_array.py)
     * [Pairs With Given Sum](data_structures/arrays/pairs_with_given_sum.py)
     * [Permutations](data_structures/arrays/permutations.py)
     * [Prefix Sum](data_structures/arrays/prefix_sum.py)
@@ -368,6 +372,7 @@
 ## Electronics
   * [Apparent Power](electronics/apparent_power.py)
   * [Builtin Voltage](electronics/builtin_voltage.py)
+  * [Capacitor Equivalence](electronics/capacitor_equivalence.py)
   * [Carrier Concentration](electronics/carrier_concentration.py)
   * [Charging Capacitor](electronics/charging_capacitor.py)
   * [Charging Inductor](electronics/charging_inductor.py)
@@ -531,12 +536,14 @@
 ## Machine Learning
   * [Apriori Algorithm](machine_learning/apriori_algorithm.py)
   * [Astar](machine_learning/astar.py)
+  * [Automatic Differentiation](machine_learning/automatic_differentiation.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)
   * [Frequent Pattern Growth](machine_learning/frequent_pattern_growth.py)
+  * [Gradient Boosting Classifier](machine_learning/gradient_boosting_classifier.py)
   * [Gradient Descent](machine_learning/gradient_descent.py)
   * [K Means Clust](machine_learning/k_means_clust.py)
   * [K Nearest Neighbours](machine_learning/k_nearest_neighbours.py)
@@ -598,6 +605,7 @@
   * [Extended Euclidean Algorithm](maths/extended_euclidean_algorithm.py)
   * [Factorial](maths/factorial.py)
   * [Factors](maths/factors.py)
+  * [Fast Inverse Sqrt](maths/fast_inverse_sqrt.py)
   * [Fermat Little Theorem](maths/fermat_little_theorem.py)
   * [Fibonacci](maths/fibonacci.py)
   * [Find Max](maths/find_max.py)
@@ -648,6 +656,7 @@
     * [Numerical Integration](maths/numerical_analysis/numerical_integration.py)
     * [Runge Kutta](maths/numerical_analysis/runge_kutta.py)
     * [Runge Kutta Fehlberg 45](maths/numerical_analysis/runge_kutta_fehlberg_45.py)
+    * [Runge Kutta Gills](maths/numerical_analysis/runge_kutta_gills.py)
     * [Secant Method](maths/numerical_analysis/secant_method.py)
     * [Simpson Rule](maths/numerical_analysis/simpson_rule.py)
     * [Square Root](maths/numerical_analysis/square_root.py)
@@ -814,6 +823,7 @@
   * [Ideal Gas Law](physics/ideal_gas_law.py)
   * [In Static Equilibrium](physics/in_static_equilibrium.py)
   * [Kinetic Energy](physics/kinetic_energy.py)
+  * [Lens Formulae](physics/lens_formulae.py)
   * [Lorentz Transformation Four Vector](physics/lorentz_transformation_four_vector.py)
   * [Malus Law](physics/malus_law.py)
   * [Mass Energy Equivalence](physics/mass_energy_equivalence.py)

maths/fast_inverse_sqrt.py

@@ -0,0 +1,54 @@
+"""
+Fast inverse square root (1/sqrt(x)) using the Quake III algorithm.
+Reference: https://en.wikipedia.org/wiki/Fast_inverse_square_root
+Accuracy: https://en.wikipedia.org/wiki/Fast_inverse_square_root#Accuracy
+"""
+
+import struct
+
+
+def fast_inverse_sqrt(number: float) -> float:
+    """
+    Compute the fast inverse square root of a floating-point number using the famous
+    Quake III algorithm.
+
+    :param float number: Input number for which to calculate the inverse square root.
+    :return float: The fast inverse square root of the input number.
+
+    Example:
+    >>> fast_inverse_sqrt(10)
+    0.3156857923527257
+    >>> fast_inverse_sqrt(4)
+    0.49915357479239103
+    >>> fast_inverse_sqrt(4.1)
+    0.4932849504615651
+    >>> fast_inverse_sqrt(0)
+    Traceback (most recent call last):
+        ...
+    ValueError: Input must be a positive number.
+    >>> fast_inverse_sqrt(-1)
+    Traceback (most recent call last):
+        ...
+    ValueError: Input must be a positive number.
+    >>> from math import isclose, sqrt
+    >>> all(isclose(fast_inverse_sqrt(i), 1 / sqrt(i), rel_tol=0.00132)
+    ...     for i in range(50, 60))
+    True
+    """
+    if number <= 0:
+        raise ValueError("Input must be a positive number.")
+    i = struct.unpack(">i", struct.pack(">f", number))[0]
+    i = 0x5F3759DF - (i >> 1)
+    y = struct.unpack(">f", struct.pack(">i", i))[0]
+    return y * (1.5 - 0.5 * number * y * y)
+
+
+if __name__ == "__main__":
+    from doctest import testmod
+
+    testmod()
+    # https://en.wikipedia.org/wiki/Fast_inverse_square_root#Accuracy
+    from math import sqrt
+
+    for i in range(5, 101, 5):
+        print(f"{i:>3}: {(1 / sqrt(i)) - fast_inverse_sqrt(i):.5f}")