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Added Fast Inverse Square Root (#11054)
* Feat: Added Fast inverse square root * Fix: Added typehint * Fix: Added doctests that break the code, changed var name * updating DIRECTORY.md * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix: fixed length of docstring * Update fast_inverse_sqrt.py --------- Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Christian Clauss <cclauss@me.com>
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DIRECTORY.md
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DIRECTORY.md
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@ -34,6 +34,7 @@
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* [Bitwise Addition Recursive](bit_manipulation/bitwise_addition_recursive.py)
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* [Bitwise Addition Recursive](bit_manipulation/bitwise_addition_recursive.py)
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* [Count 1S Brian Kernighan Method](bit_manipulation/count_1s_brian_kernighan_method.py)
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* [Count 1S Brian Kernighan Method](bit_manipulation/count_1s_brian_kernighan_method.py)
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* [Count Number Of One Bits](bit_manipulation/count_number_of_one_bits.py)
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* [Count Number Of One Bits](bit_manipulation/count_number_of_one_bits.py)
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* [Excess 3 Code](bit_manipulation/excess_3_code.py)
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* [Gray Code Sequence](bit_manipulation/gray_code_sequence.py)
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* [Gray Code Sequence](bit_manipulation/gray_code_sequence.py)
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* [Highest Set Bit](bit_manipulation/highest_set_bit.py)
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* [Highest Set Bit](bit_manipulation/highest_set_bit.py)
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* [Index Of Rightmost Set Bit](bit_manipulation/index_of_rightmost_set_bit.py)
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* [Index Of Rightmost Set Bit](bit_manipulation/index_of_rightmost_set_bit.py)
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@ -170,7 +171,10 @@
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* Arrays
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* Arrays
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* [Equilibrium Index In Array](data_structures/arrays/equilibrium_index_in_array.py)
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* [Equilibrium Index In Array](data_structures/arrays/equilibrium_index_in_array.py)
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* [Find Triplets With 0 Sum](data_structures/arrays/find_triplets_with_0_sum.py)
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* [Find Triplets With 0 Sum](data_structures/arrays/find_triplets_with_0_sum.py)
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* [Index 2D Array In 1D](data_structures/arrays/index_2d_array_in_1d.py)
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* [Kth Largest Element](data_structures/arrays/kth_largest_element.py)
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* [Median Two Array](data_structures/arrays/median_two_array.py)
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* [Median Two Array](data_structures/arrays/median_two_array.py)
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* [Monotonic Array](data_structures/arrays/monotonic_array.py)
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* [Pairs With Given Sum](data_structures/arrays/pairs_with_given_sum.py)
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* [Pairs With Given Sum](data_structures/arrays/pairs_with_given_sum.py)
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* [Permutations](data_structures/arrays/permutations.py)
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* [Permutations](data_structures/arrays/permutations.py)
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* [Prefix Sum](data_structures/arrays/prefix_sum.py)
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* [Prefix Sum](data_structures/arrays/prefix_sum.py)
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@ -368,6 +372,7 @@
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## Electronics
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## Electronics
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* [Apparent Power](electronics/apparent_power.py)
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* [Apparent Power](electronics/apparent_power.py)
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* [Builtin Voltage](electronics/builtin_voltage.py)
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* [Builtin Voltage](electronics/builtin_voltage.py)
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* [Capacitor Equivalence](electronics/capacitor_equivalence.py)
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* [Carrier Concentration](electronics/carrier_concentration.py)
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* [Carrier Concentration](electronics/carrier_concentration.py)
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* [Charging Capacitor](electronics/charging_capacitor.py)
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* [Charging Capacitor](electronics/charging_capacitor.py)
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* [Charging Inductor](electronics/charging_inductor.py)
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* [Charging Inductor](electronics/charging_inductor.py)
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## Machine Learning
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## Machine Learning
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* [Apriori Algorithm](machine_learning/apriori_algorithm.py)
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* [Apriori Algorithm](machine_learning/apriori_algorithm.py)
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* [Astar](machine_learning/astar.py)
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* [Astar](machine_learning/astar.py)
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* [Automatic Differentiation](machine_learning/automatic_differentiation.py)
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* [Data Transformations](machine_learning/data_transformations.py)
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* [Data Transformations](machine_learning/data_transformations.py)
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* [Decision Tree](machine_learning/decision_tree.py)
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* [Decision Tree](machine_learning/decision_tree.py)
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* [Dimensionality Reduction](machine_learning/dimensionality_reduction.py)
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* [Dimensionality Reduction](machine_learning/dimensionality_reduction.py)
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* Forecasting
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* Forecasting
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* [Run](machine_learning/forecasting/run.py)
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* [Run](machine_learning/forecasting/run.py)
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* [Frequent Pattern Growth](machine_learning/frequent_pattern_growth.py)
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* [Frequent Pattern Growth](machine_learning/frequent_pattern_growth.py)
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* [Gradient Boosting Classifier](machine_learning/gradient_boosting_classifier.py)
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* [Gradient Descent](machine_learning/gradient_descent.py)
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* [Gradient Descent](machine_learning/gradient_descent.py)
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* [K Means Clust](machine_learning/k_means_clust.py)
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* [K Means Clust](machine_learning/k_means_clust.py)
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* [K Nearest Neighbours](machine_learning/k_nearest_neighbours.py)
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* [K Nearest Neighbours](machine_learning/k_nearest_neighbours.py)
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* [Extended Euclidean Algorithm](maths/extended_euclidean_algorithm.py)
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* [Extended Euclidean Algorithm](maths/extended_euclidean_algorithm.py)
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* [Factorial](maths/factorial.py)
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* [Factorial](maths/factorial.py)
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* [Factors](maths/factors.py)
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* [Factors](maths/factors.py)
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* [Fast Inverse Sqrt](maths/fast_inverse_sqrt.py)
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* [Fermat Little Theorem](maths/fermat_little_theorem.py)
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* [Fermat Little Theorem](maths/fermat_little_theorem.py)
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* [Fibonacci](maths/fibonacci.py)
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* [Fibonacci](maths/fibonacci.py)
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* [Find Max](maths/find_max.py)
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* [Find Max](maths/find_max.py)
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@ -648,6 +656,7 @@
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* [Numerical Integration](maths/numerical_analysis/numerical_integration.py)
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* [Numerical Integration](maths/numerical_analysis/numerical_integration.py)
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* [Runge Kutta](maths/numerical_analysis/runge_kutta.py)
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* [Runge Kutta](maths/numerical_analysis/runge_kutta.py)
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* [Runge Kutta Fehlberg 45](maths/numerical_analysis/runge_kutta_fehlberg_45.py)
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* [Runge Kutta Fehlberg 45](maths/numerical_analysis/runge_kutta_fehlberg_45.py)
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* [Runge Kutta Gills](maths/numerical_analysis/runge_kutta_gills.py)
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* [Secant Method](maths/numerical_analysis/secant_method.py)
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* [Secant Method](maths/numerical_analysis/secant_method.py)
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* [Simpson Rule](maths/numerical_analysis/simpson_rule.py)
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* [Simpson Rule](maths/numerical_analysis/simpson_rule.py)
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* [Square Root](maths/numerical_analysis/square_root.py)
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* [Square Root](maths/numerical_analysis/square_root.py)
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* [Ideal Gas Law](physics/ideal_gas_law.py)
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* [Ideal Gas Law](physics/ideal_gas_law.py)
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* [In Static Equilibrium](physics/in_static_equilibrium.py)
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* [In Static Equilibrium](physics/in_static_equilibrium.py)
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* [Kinetic Energy](physics/kinetic_energy.py)
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* [Kinetic Energy](physics/kinetic_energy.py)
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* [Lens Formulae](physics/lens_formulae.py)
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* [Lorentz Transformation Four Vector](physics/lorentz_transformation_four_vector.py)
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* [Lorentz Transformation Four Vector](physics/lorentz_transformation_four_vector.py)
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* [Malus Law](physics/malus_law.py)
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* [Malus Law](physics/malus_law.py)
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* [Mass Energy Equivalence](physics/mass_energy_equivalence.py)
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* [Mass Energy Equivalence](physics/mass_energy_equivalence.py)
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54
maths/fast_inverse_sqrt.py
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maths/fast_inverse_sqrt.py
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"""
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Fast inverse square root (1/sqrt(x)) using the Quake III algorithm.
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Reference: https://en.wikipedia.org/wiki/Fast_inverse_square_root
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Accuracy: https://en.wikipedia.org/wiki/Fast_inverse_square_root#Accuracy
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"""
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import struct
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def fast_inverse_sqrt(number: float) -> float:
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"""
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Compute the fast inverse square root of a floating-point number using the famous
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Quake III algorithm.
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:param float number: Input number for which to calculate the inverse square root.
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:return float: The fast inverse square root of the input number.
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Example:
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>>> fast_inverse_sqrt(10)
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0.3156857923527257
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>>> fast_inverse_sqrt(4)
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0.49915357479239103
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>>> fast_inverse_sqrt(4.1)
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0.4932849504615651
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>>> fast_inverse_sqrt(0)
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Traceback (most recent call last):
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...
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ValueError: Input must be a positive number.
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>>> fast_inverse_sqrt(-1)
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Traceback (most recent call last):
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...
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ValueError: Input must be a positive number.
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>>> from math import isclose, sqrt
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>>> all(isclose(fast_inverse_sqrt(i), 1 / sqrt(i), rel_tol=0.00132)
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... for i in range(50, 60))
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True
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"""
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if number <= 0:
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raise ValueError("Input must be a positive number.")
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i = struct.unpack(">i", struct.pack(">f", number))[0]
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i = 0x5F3759DF - (i >> 1)
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y = struct.unpack(">f", struct.pack(">i", i))[0]
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return y * (1.5 - 0.5 * number * y * y)
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if __name__ == "__main__":
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from doctest import testmod
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testmod()
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# https://en.wikipedia.org/wiki/Fast_inverse_square_root#Accuracy
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from math import sqrt
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for i in range(5, 101, 5):
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print(f"{i:>3}: {(1 / sqrt(i)) - fast_inverse_sqrt(i):.5f}")
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