We'll start by adding physics equations to the complexity functions. Here's how we can integrate some basic physics concepts.
import numpy as np
Reinforced complexity functions with physics equations
def unknown_forces(data):
Applying Newton's second law: F = m * a (assuming unit mass and random acceleration)
acceleration = np.random.random()
return data * acceleration
def energy_infusion(data):
Applying E = mc^2 (assuming unit mass and speed of light, c)
c = 3e8 # speed of light in m/s
return data * (c ** 2)
def creation_of_time(data):
Applying time dilation equation: t' = t / sqrt(1 - v^2/c^2) (assuming random velocity)
c = 3e8 # speed of light in m/s
velocity = np.random.random() * c
time_dilation = 1 / np.sqrt(1 - (velocity ** 2 / c ** 2))
return data * time_dilation
Add similar physics-based implementations for other complexity functions
We'll start by adding physics equations to the complexity functions. Here's how we can integrate some basic physics concepts.
import numpy as np
Reinforced complexity functions with physics equations
def unknown_forces(data):
Applying Newton's second law: F = m * a (assuming unit mass and random acceleration)
def energy_infusion(data):
Applying E = mc^2 (assuming unit mass and speed of light, c)
def creation_of_time(data):
Applying time dilation equation: t' = t / sqrt(1 - v^2/c^2) (assuming random velocity)
Add similar physics-based implementations for other complexity functions