gradientdescent Namespace Reference

Functions
int	derivative (float x)
float	func (int x)
float	learn (int x)

Variables
int	case = 99
dict	grad = {}

Function Documentation

derivative()

int gradientdescent.derivative

(

float

x

)

Definition at line 2 of file gradientdescent.py.

def derivative(x: float) -> int:  # derivative of f(x) as given by the question
    if x > 0:
        return 1
    elif x < 0:
        return -1
    else:
        return 0  # given
 
 

Referenced by func().

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func()

float gradientdescent.func

(

int

x

)

Definition at line 15 of file gradientdescent.py.

def func(x: int) -> float:
    if x == 0:  # initial state x^(0)
        return 2.5
    elif x in grad:  # cache of x^(t)
        return grad[x]
    else:  # not in cache
        return func(x - 1) - learn(x - 1) * derivative(func(x - 1))
 
 

References derivative(), func(), and learn().

Referenced by func().

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learn()

float gradientdescent.learn

(

int

x

)

Definition at line 11 of file gradientdescent.py.

def learn(x: int) -> float:  # learning rate n
    return 1 / (x + 1)
 
 

Referenced by func().

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Variable Documentation

case

int gradientdescent.case = 99

Definition at line 25 of file gradientdescent.py.

grad

dict gradientdescent.grad = {}

Definition at line 24 of file gradientdescent.py.