#newton_method_with_gradient_descent
import exemplos
from diff.autodiff import var, df
err = 0.01
f = lambda x: x**2 - 9
x = var(1)
lr = 0.01 # tamanho do passos
n = 1000 #numero máximo de iterações
for i in range(n):
y = f(x.a)
dy = df(f, x)
x = var(x - dy * lr)
if abs(dy) <= err:
print("stop at it: {} x = {} : f(x) = {}".format(i + 1, x, y))
break
if i == (n - 1):
print("não houve convergência>: it{}".format(n))
max_iter = 1000
#x = x - 1
x = x + 1
for iter in range(max_iter):
y = f(x.a)
dy = df(f, x)
x = var(x - y / dy)
if abs(y) <= err:
print("stop at iter {}: x = {} f(x) = {}".format(iter, x, y))
import exemplos
from diff.autodiff import var, df, const
err = 0.01
f = lambda pv, i, n: pv * (i / (1 - (1 / (1 + i)**n))) - 289.62
pv = var(10)
i = const(4.20297 / 100)
n = const(6)
max_iter = 1000
for iter in range(max_iter):
y = f(pv.a, i.a, n.a)
dy = df(f, pv, i, n)
pv = var(pv - y / dy)
if abs(y) <= err:
print(pv, iter)
break
if iter == max_iter:
print("max_iter is complete: x = {}".format(pv))
import exemplos
from diff.autodiff import var, df
err = 0.01
f = lambda x: x**2 - 9
x = var(1)
max_iter = 1000
for iter in range(max_iter):
y = f(x.a)
dy = df(f, x)
x = var(x - y / dy)
if abs(y) <= err:
print("stop at iter {}: x = {} f(x) = {}".format(iter, x))
break
if iter == max_iter:
print("max_iter is complete: x = {}".format(x))
import exemplos
from diff.autodiff import var, const, df
f = lambda x: x**2 - 3 * x + 1
## gradiente descent para encontrar df(x) = 0
x = var(0) # chute inicial
lr = 0.01 # tamanho do passos
err = 0.001 # |df(x) - df(xi)|
n = 1000 #numero máximo de iterações
for i in range(n):
dy = df(f, x) # return dy
x = var(x - dy * lr)
# ajustando os passos
if abs(dy) <= err: # verificando se
print("stop at it: {} x = {}".format(i + 1, x))
break
if i == (n - 1):
print("não houve convergência>: it{}".format(n))