def fixed_point(self, expr_g, x0, tolerance, n):
'''
It calculates an approximation to a root through finding an intersection
between y = x and x = g(x) where g(x) is derivated from the function f.
parameters:
- x0 : initial point
- expr_g: function expresion derivated from f
- tolerance: maximum allowed error
- n: number of iterations until failure
'''
if tolerance < 0:
print(f"innapropiate tolerance = {tolerance}")
elif n < 1:
print(f"innapropiate number of iterations = {n}")
else:
fx = self.__function.eval(x0)
count = 0
error = tolerance + 1
data = {
"x": np.array([x0], dtype=np.float64),
"f(x)": np.array([fx], dtype=np.float64),
"E": np.array([np.NaN], dtype=np.float64)
}
g = Function(expr_g)
while fx != 0 and error > tolerance and count < n:
xn = g.eval(x0)
fx = self.__function.eval(xn)
error = self.__error(x0, xn)
x0 = xn
count += 1
data["x"] = np.append(data["x"], [xn])
data["f(x)"] = np.append(data["f(x)"], [fx])
data["E"] = np.append(data["E"], [error])
if fx == 0:
print(f"{x0} is a root")
elif error < tolerance:
print(
f"{x0} is an approximation to a root with tolerance = {tolerance}"
)
else:
print(f"failure in {n} iterations")
data_frame = pd.DataFrame(data,
columns=("x", "f(x)", "E"),
dtype=np.float64)
data_frame.index.name = "n"
print(data_frame)
def run(step, point_type, lambda_p, method):
f = Function(lambda_p)
n = f.get_size() # x size
# Initial point
if point_type == "const":
x0 = np.ones(n)
else:
x0 = np.random.uniform(-2, 2, n)
mxitr = 50000 # Max number of iterations
tol_g = 1e-8 # Tolerance for gradient
tol_x = 1e-8 # Tolerance for x
tol_f = 1e-8 # Tolerance for function
# Method for step update
if step == "fijo":
msg = "StepFijo"
elif step == "hess":
msg = "StepHess"
else:
msg = "Backtracking"
step_size = 1 # Gradient step size for "StepFijo" method
# Estimate minimum point through optimization method chosen
if method == "gd":
alg = GD()
elif method == "newton":
alg = Newton()
else:
print("\n Error: Invalid optimization method: %s\n" % method)
return
xs = alg.iterate(x0, mxitr, tol_g, tol_x, tol_f, f, msg, step_size)
# Print point x found and function value f(x)
# print("\nPoint x found: ", xs[-1])
print("\nf(x) = ", f.eval(xs[-1]))
plt.plot(np.array(range(n)), xs[-1])
plt.plot(np.array(range(n)), f.y)
plt.legend(['x*', 'y'], loc = 'best')
plt.show()
class OpenMethods:
'''
This class contains the followings methods to caltulate
some root in an one variable function:
- Fixed point
- Newton's method
- Secant
- Multiple roots
Args:
- function f: function to work with
- error: function to calculate the type of error (absolute or relative).
relative is set by default.
'''
def __init__(self, expression, error=relative):
self.__function = Function(expression)
self.__error = error
def fixed_point(self, expr_g, x0, tolerance, n):
'''
It calculates an approximation to a root through finding an intersection
between y = x and x = g(x) where g(x) is derivated from the function f.
parameters:
- x0 : initial point
- expr_g: function expresion derivated from f
- tolerance: maximum allowed error
- n: number of iterations until failure
'''
if tolerance < 0:
print(f"innapropiate tolerance = {tolerance}")
elif n < 1:
print(f"innapropiate number of iterations = {n}")
else:
fx = self.__function.eval(x0)
count = 0
error = tolerance + 1
data = {
"x": np.array([x0], dtype=np.float64),
"f(x)": np.array([fx], dtype=np.float64),
"E": np.array([np.NaN], dtype=np.float64)
}
g = Function(expr_g)
while fx != 0 and error > tolerance and count < n:
xn = g.eval(x0)
fx = self.__function.eval(xn)
error = self.__error(x0, xn)
x0 = xn
count += 1
data["x"] = np.append(data["x"], [xn])
data["f(x)"] = np.append(data["f(x)"], [fx])
data["E"] = np.append(data["E"], [error])
if fx == 0:
print(f"{x0} is a root")
elif error < tolerance:
print(
f"{x0} is an approximation to a root with tolerance = {tolerance}"
)
else:
print(f"failure in {n} iterations")
data_frame = pd.DataFrame(data,
columns=("x", "f(x)", "E"),
dtype=np.float64)
data_frame.index.name = "n"
print(data_frame)
def newtons_method(self, x0, tolerance, n):
'''
It calculates an approximation to a root through finding an intersection
between dy_dx and axis x where dy_dx is the first diff of the function f.
- x0 : initial point
- tolerance: maximum allowed error
- n: number of iterations until failure
parameters:
'''
if tolerance < 0:
print(f"innapropiate tolerance = {tolerance}")
elif n < 1:
print(f"innapropiate number of iterations = {n}")
else:
fx = self.__function.eval(x0)
df = self.__function.dfn(x0, 1)
count = 0
error = tolerance + 1
data = {
"x": np.array([x0], dtype=np.float64),
"f(x)": np.array([fx], dtype=np.float64),
"f'(x)": np.array([df], dtype=np.float64),
"E": np.array([np.NaN], dtype=np.float64)
}
while fx != 0 and df != 0 and error > tolerance and count < n:
xn = x0 - (fx / df)
fx = self.__function.eval(xn)
df = self.__function.dfn(xn, 1)
error = self.__error(x0, xn)
x0 = xn
count += 1
data["x"] = np.append(data["x"], [xn])
data["f(x)"] = np.append(data["f(x)"], [fx])
data["f'(x)"] = np.append(data["f'(x)"], [df])
data["E"] = np.append(data["E"], [error])
if fx == 0:
print(f"{x0} is a root")
elif error < tolerance:
print(
f"{x0} is an approximation to a root with tolerance = {tolerance}"
)
elif df == 0:
print(f"{x0} is a possible multiple root")
else:
print(f"failure in {n} iterations")
data_frame = pd.DataFrame(data,
columns=("x", "f(x)", "f'(x)", "E"),
dtype=np.float64)
data_frame.index.name = "n"
print(data_frame)
def secant(self, x0, x1, tolerance, n):
'''
It calculates an approximation to a root by finding an intersection
between the intersection of the secant line (with x0,x1) and axis x where
x0, x1 are known initial points.
parameters:
- x0 : initial point 1
- x1 : initial point 2
- tolerance: maximum allowed error
- n: number of iterations until failure
'''
fx0 = self.__function.eval(x0)
if tolerance < 0:
print(f"innapropiate tolerance = {tolerance}")
elif n < 1:
print(f"innapropiate number of iterations = {n}")
elif fx0 == 0:
print(f"{x0} is a root")
else:
fx1 = self.__function.eval(x1)
den = fx1 - fx0
count = 0
error = tolerance + 1
data = {
"x": np.array([x0, x1], dtype=np.float64),
"f(x)": np.array([fx0, fx1], dtype=np.float64),
"den": np.array([np.NaN, den], dtype=np.float64),
"E": np.array([np.NaN, np.NaN], dtype=np.float64)
}
while fx1 != 0 and error > tolerance and den != 0 and count < n:
x2 = x1 - (fx1 * (x1 - x0)) / den
error = self.__error(x1, x2)
x0 = x1
fx0 = fx1
x1 = x2
fx1 = self.__function.eval(x1)
den = fx1 - fx0
count += 1
data["x"] = np.append(data["x"], [x1])
data["f(x)"] = np.append(data["f(x)"], [fx1])
data["den"] = np.append(data["den"], [den])
data["E"] = np.append(data["E"], [error])
if fx1 == 0:
print(f"{x1} is a root")
elif error < tolerance:
print(
f"{x1} is an approximation to a root with tolerance = {tolerance}"
)
elif den == 0:
print(f"{x1} is a possible multiple root")
else:
print(f"failure in {n} iterations")
data_frame = pd.DataFrame(data,
columns=("x", "f(x)", "den", "E"),
dtype=np.float64)
data_frame.index.name = "n"
print(data_frame)
def multiple_roots(self, x0, tolerance, n):
'''
It calculates an approximation to a multiple root by becoming it a simple root
and applying Newton's method.
parameters:
- x0 : initial point
- tolerance: maximum allowed error
- n: number of iterations until failure
'''
if tolerance < 0:
print(f"innapropiate tolerance = {tolerance}")
elif n < 1:
print(f"innapropiate number of iterations = {n}")
else:
fx = self.__function.eval(x0)
df1 = self.__function.dfn(x0, 1)
df2 = self.__function.dfn(x0, 2)
count = 0
error = tolerance + 1
data = {
"x": np.array([x0], dtype=np.float64),
"f(x)": np.array([fx], dtype=np.float64),
"f'(x)": np.array([df1], dtype=np.float64),
"f''(x)": np.array([df2], dtype=np.float64),
"E": np.array([np.NaN], dtype=np.float64)
}
while fx != 0 and error > tolerance and count < n:
xn = x0 - (fx * df1) / ((df1**2) - fx * df2)
fx = self.__function.eval(xn)
df1 = self.__function.dfn(xn, 1)
df2 = self.__function.dfn(xn, 2)
error = self.__error(x0, xn)
x0 = xn
count += 1
data["x"] = np.append(data["x"], [xn])
data["f(x)"] = np.append(data["f(x)"], [fx])
data["f'(x)"] = np.append(data["f'(x)"], [df1])
data["f''(x)"] = np.append(data["f''(x)"], [df2])
data["E"] = np.append(data["E"], [error])
if fx == 0:
print(f"{x0} is a root")
elif error < tolerance:
print(
f"{x0} is an approximation to a root with tolerance = {tolerance}"
)
else:
print(f"failure in {n} iterations")
data_frame = pd.DataFrame(data,
columns=("x", "f(x)", "f'(x)", "f''(x)",
"E"),
dtype=np.float64)
data_frame.index.name = "n"
print(data_frame)
def get_function(self):
return self.__function
class ClosedMethods:
'''
This class contains the followings methods to caltulate
some root in an one variable function:
- Incremental search
- Bisection
- False rule
parameters:
- expr: math expression to work with
- error: function to calculate the type of error (absolute or relative).
relative is set by default.
'''
def __init__(self, expr, error=relative):
self.__function = Function(expr)
self.error = error
def incremental_search(self, x0, delta, n):
'''
Tries to find out initial values beginning by a random x and going forward
incrementally until a sign change happens or n iterations are reached.
parameters:
- x0: random x value to begin
- delta: increment for x in each iteration.
- n: number of iterations.
'''
if n < 1:
print(f"innapropiate num of iterations = {n}")
else:
delta = np.abs(delta) # handling negative values
fx0 = self.__function.eval(x0)
data = {
"x": np.array([x0], dtype=np.float64),
"f(x)": np.array([fx0], dtype=np.float64)
}
if fx0 == 0:
print(f"{x0} is a root")
else:
x1 = x0 + delta
count = 1
fx1 = self.__function.eval(x1)
while (fx0 * fx1 > 0) and (count < n):
data["x"] = np.append(data["x"], [x1])
data["f(x)"] = np.append(data["f(x)"], [fx1])
x0 = x1
fx0 = fx1
x1 += delta
fx1 = self.__function.eval(x1)
count += 1
data["x"] = np.append(data["x"], [x1])
data["f(x)"] = np.append(data["f(x)"], [fx1])
if fx1 == 0:
print(f"{x1} is a root")
elif fx0 * fx1 < 0:
print(f" There is a root between {x0} and {x1}")
else:
print(f"Failed in {n} iterations")
data_frame = pd.DataFrame(data,
columns=("x", "f(x)"),
dtype=np.float64)
data_frame.index.name = "n"
print(data_frame)
def bisection(self, xi, xs, tolerance, n, false_rule=False):
'''
Given a continuos function within [xi, xs] interval and a sign change between it,
it tries to find out the closest value to a root by calculating a mean of x.
parameters:
- xi: lower end of the interval.
- xs: higher end of the interval.
- tolerance: maximun allowed error.
- n: number of iterations.
- false_rule: set True if you want to choose false rule method
'''
if tolerance < 0:
print(f"innapropiate tolerance = {tolerance}")
elif n < 1:
print(f"innapropiate num of iterations = {n}")
else:
data = {
"xi": np.array([], dtype=np.float64),
"xs": np.array([], dtype=np.float64),
"xm": np.array([], dtype=np.float64),
"f(xm)": np.array([], dtype=np.float64),
"E": np.array([], dtype=np.float64)
}
fxi = self.__function.eval(xi)
fxs = self.__function.eval(xs)
if fxi == 0:
print(f"{xi} is a root")
elif fxs == 0:
print(f"{xs} is a root")
elif fxi * fxs < 0:
xm = np.float64(xi - (fxi * (xs - xi)) /
(fxs - fxi)) if false_rule else np.mean(
np.array((xi, xs), dtype=np.float64))
fxm = self.__function.eval(xm)
count = 1
error = tolerance + 1
data["xi"] = np.append(data["xi"], [xi])
data["xs"] = np.append(data["xs"], [xs])
data["xm"] = np.append(data["xm"], [xm])
data["f(xm)"] = np.append(data["f(xm)"], [fxm])
data["E"] = np.append(data["E"], [np.NaN])
while error > tolerance and fxm != 0 and count < n:
if fxi * fxm < 0:
xs = xm
fxs = fxm
else:
xi = xm
fxi = fxm
x_aux = xm
xm = np.float64(xi - (fxi * (xs - xi)) /
(fxs - fxi)) if false_rule else np.mean(
np.array((xi, xs), dtype=np.float64))
fxm = self.__function.eval(xm)
error = self.error(xm, x_aux)
count += 1
data["xi"] = np.append(data["xi"], [xi])
data["xs"] = np.append(data["xs"], [xs])
data["xm"] = np.append(data["xm"], [xm])
data["f(xm)"] = np.append(data["f(xm)"], [fxm])
data["E"] = np.append(data["E"], [error])
if fxm == 0:
print(f"{xm} is a root")
elif error < tolerance:
print(
f"{xm} is an approximation to a root with tolerance = {tolerance}"
)
else:
print(f"Failure in {n} iterations")
else:
print("The interval is inappropriate")
data_frame = pd.DataFrame(data,
columns=("xi", "xs", "xm", "f(xm)", "E"),
dtype=np.float64)
data_frame.index.name = "n"
print(data_frame)
def get_function(self):
return self.__function
