def simulateHestonModel( T, N, R, mu, kappa, vBar, sigmaV, rho, x0, v0 ) :
deltaT = T / (float)(N - 1)
x = [af.constant(x0, R, dtype=af.Dtype.f32), af.constant(0, R, dtype=af.Dtype.f32)]
v = [af.constant(v0, R, dtype=af.Dtype.f32), af.constant(0, R, dtype=af.Dtype.f32)]
sqrtDeltaT = math.sqrt(deltaT)
sqrtOneMinusRhoSquare = math.sqrt(1-rho**2)
m = af.constant(0, 2, dtype=af.Dtype.f32)
m[0] = rho
m[1] = sqrtOneMinusRhoSquare
zeroArray = af.constant(0, R, 1, dtype=af.Dtype.f32)
for t in range(1, N) :
tPrevious = (t + 1) % 2
tCurrent = t % 2
dBt = af.randn(R, 2, dtype=af.Dtype.f32) * sqrtDeltaT
vLag = af.maxof(v[tPrevious], zeroArray)
sqrtVLag = af.sqrt(vLag)
x[tCurrent] = x[tPrevious] + (mu - 0.5 * vLag) * deltaT + sqrtVLag * dBt[:, 0]
v[tCurrent] = vLag + kappa * (vBar - vLag) * deltaT + sigmaV * (sqrtVLag * af.matmul(dBt, m))
return (x[tCurrent], af.maxof(v[tCurrent], zeroArray))
def relu(image):
#TODO: Remove assumption of image.dims()[3] == 1
if len(image.dims()) is 1:
return af.maxof(image, af.constant(0, image.dims()[0]))
elif len(image.dims()) is 3:
d0, d1, d2 = image.dims()
return af.maxof(image, af.constant(0, d0, d1, d2))
print "error, bad val num dims"
return
def main():
T = 1
nT = 20 * T
R_first = 1000
R = 5000000
x0 = 0 # initial log stock price
v0 = 0.087**2 # initial volatility
r = math.log(1.0319) # risk-free rate
rho = -0.82 # instantaneous correlation between Brownian motions
sigmaV = 0.14 # variance of volatility
kappa = 3.46 # mean reversion speed
vBar = 0.008 # mean variance
k = math.log(0.95) # strike price
# first run
(x, v) = simulateHestonModel(T, nT, R_first, r, kappa, vBar, sigmaV, rho,
x0, v0)
# Price plain vanilla call option
tic = time.time()
(x, v) = simulateHestonModel(T, nT, R, r, kappa, vBar, sigmaV, rho, x0, v0)
af.sync()
toc = time.time() - tic
K = math.exp(k)
zeroConstant = af.constant(0, R, dtype=af.Dtype.f32)
C_CPU = math.exp(-r * T) * af.mean(af.maxof(af.exp(x) - K, zeroConstant))
print("Time elapsed = {} secs".format(toc))
print("Call price = {}".format(C_CPU))
print(af.mean(v))
def monte_carlo_options(N,
K,
t,
vol,
r,
strike,
steps,
use_barrier=True,
B=None,
ty=af.Dtype.f32):
payoff = af.constant(0, N, 1, dtype=ty)
dt = t / float(steps - 1)
s = af.constant(strike, N, 1, dtype=ty)
randmat = af.randn(N, steps - 1, dtype=ty)
randmat = af.exp((r - (vol * vol * 0.5)) * dt +
vol * math.sqrt(dt) * randmat)
S = af.product(af.join(1, s, randmat), 1)
if (use_barrier):
S = S * af.all_true(S < B, 1)
payoff = af.maxof(0, S - K)
return af.mean(payoff) * math.exp(-r * t)
def main():
T = 1
nT = 20 * T
R_first = 1000
R = 5000000
x0 = 0 # initial log stock price
v0 = 0.087**2 # initial volatility
r = math.log(1.0319) # risk-free rate
rho = -0.82 # instantaneous correlation between Brownian motions
sigmaV = 0.14 # variance of volatility
kappa = 3.46 # mean reversion speed
vBar = 0.008 # mean variance
k = math.log(0.95) # strike price
# first run
( x, v ) = simulateHestonModel( T, nT, R_first, r, kappa, vBar, sigmaV, rho, x0, v0 )
# Price plain vanilla call option
tic = time.time()
( x, v ) = simulateHestonModel( T, nT, R, r, kappa, vBar, sigmaV, rho, x0, v0 )
af.sync()
toc = time.time() - tic
K = math.exp(k)
zeroConstant = af.constant(0, R, dtype=af.Dtype.f32)
C_CPU = math.exp(-r * T) * af.mean(af.maxof(af.exp(x) - K, zeroConstant))
print("Time elapsed = {} secs".format(toc))
print("Call price = {}".format(C_CPU))
print(af.mean(v))
def forward(nn_np, data):
"""
Given a dictionary representing a feed forward neural net and an input data matrix compute the network's output and store it within the dictionary
:param nn: neural network dictionary
:param data: a numpy n by m matrix where m in the number of input units in nn
:return: the output layer activations
"""
nn = nn_np
nn['activations'] = []
#prct = []
#prct.append(data[0][0])
#prct.append(data[1][0])
#src = data.astype(float)
nsrc = data#af.Array(prct)
nn['activations'].append(nsrc)
#(af.Array(src.ctypes.data, src.shape, src.dtype.char))
nn['zs'] = []
for w, s, b in map(None, nn['weights'], nn['nonlin'], nn['biases']):
#:wq
#print af.transpose(nn['activations'][-1])
#print(type(nn['activations'][0]))
#print(nn['activations'][0])
z = af.matmul(w, nn['activations'][-1])
#for i in range(w.shape[1]-1):
# newB = af.join(1,newB,b)
p = z.T + b
nn['zs'].append(p.T)
if len(p.shape) == 2:
maxVal = af.constant(1e-5,p.shape[0],p.shape[1]).T
else:
maxVal = af.constant(1e-5,p.shape[0]).T
q = af.maxof(p.T,1e-5)#p(i) = 1e-5
nn['activations'].append(q)
return nn['activations'][-1]
def simulateHestonModel(T, N, R, mu, kappa, vBar, sigmaV, rho, x0, v0):
deltaT = T / (float)(N - 1)
x = [
af.constant(x0, R, dtype=af.Dtype.f32),
af.constant(0, R, dtype=af.Dtype.f32)
]
v = [
af.constant(v0, R, dtype=af.Dtype.f32),
af.constant(0, R, dtype=af.Dtype.f32)
]
sqrtDeltaT = math.sqrt(deltaT)
sqrtOneMinusRhoSquare = math.sqrt(1 - rho**2)
m = af.constant(0, 2, dtype=af.Dtype.f32)
m[0] = rho
m[1] = sqrtOneMinusRhoSquare
zeroArray = af.constant(0, R, 1, dtype=af.Dtype.f32)
for t in range(1, N):
tPrevious = (t + 1) % 2
tCurrent = t % 2
dBt = af.randn(R, 2, dtype=af.Dtype.f32) * sqrtDeltaT
vLag = af.maxof(v[tPrevious], zeroArray)
sqrtVLag = af.sqrt(vLag)
x[tCurrent] = x[tPrevious] + (
mu - 0.5 * vLag) * deltaT + sqrtVLag * dBt[:, 0]
v[tCurrent] = vLag + kappa * (vBar - vLag) * deltaT + sigmaV * (
sqrtVLag * af.matmul(dBt, m))
return (x[tCurrent], af.maxof(v[tCurrent], zeroArray))
def monte_carlo_options(N, K, t, vol, r, strike, steps, use_barrier = True, B = None, ty = af.Dtype.f32):
payoff = af.constant(0, N, 1, dtype = ty)
dt = t / float(steps - 1)
s = af.constant(strike, N, 1, dtype = ty)
randmat = af.randn(N, steps - 1, dtype = ty)
randmat = af.exp((r - (vol * vol * 0.5)) * dt + vol * math.sqrt(dt) * randmat);
S = af.product(af.join(1, s, randmat), 1)
if (use_barrier):
S = S * af.all_true(S < B, 1)
payoff = af.maxof(0, S - K)
return af.mean(payoff) * math.exp(-r * t)
def forward(nn_np, data):
"""
Given a dictionary representing a feed forward neural net and an input data matrix compute the network's output and store it within the dictionary
:param nn: neural network dictionary
:param data: a numpy n by m matrix where m in the number of input units in nn
:return: the output layer activations
"""
nn = nn_np
nn['activations'] = []
#prct = []
#prct.append(data[0][0])
#prct.append(data[1][0])
#src = data.astype(float)
nsrc = data #af.Array(prct)
nn['activations'].append(nsrc)
#(af.Array(src.ctypes.data, src.shape, src.dtype.char))
nn['zs'] = []
for w, s, b in map(None, nn['weights'], nn['nonlin'], nn['biases']):
#:wq
#print af.transpose(nn['activations'][-1])
#print(type(nn['activations'][0]))
#print(nn['activations'][0])
z = af.matmul(w, nn['activations'][-1])
#for i in range(w.shape[1]-1):
# newB = af.join(1,newB,b)
p = z.T + b
nn['zs'].append(p.T)
if len(p.shape) == 2:
maxVal = af.constant(1e-5, p.shape[0], p.shape[1]).T
else:
maxVal = af.constant(1e-5, p.shape[0]).T
q = af.maxof(p.T, 1e-5) #p(i) = 1e-5
nn['activations'].append(q)
return nn['activations'][-1]
def mandelbrot(data, it, maxval):
C = data
Z = data
mag = af.constant(0, *C.dims())
for ii in range(1, 1 + it):
# Doing the calculation
Z = Z * Z + C
# Get indices where abs(Z) crosses maxval
cond = ((af.abs(Z) > maxval)).as_type(af.Dtype.f32)
mag = af.maxof(mag, cond * ii)
C = C * (1 - cond)
Z = Z * (1 - cond)
af.eval(C)
af.eval(Z)
return mag / maxval
def mandelbrot(data, it, maxval):
C = data
Z = data
mag = af.constant(0, *C.dims())
for ii in range(1, 1 + it):
# Doing the calculation
Z = Z * Z + C
# Get indices where abs(Z) crosses maxval
cond = ((af.abs(Z) > maxval)).as_type(af.Dtype.f32)
mag = af.maxof(mag, cond * ii)
C = C * (1 - cond)
Z = Z * (1 - cond)
af.eval(C)
af.eval(Z)
return mag / maxval
def simple_arith(verbose = False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(3,3,dtype=af.Dtype.u32)
b = af.constant(4, 3, 3, dtype=af.Dtype.u32)
display_func(a)
display_func(b)
c = a + b
d = a
d += b
display_func(c)
display_func(d)
display_func(a + 2)
display_func(3 + a)
c = a - b
d = a
d -= b
display_func(c)
display_func(d)
display_func(a - 2)
display_func(3 - a)
c = a * b
d = a
d *= b
display_func(c * 2)
display_func(3 * d)
display_func(a * 2)
display_func(3 * a)
c = a / b
d = a
d /= b
display_func(c / 2.0)
display_func(3.0 / d)
display_func(a / 2)
display_func(3 / a)
c = a % b
d = a
d %= b
display_func(c % 2.0)
display_func(3.0 % d)
display_func(a % 2)
display_func(3 % a)
c = a ** b
d = a
d **= b
display_func(c ** 2.0)
display_func(3.0 ** d)
display_func(a ** 2)
display_func(3 ** a)
display_func(a < b)
display_func(a < 0.5)
display_func(0.5 < a)
display_func(a <= b)
display_func(a <= 0.5)
display_func(0.5 <= a)
display_func(a > b)
display_func(a > 0.5)
display_func(0.5 > a)
display_func(a >= b)
display_func(a >= 0.5)
display_func(0.5 >= a)
display_func(a != b)
display_func(a != 0.5)
display_func(0.5 != a)
display_func(a == b)
display_func(a == 0.5)
display_func(0.5 == a)
display_func(a & b)
display_func(a & 2)
c = a
c &= 2
display_func(c)
display_func(a | b)
display_func(a | 2)
c = a
c |= 2
display_func(c)
display_func(a >> b)
display_func(a >> 2)
c = a
c >>= 2
display_func(c)
display_func(a << b)
display_func(a << 2)
c = a
c <<= 2
display_func(c)
display_func(-a)
display_func(+a)
display_func(~a)
display_func(a)
display_func(af.cast(a, af.Dtype.c32))
display_func(af.maxof(a,b))
display_func(af.minof(a,b))
display_func(af.rem(a,b))
a = af.randu(3,3) - 0.5
b = af.randu(3,3) - 0.5
display_func(af.abs(a))
display_func(af.arg(a))
display_func(af.sign(a))
display_func(af.round(a))
display_func(af.trunc(a))
display_func(af.floor(a))
display_func(af.ceil(a))
display_func(af.hypot(a, b))
display_func(af.sin(a))
display_func(af.cos(a))
display_func(af.tan(a))
display_func(af.asin(a))
display_func(af.acos(a))
display_func(af.atan(a))
display_func(af.atan2(a, b))
c = af.cplx(a)
d = af.cplx(a,b)
display_func(c)
display_func(d)
display_func(af.real(d))
display_func(af.imag(d))
display_func(af.conjg(d))
display_func(af.sinh(a))
display_func(af.cosh(a))
display_func(af.tanh(a))
display_func(af.asinh(a))
display_func(af.acosh(a))
display_func(af.atanh(a))
a = af.abs(a)
b = af.abs(b)
display_func(af.root(a, b))
display_func(af.pow(a, b))
display_func(af.pow2(a))
display_func(af.exp(a))
display_func(af.expm1(a))
display_func(af.erf(a))
display_func(af.erfc(a))
display_func(af.log(a))
display_func(af.log1p(a))
display_func(af.log10(a))
display_func(af.log2(a))
display_func(af.sqrt(a))
display_func(af.cbrt(a))
a = af.round(5 * af.randu(3,3) - 1)
b = af.round(5 * af.randu(3,3) - 1)
display_func(af.factorial(a))
display_func(af.tgamma(a))
display_func(af.lgamma(a))
display_func(af.iszero(a))
display_func(af.isinf(a/b))
display_func(af.isnan(a/a))
a = af.randu(5, 1)
b = af.randu(1, 5)
c = af.broadcast(lambda x,y: x+y, a, b)
display_func(a)
display_func(b)
display_func(c)
@af.broadcast
def test_add(aa, bb):
return aa + bb
display_func(test_add(a, b))
def simple_arith(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(3, 3)
b = af.constant(4, 3, 3)
display_func(a)
display_func(b)
c = a + b
d = a
d += b
display_func(c)
display_func(d)
display_func(a + 2)
display_func(3 + a)
c = a - b
d = a
d -= b
display_func(c)
display_func(d)
display_func(a - 2)
display_func(3 - a)
c = a * b
d = a
d *= b
display_func(c * 2)
display_func(3 * d)
display_func(a * 2)
display_func(3 * a)
c = a / b
d = a
d /= b
display_func(c / 2.0)
display_func(3.0 / d)
display_func(a / 2)
display_func(3 / a)
c = a % b
d = a
d %= b
display_func(c % 2.0)
display_func(3.0 % d)
display_func(a % 2)
display_func(3 % a)
c = a**b
d = a
d **= b
display_func(c**2.0)
display_func(3.0**d)
display_func(a**2)
display_func(3**a)
display_func(a < b)
display_func(a < 0.5)
display_func(0.5 < a)
display_func(a <= b)
display_func(a <= 0.5)
display_func(0.5 <= a)
display_func(a > b)
display_func(a > 0.5)
display_func(0.5 > a)
display_func(a >= b)
display_func(a >= 0.5)
display_func(0.5 >= a)
display_func(a != b)
display_func(a != 0.5)
display_func(0.5 != a)
display_func(a == b)
display_func(a == 0.5)
display_func(0.5 == a)
a = af.randu(3, 3, dtype=af.Dtype.u32)
b = af.constant(4, 3, 3, dtype=af.Dtype.u32)
display_func(a & b)
display_func(a & 2)
c = a
c &= 2
display_func(c)
display_func(a | b)
display_func(a | 2)
c = a
c |= 2
display_func(c)
display_func(a >> b)
display_func(a >> 2)
c = a
c >>= 2
display_func(c)
display_func(a << b)
display_func(a << 2)
c = a
c <<= 2
display_func(c)
display_func(-a)
display_func(+a)
display_func(~a)
display_func(a)
display_func(af.cast(a, af.Dtype.c32))
display_func(af.maxof(a, b))
display_func(af.minof(a, b))
display_func(af.rem(a, b))
a = af.randu(3, 3) - 0.5
b = af.randu(3, 3) - 0.5
display_func(af.abs(a))
display_func(af.arg(a))
display_func(af.sign(a))
display_func(af.round(a))
display_func(af.trunc(a))
display_func(af.floor(a))
display_func(af.ceil(a))
display_func(af.hypot(a, b))
display_func(af.sin(a))
display_func(af.cos(a))
display_func(af.tan(a))
display_func(af.asin(a))
display_func(af.acos(a))
display_func(af.atan(a))
display_func(af.atan2(a, b))
c = af.cplx(a)
d = af.cplx(a, b)
display_func(c)
display_func(d)
display_func(af.real(d))
display_func(af.imag(d))
display_func(af.conjg(d))
display_func(af.sinh(a))
display_func(af.cosh(a))
display_func(af.tanh(a))
display_func(af.asinh(a))
display_func(af.acosh(a))
display_func(af.atanh(a))
a = af.abs(a)
b = af.abs(b)
display_func(af.root(a, b))
display_func(af.pow(a, b))
display_func(af.pow2(a))
display_func(af.sigmoid(a))
display_func(af.exp(a))
display_func(af.expm1(a))
display_func(af.erf(a))
display_func(af.erfc(a))
display_func(af.log(a))
display_func(af.log1p(a))
display_func(af.log10(a))
display_func(af.log2(a))
display_func(af.sqrt(a))
display_func(af.cbrt(a))
a = af.round(5 * af.randu(3, 3) - 1)
b = af.round(5 * af.randu(3, 3) - 1)
display_func(af.factorial(a))
display_func(af.tgamma(a))
display_func(af.lgamma(a))
display_func(af.iszero(a))
display_func(af.isinf(a / b))
display_func(af.isnan(a / a))
a = af.randu(5, 1)
b = af.randu(1, 5)
c = af.broadcast(lambda x, y: x + y, a, b)
display_func(a)
display_func(b)
display_func(c)
@af.broadcast
def test_add(aa, bb):
return aa + bb
display_func(test_add(a, b))
# Copyright (c) 2015, ArrayFire
# All rights reserved.
#
# This file is distributed under 3-clause BSD license.
# The complete license agreement can be obtained at:
# http://arrayfire.com/licenses/BSD-3-Clause
########################################################
import arrayfire as af
af.info()
ITERATIONS = 200
POINTS = int(10.0 * ITERATIONS)
Z = 1 + af.range(POINTS) / ITERATIONS
win = af.Window(800, 800, "3D Plot example using ArrayFire")
t = 0.1
while not win.close():
X = af.cos(Z * t + t) / Z
Y = af.sin(Z * t + t) / Z
X = af.maxof(af.minof(X, 1), -1)
Y = af.maxof(af.minof(Y, 1), -1)
Pts = af.join(1, X, Y, Z)
win.plot3(Pts)
t = t + 0.01
c >>= 2 af.display(c) af.display(a << b) af.display(a << 2) c = a c <<= 2 af.display(c) af.display(-a) af.display(+a) af.display(~a) af.display(a) af.display(af.cast(a, af.c32)) af.display(af.maxof(a, b)) af.display(af.minof(a, b)) af.display(af.rem(a, b)) a = af.randu(3, 3) - 0.5 b = af.randu(3, 3) - 0.5 af.display(af.abs(a)) af.display(af.arg(a)) af.display(af.sign(a)) af.display(af.round(a)) af.display(af.trunc(a)) af.display(af.floor(a)) af.display(af.ceil(a)) af.display(af.hypot(a, b)) af.display(af.sin(a))
def advection_step(u, v):
'''
done - Matching u_new and v_new
'''
u_w_bc, v_w_bc = add_boundary_conditions(u, v)
u = af.moddims(u_w_bc, params.n, params.n)
v = af.moddims(v_w_bc, params.n, params.n)
u_tile = u[1:-1, 1:-1]
v_tile = v[1:-1, 1:-1]
u_new = af.np_to_af_array(np.zeros([params.n, params.n]))
v_new = af.np_to_af_array(np.zeros([params.n, params.n]))
x0_tile = af.tile(af.np_to_af_array(np.linspace(0, 1, params.n)), 1,
params.n)[1:-1, 1:-1]
y0_tile = af.transpose(x0_tile)
reference_0_tile = af.constant(0,
params.n - 2,
params.n - 2,
dtype=af.Dtype.f64)
reference_1_tile = af.constant(1,
params.n - 2,
params.n - 2,
dtype=af.Dtype.f64)
x1_tile = af.minof(
af.maxof(x0_tile - params.delta_t * u_tile, reference_0_tile),
reference_1_tile)
y1_tile = af.minof(
af.maxof(y0_tile - params.delta_t * v_tile, reference_0_tile),
reference_1_tile)
i_left_tile = af.minof(af.cast(x1_tile * (params.n - 1), af.Dtype.s64),
(params.n - 2) * reference_1_tile)
i_left_flat = af.flat(i_left_tile)
i_right_tile = i_left_tile + 1
i_right_flat = af.flat(i_right_tile)
j_bottom_tile = af.minof(af.cast(y1_tile * (params.n - 1), af.Dtype.s64),
(params.n - 2) * reference_1_tile)
j_bottom_flat = af.flat(j_bottom_tile * params.n)
j_top_tile = j_bottom_tile + 1
j_top_flat = af.flat(j_top_tile * params.n)
x_left_tile = i_left_tile / (params.n - 1)
x_right_tile = i_right_tile / (params.n - 1)
y_bottom_tile = j_bottom_tile / (params.n - 1)
y_top_tile = j_top_tile / (params.n - 1)
print(x_left_tile)
flat_u = af.flat(u)
flat_v = af.flat(v)
u_top_left_tile = af.moddims(flat_u[i_left_flat + j_top_flat],
params.n - 2, params.n - 2)
u_top_right_tile = af.moddims(flat_u[i_right_flat + j_top_flat],
params.n - 2, params.n - 2)
u_bottom_left_tile = af.moddims(flat_u[i_left_flat + j_bottom_flat],
params.n - 2, params.n - 2)
u_bottom_right_tile = af.moddims(flat_u[i_right_flat + j_bottom_flat],
params.n - 2, params.n - 2)
v_top_left_tile = af.moddims(flat_v[i_left_flat + j_top_flat],
params.n - 2, params.n - 2)
v_top_right_tile = af.moddims(flat_v[i_right_flat + j_top_flat],
params.n - 2, params.n - 2)
v_bottom_left_tile = af.moddims(flat_v[i_left_flat + j_bottom_flat],
params.n - 2, params.n - 2)
v_bottom_right_tile = af.moddims(flat_v[i_right_flat + j_bottom_flat],
params.n - 2, params.n - 2)
u_upper_tile = u_top_left_tile\
+ (u_top_right_tile - u_top_left_tile)\
* (x1_tile - x_left_tile) / params.delta_x
u_lower_tile = u_bottom_right_tile\
+ (u_bottom_left_tile - u_bottom_right_tile)\
* (x1_tile - x_left_tile) / params.delta_x
u_new_tile = u_lower_tile + (u_upper_tile - u_lower_tile)\
* (y1_tile - y_bottom_tile) / params.delta_x
v_upper_tile = v_top_left_tile + (v_top_right_tile - v_top_left_tile)\
* (x1_tile - x_left_tile) / params.delta_x
v_lower_tile = v_bottom_right_tile + (v_bottom_left_tile - v_bottom_right_tile)\
* (x1_tile - x_left_tile) / params.delta_x
v_new_tile = v_lower_tile + (v_upper_tile - v_lower_tile)\
* (y1_tile - y_bottom_tile) / params.delta_x
u_new = af.flat(u_new_tile)
v_new = af.flat(v_new_tile)
return u_new, v_new
c >>= 2 af.display(c) af.display(a << b) af.display(a << 2) c = a c <<= 2 af.display(c) af.display(-a) af.display(+a) af.display(~a) af.display(a) af.display(af.cast(a, af.c32)) af.display(af.maxof(a,b)) af.display(af.minof(a,b)) af.display(af.rem(a,b)) a = af.randu(3,3) - 0.5 b = af.randu(3,3) - 0.5 af.display(af.abs(a)) af.display(af.arg(a)) af.display(af.sign(a)) af.display(af.round(a)) af.display(af.trunc(a)) af.display(af.floor(a)) af.display(af.ceil(a)) af.display(af.hypot(a, b)) af.display(af.sin(a))
