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 prod(a: ndarray,
axis: tp.Optional[int] = None) -> tp.Union[float, ndarray]:
"""
Return the product of array elements over a given axis.
"""
return _wrap_af_array(af.product(a._af_array, dim=axis))
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 prod(self, s, axis):
if self.dtype == numpy.bool:
s = s.astype(pu.typemap(numpy.int64))
return arrayfire.product(s, dim=axis)
def simple_algorithm(verbose = False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(3, 3)
print_func(af.sum(a), af.product(a), af.min(a), af.max(a),
af.count(a), af.any_true(a), af.all_true(a))
display_func(af.sum(a, 0))
display_func(af.sum(a, 1))
display_func(af.product(a, 0))
display_func(af.product(a, 1))
display_func(af.min(a, 0))
display_func(af.min(a, 1))
display_func(af.max(a, 0))
display_func(af.max(a, 1))
display_func(af.count(a, 0))
display_func(af.count(a, 1))
display_func(af.any_true(a, 0))
display_func(af.any_true(a, 1))
display_func(af.all_true(a, 0))
display_func(af.all_true(a, 1))
display_func(af.accum(a, 0))
display_func(af.accum(a, 1))
display_func(af.sort(a, is_ascending=True))
display_func(af.sort(a, is_ascending=False))
b = (a > 0.1) * a
c = (a > 0.4) * a
d = b / c
print_func(af.sum(d));
print_func(af.sum(d, nan_val=0.0));
display_func(af.sum(d, dim=0, nan_val=0.0));
val,idx = af.sort_index(a, is_ascending=True)
display_func(val)
display_func(idx)
val,idx = af.sort_index(a, is_ascending=False)
display_func(val)
display_func(idx)
b = af.randu(3,3)
keys,vals = af.sort_by_key(a, b, is_ascending=True)
display_func(keys)
display_func(vals)
keys,vals = af.sort_by_key(a, b, is_ascending=False)
display_func(keys)
display_func(vals)
c = af.randu(5,1)
d = af.randu(5,1)
cc = af.set_unique(c, is_sorted=False)
dd = af.set_unique(af.sort(d), is_sorted=True)
display_func(cc)
display_func(dd)
display_func(af.set_union(cc, dd, is_unique=True))
display_func(af.set_union(cc, dd, is_unique=False))
display_func(af.set_intersect(cc, cc, is_unique=True))
display_func(af.set_intersect(cc, cc, is_unique=False))
def prod(self, s, axis):
if self.dtype == numpy.bool:
s = s.astype(pu.typemap(numpy.int64))
return arrayfire.product(s, dim=axis)
#!/usr/bin/python
#######################################################
# 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
a = af.randu(3, 3)
print(af.sum(a), af.product(a), af.min(a), af.max(a), af.count(a),
af.any_true(a), af.all_true(a))
af.display(af.sum(a, 0))
af.display(af.sum(a, 1))
af.display(af.product(a, 0))
af.display(af.product(a, 1))
af.display(af.min(a, 0))
af.display(af.min(a, 1))
af.display(af.max(a, 0))
af.display(af.max(a, 1))
af.display(af.count(a, 0))
af.display(af.count(a, 1))
#!/usr/bin/python import arrayfire as af a = af.randu(3, 3) print(af.sum(a), af.product(a), af.min(a), af.max(a), af.count(a), af.any_true(a), af.all_true(a)) af.print_array(af.sum(a, 0)) af.print_array(af.sum(a, 1)) af.print_array(af.product(a, 0)) af.print_array(af.product(a, 1)) af.print_array(af.min(a, 0)) af.print_array(af.min(a, 1)) af.print_array(af.max(a, 0)) af.print_array(af.max(a, 1)) af.print_array(af.count(a, 0)) af.print_array(af.count(a, 1)) af.print_array(af.any_true(a, 0)) af.print_array(af.any_true(a, 1)) af.print_array(af.all_true(a, 0)) af.print_array(af.all_true(a, 1)) af.print_array(af.accum(a, 0)) af.print_array(af.accum(a, 1))
def simple_algorithm(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(3, 3)
k = af.constant(1, 3, 3, dtype=af.Dtype.u32)
af.eval(k)
print_func(af.sum(a), af.product(a), af.min(a), af.max(a), af.count(a),
af.any_true(a), af.all_true(a))
display_func(af.sum(a, 0))
display_func(af.sum(a, 1))
rk = af.constant(1, 3, dtype=af.Dtype.u32)
rk[2] = 0
af.eval(rk)
display_func(af.sumByKey(rk, a, dim=0))
display_func(af.sumByKey(rk, a, dim=1))
display_func(af.productByKey(rk, a, dim=0))
display_func(af.productByKey(rk, a, dim=1))
display_func(af.minByKey(rk, a, dim=0))
display_func(af.minByKey(rk, a, dim=1))
display_func(af.maxByKey(rk, a, dim=0))
display_func(af.maxByKey(rk, a, dim=1))
display_func(af.anyTrueByKey(rk, a, dim=0))
display_func(af.anyTrueByKey(rk, a, dim=1))
display_func(af.allTrueByKey(rk, a, dim=0))
display_func(af.allTrueByKey(rk, a, dim=1))
display_func(af.countByKey(rk, a, dim=0))
display_func(af.countByKey(rk, a, dim=1))
display_func(af.product(a, 0))
display_func(af.product(a, 1))
display_func(af.min(a, 0))
display_func(af.min(a, 1))
display_func(af.max(a, 0))
display_func(af.max(a, 1))
display_func(af.count(a, 0))
display_func(af.count(a, 1))
display_func(af.any_true(a, 0))
display_func(af.any_true(a, 1))
display_func(af.all_true(a, 0))
display_func(af.all_true(a, 1))
display_func(af.accum(a, 0))
display_func(af.accum(a, 1))
display_func(af.scan(a, 0, af.BINARYOP.ADD))
display_func(af.scan(a, 1, af.BINARYOP.MAX))
display_func(af.scan_by_key(k, a, 0, af.BINARYOP.ADD))
display_func(af.scan_by_key(k, a, 1, af.BINARYOP.MAX))
display_func(af.sort(a, is_ascending=True))
display_func(af.sort(a, is_ascending=False))
b = (a > 0.1) * a
c = (a > 0.4) * a
d = b / c
print_func(af.sum(d))
print_func(af.sum(d, nan_val=0.0))
display_func(af.sum(d, dim=0, nan_val=0.0))
val, idx = af.sort_index(a, is_ascending=True)
display_func(val)
display_func(idx)
val, idx = af.sort_index(a, is_ascending=False)
display_func(val)
display_func(idx)
b = af.randu(3, 3)
keys, vals = af.sort_by_key(a, b, is_ascending=True)
display_func(keys)
display_func(vals)
keys, vals = af.sort_by_key(a, b, is_ascending=False)
display_func(keys)
display_func(vals)
c = af.randu(5, 1)
d = af.randu(5, 1)
cc = af.set_unique(c, is_sorted=False)
dd = af.set_unique(af.sort(d), is_sorted=True)
display_func(cc)
display_func(dd)
display_func(af.set_union(cc, dd, is_unique=True))
display_func(af.set_union(cc, dd, is_unique=False))
display_func(af.set_intersect(cc, cc, is_unique=True))
display_func(af.set_intersect(cc, cc, is_unique=False))