class Option_Price:
energy_list: np.ndarray
S = 50
K = 50
r = 0.05
v = 0.5
T = 1
CorP = "C"
Steps = 100
samples = 10000
def __init__(self):
#steps and dimension
self.ndim = self.Steps #proxy for number of time steps, linearly the system in one dimensional
self.samples = self.samples
self.potential = Option(self.S, self.K, self.r, self.v, self.T,
self.CorP, self.Steps)
#setting up the simulator
self.mc = MC(self.potential, np.zeros(self.ndim), 1)
#generating coords(normal random numbers)
self.takestep = SimpleGaussian(42, self.ndim)
self.mc.set_take_step(self.takestep)
self.Recorded_Energy = RecordEnergyTimeSeries(1, 1)
#collecting all energies of each simulation
self.mc.add_action(self.Recorded_Energy)
self.mc.run(self.samples)
self.energy_list = np.array(
self.Recorded_Energy.get_energy_time_series())
class TestRecordCoordsTimeSeries(unittest.TestCase):
def setUp(self):
self.bdim = 3
self.origin: np.ndarray = [5, 6, 7]
self.temp = 1
self.potential = TestPotential()
self.mc = MC(self.potential, self.origin, self.temp)
self.nrsteps = 100
def test_frequencies(self):
self.step = NoTakeStep()
self.mc.set_take_step(self.step)
self.Record_Hist = RecordEnergyHistogram(0, 100, 50, 0)
self.mc.add_action(self.Record_Hist)
self.mc.run(self.nrsteps)
#self.coords_list = np.array(self.Record_Coords.get_timeseries())
ans = np.asarray(self.origin * self.nrsteps).reshape(self.nrsteps, -1)
print(self.Record_Hist.get_histogram(),
len(self.Record_Hist.get_histogram()))
class TestRecordCoordsTimeSeries(unittest.TestCase):
def setUp(self):
self.bdim = 3
self.origin: np.ndarray= [5, 6, 7]
self.temp = 1
self.potential = NoPotential()
self.mc = MC(self.potential, self.origin, self.temp)
self.nrsteps = 5
def test_frequencies(self) :
self.step = NoTakeStep()
self.mc.set_take_step(self.step)
self.Record_Coords = RecordsCoordsTimeSeries(self.bdim, 1, 1)
self.mc.add_action(self.Record_Coords)
self.mc.run(self.nrsteps)
self.coords_list = np.array(self.Record_Coords.get_timeseries())
ans = np.asarray(self.origin*self.nrsteps).reshape(self.nrsteps, -1)
np.allclose(self.coords_list, ans)
class IsingMetropolisMonteCarlo(MC):
""" Single flip montecarlo runner for the ising model
"""
def __init__(self,
potential,
initial_coords,
temperature,
niter,
hEmin=0,
hEmax=100,
hbinsize=0.01,
radius=2.5,
acceptance=0.5,
adjustf=0.9,
adjustf_niter=1e4,
adjustf_navg=100,
bdim=3,
single=False,
seeds=None):
#calling the base class
hEmin = 0
print("initial", hEmin)
super(IsingMetropolisMonteCarlo,
self).__init__(potential, initial_coords, temperature)
random.seed(10)
#initial_coords = random.randint(2,
# size=(N, N),
# dtype=bool) # we're starting
# with a random initial configuration
# define the monte carlo runner with
# the potential function , initial coordinates and temperature
#ising_model = IsingModel(N, N)
self.mc = MC(potential, initial_coords, temperature)
# add the step type
#self.take_step = IsingFlip(10, N, N)
seeds = range(np.iinfo(np.int32).max)
# accept test
self.accepttest = MetropolisTest(seeds)
self.mc.take_steps = SimpleGaussian(43, 4)
self.conftest = CheckSphericalContainer(radius, bdim)
print("Here", self.mc.take_steps)
# action
self.binsize = hbinsize
# record_energy
print("min1", hEmin, "max1", hEmax)
self.histogram = RecordEnergyHistogram(hEmin, hEmax, self.binsize,
adjustf_niter)
#self.action = RecordEnergyTimeSeries(record_every, stepskip)
self.config = 0
self.mc.set_take_step(self.mc.take_steps)
self.mc.add_accept_test(self.accepttest)
self.mc.add_action(self.histogram)
self.mc.add_conf_test(self.conftest)
self.mc.set_report_steps(adjustf_niter)
self.nsteps = niter
def run(self):
""" Runs the Monte Carlo simulation
"""
#print(self.nsteps)
#print(self.mc.trial_coords)
self.mc.run(self.nsteps)
def get_et_series(self):
""" gets the array of energies at every iteration
"""
return np.array(self.action.get_energy_time_series())
def set_control(self, c):
"""
sets the temperature or whichever control parameter that takes
the role of the temperature, such as the stifness of an harmonic
"""
self.temperature = c
def get_stepsize(self):
return self.step.get_stepsize()
def dump_histogram(self, fname):
"""write histogram to fname"""
Emin, Emax = self.histogram.get_bounds_val()
histl = self.histogram.get_histogram()
hist = np.array(histl)
Energies, step = np.linspace(Emin,
Emax,
num=len(hist),
endpoint=False,
retstep=True)
assert (abs(step - self.binsize) < self.binsize / 100)
np.savetxt(fname, np.column_stack((Energies, hist)), delimiter='\t')
mean, variance = self.histogram.get_mean_variance()
return mean, variance
def get_histogram(self):
"""returns a energy list and a histogram list"""
Emin, Emax = self.histogram.get_bounds_val()
histl = self.histogram.get_histogram()
hist = np.array(histl)
Energies, step = np.linspace(Emin,
Emax,
num=len(hist),
endpoint=False,
retstep=True)
mean, variance = self.histogram.get_mean_variance()
assert (abs(step - self.binsize) < self.binsize / 100)
return Energies, hist, mean, variance
def show_histogram(self):
"""shows the histogram"""
hist = self.histogram.get_histogram()
val = [i * self.binsize for i in xrange(len(hist))]
plt.hist(val, weights=hist, bins=len(hist))
plt.show()