def test_lagfit(self):
def f(x):
return x*(x - 1)*(x - 2)
# Test exceptions
assert_raises(ValueError, lag.lagfit, [1], [1], -1)
assert_raises(TypeError, lag.lagfit, [[1]], [1], 0)
assert_raises(TypeError, lag.lagfit, [], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [[[1]]], 0)
assert_raises(TypeError, lag.lagfit, [1, 2], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [1, 2], 0)
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[[1]])
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[1, 1])
assert_raises(ValueError, lag.lagfit, [1], [1], [-1,])
assert_raises(ValueError, lag.lagfit, [1], [1], [2, -1, 6])
assert_raises(TypeError, lag.lagfit, [1], [1], [])
# Test fit
x = np.linspace(0, 2)
y = f(x)
#
coef3 = lag.lagfit(x, y, 3)
assert_equal(len(coef3), 4)
assert_almost_equal(lag.lagval(x, coef3), y)
coef3 = lag.lagfit(x, y, [0, 1, 2, 3])
assert_equal(len(coef3), 4)
assert_almost_equal(lag.lagval(x, coef3), y)
#
coef4 = lag.lagfit(x, y, 4)
assert_equal(len(coef4), 5)
assert_almost_equal(lag.lagval(x, coef4), y)
coef4 = lag.lagfit(x, y, [0, 1, 2, 3, 4])
assert_equal(len(coef4), 5)
assert_almost_equal(lag.lagval(x, coef4), y)
#
coef2d = lag.lagfit(x, np.array([y, y]).T, 3)
assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
coef2d = lag.lagfit(x, np.array([y, y]).T, [0, 1, 2, 3])
assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
# test weighting
w = np.zeros_like(x)
yw = y.copy()
w[1::2] = 1
y[0::2] = 0
wcoef3 = lag.lagfit(x, yw, 3, w=w)
assert_almost_equal(wcoef3, coef3)
wcoef3 = lag.lagfit(x, yw, [0, 1, 2, 3], w=w)
assert_almost_equal(wcoef3, coef3)
#
wcoef2d = lag.lagfit(x, np.array([yw, yw]).T, 3, w=w)
assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
wcoef2d = lag.lagfit(x, np.array([yw, yw]).T, [0, 1, 2, 3], w=w)
assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
# test scaling with complex values x points whose square
# is zero when summed.
x = [1, 1j, -1, -1j]
assert_almost_equal(lag.lagfit(x, x, 1), [1, -1])
assert_almost_equal(lag.lagfit(x, x, [0, 1]), [1, -1])
def test_lagfit(self):
def f(x):
return x*(x - 1)*(x - 2)
# Test exceptions
assert_raises(ValueError, lag.lagfit, [1], [1], -1)
assert_raises(TypeError, lag.lagfit, [[1]], [1], 0)
assert_raises(TypeError, lag.lagfit, [], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [[[1]]], 0)
assert_raises(TypeError, lag.lagfit, [1, 2], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [1, 2], 0)
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[[1]])
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[1, 1])
assert_raises(ValueError, lag.lagfit, [1], [1], [-1,])
assert_raises(ValueError, lag.lagfit, [1], [1], [2, -1, 6])
assert_raises(TypeError, lag.lagfit, [1], [1], [])
# Test fit
x = np.linspace(0, 2)
y = f(x)
#
coef3 = lag.lagfit(x, y, 3)
assert_equal(len(coef3), 4)
assert_almost_equal(lag.lagval(x, coef3), y)
coef3 = lag.lagfit(x, y, [0, 1, 2, 3])
assert_equal(len(coef3), 4)
assert_almost_equal(lag.lagval(x, coef3), y)
#
coef4 = lag.lagfit(x, y, 4)
assert_equal(len(coef4), 5)
assert_almost_equal(lag.lagval(x, coef4), y)
coef4 = lag.lagfit(x, y, [0, 1, 2, 3, 4])
assert_equal(len(coef4), 5)
assert_almost_equal(lag.lagval(x, coef4), y)
#
coef2d = lag.lagfit(x, np.array([y, y]).T, 3)
assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
coef2d = lag.lagfit(x, np.array([y, y]).T, [0, 1, 2, 3])
assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
# test weighting
w = np.zeros_like(x)
yw = y.copy()
w[1::2] = 1
y[0::2] = 0
wcoef3 = lag.lagfit(x, yw, 3, w=w)
assert_almost_equal(wcoef3, coef3)
wcoef3 = lag.lagfit(x, yw, [0, 1, 2, 3], w=w)
assert_almost_equal(wcoef3, coef3)
#
wcoef2d = lag.lagfit(x, np.array([yw, yw]).T, 3, w=w)
assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
wcoef2d = lag.lagfit(x, np.array([yw, yw]).T, [0, 1, 2, 3], w=w)
assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
# test scaling with complex values x points whose square
# is zero when summed.
x = [1, 1j, -1, -1j]
assert_almost_equal(lag.lagfit(x, x, 1), [1, -1])
assert_almost_equal(lag.lagfit(x, x, [0, 1]), [1, -1])
def eval(self, x, u, output_array=None):
x = np.atleast_1d(x)
if output_array is None:
output_array = np.zeros(x.shape)
w_hat = work[(u, 0, True)]
w_hat[1:] = u[:-1]
output_array[:] = lag.lagval(x, u) - lag.lagval(x, w_hat)
output_array *= np.exp(-x/2)
return output_array
def test_lagmul(self):
# check values of result
for i in range(5):
pol1 = [0] * i + [1]
val1 = lag.lagval(self.x, pol1)
for j in range(5):
msg = "At i=%d, j=%d" % (i, j)
pol2 = [0] * j + [1]
val2 = lag.lagval(self.x, pol2)
pol3 = lag.lagmul(pol1, pol2)
val3 = lag.lagval(self.x, pol3)
assert_(len(pol3) == i + j + 1, msg)
assert_almost_equal(val3, val1 * val2, err_msg=msg)
def test_lagmul(self) :
# check values of result
for i in range(5) :
pol1 = [0]*i + [1]
val1 = lag.lagval(self.x, pol1)
for j in range(5) :
msg = "At i=%d, j=%d" % (i, j)
pol2 = [0]*j + [1]
val2 = lag.lagval(self.x, pol2)
pol3 = lag.lagmul(pol1, pol2)
val3 = lag.lagval(self.x, pol3)
assert_(len(pol3) == i + j + 1, msg)
assert_almost_equal(val3, val1*val2, err_msg=msg)
def laguerre_feature_func(x: float,
i: int,
ident=ident,
strike=strike) -> float:
# noinspection PyTypeChecker
xp = x / strike
return np.exp(-xp / 2) * lagval(xp, ident[i])
def test_lagvander(self) :
# check for 1d x
x = np.arange(3)
v = lag.lagvander(x, 3)
assert_(v.shape == (3, 4))
for i in range(4) :
coef = [0]*i + [1]
assert_almost_equal(v[..., i], lag.lagval(x, coef))
# check for 2d x
x = np.array([[1, 2], [3, 4], [5, 6]])
v = lag.lagvander(x, 3)
assert_(v.shape == (3, 2, 4))
for i in range(4) :
coef = [0]*i + [1]
assert_almost_equal(v[..., i], lag.lagval(x, coef))
def test_lagvander(self):
# check for 1d x
x = np.arange(3)
v = lag.lagvander(x, 3)
assert_(v.shape == (3, 4))
for i in range(4):
coef = [0] * i + [1]
assert_almost_equal(v[..., i], lag.lagval(x, coef))
# check for 2d x
x = np.array([[1, 2], [3, 4], [5, 6]])
v = lag.lagvander(x, 3)
assert_(v.shape == (3, 2, 4))
for i in range(4):
coef = [0] * i + [1]
assert_almost_equal(v[..., i], lag.lagval(x, coef))
def fitted_lspi_put_option(
obj: OptimalExerciseRL, strike: float, expiry: float,
training_data: Sequence[TrainingDataType], training_iters: int,
split: int) -> LinearFunctionApprox[Tuple[float, float]]:
num_laguerre: int = 3
lspi_reg: float = 0.001
ident: np.ndarray = np.eye(num_laguerre)
features: List[Callable[[Tuple[float, float]], float]] = [lambda _: 1.]
features += [
(lambda t_s: np.exp(-t_s[1] /
(2 * strike)) * lagval(t_s[1] / strike, ident[i]))
for i in range(num_laguerre)
]
features += [
lambda t_s: np.cos(-t_s[0] * np.pi / (2 * expiry)),
lambda t_s: np.log(expiry - t_s[0])
if t_s[0] != expiry else 0., lambda t_s: (t_s[0] / expiry)**2
]
linear_approx: LinearFunctionApprox[Tuple[float, float]] = \
obj.linear_func_approx(features=features, reg=lspi_reg)
return obj.train_lspi(training_data=training_data,
init_fa=linear_approx,
training_iters=training_iters,
split=split)
def laguerre_feature_func(x: float,
i: int,
ident=ident,
strike=strike) -> float:
# noinspection PyTypeChecker
return np.exp(-x / (strike * 2)) * \
lagval(x / strike, ident[i])
def fitted_lspi_put_option(
expiry: float, num_steps: int, num_paths: int, spot_price: float,
spot_price_frac: float, rate: float, vol: float, strike: float,
training_iters: int) -> LinearFunctionApprox[Tuple[float, float]]:
num_laguerre: int = 4
epsilon: float = 1e-3
ident: np.ndarray = np.eye(num_laguerre)
features: List[Callable[[Tuple[float, float]], float]] = [lambda _: 1.]
features += [(lambda t_s, i=i: np.exp(-t_s[1] / (2 * strike)) * lagval(
t_s[1] / strike, ident[i])) for i in range(num_laguerre)]
features += [
lambda t_s: np.cos(-t_s[0] * np.pi / (2 * expiry)),
lambda t_s: np.log(expiry - t_s[0])
if t_s[0] != expiry else 0., lambda t_s: (t_s[0] / expiry)**2
]
training_data: Sequence[TrainingDataType] = training_sim_data(
expiry=expiry,
num_steps=num_steps,
num_paths=num_paths,
spot_price=spot_price,
spot_price_frac=spot_price_frac,
rate=rate,
vol=vol)
dt: float = expiry / num_steps
gamma: float = np.exp(-rate * dt)
num_features: int = len(features)
states: Sequence[Tuple[float,
float]] = [(i * dt, s) for i, s, _ in training_data]
next_states: Sequence[Tuple[float, float]] = \
[((i + 1) * dt, s1) for i, _, s1 in training_data]
feature_vals: np.ndarray = np.array([[f(x) for f in features]
for x in states])
next_feature_vals: np.ndarray = np.array([[f(x) for f in features]
for x in next_states])
non_terminal: np.ndarray = np.array(
[i < num_steps - 1 for i, _, _ in training_data])
exer: np.ndarray = np.array([max(strike - s1, 0) for _, s1 in next_states])
wts: np.ndarray = np.zeros(num_features)
for _ in range(training_iters):
a_inv: np.ndarray = np.eye(num_features) / epsilon
b_vec: np.ndarray = np.zeros(num_features)
cont: np.ndarray = np.dot(next_feature_vals, wts)
cont_cond: np.ndarray = non_terminal * (cont > exer)
for i in range(len(training_data)):
phi1: np.ndarray = feature_vals[i]
phi2: np.ndarray = phi1 - \
cont_cond[i] * gamma * next_feature_vals[i]
temp: np.ndarray = a_inv.T.dot(phi2)
a_inv -= np.outer(a_inv.dot(phi1), temp) / (1 + phi1.dot(temp))
b_vec += phi1 * (1 - cont_cond[i]) * exer[i] * gamma
wts = a_inv.dot(b_vec)
return LinearFunctionApprox.create(feature_functions=features,
weights=Weights.create(wts))
def test_lagval(self):
#check empty input
assert_equal(lag.lagval([], [1]).size, 0)
#check normal input)
x = np.linspace(-1, 1)
y = [polyval(x, c) for c in Llist]
for i in range(7):
msg = "At i=%d" % i
tgt = y[i]
res = lag.lagval(x, [0]*i + [1])
assert_almost_equal(res, tgt, err_msg=msg)
#check that shape is preserved
for i in range(3):
dims = [2]*i
x = np.zeros(dims)
assert_equal(lag.lagval(x, [1]).shape, dims)
assert_equal(lag.lagval(x, [1, 0]).shape, dims)
assert_equal(lag.lagval(x, [1, 0, 0]).shape, dims)
def test_lagval(self):
#check empty input
assert_equal(lag.lagval([], [1]).size, 0)
#check normal input)
x = np.linspace(-1, 1)
y = [polyval(x, c) for c in Llist]
for i in range(7):
msg = "At i=%d" % i
tgt = y[i]
res = lag.lagval(x, [0]*i + [1])
assert_almost_equal(res, tgt, err_msg=msg)
#check that shape is preserved
for i in range(3):
dims = [2]*i
x = np.zeros(dims)
assert_equal(lag.lagval(x, [1]).shape, dims)
assert_equal(lag.lagval(x, [1, 0]).shape, dims)
assert_equal(lag.lagval(x, [1, 0, 0]).shape, dims)
def test_lagfromroots(self) :
res = lag.lagfromroots([])
assert_almost_equal(trim(res), [1])
for i in range(1, 5) :
roots = np.cos(np.linspace(-np.pi, 0, 2*i + 1)[1::2])
pol = lag.lagfromroots(roots)
res = lag.lagval(roots, pol)
tgt = 0
assert_(len(pol) == i + 1)
assert_almost_equal(lag.lag2poly(pol)[-1], 1)
assert_almost_equal(res, tgt)
def test_lagfromroots(self):
res = lag.lagfromroots([])
assert_almost_equal(trim(res), [1])
for i in range(1, 5):
roots = np.cos(np.linspace(-np.pi, 0, 2 * i + 1)[1::2])
pol = lag.lagfromroots(roots)
res = lag.lagval(roots, pol)
tgt = 0
assert_(len(pol) == i + 1)
assert_almost_equal(lag.lag2poly(pol)[-1], 1)
assert_almost_equal(res, tgt)
def getAbscissasAndWeights(N=5):
# Laguerre polynomial roots and weights for Laguerre-Guass quadrature
coef = np.concatenate([np.zeros(N), [1]])
roots = laguerre.lagroots(coef)
weights = []
for n, root in enumerate(roots):
n = n + 1
array = np.concatenate([np.zeros(N + 1), [1]])
value = laguerre.lagval(root, array)
weight = root / ((N + 1) * value)**2
weights.append(weight)
return roots, weights
def test_lagval(self) :
def f(x) :
return x*(x**2 - 1)
#check empty input
assert_equal(lag.lagval([], [1]).size, 0)
#check normal input)
for i in range(7) :
msg = "At i=%d" % i
ser = np.zeros
tgt = self.y[i]
res = lag.lagval(self.x, [0]*i + [1])
assert_almost_equal(res, tgt, err_msg=msg)
#check that shape is preserved
for i in range(3) :
dims = [2]*i
x = np.zeros(dims)
assert_equal(lag.lagval(x, [1]).shape, dims)
assert_equal(lag.lagval(x, [1,0]).shape, dims)
assert_equal(lag.lagval(x, [1,0,0]).shape, dims)
def test_lagval(self) :
def f(x) :
return x*(x**2 - 1)
#check empty input
assert_equal(lag.lagval([], [1]).size, 0)
#check normal input)
for i in range(7) :
msg = "At i=%d" % i
ser = np.zeros
tgt = self.y[i]
res = lag.lagval(self.x, [0]*i + [1])
assert_almost_equal(res, tgt, err_msg=msg)
#check that shape is preserved
for i in range(3) :
dims = [2]*i
x = np.zeros(dims)
assert_equal(lag.lagval(x, [1]).shape, dims)
assert_equal(lag.lagval(x, [1,0]).shape, dims)
assert_equal(lag.lagval(x, [1,0,0]).shape, dims)
def test_lagfit(self) :
def f(x) :
return x*(x - 1)*(x - 2)
# Test exceptions
assert_raises(ValueError, lag.lagfit, [1], [1], -1)
assert_raises(TypeError, lag.lagfit, [[1]], [1], 0)
assert_raises(TypeError, lag.lagfit, [], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [[[1]]], 0)
assert_raises(TypeError, lag.lagfit, [1, 2], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [1, 2], 0)
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[[1]])
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[1,1])
# Test fit
x = np.linspace(0,2)
y = f(x)
#
coef3 = lag.lagfit(x, y, 3)
assert_equal(len(coef3), 4)
assert_almost_equal(lag.lagval(x, coef3), y)
#
coef4 = lag.lagfit(x, y, 4)
assert_equal(len(coef4), 5)
assert_almost_equal(lag.lagval(x, coef4), y)
#
coef2d = lag.lagfit(x, np.array([y,y]).T, 3)
assert_almost_equal(coef2d, np.array([coef3,coef3]).T)
# test weighting
w = np.zeros_like(x)
yw = y.copy()
w[1::2] = 1
y[0::2] = 0
wcoef3 = lag.lagfit(x, yw, 3, w=w)
assert_almost_equal(wcoef3, coef3)
#
wcoef2d = lag.lagfit(x, np.array([yw,yw]).T, 3, w=w)
assert_almost_equal(wcoef2d, np.array([coef3,coef3]).T)
def test_lagfit(self) :
def f(x) :
return x*(x - 1)*(x - 2)
# Test exceptions
assert_raises(ValueError, lag.lagfit, [1], [1], -1)
assert_raises(TypeError, lag.lagfit, [[1]], [1], 0)
assert_raises(TypeError, lag.lagfit, [], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [[[1]]], 0)
assert_raises(TypeError, lag.lagfit, [1, 2], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [1, 2], 0)
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[[1]])
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[1,1])
# Test fit
x = np.linspace(0,2)
y = f(x)
#
coef3 = lag.lagfit(x, y, 3)
assert_equal(len(coef3), 4)
assert_almost_equal(lag.lagval(x, coef3), y)
#
coef4 = lag.lagfit(x, y, 4)
assert_equal(len(coef4), 5)
assert_almost_equal(lag.lagval(x, coef4), y)
#
coef2d = lag.lagfit(x, np.array([y,y]).T, 3)
assert_almost_equal(coef2d, np.array([coef3,coef3]).T)
# test weighting
w = np.zeros_like(x)
yw = y.copy()
w[1::2] = 1
y[0::2] = 0
wcoef3 = lag.lagfit(x, yw, 3, w=w)
assert_almost_equal(wcoef3, coef3)
#
wcoef2d = lag.lagfit(x, np.array([yw,yw]).T, 3, w=w)
assert_almost_equal(wcoef2d, np.array([coef3,coef3]).T)
def mainPricing(I, M, df):
S = GenS(I, M) # generate stock price paths
h = IV(S) # inner value matrix
V = IV(S) # value matrix
for t in range(M - 1, -1, -1):
# rg = polyfit(S[t,:], V[t+1,:]*df, reg) # regression at time t
rg = a.lagfit(S[t, :], V[t + 1, :] * df, reg)
C = a.lagval(S[t, :], rg, True)
# C = polyval(rg, S[t, :])
##C = polyval(rg,S[t,:]) # continuation values
V[t, :] = where(h[t, :] > C, h[t, :],
V[t + 1, :] * df) # exercise decision
V0 = sum(V[0, :]) / I # LSM estimator
return V0
def GaussLaguerre(N):
"""
function to return roots and weights for Gauss-Laguerre integration
with N mesh points
no multiplication for weights for Laguerre!
"""
#initialize coeffitents for Laguerre series
coeff = np.zeros(N+1)
#find root of N-th Laguerre polynomial
coeff[N] = 1
roots = laguerre.lagroots(coeff)
#reset coeff.; initalize L matrix
L = np.zeros((N,N), dtype = np.float64)
coeff = np.zeros(N)
for i in range(N):
#fill j-th Laguerre poly with the i-th root.
for j in range(N):
coeff[j] = 1
L[i, j] = laguerre.lagval(roots[i], coeff)
coeff[j] = 0
L_inv = inv(L)
return L_inv[0,:], roots
def plot_fitted_call_prices(is_call: bool, strike: float, expiry: float,
r: float, sigma: float) -> None:
spot_prices = np.linspace(strike * 0.5, strike * 1.5, 1001)
option_prices = [
EuropeanBSPricing(is_call, s, strike, expiry, r,
sigma).get_option_price() for s in spot_prices
]
def fit_func(x: np.ndarray, a: float, b: float, c: float) -> np.ndarray:
return a * np.exp(b * x + c)
def jac_func(x: np.ndarray, a: float, b: float, c: float) -> np.ndarray:
t = np.exp(b * x + c)
da = t
db = a * t * x
dc = a * t
return np.transpose([da, db, dc])
fp = curve_fit(f=fit_func,
xdata=spot_prices,
ydata=option_prices,
jac=jac_func)[0]
pred1_option_prices = fit_func(spot_prices, fp[0], fp[1], fp[2])
num_laguerre = 10
ident = np.eye(num_laguerre)
spot_features = np.array([[1.] + [
np.exp(-s / (strike * 2)) * lagval(s / strike, ident[i])
for i in range(num_laguerre)
] for s in spot_prices])
lp = np.linalg.lstsq(spot_features, np.array(option_prices), rcond=None)[0]
pred2_option_prices = spot_features.dot(lp)
plt.plot(spot_prices, option_prices, 'r')
plt.plot(spot_prices, pred1_option_prices, 'b')
plt.plot(spot_prices, pred2_option_prices, 'g')
plt.show()
def test_lagint(self) :
# check exceptions
assert_raises(ValueError, lag.lagint, [0], .5)
assert_raises(ValueError, lag.lagint, [0], -1)
assert_raises(ValueError, lag.lagint, [0], 1, [0, 0])
# test integration of zero polynomial
for i in range(2, 5):
k = [0]*(i - 2) + [1]
res = lag.lagint([0], m=i, k=k)
assert_almost_equal(res, [1, -1])
# check single integration with integration constant
for i in range(5) :
scl = i + 1
pol = [0]*i + [1]
tgt = [i] + [0]*i + [1/scl]
lagpol = lag.poly2lag(pol)
lagint = lag.lagint(lagpol, m=1, k=[i])
res = lag.lag2poly(lagint)
assert_almost_equal(trim(res), trim(tgt))
# check single integration with integration constant and lbnd
for i in range(5) :
scl = i + 1
pol = [0]*i + [1]
lagpol = lag.poly2lag(pol)
lagint = lag.lagint(lagpol, m=1, k=[i], lbnd=-1)
assert_almost_equal(lag.lagval(-1, lagint), i)
# check single integration with integration constant and scaling
for i in range(5) :
scl = i + 1
pol = [0]*i + [1]
tgt = [i] + [0]*i + [2/scl]
lagpol = lag.poly2lag(pol)
lagint = lag.lagint(lagpol, m=1, k=[i], scl=2)
res = lag.lag2poly(lagint)
assert_almost_equal(trim(res), trim(tgt))
# check multiple integrations with default k
for i in range(5) :
for j in range(2, 5) :
pol = [0]*i + [1]
tgt = pol[:]
for k in range(j) :
tgt = lag.lagint(tgt, m=1)
res = lag.lagint(pol, m=j)
assert_almost_equal(trim(res), trim(tgt))
# check multiple integrations with defined k
for i in range(5) :
for j in range(2, 5) :
pol = [0]*i + [1]
tgt = pol[:]
for k in range(j) :
tgt = lag.lagint(tgt, m=1, k=[k])
res = lag.lagint(pol, m=j, k=list(range(j)))
assert_almost_equal(trim(res), trim(tgt))
# check multiple integrations with lbnd
for i in range(5) :
for j in range(2, 5) :
pol = [0]*i + [1]
tgt = pol[:]
for k in range(j) :
tgt = lag.lagint(tgt, m=1, k=[k], lbnd=-1)
res = lag.lagint(pol, m=j, k=list(range(j)), lbnd=-1)
assert_almost_equal(trim(res), trim(tgt))
# check multiple integrations with scaling
for i in range(5) :
for j in range(2, 5) :
pol = [0]*i + [1]
tgt = pol[:]
for k in range(j) :
tgt = lag.lagint(tgt, m=1, k=[k], scl=2)
res = lag.lagint(pol, m=j, k=list(range(j)), scl=2)
assert_almost_equal(trim(res), trim(tgt))
def laguerre_feature_func(x: float, i: int) -> float:
xp = x / strike
return np.exp(-xp / 2) * lagval(xp, eye[i])
def laguerre_state_ff(x: Tuple[NonTerminal[S], A], i=i) -> float:
return lagval(float(x[0].state), states_ident[i])
def laguerre_action_ff(x: Tuple[NonTerminal[S], A], j=j) -> float:
return lagval(float(x[1]), actions_ident[j])
cont += 1
r = r * x + polinomio[i]
print("El numero de multiplicaciones con horner es {}".format(cont))
print(r)
return r
def hornerd2(polinomio, x):
r = 0
cont = 0
polinomio = Derivada(polinomio)
polinomio = Derivada(polinomio)
for i in range(len(polinomio)):
# multiplica el valor de la x
# luego suma el coeficinte
cont += 1
r = r * x + polinomio[i]
print("El numero de multiplicaciones con horner es {}".format(cont))
print(r)
return r
if __name__ == "__main__":
polinomio = [1, -5, -9, 155, -250]
horner(polinomio, complex(1))
hornerd1(polinomio, complex(1))
hornerd2(polinomio, complex(1))
p = np.array(polinomio)
r = lagval(x=1, c=p) #no tiene sentido lo que da
print(r)
def apply_fitting_fields(plotgui):
"""
Apply the set fitting parameters from the window.
This routine reads the fitting parameters from the window and then
applies the fitting. Each time it is called a new data set should
be generated, unless the fit order is too large for the number of
points. The fit order must be at most 1 less than the number
of points, otherwise the routine just shows an error message
pop-up and returns.
Parameters
----------
plotgui: by assumption a matplotlib_user_interface object
Returns
-------
None
"""
fit_type = plotgui.set_fitting_fields[0].get()
if 'Cubic Spline' in fit_type:
fit_order = float(plotgui.set_fitting_fields[1].get())
else:
try:
fit_order = int(plotgui.set_fitting_fields[1].get())
except ValueError:
str1 = 'Error: bad fit order (%s). Settng to 4.' % (
plotgui.set_fitting_fields[1].get())
plotgui.fit_text.insert(Tk.END, str1)
plotgui.fit_text.see(Tk.END)
fit_order = 4
set_number = plotgui.set_fitting_list_area.current()
fit_flag = plotgui.fit_option.get()
if fit_flag == 0:
xvalues = numpy.copy(plotgui.xdata[set_number]['values'])
yvalues = numpy.copy(plotgui.ydata[set_number]['values'])
else:
xvalues = numpy.copy(plotgui.ydata[set_number]['values'])
yvalues = numpy.copy(plotgui.xdata[set_number]['values'])
inds = numpy.argsort(xvalues)
xvalues = xvalues[inds]
yvalues = yvalues[inds]
npoints = len(xvalues)
if ('Spline' not in fit_type) and ('Internal' not in fit_type):
if npoints + 1 <= fit_order:
tkinter.messagebox.showinfo(
'Error', 'The number of points is too few for the fit order.' +
' Please check your inputs.')
return
xmin = numpy.min(xvalues)
xmax = numpy.max(xvalues)
delx = xmax - xmin
xstep = 1.2 * delx / 1201.
xout = numpy.arange(xmin - delx / 10., xmax + delx / 10., xstep)
if 'Internal' in fit_type:
if npoints < 2:
tkinter.messagebox.showinfo(
'Error', 'The number of points is too few for a linear fit.' +
' Please check your inputs.')
return
if fit_flag == 0:
yerrors = (plotgui.ydata[set_number]['lowerror'] +
plotgui.ydata[set_number]['higherror']) / 2.
else:
yerrors = (plotgui.xdata[set_number]['lowerror'] +
plotgui.xdata[set_number]['higherror']) / 2.
if (numpy.min(yerrors) == 0.) and (numpy.max(yerrors) == 0.):
yerrors = yerrors + 1.
slope, intercept, slope_error, intercept_error, covariance, \
correlation = general_utilities.slope_calculation(
xvalues, yvalues, yerrors)
if slope is None:
tkinter.messagebox.showinfo('Error',
'Error in the standard slope fit.\n')
return
yfit = intercept + xvalues * slope
yout = intercept + xout * slope
errorterm1 = xout * 0. + intercept_error
errorterm2 = xout * slope_error
youterror = numpy.sqrt(errorterm1 * errorterm1 +
errorterm2 * errorterm2)
labelstring = 'Standard linear fit'
rms = numpy.sqrt(numpy.mean((yvalues - yfit) * (yvalues - yfit)))
str1 = 'Regression calculation results:\n'
str1 = str1 + 'Slope: %g +/- %g\n' % (slope, slope_error)
str1 = str1 + 'Intercept: %g +/- %g\n' % (intercept, intercept_error)
str1 = str1 + 'Covariance: %f\n' % (covariance)
str1 = str1 + 'Correlation: %f\n' % (correlation)
str1 = str1 + 'RMS deviation: %f\n' % (rms)
tkinter.messagebox.showinfo('Information', str1)
outfile = open('fit_values.txt', 'a')
print(str1, file=outfile)
print(' ', file=outfile)
outfile.close()
if fit_type == 'Polynomial':
fitpars = polynomial.polyfit(xvalues, yvalues, fit_order)
yout = polynomial.polyval(xout, fitpars)
yfit = polynomial.polyval(xvalues, fitpars)
labelstring = 'Order %d polynomial fit' % (fit_order)
general_utilities.list_polynomial_fitpars(fit_type, fit_order, fitpars)
if fit_type == 'Legendre':
fitpars = legendre.legfit(xvalues, yvalues, fit_order)
yout = legendre.legval(xout, fitpars)
yfit = legendre.legval(xvalues, fitpars)
labelstring = 'Order %d Legendre polynomial fit' % (fit_order)
general_utilities.list_polynomial_fitpars(fit_type, fit_order, fitpars)
if fit_type == 'Laguerre':
fitpars = laguerre.lagfit(xvalues, yvalues, fit_order)
yout = laguerre.lagval(xout, fitpars)
yfit = laguerre.lagval(xvalues, fitpars)
labelstring = 'Order %d Laguerre polynomial fit' % (fit_order)
general_utilities.list_polynomial_fitpars(fit_type, fit_order, fitpars)
if fit_type == 'Chebyshev':
fitpars = chebyshev.chebfit(xvalues, yvalues, fit_order)
yout = chebyshev.chebval(xout, fitpars)
yfit = chebyshev.chebval(xvalues, fitpars)
labelstring = 'Order %d Chebyshev polynomial fit' % (fit_order)
general_utilities.list_polynomial_fitpars(fit_type, fit_order, fitpars)
if fit_type == 'Least-Squares Spline':
if fit_flag == 0:
yerrors = (plotgui.ydata[set_number]['lowerror'] +
plotgui.ydata[set_number]['higherror']) / 2.
else:
yerrors = (plotgui.xdata[set_number]['lowerror'] +
plotgui.xdata[set_number]['higherror']) / 2.
if (numpy.min(yerrors) == 0.) and (numpy.max(yerrors) == 0.):
yerrors = yerrors + 1.
xmin1 = numpy.min(xvalues)
xmax1 = numpy.max(xvalues)
xrange = xmax1 - xmin1
nknots = int(fit_order)
if (nknots < 3) or (nknots > int(len(xvalues) / 2)):
nknots = 3
xstep = xrange / (nknots - 2)
xknots = numpy.arange(
numpy.min(xvalues) + xstep,
numpy.max(xvalues) * 0.999999999, xstep)
k = 3
# Use cubic splines
knotedges = numpy.r_[(xmin, ) * (k + 1), xknots, (xmax, ) * (k + 1)]
weights = 1. / yerrors
weights[yerrors == 0.] = 0.
fitobject = make_lsq_spline(xvalues, yvalues, knotedges, k, w=weights)
yout = fitobject(xout)
yfit = fitobject(xvalues)
labelstring = 'Least squares spline fit, sections = %d' % (nknots)
if fit_type == 'Spline':
fitpars = UnivariateSpline(xvalues,
yvalues,
k=1,
s=None,
bbox=[xmin - delx, xmax + delx])
labelstring = 'Default spline fit'
yout = fitpars(xout)
yfit = fitpars(xvalues)
if fit_type == 'Cubic Spline':
if fit_order < 0.:
str1 = 'Error: smoothing value %f (< 0) is not allowed.'\
+ ' Settng to 0.0' % (fit_order)
plotgui.fit_text.insert(Tk.END, str1)
plotgui.fit_text.see(Tk.END)
fit_order = 0.0
fitpars = UnivariateSpline(xvalues,
yvalues,
k=3,
bbox=[xmin - delx, xmax + delx],
s=fit_order)
yout = fitpars(xout)
yfit = fitpars(xvalues)
labelstring = 'Cubic spline fit, smoothing = %f' % (fit_order)
rms = fit_statistics(yvalues, yfit)
if 'Internal' in fit_type:
if fit_flag == 0:
xlowerror = youterror * 0.
xhigherror = youterror * 0.
ylowerror = youterror
yhigherror = youterror
else:
xlowerror = youterror
xhigherror = youterror
ylowerror = youterror * 0.
yhigherror = youterror * 0.
else:
xlowerror = xout * 0.
xhigherror = xout * 0.
ylowerror = yout * 0.
yhigherror = yout * 0.
xmin = numpy.min(xout)
xmax = numpy.max(xout)
ymin = numpy.min(yfit)
ymax = numpy.max(yfit)
if rms is not None:
str1 = 'Fit: RMS = %g for %d points\n' % (rms, len(yfit))
plotgui.fit_text.insert(Tk.END, str1)
plotgui.fit_text.see(Tk.END)
if fit_flag == 0:
plotgui.xdata[plotgui.nsets] = {
'values': xout,
'lowerror': xlowerror,
'higherror': xhigherror,
'minimum': xmin,
'maximum': xmax,
'errors': False,
'legend': True
}
plotgui.ydata[plotgui.nsets] = {
'values': yout,
'lowerror': ylowerror,
'higherror': yhigherror,
'minimum': ymin,
'maximum': ymax,
'errors': True,
'legend': True
}
else:
plotgui.xdata[plotgui.nsets] = {
'values': yout,
'lowerror': ylowerror,
'higherror': yhigherror,
'minimum': ymin,
'maximum': ymax,
'errors': False,
'legend': True
}
plotgui.ydata[plotgui.nsets] = {
'values': xout,
'lowerror': xlowerror,
'higherror': xhigherror,
'minimum': xmin,
'maximum': xmax,
'errors': False,
'legend': True
}
m = plotgui.nsets % 10
n = int(math.floor(plotgui.nsets / 10))
plotgui.set_properties[plotgui.nsets]['symbol'] = None
plotgui.set_properties[plotgui.nsets]['linestyle'] = '-'
plotgui.set_properties[plotgui.nsets]['colour'] = plotgui.colourset[m]
plotgui.set_properties[plotgui.nsets]['symbolsize'] = 4.0 + 0.3 * n
plotgui.set_properties[plotgui.nsets]['label'] = labelstring
plotgui.nsets = plotgui.nsets + 1
make_plot.make_plot(plotgui)
opt_ex_bi: OptimalExerciseBI = OptimalExerciseBI(
spot_price=spot_price_val,
payoff=lambda x: max(strike - x, 0.),
expiry=expiry_val,
rate=rate_val,
vol=vol_val,
num_steps=num_steps_val,
spot_price_frac=spot_price_frac_val)
num_laguerre: int = 4
reglr_coeff: float = 0.001
ident: np.ndarray = np.eye(num_laguerre)
ffs: List[Callable[[NonTerminal[float]], float]] = [lambda _: 1.]
ffs += [(lambda s: np.log(1 + np.exp(-s.state / (2 * strike))) * lagval(
s.state / strike, ident[i])) for i in range(num_laguerre)]
it_vf = opt_ex_bi.backward_induction_vf_and_pi(features=ffs,
reg_coeff=reglr_coeff)
prices: np.ndarray = np.arange(120.0)
print("Backward Induction: VF And Policy")
print("---------------------------------")
print()
all_funcs: List[FunctionApprox[NonTerminal[float]]] = []
for t, (v, p) in enumerate(it_vf):
print(f"Time {t:d}")
print()
if t == 0 or t == int(num_steps_val / 2) or t == num_steps_val - 1:
def test_lagfit(self):
def f(x):
return x * (x - 1) * (x - 2)
# Test exceptions
assert_raises(ValueError, lag.lagfit, [1], [1], -1)
assert_raises(TypeError, lag.lagfit, [[1]], [1], 0)
assert_raises(TypeError, lag.lagfit, [], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [[[1]]], 0)
assert_raises(TypeError, lag.lagfit, [1, 2], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [1, 2], 0)
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[[1]])
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[1, 1])
# Test fit
x = np.linspace(0, 2)
y = f(x)
#
coef3 = lag.lagfit(x, y, 3)
assert_equal(len(coef3), 4)
assert_almost_equal(lag.lagval(x, coef3), y)
#
coef4 = lag.lagfit(x, y, 4)
assert_equal(len(coef4), 5)
assert_almost_equal(lag.lagval(x, coef4), y)
#
coef2d = lag.lagfit(x, np.array([y, y]).T, 3)
assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
# test weighting
w = np.zeros_like(x)
yw = y.copy()
w[1::2] = 1
y[0::2] = 0
wcoef3 = lag.lagfit(x, yw, 3, w=w)
assert_almost_equal(wcoef3, coef3)
#
wcoef2d = lag.lagfit(x, np.array([yw, yw]).T, 3, w=w)
assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
#test NA
y = f(x)
y[10] = 100
xm = x.view(maskna=1)
xm[10] = np.NA
res = lag.lagfit(xm, y, 3)
assert_almost_equal(res, coef3)
ym = y.view(maskna=1)
ym[10] = np.NA
res = lag.lagfit(x, ym, 3)
assert_almost_equal(res, coef3)
y2 = np.vstack((y, y)).T
y2[10, 0] = 100
y2[15, 1] = 100
y2m = y2.view(maskna=1)
y2m[10, 0] = np.NA
y2m[15, 1] = np.NA
res = lag.lagfit(x, y2m, 3).T
assert_almost_equal(res[0], coef3)
assert_almost_equal(res[1], coef3)
wm = np.ones_like(x, maskna=1)
wm[10] = np.NA
res = lag.lagfit(x, y, 3, w=wm)
assert_almost_equal(res, coef3)
for m in range(1, 10):
# Simulation Parameters
I = 25000
M = 50
dt=T/M
df = exp(-r * dt)
# Stock Price Paths
S = S0 * np.exp(np.cumsum((r - 0.5 * sigma ** 2) * dt
+ sigma * math.sqrt(dt) * np.random.standard_normal((M + 1, I)), axis=0))
S[0] = S0
# Inner Values
h = np.maximum(K - S, 0)
# Present Value Vector (Initialization)
V = h[-1]
# American Option Valuation by Backwards Induction
for t in xrange(M - 1, 0, -1):
rg= laguerre.lagfit(S[t], V * df, m)
C = laguerre.lagval(S[t], rg)# continuation values V = np.where(h[t] > C, h[t], V * df)
# exercise decision
V0 = df*np.sum(V)/I #LSMestimator
error.append(abs(V0 - Vbsm))
print error
plt.xlabel('Order of Polynomial for LS regression')
plt.ylabel('Errors')
plt.plot(range(1, 10), error)
plt.show()
def fitted_dql_put_option(
expiry: float, num_steps: int, num_paths: int, spot_price: float,
spot_price_frac: float, rate: float, vol: float, strike: float,
training_iters: int) -> DNNApprox[Tuple[float, float]]:
reg_coeff: float = 1e-2
neurons: Sequence[int] = [6]
# features: List[Callable[[Tuple[float, float]], float]] = [
# lambda t_s: 1.,
# lambda t_s: t_s[0] / expiry,
# lambda t_s: t_s[1] / strike,
# lambda t_s: t_s[0] * t_s[1] / (expiry * strike)
# ]
num_laguerre: int = 2
ident: np.ndarray = np.eye(num_laguerre)
features: List[Callable[[Tuple[float, float]], float]] = [lambda _: 1.]
features += [(lambda t_s, i=i: np.exp(-t_s[1] / (2 * strike)) * lagval(
t_s[1] / strike, ident[i])) for i in range(num_laguerre)]
features += [
lambda t_s: np.cos(-t_s[0] * np.pi / (2 * expiry)),
lambda t_s: np.log(expiry - t_s[0])
if t_s[0] != expiry else 0., lambda t_s: (t_s[0] / expiry)**2
]
ds: DNNSpec = DNNSpec(neurons=neurons,
bias=True,
hidden_activation=lambda x: np.log(1 + np.exp(-x)),
hidden_activation_deriv=lambda y: np.exp(-y) - 1,
output_activation=lambda x: x,
output_activation_deriv=lambda y: np.ones_like(y))
fa: DNNApprox[Tuple[float, float]] = DNNApprox.create(
feature_functions=features,
dnn_spec=ds,
adam_gradient=AdamGradient(learning_rate=0.1, decay1=0.9,
decay2=0.999),
regularization_coeff=reg_coeff)
dt: float = expiry / num_steps
gamma: float = np.exp(-rate * dt)
training_data: Sequence[TrainingDataType] = training_sim_data(
expiry=expiry,
num_steps=num_steps,
num_paths=num_paths,
spot_price=spot_price,
spot_price_frac=spot_price_frac,
rate=rate,
vol=vol)
for _ in range(training_iters):
t_ind, s, s1 = training_data[randrange(len(training_data))]
t = t_ind * dt
x_val: Tuple[float, float] = (t, s)
val: float = max(strike - s1, 0)
if t_ind < num_steps - 1:
val = max(val, fa.evaluate([(t + dt, s1)])[0])
y_val: float = gamma * val
fa = fa.update([(x_val, y_val)])
# for w in fa.weights:
# pprint(w.weights)
return fa
opt_ex_bi: OptimalExerciseBI = OptimalExerciseBI(
spot_price=spot_price_val,
payoff=lambda x: max(strike - x, 0.),
expiry=expiry_val,
rate=rate_val,
vol=vol_val,
num_steps=num_steps_val,
spot_price_frac=spot_price_frac_val)
num_laguerre: int = 4
reglr_coeff: float = 0.001
ident: np.ndarray = np.eye(num_laguerre)
ffs: List[Callable[[StateType], float]] = [lambda _: 1.]
ffs += [(lambda s_e: np.log(1 + np.exp(-s_e[0] / (2 * strike))) * lagval(
s_e[0] / strike, ident[i])) for i in range(num_laguerre)]
it_vf = opt_ex_bi.backward_induction_vf_and_pi(features=ffs,
reg_coeff=reglr_coeff)
prices: np.ndarray = np.arange(120.0)
print("Backward Induction: VF And Policy")
print("---------------------------------")
print()
all_funcs: List[FunctionApprox[StateType]] = []
for t, (v, p) in enumerate(it_vf):
print(f"Time {t:d}")
print()
if t == 0 or t == int(num_steps_val / 2) or t == num_steps_val - 1:
def laguerre_feature_function(state: State, num_feature: int) -> float:
xp = state.price / strike
return np.exp(-xp / 2) * lagval(xp, eye[num_feature])
def laguerre_ff(x: NonTerminal[S], i=i) -> float:
return lagval(float(x.state), ident[i])
def laguerre_func(x: float, i=i) -> float:
return lagval(x, ident[i])
def test_lagint(self):
# check exceptions
assert_raises(ValueError, lag.lagint, [0], .5)
assert_raises(ValueError, lag.lagint, [0], -1)
assert_raises(ValueError, lag.lagint, [0], 1, [0, 0])
# test integration of zero polynomial
for i in range(2, 5):
k = [0] * (i - 2) + [1]
res = lag.lagint([0], m=i, k=k)
assert_almost_equal(res, [1, -1])
# check single integration with integration constant
for i in range(5):
scl = i + 1
pol = [0] * i + [1]
tgt = [i] + [0] * i + [1 / scl]
lagpol = lag.poly2lag(pol)
lagint = lag.lagint(lagpol, m=1, k=[i])
res = lag.lag2poly(lagint)
assert_almost_equal(trim(res), trim(tgt))
# check single integration with integration constant and lbnd
for i in range(5):
scl = i + 1
pol = [0] * i + [1]
lagpol = lag.poly2lag(pol)
lagint = lag.lagint(lagpol, m=1, k=[i], lbnd=-1)
assert_almost_equal(lag.lagval(-1, lagint), i)
# check single integration with integration constant and scaling
for i in range(5):
scl = i + 1
pol = [0] * i + [1]
tgt = [i] + [0] * i + [2 / scl]
lagpol = lag.poly2lag(pol)
lagint = lag.lagint(lagpol, m=1, k=[i], scl=2)
res = lag.lag2poly(lagint)
assert_almost_equal(trim(res), trim(tgt))
# check multiple integrations with default k
for i in range(5):
for j in range(2, 5):
pol = [0] * i + [1]
tgt = pol[:]
for k in range(j):
tgt = lag.lagint(tgt, m=1)
res = lag.lagint(pol, m=j)
assert_almost_equal(trim(res), trim(tgt))
# check multiple integrations with defined k
for i in range(5):
for j in range(2, 5):
pol = [0] * i + [1]
tgt = pol[:]
for k in range(j):
tgt = lag.lagint(tgt, m=1, k=[k])
res = lag.lagint(pol, m=j, k=list(range(j)))
assert_almost_equal(trim(res), trim(tgt))
# check multiple integrations with lbnd
for i in range(5):
for j in range(2, 5):
pol = [0] * i + [1]
tgt = pol[:]
for k in range(j):
tgt = lag.lagint(tgt, m=1, k=[k], lbnd=-1)
res = lag.lagint(pol, m=j, k=list(range(j)), lbnd=-1)
assert_almost_equal(trim(res), trim(tgt))
# check multiple integrations with scaling
for i in range(5):
for j in range(2, 5):
pol = [0] * i + [1]
tgt = pol[:]
for k in range(j):
tgt = lag.lagint(tgt, m=1, k=[k], scl=2)
res = lag.lagint(pol, m=j, k=list(range(j)), scl=2)
assert_almost_equal(trim(res), trim(tgt))
def test_lagfit(self) :
def f(x) :
return x*(x - 1)*(x - 2)
# Test exceptions
assert_raises(ValueError, lag.lagfit, [1], [1], -1)
assert_raises(TypeError, lag.lagfit, [[1]], [1], 0)
assert_raises(TypeError, lag.lagfit, [], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [[[1]]], 0)
assert_raises(TypeError, lag.lagfit, [1, 2], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [1, 2], 0)
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[[1]])
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[1,1])
# Test fit
x = np.linspace(0,2)
y = f(x)
#
coef3 = lag.lagfit(x, y, 3)
assert_equal(len(coef3), 4)
assert_almost_equal(lag.lagval(x, coef3), y)
#
coef4 = lag.lagfit(x, y, 4)
assert_equal(len(coef4), 5)
assert_almost_equal(lag.lagval(x, coef4), y)
#
coef2d = lag.lagfit(x, np.array([y,y]).T, 3)
assert_almost_equal(coef2d, np.array([coef3,coef3]).T)
# test weighting
w = np.zeros_like(x)
yw = y.copy()
w[1::2] = 1
y[0::2] = 0
wcoef3 = lag.lagfit(x, yw, 3, w=w)
assert_almost_equal(wcoef3, coef3)
#
wcoef2d = lag.lagfit(x, np.array([yw,yw]).T, 3, w=w)
assert_almost_equal(wcoef2d, np.array([coef3,coef3]).T)
#test NA
y = f(x)
y[10] = 100
xm = x.view(maskna=1)
xm[10] = np.NA
res = lag.lagfit(xm, y, 3)
assert_almost_equal(res, coef3)
ym = y.view(maskna=1)
ym[10] = np.NA
res = lag.lagfit(x, ym, 3)
assert_almost_equal(res, coef3)
y2 = np.vstack((y,y)).T
y2[10,0] = 100
y2[15,1] = 100
y2m = y2.view(maskna=1)
y2m[10,0] = np.NA
y2m[15,1] = np.NA
res = lag.lagfit(x, y2m, 3).T
assert_almost_equal(res[0], coef3)
assert_almost_equal(res[1], coef3)
wm = np.ones_like(x, maskna=1)
wm[10] = np.NA
res = lag.lagfit(x, y, 3, w=wm)
assert_almost_equal(res, coef3)
def eval(self, x, u, output_array=None):
x = np.atleast_1d(x)
if output_array is None:
output_array = np.zeros(x.shape)
output_array[:] = lag.lagval(x, u)*np.exp(-x/2)
return output_array
