def calculate_rst(b_minus,
b_plus,
a_minus,
a_plus,
a_m,
a0=np.ones(1),
d=1,
p=0,
r_e=0):
"""Calculate the coefficient of the rst"""
perturbation = P.polypow(zero(1), p)
s2, r0 = solve_diophantine(P.polymul(perturbation, a_minus),
P.polymul(delay(d), b_minus),
P.polymul(a_m, a0))
b_m_plus, _ = solve_diophantine(P.polymul(delay(d), b_minus),
P.polypow(zero(1), r_e + 1), a_m)
t0 = P.polymul(a0, b_m_plus)
r = P.polymul(r0, a_plus)
s = P.polymul(s2, P.polymul(b_plus, perturbation))
t = P.polymul(t0, a_plus)
print("R = ", ", ".join(map(str, r)))
print("S = ", ", ".join(map(str, s)))
print("T = ", ", ".join(map(str, t)))
return r, s, t
def d2d(b, a, T):
nb = len(b)
na = len(a)
b_new = _polytustin(b, T)
a_new = _polytustin(a, T)
if nb > na:
a_new = polymul(a_new, polypow([1, (T - 1) / (T + 1)], nb - na))
elif na > nb:
b_new = polymul(b_new, polypow([1, (T - 1) / (T + 1)], na - nb))
return b_new / a_new[0], a_new / a_new[0]
def _polytustin(a, T):
n = len(a)
if sum(a[1:]) == 0:
return a
a_new = 0
num = [(T - 1) / (T + 1), 1]
den = [1, (T - 1) / (T + 1)]
for i in range(n):
pnum = polypow(num, i)
pden = polypow(den, n - 1 - i)
a_new = polyadd(a_new, a[i] * polymul(pnum, pden))
return a_new
def smoothing_poly_lnprior(poly, degree, xmin, xmax, gamma=1):
"""
A smoothing prior that suppresses higher order derivatives of a polynomial,
poly = a + b x + c x*x + ..., described by a vector of its coefficients,
[a, b, c, ...].
Functional form is:
ln p(poly coeffs) =
-gamma * integrate( (diff(poly(x), x, degree))^2, x, xmin, xmax)
So it takes the `degree`th derivative of the polynomial, squares it,
integrates that from xmin to xmax, and scales by -gamma.
"""
# Take the `degree`th derivative of the polynomial.
poly_diff = P.polyder(poly, m=degree)
# Square the polynomial.
poly_diff_sq = P.polypow(poly_diff, 2)
# Take the indefinite integral of the polynomial.
poly_int_indef = P.polyint(poly_diff_sq)
# Evaluate the integral at xmin and xmax to get the definite integral.
poly_int_def = (
P.polyval(xmax, poly_int_indef) - P.polyval(xmin, poly_int_indef)
)
# Scale by -gamma to get the log prior
lnp = -gamma * poly_int_def
return lnp
def smoothing_poly_lnprior(poly, degree, xmin, xmax, gamma=1):
"""
A smoothing prior that suppresses higher order derivatives of a polynomial,
poly = a + b x + c x*x + ..., described by a vector of its coefficients,
[a, b, c, ...].
Functional form is:
ln p(poly coeffs) =
-gamma * integrate( (diff(poly(x), x, degree))^2, x, xmin, xmax)
So it takes the `degree`th derivative of the polynomial, squares it,
integrates that from xmin to xmax, and scales by -gamma.
"""
# Take the `degree`th derivative of the polynomial.
poly_diff = P.polyder(poly, m=degree)
# Square the polynomial.
poly_diff_sq = P.polypow(poly_diff, 2)
# Take the indefinite integral of the polynomial.
poly_int_indef = P.polyint(poly_diff_sq)
# Evaluate the integral at xmin and xmax to get the definite integral.
poly_int_def = (P.polyval(xmax, poly_int_indef) -
P.polyval(xmin, poly_int_indef))
# Scale by -gamma to get the log prior
lnp = -gamma * poly_int_def
return lnp
def probability(dice_number, sides, target):
powers = [0] + [1] * sides
poly = polypow(powers, dice_number)
try:
return round(poly[target] / sides ** dice_number, 4)
except IndexError:
return 0
def __init__(self, history, order, order_s, diff, diff_s, period, seed=42):
self.order = order # AR order p
self.order_s = order_s # seasonal AR order P
self.diff = diff # non-seasonal difference order d
self.diff_s = diff_s # seasonal difference order D
self.period = period # seasonal period s
# original series [X_{t-1}, X_{t-2}, ..., X_{t-(p+s*P+d+s*D)}]
self.X = np.zeros(order + period * order_s + diff + period * diff_s)
trunc = list(reversed(history))[:order + period * order_s + diff +
period * diff_s]
self.X[:len(trunc)] = trunc
self.X = deque(self.X,
maxlen=order + period * order_s + diff +
period * diff_s)
# compute (1-B)^d (1-B^s)^D
self.diff_polycoef = polypow([1, -1], diff) # (1-B)^D
self.diff_s_polycoef = polypow([1] + [0] * (period - 1) + [-1],
diff_s) # (1-B^s)^D
self.diff_multiply = self._polymul(self.diff_polycoef,
self.diff_s_polycoef)
# differenced series [Y_{t-1}, Y_{t-2}, ..., Y_{t-(p+s*P)}]
self.Y = self._compute_backshift(self.diff_multiply, self.X)
assert len(self.Y) == order + period * order_s
self.Y = deque(self.Y, maxlen=order + period * order_s)
self.c = 1
self.X_max = 2
self.D = 2 * self.c * np.sqrt(order + order_s)
self.G = self.D * (self.X_max**2)
self.lambda_ = 1.0 / (order + order_s) if order + order_s != 0 else 1.0
self.eta = 0.5 * min(4 * self.G * self.D,
self.lambda_) # learning rate
self.epsilon = 1.0 / (self.eta * self.D)**2
self.A = np.matrix(np.diag([1] * (order + order_s)) *
self.epsilon) # hessian matrix
if seed:
np.random.seed(seed)
self.gamma = np.matrix(np.random.uniform(
-self.c, self.c, (order, 1))) # parameters g_1, g_2, ..., g_p
if seed:
np.random.seed(seed)
self.gamma_s = np.matrix(
np.random.uniform(
-self.c, self.c,
(order_s, 1))) # seasonal parameters gs_1, gs_2, ..., gs_P
def test_polypow(self):
for i in range(5):
for j in range(5):
msg = "At i=%d, j=%d" % (i, j)
c = np.arange(i + 1)
tgt = reduce(poly.polymul, [c] * j, np.array([1]))
res = poly.polypow(c, j)
assert_equal(trim(res), trim(tgt), err_msg=msg)
def test_polypow(self):
for i in range(5):
for j in range(5):
msg = "At i=%d, j=%d" % (i, j)
c = np.arange(i + 1)
tgt = reduce(poly.polymul, [c]*j, np.array([1]))
res = poly.polypow(c, j)
assert_equal(trim(res), trim(tgt), err_msg=msg)
def YofS(YZn, YZd, omega1, omega2):
#Convert Y(Z^2) to Y(S)/S by using EQN(2)
Z2n = np.array([1, 0, omega2**2])
Z2d = np.array([1, 0, omega1**2])
#Numerator and denominator of Y(Z^2) will have the same even order
YSn = np.array([])
YSd = np.array([])
N_Order = len(YZd)//2
for i in range(N_Order + 1):
temp = np.polymul(np_pp.polypow(Z2n, N_Order - i), np_pp.polypow(Z2d, i))
YSn = np.polyadd(YSn, YZn[2*i]*temp)
YSd = np.polyadd(YSd, YZd[2*i]*temp)
#Ysd constant term is always zero and therefore..
YSd = YSd[:-1]
return YSn, YSd
def berlekampTest(prime, poly):
m = len(poly) - 1
u = [0, 1]
for i in range(0, m // 2):
u = P.polydiv(P.polypow(u, prime), poly)[1] % prime
d = gcd(P.polysub(u, [0, 1]) % prime, poly, prime)
#print(P.polysub(u, [0, 1]) % prime, poly, d)
if not np.array_equal(np.array([1.]), d):
return False
return True
def diePmf(dice, sides):
# Source: http://www.johndcook.com/blog/2013/04/29/rolling-dice-for-normal-samples-python-version/
# Create an array of polynomial coefficients for
# x + x^2 + ... + x^sides
p = ones(sides + 1)
p[0] = 0
p /= sides
# Extract the coefficients of p(x)**dice and divide by sides**dice
pmf = polypow(p, dice)
#cdf = pmf.cumsum()
return pmf
def magic(sides, dices, health, points):
# Prob of getting a sum of k is the coefficient of x^k in
# ((x + x^2 + ... + x^m)/m)^n
# m = sides per dice
# n = number of dices
p = ones(sides + 1)
p[0] = 0
p /= sides
p = polypow(p, dices)
# Get cumulative sum to get prob
cdf = p.cumsum()
k = health - points - 1
return 1 - cdf[k] if k <= len(cdf) and k > 0 else 1 if k < 0 else 0
def roll(d_num, sides, target):
"""
:param d_num: number of dices OR max size of each part of composition
:param sides: sides of dice OR number of parts of composition
:param target: number to get through rolling a single roll OR power in power
series (or integer n for composition)
:return: rounded probability
"""
polynomial = (poly1d([1 for e in range(0, sides + 1)]) - 1)
poly_coeffs = polypow(polynomial.coefficients[::-1], d_num)
if target + 1 > len(poly_coeffs):
return 0
num_of_compositions = poly_coeffs[target]
probability = num_of_compositions / (sides**d_num)
return round(probability, 4)
def probability(dice_number, sides, target):
"""
Using numpy polynomial
The number of ways to obtain x as a sum of n s-sided dice
is given by the coefficients of the polynomial:
f(x) = (x + x^2 + ... + x^s)^n
"""
# power series (note that the power series starts from x^1, therefore
# the first coefficient is zero)
powers = [0] + [1] * sides
# f(x) polynomial, computed used polypow in numpy
poly = polypow(powers, dice_number)
return poly[target] / sides**dice_number if target < len(poly) else 0
def __init__(self, pol1, pol2):
"""
Composes two polynomials (i.e., `pol1'(`pol2')) with distinct covariance
matrices.
Considering we have polynomials
f(x) = \\sum_i a_i x^i,
g(x) = \\sum_j b_j x^j,
with variances \\sigma_f and \\sigma_g when evaluated (see
coll_dyn_activem.maths.Polynomial), we compute
\\sigma_fg(x) = \\sigma_f(g(x))
+ \\sigma_g(x) \\times [ \\sum_i i a_i g(x)^{i-1} ]^2,
as the variance of the composed polynomial f(g) evaluated at x. We
stress that this considers no correlations between the coefficients of
the polynomials.
(see https://en.wikipedia.org/wiki/Propagation_of_uncertainty#Non-linear_combinations)
Parameters
----------
pol1 : coll_dyn_activem.maths.Polynomial
First polynomial.
pol2 : coll_dyn_activem.maths.Polynomial
Second polynomial.
Returns
-------
pol : coll_dyn_activem.maths.Polynomial
Composed polynomial.
"""
self._pol1, self._pol2 = pol1, pol2
self.deg = self._pol1.deg * self._pol2.deg # degree of composed polynomial
# WARNING: numpy.polynomial.polynomial.polyadd and polypow considers
# arrays as polynomials with lowest coefficient first,
# contrarily to polyval and polyfit.
_pol1, _pol2 = self._pol1.pol[::-1], self._pol2.pol[::-1]
self.pol = np.zeros((1, )) # composed polynomial
for i in range(pol1.deg + 1):
self.pol = polyadd(self.pol, _pol1[i] * polypow(_pol2, i))
self.pol = self.pol[::-1]
def starsbars(totalLimit,boxLimit,boxNum):
# Init a list of coeffs
polyCoeff = [0]*(boxLimit+1)
polyCoeff[boxLimit] = -1
polyCoeff[0] = 1
# Poly Coeff should be 1-x^boxLimit
# Expand the polynomial to be (1-x^boxLimit)^boxNum
expandedPolyCoeff = poly.polypow(polyCoeff,boxNum)
# Get terms that are valid
validPolyCoeff = expandedPolyCoeff[:totalLimit]
# Get teh powers of the valid terms
powers = [i for i,v in enumerate(validPolyCoeff) if (v)]
# Get the coeffs of the valid terms
coeffs = [v for i,v in enumerate(validPolyCoeff) if (v)]
# substituting the generative function
finalpowers = [(i * -1)+totalLimit for i in powers]
# print(finalpowers)
# Better explanation: https://math.stackexchange.com/questions/1922819/stars-and-bars-with-bounds
# Here we have limit = 127, cap = 63, entities = 6
# [x^limit](1-x^cap)^entities * sum(k+entities-1/entities-1)x^k
# expressing the above you get the following
# [x^limit]x^{384}-6x^{320}+15x^{256}-20x^{192}+15x^{128}-6x^{64}+1 * sum(k+entities-1/entities-1)x^k
# we can then apply [x^{p-q}]A(x)=[x^p]x^{q}A(x)
# [x^{-257}]-6[x^{-193}]+15[x^{-129}]-20[x^{-65}]+15[x^{-1}]-6x^{63}+[x^127] * sum(k+entities-1/entities-1)x^k
# because negative exponents dont do anything we can simplify to
# -6[x^{63}]+[x^127] * sum(k+entities-1/entities-1)x^k
# which simplifies to -6(68 nCr 5)+(132 nCr 5)
# 241888308
# For this case we need to
# sum all posibilites from 0 to limit
finalAnswer = 0
for idx,value in enumerate(finalpowers):
finalAnswer+= coeffs[idx]*C(finalpowers[idx]+boxNum-1, boxNum-1,False)
if finalAnswer == 0:
# This is just to count the posibility of no EVs
finalAnswer+=1
# print(finalAnswer)
return finalAnswer
def _get_sufficient_sk_power(self, max_power):
"""Generate an list of secret key polynomial raised to 1...max_power.
Args:
max_power: heighest power up to which we want to raise secretkey.
Returns:
A 2-dim list having secretkey powers.
"""
sk_power = [[] for _ in range(max_power)]
sk_power[0] = self._secret_key
for i in range(2, max_power + 1):
for j in range(len(self._coeff_modulus)):
sk_power[i - 1].append(
poly.polypow(self._secret_key[j], i).astype(int).tolist())
return sk_power
def initControl(self):
self.ts = 0.005
# Right motor
# self.k = 45
# self.tau = 0.100
# Left motor
self.k = 48
self.tau = 0.095
self.gd = cnt.tf(self.k, [self.tau, 1, 0]).sample(self.ts)
c = np.exp(-self.ts / self.tau)
b_minus = self.k * np.array(
[self.ts - self.tau * (1 - c), self.tau * (1 - c) - c * self.ts])
b_plus = np.ones(1)
a_minus = zero(1)
a_plus = zero(c)
a_m = P.polypow(zero(0.7), 2)
self.r, self.s, self.t =\
map(poly2tf, calculate_rst(b_minus, b_plus, a_minus, a_plus, a_m,
d=1, p=1))
self.time = np.linspace(0, 0.99, 100)
def dice_polypow_gmpy2(faces, num):
""" Numpy polynomial power function (iterated convolve). With gmpy2. """
return P.polypow(array([mpz(1)]*faces, dtype=object), num)
def polypow(cs, pow, maxpower=None):
from numpy.polynomial.polynomial import polypow
return polypow(cs, pow, maxpower)
]
gr = re.split("\+|-", comps[k])
gr = [
re.search("\d*$", g).group() if "x" in g else "0"
for g in gr
]
gr = [int(g) if g != "" else 1 for g in gr]
fCoef = [
cf.pop() if g in gr else 0 for g in range(max(gr) + 1)
]
c = npp.Polynomial(coef=fCoef)
k += 1
pol = npp.polymul(pol, c).any()
elif op.startswith("^"):
g = int(op[1:])
pol = npp.polypow(pol, g).any()
res = ""
g = pol.degree()
for i in [int(q) for q in pol.coef[::-1]]:
if i != 0:
if g > 0:
a = ""
if g != pol.degree():
a += "+" if i > 0 else ""
a += "" if i == 1 else "-" if i == -1 else str(i)
b = a + "x" + ("" if g == 1 else "^" + str(g))
res += b
else:
res += str(i) if i < 0 else "+" + str(i)
g -= 1
def polypow(cs, pow, maxpower=None) :
from numpy.polynomial.polynomial import polypow
return polypow(cs, pow, maxpower)
def rolldice_sum_prob(sum_, dice_amount):
return (P.polypow([1, 1, 1, 1, 1, 1], dice_amount)
).item(sum_ - dice_amount) / (6**dice_amount) if (
sum_ <= 6 * dice_amount) else 0.0
def probability(dice_number, sides, target):
if target < 1 or target > dice_number * sides:
return 0.0000
prob = polynomial.polypow([0] + [1] * sides, dice_number)
return round(prob[target] / sides ** dice_number, 4)
def get_beta(self, p, p0):
p2 = nppoly.polypow(p, 2)
p02 = nppoly.polypow(p0, 2)
return (self.integrate(p2, self.intlims[0], self.intlims[1]) /
self.integrate(p02, self.intlims[0], self.intlims[1]))
def get_probs(n_dice, n_turns):
p = [1 / n_dice] * n_dice
return polypow(p, n_turns)
from numpy.polynomial.polynomial import polypow from numpy import ones sides = 6 dice = 2 # Create an array of polynomial coefficients for # x + x^2 + ... + x^sides p = ones(sides + 1) p[0] = 0 # Extract the coefficients of p(x)**dice and divide by sides**dice pmf = sides**(-dice) * polypow(p, dice) cdf = pmf.cumsum() print cdf
def get_alpha(self, p):
p2 = nppoly.polypow(p, 2)
xp2 = nppoly.polymulx(p2)
return (self.integrate(xp2, self.intlims[0], self.intlims[1]) /
self.integrate(p2, self.intlims[0], self.intlims[1]))
def eof_p_opt(sch, max_degree=None, debug=False, capacity=None):
"""
:param sch: Input scheme.
:param max_degree: Max degree of result polinomial.
:param debug: If True debug information will be printed due to process.
:return: First max_degree+1 members of polinomial EOF(p) for input scheme.
"""
n = sch.inputs()
m = sch.elements()
if capacity is None:
capacity = min([2 ** sch.inputs(), 32])
nonerror = (0,) * m
if max_degree is None:
max_degree = m
sch_process = d.make_process_func(sch, capacity=capacity)
if debug:
start = time.time()
counter = 0
while time.time() - start < 1:
output_true = sch_process((0,) * n, (0,) * m)
vec2num(output_true)
vec2num(output_true)
counter += 1
process_time = (time.time() - start) / counter
inputs = 2 ** n / capacity
errors = sum(choose(m, degree) for degree in range(max_degree + 1))
estimated_time = process_time * inputs * errors
print("Estimated time eof_p: ", estimated_time)
poly = Polynomial([Fraction(0, 1)])
input_prob = Fraction(1, 2 ** n)
p = Polynomial([Fraction(0, 1), Fraction(1, 1)])
sum_poly = Polynomial([Fraction(0, 1)])
for input_values in inputs_combinations(n, capacity=capacity):
output_true = sch_process(input_values, nonerror)
if debug:
print(input_values)
for degree in range(max_degree + 1):
error_prob = Polynomial(polypow(p.coef, degree, maxpower=100)) * Polynomial(
polypow((1 - p).coef, m - degree, maxpower=100)
)
# print(error_prob.coef)
errors_number = 0
if debug:
print(len(list(combinations(range(m), r=degree))))
for error_comb in combinations(range(m), r=degree):
error_vec = [0] * m
for i in error_comb:
error_vec[i] = 2 ** capacity - 1
output_error = sch_process(input_values, error_vec)
errors = [i ^ j for i, j in zip(output_true, output_error)]
errors_number += ones(reduce(or_, errors))
if debug:
print(errors_number)
if errors_number:
poly += errors_number * input_prob * error_prob
result = list(poly.coef)
# removing trailing zeros
while not result[-1]:
result.pop()
return result
