def align_magnetism(m, vectors):
""" Rotates a matrix, to align its components with the direction
of the magnetism """
if not len(m) == 2 * len(vectors): # stop if they don't have
# compatible dimensions
raise
# pauli matrices
from scipy.sparse import csc_matrix, bmat
sx = csc_matrix([[0.0, 1.0], [1.0, 0.0]])
sy = csc_matrix([[0.0, -1j], [1j, 0.0]])
sz = csc_matrix([[1.0, 0.0], [0.0, -1.0]])
n = len(m) / 2 # number of sites
R = [[None for i in range(n)] for j in range(n)] # rotation matrix
from scipy.linalg import expm # exponenciate matrix
for (i, v) in zip(range(n), vectors): # loop over sites
vv = np.sqrt(v.dot(v)) # norm of v
if vv > 0.000001: # if nonzero scale
u = v / vv
else: # if zero put to zero
u = np.array([0.0, 0.0, 0.0])
# rot = u[0]*sx + u[1]*sy + u[2]*sz
uxy = np.sqrt(u[0] ** 2 + u[1] ** 2) # component in xy plane
phi = np.arctan2(u[1], u[0])
theta = np.arctan2(uxy, u[2])
r1 = phi * sz / 2.0 # rotate along z
r2 = theta * sy / 2.0 # rotate along y
# a factor 2 is taken out due to 1/2 of S
rot = expm(1j * r2) * expm(1j * r1)
R[i][i] = rot # save term
R = bmat(R) # convert to full sparse matrix
mout = R * csc_matrix(m) * R.H # rotate matrix
return mout.todense() # return dense matrix
def correlation_matrix_quadrature(a1, a2, rho=None):
"""
Calculate the quadrature correlation matrix with given field operators
:math:`a_1` and :math:`a_2`. If a density matrix is given the expectation
values are calculated, otherwise a matrix with operators is returned.
Parameters
----------
a1 : :class:`qutip.qobj.Qobj`
Field operator for mode 1.
a2 : :class:`qutip.qobj.Qobj`
Field operator for mode 2.
rho : :class:`qutip.qobj.Qobj`
Density matrix for which to calculate the covariance matrix.
Returns
-------
corr_mat: *array* of complex numbers or :class:`qutip.qobj.Qobj`
A 2-dimensional *array* of covariance values for the field quadratures,
or, if rho=0, a matrix of operators.
"""
x1 = (a1 + a1.dag()) / np.sqrt(2)
p1 = -1j * (a1 - a1.dag()) / np.sqrt(2)
x2 = (a2 + a2.dag()) / np.sqrt(2)
p2 = -1j * (a2 - a2.dag()) / np.sqrt(2)
basis = [x1, p1, x2, p2]
return correlation_matrix(basis, rho)
def affine_grid(self,Hz,rhoz,Lam):
"""
Get data on regular spatial grid
"""
#First find dimensionless density params
Om0 = 8*pi*rhoz[0]/(3*Hz[0]**2)
OL0 = Lam/(3*Hz[0]**2)
Ok0 = 1-Om0-OL0
#Get t0
t0 = self.get_age(Om0,Ok0,OL0,Hz[0])
#Set affine parameter vals
dvo = uvs(self.z,1/(self.uz**2*Hz),k=3,s=0.0)
vzo = dvo.antiderivative()
vz = vzo(self.z)
vz[0] = 0.0
#Compute grid sizes that gives num error od err
NJ = int(ceil(vz[-1]/sqrt(self.err) + 1))
NI = int(ceil(3.0*(NJ - 1)*(t0 - self.tmin)/vz[-1] + 1))
#Get functions on regular grid
v = linspace(0,vz[-1],NJ)
delv = (v[-1] - v[0])/(NJ-1)
if delv > sqrt(self.err):
print 'delv > sqrt(err)'
Ho = uvs(vz,Hz,s=0.0,k=3)
H = Ho(v)
rhoo = uvs(vz,rhoz,s=0.0,k=3)
rho = rhoo(v)
uo = uvs(vz,self.uz,s=0.0,k=3)
u = uo(v)
u[0] = 1.0
return v,vzo,H,rho,u,NJ,NI,delv,Om0,OL0,Ok0,t0
def get_tracedata(self, format = 'AmpPha', single=False):
'''
Get the data of the current trace
Input:
format (string) : 'AmpPha': Amp in dB and Phase, 'RealImag',
Output:
'AmpPha':_ Amplitude and Phase
'''
#data = self._visainstrument.ask_for_values(':FORMAT REAL,32;*CLS;CALC1:DATA:NSW? SDAT,1;*OPC',format=1)
data = self._visainstrument.ask_for_values('FORM:DATA REAL; FORM:BORD SWAPPED; CALC%i:SEL:DATA:SDAT?'%(self._ci), format = visa.double)
data_size = numpy.size(data)
datareal = numpy.array(data[0:data_size:2])
dataimag = numpy.array(data[1:data_size:2])
if format.upper() == 'REALIMAG':
if self._zerospan:
return numpy.mean(datareal), numpy.mean(dataimag)
else:
return datareal, dataimag
elif format.upper() == 'AMPPHA':
if self._zerospan:
datareal = numpy.mean(datareal)
dataimag = numpy.mean(dataimag)
dataamp = numpy.sqrt(datareal*datareal+dataimag*dataimag)
datapha = numpy.arctan(dataimag/datareal)
return dataamp, datapha
else:
dataamp = numpy.sqrt(datareal*datareal+dataimag*dataimag)
datapha = numpy.arctan2(dataimag,datareal)
return dataamp, datapha
else:
raise ValueError('get_tracedata(): Format must be AmpPha or RealImag')
def __init__(self, class_dim, word_dim, hidden_dim, sen_len, batch_size, truncate=-1):
# Assign instance variables
self.class_dim = class_dim
self.word_dim = word_dim
self.hidden_dim = hidden_dim
self.sen_len = sen_len
self.batch_size = batch_size
self.truncate = truncate
params = {}
# Initialize the network parameters
params["E"] = np.random.uniform(-np.sqrt(1./hidden_dim), np.sqrt(1./hidden_dim), (word_dim, hidden_dim)) #Ebdding Matirx
params["W"] = np.random.uniform(-np.sqrt(1./hidden_dim), np.sqrt(1./hidden_dim), (4, hidden_dim, hidden_dim * 4)) #W[0-1].dot(x), W[2-3].(i,f,o,c)
params["B"] = np.random.uniform(-np.sqrt(1./hidden_dim), np.sqrt(1./hidden_dim), (2, hidden_dim * 4)) #B[0-1] for W[0-1]
params["lrW"] = np.random.uniform(-np.sqrt(1./hidden_dim), np.sqrt(1./hidden_dim), (2, hidden_dim, class_dim)) #LR W and b
params["lrb"] = np.random.uniform(-np.sqrt(1./hidden_dim), np.sqrt(1./hidden_dim), (class_dim))
# Assign paramenters' names
self.param_names = {"orign":["E", "W", "B", "lrW", "lrb"],
"cache":["mE", "mW", "mB", "mlrW", "mlrb"]}
# Theano: Created shared variables
self.params = {}
# Model's shared variables
for _n in self.param_names["orign"]:
self.params[_n] = theano.shared(value=params[_n].astype(theano.config.floatX), name=_n)
# Shared variables for RMSProp
for _n in self.param_names["cache"]:
self.params[_n] = theano.shared(value=np.zeros(params[_n[1:]].shape).astype(theano.config.floatX), name=_n)
# Build model graph
self.__theano_build__()
def __init__(self, x, y, in_size, out_size, prefix='lr_'):
self.W = theano.shared(
value=np.random.uniform(
low=-np.sqrt(6. / (in_size + out_size)),
high=np.sqrt(6. / (in_size + out_size)),
size=(in_size, out_size)
).astype(theano.config.floatX),
name='W',
borrow=True
)
self.b = theano.shared(
value=np.random.uniform(
low=-np.sqrt(6. / (in_size + out_size)),
high=np.sqrt(6. / (in_size + out_size)),
size=(out_size,)
).astype(theano.config.floatX),
name='b',
borrow=True
)
self.y_given_x = T.nnet.softmax(T.dot(x, self.W) + self.b)
self.y_d = T.argmax(self.y_given_x, axis=1)
self.loss = -T.mean(T.log(self.y_given_x)[T.arange(y.shape[0]), y])
self.error = T.mean(T.neq(self.y_d, y))
self.params = {prefix+'W': self.W, prefix+'b': self.b}
def __init__(self, n_in, n_out, W_init=None, b_init=None,
activation=T.tanh):
self.activation = activation
if W_init is None:
rng = numpy.random.RandomState(1234)
W_values = numpy.asarray(rng.uniform(
low=-numpy.sqrt(6. / (n_in + n_out)),
high=numpy.sqrt(6. / (n_in + n_out)),
size=(n_in, n_out)
),
dtype=theano.config.floatX
)
if activation == theano.tensor.nnet.sigmoid:
W_values *= 4
W_init = theano.shared(value=W_values, name='W', borrow=True)
if b_init is None:
b_values = numpy.zeros((n_out,), dtype=theano.config.floatX)
b_init = theano.shared(value=b_values, name='b', borrow=True)
self.W = W_init
self.b = b_init
# parameters of the model
self.params = [self.W, self.b]
def __init__(self, sess, ob_space, ac_space, nbatch, nsteps, reuse=False): #pylint: disable=W0613
self.pdtype = make_pdtype(ac_space)
with tf.variable_scope("model", reuse=reuse):
X, processed_x = observation_input(ob_space, nbatch)
activ = tf.tanh
processed_x = tf.layers.flatten(processed_x)
pi_h1 = activ(fc(processed_x, 'pi_fc1', nh=64, init_scale=np.sqrt(2)))
pi_h2 = activ(fc(pi_h1, 'pi_fc2', nh=64, init_scale=np.sqrt(2)))
vf_h1 = activ(fc(processed_x, 'vf_fc1', nh=64, init_scale=np.sqrt(2)))
vf_h2 = activ(fc(vf_h1, 'vf_fc2', nh=64, init_scale=np.sqrt(2)))
vf = fc(vf_h2, 'vf', 1)[:,0]
self.pd, self.pi = self.pdtype.pdfromlatent(pi_h2, init_scale=0.01)
a0 = self.pd.sample()
neglogp0 = self.pd.neglogp(a0)
self.initial_state = None
def step(ob, *_args, **_kwargs):
a, v, neglogp = sess.run([a0, vf, neglogp0], {X:ob})
return a, v, self.initial_state, neglogp
def value(ob, *_args, **_kwargs):
return sess.run(vf, {X:ob})
self.X = X
self.vf = vf
self.step = step
self.value = value
def fix_poly(polygon):
ret = np.array([ [0,0],[0,0],[0,0],[0,0] ],np.float32)
min_ = np.sqrt(polygon[0][0][0]**2 + polygon[0][0][1]**2)
minc = 0
for i in range(1,4):
if np.sqrt(polygon[i][0][0]**2 + polygon[i][0][1]**2) < min_:
min_ = np.sqrt(polygon[i][0][0]**2 + polygon[i][0][1]**2)
minc = i
#found top left vertex, rotate until it's on the top left
for i in range(minc):
polygon = np.roll(polygon,-1,axis=0)
#if needed, "invert" the order.
dist1 = dist_line(polygon[0],polygon[2],polygon[1])
dist3 = dist_line(polygon[0],polygon[2],polygon[3])
if dist3 > dist1:
x = polygon[3][0][0]
y = polygon[3][0][1]
polygon[3][0][0] = polygon[1][0][0]
polygon[3][0][1] = polygon[1][0][1]
polygon[1][0][0] = x
polygon[1][0][1] = y
ret[0] = polygon[0][0]
ret[1] = polygon[1][0]
ret[2] = polygon[2][0]
ret[3] = polygon[3][0]
return ret
def discrepancy(observed, simulated, expected):
"""Calculates Freeman-Tukey statistics (Freeman and Tukey 1950) as
a measure of discrepancy between observed and r replicates of simulated data. This
is a convenient method for assessing goodness-of-fit (see Brooks et al. 2000).
D(x|\theta) = \sum_j (\sqrt{x_j} - \sqrt{e_j})^2
:Parameters:
observed : Iterable of observed values (length n)
simulated : Iterable of simulated values (length rxn)
expected : Iterable of expected values (length rxn)
:Returns:
D_obs : Discrepancy of observed values
D_sim : Discrepancy of simulated values
"""
try:
simulated = simulated.astype(float)
except AttributeError:
simulated = simulated.trace().astype(float)
try:
expected = expected.astype(float)
except AttributeError:
expected = expected.trace().astype(float)
D_obs = np.sum([(np.sqrt(observed)-np.sqrt(e))**2 for e in expected], 1)
D_sim = np.sum([(np.sqrt(s)-np.sqrt(e))**2 for s,e in zip(simulated, expected)], 1)
# Print p-value
count = sum(s>o for o,s in zip(D_obs,D_sim))
print_('Bayesian p-value: p=%.3f' % (1.*count/len(D_obs)))
return D_obs, D_sim
def GetCorV(self, inAmpField):
lplcCo=inAmpField.lplcCo
try:
self.CorV=1./ np.sqrt(1./(self.AppV**2) - lplcCo)
except:
self.CorV=1./ np.sqrt(1./(self.AppV[1:-1, 1:-1]**2) - lplcCo)
return
def get_nxnyr_cd():
box = cfg.pms['shape']
boxMpc = np.array([cfg.pms['xyMpc'],cfg.pms['xyMpc'],cfg.pms['zMpc']])
lx = boxMpc[0]/2.; ly = boxMpc[1]/2.; lz = boxMpc[2]
z,d = get_z_d(cfg.pms['zi'],cfg.pms['zf'])
# front of box -- don't use bc grid will extend
# outside the standard box
# nx_max = lx / np.sqrt(lx*lx+d[0]*d[0]) # nx_min = - nx_max
# ny_max = ly / np.sqrt(ly*ly+d[0]*d[0]) # ny_min = - ny_max
# r_max = np.sqrt(lx*lx+ly*ly+(d[0]+lz)*(d[0]+lz)) # r_min = d[0]
# back of box -- throws away half the box but whatever
df = d[0]+lz
nx_max = lx / np.sqrt(lx*lx+df*df) # nx_min = - nx_max
ny_max = ly / np.sqrt(ly*ly+df*df) # ny_min = - ny_max
r_max = np.sqrt(lx*lx+ly*ly+df*df) # r_min = d[0]
print nx_max, ny_max
nxcd = np.linspace(-nx_max,nx_max,box[0])
nycd = np.linspace(-ny_max,ny_max,box[1])
print 2*nx_max/box[0], 2*ny_max/box[1]
rcd = np.linspace(d[0],r_max,box[2])
return nxcd,nycd,rcd
def logarithmic_negativity(V):
"""
Calculate the logarithmic negativity given the symmetrized covariance
matrix, see :func:`qutip.continous_variables.covariance_matrix`. Note that
the two-mode field state that is described by `V` must be Gaussian for this
function to applicable.
Parameters
----------
V : *2d array*
The covariance matrix.
Returns
-------
N: *float*, the logarithmic negativity for the two-mode Gaussian state
that is described by the the Wigner covariance matrix V.
"""
A = V[0:2, 0:2]
B = V[2:4, 2:4]
C = V[0:2, 2:4]
sigma = np.linalg.det(A) + np.linalg.det(B) - 2 * np.linalg.det(C)
nu_ = sigma / 2 - np.sqrt(sigma ** 2 - 4 * np.linalg.det(V)) / 2
if nu_ < 0.0:
return 0.0
nu = np.sqrt(nu_)
lognu = -np.log(2 * nu)
logneg = max(0, lognu)
return logneg
def screen_potential(r, v, charge):
"""Split long-range potential into short-ranged contributions.
The potential v is a long-ranted potential with the asymptotic form Z/r
corresponding to the given charge.
Return a potential vscreened and charge distribution rhocomp such that
v(r) = vscreened(r) + vHartree[rhocomp](r).
The returned quantities are truncated to a reasonable cutoff radius.
"""
vr = v * r + charge
err = 0.0
i = len(vr)
while err < 1e-5:
# Things can be a bit sensitive to the threshold. The O.pz-mt setup
# gets 20-30 Bohr long compensation charges if it's 1e-6.
i -= 1
err = abs(vr[i])
i += 1
icut = np.searchsorted(r, r[i] * 1.1)
rcut = r[icut]
rshort = r[:icut]
a = rcut / 5.0 # XXX why is this so important?
vcomp = charge * erf(rshort / (np.sqrt(2.0) * a)) / rshort
# XXX divide by r
rhocomp = charge * (np.sqrt(2.0 * np.pi) * a)**(-3) * \
np.exp(-0.5 * (rshort / a)**2)
vscreened = v[:icut] + vcomp
return vscreened, rhocomp
def test_ogamma():
"""Tests the effects of changing the temperature of the CMB"""
# Tested against Ned Wright's advanced cosmology calculator,
# Sep 7 2012. The accuracy of our comparision is limited by
# how many digits it outputs, which limits our test to about
# 0.2% accuracy. The NWACC does not allow one
# to change the number of nuetrino species, fixing that at 3.
# Also, inspection of the NWACC code shows it uses inaccurate
# constants at the 0.2% level (specifically, a_B),
# so we shouldn't expect to match it that well. The integral is
# also done rather crudely. Therefore, we should not expect
# the NWACC to be accurate to better than about 0.5%, which is
# unfortunate, but reflects a problem with it rather than this code.
# More accurate tests below using Mathematica
z = np.array([1.0, 10.0, 500.0, 1000.0])
cosmo = core.FlatLambdaCDM(H0=70, Om0=0.3, Tcmb0=0, Neff=3)
assert np.allclose(cosmo.angular_diameter_distance(z).value,
[1651.9, 858.2, 26.855, 13.642], rtol=5e-4)
cosmo = core.FlatLambdaCDM(H0=70, Om0=0.3, Tcmb0=2.725, Neff=3)
assert np.allclose(cosmo.angular_diameter_distance(z).value,
[1651.8, 857.9, 26.767, 13.582], rtol=5e-4)
cosmo = core.FlatLambdaCDM(H0=70, Om0=0.3, Tcmb0=4.0, Neff=3)
assert np.allclose(cosmo.angular_diameter_distance(z).value,
[1651.4, 856.6, 26.489, 13.405], rtol=5e-4)
# Next compare with doing the integral numerically in Mathematica,
# which allows more precision in the test. It is at least as
# good as 0.01%, possibly better
cosmo = core.FlatLambdaCDM(H0=70, Om0=0.3, Tcmb0=0, Neff=3.04)
assert np.allclose(cosmo.angular_diameter_distance(z).value,
[1651.91, 858.205, 26.8586, 13.6469], rtol=1e-5)
cosmo = core.FlatLambdaCDM(H0=70, Om0=0.3, Tcmb0=2.725, Neff=3.04)
assert np.allclose(cosmo.angular_diameter_distance(z).value,
[1651.76, 857.817, 26.7688, 13.5841], rtol=1e-5)
cosmo = core.FlatLambdaCDM(H0=70, Om0=0.3, Tcmb0=4.0, Neff=3.04)
assert np.allclose(cosmo.angular_diameter_distance(z).value,
[1651.21, 856.411, 26.4845, 13.4028], rtol=1e-5)
# Just to be really sure, we also do a version where the integral
# is analytic, which is a Ode = 0 flat universe. In this case
# Integrate(1/E(x),{x,0,z}) = 2 ( sqrt((1+Or z)/(1+z)) - 1 )/(Or - 1)
# Recall that c/H0 * Integrate(1/E) is FLRW.comoving_distance.
Ogamma0h2 = 4 * 5.670373e-8 / 299792458.0 ** 3 * 2.725 ** 4 / 1.87837e-26
Onu0h2 = Ogamma0h2 * 7.0 / 8.0 * (4.0 / 11.0) ** (4.0 / 3.0) * 3.04
Or0 = (Ogamma0h2 + Onu0h2) / 0.7 ** 2
Om0 = 1.0 - Or0
hubdis = 299792.458 / 70.0
cosmo = core.FlatLambdaCDM(H0=70, Om0=Om0, Tcmb0=2.725, Neff=3.04)
targvals = 2.0 * hubdis * \
(np.sqrt((1.0 + Or0 * z) / (1.0 + z)) - 1.0) / (Or0 - 1.0)
assert np.allclose(cosmo.comoving_distance(z).value, targvals, rtol=1e-5)
# Try Tcmb0 = 4
Or0 *= (4.0 / 2.725) ** 4
Om0 = 1.0 - Or0
cosmo = core.FlatLambdaCDM(H0=70, Om0=Om0, Tcmb0=4.0, Neff=3.04)
targvals = 2.0 * hubdis * \
(np.sqrt((1.0 + Or0 * z) / (1.0 + z)) - 1.0) / (Or0 - 1.0)
assert np.allclose(cosmo.comoving_distance(z).value, targvals, rtol=1e-5)
def reg_score_function(X, y, mean, scale, shape, skewness):
""" GAS Skew t Regression Update term using gradient only - native Python function
Parameters
----------
X : float
datapoint for the right hand side variable
y : float
datapoint for the time series
mean : float
location parameter for the Skew t distribution
scale : float
scale parameter for the Skew t distribution
shape : float
tail thickness parameter for the Skew t distribution
skewness : float
skewness parameter for the Skew t distribution
Returns
----------
- Score of the Skew t family
"""
m1 = (np.sqrt(shape)*sp.gamma((shape-1.0)/2.0))/(np.sqrt(np.pi)*sp.gamma(shape/2.0))
mean = mean + (skewness - (1.0/skewness))*scale*m1
if (y-mean)>=0:
return ((shape+1)/shape)*((y-mean)*X)/(np.power(skewness*scale,2) + (np.power(y-mean,2)/shape))
else:
return ((shape+1)/shape)*((y-mean)*X)/(np.power(scale,2) + (np.power(skewness*(y-mean),2)/shape))
def second_order_score(y, mean, scale, shape, skewness):
""" GAS Skew t Update term potentially using second-order information - native Python function
Parameters
----------
y : float
datapoint for the time series
mean : float
location parameter for the Skew t distribution
scale : float
scale parameter for the Skew t distribution
shape : float
tail thickness parameter for the Skew t distribution
skewness : float
skewness parameter for the Skew t distribution
Returns
----------
- Adjusted score of the Skew t family
"""
m1 = (np.sqrt(shape)*sp.gamma((shape-1.0)/2.0))/(np.sqrt(np.pi)*sp.gamma(shape/2.0))
mean = mean + (skewness - (1.0/skewness))*scale*m1
if (y-mean)>=0:
return ((shape+1)/shape)*(y-mean)/(np.power(skewness*scale,2) + (np.power(y-mean,2)/shape))
else:
return ((shape+1)/shape)*(y-mean)/(np.power(scale,2) + (np.power(skewness*(y-mean),2)/shape))
def markov_blanket(y, mean, scale, shape, skewness):
""" Markov blanket for each likelihood term
Parameters
----------
y : np.ndarray
univariate time series
mean : np.ndarray
array of location parameters for the Skew t distribution
scale : float
scale parameter for the Skew t distribution
shape : float
tail thickness parameter for the Skew t distribution
skewness : float
skewness parameter for the Skew t distribution
Returns
----------
- Markov blanket of the Skew t family
"""
m1 = (np.sqrt(shape)*sp.gamma((shape-1.0)/2.0))/(np.sqrt(np.pi)*sp.gamma(shape/2.0))
mean = mean + (skewness - (1.0/skewness))*scale*m1
return Skewt.logpdf_internal(x=y, df=shape, loc=mean, gamma=skewness, scale=scale)
def svdUpdate(U, S, V, a, b):
"""
Update SVD of an (m x n) matrix `X = U * S * V^T` so that
`[X + a * b^T] = U' * S' * V'^T`
and return `U'`, `S'`, `V'`.
`a` and `b` are (m, 1) and (n, 1) rank-1 matrices, so that svdUpdate can simulate
incremental addition of one new document and/or term to an already existing
decomposition.
"""
rank = U.shape[1]
m = U.T * a
p = a - U * m
Ra = numpy.sqrt(p.T * p)
assert float(Ra) > 1e-10
P = (1.0 / float(Ra)) * p
n = V.T * b
q = b - V * n
Rb = numpy.sqrt(q.T * q)
assert float(Rb) > 1e-10
Q = (1.0 / float(Rb)) * q
K = numpy.matrix(numpy.diag(list(numpy.diag(S)) + [0.0])) + numpy.bmat("m ; Ra") * numpy.bmat(" n; Rb").T
u, s, vt = numpy.linalg.svd(K, full_matrices=False)
tUp = numpy.matrix(u[:, :rank])
tVp = numpy.matrix(vt.T[:, :rank])
tSp = numpy.matrix(numpy.diag(s[:rank]))
Up = numpy.bmat("U P") * tUp
Vp = numpy.bmat("V Q") * tVp
Sp = tSp
return Up, Sp, Vp
def neg_loglikelihood(y, mean, scale, shape, skewness):
""" Negative loglikelihood function
Parameters
----------
y : np.ndarray
univariate time series
mean : np.ndarray
array of location parameters for the Skew t distribution
scale : float
scale parameter for the Skew t distribution
shape : float
tail thickness parameter for the Skew t distribution
skewness : float
skewness parameter for the Skew t distribution
Returns
----------
- Negative loglikelihood of the Skew t family
"""
m1 = (np.sqrt(shape)*sp.gamma((shape-1.0)/2.0))/(np.sqrt(np.pi)*sp.gamma(shape/2.0))
mean = mean + (skewness - (1.0/skewness))*scale*m1
return -np.sum(Skewt.logpdf_internal(x=y, df=shape, loc=mean, gamma=skewness, scale=scale))
def test_ParameterizedAberration():
# verify that we can reproduce the same behavior as ZernikeAberration
# using ParameterizedAberration
NWAVES = 0.5
WAVELENGTH = 1e-6
RADIUS = 1.0
pupil = optics.CircularAperture(radius=RADIUS)
zern_wave = poppy_core.Wavefront(npix=NPIX, diam=DIAM, wavelength=1e-6)
zernike_wfe = wfe.ZernikeWFE(
coefficients=[0, 0, 2e-7, NWAVES * WAVELENGTH / (2 * np.sqrt(3)), 0, 3e-8],
radius=RADIUS
)
zern_wave *= pupil
zern_wave *= zernike_wfe
parameterized_distortion = wfe.ParameterizedWFE(
coefficients=[0, 0, 2e-7, NWAVES * WAVELENGTH / (2 * np.sqrt(3)), 0, 3e-8],
basis_factory=zernike.zernike_basis,
radius=RADIUS
)
pd_wave = poppy_core.Wavefront(npix=NPIX, diam=3.0, wavelength=1e-6)
pd_wave *= pupil
pd_wave *= parameterized_distortion
np.testing.assert_allclose(pd_wave.phase, zern_wave.phase,
err_msg="ParameterizedAberration disagrees with ZernikeAberration")
def CA(self):
# return NPortZ(self).CA
z0 = self.z0
A = np.mat(self.A)
T = np.matrix([[np.sqrt(z0), -(A[0,1]+A[0,0]*z0)/np.sqrt(z0)],
[-1/np.sqrt(z0), -(A[1,1]+A[1,0]*z0)/np.sqrt(z0)]])
return np.array(T * np.mat(self.CS) * T.H)
def __init__(self, rng, input, n_in, n_out, W=None, b=None,
activation=T.tanh):
self.input = input[0]
# initialize weights into this layer
if W is None:
W_values = np.asarray(
rng.uniform(
size=(n_in, n_out),
low=-np.sqrt(6. / (n_in + n_out)),
high=np.sqrt(6. / (n_in + n_out)),
),
dtype=theano.config.floatX
)
if activation == theano.tensor.nnet.sigmoid:
W_values *= 4
W = theano.shared(value=W_values, name='W', borrow=True)
# initialize bias term weights into this layer
if b is None:
b_values = np.zeros((n_out,), dtype=theano.config.floatX)
b = theano.shared(value=b_values, name='b', borrow=True)
self.W = W
self.b = b
lin_output = T.dot(self.input, self.W) + self.b
self.output = (
lin_output if activation is None
else activation(lin_output)
)
self.params = [self.W, self.b]
def testStudentLogPDFMultidimensional(self):
with self.test_session():
batch_size = 6
df = constant_op.constant([[1.5, 7.2]] * batch_size)
mu = constant_op.constant([[3., -3.]] * batch_size)
sigma = constant_op.constant([[-math.sqrt(10.), math.sqrt(15.)]] *
batch_size)
df_v = np.array([1.5, 7.2])
mu_v = np.array([3., -3.])
sigma_v = np.array([np.sqrt(10.), np.sqrt(15.)])
t = np.array([[-2.5, 2.5, 4., 0., -1., 2.]], dtype=np.float32).T
student = student_t.StudentT(df, loc=mu, scale=sigma)
log_pdf = student.log_prob(t)
log_pdf_values = self.evaluate(log_pdf)
self.assertEqual(log_pdf.get_shape(), (6, 2))
pdf = student.prob(t)
pdf_values = self.evaluate(pdf)
self.assertEqual(pdf.get_shape(), (6, 2))
if not stats:
return
expected_log_pdf = stats.t.logpdf(t, df_v, loc=mu_v, scale=sigma_v)
expected_pdf = stats.t.pdf(t, df_v, loc=mu_v, scale=sigma_v)
self.assertAllClose(expected_log_pdf, log_pdf_values)
self.assertAllClose(np.log(expected_pdf), log_pdf_values)
self.assertAllClose(expected_pdf, pdf_values)
self.assertAllClose(np.exp(expected_log_pdf), pdf_values)
def getEff(s, cut, comp='joint', reco=True):
eff, sig, relerr = {},{},{}
a = np.log10(s['MC_energy'])
Ebins = getEbins()
Emids = getMids(Ebins)
erangeDict = getErange()
c0 = cut
if comp != 'joint':
compcut = s['comp'] == comp
c0 = cut * compcut
# Set radii for finding effective area
rDict = {}
keys = ['low', 'mid', 'high']
for key in keys:
rDict[key] = np.array([600, 800, 1100, 1700, 2600, 2900])
rDict['low'][1] = 600
Ebreaks = np.array([4, 5, 6, 7, 8, 9])
rgrp = np.digitize(Emids, Ebreaks) - 1
for key in keys:
# Get efficiency and sigma
simcut = np.array([sim in erangeDict[key] for sim in s['sim']])
k = np.histogram(a[c0*simcut], bins=Ebins)[0]
#k = Nfinder(a, c0*simcut)
n = s['MC'][comp][key].astype('float')
eff[key], sig[key], relerr[key] = np.zeros((3, len(k)))
with np.errstate(divide='ignore', invalid='ignore'):
eff[key] = k / n
var = (k+1)*(k+2)/((n+2)*(n+3)) - (k+1)**2/((n+2)**2)
sig[key] = np.sqrt(var)
# Multiply by throw area
r = np.array([rDict[key][i] for i in rgrp])
eff[key] *= np.pi*(r**2)
sig[key] *= np.pi*(r**2)
# Deal with parts of the arrays with no information
for i in range(len(eff[key])):
if n[i] == 0:
eff[key][i] = 0
sig[key][i] = np.inf
# Combine low, mid, and high energy datasets
eff_tot = (np.sum([eff[key]/sig[key] for key in keys], axis=0) /
np.sum([1/sig[key] for key in keys], axis=0))
sig_tot = np.sqrt(1 / np.sum([1/sig[key]**2 for key in keys], axis=0))
with np.errstate(divide='ignore'):
relerr = sig_tot / eff_tot
# UGH : find better way to do this
if reco:
eff_tot = eff_tot[20:]
sig_tot = sig_tot[20:]
relerr = relerr[20:]
return eff_tot, sig_tot, relerr
def EN_CID(y):
"""
CID measure from Batista, G. E. A. P. A., Keogh, E. J., Tataw, O. M. & de
Souza, V. M. A. CID: an efficient complexity-invariant distance for time
series. Data Min Knowl. Disc. 28, 634-669 (2014).
Arguments
---------
y: a nitime time-series object, or numpy vector
"""
# Make the input a row vector of numbers:
y = makeRowVector(vectorize(y))
# Prepare the output dictionary
out = {}
# Original definition (in Table 2 of paper cited above)
out['CE1'] = np.sqrt(np.mean(np.power(np.diff(y),2))); # sum -> mean to deal with non-equal time-series lengths
# Definition corresponding to the line segment example in Fig. 9 of the paper
# cited above (using Pythagoras's theorum):
out['CE2'] = np.mean(np.sqrt(1 + np.power(np.diff(y),2)));
return out
def testStudentSampleMultiDimensional(self):
with self.test_session():
batch_size = 7
df = constant_op.constant([[3., 7.]] * batch_size)
mu = constant_op.constant([[3., -3.]] * batch_size)
sigma = constant_op.constant([[math.sqrt(10.), math.sqrt(15.)]] *
batch_size)
df_v = [3., 7.]
mu_v = [3., -3.]
sigma_v = [np.sqrt(10.), np.sqrt(15.)]
n = constant_op.constant(200000)
student = student_t.StudentT(df=df, loc=mu, scale=sigma)
samples = student.sample(n, seed=123456)
sample_values = self.evaluate(samples)
self.assertEqual(samples.get_shape(), (200000, batch_size, 2))
self.assertAllClose(
sample_values[:, 0, 0].mean(), mu_v[0], rtol=1e-2, atol=0)
self.assertAllClose(
sample_values[:, 0, 0].var(),
sigma_v[0]**2 * df_v[0] / (df_v[0] - 2),
rtol=1e-1,
atol=0)
self._checkKLApprox(df_v[0], mu_v[0], sigma_v[0], sample_values[:, 0, 0])
self.assertAllClose(
sample_values[:, 0, 1].mean(), mu_v[1], rtol=1e-2, atol=0)
self.assertAllClose(
sample_values[:, 0, 1].var(),
sigma_v[1]**2 * df_v[1] / (df_v[1] - 2),
rtol=1e-1,
atol=0)
self._checkKLApprox(df_v[0], mu_v[0], sigma_v[0], sample_values[:, 0, 1])
def test_decimate():
"""Test decimation of digitizer headshapes with too many points."""
# load headshape and convert to meters
hsp_mm = _get_ico_surface(5)['rr'] * 100
hsp_m = hsp_mm / 1000.
# save headshape to a file in mm in temporary directory
tempdir = _TempDir()
sphere_hsp_path = op.join(tempdir, 'test_sphere.txt')
np.savetxt(sphere_hsp_path, hsp_mm)
# read in raw data using spherical hsp, and extract new hsp
with warnings.catch_warnings(record=True) as w:
raw = read_raw_kit(sqd_path, mrk_path, elp_txt_path, sphere_hsp_path)
assert_true(any('more than' in str(ww.message) for ww in w))
# collect headshape from raw (should now be in m)
hsp_dec = np.array([dig['r'] for dig in raw.info['dig']])[8:]
# with 10242 points and _decimate_points set to resolution of 5 mm, hsp_dec
# should be a bit over 5000 points. If not, something is wrong or
# decimation resolution has been purposefully changed
assert_true(len(hsp_dec) > 5000)
# should have similar size, distance from center
dist = np.sqrt(np.sum((hsp_m - np.mean(hsp_m, axis=0))**2, axis=1))
dist_dec = np.sqrt(np.sum((hsp_dec - np.mean(hsp_dec, axis=0))**2, axis=1))
hsp_rad = np.mean(dist)
hsp_dec_rad = np.mean(dist_dec)
assert_almost_equal(hsp_rad, hsp_dec_rad, places=3)
def DM(self, z):
"""Transverse Comoving Distance (Mpc)
Parameters
----------
z : float
redshift
Returns
-------
y : float
The transverse comoving distance in Mpc, given by Hogg eqn 16
Examples
--------
>>> cosmo = Cosmology()
>>> cosmo.DM(1.0)
3303.8288058874678
"""
# Compute the transverse comoving distance in Mpc (Eqn 16)
if self.OmegaK > 0.0:
return self.DH / np.sqrt(self.OmegaK) * \
np.sinh(np.sqrt(self.OmegaK)*self.DC(z)/self.DH)
elif self.OmegaK == 0.0:
return self.DC(z)
elif self.OmegaK < 0.0:
return self.DH / np.sqrt(np.abs(self.OmegaK)) * \
np.sin(np.sqrt(np.abs(self.OmegaK))*self.DC(z)/self.DH)
def delta(phase,inc, ecc = 0, omega=0):
"""
Compute the distance center-to-center between planet and host star.
___
INPUT:
phase: orbital phase in radian
inc: inclination of the system in radian
OPTIONAL INPUT:
ecc:
omega:
//
OUTPUT:
distance center-to-center, double-float number.
___
"""
phase = 2*np.pi*phase
if ecc == 0 and omega == 0:
delta = np.sqrt(1-(np.cos(phase)**2)*(np.sin(inc)**2))
else:
delta = (1.-ecc**2.)/(1.-ecc*np.sin(phase-omega))* np.sqrt((1.-(np.cos(phase))**2.*(np.sin(inc))**2))
return delta
# test features
feats = ad.csv_feats_dict
ft = feats['colors'] + feats['other'][0:2] + feats['mags'] # features to use
# filter for large,small classes, high class_probability
nLarge, nSmall, cprob = 10000, 50, 0.99
d = ad.filter_dfcsv(dfcsv, feats=ft, nLarge=nLarge, nSmall=nSmall,
cprob=cprob) # filter dfcsv
lclasses = d.loc[d.numinType > nLarge, 'newType'].unique()
sclasses = d.loc[d.numinType < nSmall, 'newType'].unique()
# dataframe for results
idxs = pd.MultiIndex.from_product(
[list(sclasses), list(lclasses)], names=['small_class', 'large_class'])
simdf = pd.DataFrame(data=None, index=idxs, columns=['dist', 'dist_std'])
# calc distance between classes
# using Euclidean distance between feature means
davg = d[ft + ['newType']].groupby('newType').mean()
dstd = d[ft + ['newType']].groupby('newType').std()
for sc in sclasses:
for lc in lclasses:
dist, dist_std = 0, 0
for f in ft:
dist = dist + (davg.loc[sc, f] - davg.loc[lc, f])**2
dist_std = dist_std + (davg.loc[sc, f] / dstd.loc[sc, f] -
davg.loc[lc, f] / dstd.loc[lc, f])**2
simdf.loc[(sc, lc), 'dist'] = np.sqrt(dist)
simdf.loc[(sc, lc), 'dist_std'] = np.sqrt(dist_std)
def plotResults(data):
means = np.mean(data, axis=0)
std_errors = np.std(data, axis=0) / np.sqrt(len(data))
plt.errorbar(range(len(means)), means, yerr=std_errors)
plt.ylim(0, 100)
def constrained_oasisAR2(y,
g,
sn,
optimize_b=True,
b_nonneg=True,
optimize_g=0,
decimate=5,
shift=100,
window=None,
tol=1e-9,
max_iter=1,
penalty=1):
""" Infer the most likely discretized spike train underlying an AR(2)
fluorescence trace. Solves the noise constrained sparse non-negative
deconvolution problem min (s)_1 subject to (c-y)^2 = sn^2 T and
s_t = c_t-g1 c_{t-1}-g2 c_{t-2} >= 0
Args:
y : array of float
One dimensional array containing the fluorescence intensities
(with baseline already subtracted) with one entry per time-bin.
g : (float, float)
Parameters of the AR(2) process that models the fluorescence
impulse response.
sn : float
Standard deviation of the noise distribution.
optimize_b : bool, optional, default True
Optimize baseline if True else it is set to 0, see y.
b_nonneg: bool, optional, default True
Enforce strictly non-negative baseline if True.
optimize_g : int, optional, default 0
Number of large, isolated events to consider for optimizing g.
No optimization if optimize_g=0.
decimate : int, optional, default 5
Decimation factor for estimating hyper-parameters faster on
decimated data.
shift : int, optional, default 100
Number of frames by which to shift window from on run of NNLS to
the next.
window : int, optional, default None (200 or larger dependend on g)
Window size.
tol : float, optional, default 1e-9
Tolerance parameter.
max_iter : int, optional, default 1
Maximal number of iterations.
penalty : int, optional, default 1
Sparsity penalty. 1: min (s)_1 0: min (s)_0
Returns:
c : array of float
The inferred denoised fluorescence signal at each time-bin.
s : array of float
Discretized deconvolved neural activity (spikes).
b : float
Fluorescence baseline value.
(g1, g2) : tuple of float
Parameters of the AR(2) process that models the fluorescence
impulse response.
lam : float
Sparsity penalty parameter lambda of dual problem.
References:
Friedrich J and Paninski L, NIPS 2016
Friedrich J, Zhou P, and Paninski L, arXiv 2016
"""
T = len(y)
d = (g[0] + np.sqrt(g[0] * g[0] + 4 * g[1])) / 2
r = (g[0] - np.sqrt(g[0] * g[0] + 4 * g[1])) / 2
if window is None:
window = int(min(T, max(200, -5 / np.log(d))))
if not optimize_g:
g11 = (np.exp(np.log(d) * np.arange(1, T + 1)) * np.arange(1, T + 1)) if d == r else \
(np.exp(np.log(d) * np.arange(1, T + 1)) -
np.exp(np.log(r) * np.arange(1, T + 1))) / (d - r)
g12 = np.append(0, g[1] * g11[:-1])
g11g11 = np.cumsum(g11 * g11)
g11g12 = np.cumsum(g11 * g12)
Sg11 = np.cumsum(g11)
f_lam = 1 - g[0] - g[1]
elif decimate == 0: # need to run AR1 anyways for estimating AR coeffs
decimate = 1
thresh = sn * sn * T
# get initial estimate of b and lam on downsampled data using AR1 model
if decimate > 0:
_, s, b, aa, lam = constrained_oasisAR1(
y[:len(y) // decimate * decimate].reshape(-1, decimate).mean(1),
d**decimate,
sn / np.sqrt(decimate),
optimize_b=optimize_b,
b_nonneg=b_nonneg,
optimize_g=optimize_g)
if optimize_g:
from scipy.optimize import minimize
d = aa**(1. / decimate)
if decimate > 1:
s = oasisAR1(y - b, d, lam=lam * (1 - aa) / (1 - d))[1]
r = estimate_time_constant(s, 1, fudge_factor=.98)[0]
g[0] = d + r
g[1] = -d * r
g11 = (np.exp(np.log(d) * np.arange(1, T + 1)) -
np.exp(np.log(r) * np.arange(1, T + 1))) / (d - r)
g12 = np.append(0, g[1] * g11[:-1])
g11g11 = np.cumsum(g11 * g11)
g11g12 = np.cumsum(g11 * g12)
Sg11 = np.cumsum(g11)
f_lam = 1 - g[0] - g[1]
elif decimate > 1:
s = oasisAR1(y - b, d, lam=lam * (1 - aa) / (1 - d))[1]
lam *= (1 - d**decimate) / f_lam
# this window size seems necessary and sufficient
possible_spikes = [
x + np.arange(-2, 3) for x in np.where(s > s.max() / 10.)[0]
]
ff = np.array(possible_spikes, dtype=np.int).ravel()
ff = np.unique(ff[(ff >= 0) * (ff < T)])
mask = np.zeros(T, dtype=bool)
mask[ff] = True
else:
b = np.percentile(y, 15) if optimize_b else 0
lam = 2 * sn * np.linalg.norm(g11)
mask = None
if b_nonneg:
b = max(b, 0)
# run ONNLS
c, s = onnls(y - b,
g,
lam=lam,
mask=mask,
shift=shift,
window=window,
tol=tol)
if not optimize_b: # don't optimize b, just the dual variable lambda
for _ in range(max_iter - 1):
res = y - c
RSS = res.dot(res)
if np.abs(RSS - thresh) < 1e-4 * thresh:
break
# calc shift dlam, here attributed to sparsity penalty
tmp = np.empty(T)
ls = np.append(np.where(s > 1e-6)[0], T)
l = ls[0]
tmp[:l] = (1 + d) / (1 + d**l) * \
np.exp(np.log(d) * np.arange(l)) # first pool
for i, f in enumerate(ls[:-1]): # all other pools
l = ls[i + 1] - f - 1
# if and elif correct last 2 time points for |s|_1 instead |c|_1
if i == len(ls) - 2: # last pool
tmp[f] = (1. / f_lam if l == 0 else
(Sg11[l] + g[1] / f_lam * g11[l - 1] +
(g[0] + g[1]) / f_lam * g11[l] -
g11g12[l] * tmp[f - 1]) / g11g11[l])
# secondlast pool if last one has length 1
elif i == len(ls) - 3 and ls[-2] == T - 1:
tmp[f] = (Sg11[l] + g[1] / f_lam * g11[l] -
g11g12[l] * tmp[f - 1]) / g11g11[l]
else: # all other pools
tmp[f] = (Sg11[l] - g11g12[l] * tmp[f - 1]) / g11g11[l]
l += 1
tmp[f + 1:f + l] = g11[1:l] * tmp[f] + g12[1:l] * tmp[f - 1]
aa = tmp.dot(tmp)
bb = res.dot(tmp)
cc = RSS - thresh
try:
db = (-bb + np.sqrt(bb * bb - aa * cc)) / aa
except:
db = -bb / aa
# perform shift
b += db
c, s = onnls(y - b,
g,
lam=lam,
mask=mask,
shift=shift,
window=window,
tol=tol)
db = np.mean(y - c) - b
b += db
lam -= db / f_lam
else: # optimize b
db = max(np.mean(y - c), 0 if b_nonneg else -np.inf) - b
b += db
lam -= db / (1 - g[0] - g[1])
g_converged = False
for _ in range(max_iter - 1):
res = y - c - b
RSS = res.dot(res)
if np.abs(RSS - thresh) < 1e-4 * thresh:
break
# calc shift db, here attributed to baseline
tmp = np.empty(T)
ls = np.append(np.where(s > 1e-6)[0], T)
l = ls[0]
tmp[:l] = (1 + d) / (1 + d**l) * \
np.exp(np.log(d) * np.arange(l)) # first pool
for i, f in enumerate(ls[:-1]): # all other pools
l = ls[i + 1] - f
tmp[f] = (Sg11[l - 1] -
g11g12[l - 1] * tmp[f - 1]) / g11g11[l - 1]
tmp[f + 1:f + l] = g11[1:l] * tmp[f] + g12[1:l] * tmp[f - 1]
tmp -= tmp.mean()
aa = tmp.dot(tmp)
bb = res.dot(tmp)
cc = RSS - thresh
try:
db = (-bb + np.sqrt(bb * bb - aa * cc)) / aa
except:
db = -bb / aa
# perform shift
if b_nonneg:
db = np.max(db, -b)
b += db
c, s = onnls(y - b,
g,
lam=lam,
mask=mask,
shift=shift,
window=window,
tol=tol)
# update b and lam
db = np.max(np.mean(y - c), 0 if b_nonneg else -np.inf) - b
b += db
lam -= db / f_lam
# update g and b
if optimize_g and (not g_converged):
def getRSS(y, opt):
b, ld, lr = opt
if ld < lr:
return 1e3 * thresh
d, r = np.exp(ld), np.exp(lr)
g1, g2 = d + r, -d * r
tmp = b + onnls(
y - b, [g1, g2], lam, mask=(s > 1e-2 * s.max()))[0] - y
return tmp.dot(tmp)
result = minimize(lambda x: getRSS(y, x),
(b, np.log(d), np.log(r)),
bounds=((0 if b_nonneg else None, None),
(None, -1e-4), (None, -1e-3)),
method='L-BFGS-B',
options={
'gtol': 1e-04,
'maxiter': 10,
'ftol': 1e-05
})
if abs(result['x'][1] - np.log(d)) < 1e-3:
g_converged = True
b, ld, lr = result['x']
d, r = np.exp(ld), np.exp(lr)
g = (d + r, -d * r)
c, s = onnls(y - b,
g,
lam=lam,
mask=mask,
shift=shift,
window=window,
tol=tol)
# update b and lam
db = np.max(np.mean(y - c), 0 if b_nonneg else -np.inf) - b
b += db
lam -= db
if penalty == 0: # get (locally optimal) L0 solution
def c4smin(y, s, s_min):
ls = np.append(np.where(s > s_min)[0], T)
tmp = np.zeros_like(s)
l = ls[0] # first pool
tmp[:l] = np.max(
0,
np.exp(np.log(d) * np.arange(l)).dot(y[:l]) * (1 - d * d) /
(1 - d**(2 * l))) * np.exp(np.log(d) * np.arange(l))
for i, f in enumerate(ls[:-1]): # all other pools
l = ls[i + 1] - f
tmp[f] = (g11[:l].dot(y[f:f + l]) -
g11g12[l - 1] * tmp[f - 1]) / g11g11[l - 1]
tmp[f + 1:f + l] = g11[1:l] * tmp[f] + g12[1:l] * tmp[f - 1]
return tmp
spikesizes = np.sort(s[s > 1e-6])
i = len(spikesizes) // 2
l = 0
u = len(spikesizes) - 1
while u - l > 1:
s_min = spikesizes[i]
tmp = c4smin(y - b, s, s_min)
res = y - b - tmp
RSS = res.dot(res)
if RSS < thresh or i == 0:
l = i
i = (l + u) // 2
res0 = tmp
else:
u = i
i = (l + u) // 2
if i > 0:
c = res0
s = np.append([0, 0], c[2:] - g[0] * c[1:-1] - g[1] * c[:-2])
return c, s, b, g, lam
def onnls(y,
g,
lam=0,
shift=100,
window=None,
mask=None,
tol=1e-9,
max_iter=None):
""" Infer the most likely discretized spike train underlying an AR(2)
fluorescence trace. Solves the sparse non-negative deconvolution problem
``argmin_s 1/2|Ks-y|^2 + lam |s|_1`` for ``s>=0``
Args:
y : array of float, shape (T,)
One dimensional array containing the fluorescence intensities with
one entry per time-bin.
g : array, shape (p,)
if p in (1,2):
Parameter(s) of the AR(p) process that models the fluorescence
impulse response.
else:
Kernel that models the fluorescence impulse response.
lam : float, optional, default 0
Sparsity penalty parameter lambda.
shift : int, optional, default 100
Number of frames by which to shift window from on run of NNLS
to the next.
window : int, optional, default None (200 or larger dependend on g)
Window size.
mask : array of bool, shape (n,), optional, default (True,)*n
Mask to restrict potential spike times considered.
tol : float, optional, default 1e-9
Tolerance parameter.
max_iter : None or int, optional, default None
Maximum number of iterations before termination.
If None (default), it is set to window size.
Returns:
c : array of float, shape (T,)
The inferred denoised fluorescence signal at each time-bin.
s : array of float, shape (T,)
Discretized deconvolved neural activity (spikes).
References:
Friedrich J and Paninski L, NIPS 2016
Bro R and DeJong S, J Chemometrics 1997
"""
T = len(y)
if mask is None:
mask = np.ones(T, dtype=bool)
if window is None:
w = max(
200,
len(g) if len(g) > 2 else int(
-5 / np.log(g[0] if len(g) == 1 else
(g[0] + np.sqrt(g[0] * g[0] + 4 * g[1])) / 2)))
else:
w = window
w = min(T, w)
K = np.zeros((w, w))
if len(g) == 1: # kernel for AR(1)
_y = y - lam * (1 - g[0])
_y[-1] = y[-1] - lam
h = np.exp(np.log(g[0]) * np.arange(w))
for i in range(w):
K[i:, i] = h[:w - i]
elif len(g) == 2: # kernel for AR(2)
_y = y - lam * (1 - g[0] - g[1])
_y[-2] = y[-2] - lam * (1 - g[0])
_y[-1] = y[-1] - lam
d = (g[0] + np.sqrt(g[0] * g[0] + 4 * g[1])) / 2
r = (g[0] - np.sqrt(g[0] * g[0] + 4 * g[1])) / 2
if d == r:
h = np.exp(np.log(d) * np.arange(1, w + 1)) * np.arange(1, w + 1)
else:
h = (np.exp(np.log(d) * np.arange(1, w + 1)) -
np.exp(np.log(r) * np.arange(1, w + 1))) / (d - r)
for i in range(w):
K[i:, i] = h[:w - i]
else: # arbitrary kernel
h = g
for i in range(w):
K[i:, i] = h[:w - i]
a = np.linalg.inv(K).sum(0)
_y = y - lam * a[0]
_y[-w:] = y[-w:] - lam * a
s = np.zeros(T)
KK = K.T.dot(K)
for i in range(0, max(1, T - w), shift):
s[i:i + w] = _nnls(KK,
K.T.dot(_y[i:i + w]),
s[i:i + w],
mask=mask[i:i + w],
tol=tol,
max_iter=max_iter)[:w]
# subtract contribution of spikes already committed to
_y[i:i + w] -= K[:, :shift].dot(s[i:i + shift])
s[i + shift:] = _nnls(KK[-(T - i - shift):, -(T - i - shift):],
K[:T - i - shift, :T - i - shift].T.dot(_y[i +
shift:]),
s[i + shift:],
mask=mask[i + shift:])
c = np.zeros_like(s)
for t in np.where(s > tol)[0]:
c[t:t + w] += s[t] * h[:min(w, T - t)]
return c, s
def constrained_foopsi(fluor,
bl=None,
c1=None,
g=None,
sn=None,
p=1,
method_deconvolution='oasis',
bas_nonneg=True,
noise_range=[.25, .5],
noise_method='logmexp',
lags=5,
fudge_factor=1.,
verbosity=False,
solvers=None,
optimize_g=0,
s_min=None,
**kwargs):
"""
Infer the most likely discretized spike train underlying a fluorescence
trace. It relies on a noise constrained deconvolution approach.
Args:
fluor: np.ndarray
One dimensional array containing the fluorescence intensities with
one entry per time-bin.
bl: [optional] float
Fluorescence baseline value. If no value is given, then bl is
estimated from the data.
c1: [optional] float
value of calcium at time 0
g: [optional] list,float
Parameters of the AR process that models the fluorescence impulse
response. Estimated from the data if no value is given
sn: float, optional
Standard deviation of the noise distribution. If no value is
given, then sn is estimated from the data.
p: int
order of the autoregression model
method_deconvolution: [optional] string
solution method for basis projection pursuit 'cvx' or 'cvxpy' or
'oasis'
bas_nonneg: bool
baseline strictly non-negative
noise_range: list of two elms
frequency range for averaging noise PSD
noise_method: string
method of averaging noise PSD
lags: int
number of lags for estimating time constants
fudge_factor: float
fudge factor for reducing time constant bias
verbosity: bool
display optimization details
solvers: list string
primary and secondary (if problem unfeasible for approx solution)
solvers to be used with cvxpy, default is ['ECOS','SCS']
optimize_g : [optional] int, only applies to method 'oasis'
Number of large, isolated events to consider for optimizing g.
If optimize_g=0 (default) the provided or estimated g is not
further optimized.
s_min : float, optional, only applies to method 'oasis'
Minimal non-zero activity within each bin (minimal 'spike size').
For negative values the threshold is abs(s_min) * sn * sqrt(1-g)
If None (default) the standard L1 penalty is used
If 0 the threshold is determined automatically such that
RSS <= sn^2 T
Returns:
c: np.ndarray float
The inferred denoised fluorescence signal at each time-bin.
bl, c1, g, sn : As explained above
sp: ndarray of float
Discretized deconvolved neural activity (spikes)
lam: float
Regularization parameter
Raises:
Exception("You must specify the value of p")
Exception('OASIS is currently only implemented for p=1 and p=2')
Exception('Undefined Deconvolution Method')
References:
* Pnevmatikakis et al. 2016. Neuron, in press,
http://dx.doi.org/10.1016/j.neuron.2015.11.037
* Machado et al. 2015. Cell 162(2):338-350
\image: docs/img/deconvolution.png
\image: docs/img/evaluationcomponent.png
"""
if g is None or sn is None:
# Estimate noise standard deviation and AR coefficients,
# if they are not present
g, sn = estimate_parameters(fluor,
p=p,
sn=sn,
g=g,
range_ff=noise_range,
method=noise_method,
lags=lags,
fudge_factor=fudge_factor)
lam = None
if method_deconvolution == 'cvx':
c, bl, c1, g, sn, sp = cvxopt_foopsi(fluor,
b=bl,
c1=c1,
g=g,
sn=sn,
p=p,
bas_nonneg=bas_nonneg,
verbosity=verbosity)
elif method_deconvolution == 'cvxpy':
c, bl, c1, g, sn, sp = cvxpy_foopsi(fluor,
g,
sn,
b=bl,
c1=c1,
bas_nonneg=bas_nonneg,
solvers=solvers)
elif method_deconvolution == 'oasis':
from cnmf_oasis import constrained_oasisAR1
penalty = 1 if s_min is None else 0
if p == 1:
if bl is None:
# Infer the most likely discretized spike train underlying
# an AR(1) fluorescence trace.
# Solves the noise constrained sparse
# non-negative deconvolution problem. min |s|_1 subject to:
# |c-y|^2 = sn^2 T and s_t = c_t-g c_{t-1} >= 0
c, sp, bl, g, lam = constrained_oasisAR1(
fluor.astype(np.float32),
g[0],
sn,
optimize_b=True,
b_nonneg=bas_nonneg,
optimize_g=optimize_g,
penalty=penalty,
s_min=0 if s_min is None else s_min)
else:
c, sp, _, g, lam = constrained_oasisAR1(
(fluor - bl).astype(np.float32),
g[0],
sn,
optimize_b=False,
penalty=penalty)
c1 = c[0]
# remove intial calcium to align with the other foopsi methods
# it is added back in function constrained_foopsi_parallel of
# temporal.py
c -= c1 * g**np.arange(len(fluor))
elif p == 2:
from cnmf_oasis import constrained_oasisAR1, constrained_oasisAR2
if bl is None:
c, sp, bl, g, lam = constrained_oasisAR2(fluor.astype(
np.float32),
g,
sn,
optimize_b=True,
b_nonneg=bas_nonneg,
optimize_g=optimize_g,
penalty=penalty)
else:
c, sp, _, g, lam = constrained_oasisAR2(
(fluor - bl).astype(np.float32),
g,
sn,
optimize_b=False,
penalty=penalty)
c1 = c[0]
d = (g[0] + np.sqrt(g[0] * g[0] + 4 * g[1])) / 2
c -= c1 * d**np.arange(len(fluor))
else:
raise Exception(
'OASIS is currently only implemented for p=1 and p=2')
g = np.ravel(g)
return c, bl, c1, g, sn, sp, lam
def cvxpy_foopsi(fluor, g, sn, b=None, c1=None, bas_nonneg=True, solvers=None):
"""
Solves the deconvolution problem using the cvxpy package and the
ECOS/SCS library.
Args:
fluor: ndarray
fluorescence trace
g: list of doubles
parameters of the autoregressive model, cardinality equivalent to p
sn: double
estimated noise level
b: double
baseline level. If None it is estimated.
c1: double
initial value of calcium. If None it is estimated.
bas_nonneg: boolean
should the baseline be estimated
solvers: tuple of two strings
primary and secondary solvers to be used. Can be choosen between
ECOS, SCS, CVXOPT
Returns:
c: estimated calcium trace
b: estimated baseline
c1: esimtated initial calcium value
g: esitmated parameters of the autoregressive model
sn: estimated noise level
sp: estimated spikes
Raises:
ImportError 'cvxpy solver requires installation of cvxpy. Not working
in windows at the moment.'
ValueError 'Problem solved suboptimally or unfeasible'
"""
# todo: check the result and gen_vector vars
try:
import cvxpy as cvx
except ImportError: # XXX Is the below still true?
raise ImportError('''cvxpy solver requires installation of cvxpy.
Not working in windows at the moment.''')
if solvers is None:
solvers = ['ECOS', 'SCS']
T = fluor.size
# construct deconvolution matrix (sp = G*c)
G = scipy.sparse.dia_matrix((np.ones((1, T)), [0]), (T, T))
for i, gi in enumerate(g):
G = G + \
scipy.sparse.dia_matrix((-gi * np.ones((1, T)), [-1 - i]), (T, T))
gr = np.roots(np.concatenate([np.array([1]), -g.flatten()]))
gd_vec = np.max(gr)**np.arange(T) # decay vector for initial fluorescence
gen_vec = G.dot(scipy.sparse.coo_matrix(np.ones((T, 1))))
c = cvx.Variable(T) # calcium at each time step
constraints = []
cnt = 0
if b is None:
flag_b = True
cnt += 1
b = cvx.Variable(1) # baseline value
if bas_nonneg:
b_lb = 0
else:
b_lb = np.min(fluor)
constraints.append(b >= b_lb)
else:
flag_b = False
if c1 is None:
flag_c1 = True
cnt += 1
c1 = cvx.Variable(1) # baseline value
constraints.append(c1 >= 0)
else:
flag_c1 = False
thrNoise = sn * np.sqrt(fluor.size)
try:
# minimize number of spikes
objective = cvx.Minimize(cvx.norm(G * c, 1))
constraints.append(G * c >= 0)
constraints.append(
cvx.norm(-c + fluor - b - gd_vec * c1, 2) <= thrNoise)
prob = cvx.Problem(objective, constraints)
result = prob.solve(solver=solvers[0])
if not (prob.status == 'optimal'
or prob.status == 'optimal_inaccurate'):
raise ValueError('Problem solved suboptimally or unfeasible')
print(('PROBLEM STATUS:' + prob.status))
sys.stdout.flush()
except (ValueError, cvx.SolverError):
# if solvers fail to solve the problem
lam = old_div(sn, 500)
constraints = constraints[:-1]
objective = cvx.Minimize(
cvx.norm(-c + fluor - b - gd_vec * c1, 2) +
lam * cvx.norm(G * c, 1))
prob = cvx.Problem(objective, constraints)
try: # in case scs was not installed properly
try:
print('TRYING AGAIN ECOS')
sys.stdout.flush()
result = prob.solve(solver=solvers[0])
except:
print((solvers[0] + ' DID NOT WORK TRYING ' + solvers[1]))
result = prob.solve(solver=solvers[1])
except:
sys.stderr.write(
'''***** SCS solver failed, try installing and compiling SCS
for much faster performance. Otherwise set the solvers in
tempora_params to ["ECOS","CVXOPT"]''')
sys.stderr.flush()
raise
if not (prob.status == 'optimal'
or prob.status == 'optimal_inaccurate'):
print(('PROBLEM STATUS:' + prob.status))
sp = fluor
c = fluor
b = 0
c1 = 0
return c, b, c1, g, sn, sp
sp = np.squeeze(np.asarray(G * c.value))
c = np.squeeze(np.asarray(c.value))
if flag_b:
b = np.squeeze(b.value)
if flag_c1:
c1 = np.squeeze(c1.value)
return c, b, c1, g, sn, sp
# Model 2 (a_0 + a_1 K + a_2 K^2 + a_3 T + a_5 K*T)
X = np.column_stack((data['StrikePrice'], data['StrikePrice']**2, data['t_delta'], data['StrikePrice']*data['t_delta']))
X = sm.add_constant(X)
y = data['ImpliedVola']
model = sm.OLS(y, X)
results = model.fit()
DVF_Model2_result['a_0'][i] = results.params[0]
DVF_Model2_result['a_1'][i] = results.params[1]
DVF_Model2_result['a_2'][i] = results.params[2]
DVF_Model2_result['a_3'][i] = results.params[3]
DVF_Model2_result['a_5'][i] = results.params[4]
# Create RMSE
DVF_Model2_result['RMSE_S'][i] = np.sqrt(np.mean((data['TaeglicherAbrechnungspreis'] - vBSMOption(data['OptionType'], data['SchlusspreisBasiswert'], data['t_delta'], data['StrikePrice'], data['EONIA'], vDVF_Model2_sigma(data['t_delta'], data['StrikePrice'], DVF_Model2_result['a_0'][i], DVF_Model2_result['a_1'][i], DVF_Model2_result['a_2'][i], DVF_Model2_result['a_3'][i], DVF_Model2_result['a_5'][i]), Greek='Price'))**2)) / data['SchlusspreisBasiswert'][0]
if i > 0:
DVF_Model2_result['RMSE_S_Previous_Vs_Current'][i] = np.sqrt(np.mean((data['TaeglicherAbrechnungspreis'] - vBSMOption(data['OptionType'], data['SchlusspreisBasiswert'], data['t_delta'], data['StrikePrice'], data['EONIA'], vDVF_Model2_sigma(data['t_delta'], data['StrikePrice'], DVF_Model2_result['a_0'][i-1], DVF_Model2_result['a_1'][i-1], DVF_Model2_result['a_2'][i-1], DVF_Model2_result['a_3'][i-1], DVF_Model2_result['a_5'][i-1]), Greek='Price'))**2)) / data['SchlusspreisBasiswert'][0]
# Model 3 (a_0 + a_1 K + a_2 K^2 + a_3 T + a_4 T^2 + a_5 K*T)
X = np.column_stack((data['StrikePrice'], data['StrikePrice']**2, data['t_delta'], data['t_delta']**2, data['StrikePrice']*data['t_delta']))
X = sm.add_constant(X)
y = data['ImpliedVola']
model = sm.OLS(y, X)
results = model.fit()
DVF_Model3_result['a_0'][i] = results.params[0]
DVF_Model3_result['a_1'][i] = results.params[1]
DVF_Model3_result['a_2'][i] = results.params[2]
from common import load, load_args
import numpy
import os
import matplotlib.pyplot as plt
input_list = []
target_list = []
for i in range(101):
input_list.append(i)
target_list.append(numpy.sqrt(i))
directory = os.fsencode('./output/saves/')
if not os.path.exists('./output/charts'):
os.makedirs('./output/charts')
output = []
temp = []
for file in os.listdir(directory):
filename = os.fsdecode(file)
filename_split = filename.split('_')
if filename_split[0] == '0.01' and filename_split[5] == '50.ser':
network = load('./output/saves/' + filename)
output.clear()
for number in input_list:
temp.clear()
temp.append(number)
output.append(network.query(temp)[0][0])
plt.figure(0)
plt.suptitle('Aproksymacja funkcji')
plt.title('lr=' + filename_split[1] + ', momentum=' +
filename_split[2] + ', neurony ukryte=' + filename_split[3] +
plt.xlabel(r'$\mu$' "(" r'$\phi$' ")")
plt.ylabel('Height (km)')
if n_part == 1:
plt.subplot(1, 4, 2)
sigma = np.zeros((results.shape[0], 1))
M0 = np.asarray(moments[:, 0, 0], dtype=float).reshape((-1, 1))
M1 = np.asarray(moments[:, 1, 0], dtype=float).reshape((-1, 1))
M2 = np.asarray(moments[:, 2, 0], dtype=float).reshape((-1, 1))
M3 = np.asarray(moments[:, 3, 0], dtype=float).reshape((-1, 1))
sigma[:, 0] = np.sqrt(M2[:, 0] / M0[:, 0] - (M1[:, 0] / M0[:, 0])**2)
plt.plot(sigma, z)
plt.xlabel(r'$\sigma$' "(" r'$\phi$' ")")
plt.ylabel('Height (km)')
plt.subplot(1, 4, 3)
skew = np.zeros((results.shape[0], 1))
skew[:, 0] = M3[:, 0] - 3 * M1[:, 0] * M2[:, 0] + 2 * M1[:, 0]**3
plt.plot(skew, z)
plt.xlabel('Skew (\phi)' "(" r'$\phi$' ")")
def GP_forecast(
df: pd.DataFrame,
days_in_past: int = 2,
days_in_future: int = 1,
detectors: list = None,
kern=None,
) -> pd.DataFrame:
"""Forecast using Gaussian Processes
Args:
df: Dataframe of JamCam data
days_in_past: Integer number of previous days to use for forecast
days_in_future: Days in future produce a for forecast for
detectors: List of detectors to look at
Returns:
Dataframe forecast in same format as JamCam input dataframe
"""
# extract numpy array of detector ID's
if detectors is None:
detectors = df["detector_id"].drop_duplicates().to_numpy()
framelist = []
i = 0
for detector in detectors:
i += 1
dataset = df[df["detector_id"] == detector].tail(n=26 * days_in_past)
Y = dataset["n_vehicles_in_interval"].to_numpy().reshape(-1, 1)
Y = Y.astype(float)
X = (
(dataset["measurement_end_utc"] - dataset["measurement_end_utc"].min())
.astype("timedelta64[h]")
.to_numpy()
.reshape(-1, 1)
)
scaler = MinMaxScaler(feature_range=(-1, 1))
y = scaler.fit_transform(Y)
if kern is None:
kern_pD = gpflow.kernels.Periodic(gpflow.kernels.SquaredExponential())
kern_pW = gpflow.kernels.Periodic(gpflow.kernels.SquaredExponential())
kern_SE = gpflow.kernels.SquaredExponential()
kern_W = gpflow.kernels.White()
kern_M = gpflow.kernels.Matern32()
kern_pD.period.assign(24.0)
# kern_pD.base_kernel.variance.assign(10)
kern_pW.period.assign(168.0)
# kern_pW.base_kernel.variance.assign(10)
k = kern_pD + kern_pW + kern_M
else:
k = kern
m = gpflow.models.GPR(data=(X, y), kernel=k, mean_function=None)
opt = gpflow.optimizers.Scipy()
try:
opt.minimize(
m.training_loss,
m.trainable_variables,
options=dict(maxiter=500),
)
except:
print(detector, " Covariance matrix not invertible, skipping to next detector")
del m
continue
print("please wait: ", i, "/", len(detectors), end="\r")
time_shift=24 - dataset["measurement_end_utc"].max().hour
## generate test points for prediction
xx = np.linspace(
X.max() + time_shift, X.max() + time_shift + (days_in_future * 24) + 1, (days_in_future * 24)
).reshape(
(days_in_future * 24), 1
) # test points must be of shape (N, D)
## predict mean and variance of latent GP at test points
mean, var = m.predict_f(xx)
# reverse min_max scaler
testPredict = scaler.inverse_transform(mean)
testVar = scaler.inverse_transform(var)
# find the time period for our testPredictions
start_date = dataset["measurement_end_utc"].max() + np.timedelta64(time_shift, "h")
end_date = start_date + np.timedelta64(24 * (days_in_future) -1, "h")
T = pd.date_range(start_date, end_date, freq="H")
# organise data into dataframe similar to the SCOOT outputs
df2 = pd.DataFrame(
{
"detector_id": detector,
"lon": df[df["detector_id"] == detector]["lon"].iloc[0],
"lat": df[df["detector_id"] == detector]["lat"].iloc[0],
"measurement_start_utc": T,
"measurement_end_utc": T + np.timedelta64(1, "h"),
"n_vehicles_in_interval": testPredict.flatten(),
"prediction_variance": testVar.flatten(),
"baseline_upper": testPredict.flatten() + 3 * np.sqrt(testVar.flatten()),
"baseline_lower": testPredict.flatten() - 3 * np.sqrt(testVar.flatten()),
}
)
del m
framelist.append(df2)
return pd.concat(framelist)
def G_synthesis(
dlatents_in, # Input: Disentangled latents (W) [minibatch, num_layers, dlatent_size].
dlatent_size=512, # Disentangled latent (W) dimensionality.
num_channels=3, # Number of output color channels.
resolution=1024, # Output resolution.
fmap_base=8192, # Overall multiplier for the number of feature maps.
fmap_decay=1.0, # log2 feature map reduction when doubling the resolution.
fmap_max=512, # Maximum number of feature maps in any layer.
use_styles=True, # Enable style inputs?
const_input_layer=True, # First layer is a learned constant?
use_noise=True, # Enable noise inputs?
randomize_noise=True, # True = randomize noise inputs every time (non-deterministic), False = read noise inputs from variables.
nonlinearity='lrelu', # Activation function: 'relu', 'lrelu'
use_wscale=True, # Enable equalized learning rate?
use_pixel_norm=False, # Enable pixelwise feature vector normalization?
use_instance_norm=True, # Enable instance normalization?
dtype='float32', # Data type to use for activations and outputs.
fused_scale='auto', # True = fused convolution + scaling, False = separate ops, 'auto' = decide automatically.
blur_filter=[
1, 2, 1
], # Low-pass filter to apply when resampling activations. None = no filtering.
# structure = 'auto', # 'fixed' = no progressive growing, 'linear' = human-readable, 'recursive' = efficient, 'auto' = select automatically.
structure='fixed', # 'fixed' = no progressive growing, 'linear' = human-readable, 'recursive' = efficient, 'auto' = select automatically.
is_template_graph=False, # True = template graph constructed by the Network class, False = actual evaluation.
force_clean_graph=False, # True = construct a clean graph that looks nice in TensorBoard, False = default behavior.
**_kwargs): # Ignore unrecognized keyword args.
resolution_log2 = int(np.log2(resolution))
assert resolution == 2**resolution_log2 and resolution >= 4
def nf(stage):
return min(int(fmap_base / (2.0**(stage * fmap_decay))), fmap_max)
def blur(x):
return blur2d(x, blur_filter) if blur_filter else x
if is_template_graph: force_clean_graph = True
if force_clean_graph: randomize_noise = False
if structure == 'auto':
structure = 'linear' if force_clean_graph else 'recursive'
act, gain = {
'relu': (tf.nn.relu, np.sqrt(2)),
'lrelu': (leaky_relu, np.sqrt(2))
}[nonlinearity]
num_layers = resolution_log2 * 2 - 2
num_styles = num_layers if use_styles else 1
images_out = None
# Primary inputs.
dlatents_in.set_shape([None, num_styles, dlatent_size])
dlatents_in = tf.cast(dlatents_in, dtype)
lod_in = tf.cast(
tf.get_variable('lod', initializer=np.float32(0), trainable=False),
dtype)
# Noise inputs.
noise_inputs = []
if use_noise:
for layer_idx in range(num_layers):
res = layer_idx // 2 + 2
shape = [1, use_noise, 2**res, 2**res]
noise_inputs.append(
tf.get_variable('noise%d' % layer_idx,
shape=shape,
initializer=tf.initializers.random_normal(),
trainable=False))
# Things to do at the end of each layer.
def layer_epilogue(x, layer_idx):
if use_noise:
x = apply_noise(x,
noise_inputs[layer_idx],
randomize_noise=randomize_noise)
x = apply_bias(x)
x = act(x)
if use_pixel_norm:
x = pixel_norm(x)
if use_instance_norm:
x = instance_norm(x)
if use_styles:
x = style_mod(x, dlatents_in[:, layer_idx], use_wscale=use_wscale)
return x
# Early layers.
with tf.variable_scope('4x4'):
if const_input_layer:
with tf.variable_scope('Const'):
x = tf.get_variable('const',
shape=[1, nf(1), 4, 4],
initializer=tf.initializers.ones())
x = layer_epilogue(
tf.tile(tf.cast(x, dtype),
[tf.shape(dlatents_in)[0], 1, 1, 1]), 0)
else:
with tf.variable_scope('Dense'):
x = dense(
dlatents_in[:, 0],
fmaps=nf(1) * 16,
gain=gain / 4,
use_wscale=use_wscale
) # tweak gain to match the official implementation of Progressing GAN
x = layer_epilogue(tf.reshape(x, [-1, nf(1), 4, 4]), 0)
with tf.variable_scope('Conv'):
x = layer_epilogue(
conv2d(x,
fmaps=nf(1),
kernel=3,
gain=gain,
use_wscale=use_wscale), 1)
# Building blocks for remaining layers.
def block(res, x): # res = 3..resolution_log2
with tf.variable_scope('%dx%d' % (2**res, 2**res)):
with tf.variable_scope('Conv0_up'):
x = layer_epilogue(
blur(
upscale2d_conv2d(x,
fmaps=nf(res - 1),
kernel=3,
gain=gain,
use_wscale=use_wscale,
fused_scale=fused_scale)),
res * 2 - 4)
with tf.variable_scope('Conv1'):
x = layer_epilogue(
conv2d(x,
fmaps=nf(res - 1),
kernel=3,
gain=gain,
use_wscale=use_wscale), res * 2 - 3)
return x
def torgb(res, x): # res = 2..resolution_log2
lod = resolution_log2 - res
with tf.variable_scope('ToRGB_lod%d' % lod):
return apply_bias(
conv2d(x,
fmaps=num_channels,
kernel=1,
gain=1,
use_wscale=use_wscale))
# Fixed structure: simple and efficient, but does not support progressive growing.
if structure == 'fixed':
for res in range(3, resolution_log2 + 1):
x = block(res, x)
images_out = torgb(resolution_log2, x)
# Linear structure: simple but inefficient.
if structure == 'linear':
images_out = torgb(2, x)
for res in range(3, resolution_log2 + 1):
lod = resolution_log2 - res
x = block(res, x)
img = torgb(res, x)
images_out = upscale2d(images_out)
with tf.variable_scope('Grow_lod%d' % lod):
images_out = tflib.lerp_clip(img, images_out, lod_in - lod)
# Recursive structure: complex but efficient.
if structure == 'recursive':
def cset(cur_lambda, new_cond, new_lambda):
return lambda: tf.cond(new_cond, new_lambda, cur_lambda)
def grow(x, res, lod):
y = block(res, x)
img = lambda: upscale2d(torgb(res, y), 2**lod)
img = cset(
img, (lod_in > lod), lambda: upscale2d(
tflib.lerp(torgb(res, y), upscale2d(torgb(res - 1, x)),
lod_in - lod), 2**lod))
if lod > 0:
img = cset(img, (lod_in < lod),
lambda: grow(y, res + 1, lod - 1))
return img()
images_out = grow(x, 3, resolution_log2 - 3)
assert images_out.dtype == tf.as_dtype(dtype)
return tf.identity(images_out, name='images_out')
def generate_cone(direction_vec, lim_angle, xxx_todo_changeme2, num_pts_dir):
"""
Generates cone for direction vector on real S_6
(x^2 + y^2 + z^2 + u^2 + v^2 + w^2 = 1)
and generates cartesian raster for x and y
"""
(dx, dy) = xxx_todo_changeme2
num_pts_lspace = num_pts_dir
if num_pts_dir % 2 == 1:
num_pts_lspace -= 1
lspace = np.hstack(
(np.linspace(-1, 0, num_pts_lspace//2, endpoint=False),
np.linspace(1, 0, num_pts_lspace//2, endpoint=False)
)
)
lspace = np.hstack((0, lspace))
# generate vectors in z direction
x = dx*lspace
y = dy*lspace
kxr = lim_angle*lspace
kxi = lim_angle*lspace
kyr = lim_angle*lspace
kyi = lim_angle*lspace
kzi = lim_angle*lspace
(X, Y, KXR, KXI, KYR, KYI, KZI) = np.meshgrid(x, y, kxr, kxi, kyr, kyi, kzi)
KZR = np.sqrt(1. - KXR**2 - KXI**2 - KYR**2 - KYI**2 - KZI**2)
complex_ek = np.vstack((KXR.flatten() + 1j*KXI.flatten(),
KYR.flatten() + 1j*KYI.flatten(),
KZR.flatten() + 1j*KZI.flatten()))
start_pts = np.vstack((X.flatten(), Y.flatten(), np.zeros_like(X.flatten())))
# TODO: complex rotate complex_ek into right direction
# this means: generalize rodrigues to unitary matrices
# and get 5 angles from dir_vector
#print(np.linalg.norm(complex_ek, axis=0))
#print(complex_ek)
#kz = np.cos(lim_angle)
#kinpl = np.sin(lim_angle)
# rotate back into direction_vec direction
#phi = np.arctan2(direction_vec[1], direction_vec[0])
#theta = np.arcsin(np.sqrt(direction_vec[1]**2 + direction_vec[0]**2))
return (start_pts, complex_ek)
def normfun(x,mu, sigma):
pdf = np.exp(-((x - mu)**2) / (2* sigma**2)) / (sigma * np.sqrt(2*np.pi))
return pdf
def polynomial_regression():
# Check if user is loggedin
if ('loggedin' in session):
# Init variables
(data_to_html, feature, graph_title,
msg_suc, msg_err, msg_warn) = (None,) * 6
# Init list
(columns, res_list, score_list) = (list(), ) * 3
# Get session details
username = session['username']
lang = session['lang']
# Define tag category + model
cat_tag = 'REG'
mod_tag = 'PR'
# Connect to database
cursor = mysql.connection.cursor(MySQLdb.cursors.DictCursor)
# Get categories of navbar
navbar_cat = datas_cat_nav(cursor, lang)
navbar_cat_name = navbar_cat[0]
navbar_cat_tag = navbar_cat[1]
navbar_cat_icon = navbar_cat[2]
navbar_cat_link = navbar_cat[3]
# Get models of navbar
navbar_models = datas_mod_nav(cursor, lang, navbar_cat_tag)
# Get settings of navbar
navbar_settings = datas_set_nav(cursor, lang)
navbar_set_name = navbar_settings[0]
navbar_set_icon = navbar_settings[1]
navbar_set_link = navbar_settings[2]
# Get category details for breadcrumb
cat_details = cards_categories(cursor, lang, cat_tag)
cat_name = cat_details[0]
cat_link = cat_details[3]
# Get model details for breadcrumb
model_details = datas_model(cursor, lang, mod_tag)
model_name = model_details[0]
model_link = model_details[1]
# Break connection
cursor.close()
if (request.method == 'POST'):
# Upload file
if (request.form['submit_btn'] == 'Upload Now'
or request.form['submit_btn'] == 'Envoyer maintenant'):
# All fields was complete
if (bool(request.files['file']) == 1
and bool(request.form['sep_select']) == 1
):
get_upload_datas = upload_file(lang, False)
msg_err = get_upload_datas[0]
msg_suc = get_upload_datas[1]
msg_warn = get_upload_datas[2]
global new_tmp_path
new_tmp_path = get_upload_datas[3]
global colname_list
colname_list = get_upload_datas[4]
columns = colname_list
data_to_html = get_upload_datas[5]
global df
df = get_upload_datas[6]
else:
if (lang == 'en'):
# Submit without upload file
msg_err = (
'Please upload your data and select a separator.'
)
else:
msg_err = (
'Veuillez télécharger vos données et '
'choisir un séparateur.'
)
# Model compute
if (request.form['submit_btn'] == 'Launch the model'
or request.form['submit_btn'] == 'Lancer le modèle'):
feature = request.form['feature']
# Get colname list
columns = colname_list
# Show uploading files
data_to_html = df_html_show(df)
# Delete feature from columns
columns.remove(feature)
for i in columns:
x_feat = df[feature].values.reshape(-1, 1)
y_targ = df[i].values.reshape(-1, 1)
# Train Test
X_train, X_test, y_train, y_test = train_test_split(
x_feat,
y_targ,
test_size=0.33,
random_state=42
)
score_rmse = list()
min_rmse, min_deg = (math.inf,) * 2
for deg in range(1, 11):
# Train features
poly_features = PolynomialFeatures(degree=deg, include_bias=False)
x_poly_train = poly_features.fit_transform(X_train)
# Linear regression
poly_reg = LinearRegression().fit(x_poly_train, y_train)
# Compare with test data
x_poly_test = poly_features.fit_transform(X_test)
poly_predict = poly_reg.predict(x_poly_test)
poly_rmse = np.sqrt(mean_squared_error(y_test, poly_predict))
score_rmse.append(poly_rmse)
# Cross-validation of degree
if (min_rmse > poly_rmse):
min_rmse = poly_rmse
min_deg = deg
# Create Polynomial model
polynomial = PolynomialFeatures(degree=min_deg)
# Fit polynomial model
X_train = polynomial.fit_transform(X_train)
X_test = polynomial.fit_transform(X_test)
# Create linear model and fit
regressor = linear_model.LinearRegression().fit(X_train, y_train)
# Predicting test set results
y_test_pred = regressor.predict(X_test)
# Prediction
y_pred = regressor.predict(X_train)
y_pred = y_pred.tolist()
# Accuracy
r2_test = r2_score(y_test , y_test_pred) * 100
r2_train = r2_score(y_train, y_pred) * 100
res = [i, round(statistics.mean([r2_test, r2_train]), 2)]
res_list.append(res)
# Save scoring
score_list = [score[1] for score in res_list]
if (lang == 'en'):
# Add graph title
graph_title = (
'Comparison of the correlation between ' + feature +
' and the columns :'
)
# Success
msg_suc = (
'The model was successfully calculated. '
'Your data was automatically deleted.'
)
else:
graph_title = (
'Comparaison de la corrélation entre ' + feature +
' et les colonnes :'
)
msg_suc = (
'Le modèle a été calculé avec succès. '
'Vos données ont été automatiquement supprimées.'
)
# Delete file
file_remove(new_tmp_path)
return render_template(
'regression/pol_reg.html',
title = model_name,
username = username,
lang = lang,
nav_cat_name = navbar_cat_name,
nav_cat_tag = navbar_cat_tag,
nav_cat_icon = navbar_cat_icon,
nav_cat_lnk = navbar_cat_link,
nav_models = navbar_models,
nav_set_name = navbar_set_name,
nav_set_icon = navbar_set_icon,
nav_set_lnk = navbar_set_link,
cat_name = cat_name,
cat_tag = cat_tag,
cat_link = cat_link,
model_name = model_name,
model_link = model_link,
msg_err = msg_err,
msg_suc = msg_suc,
msg_warn = msg_warn,
data_show = data_to_html,
df_columns = columns,
feature = feature,
score_list = score_list,
graph_title = graph_title
)
else:
return redirect('404')
def sgd_recur(X,
y,
grad,
batch_size,
n_epoch,
L,
init_step=2.0,
R=1.0,
reg_coeff=0.001,
verbose=False,
loss_func=None,
test_func=None):
N, dim = X.shape
batch_idx = get_batch_index(N, batch_size)
m = len(batch_idx) - 1
mu = reg_coeff
niter = n_epoch * m + 1
it = 0
# initialization
w = np.zeros((niter, dim))
sens = np.zeros((niter, m))
step_size = init_step
for t in range(n_epoch):
if m > 1:
step_size = init_step / np.sqrt(t + 1)
# recurrence coefficient
contr_coeff = max(np.abs(1. - step_size * mu),
np.abs(1. - step_size * L))
b = (2.0 * R * step_size) / batch_size
for j in range(m):
mini_X = X[batch_idx[j]:batch_idx[j + 1], :]
mini_y = y[batch_idx[j]:batch_idx[j + 1]]
# gradient desecent update
gt = grad(w[it], mini_X, mini_y) / batch_size
gt += reg_coeff * w[it]
w[it + 1, :] = w[it] - step_size * gt
for k in range(m):
sens[it + 1, k] = contr_coeff * sens[it, k]
if k == j:
sens[it + 1, k] += b
it += 1
if verbose:
objval = loss_func(w[it], X, y)
acc = test_func(w[it], X, y) * 100
print("[{0}] loss={1:.5f} acc={2:7.3f}".format(t + 1, objval, acc))
# avg_sens = sens[-1, :]
# last_it = w[-1, :]
# return last_it, avg_sens
return w[1:, ], sens[1:, :]
def acf_to_acorr(acf):
diag = np.diag(acf[0])
# numpy broadcasting sufficient
return acf / np.sqrt(np.outer(diag, diag))
#get VCSS data
#freq_arr, flux_arr, eflux_arr, labels = get_VCSS_data(sname, wise_row,
# sp_row, labels, freq_arr, flux_arr, eflux_arr)
freq_arr, flux_arr, eflux_arr, labels = sedutils.VCSS_data_cleanup(
sname, wise_row, sp_row, labels, freq_arr, flux_arr, eflux_arr)
flux_arr, eflux_arr, labels = sedutils.mod_data_flux_density_scale(
flux_arr, eflux_arr, labels)
if 'VLASS' in labels and sp_row['VLASS_Limit'] != 'U':
ind = labels.index('VLASS')
vflux = flux_arr[ind]
eflux = eflux_arr[ind]
if eflux < 0.2 * vflux:
eflux_arr[ind] = np.sqrt(eflux**2 + (vflux / 5)**2)
if useInBand:
if 'BX' in labels and np.any(jvla_BX['snr'] > 50):
freq_arr, flux_arr, eflux_arr = rmfit.prep_fit_arr(
freq_arr, flux_arr, eflux_arr, alpha_BX)
if 'AX' in labels and np.any(jvla_AX['snr'] > 50):
freq_arr, flux_arr, eflux_arr = rmfit.prep_fit_arr(
freq_arr, flux_arr, eflux_arr, alpha_AX)
#create dict for online table:
mydict = {}
for snu, esnu, cat in zip(flux_arr, eflux_arr, labels):
mydict['F' + cat] = np.round(snu, 2)
mydict['E' + cat] = np.round(esnu, 2)
def momentum(X,
y,
grad,
batch_size,
beta,
L,
sigma,
alpha,
n_epoch,
reg_coeff=0.001,
verbose=False):
N, dim = X.shape
n_batch = int(N / batch_size)
rem = N % batch_size
extra = rem / n_batch
batch_size += extra
rem = N % batch_size
m = n_batch
mu = reg_coeff
n_alpha = len(alpha)
sig_sq = 2.0 * (sigma**2)
batches = np.arange(N)
np.random.shuffle(batches)
# initialization
w = np.zeros(dim)
v = np.zeros_like(w)
sens = np.zeros((m, n_alpha))
sens_p = np.zeros_like(sens)
for t in range(n_epoch):
step_size = 2. / (t + 1)
for j in range(m):
batch_start = batch_start_idx(j, batch_size, rem)
batch_finish = batch_start_idx(j + 1, batch_size, rem)
rand_idx = batches[batch_start:batch_finish]
mini_X = X[rand_idx, :]
mini_y = y[rand_idx]
# gradient desecent update
gt = grad(w, mini_X, mini_y)
gt /= batch_size
gt += mu * w
v[:] = beta * v + step_size * gt
w -= v
if verbose:
loss = logistic_loss(w, X, y) / N + 0.5 * reg_coeff * np.dot(
w, w)
print("[{0}] loss={1}".format(t + 1, loss))
# recurrence coefficient
contr_coeff = max(np.absolute(1. - np.sqrt(step_size * mu)),
np.absolute(1. - np.sqrt(step_size * L)))
expan = 2.0 * step_size / batch_size
if t == 0:
for j in range(1, m):
sens[0, :] = (expan**2) * (contr_coeff**(2 * (m - j - 1)))
else:
for j in range(m):
sens[j, :] = (contr_coeff**(2 * m) *
(sens[j, :] + sens_p[j, :]) + (2.0 * expan) *
(contr_coeff**(2 * (m - 1) - j)) *
np.sqrt(sens[j, :] + sens_p[j, :]) + (expan**2) *
(contr_coeff**(2 * (m - j - 1))))
expo = alpha * (alpha - 1) * sens / sig_sq
log_eta = logsumexp(expo, axis=0) - np.log(m)
return w, log_eta
def vega(self):
self.BSM()
self.vega = np.exp(-self.r*self.T)*self.S0*np.sqrt(self.T)*norm.pdf(self.d1)
return self.vega[0]
def QuatSqrt(Q) :
return (one+Q)/np.sqrt(2+2*Q[0])
def D_basic(
images_in, # First input: Images [minibatch, channel, height, width].
labels_in, # Second input: Labels [minibatch, label_size].
num_channels=1, # Number of input color channels. Overridden based on dataset.
resolution=32, # Input resolution. Overridden based on dataset.
label_size=0, # Dimensionality of the labels, 0 if no labels. Overridden based on dataset.
fmap_base=8192, # Overall multiplier for the number of feature maps.
fmap_decay=1.0, # log2 feature map reduction when doubling the resolution.
fmap_max=512, # Maximum number of feature maps in any layer.
nonlinearity='lrelu', # Activation function: 'relu', 'lrelu',
use_wscale=True, # Enable equalized learning rate?
mbstd_group_size=4, # Group size for the minibatch standard deviation layer, 0 = disable.
mbstd_num_features=1, # Number of features for the minibatch standard deviation layer.
dtype='float32', # Data type to use for activations and outputs.
fused_scale='auto', # True = fused convolution + scaling, False = separate ops, 'auto' = decide automatically.
blur_filter=[
1, 2, 1
], # Low-pass filter to apply when resampling activations. None = no filtering.
structure='auto', # 'fixed' = no progressive growing, 'linear' = human-readable, 'recursive' = efficient, 'auto' = select automatically.
is_template_graph=False, # True = template graph constructed by the Network class, False = actual evaluation.
**_kwargs): # Ignore unrecognized keyword args.
resolution_log2 = int(np.log2(resolution))
assert resolution == 2**resolution_log2 and resolution >= 4
def nf(stage):
return min(int(fmap_base / (2.0**(stage * fmap_decay))), fmap_max)
def blur(x):
return blur2d(x, blur_filter) if blur_filter else x
if structure == 'auto':
structure = 'linear' if is_template_graph else 'recursive'
act, gain = {
'relu': (tf.nn.relu, np.sqrt(2)),
'lrelu': (leaky_relu, np.sqrt(2))
}[nonlinearity]
images_in.set_shape([None, num_channels, resolution, resolution])
labels_in.set_shape([None, label_size])
images_in = tf.cast(images_in, dtype)
labels_in = tf.cast(labels_in, dtype)
lod_in = tf.cast(
tf.get_variable('lod', initializer=np.float32(0.0), trainable=False),
dtype)
scores_out = None
# Building blocks.
def fromrgb(x, res): # res = 2..resolution_log2
with tf.variable_scope('FromRGB_lod%d' % (resolution_log2 - res)):
return act(
apply_bias(
conv2d(x,
fmaps=nf(res - 1),
kernel=1,
gain=gain,
use_wscale=use_wscale)))
def block(x, res): # res = 2..resolution_log2
with tf.variable_scope('%dx%d' % (2**res, 2**res)):
if res >= 3: # 8x8 and up
with tf.variable_scope('Conv0'):
x = act(
apply_bias(
conv2d(x,
fmaps=nf(res - 1),
kernel=3,
gain=gain,
use_wscale=use_wscale)))
with tf.variable_scope('Conv1_down'):
x = act(
apply_bias(
conv2d_downscale2d(blur(x),
fmaps=nf(res - 2),
kernel=3,
gain=gain,
use_wscale=use_wscale,
fused_scale=fused_scale)))
else: # 4x4
if mbstd_group_size > 1:
x = minibatch_stddev_layer(x, mbstd_group_size,
mbstd_num_features)
with tf.variable_scope('Conv'):
x = act(
apply_bias(
conv2d(x,
fmaps=nf(res - 1),
kernel=3,
gain=gain,
use_wscale=use_wscale)))
with tf.variable_scope('Dense0'):
x = act(
apply_bias(
dense(x,
fmaps=nf(res - 2),
gain=gain,
use_wscale=use_wscale)))
with tf.variable_scope('Dense1'):
x = apply_bias(
dense(x,
fmaps=max(label_size, 1),
gain=1,
use_wscale=use_wscale))
return x
# Fixed structure: simple and efficient, but does not support progressive growing.
if structure == 'fixed':
x = fromrgb(images_in, resolution_log2)
for res in range(resolution_log2, 2, -1):
x = block(x, res)
scores_out = block(x, 2)
# Linear structure: simple but inefficient.
if structure == 'linear':
img = images_in
x = fromrgb(img, resolution_log2)
for res in range(resolution_log2, 2, -1):
lod = resolution_log2 - res
x = block(x, res)
img = downscale2d(img)
y = fromrgb(img, res - 1)
with tf.variable_scope('Grow_lod%d' % lod):
x = tflib.lerp_clip(x, y, lod_in - lod)
scores_out = block(x, 2)
# Recursive structure: complex but efficient.
if structure == 'recursive':
def cset(cur_lambda, new_cond, new_lambda):
return lambda: tf.cond(new_cond, new_lambda, cur_lambda)
def grow(res, lod):
x = lambda: fromrgb(downscale2d(images_in, 2**lod), res)
if lod > 0:
x = cset(x, (lod_in < lod), lambda: grow(res + 1, lod - 1))
x = block(x(), res)
y = lambda: x
if res > 2:
y = cset(
y, (lod_in > lod), lambda: tflib.lerp(
x,
fromrgb(downscale2d(images_in, 2**(lod + 1)), res - 1),
lod_in - lod))
return y()
scores_out = grow(2, resolution_log2 - 2)
# Label conditioning from "Which Training Methods for GANs do actually Converge?"
if label_size:
with tf.variable_scope('LabelSwitch'):
scores_out = tf.reduce_sum(scores_out * labels_in,
axis=1,
keepdims=True)
assert scores_out.dtype == tf.as_dtype(dtype)
scores_out = tf.identity(scores_out, name='scores_out')
return scores_out
def parallel_modelbuilding(sess,
tf_cluster,
masks,
datasets,
gtabs,
n_parts=16):
ten_model = dti.TensorModel(gtabs[0])
design_matrix = ten_model.design_matrix
nonzero_indices_list = [np.nonzero(masks[i]) for i in range(len(masks))]
max_length = np.max([
len(nonzero_indices_list[i][0])
for i in range(len(nonzero_indices_list))
])
stride = int(np.ceil(max_length / float(n_parts)))
dim_sh = [stride, datasets[0].shape[-1]]
waves = tf_cluster.partition_work_waves(int(np.ceil(max_length / stride)),
use_host=False)
dim_inputs = []
cnt_inputs = []
work = []
dm_input = tf.placeholder(tf.float64, shape=design_matrix.shape, name="dm")
for i_worker in range(len(waves[0])):
with tf.device(waves[0][i_worker]):
dim_inputs.append(
tf.placeholder(tf.float64,
shape=dim_sh,
name="dim_%d" % i_worker))
cnt_inputs.append(
tf.placeholder(tf.int32, shape=1,
name="counter_%d" % i_worker))
work.append(
mb.model_building(dim_inputs[-1], dm_input, cnt_inputs[-1]))
fas = []
for i_data in range(len(datasets)):
ten_model = dti.TensorModel(gtabs[i_data])
design_matrix = ten_model.design_matrix
nonzero_indices = nonzero_indices_list[i_data]
dti_params = np.zeros(datasets[i_data].shape[:-1] + (12, ))
cnt = 1
thread_mask = np.zeros(masks[i_data].shape, dtype=np.int)
data = datasets[i_data]
waves = tf_cluster.partition_work_waves(int(
np.ceil(len(nonzero_indices[0]) / stride)),
use_host=False)
for i_wave in range(len(waves)):
counter = []
data_in_mask_list = []
for i_worker in range(len(waves[0])):
step = (cnt - 1) * stride
thread_mask[nonzero_indices[0][step:step + stride],
nonzero_indices[1][step:step + stride],
nonzero_indices[2][step:step + stride]] = cnt
data_in_mask = \
np.reshape(data[nonzero_indices[0][step: step + stride],
nonzero_indices[1][step: step + stride],
nonzero_indices[2][step: step + stride]],
(-1, data.shape[-1]))
data_in_mask = np.maximum(data_in_mask, 0.0001)
data_in_mask_list.append(data_in_mask)
counter.append(np.array([cnt]))
cnt += 1
feed_dict = {
i: d
for i, d in zip(dim_inputs[:len(waves[i_wave])],
data_in_mask_list)
}
feed_dict.update(
{a: d
for a, d in zip([dm_input], [design_matrix])})
feed_dict.update({
a: d
for a, d in zip(cnt_inputs[:len(waves[i_wave])], counter)
})
results = []
results += sess.run(work[:len(waves[i_wave])], feed_dict=feed_dict)
for result in results:
dti_params[thread_mask == result[1][0]] = \
result[0].reshape(result[0].shape[0], 12)
evals = dti_params[..., :3]
evals = mb._roll_evals(evals, -1)
all_zero = (evals == 0).all(axis=0)
ev1, ev2, ev3 = evals
fa = np.sqrt(0.5 * ((ev1 - ev2)**2 + (ev2 - ev3)**2 + (ev3 - ev1)**2) /
((evals * evals).sum(0) + all_zero))
fas.append(fa)
return fas
def sgd_adv(D, sideBilinear, U0, UBias0, ULatentScaler0, WBilinear0,
eta_Latent, eta_LatentScaler, eta_Bilinear, eta_RowBias, epochs,
plotAdv, plotAdvHR, validationSet, alpha, epsilon):
pairs = len(D[1, :])
testOnes = []
for index in range(0, len(validationSet[0, :])):
if validationSet[2, index] == 1.0:
testOnes.append(index)
#load model parameter, passed from the initial training SGD function
U = np.copy(U0)
UBias = np.copy(UBias0)
ULatentScaler = np.copy(ULatentScaler0)
WBilinear = np.copy(WBilinear0)
#load starting learning rates
etaLatent = eta_Latent # / ((1 + etaLatent0 * lambdaLatent) * e)
etaRowBias = eta_RowBias # / ((1 + etaRowBias0 * lambdaRowBias) * e)
etaLatentScaler = eta_LatentScaler # / ((1 + etaLatentScaler0 * lambdaLatentScaler) * e)
etaBilinear = eta_Bilinear # / ((1 + etaBilinear0 * lambdaBilinear) * e)
limit = int(1 * pairs)
#initalize perturbations
DeltaI = np.zeros(len(U[:, 0]))
DeltaJ = np.zeros(len(U[:, 0]))
DeltaXI = np.zeros(len(sideBilinear[:, 0]))
DeltaXJ = np.zeros(len(sideBilinear[:, 0]))
# np.sqrt(np.power(epsilon, 2)/len(U[:, 0]))
# here we set the bound (if starting perturbation is random)for the random noise that could be 0 if we want to add Delta=0 or the commented value if we want ||Delta||<epsilon
aucCounter = 0
# Main SGD body
for e in range(1, epochs):
for t in range(0, limit):
i = int(D[0, t]) - 1
j = int(D[1, t]) - 1
truth = int(D[2, t])
# Procedure for the adversarial perturbation buidling
predictionDelta = (U[:, i].T + DeltaI) @ ULatentScaler @ (
U[:, j].T + DeltaJ).T + UBias[i] + UBias[j] + (
sideBilinear[:, i].T + DeltaXI) @ WBilinear @ (
sideBilinear[:, j].T +
DeltaXJ).T # WPair @ sidePair[:, i, j] # + WBias
sigmaDelta = 1 / (1 + np.exp(-predictionDelta)
) # this must be a matrix of link probabilities.
GammaI = alpha * (sigmaDelta - truth) * (
ULatentScaler @ (U[:, j].T + DeltaJ).T).T
GammaJ = alpha * (sigmaDelta - truth) * (
(U[:, i].T + DeltaI) @ ULatentScaler)
GammaXI = alpha * (sigmaDelta - truth) * (
WBilinear @ (sideBilinear[:, j].T + DeltaXJ).T).T
GammaXJ = alpha * (sigmaDelta - truth) * (
(sideBilinear[:, i].T + DeltaXI) @ WBilinear)
DeltaAdvI = epsilon * GammaI / np.sqrt(
max(np.sum(np.power(GammaI, 2)), 0.000001))
DeltaAdvJ = epsilon * GammaJ / np.sqrt(
max(np.sum(np.power(GammaJ, 2)), 0.000001))
DeltaAdvXI = epsilon * GammaXI / np.sqrt(
max(np.sum(np.power(GammaXI, 2)), 0.000001))
DeltaAdvXJ = epsilon * GammaXJ / np.sqrt(
max(np.sum(np.power(GammaXJ, 2)), 0.000001))
predictionAdv = (U[:, i].T + DeltaAdvI) @ ULatentScaler @ (U[:, j].T + DeltaAdvJ).T + UBias[i] + UBias[j] \
+ (sideBilinear[:, i].T + DeltaAdvXI) @ WBilinear @ (sideBilinear[:, j].T + DeltaAdvXJ).T #+ WPair @ sidePair[:, i, j]
prediction = (
U[:, i]).T @ ULatentScaler @ U[:, j] + UBias[i] + UBias[
j] + sideBilinear[:, i].T @ WBilinear @ sideBilinear[:, j]
sigmaAdv = 1 / (1 + np.exp(-predictionAdv))
sigma = 1 / (1 + np.exp(-prediction))
#cost = -(truth * np.log(sigma) + (1 - truth) * (np.log(1-sigma))) - alpha * ((truth * np.log(sigmaAdv)) + (1 - truth) * (np.log(1-sigmaAdv)))
gradscalerAdv = float(sigmaAdv - truth)
gradscaler = float(sigma - truth)
#gradients computation
gradIAdv = ULatentScaler @ (
U[:, j].T + DeltaAdvJ
).T # + np.dot(ULatentScalerAdv, np.transpose(DeltaAdvJ))
gradI = ULatentScaler @ U[:, j]
gradJAdv = ((U[:, i].T + DeltaAdvI) @ ULatentScaler).T
gradJ = (U[:, i].T @ ULatentScaler).T
gradBilinear = sideBilinear[:, i] @ sideBilinear[:, j].T
gradBilinearAdv = sideBilinear[:,
i] @ sideBilinear[:,
j].T + sideBilinear[:,
j] @ DeltaAdvXI + sideBilinear[:,
i] @ DeltaAdvXJ + DeltaAdvXI @ DeltaAdvXJ.T
gradBias = 1
gradLatentScaler = U[:, i] @ U[:, j].T
gradLatentScalerAdv = U[:,
i] @ U[:,
j].T + U[:,
j] @ DeltaAdvI + U[:,
i] @ DeltaAdvJ + DeltaAdvI @ DeltaAdvJ.T
#updates
U[:, i] = U[:, i] - etaLatent * (
gradscaler * gradI + alpha * gradscalerAdv * gradIAdv
) # U_i è di dimensione 2x1
U[:, j] = U[:, j] - etaLatent * (gradscaler * gradJ +
alpha * gradscalerAdv * gradJAdv)
UBias[i] = UBias[i] - etaRowBias * (gradscaler * gradBias)
UBias[j] = UBias[j] - etaRowBias * (gradscaler * gradBias)
ULatentScaler = ULatentScaler - etaLatentScaler * (
gradscaler * gradLatentScaler +
alpha * gradscalerAdv * gradLatentScalerAdv)
WBilinear = WBilinear - etaBilinear * (
gradscaler * gradBilinear +
alpha * gradscalerAdv * gradBilinearAdv)
if e % aucStep == 0:
prediction = predict(U, UBias, ULatentScaler, WBilinear,
sideBilinear)
acc, rec, prec, auc, f1 = test(prediction, validationSet)
hr = hitratio(U, ULatentScaler, UBias, WBilinear, validationSet,
testOnes)
plotAdvHR[aucCounter, 0] = hr
plotAdv[aucCounter, 0] = auc
aucCounter += 1
return U, UBias, ULatentScaler, WBilinear, plotAdv, plotAdvHR
def __compute(self):
self.CAR = np.cumsum(self.AR)
self.var_CAR = [(i * var) for i, var in enumerate(self.var_AR, 1)]
self.tstat = self.CAR / np.sqrt(self.var_CAR)
self.pvalue = 1.0 - t.cdf(abs(self.tstat), self.df)
#Berechnung der Energien,nach linearer Skala in MeV
E0=4
E1=E0/796
E=E1*Emax
print('Energien der alpha-Teilchen=', E)
#Berechnung der effektiven Länge
p0=1013
x0=2
x=x0*(p/p0)
print('effektive Länge=',x)
def f(x, a, b):
return a*x+b
params, cov = curve_fit(f, x1, N1)
errors = np.sqrt(np.diag(cov))
print('a =', params[0], '±', errors[0])
print('b =', params[1], '±', errors[1])
a=ufloat(params[0], errors[0])
b=ufloat(params[1], errors[1])
t=np.linspace(0.6, 8)
#plt.plot (x, y, 'rx', label='Messwerte')
plt.plot(x,N , 'rx', label='Messwerte')
#plt.plot(t,f(t, *params), 'b-' ,label='Ausgleichsgerade')
plt.axhline(y=67691)
plt.text(-0.1, 65707,'N/2')
#plt.axvline(x=7.4)
#plt.yscale('log')
plt.xlabel(r'$ x/cm$')
#程序文件Pex3_20.py
import numpy as np
from scipy.integrate import tplquad
f = lambda z, y, x: z * np.sqrt(x**2 + y**2 + 1)
ybd = lambda x: np.sqrt(2 * x - x**2)
print("I=", tplquad(f, 0, 2, lambda x: -ybd(x), ybd, 0, 6))
def draw(m_, V_, z):
raise NotImplementedError
ns = V_.shape[0]
m = sp.array([[i for i in (m_)]])
V = copy.copy(V_)
R = sp.empty([ns, z])
libGP.drawk(V.ctypes.data_as(ctpd), cint(ns), R.ctypes.data_as(ctpd),
cint(z))
R += sp.hstack([m.T] * z)
#R=sp.random.multivariate_normal(m.flatten(),V,z)
return copy.copy(R).T
from scipy import stats
SQRT_1_2PI = 1 / np.sqrt(2 * np.pi)
def EI(m, s, y):
N = len(m)
R = sp.empty(N)
for i in xrange(N):
S = (y - m[i]) / s[i]
c = stats.norm.cdf(S)
p = stats.norm.pdf(S)
R[i] = (y - m[i]) * c + s[i] * p
return R
def lEI(m, s, y):
N = len(m)
def infer_LCB(self, X, D, p):
m, v = self.infer_diag(X, D)
return m - p * np.sqrt(v)
def G_mapping(
latents_in, # First input: Latent vectors (Z) [minibatch, latent_size].
labels_in, # Second input: Conditioning labels [minibatch, label_size].
latent_size=512, # Latent vector (Z) dimensionality.
label_size=0, # Label dimensionality, 0 if no labels.
dlatent_size=512, # Disentangled latent (W) dimensionality.
dlatent_broadcast=None, # Output disentangled latent (W) as [minibatch, dlatent_size] or [minibatch, dlatent_broadcast, dlatent_size].
mapping_layers=8, # Number of mapping layers.
mapping_fmaps=512, # Number of activations in the mapping layers.
mapping_lrmul=0.01, # Learning rate multiplier for the mapping layers.
mapping_nonlinearity='lrelu', # Activation function: 'relu', 'lrelu'.
use_wscale=True, # Enable equalized learning rate?
normalize_latents=True, # Normalize latent vectors (Z) before feeding them to the mapping layers?
dtype='float32', # Data type to use for activations and outputs.
**_kwargs): # Ignore unrecognized keyword args.
act, gain = {
'relu': (tf.nn.relu, np.sqrt(2)),
'lrelu': (leaky_relu, np.sqrt(2))
}[mapping_nonlinearity]
# Inputs.
latents_in.set_shape([None, latent_size])
labels_in.set_shape([None, label_size])
latents_in = tf.cast(latents_in, dtype)
labels_in = tf.cast(labels_in, dtype)
x = latents_in
# Embed labels and concatenate them with latents.
if label_size:
with tf.variable_scope('LabelConcat'):
w = tf.get_variable('weight',
shape=[label_size, latent_size],
initializer=tf.initializers.random_normal())
y = tf.matmul(labels_in, tf.cast(w, dtype))
x = tf.concat([x, y], axis=1)
# Normalize latents.
if normalize_latents:
x = pixel_norm(x)
# Mapping layers.
for layer_idx in range(mapping_layers):
with tf.variable_scope('Dense%d' % layer_idx):
fmaps = dlatent_size if layer_idx == mapping_layers - 1 else mapping_fmaps
x = dense(x,
fmaps=fmaps,
gain=gain,
use_wscale=use_wscale,
lrmul=mapping_lrmul)
x = apply_bias(x, lrmul=mapping_lrmul)
x = act(x)
# Broadcast.
if dlatent_broadcast is not None:
with tf.variable_scope('Broadcast'):
x = tf.tile(x[:, np.newaxis], [1, dlatent_broadcast, 1])
# Output.
assert x.dtype == tf.as_dtype(dtype)
return tf.identity(x, name='dlatents_out')
def __init__(self, fwhm, ratio=1.0, theta=0.0, sigma_radius=1.5,
normalize_zerosum=True):
if fwhm < 0:
raise ValueError('fwhm must be positive.')
if ratio <= 0 or ratio > 1:
raise ValueError('ratio must be positive and less or equal '
'than 1.')
if sigma_radius <= 0:
raise ValueError('sigma_radius must be positive.')
self.fwhm = fwhm
self.ratio = ratio
self.theta = theta
self.sigma_radius = sigma_radius
self.xsigma = self.fwhm * gaussian_fwhm_to_sigma
self.ysigma = self.xsigma * self.ratio
theta_radians = np.deg2rad(self.theta)
cost = np.cos(theta_radians)
sint = np.sin(theta_radians)
xsigma2 = self.xsigma**2
ysigma2 = self.ysigma**2
self.a = (cost**2 / (2.0 * xsigma2)) + (sint**2 / (2.0 * ysigma2))
# CCW
self.b = 0.5 * cost * sint * ((1.0 / xsigma2) - (1.0 / ysigma2))
self.c = (sint**2 / (2.0 * xsigma2)) + (cost**2 / (2.0 * ysigma2))
# find the extent of an ellipse with radius = sigma_radius*sigma;
# solve for the horizontal and vertical tangents of an ellipse
# defined by g(x,y) = f
self.f = self.sigma_radius**2 / 2.0
denom = (self.a * self.c) - self.b**2
# nx and ny are always odd
self.nx = 2 * int(max(2, math.sqrt(self.c * self.f / denom))) + 1
self.ny = 2 * int(max(2, math.sqrt(self.a * self.f / denom))) + 1
self.xc = self.xradius = self.nx // 2
self.yc = self.yradius = self.ny // 2
# define the kernel on a 2D grid
yy, xx = np.mgrid[0:self.ny, 0:self.nx]
self.circular_radius = np.sqrt((xx - self.xc)**2 + (yy - self.yc)**2)
self.elliptical_radius = (self.a * (xx - self.xc)**2
+ 2.0 * self.b * (xx - self.xc)
* (yy - self.yc)
+ self.c * (yy - self.yc)**2)
self.mask = np.where(
(self.elliptical_radius <= self.f)
| (self.circular_radius <= 2.0), 1, 0).astype(int)
self.npixels = self.mask.sum()
# NOTE: the central (peak) pixel of gaussian_kernel has a value of 1.
self.gaussian_kernel_unmasked = np.exp(-self.elliptical_radius)
self.gaussian_kernel = self.gaussian_kernel_unmasked * self.mask
# denom = variance * npixels
denom = ((self.gaussian_kernel**2).sum()
- (self.gaussian_kernel.sum()**2 / self.npixels))
self.relerr = 1.0 / np.sqrt(denom)
# normalize the kernel to zero sum
if normalize_zerosum:
self.data = ((self.gaussian_kernel
- (self.gaussian_kernel.sum() / self.npixels))
/ denom) * self.mask
else:
self.data = self.gaussian_kernel
self.shape = self.data.shape
