def change_pmin_by_innovation(self, x, f):
Lx, _ = self._gp_innovation_local(x)
dMdb = Lx
dVdb = -Lx.dot(Lx.T)
stoch_changes = dMdb.dot(self.W)
Mb_new = self.Mb[:, None] + stoch_changes
Vb_new = self.Vb + dVdb
Vb_new[np.diag_indices(Vb_new.shape[0])] = np.clip(Vb_new[np.diag_indices(Vb_new.shape[0])], np.finfo(Vb_new.dtype).eps, np.inf)
Vb_new[np.where((Vb_new < np.finfo(Vb_new.dtype).eps) & (Vb_new > -np.finfo(Vb_new.dtype).eps))] = 0
try:
cVb_new = np.linalg.cholesky(Vb_new)
except np.linalg.LinAlgError:
try:
cVb_new = np.linalg.cholesky(Vb_new + 1e-10 * np.eye(Vb_new.shape[0]))
except np.linalg.LinAlgError:
try:
cVb_new = np.linalg.cholesky(Vb_new + 1e-6 * np.eye(Vb_new.shape[0]))
except np.linalg.LinAlgError:
cVb_new = np.linalg.cholesky(Vb_new + 1e-3 * np.eye(Vb_new.shape[0]))
f_new = np.dot(cVb_new, self.F.T)
f_new = f_new[:, :, None]
Mb_new = Mb_new[:, None, :]
f_new = Mb_new + f_new
return self.calc_pmin(f_new)
def predict(self, X_test, **kwargs):
# Normalize input data to 0 mean and unit std
X_ = (X_test - self.X_mean) / self.X_std
# Get features from the net
layers = lasagne.layers.get_all_layers(self.network)
theta = lasagne.layers.get_output(layers[:-1], X_)[-1].eval()
# Marginalise predictions over hyperparameters of the BLR
mu = np.zeros([len(self.models), X_test.shape[0]])
var = np.zeros([len(self.models), X_test.shape[0]])
for i, m in enumerate(self.models):
mu[i], var[i] = m.predict(theta)
# See the algorithm runtime prediction paper by Hutter et al
# for the derivation of the total variance
m = np.array([[mu.mean()]])
v = np.mean(mu ** 2 + var) - m ** 2
# Clip negative variances and set them to the smallest
# positive float value
if v.shape[0] == 1:
v = np.clip(v, np.finfo(v.dtype).eps, np.inf)
else:
v[np.diag_indices(v.shape[0])] = \
np.clip(v[np.diag_indices(v.shape[0])],
np.finfo(v.dtype).eps, np.inf)
v[np.where((v < np.finfo(v.dtype).eps) & (v > -np.finfo(v.dtype).eps))] = 0
return m, v
def _laplacian_dense(csgraph, normed='geometric', symmetrize=True, scaling_epps=0., renormalization_exponent=1, return_diag=False, return_lapsym = False):
n_nodes = csgraph.shape[0]
if symmetrize:
lap = (csgraph + csgraph.T)/2.
else:
lap = csgraph.copy()
degrees = np.asarray(lap.sum(axis=1)).squeeze()
di = np.diag_indices( lap.shape[0] ) # diagonal indices
if normed == 'symmetricnormalized':
w = np.sqrt(degrees)
w_zeros = (w == 0)
w[w_zeros] = 1
lap /= w
lap /= w[:, np.newaxis]
di = np.diag_indices( lap.shape[0] )
lap[di] -= (1 - w_zeros).astype(lap.dtype)
if normed == 'geometric':
w = degrees.copy() # normalize once symmetrically by d
w_zeros = (w == 0)
w[w_zeros] = 1
lap /= w
lap /= w[:, np.newaxis]
w = np.asarray(lap.sum(axis=1)).squeeze() #normalize again asymmetricall
if return_lapsym:
lapsym = lap.copy()
lap /= w[:, np.newaxis]
lap[di] -= (1 - w_zeros).astype(lap.dtype)
if normed == 'renormalized':
w = degrees**renormalization_exponent;
# same as 'geometric' from here on
w_zeros = (w == 0)
w[w_zeros] = 1
lap /= w
lap /= w[:, np.newaxis]
w = np.asarray(lap.sum(axis=1)).squeeze() #normalize again asymmetricall
if return_lapsym:
lapsym = lap.copy()
lap /= w[:, np.newaxis]
lap[di] -= (1 - w_zeros).astype(lap.dtype)
if normed == 'unnormalized':
dum = lap[di]-degrees[np.newaxis,:]
lap[di] = dum[0,:]
if normed == 'randomwalk':
lap /= degrees[:,np.newaxis]
lap -= np.eye(lap.shape[0])
if scaling_epps > 0.:
lap *= 4/(scaling_epps**2)
if return_diag:
diag = np.array( lap[di] )
if return_lapsym:
return lap, diag, lapsym, w
else:
return lap, diag
elif return_lapsym:
return lap, lapsym, w
else:
return lap
def _gamma1_intermediates(mycc, t1, t2, l1, l2, eris=None):
doo, dov, dvo, dvv = gccsd_rdm._gamma1_intermediates(mycc, t1, t2, l1, l2)
if eris is None: eris = mycc.ao2mo()
nocc, nvir = t1.shape
bcei = numpy.asarray(eris.ovvv).conj().transpose(3,2,1,0)
majk = numpy.asarray(eris.ooov).conj().transpose(2,3,0,1)
bcjk = numpy.asarray(eris.oovv).conj().transpose(2,3,0,1)
mo_e = eris.mo_energy
eia = mo_e[:nocc,None] - mo_e[nocc:]
d3 = lib.direct_sum('ia+jb+kc->ijkabc', eia, eia, eia)
t3c =(numpy.einsum('jkae,bcei->ijkabc', t2, bcei)
- numpy.einsum('imbc,majk->ijkabc', t2, majk))
t3c = t3c - t3c.transpose(0,1,2,4,3,5) - t3c.transpose(0,1,2,5,4,3)
t3c = t3c - t3c.transpose(1,0,2,3,4,5) - t3c.transpose(2,1,0,3,4,5)
t3c /= d3
t3d = numpy.einsum('ia,bcjk->ijkabc', t1, bcjk)
t3d += numpy.einsum('ai,jkbc->ijkabc', eris.fock[nocc:,:nocc], t2)
t3d = t3d - t3d.transpose(0,1,2,4,3,5) - t3d.transpose(0,1,2,5,4,3)
t3d = t3d - t3d.transpose(1,0,2,3,4,5) - t3d.transpose(2,1,0,3,4,5)
t3d /= d3
goo = numpy.einsum('iklabc,jklabc->ij', (t3c+t3d).conj(), t3c) * (1./12)
gvv = numpy.einsum('ijkacd,ijkbcd->ab', t3c+t3d, t3c.conj()) * (1./12)
doo[numpy.diag_indices(nocc)] -= goo.diagonal()
dvv[numpy.diag_indices(nvir)] += gvv.diagonal()
dvo += numpy.einsum('ijab,ijkabc->ck', t2.conj(), t3c) * (1./4)
return doo, dov, dvo, dvv
def test_eigenvectors(self):
P = self.bdc.transition_matrix()
# k==None
ev = eigvals(P)
ev = ev[np.argsort(np.abs(ev))[::-1]]
Dn = np.diag(ev)
# right eigenvectors
Rn = eigenvectors(P)
assert_allclose(np.dot(P,Rn),np.dot(Rn,Dn))
# left eigenvectors
Ln = eigenvectors(P, right=False)
assert_allclose(np.dot(Ln.T,P),np.dot(Dn,Ln.T))
# orthogonality
Xn = np.dot(Ln.T, Rn)
di = np.diag_indices(Xn.shape[0])
Xn[di] = 0.0
assert_allclose(Xn,0)
# k!=None
Dnk = Dn[:,0:self.k][0:self.k,:]
# right eigenvectors
Rn = eigenvectors(P, k=self.k)
assert_allclose(np.dot(P,Rn),np.dot(Rn,Dnk))
# left eigenvectors
Ln = eigenvectors(P, right=False, k=self.k)
assert_allclose(np.dot(Ln.T,P),np.dot(Dnk,Ln.T))
# orthogonality
Xn = np.dot(Ln.T, Rn)
di = np.diag_indices(self.k)
Xn[di] = 0.0
assert_allclose(Xn,0)
def test_diag_indices():
di = diag_indices(4)
a = array([[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16]])
a[di] = 100
yield (assert_array_equal, a,
array([[100, 2, 3, 4],
[ 5, 100, 7, 8],
[ 9, 10, 100, 12],
[ 13, 14, 15, 100]]))
# Now, we create indices to manipulate a 3-d array:
d3 = diag_indices(2, 3)
# And use it to set the diagonal of a zeros array to 1:
a = zeros((2, 2, 2),int)
a[d3] = 1
yield (assert_array_equal, a,
array([[[1, 0],
[0, 0]],
[[0, 0],
[0, 1]]]) )
def _make_rdm1(mycc, d1, with_frozen=True, ao_repr=False):
'''dm1[p,q] = <q_alpha^\dagger p_alpha> + <q_beta^\dagger p_beta>
The convention of 1-pdm is based on McWeeney's book, Eq (5.4.20).
The contraction between 1-particle Hamiltonian and rdm1 is
E = einsum('pq,qp', h1, rdm1)
'''
doo, dov, dvo, dvv = d1
nocc, nvir = dov.shape
nmo = nocc + nvir
dm1 = numpy.empty((nmo,nmo), dtype=doo.dtype)
dm1[:nocc,:nocc] = doo + doo.conj().T
dm1[:nocc,nocc:] = dov + dvo.conj().T
dm1[nocc:,:nocc] = dm1[:nocc,nocc:].conj().T
dm1[nocc:,nocc:] = dvv + dvv.conj().T
dm1[numpy.diag_indices(nocc)] += 2
if with_frozen and not (mycc.frozen is 0 or mycc.frozen is None):
nmo = mycc.mo_occ.size
nocc = numpy.count_nonzero(mycc.mo_occ > 0)
rdm1 = numpy.zeros((nmo,nmo), dtype=dm1.dtype)
rdm1[numpy.diag_indices(nocc)] = 2
moidx = numpy.where(mycc.get_frozen_mask())[0]
rdm1[moidx[:,None],moidx] = dm1
dm1 = rdm1
if ao_repr:
mo = mycc.mo_coeff
dm1 = lib.einsum('pi,ij,qj->pq', mo, dm1, mo.conj())
return dm1
def predict_wishard_embedding(self, Xnew, kern=None, mean=True, covariance=True):
"""
Predict the wishard embedding G of the GP. This is the density of the
input of the GP defined by the probabilistic function mapping f.
G = J_mean.T*J_mean + output_dim*J_cov.
:param array-like Xnew: The points at which to evaluate the magnification.
:param :py:class:`~GPy.kern.Kern` kern: The kernel to use for the magnification.
Supplying only a part of the learning kernel gives insights into the density
of the specific kernel part of the input function. E.g. one can see how dense the
linear part of a kernel is compared to the non-linear part etc.
"""
if kern is None:
kern = self.kern
mu_jac, var_jac = self.predict_jacobian(Xnew, kern, full_cov=False)
mumuT = np.einsum('iqd,ipd->iqp', mu_jac, mu_jac)
Sigma = np.zeros(mumuT.shape)
if var_jac.ndim == 3:
Sigma[(slice(None), )+np.diag_indices(Xnew.shape[1], 2)] = var_jac.sum(-1)
else:
Sigma[(slice(None), )+np.diag_indices(Xnew.shape[1], 2)] = self.output_dim*var_jac
G = 0.
if mean:
G += mumuT
if covariance:
G += Sigma
return G
def compute_genetic_distance(X, **kwargs):
"""Given genotype matrix X, returns pairwise genetic distance between individuals
using the estimator described in Theorem 1.
Args:
X: n * p matrix of 0/1/2/nan, n is #individuals, p is #SNPs
"""
n, p = X.shape
missing = np.isnan(X)
col_sums = np.nansum(X, axis=0)
col_counts = np.sum(~missing, axis=0)
mu_hat = col_sums / 2. / col_counts # p dimensional
eta0_hat = np.nansum(X**2 - X, axis=0) / 2. / col_counts - mu_hat**2
X_tmp = X/2.
X_tmp[missing] = 0
non_missing = np.array(~missing, dtype=float)
X_shifted = X_tmp - mu_hat
gdm_squared = 2. * np.mean(eta0_hat) - 2. * np.dot(X_shifted, X_shifted.T) / np.dot(non_missing, non_missing.T)
gdm_squared[np.diag_indices(n)] = 0.
if len(gdm_squared[gdm_squared < 0]) > 0:
# shift all entries by the smallest amount that makes them non-negative
shift = - np.min(gdm_squared[gdm_squared < 0])
gdm_squared += shift
gdm_squared[np.diag_indices(n)] = 0.
gdm = np.sqrt(np.maximum(gdm_squared, 0.))
return gdm
def test_calculate2():
# Two mutations in one cluster, two in second
c = np.zeros((4,2))
c[0:2,0] = 1
c[2:4,1] = 1
c = np.dot(c,c.T)
# Identical
assert round(calculate2(c,c), 2) == 1.00
#Inverted
c2 = np.abs(c-1)
c2[np.diag_indices(4)] = 1
assert round(calculate2(c,c2), 2) == -0.68
# Differences first 3 SSMs in first cluster, 4th ssm in second cluster
c3 = np.zeros((4,2))
c3[0:3,0] = 1
c3[3:4,1] = 1
c3 = np.dot(c3,c3.T)
assert round(calculate2(c,c3), 2) == -0.20
# Metric doesn't count the diagnonal
c4 = c+0
c4[np.diag_indices(4)] = 0
assert round(calculate2(c,c4), 2) == 0.74
c2[np.diag_indices(4)] = 0
assert round(calculate2(c,c2), 2) == -0.23
def kNN_graph(self, k, metric, mutual=False):
# self.latex = []
nn = NearestNeighbors(k, algorithm="brute", metric=metric, n_jobs=-1).fit(self.X)
UAM = nn.kneighbors_graph(self.X).toarray() #unweighted adjacency matrix
m = UAM.shape[0]
self.W = np.zeros((m, m)) #(weighted) adjecancy matrix
self.D = np.zeros((m, m)) #degree matrix
if mutual == False:
if self.full_calculated:
indices = np.where(UAM == 1)
self.W[indices] = self.full_W[indices]
self.D[np.diag_indices(m)] = np.sum(self.W, 1)
else:
for i in range(m):
for j in range(m):
if UAM[i,j] == 1:
sim = self.s(self.X[i], self.X[j], self.d)
self.W[i,j] = sim
self.D[i,i] += sim
else:
if self.full_calculated:
indices = np.where(np.logical_and(UAM == 1, UAM.T == 1).astype(int) == 1)
self.W[indices] = self.full_W[indices]
self.D[np.diag_indices(m)] = np.sum(self.W != 0, 1)
else:
for i in range(m):
for j in range(m):
if UAM[i,j] == 1 and UAM[j,i] == 1:
sim = self.s(self.X[i], self.X[j], self.d)
self.W[i,j] = sim
self.D[i,i] += sim
self.W = np.nan_to_num(self.W)
self.graph = "kNN graph, k = " + str(k) + ", mutual:" + str(mutual)
def test_naf_layer_full():
batch_size = 2
for nb_actions in (1, 3):
# Construct single model with NAF as the only layer, hence it is fully deterministic
# since no weights are used, which would be randomly initialized.
L_flat_input = Input(shape=((nb_actions * nb_actions + nb_actions) // 2,))
mu_input = Input(shape=(nb_actions,))
action_input = Input(shape=(nb_actions,))
x = NAFLayer(nb_actions, mode='full')([L_flat_input, mu_input, action_input])
model = Model(inputs=[L_flat_input, mu_input, action_input], outputs=x)
model.compile(loss='mse', optimizer='sgd')
# Create random test data.
L_flat = np.random.random((batch_size, (nb_actions * nb_actions + nb_actions) // 2)).astype('float32')
mu = np.random.random((batch_size, nb_actions)).astype('float32')
action = np.random.random((batch_size, nb_actions)).astype('float32')
# Perform reference computations in numpy since these are much easier to verify.
L = np.zeros((batch_size, nb_actions, nb_actions)).astype('float32')
LT = np.copy(L)
for l, l_T, l_flat in zip(L, LT, L_flat):
l[np.tril_indices(nb_actions)] = l_flat
l[np.diag_indices(nb_actions)] = np.exp(l[np.diag_indices(nb_actions)])
l_T[:, :] = l.T
P = np.array([np.dot(l, l_T) for l, l_T in zip(L, LT)]).astype('float32')
A_ref = np.array([np.dot(np.dot(a - m, p), a - m) for a, m, p in zip(action, mu, P)]).astype('float32')
A_ref *= -.5
# Finally, compute the output of the net, which should be identical to the previously
# computed reference.
A_net = model.predict([L_flat, mu, action]).flatten()
assert_allclose(A_net, A_ref, rtol=1e-5)
def beam_search(dec,state,y,data,beam_width,mydict_inv):
beam_width=beam_width
xp=cuda.cupy
batchsize=data.shape[0]
vocab_size=len(mydict_inv)
topk=20
route = np.zeros((batchsize,beam_width,50)).astype(np.int32)
for j in range(50):
if j == 0:
y = Variable(xp.array(np.argmax(y.data.get(), axis=1)).astype(xp.int32))
state,y = dec(y, state, train=False)
h=state['h1'].data
c=state['c1'].data
h=xp.tile(h.reshape(batchsize,1,-1), (1,beam_width,1))
c=xp.tile(c.reshape(batchsize,1,-1), (1,beam_width,1))
ptr=F.log_softmax(y).data.get()
pred_total_city = np.argsort(ptr)[:,::-1][:,:beam_width]
pred_total_score = np.sort(ptr)[:,::-1][:,:beam_width]
route[:,:,j] = pred_total_city
pred_total_city=pred_total_city.reshape(batchsize,beam_width,1)
else:
pred_next_score=np.zeros((batchsize,beam_width,topk))
pred_next_city=np.zeros((batchsize,beam_width,topk)).astype(np.int32)
score2idx=np.zeros((batchsize,beam_width,topk)).astype(np.int32)
for b in range(beam_width):
state={'c1':Variable(c[:,b,:]), 'h1':Variable(h[:,b,:])}
cur_city = xp.array([pred_total_city[i,b,j-1] for i in range(batchsize)]).astype(xp.int32)
state,y = dec(cur_city,state, train=False)
h[:,b,:]=state['h1'].data
c[:,b,:]=state['c1'].data
ptr=F.log_softmax(y).data.get()
pred_next_score[:,b,:]=np.sort(ptr, axis=1)[:,::-1][:,:topk]
pred_next_city[:,b,:]=np.argsort(ptr, axis=1)[:,::-1][:,:topk]
h=F.stack([h for i in range(topk)], axis=2).data
c=F.stack([c for i in range(topk)], axis=2).data
pred_total_city = np.tile(route[:,:,:j],(1,1,topk)).reshape(batchsize,beam_width,topk,j)
pred_next_city = pred_next_city.reshape(batchsize,beam_width,topk,1)
pred_total_city = np.concatenate((pred_total_city,pred_next_city),axis=3)
pred_total_score = np.tile(pred_total_score.reshape(batchsize,beam_width,1),(1,1,topk)).reshape(batchsize,beam_width,topk,1)
pred_next_score = pred_next_score.reshape(batchsize,beam_width,topk,1)
pred_total_score += pred_next_score
idx = pred_total_score.reshape(batchsize,beam_width * topk).argsort(axis=1)[:,::-1][:,:beam_width]
pred_total_city = pred_total_city[:,idx//topk, np.mod(idx,topk), :][np.diag_indices(batchsize,ndim=2)].reshape(batchsize,beam_width,j+1)
pred_total_score = pred_total_score[:,idx//topk, np.mod(idx,topk), :][np.diag_indices(batchsize,ndim=2)].reshape(batchsize,beam_width,1)
h = h[:,idx//topk, np.mod(idx,topk), :][np.diag_indices(batchsize,ndim=2)].reshape(batchsize,beam_width,-1)
c = c[:,idx//topk, np.mod(idx,topk), :][np.diag_indices(batchsize,ndim=2)].reshape(batchsize,beam_width,-1)
route[:,:,:j+1] =pred_total_city
if (pred_total_city[:,:,j] == 15).all():
break
return route[:,0,:j+1].tolist()
def derivative_of_marginalLikelihood(self, hyp):
"""
This method calculates the derivative of marginal likelihood.
You may refer to this code to see what methods in numpy is useful
while you are implementing other functions.
Do not modify this method.
"""
trainingData = self.trainingData
trainingLabels = self.trainingLabels
c = len(self.legalLabels)
[n,d] = self.trainingShape
hyp = np.reshape(hyp, self.hypSize)
[mode,_] = self.findMode(trainingData, trainingLabels, hyp)
[t,_] = self.trainingLabels2t(trainingLabels)
Ks = self.calculateCovariance(trainingData, hyp)
[_,[E, M, R, b, totpi, K],_] = self.calculateIntermediateValues(t, mode, Ks)
MRE = np.linalg.solve(M,R.T.dot(E))
MMRE = np.linalg.solve(M.T,MRE)
KWinvinv = E-E.dot(R.dot(MMRE))
KinvWinv = K-K.dot(KWinvinv.dot(K))
partitioned_KinvWinv = np.transpose(np.array(np.split(np.array(np.split(KinvWinv, c)),c,2)),[2,3,1,0])
s2 = np.zeros([n,c])
for i in range(n):
pi_n = softmax(np.reshape(mode,[c,n])[:,i:i+1].T).T
pipj = pi_n.dot(pi_n.T)
pi_3d = np.zeros([c,c,c])
pi_3d[np.diag_indices(c,3)] = pi_n.ravel()
pipjpk = np.tensordot(pi_n,np.reshape(pipj,(1,c,c)),(1,0))
pipj_3d = np.zeros([c,c,c])
pipj_3d[np.diag_indices(c)] = pipj
W_3d = pi_3d + 2 * pipjpk - pipj_3d - np.transpose(pipj_3d,[2,1,0]) - np.transpose(pipj_3d,[1,2,0])
s2[i,:] = -0.5*np.trace(partitioned_KinvWinv[i,i].dot(W_3d))
b_rs = np.reshape(b, [c,n])
dZ = np.zeros(hyp.shape)
for j in range(2):
cs = []
zeroCs = [np.zeros([n,n]) for i in range(c)]
for i in range(c):
C = self.covARD(hyp[i,:],trainingData,None,j)
dZ[i,j] = 0.5*b_rs[i,:].T.dot(C.dot(b_rs[i,:]))
zeroCs[i] = C
cs.append(self.block_diag(zeroCs))
zeroCs[i] = np.zeros([n,n])
for i in range(c):
dd = cs[i].dot(t-totpi)
s3 = dd - K.dot(KWinvinv.dot(dd))
dZ[i,j] += - 0.5 * np.trace(KWinvinv.dot(cs[i])) + s2.T.ravel().dot(s3) #
return -dZ.ravel()
def main():
order = 4
cov, inv_cov, inv_cov_est, t_direct, t_patches = get_mats(order)
header = '=== inv_scale_length:{:.1f}: ==='
header = header.format(inv_scale_length)
print(header, file=logf)
print('Time to invert directly: {:.4f} s'.format(t_direct), file=logf)
maybe_zero = inv_cov_est - inv_cov_est.T
for i in range(npix):
for j in range(npix):
print(maybe_zero[i][j])
return 0
#patch_identity.shape = shape
t2 = time()
print('Time to invert by patches: {:.4f} s'.format(t2-t1), file=logf)
I = np.dot(inv_cov, cov)
I_est = np.dot(inv_cov_est, cov)
t3 = time()
print('Time to calculate checks: {:.4f} s'.format(t3-t2), file=logf)
I_diag = I[np.diag_indices(I.shape[0])]
I_diag_dev = np.max(np.abs(I_diag-1.))
I[np.diag_indices(I.shape[0])] = 0.
I_off_diag_dev = np.max(np.abs(I))
print('C^-1 C:', file=logf)
print(' * max. abs. dev. along diagonal: {:.3g}'.format(I_diag_dev), file=logf)
print(' * max. abs. dev. off diagonal: {:.3g}'.format(I_off_diag_dev), file=logf)
I_diag = I_est[np.diag_indices(I_est.shape[0])]
I_diag_dev = np.max(np.abs(I_diag-1.))
I_est[np.diag_indices(I_est.shape[0])] = 0.
I_off_diag_dev = np.max(np.abs(I_est))
print('(C^-1)_{est} C:', file=logf)
print(' * max. abs. dev. along diagonal: {:.3g}'.format(I_diag_dev), file=logf)
print(' * max. abs. dev. off diagonal: {:.3g}'.format(I_off_diag_dev), file=logf)
#plt.imsave('dist.png', dist, cmap=colors.inferno_r)
row2fig('dist.png', dist)
plt.imsave('dist_matrix.png', dist_mat, cmap=colors.inferno_r)
plt.imsave('cov.png', cov, cmap=colors.inferno_r)
vmax = np.max(np.abs(inv_cov))
plt.imsave('inv_cov.png', inv_cov, cmap='coolwarm_r', vmin=-vmax, vmax=vmax)
vmax = np.max(np.abs(inv_cov_est))
plt.imsave('inv_cov_est.png', inv_cov_est, cmap='coolwarm_r', vmin=-vmax, vmax=vmax)
row2fig('inv_cov_est_row.png', inv_cov_est[1387])
with open('inv_cov_est_diag.txt', 'w') as icer:
for i in range(npix):
print(np.max(inv_cov_est[i]), file=icer)
with open('inv_cov_diag.txt', 'w') as icer:
for i in range(npix):
print(np.max(inv_cov[i]), file=icer)
#plt.imsave('patches.png', patch_identity, cmap=colors.inferno_r)
row2fig('patches.png', patch_identity)
return 0
def convert(self, network):
""" Generates an n-dimensional connectivity matrix. """
if not isinstance(network, NeuralNetwork):
network = NeuralNetwork(network)
os = np.atleast_1d(self.substrate_shape)
# Unpack the genotype
w, f = network.cm.copy(), network.node_types[:]
# Create substrate
if len(os) == 1:
cm = np.mgrid[-1:1:os[0]*1j,-1:1:os[0]*1j].transpose((1,2,0))
elif len(os) == 2:
cm = np.mgrid[-1:1:os[0]*1j,-1:1:os[1]*1j,-1:1:os[0]*1j,-1:1:os[1]*1j].transpose(1,2,3,4,0)
else:
raise NotImplementedError("3+D substrates not supported yet.")
# Insert a bias
cm = np.insert(cm, 0, 1.0, -1)
# Check if the genotype has enough weights
if w.shape[0] < cm.shape[-1]:
raise Exception("Genotype weight matrix is too small (%s)" % (w.shape,) )
# Append zeros
n_elems = len(f)
nvals = np.zeros(cm.shape[:-1] + (n_elems - cm.shape[-1],))
cm = np.concatenate((cm, nvals), -1)
shape = cm.shape
# Fix the input elements
frozen = len(os) * 2 + 1
w[:frozen] = 0.0
w[np.diag_indices(frozen, 2)] = 1.0
f[:frozen] = [lambda x: x] * frozen
w[np.diag_indices(n_elems)] = (1 - self.recursion) * w[np.diag_indices(n_elems)] + self.recursion
# Compute the reaction
self._steps = []
laplacian = np.empty_like(cm[..., frozen:])
kernel = self.diffusion * np.array([1.,2.,1.])
for _ in range(self.reaction_steps):
cm = np.dot(w, cm.reshape((-1, n_elems)).T)
cm = np.clip(cm, self.cm_range[0], self.cm_range[1])
for el in xrange(cm.shape[0]):
cm[el,:] = f[el](cm[el,:])
cm = cm.T.reshape(shape)
# apply diffusion
laplacian[:] = 0.0
for ax in xrange(cm.ndim - 1):
laplacian += scipy.ndimage.filters.convolve1d(cm[..., frozen:], kernel, axis=ax, mode='constant')
cm[..., frozen:] += laplacian
self._steps.append(cm[...,-1])
# Return the values of the last element (indicating connectivity strength)
output = cm[..., -1]
# Build a network object
net = NeuralNetwork().from_matrix(output)
if self.sandwich:
net.make_sandwich()
return net
def update_amps(cc, t1, t2, eris):
assert(isinstance(eris, _PhysicistsERIs))
nocc, nvir = t1.shape
fock = eris.fock
fov = fock[:nocc,nocc:]
mo_e_o = eris.mo_energy[:nocc]
mo_e_v = eris.mo_energy[nocc:] + cc.level_shift
tau = imd.make_tau(t2, t1, t1)
Fvv = imd.cc_Fvv(t1, t2, eris)
Foo = imd.cc_Foo(t1, t2, eris)
Fov = imd.cc_Fov(t1, t2, eris)
Woooo = imd.cc_Woooo(t1, t2, eris)
Wvvvv = imd.cc_Wvvvv(t1, t2, eris)
Wovvo = imd.cc_Wovvo(t1, t2, eris)
# Move energy terms to the other side
Fvv[np.diag_indices(nvir)] -= mo_e_v
Foo[np.diag_indices(nocc)] -= mo_e_o
# T1 equation
t1new = einsum('ie,ae->ia', t1, Fvv)
t1new += -einsum('ma,mi->ia', t1, Foo)
t1new += einsum('imae,me->ia', t2, Fov)
t1new += -einsum('nf,naif->ia', t1, eris.ovov)
t1new += -0.5*einsum('imef,maef->ia', t2, eris.ovvv)
t1new += -0.5*einsum('mnae,mnie->ia', t2, eris.ooov)
t1new += fov.conj()
# T2 equation
Ftmp = Fvv - 0.5*einsum('mb,me->be', t1, Fov)
tmp = einsum('ijae,be->ijab', t2, Ftmp)
t2new = tmp - tmp.transpose(0,1,3,2)
Ftmp = Foo + 0.5*einsum('je,me->mj', t1, Fov)
tmp = einsum('imab,mj->ijab', t2, Ftmp)
t2new -= tmp - tmp.transpose(1,0,2,3)
t2new += np.asarray(eris.oovv).conj()
t2new += 0.5*einsum('mnab,mnij->ijab', tau, Woooo)
t2new += 0.5*einsum('ijef,abef->ijab', tau, Wvvvv)
tmp = einsum('imae,mbej->ijab', t2, Wovvo)
tmp -= -einsum('ie,ma,mbje->ijab', t1, t1, eris.ovov)
tmp = tmp - tmp.transpose(1,0,2,3)
tmp = tmp - tmp.transpose(0,1,3,2)
t2new += tmp
tmp = einsum('ie,jeba->ijab', t1, np.array(eris.ovvv).conj())
t2new += (tmp - tmp.transpose(1,0,2,3))
tmp = einsum('ma,ijmb->ijab', t1, np.asarray(eris.ooov).conj())
t2new -= (tmp - tmp.transpose(0,1,3,2))
eia = mo_e_o[:,None] - mo_e_v
eijab = lib.direct_sum('ia,jb->ijab', eia, eia)
t1new /= eia
t2new /= eijab
return t1new, t2new
def test_general_metric(seed=1234, N=2, ndim=3):
np.random.seed(seed)
_general_metric(np.eye(ndim), N=N, ndim=ndim)
L = np.random.randn(ndim, ndim)
L[np.diag_indices(ndim)] = np.exp(L[np.diag_indices(ndim)])
L[np.triu_indices(ndim, 1)] = 0.0
metric = np.dot(L, L.T)
_general_metric(metric, N=N, ndim=ndim)
def parse_dataFrame(df, df_name):
"""
removes overlapping cells by x, y, and source image.
"""
x = np.array(df['x'])
y = np.array(df['y'])
i = np.array(df['image'])
# create linkage table
dx = x[:, np.newaxis] - x
dy = y[:, np.newaxis] - y
di = (i[:, np.newaxis] != i)*1e9
# set diagonal of linkage table to large number
dx[np.diag_indices(len(dx))] = 1e9
dy[np.diag_indices(len(dy))] = 1e9
di[np.diag_indices(len(di))] = 1e9
# get absolute values
dx = np.abs(dx)
dy = np.abs(dy)
# sum vector of x, y, and image
d = dx + dy + di
# set variable = to min of 0 for vector summation
b = d.min(0)
# create array with zeros of length b
exclude = np.zeros(len(b))
# iterate through index of length b
for i in range(len(b)):
# if True/anything present at exclude[i]; skip
if exclude[i]:
continue
# add True to index position of exclude if d[i] < 30
exclude[(d[i] < 20).nonzero()] = True
d = {}
# create overlap frame
df_overlap = df[exclude == True]
df_overlap = df_overlap.reset_index(drop=True)
# create non_overlap frame
df_nonoverlap = df[exclude == False]
df_nonoverlap = df_nonoverlap.reset_index(drop=True)
d['{}_non_overlap'.format(df_name)] = df_nonoverlap
return d
def on_stiffness(self, factor):
diag3 = np.diag_indices( 3 )
diag6 = np.diag_indices( 6 )
# diagonal
self.stiffness[ diag6 ] = - factor * self.k
# off-diagonal
self.stiffness[ 3:, :3 ][diag3] = factor * self.k
self.stiffness[ :3, 3: ][diag3] = factor * self.k
def do_test(num):
#x_coords,y_coords = np.loadtxt('cats/src_336.tab',comments='#',usecols=(0,1),unpack=True) # read in phot results as arrays
x_coords = np.array(np.random.rand(num)*500.,dtype=np.float32)
y_coords = np.array(np.random.rand(num)*500.,dtype=np.float32)
j_t1 = time.time()
length = len(y_coords)
a = range(length)
j_min_distances = []
for i in a:
indiv_dist = []
for j in a:
if i != j:
indiv_dist.append(np.sqrt((x_coords[i]-x_coords[j])**2+(y_coords[i]-y_coords[j])**2))
j_min_distances.append(np.amin(indiv_dist))
j_t2 = time.time()
####################
a_t1 =time.time()
'''the padding is necessary to allow us to take the transpose.
xpad is just [x]'''
xpad = x_coords[None,:]
'''xpad - xpad.T produces a 2D array with the difference between every
combination of points in xpad. We then wrap in in a masked array.
It's a tad bit faster to square these now.'''
xdiff = np.ma.array(xpad - xpad.T)**2
'''now mask out the diagonal values because those will be zero'''
xdiff[np.diag_indices(xdiff.shape[0])] = np.ma.masked
ypad = y_coords[None, :]
ydiff = np.ma.array(ypad - ypad.T)**2
ydiff[np.diag_indices(ydiff.shape[0])] = np.ma.masked
'''find the minima. The .data makes this a standard numpy
array rather than a masked array'''
a_min_distances = np.min(np.sqrt(xdiff + ydiff),axis=1).data
a_t2 = time.time()
#####################
avgdiff = np.mean(np.abs(j_min_distances - a_min_distances))
stddiff = np.std(np.abs(j_min_distances - a_min_distances))
print "Loop method took {:4.4f} seconds\nNumpy method took {:4.4f} seconds".format(j_t2 - j_t1, a_t2 - a_t1)
print "Results vary by an average of {:4.4f} with a std of {:4.4f}".format(avgdiff,stddiff)
return
def predict(self, X, **kwargs):
r"""
Returns the predictive mean and variance of the objective function
at X average over all hyperparameter samples.
The mean is computed by:
:math \mu(x) = \frac{1}{M}\sum_{i=1}^{M}\mu_m(x)
And the variance by:
:math \sigma^2(x) = (\frac{1}{M}\sum_{i=1}^{M}(\sigma^2_m(x) + \mu_m(x)^2) - \mu^2
Parameters
----------
X: np.ndarray (N, D)
Input test points
Returns
----------
np.array(N,1)
predictive mean
np.array(N,1)
predictive variance
"""
# For EnvES we transform s to (1 - s)^2
if self.basis_func is not None:
X_test = deepcopy(X)
X_test[:, self.dim] = self.basis_func(X_test[:, self.dim])
else:
X_test = X
mu = np.zeros([self.n_hypers, X_test.shape[0]])
var = np.zeros([self.n_hypers, X_test.shape[0]])
for i, model in enumerate(self.models):
mu[i], var[i] = model.predict(X_test)
# See the algorithm runtime prediction paper by Hutter et al
# for the derivation of the total variance
m = np.array([[mu.mean()]])
v = np.mean(mu ** 2 + var) - m ** 2
# Clip negative variances and set them to the smallest
# positive float value
if v.shape[0] == 1:
v = np.clip(v, np.finfo(v.dtype).eps, np.inf)
else:
v[np.diag_indices(v.shape[0])] = \
np.clip(v[np.diag_indices(v.shape[0])],
np.finfo(v.dtype).eps, np.inf)
v[np.where((v < np.finfo(v.dtype).eps) & (v > -np.finfo(v.dtype).eps))] = 0
return m, v
def predict(self, X, full_cov=False):
if self.m == None:
print "ERROR: Model needs to be trained first."
return None
mean, var = self.m.predict(X, full_cov=full_cov)
if not full_cov:
return mean[:, 0], np.clip(var[:, 0], np.finfo(var.dtype).eps, np.inf)
#return mean
else:
var[np.diag_indices(var.shape[0])] = np.clip(var[np.diag_indices(var.shape[0])], np.finfo(var.dtype).eps, np.inf)
var[np.where((var < np.finfo(var.dtype).eps) & (var > -np.finfo(var.dtype).eps))] = 0
return mean[:, 0], var
def _make_rdm1(mycc, d1, with_frozen=True, ao_repr=False):
doo, dOO = d1[0]
dov, dOV = d1[1]
dvo, dVO = d1[2]
dvv, dVV = d1[3]
nocca, nvira = dov.shape
noccb, nvirb = dOV.shape
nmoa = nocca + nvira
nmob = noccb + nvirb
dm1a = numpy.empty((nmoa,nmoa), dtype=doo.dtype)
dm1a[:nocca,:nocca] = doo + doo.conj().T
dm1a[:nocca,nocca:] = dov + dvo.conj().T
dm1a[nocca:,:nocca] = dm1a[:nocca,nocca:].conj().T
dm1a[nocca:,nocca:] = dvv + dvv.conj().T
dm1a *= .5
dm1a[numpy.diag_indices(nocca)] += 1
dm1b = numpy.empty((nmob,nmob), dtype=dOO.dtype)
dm1b[:noccb,:noccb] = dOO + dOO.conj().T
dm1b[:noccb,noccb:] = dOV + dVO.conj().T
dm1b[noccb:,:noccb] = dm1b[:noccb,noccb:].conj().T
dm1b[noccb:,noccb:] = dVV + dVV.conj().T
dm1b *= .5
dm1b[numpy.diag_indices(noccb)] += 1
if with_frozen and not (mycc.frozen is 0 or mycc.frozen is None):
nmoa = mycc.mo_occ[0].size
nmob = mycc.mo_occ[1].size
nocca = numpy.count_nonzero(mycc.mo_occ[0] > 0)
noccb = numpy.count_nonzero(mycc.mo_occ[1] > 0)
rdm1a = numpy.zeros((nmoa,nmoa), dtype=dm1a.dtype)
rdm1b = numpy.zeros((nmob,nmob), dtype=dm1b.dtype)
rdm1a[numpy.diag_indices(nocca)] = 1
rdm1b[numpy.diag_indices(noccb)] = 1
moidx = mycc.get_frozen_mask()
moidxa = numpy.where(moidx[0])[0]
moidxb = numpy.where(moidx[1])[0]
rdm1a[moidxa[:,None],moidxa] = dm1a
rdm1b[moidxb[:,None],moidxb] = dm1b
dm1a = rdm1a
dm1b = rdm1b
if ao_repr:
mo_a, mo_b = mycc.mo_coeff
dm1a = lib.einsum('pi,ij,qj->pq', mo_a, dm1a, mo_a)
dm1b = lib.einsum('pi,ij,qj->pq', mo_b, dm1b, mo_b)
return dm1a, dm1b
def oas_cov(pts):
r"""
Estimate the covariance matrix using the Oracle Approximating Shrinkage algorithm
.. math::
(1 - s)\Sigma + s \mu \mathcal{I}_d
where :math:`\mu = \mathrm{tr}(\Sigma) / d`. This ensures the covariance matrix estimate is
well behaved for small sample sizes.
:param pts:
An `(N, ndim)`-shaped array, containing `N` samples from the target distribution.
This follows the implementation in `scikit-learn
<https://github.com/scikit-learn/scikit-learn/blob/31c5497/
sklearn/covariance/shrunk_covariance_.py>`_.
"""
pts = np.atleast_2d(pts)
npts, ndim = pts.shape
emperical_cov = np.cov(pts.T)
mean = np.trace(emperical_cov) / ndim
alpha = np.mean(emperical_cov * emperical_cov)
num = alpha + mean * mean
den = (npts + 1) * (alpha - (mean * mean) / ndim)
shrinkage = min(num / den, 1)
shrunk_cov = (1 - shrinkage) * emperical_cov
shrunk_cov[np.diag_indices(ndim)] += shrinkage * mean
return shrunk_cov
def is_diagonal(A):
"""Returns true iff A is a diagonal matrix."""
A = to_square_array(A)
u = np.diag_indices(len(A))
check = np.zeros_like(A)
check[u] = A[u]
return np.allclose(A, check)
def decompose_tensor(self, rank, X=None, init='nvecs', lambda_A=10, lambda_R=10, *args, **kwargs):
"""Decompose adjacency tensor using RESCAL_ALS
"""
# if X not specified, used X attribute if exists
if X is None:
if hasattr(self, 'X'):
X = self.X
else:
print("netCreate object has no X attribute. Need to Provide X argument.")
# Set logging to INFO to see RESCAL information
logging.basicConfig(level=logging.INFO)
A, R, fit, itr, exectimes = als(X, rank=rank, init=init, lambda_A=lambda_A, lambda_R=lambda_R)
self.rescal_params = {'rank':rank,'fit':fit,'lambda_A':lambda_A,'lambda_R':lambda_R}
self.A = A
self.R = R
self.AAT = AATnn = np.dot(A,A.T)
AATnn[AATnn < 0] = 0
#make SIF: zeros on diags, non-negative values both upper & lower triangles
# not row-normalized since some individuals are more likely to have more ties
SIF = AATnn
SIF[np.diag_indices(SIF.shape[0])] = 0
self.SIF = SIF
# remove upper triangle by keeping only lower triangle indexed values
self.AATnn = np.tril(AATnn, k= -1) # k = -1 to keep only below diagonal
def dirichlet_covariance(alpha):
r"""Covariance matrix for Dirichlet distribution.
Parameters
----------
alpha : (M, ) ndarray
Parameters of Dirichlet distribution
Returns
-------
cov : (M, M) ndarray
Covariance matrix
"""
alpha0 = alpha.sum()
norm = alpha0 ** 2 * (alpha0 + 1.0)
"""Non normalized covariance"""
Z = -alpha[:, np.newaxis] * alpha[np.newaxis, :]
"""Correct diagonal"""
ind = np.diag_indices(Z.shape[0])
Z[ind] += alpha0 * alpha
"""Covariance matrix"""
cov = Z / norm
return cov
def _translation_matrix_from_vector(v):
"""
Compute a translation matrix T for vector v. If x is a 3-vector, then
dot(T, y)[:3] = x + v
where y is a 4-vector with an appended one: y[0:3] = x, y[3] = 1.
Parameters
----------
v : np.ndarray
3-vector of a translation
Returns
-------
T : np.ndarray
A 4x4 translation matrix
Citation
--------
..[1] http://en.wikipedia.org/wiki/Translation_%28geometry%29#Matrix_
representation
"""
T = np.zeros((4,4), dtype=np.float64)
di = np.diag_indices(4)
T[di] = 1.0
T[0:3,3] = v.copy()
return T
def clean_diagonal(H):
"""
Removes the infinities along the diagonal, which makes the
matrix useable but doesn't change anything important
"""
H[np.diag_indices(H.shape[0])] = 0
return H
def convert_to_matrix(x):
G = np.zeros((n, n, k))
G[np.triu_indices(n)] = x[-n*(n+1)//2:, :]
G = G + np.transpose(G, [1, 0, 2])
G[np.diag_indices(n)] = G[np.diag_indices(n)]/2
return G
def quasiOT_sample():
p = int(input("Number of levels ?\n"))
r = int(input("Number of sampled random trajectories ?\n"))
M = int(input("Number of generated random trajectories ?\n"))
# p= 4
# r= 50
# M= 200
datafile = easygui.fileopenbox(msg="Choose the input file (*.csv)",
title="Open File",
default="*.csv")
while datafile is None:
easygui.msgbox("No file is selected, please try again !")
datafile = easygui.fileopenbox(msg="Choose the input file (*.csv)",
title="Open File",
default="*.csv")
data = pd.read_csv(datafile, sep=',')
datamatrix = data[['Min', 'Max']].values
ColName = data['Parameter'].values
k = len(ColName)
datamatrix_diff = datamatrix[:, 1] - datamatrix[:, 0]
B = np.append(np.zeros([1, k]), np.tril(np.ones([k, k])), axis=0)
delta = p / (2 * (p - 1))
J_1 = np.ones([k + 1, k])
J_k = np.ones([k + 1, k])
T = [0 for i in range(M)]
i = 1
A = np.eye(k)
while i <= M:
x_Star = np.random.randint(0, p - 1, [1, k]) / (p - 1)
d = np.ones([1, k])
d[np.random.rand(1, k) <= 0.5] = -1
D_Star = np.eye(k)
D_Star[np.diag_indices(k)] = d
P_Star = A[np.random.permutation(k), :]
B_Star = (J_1 * x_Star + (delta / 2) *
((2 * B - J_k).dot(D_Star) + J_k)).dot(P_Star)
if k > 10:
for cp in np.nditer(np.arange(p / 2)):
B_Star[B_Star == (cp - 0.5 * p) /
(p - 1)] = (cp + 0.5 * p) / (p - 1)
for cp in np.nditer(np.arange(p / 2) + p / 2):
B_Star[B_Star == (cp + 0.5 * p) /
(p - 1)] = (cp - 0.5 * p) / (p - 1)
if np.min(B_Star) >= 0 and np.max(B_Star) <= 1:
T[i - 1] = B_Star
i = i + 1
DIST = np.zeros([M, M])
for i in range(2, M + 1):
for j in range(1, i):
DIST[i - 1,
j - 1] = np.sum(sdis.cdist(T[i - 1], T[j - 1], 'euclidean'))
DIST[j - 1, i - 1] = DIST[i - 1, j - 1]
vector = np.arange(1, M + 1)
CombDist = np.zeros([M, 1])
CombMatrix0 = nchoosek(vector, M - 1)
for i in range(M, r, -1):
if i == M:
r_comb_matrix = nchoosek(vector, 2)
CombDist_total = 0.
for index in range(np.size(r_comb_matrix, 0)):
CombDist_total = CombDist_total + (
DIST[r_comb_matrix[index, 1] - 1,
r_comb_matrix[index, 0] - 1])**2
discard_element = np.arange(M, 0, -1)
for j in range(i):
CombDist[j] = CombDist_total - np.sum(
DIST[CombMatrix0[j, :] - 1, discard_element[j] - 1]**2)
else:
for j in range(i):
CombDist[j] = CombDist[j] - np.sum(
DIST[CombMatrix0[j, :] - 1, discard_element - 1]**2)
index = np.argmax(CombDist)
old_vector = vector[:]
vector = CombMatrix0[index, :]
discard_element = np.setdiff1d(old_vector, vector)
CombDist = np.delete(CombDist, index)
CombMatrix0 = np.delete(CombMatrix0, index, 0)
CombMatrix0 = CombMatrix0[CombMatrix0 != discard_element]
CombMatrix0 = np.reshape(CombMatrix0, (i - 1, i - 2))
best_comb = np.sort(vector)
TrajectorySet = [T[i] for i in best_comb - 1]
ParameterSet = TrajectorySet[:]
datamatrix_diff_transver = np.matlib.repmat(datamatrix_diff, k + 1, 1)
datamatrix_transver = np.matlib.repmat(np.transpose(datamatrix[:, 0]),
k + 1, 1)
for i in range(r):
trajectory = TrajectorySet[i]
ParameterSet[
i] = datamatrix_transver + trajectory * datamatrix_diff_transver
t = pd.DataFrame([], columns=ColName)
for i in ParameterSet:
t = t.append(pd.DataFrame(i, columns=ColName))
t.to_csv(os.path.join(os.path.dirname(datafile), "quasiOT_sample.csv"),
index=False,
float_format='%.5f')
for (l_x, l_y, L_x, L_y, z_range, z_bearing, Z_range, Z_bearing) in zip(detected_x, detected_y, real_x, real_y, z_range_array, z_bearing_array, Z_range_array, Z_bearing_array):
q = (L_x - x_t)**2 + (L_y - y_t)**2
if H_t.size == 0:
H_t = np.matrix([[-(L_x-x_t)/np.sqrt(q),-(L_y-y_t)/np.sqrt(q),0],[(L_y-y_t)/q,-(L_x-x_t)/q,-1]])
Q_t_array = np.array([sigma_l_d**2,sigma_l_theta**2])
zsensor_t = np.matrix([[z_range],[z_bearing]])
zreal_t = np.matrix([[Z_range],[Z_bearing]])
else:
H_t = np.vstack((H_t, np.matrix([[-(L_x-x_t)/np.sqrt(q),-(L_y-y_t)/np.sqrt(q),0],[(L_y-y_t)/q,-(L_x-x_t)/q,-1]])))
Q_t_array = np.hstack((Q_t_array,np.array([sigma_l_d**2,sigma_l_theta**2])))
zsensor_t = np.vstack((zsensor_t,np.matrix([[z_range],[z_bearing]])))
zreal_t = np.vstack((zreal_t,np.matrix([[Z_range],[Z_bearing]])))
Q_t = np.eye(Q_t_array.size)
row, col = np.diag_indices(Q_t_array.shape[0])
Q_t[row,col] = Q_t_array
K_t = np.dot(np.dot(sigma_t_,H_t.T),np.linalg.inv(np.dot(np.dot(H_t,sigma_t_),H_t.T)+Q_t))
INOVA = zsensor_t - zreal_t
DeltaP = np.dot(K_t,INOVA)
PoseR = getPose()
DeltaP = {
"th": DeltaP[2].item(),
"x": DeltaP[0].item(),
"y": DeltaP[1].item()
}
def _fit_newton(f,
score,
start_params,
fargs,
kwargs,
disp=True,
maxiter=100,
callback=None,
retall=False,
full_output=True,
hess=None,
ridge_factor=1e-10):
tol = kwargs.setdefault('tol', 1e-8)
iterations = 0
oldparams = np.inf
newparams = np.asarray(start_params)
if retall:
history = [oldparams, newparams]
while (iterations < maxiter
and np.any(np.abs(newparams - oldparams) > tol)):
H = np.asarray(hess(newparams))
# regularize Hessian, not clear what ridge factor should be
# keyword option with absolute default 1e-10, see #1847
if not np.all(ridge_factor == 0):
H[np.diag_indices(H.shape[0])] += ridge_factor
oldparams = newparams
newparams = oldparams - np.dot(np.linalg.inv(H), score(oldparams))
if retall:
history.append(newparams)
if callback is not None:
callback(newparams)
iterations += 1
fval = f(newparams, *fargs) # this is the negative likelihood
if iterations == maxiter:
warnflag = 1
if disp:
print("Warning: Maximum number of iterations has been "
"exceeded.")
print(" Current function value: %f" % fval)
print(" Iterations: %d" % iterations)
else:
warnflag = 0
if disp:
print("Optimization terminated successfully.")
print(" Current function value: %f" % fval)
print(" Iterations %d" % iterations)
if full_output:
(xopt, fopt, niter, gopt, hopt) = (newparams, f(newparams,
*fargs), iterations,
score(newparams), hess(newparams))
converged = not warnflag
retvals = {
'fopt': fopt,
'iterations': niter,
'score': gopt,
'Hessian': hopt,
'warnflag': warnflag,
'converged': converged
}
if retall:
retvals.update({'allvecs': history})
else:
retvals = newparams
xopt = None
return xopt, retvals
test = test[1] #test part of the test set
print "Train."
clf.fit(X_train[train], y_train[train])
print "Predict."
y_pred_proba = clf.predict_proba(X[test])
predicted_probability[test] = y_pred_proba.copy()
y_pred[test] = np.array([best_decision(prob_configuration, score_matrix=score_matrix)[0] for prob_configuration in y_pred_proba])
y_pred_conf = np.zeros((nTrial, nCh, nCh))
for i_trial in range(nTrial):
for i_comb in range(order_combinations.shape[0]):
y_pred_conf[i_trial][order_combinations[i_comb][:,None], order_combinations[i_comb]] += class_to_configuration(y_pred[i_trial*nComb+i_comb])
print "Set zero the diagonal"
for i_trial in range(nTrial):
y_pred_conf[i_trial][np.diag_indices(nCh)] = 0
y_level2_conf[i_trial][np.diag_indices(nCh)] = 0
pwd_dest = 'data/'
filename_save = '%ssimulated_Ldataset_tws%d_r2_mse_granger_binary_class_rowNorm_fEng_cv.pickle' % (pwd_dest, time_window_size_test)
print "Saving %s" % filename_save
pickle.dump({'y_test_pred': y_pred_conf,
'y_test_true': y_level2_conf,
'predicted_probability': predicted_probability,
'score_matrix': score_matrix,
},
open(filename_save, 'w'),
protocol = pickle.HIGHEST_PROTOCOL)
def call(self, x, mask=None):
# TODO: validate input shape
# The input of this layer is [L, mu, a] in concatenated form. We first split
# those up.
idx = 0
if self.mode == 'full':
L_flat = x[:, idx:idx + (self.nb_actions * self.nb_actions + self.nb_actions) // 2]
idx += (self.nb_actions * self.nb_actions + self.nb_actions) // 2
elif self.mode == 'diag':
L_flat = x[:, idx:idx + self.nb_actions]
idx += self.nb_actions
else:
L_flat = None
assert L_flat is not None
mu = x[:, idx:idx + self.nb_actions]
idx += self.nb_actions
a = x[:, idx:idx + self.nb_actions]
idx += self.nb_actions
if self.mode == 'full':
# Create L and L^T matrix, which we use to construct the positive-definite matrix P.
L = None
LT = None
if K._BACKEND == 'theano':
import theano.tensor as T
import theano
def fn(x, L_acc, LT_acc):
x_ = K.zeros((self.nb_actions, self.nb_actions))
x_ = T.set_subtensor(x_[np.tril_indices(self.nb_actions)], x)
diag = K.exp(T.diag(x_))
x_ = T.set_subtensor(x_[np.diag_indices(self.nb_actions)], diag)
return x_, x_.T
outputs_info = [
K.zeros((self.nb_actions, self.nb_actions)),
K.zeros((self.nb_actions, self.nb_actions)),
]
results, _ = theano.scan(fn=fn, sequences=L_flat, outputs_info=outputs_info)
L, LT = results
elif K._BACKEND == 'tensorflow':
import tensorflow as tf
# Number of elements in a triangular matrix.
nb_elems = (self.nb_actions * self.nb_actions + self.nb_actions) // 2
# Create mask for the diagonal elements in L_flat. This is used to exponentiate
# only the diagonal elements, which is done before gathering.
diag_indeces = [0]
for row in range(1, self.nb_actions):
diag_indeces.append(diag_indeces[-1] + (row + 1))
diag_mask = np.zeros(1 + nb_elems) # +1 for the leading zero
diag_mask[np.array(diag_indeces) + 1] = 1
diag_mask = K.variable(diag_mask)
# Add leading zero element to each element in the L_flat. We use this zero
# element when gathering L_flat into a lower triangular matrix L.
nb_rows = tf.shape(L_flat)[0]
zeros = tf.expand_dims(tf.tile(K.zeros((1,)), [nb_rows]), 1)
L_flat = tf.concat(1, [zeros, L_flat])
# Create mask that can be used to gather elements from L_flat and put them
# into a lower triangular matrix.
tril_mask = np.zeros((self.nb_actions, self.nb_actions), dtype='int32')
tril_mask[np.tril_indices(self.nb_actions)] = range(1, nb_elems + 1)
# Finally, process each element of the batch.
init = [
K.zeros((self.nb_actions, self.nb_actions)),
K.zeros((self.nb_actions, self.nb_actions)),
]
def fn(a, x):
# Exponentiate everything. This is much easier than only exponentiating
# the diagonal elements, and, usually, the action space is relatively low.
x_ = K.exp(x)
# Only keep the diagonal elements.
x_ *= diag_mask
# Add the original, non-diagonal elements.
x_ += x * (1. - diag_mask)
# Finally, gather everything into a lower triangular matrix.
L_ = tf.gather(x_, tril_mask)
return [L_, tf.transpose(L_)]
tmp = tf.scan(fn, L_flat, initializer=init)
if isinstance(tmp, (list, tuple)):
# TensorFlow 0.10 now returns a tuple of tensors.
L, LT = tmp
else:
# Old TensorFlow < 0.10 returns a shared tensor.
L = tmp[:, 0, :, :]
LT = tmp[:, 1, :, :]
else:
raise RuntimeError('Unknown Keras backend "{}".'.format(K._BACKEND))
assert L is not None
assert LT is not None
P = K.batch_dot(L, LT)
elif self.mode == 'diag':
if K._BACKEND == 'theano':
import theano.tensor as T
import theano
def fn(x, P_acc):
x_ = K.zeros((self.nb_actions, self.nb_actions))
x_ = T.set_subtensor(x_[np.diag_indices(self.nb_actions)], x)
return x_
outputs_info = [
K.zeros((self.nb_actions, self.nb_actions)),
]
P, _ = theano.scan(fn=fn, sequences=L_flat, outputs_info=outputs_info)
print(P)
elif K._BACKEND == 'tensorflow':
import tensorflow as tf
# Create mask that can be used to gather elements from L_flat and put them
# into a diagonal matrix.
diag_mask = np.zeros((self.nb_actions, self.nb_actions), dtype='int32')
diag_mask[np.diag_indices(self.nb_actions)] = range(1, self.nb_actions + 1)
# Add leading zero element to each element in the L_flat. We use this zero
# element when gathering L_flat into a lower triangular matrix L.
nb_rows = tf.shape(L_flat)[0]
zeros = tf.expand_dims(tf.tile(K.zeros((1,)), [nb_rows]), 1)
L_flat = tf.concat(1, [zeros, L_flat])
# Finally, process each element of the batch.
def fn(a, x):
x_ = tf.gather(x, diag_mask)
return x_
P = tf.scan(fn, L_flat, initializer=K.zeros((self.nb_actions, self.nb_actions)))
else:
raise RuntimeError('Unknown Keras backend "{}".'.format(K._BACKEND))
assert P is not None
assert K.ndim(P) == 3
# Combine a, mu and P into a scalar (over the batches). What we compute here is
# -.5 * (a - mu)^T * P * (a - mu), where * denotes the dot-product. Unfortunately
# TensorFlow handles vector * P slightly suboptimal, hence we convert the vectors to
# 1xd/dx1 matrices and finally flatten the resulting 1x1 matrix into a scalar. All
# operations happen over the batch size, which is dimension 0.
prod = K.batch_dot(K.expand_dims(a - mu, dim=1), P)
prod = K.batch_dot(prod, K.expand_dims(a - mu, dim=-1))
A = -.5 * K.batch_flatten(prod)
assert K.ndim(A) == 2
return A
def nearestSPD(A):
"""
Nearest Symmetric Positive Definite matrix to A.
The Frobenius norm is used: the rms difference of the elements.
Parameters
----------
A : ndarray, 2-D
Matrix that should be SPD, but might not be, perhaps because
of floating point arithmetic, or limitations in the data
available to estimate its elements.
Returns
-------
Ahat : ndarray, 2-D
(Almost) nearest positive definite matrix to A
Notes
-----
From Higham: "The nearest symmetric positive semidefinite matrix in the
Frobenius norm to an arbitrary real matrix A is shown to be (B + H)/2,
where H is the symmetric polar factor of B=(A + A')/2."
http://www.sciencedirect.com/science/article/pii/0024379588902236
Code and docstring are based on the Matlab m-file by John D'Errico:
http://www.mathworks.com/matlabcentral/fileexchange/42885-nearestspd
The tweaking method was changed to be more robust in the common
pathological case, where more than one eigenvalue is near zero, and
when one eigenvalue is exactly zero. To be conservative, we are
always nudging the diagonal to larger values.
"""
# Ensure symmetry:
B = (A + A.T) / 2
# Symmetric polar factor, H: (in numpy svd, B = U S V, not U S V').
U, S, V = np.linalg.svd(B)
H = np.dot(V.T * S, V)
Ahat = (B + H) / 2
Ahat = (Ahat + Ahat.T) / 2
# At this point, given floating point errors and differences
# among algorithms, Ahat might be on the PD boundary, or too
# close to it for some numerical operations. Adjust it:
n = A.shape[0]
k = 0
# The "k == 0" is included to avoid a warning from
# np.random.multivariate_normal, which seems to have a PD-detection
# algorithm that occasionally fails on matrices that pass the
# cholesky test.
while k == 0 or not _is_PD(Ahat):
k += 1
# Tweaking strategy differs from D'Errico version. It
# is still a very small adjustment, but much larger than
# his.
# Eigvals are or can be complex dtype, so take abs().
maxeig = np.abs(np.linalg.eigvals(Ahat)).max()
Ahat[np.diag_indices(n)] += np.spacing(maxeig)
# Normally no more than one adjustment will be needed.
if k > 100:
warnings.warn("adjustment in nearestSPD did not converge; "
"returning diagonal")
return np.diag(np.diag(A))
return Ahat
def plot_connectivity_circle(con, node_names, indices=None, n_lines=None,
node_angles=None, node_width=None,
node_colors=None, facecolor='black',
textcolor='white', node_edgecolor='black',
linewidth=1.5, colormap='hot', vmin=None,
vmax=None, colorbar=True, title=None,
colorbar_size=0.2, colorbar_pos=(-0.3, 0.1),
fontsize_title=12, fontsize_names=8,
fontsize_colorbar=8, padding=6.,
fig=None, subplot=111, interactive=True,
node_linewidth=2.):
"""Visualize connectivity as a circular graph.
Note: This code is based on the circle graph example by Nicolas P. Rougier
http://www.labri.fr/perso/nrougier/coding/.
Parameters
----------
con : array
Connectivity scores. Can be a square matrix, or a 1D array. If a 1D
array is provided, "indices" has to be used to define the connection
indices.
node_names : list of str
Node names. The order corresponds to the order in con.
indices : tuple of arrays | None
Two arrays with indices of connections for which the connections
strenghts are defined in con. Only needed if con is a 1D array.
n_lines : int | None
If not None, only the n_lines strongest connections (strength=abs(con))
are drawn.
node_angles : array, shape=(len(node_names,)) | None
Array with node positions in degrees. If None, the nodes are equally
spaced on the circle. See mne.viz.circular_layout.
node_width : float | None
Width of each node in degrees. If None, the minimum angle between any
two nodes is used as the width.
node_colors : list of tuples | list of str
List with the color to use for each node. If fewer colors than nodes
are provided, the colors will be repeated. Any color supported by
matplotlib can be used, e.g., RGBA tuples, named colors.
facecolor : str
Color to use for background. See matplotlib.colors.
textcolor : str
Color to use for text. See matplotlib.colors.
node_edgecolor : str
Color to use for lines around nodes. See matplotlib.colors.
linewidth : float
Line width to use for connections.
colormap : str
Colormap to use for coloring the connections.
vmin : float | None
Minimum value for colormap. If None, it is determined automatically.
vmax : float | None
Maximum value for colormap. If None, it is determined automatically.
colorbar : bool
Display a colorbar or not.
title : str
The figure title.
colorbar_size : float
Size of the colorbar.
colorbar_pos : 2-tuple
Position of the colorbar.
fontsize_title : int
Font size to use for title.
fontsize_names : int
Font size to use for node names.
fontsize_colorbar : int
Font size to use for colorbar.
padding : float
Space to add around figure to accommodate long labels.
fig : None | instance of matplotlib.pyplot.Figure
The figure to use. If None, a new figure with the specified background
color will be created.
subplot : int | 3-tuple
Location of the subplot when creating figures with multiple plots. E.g.
121 or (1, 2, 1) for 1 row, 2 columns, plot 1. See
matplotlib.pyplot.subplot.
interactive : bool
When enabled, left-click on a node to show only connections to that
node. Right-click shows all connections.
node_linewidth : float
Line with for nodes.
Returns
-------
fig : instance of matplotlib.pyplot.Figure
The figure handle.
axes : instance of matplotlib.axes.PolarAxesSubplot
The subplot handle.
"""
import matplotlib.pyplot as plt
import matplotlib.path as m_path
import matplotlib.patches as m_patches
n_nodes = len(node_names)
if node_angles is not None:
if len(node_angles) != n_nodes:
raise ValueError('node_angles has to be the same length '
'as node_names')
# convert it to radians
node_angles = node_angles * np.pi / 180
else:
# uniform layout on unit circle
node_angles = np.linspace(0, 2 * np.pi, n_nodes, endpoint=False)
if node_width is None:
# widths correspond to the minimum angle between two nodes
dist_mat = node_angles[None, :] - node_angles[:, None]
dist_mat[np.diag_indices(n_nodes)] = 1e9
node_width = np.min(np.abs(dist_mat))
else:
node_width = node_width * np.pi / 180
if node_colors is not None:
if len(node_colors) < n_nodes:
node_colors = cycle(node_colors)
else:
# assign colors using colormap
node_colors = [plt.cm.spectral(i / float(n_nodes))
for i in range(n_nodes)]
# handle 1D and 2D connectivity information
if con.ndim == 1:
if indices is None:
raise ValueError('indices has to be provided if con.ndim == 1')
elif con.ndim == 2:
if con.shape[0] != n_nodes or con.shape[1] != n_nodes:
raise ValueError('con has to be 1D or a square matrix')
# we use the lower-triangular part
indices = tril_indices(n_nodes, -1)
con = con[indices]
else:
raise ValueError('con has to be 1D or a square matrix')
# get the colormap
if isinstance(colormap, string_types):
colormap = plt.get_cmap(colormap)
# Make figure background the same colors as axes
if fig is None:
fig = plt.figure(figsize=(8, 8), facecolor=facecolor)
# Use a polar axes
if not isinstance(subplot, tuple):
subplot = (subplot,)
axes = plt.subplot(*subplot, polar=True, axisbg=facecolor)
# No ticks, we'll put our own
plt.xticks([])
plt.yticks([])
# Set y axes limit, add additonal space if requested
plt.ylim(0, 10 + padding)
# Remove the black axes border which may obscure the labels
axes.spines['polar'].set_visible(False)
# Draw lines between connected nodes, only draw the strongest connections
if n_lines is not None and len(con) > n_lines:
con_thresh = np.sort(np.abs(con).ravel())[-n_lines]
else:
con_thresh = 0.
# get the connections which we are drawing and sort by connection strength
# this will allow us to draw the strongest connections first
con_abs = np.abs(con)
con_draw_idx = np.where(con_abs >= con_thresh)[0]
con = con[con_draw_idx]
con_abs = con_abs[con_draw_idx]
indices = [ind[con_draw_idx] for ind in indices]
# now sort them
sort_idx = np.argsort(con_abs)
con_abs = con_abs[sort_idx]
con = con[sort_idx]
indices = [ind[sort_idx] for ind in indices]
# Get vmin vmax for color scaling
if vmin is None:
vmin = np.min(con[np.abs(con) >= con_thresh])
if vmax is None:
vmax = np.max(con)
vrange = vmax - vmin
# We want to add some "noise" to the start and end position of the
# edges: We modulate the noise with the number of connections of the
# node and the connection strength, such that the strongest connections
# are closer to the node center
nodes_n_con = np.zeros((n_nodes), dtype=np.int)
for i, j in zip(indices[0], indices[1]):
nodes_n_con[i] += 1
nodes_n_con[j] += 1
# initalize random number generator so plot is reproducible
rng = np.random.mtrand.RandomState(seed=0)
n_con = len(indices[0])
noise_max = 0.25 * node_width
start_noise = rng.uniform(-noise_max, noise_max, n_con)
end_noise = rng.uniform(-noise_max, noise_max, n_con)
nodes_n_con_seen = np.zeros_like(nodes_n_con)
for i, (start, end) in enumerate(zip(indices[0], indices[1])):
nodes_n_con_seen[start] += 1
nodes_n_con_seen[end] += 1
start_noise[i] *= ((nodes_n_con[start] - nodes_n_con_seen[start]) /
float(nodes_n_con[start]))
end_noise[i] *= ((nodes_n_con[end] - nodes_n_con_seen[end]) /
float(nodes_n_con[end]))
# scale connectivity for colormap (vmin<=>0, vmax<=>1)
con_val_scaled = (con - vmin) / vrange
# Finally, we draw the connections
for pos, (i, j) in enumerate(zip(indices[0], indices[1])):
# Start point
t0, r0 = node_angles[i], 10
# End point
t1, r1 = node_angles[j], 10
# Some noise in start and end point
t0 += start_noise[pos]
t1 += end_noise[pos]
verts = [(t0, r0), (t0, 5), (t1, 5), (t1, r1)]
codes = [m_path.Path.MOVETO, m_path.Path.CURVE4, m_path.Path.CURVE4,
m_path.Path.LINETO]
path = m_path.Path(verts, codes)
color = colormap(con_val_scaled[pos])
# Actual line
patch = m_patches.PathPatch(path, fill=False, edgecolor=color,
linewidth=linewidth, alpha=1.)
axes.add_patch(patch)
# Draw ring with colored nodes
height = np.ones(n_nodes) * 1.0
bars = axes.bar(node_angles, height, width=node_width, bottom=9,
edgecolor=node_edgecolor, lw=node_linewidth,
facecolor='.9', align='center')
for bar, color in zip(bars, node_colors):
bar.set_facecolor(color)
# Draw node labels
angles_deg = 180 * node_angles / np.pi
for name, angle_rad, angle_deg in zip(node_names, node_angles, angles_deg):
if angle_deg >= 270:
ha = 'left'
else:
# Flip the label, so text is always upright
angle_deg += 180
ha = 'right'
axes.text(angle_rad, 10.4, name, size=fontsize_names,
rotation=angle_deg, rotation_mode='anchor',
horizontalalignment=ha, verticalalignment='center',
color=textcolor)
if title is not None:
plt.title(title, color=textcolor, fontsize=fontsize_title,
axes=axes)
if colorbar:
norm = normalize_colors(vmin=vmin, vmax=vmax)
sm = plt.cm.ScalarMappable(cmap=colormap, norm=norm)
sm.set_array(np.linspace(vmin, vmax))
cb = plt.colorbar(sm, ax=axes, use_gridspec=False,
shrink=colorbar_size,
anchor=colorbar_pos)
cb_yticks = plt.getp(cb.ax.axes, 'yticklabels')
cb.ax.tick_params(labelsize=fontsize_colorbar)
plt.setp(cb_yticks, color=textcolor)
# Add callback for interaction
if interactive:
callback = partial(_plot_connectivity_circle_onpick, fig=fig,
axes=axes, indices=indices, n_nodes=n_nodes,
node_angles=node_angles)
fig.canvas.mpl_connect('button_press_event', callback)
return fig, axes
def make_rdm2(mp, t2=None):
r'''
Two-particle spin density matrices dm2aa, dm2ab, dm2bb in MO basis
dm2aa[p,q,r,s] = <q_alpha^\dagger s_alpha^\dagger r_alpha p_alpha>
dm2ab[p,q,r,s] = <q_alpha^\dagger s_beta^\dagger r_beta p_alpha>
dm2bb[p,q,r,s] = <q_beta^\dagger s_beta^\dagger r_beta p_beta>
(p,q correspond to one particle and r,s correspond to another particle)
Two-particle density matrix should be contracted to integrals with the
pattern below to compute energy
E = numpy.einsum('pqrs,pqrs', eri_aa, dm2_aa)
E+= numpy.einsum('pqrs,pqrs', eri_ab, dm2_ab)
E+= numpy.einsum('pqrs,rspq', eri_ba, dm2_ab)
E+= numpy.einsum('pqrs,pqrs', eri_bb, dm2_bb)
where eri_aa[p,q,r,s] = (p_alpha q_alpha | r_alpha s_alpha )
eri_ab[p,q,r,s] = ( p_alpha q_alpha | r_beta s_beta )
eri_ba[p,q,r,s] = ( p_beta q_beta | r_alpha s_alpha )
eri_bb[p,q,r,s] = ( p_beta q_beta | r_beta s_beta )
'''
if t2 is None: t2 = mp.t2
nmoa, nmob = nmoa0, nmob0 = mp.nmo
nocca, noccb = nocca0, noccb0 = mp.nocc
t2aa, t2ab, t2bb = t2
if not (mp.frozen is 0 or mp.frozen is None):
nmoa0 = mp.mo_occ[0].size
nmob0 = mp.mo_occ[1].size
nocca0 = numpy.count_nonzero(mp.mo_occ[0] > 0)
noccb0 = numpy.count_nonzero(mp.mo_occ[1] > 0)
moidxa, moidxb = mp.get_frozen_mask()
oidxa = numpy.where(moidxa & (mp.mo_occ[0] > 0))[0]
vidxa = numpy.where(moidxa & (mp.mo_occ[0] == 0))[0]
oidxb = numpy.where(moidxb & (mp.mo_occ[1] > 0))[0]
vidxb = numpy.where(moidxb & (mp.mo_occ[1] == 0))[0]
dm2aa = numpy.zeros((nmoa0, nmoa0, nmoa0, nmoa0), dtype=t2aa.dtype)
dm2ab = numpy.zeros((nmoa0, nmoa0, nmob0, nmob0), dtype=t2aa.dtype)
dm2bb = numpy.zeros((nmob0, nmob0, nmob0, nmob0), dtype=t2aa.dtype)
tmp = t2aa.transpose(0, 2, 1, 3)
dm2aa[oidxa[:, None, None, None], vidxa[:, None, None], oidxa[:, None],
vidxa] = tmp
dm2aa[vidxa[:, None, None, None], oidxa[:, None, None], vidxa[:, None],
oidxa] = tmp.conj().transpose(1, 0, 3, 2)
tmp = t2bb.transpose(0, 2, 1, 3)
dm2bb[oidxb[:, None, None, None], vidxb[:, None, None], oidxb[:, None],
vidxb] = tmp
dm2bb[vidxb[:, None, None, None], oidxb[:, None, None], vidxb[:, None],
oidxb] = tmp.conj().transpose(1, 0, 3, 2)
dm2ab[oidxa[:, None, None, None], vidxa[:, None, None], oidxb[:, None],
vidxb] = t2ab.transpose(0, 2, 1, 3)
dm2ab[vidxa[:, None, None, None], oidxa[:, None, None], vidxb[:, None],
oidxb] = t2ab.conj().transpose(2, 0, 3, 1)
else:
dm2aa = numpy.zeros((nmoa0, nmoa0, nmoa0, nmoa0), dtype=t2aa.dtype)
dm2ab = numpy.zeros((nmoa0, nmoa0, nmob0, nmob0), dtype=t2aa.dtype)
dm2bb = numpy.zeros((nmob0, nmob0, nmob0, nmob0), dtype=t2aa.dtype)
#:tmp = (t2aa.transpose(0,2,1,3) - t2aa.transpose(0,3,1,2)) * .5
#: t2aa.transpose(0,2,1,3) == -t2aa.transpose(0,3,1,2)
tmp = t2aa.transpose(0, 2, 1, 3)
dm2aa[:nocca0, nocca0:, :nocca0, nocca0:] = tmp
dm2aa[nocca0:, :nocca0,
nocca0:, :nocca0] = tmp.conj().transpose(1, 0, 3, 2)
tmp = t2bb.transpose(0, 2, 1, 3)
dm2bb[:noccb0, noccb0:, :noccb0, noccb0:] = tmp
dm2bb[noccb0:, :noccb0,
noccb0:, :noccb0] = tmp.conj().transpose(1, 0, 3, 2)
dm2ab[:nocca0, nocca0:, :noccb0, noccb0:] = t2ab.transpose(0, 2, 1, 3)
dm2ab[nocca0:, :nocca0, noccb0:, :noccb0] = t2ab.transpose(2, 0, 3,
1).conj()
dm1a, dm1b = make_rdm1(mp, t2)
dm1a[numpy.diag_indices(nocca0)] -= 1
dm1b[numpy.diag_indices(noccb0)] -= 1
for i in range(nocca0):
dm2aa[i, i, :, :] += dm1a.T
dm2aa[:, :, i, i] += dm1a.T
dm2aa[:, i, i, :] -= dm1a.T
dm2aa[i, :, :, i] -= dm1a
dm2ab[i, i, :, :] += dm1b.T
for i in range(noccb0):
dm2bb[i, i, :, :] += dm1b.T
dm2bb[:, :, i, i] += dm1b.T
dm2bb[:, i, i, :] -= dm1b.T
dm2bb[i, :, :, i] -= dm1b
dm2ab[:, :, i, i] += dm1a.T
for i in range(nocca0):
for j in range(nocca0):
dm2aa[i, i, j, j] += 1
dm2aa[i, j, j, i] -= 1
for i in range(noccb0):
for j in range(noccb0):
dm2bb[i, i, j, j] += 1
dm2bb[i, j, j, i] -= 1
for i in range(nocca0):
for j in range(noccb0):
dm2ab[i, i, j, j] += 1
return dm2aa, dm2ab, dm2bb
def __init__(self, endog, exog=None, order=(1, 0), trend='c',
error_cov_type='unstructured', measurement_error=False,
enforce_stationarity=True, enforce_invertibility=True,
trend_offset=1, **kwargs):
# Model parameters
self.error_cov_type = error_cov_type
self.measurement_error = measurement_error
self.enforce_stationarity = enforce_stationarity
self.enforce_invertibility = enforce_invertibility
# Save the given orders
self.order = order
# Model orders
self.k_ar = int(order[0])
self.k_ma = int(order[1])
# Check for valid model
if error_cov_type not in ['diagonal', 'unstructured']:
raise ValueError('Invalid error covariance matrix type'
' specification.')
if self.k_ar == 0 and self.k_ma == 0:
raise ValueError('Invalid VARMAX(p,q) specification; at least one'
' p,q must be greater than zero.')
# Warn for VARMA model
if self.k_ar > 0 and self.k_ma > 0:
warn('Estimation of VARMA(p,q) models is not generically robust,'
' due especially to identification issues.',
EstimationWarning)
# Trend
self.trend = trend
self.trend_offset = trend_offset
self.polynomial_trend, self.k_trend = prepare_trend_spec(self.trend)
self._trend_is_const = (self.polynomial_trend.size == 1 and
self.polynomial_trend[0] == 1)
# Exogenous data
(self.k_exog, exog) = prepare_exog(exog)
# Note: at some point in the future might add state regression, as in
# SARIMAX.
self.mle_regression = self.k_exog > 0
# We need to have an array or pandas at this point
if not _is_using_pandas(endog, None):
endog = np.asanyarray(endog)
# Model order
# Used internally in various places
_min_k_ar = max(self.k_ar, 1)
self._k_order = _min_k_ar + self.k_ma
# Number of states
k_endog = endog.shape[1]
k_posdef = k_endog
k_states = k_endog * self._k_order
# By default, initialize as stationary
kwargs.setdefault('initialization', 'stationary')
# By default, use LU decomposition
kwargs.setdefault('inversion_method', INVERT_UNIVARIATE | SOLVE_LU)
# Initialize the state space model
super(VARMAX, self).__init__(
endog, exog=exog, k_states=k_states, k_posdef=k_posdef, **kwargs
)
# Set as time-varying model if we have time-trend or exog
if self.k_exog > 0 or (self.k_trend > 0 and not self._trend_is_const):
self.ssm._time_invariant = False
# Initialize the parameters
self.parameters = OrderedDict()
self.parameters['trend'] = self.k_endog * self.k_trend
self.parameters['ar'] = self.k_endog**2 * self.k_ar
self.parameters['ma'] = self.k_endog**2 * self.k_ma
self.parameters['regression'] = self.k_endog * self.k_exog
if self.error_cov_type == 'diagonal':
self.parameters['state_cov'] = self.k_endog
# These parameters fill in a lower-triangular matrix which is then
# dotted with itself to get a positive definite matrix.
elif self.error_cov_type == 'unstructured':
self.parameters['state_cov'] = (
int(self.k_endog * (self.k_endog + 1) / 2)
)
self.parameters['obs_cov'] = self.k_endog * self.measurement_error
self.k_params = sum(self.parameters.values())
# Initialize trend data
self._trend_data = prepare_trend_data(
self.polynomial_trend, self.k_trend, self.nobs,
offset=self.trend_offset)
# Initialize known elements of the state space matrices
# If we have exog effects, then the state intercept needs to be
# time-varying
if (self.k_trend > 0 and not self._trend_is_const) or self.k_exog > 0:
self.ssm['state_intercept'] = np.zeros((self.k_states, self.nobs))
# self.ssm['obs_intercept'] = np.zeros((self.k_endog, self.nobs))
# The design matrix is just an identity for the first k_endog states
idx = np.diag_indices(self.k_endog)
self.ssm[('design',) + idx] = 1
# The transition matrix is described in four blocks, where the upper
# left block is in companion form with the autoregressive coefficient
# matrices (so it is shaped k_endog * k_ar x k_endog * k_ar) ...
if self.k_ar > 0:
idx = np.diag_indices((self.k_ar - 1) * self.k_endog)
idx = idx[0] + self.k_endog, idx[1]
self.ssm[('transition',) + idx] = 1
# ... and the lower right block is in companion form with zeros as the
# coefficient matrices (it is shaped k_endog * k_ma x k_endog * k_ma).
idx = np.diag_indices((self.k_ma - 1) * self.k_endog)
idx = (idx[0] + (_min_k_ar + 1) * self.k_endog,
idx[1] + _min_k_ar * self.k_endog)
self.ssm[('transition',) + idx] = 1
# The selection matrix is described in two blocks, where the upper
# block selects the all k_posdef errors in the first k_endog rows
# (the upper block is shaped k_endog * k_ar x k) and the lower block
# also selects all k_posdef errors in the first k_endog rows (the lower
# block is shaped k_endog * k_ma x k).
idx = np.diag_indices(self.k_endog)
self.ssm[('selection',) + idx] = 1
idx = idx[0] + _min_k_ar * self.k_endog, idx[1]
if self.k_ma > 0:
self.ssm[('selection',) + idx] = 1
# Cache some indices
if self._trend_is_const and self.k_exog == 0:
self._idx_state_intercept = np.s_['state_intercept', :k_endog, :]
elif self.k_trend > 0 or self.k_exog > 0:
self._idx_state_intercept = np.s_['state_intercept', :k_endog, :-1]
if self.k_ar > 0:
self._idx_transition = np.s_['transition', :k_endog, :]
else:
self._idx_transition = np.s_['transition', :k_endog, k_endog:]
if self.error_cov_type == 'diagonal':
self._idx_state_cov = (
('state_cov',) + np.diag_indices(self.k_endog))
elif self.error_cov_type == 'unstructured':
self._idx_lower_state_cov = np.tril_indices(self.k_endog)
if self.measurement_error:
self._idx_obs_cov = ('obs_cov',) + np.diag_indices(self.k_endog)
# Cache some slices
def _slice(key, offset):
length = self.parameters[key]
param_slice = np.s_[offset:offset + length]
offset += length
return param_slice, offset
offset = 0
self._params_trend, offset = _slice('trend', offset)
self._params_ar, offset = _slice('ar', offset)
self._params_ma, offset = _slice('ma', offset)
self._params_regression, offset = _slice('regression', offset)
self._params_state_cov, offset = _slice('state_cov', offset)
self._params_obs_cov, offset = _slice('obs_cov', offset)
# Update _init_keys attached by super
self._init_keys += ['order', 'trend', 'error_cov_type',
'measurement_error', 'enforce_stationarity',
'enforce_invertibility'] + list(kwargs.keys())
def diag_indices(*args, **kwargs):
return tuple(map(tf.convert_to_tensor, _np.diag_indices(*args, **kwargs)))
def _logm_triu(T):
"""
Compute matrix logarithm of an upper triangular matrix.
The matrix logarithm is the inverse of
expm: expm(logm(`T`)) == `T`
Parameters
----------
T : (N, N) array_like
Upper triangular matrix whose logarithm to evaluate
Returns
-------
logm : (N, N) ndarray
Matrix logarithm of `T`
References
----------
.. [1] Awad H. Al-Mohy and Nicholas J. Higham (2012)
"Improved Inverse Scaling and Squaring Algorithms
for the Matrix Logarithm."
SIAM Journal on Scientific Computing, 34 (4). C152-C169.
ISSN 1095-7197
.. [2] Nicholas J. Higham (2008)
"Functions of Matrices: Theory and Computation"
ISBN 978-0-898716-46-7
.. [3] Nicholas J. Higham and Lijing lin (2011)
"A Schur-Pade Algorithm for Fractional Powers of a Matrix."
SIAM Journal on Matrix Analysis and Applications,
32 (3). pp. 1056-1078. ISSN 0895-4798
"""
T = np.asarray(T)
if len(T.shape) != 2 or T.shape[0] != T.shape[1]:
raise ValueError('expected an upper triangular square matrix')
n, n = T.shape
# Construct T0 with the appropriate type,
# depending on the dtype and the spectrum of T.
T_diag = np.diag(T)
keep_it_real = (not _has_complex_dtype_char(T)) and (np.min(T_diag) >= 0)
if keep_it_real:
T0 = T
else:
T0 = T.astype(complex)
# Define bounds given in Table (2.1).
theta = (None, 1.59e-5, 2.31e-3, 1.94e-2, 6.21e-2, 1.28e-1, 2.06e-1,
2.88e-1, 3.67e-1, 4.39e-1, 5.03e-1, 5.60e-1, 6.09e-1, 6.52e-1,
6.89e-1, 7.21e-1, 7.49e-1)
R, s, m = _inverse_squaring_helper(T0, theta)
# Evaluate U = 2**s r_m(T - I) using the partial fraction expansion (1.1).
# This requires the nodes and weights
# corresponding to degree-m Gauss-Legendre quadrature.
# These quadrature arrays need to be transformed from the [-1, 1] interval
# to the [0, 1] interval.
nodes, weights = scipy.special.p_roots(m)
nodes = nodes.real
if nodes.shape != (m, ) or weights.shape != (m, ):
raise Exception('internal error')
nodes = 0.5 + 0.5 * nodes
weights = 0.5 * weights
ident = np.identity(n)
U = np.zeros_like(R)
for alpha, beta in zip(weights, nodes):
U += solve_triangular(ident + beta * R, alpha * R)
U *= np.exp2(s)
# Skip this step if the principal branch
# does not exist at T0; this happens when a diagonal entry of T0
# is negative with imaginary part 0.
has_principal_branch = all(x.real > 0 or x.imag != 0 for x in np.diag(T0))
if has_principal_branch:
# Recompute diagonal entries of U.
U[np.diag_indices(n)] = np.log(np.diag(T0))
# Recompute superdiagonal entries of U.
# This indexing of this code should be renovated
# when newer np.diagonal() becomes available.
for i in range(n - 1):
l1 = T0[i, i]
l2 = T0[i + 1, i + 1]
t12 = T0[i, i + 1]
U[i, i + 1] = _logm_superdiag_entry(l1, l2, t12)
# Return the logm of the upper triangular matrix.
if not np.array_equal(U, np.triu(U)):
raise Exception('internal inconsistency')
return U
def _remainder_matrix_power_triu(T, t):
"""
Compute a fractional power of an upper triangular matrix.
The fractional power is restricted to fractions -1 < t < 1.
This uses algorithm (3.1) of [1]_.
The Pade approximation itself uses algorithm (4.1) of [2]_.
Parameters
----------
T : (N, N) array_like
Upper triangular matrix whose fractional power to evaluate.
t : float
Fractional power between -1 and 1 exclusive.
Returns
-------
X : (N, N) array_like
The fractional power of the matrix.
References
----------
.. [1] Nicholas J. Higham and Lijing Lin (2013)
"An Improved Schur-Pade Algorithm for Fractional Powers
of a Matrix and their Frechet Derivatives."
.. [2] Nicholas J. Higham and Lijing lin (2011)
"A Schur-Pade Algorithm for Fractional Powers of a Matrix."
SIAM Journal on Matrix Analysis and Applications,
32 (3). pp. 1056-1078. ISSN 0895-4798
"""
m_to_theta = {
1: 1.51e-5,
2: 2.24e-3,
3: 1.88e-2,
4: 6.04e-2,
5: 1.24e-1,
6: 2.00e-1,
7: 2.79e-1,
}
n, n = T.shape
T0 = T
T0_diag = np.diag(T0)
if np.array_equal(T0, np.diag(T0_diag)):
U = np.diag(T0_diag**t)
else:
R, s, m = _inverse_squaring_helper(T0, m_to_theta)
# Evaluate the Pade approximation.
# Note that this function expects the negative of the matrix
# returned by the inverse squaring helper.
U = _fractional_power_pade(-R, t, m)
# Undo the inverse scaling and squaring.
# Be less clever about this
# if the principal branch does not exist at T0;
# this happens when a diagonal entry of T0
# is negative with imaginary part 0.
eivals = np.diag(T0)
has_principal_branch = all(x.real > 0 or x.imag != 0 for x in eivals)
for i in range(s, -1, -1):
if i < s:
U = U.dot(U)
else:
if has_principal_branch:
p = t * np.exp2(-i)
U[np.diag_indices(n)] = T0_diag**p
for j in range(n - 1):
l1 = T0[j, j]
l2 = T0[j + 1, j + 1]
t12 = T0[j, j + 1]
f12 = _fractional_power_superdiag_entry(l1, l2, t12, p)
U[j, j + 1] = f12
if not np.array_equal(U, np.triu(U)):
raise Exception('internal inconsistency')
return U
PARAMS = {
"batch_size": 1000,
"keywords": 300,
"latent_dim": 2,
"sigma": 1,
"epochs": 2,
"beta": 1,
# "kl_anneal_rate": 0.05,
# "logistic_anneal": True,
"learning_rate": 0.05,
}
with open("./result/keywords.txt", "r") as f:
keywords = [w.strip('\n') for w in f.readlines()]
#%%
di = np.diag_indices(PARAMS['keywords'])
filelist = sorted([f for f in os.listdir('/Users/anseunghwan/Documents/uos/textmining/data/') if f.endswith('.npz')])
# testn = np.argmin(np.array([os.path.getsize('/Users/anseunghwan/Documents/uos/textmining/data/' + f) for f in filelist]))
testn = len(filelist)-1
I = np.eye(PARAMS['keywords'])
'''validation(test)'''
Atest_ = sparse.load_npz('/Users/anseunghwan/Documents/uos/textmining/data/Atest.npz')
Atest = Atest_.toarray().reshape((-1, PARAMS['keywords'], PARAMS['keywords']))
'''degree matrix (every node connected to itself)'''
# D = I[None, :, :] * np.sqrt(1 / np.sum(Atest_[:, di[0], di[1]], axis=-1))[:, None, None]
D = Atest[:, di[0], di[1]] * np.sqrt(1 / np.sum(Atest[:, di[0], di[1]], axis=-1, keepdims=True))
D[np.where(D == 0)] = 1
D = I[None, :, :] * D[:, None]
Atest[:, di[0], di[1]] = 1 # diagonal element
def get_frag(c: cooler.api.Cooler,
resolution: int,
offsets: pd.core.series.Series,
chrom1: str,
start1: int,
end1: int,
chrom2: str,
start2: int,
end2: int,
width: int = 22,
height: int = -1,
padding: int = 10,
normalize: bool = True,
balanced: bool = True,
percentile: float = 100.0,
ignore_diags: int = 0,
no_normalize: bool = False) -> np.ndarray:
"""
Retrieves a matrix fragment.
Args:
c:
Cooler object.
chrom1:
Chromosome 1. E.g.: `1` or `chr1`.
start1:
First start position in base pairs relative to `chrom1`.
end1:
First end position in base pairs relative to `chrom1`.
chrom2:
Chromosome 2. E.g.: `1` or `chr1`.
start2:
Second start position in base pairs relative to `chrom2`.
end2:
Second end position in base pairs relative to `chrom2`.
offsets:
Pandas Series of chromosome offsets in bins.
width:
Width of the fragment in pixels.
height:
Height of the fragments in pixels. If `-1` `height` will equal
`width`. Defaults to `-1`.
padding: Percental padding related to the dimension of the fragment.
E.g., 10 = 10% padding (5% per side). Defaults to `10`.
normalize:
If `True` the fragment will be normalized to [0, 1].
Defaults to `True`.
balanced:
If `True` the fragment will be balanced using Cooler.
Defaults to `True`.
percentile:
Percentile clip. E.g., For 99 the maximum will be
capped at the 99-percentile. Defaults to `100.0`.
ignore_diags:
Number of diagonals to be ignored, i.e., set to 0.
Defaults to `0`.
no_normalize:
If `true` the returned matrix is not normalized.
Defaults to `False`.
Returns:
"""
if height is -1:
height = width
# Restrict padding to be [0, 100]%
padding = min(100, max(0, padding)) / 100
# Normalize chromosome names
if not chrom1.startswith('chr'):
chrom1 = 'chr{}'.format(chrom1)
if not chrom2.startswith('chr'):
chrom2 = 'chr{}'.format(chrom2)
# Get chromosome offset
offset1 = offsets[chrom1]
offset2 = offsets[chrom2]
start_bin1 = offset1 + int(round(float(start1) / resolution))
end_bin1 = offset1 + int(round(float(end1) / resolution)) + 1
start_bin2 = offset2 + int(round(float(start2) / resolution))
end_bin2 = offset2 + int(round(float(end2) / resolution)) + 1
# Apply percental padding
padding1 = int(round(((end_bin1 - start_bin1) / 2) * padding))
padding2 = int(round(((end_bin2 - start_bin2) / 2) * padding))
start_bin1 -= padding1
start_bin2 -= padding2
end_bin1 += padding1
end_bin2 += padding2
# Get the size of the region
dim1 = end_bin1 - start_bin1
dim2 = end_bin2 - start_bin2
# Get additional absolute padding if needed
padding1 = 0
if dim1 < width:
padding1 = int((width - dim1) / 2)
start_bin1 -= padding1
end_bin1 += padding1
padding2 = 0
if dim2 < height:
padding2 = int((height - dim2) / 2)
start_bin2 -= padding2
end_bin2 += padding2
# In case the final dimension does not math the desired domension we
# increase the end bin. This can be caused when the padding is not
# divisable by 2, since the padding is rounded to the nearest integer.
abs_dim1 = abs(start_bin1 - end_bin1)
if abs_dim1 < width:
end_bin1 += width - abs_dim1
abs_dim1 = width
abs_dim2 = abs(start_bin2 - end_bin2)
if abs_dim2 < height:
end_bin2 += height - abs_dim2
abs_dim2 = height
# Maximum width / height is 512
if abs_dim1 > hss.SNIPPET_MAT_MAX_DATA_DIM: raise SnippetTooLarge()
if abs_dim2 > hss.SNIPPET_MAT_MAX_DATA_DIM: raise SnippetTooLarge()
# Finally, adjust to negative values.
# Since relative bin IDs are adjusted by the start this will lead to a
# white offset.
real_start_bin1 = start_bin1 if start_bin1 >= 0 else 0
real_start_bin2 = start_bin2 if start_bin2 >= 0 else 0
# Get the data
data = c.matrix(as_pixels=True, balance=False,
max_chunk=np.inf)[real_start_bin1:end_bin1,
real_start_bin2:end_bin2]
# Annotate pixels for balancing
bins = c.bins(convert_enum=False)[['weight']]
data = cooler.annotate(data, bins, replace=False)
# Calculate relative bin IDs
rel_bin1 = np.add(data['bin1_id'].values, -start_bin1)
rel_bin2 = np.add(data['bin2_id'].values, -start_bin2)
# Balance counts
if balanced:
values = data['count'].values.astype(np.float32)
values *= data['weight1'].values * data['weight2'].values
else:
values = data['count'].values
# Get pixel IDs for the upper triangle
idx1 = np.add(np.multiply(rel_bin1, abs_dim1), rel_bin2)
# Mirror matrix
idx2_1 = np.add(data['bin2_id'].values, -start_bin1)
idx2_2 = np.add(data['bin1_id'].values, -start_bin2)
idx2 = np.add(np.multiply(idx2_1, abs_dim1), idx2_2)
validBins = np.where((idx2_1 < abs_dim1) & (idx2_2 >= 0))
# Ignore diagonals
diags_start_row = None
if ignore_diags > 0:
try:
diags_start_idx = np.min(
np.where(data['bin1_id'].values == data['bin2_id'].values))
diags_start_row = (rel_bin1[diags_start_idx] -
rel_bin2[diags_start_idx])
except ValueError:
pass
# Copy pixel values onto the final array
frag_len = abs_dim1 * abs_dim2
frag = np.zeros(frag_len, dtype=np.float32)
# Make sure we're within the bounds
idx1_f = np.where(idx1 < frag_len)
frag[idx1[idx1_f]] = values[idx1_f]
frag[idx2[validBins]] = values[validBins]
frag = frag.reshape((abs_dim1, abs_dim2))
# Store low quality bins
low_quality_bins = np.where(np.isnan(frag))
# Assign 0 for now to avoid influencing the max values
frag[low_quality_bins] = 0
# Scale fragment down if needed
scaled = False
scale_x = width / frag.shape[0]
if frag.shape[0] > width or frag.shape[1] > height:
scaledFrag = np.zeros((width, height), float)
frag = scaledFrag + zoomArray(frag, scaledFrag.shape, order=1)
scaled = True
# Normalize by minimum
if not no_normalize:
min_val = np.min(frag)
frag -= min_val
ignored_idx = None
# Remove diagonals
if ignore_diags > 0 and diags_start_row is not None:
if width == height:
scaled_row = int(np.rint(diags_start_row / scale_x))
idx = np.diag_indices(width)
scaled_idx = (idx if scaled_row == 0 else
[idx[0][scaled_row:], idx[0][:-scaled_row]])
for i in range(ignore_diags):
# First set all cells to be ignored to `-1` so that we can
# easily query for them later.
if i == 0:
frag[scaled_idx] = -1
else:
dist_to_diag = scaled_row - i
dist_neg = min(0, dist_to_diag)
off = 0 if dist_to_diag >= 0 else i - scaled_row
# Above diagonal
frag[((scaled_idx[0] - i)[off:],
(scaled_idx[1])[off:])] = -1
# Extra cutoff at the bottom right
frag[(range(
scaled_idx[0][-1] - i,
scaled_idx[0][-1] + 1 + dist_neg,
),
range(scaled_idx[1][-1],
scaled_idx[1][-1] + i + 1 + dist_neg))] = -1
# Below diagonal
frag[((scaled_idx[0] + i)[:-i], (scaled_idx[1])[:-i])] = -1
# Save the final selection of ignored cells for fast access
# later and set those values to `0` now.
ignored_idx = np.where(frag == -1)
frag[ignored_idx] = 0
else:
logger.warn(
'Ignoring the diagonal only supported for squared features')
# Capp by percentile
max_val = np.percentile(frag, percentile)
frag = np.clip(frag, 0, max_val)
# Normalize by maximum
if not no_normalize and max_val > 0:
frag /= max_val
# Set the ignored diagonal to the maximum
if ignored_idx:
frag[ignored_idx] = 1.0
if not scaled:
# Recover low quality bins
frag[low_quality_bins] = -1
return frag
def main(event):
parser = argparse.ArgumentParser()
parser.add_argument(
'--apath',
type=str,
default='/keilholz-lab/Jacob/TDABrains_00/data/FCStateClassif/anat/')
parser.add_argument('--poolSize', type=int, default=14)
parser.add_argument('--nVols', type=int, default=-1)
parser.add_argument('--display', type=bool, default=False)
args = parser.parse_args()
apath_rest = args.apath
apath_task = args.apath
vloc = args.apath
volunteers, restDirs, taskDirs, EVtxt, Blocks, Blockstxt = p_utils.getLists(
vloc=vloc)
random.shuffle(volunteers)
# saveloc
saveloc = './results/'
poolSize = args.poolSize
maxdim = p_utils.maxdim
metrics = p_utils.metrics
train_volloc = saveloc + 'ind0/'
# Make table of existing data
# indices pulled from train_volloc as previous step (p3_....py) runs over all available volunteers
all_times_vols = p_utils.getSecondList(train_volloc)
allInds = []
volInds = {}
allNames = []
allNames_dict = {}
dict_count = -1
dLen = 0
for voln in tqdm(all_times_vols, desc='Loading timing info.'):
allInds.append(
np.load(train_volloc + str(voln) + '.npy', allow_pickle=True))
volInds[voln] = np.empty(allInds[-1].shape, dtype='int')
for i in allInds[-1]:
dict_count += 1
allNames.append((voln[:5] + '_{:03d}').format(i))
allNames_dict[allNames[-1]] = dict_count
volInds[voln][i] = dict_count
dLen = i + 1
lan = len(allNames)
san = set(allNames)
# Also load data
UUs = {}
for metric in metrics:
UUs[metric] = []
for voln in tqdm(all_times_vols, desc='Loading data'):
if metric == 'diagram':
graphType = 'ripped'
elif metric == 'simplex':
graphType = 'simplex'
locU = saveloc + '{}Training/'.format(graphType)
with open(locU + str(voln) + '.pkl', 'rb') as file:
uus = pickle.load(file)
for uu in uus:
if metric == 'diagram':
UUs[metric].append(uu['dgms'])
if metric == 'simplex':
UUs[metric].append(uu)
print('UUs[-1] is shape {}'.format(UUs[metric][-1].shape))
nP = p_utils.knn
nW = 1
pool = Pool(poolSize, p_utils.getMultiscaleIndexerRanges, (
nP,
nW,
))
# Initialize data Mat
dataMat = {}
for metric in metrics:
for dim in range(maxdim + 1):
dataMat[dim] = np.full([lan, lan], -1.0)
dataMat[dim][np.diag_indices(lan)] = 0
# Load finished distances
for metric in metrics:
if metric == 'diagram':
dist_fun = partial(p_utils.slw2, normalize=False)
desc = 'calc Wasserstein distance'
elif metric == 'simplex':
dist_fun = partial(p_utils.calcWeightedJaccard)
desc = 'calc connected simplices'
locBeg = saveloc + '{}AllDist_HN/Begun/'.format(metric)
makedirs(locBeg, exist_ok=True)
begun = p_utils.getSecondList(locBeg)
locFin = saveloc + '{}AllDist_HN/Finished/'.format(metric)
makedirs(locFin, exist_ok=True)
finished = p_utils.getSecondList(locFin)
for fin in tqdm(finished):
fname = locBeg + fin + '.npy'
otherInds = np.load(fname)
fname = locFin + fin + '.npy'
tempD = np.load(fname)
for dim in range(maxdim + 1):
dataMat[dim][allNames_dict[fin],
otherInds] = tempD[:, dim].ravel()
dataMat[dim][otherInds,
allNames_dict[fin]] = tempD[:, dim].ravel()
remaining = san - set(finished) - set(begun)
while len(remaining) and not event.is_set():
print('finished {}'.format(-len(remaining) + lan))
print('remaining Vol x Times, {} of {}.'.format(
len(remaining), lan))
lineLbl = random.choice(tuple(remaining))
lineNum = allNames_dict[lineLbl]
otherInds = np.arange(lan)[(dataMat[0][lineNum, :] == -1).ravel()]
np.save(locBeg + lineLbl + '.npy', otherInds)
#print('printing dataMat histograms')
#for dim in range(maxdim+1):
# print(np.histogram(dataMat[dim].ravel()))
print('For datapoint {}, # remaining inds = {}.'.format(
lineLbl, len(otherInds)))
tempD = list(
pool.imap(dist_fun,
tqdm([[UUs[metric][lineNum], UUs[metric][oi]]
for oi in otherInds],
total=len(otherInds),
desc=desc),
chunksize=1))
#tempD = [[-1]*len(Inds)]*3
tempD = np.array(tempD)
print('Saving results for line {}'.format(lineLbl))
fname = locFin + lineLbl + '.npy'
np.save(fname, tempD)
newly_finished = p_utils.getSecondList(locFin)
for fin in list(set(newly_finished) - set(finished)):
fname = locBeg + fin + '.npy'
otherInds = np.load(fname)
fname = locFin + fin + '.npy'
tempD = np.load(fname)
for dim in range(maxdim + 1):
dataMat[dim][allNames_dict[fin],
otherInds] = tempD[:, dim].ravel()
dataMat[dim][otherInds,
allNames_dict[fin]] = tempD[:, dim].ravel()
finished = newly_finished
begun = p_utils.getSecondList(locBeg)
remaining = san - set(finished) - set(begun)
print('finished {}'.format(-len(remaining) + lan))
print('We done with {}'.format(metric))
return
def fn(x, P_acc):
x_ = K.zeros((self.nb_actions, self.nb_actions))
x_ = T.set_subtensor(x_[np.diag_indices(self.nb_actions)], x)
return x_
import numpy as np
from scipy.optimize import minimize
from sklearn.linear_model import Ridge
# 反投影矩阵
lbp_B = np.loadtxt(r'D:\Proj\EIT\EIT-py\data\lbp_B_python.csv', delimiter=",")
# 灵敏度矩阵
S = np.loadtxt(r'D:\Proj\EIT\EIT-py\data\jacobian_python.csv', delimiter=",")
inv_S = np.linalg.pinv(S)
# 灵敏度归一化——A矩阵
W = np.zeros((576, 576))
S2 = ((S**2).sum(axis=0))**(-0.5)
row, col = np.diag_indices(W.shape[0])
W[row, col] = S2
A = np.dot(W, np.linalg.pinv(np.dot(S, W)))
#迭代计算的初始值
x0 = np.zeros(576)
def lbp(proj_d):
"""线性反投影算法"""
#将28个边界值补全(8*7)
proj56 = np.hstack([
proj_d[0:7], proj_d[7:13], proj_d[0], proj_d[13:18], proj_d[1],
proj_d[7], proj_d[18:22], proj_d[2], proj_d[8], proj_d[13],
proj_d[22:25], proj_d[3], proj_d[9], proj_d[14], proj_d[18],
proj_d[25:27], proj_d[4], proj_d[10], proj_d[15], proj_d[19],
proj_d[22], proj_d[27], proj_d[5], proj_d[11], proj_d[16], proj_d[20],
def cholesky_eri_b(mol, erifile, auxbasis='weigend+etb', dataname='eri_mo',
int3c='cint3c2e_sph', aosym='s2ij', int2c='cint2c2e_sph',
comp=1, ioblk_size=256, verbose=logger.NOTE):
'''3-center 2-electron AO integrals
'''
assert(aosym in ('s1', 's2ij'))
time0 = (time.clock(), time.time())
if isinstance(verbose, logger.Logger):
log = verbose
else:
log = logger.Logger(mol.stdout, verbose)
auxmol = incore.format_aux_basis(mol, auxbasis)
j2c = incore.fill_2c2e(mol, auxmol, intor=int2c)
log.debug('size of aux basis %d', j2c.shape[0])
time1 = log.timer('2c2e', *time0)
try:
low = scipy.linalg.cholesky(j2c, lower=True)
except scipy.linalg.LinAlgError:
j2c[numpy.diag_indices(j2c.shape[1])] += 1e-14
low = scipy.linalg.cholesky(j2c, lower=True)
j2c = None
time1 = log.timer('Cholesky 2c2e', *time1)
if h5py.is_hdf5(erifile):
feri = h5py.File(erifile)
if dataname in feri:
del(feri[dataname])
else:
feri = h5py.File(erifile, 'w')
for icomp in range(comp):
feri.create_group('%s/%d'%(dataname,icomp)) # for h5py old version
def store(b, label):
cderi = scipy.linalg.solve_triangular(low, b, lower=True, overwrite_b=True)
if cderi.flags.f_contiguous:
cderi = lib.transpose(cderi.T)
feri[label] = cderi
atm, bas, env, ao_loc = incore._env_and_aoloc(int3c, mol, auxmol)
nao = ao_loc[mol.nbas]
naoaux = ao_loc[-1] - nao
if aosym == 's1':
nao_pair = nao * nao
buflen = min(max(int(ioblk_size*1e6/8/naoaux/comp), 1), nao_pair)
shranges = _guess_shell_ranges(mol, buflen, 's1')
else:
nao_pair = nao * (nao+1) // 2
buflen = min(max(int(ioblk_size*1e6/8/naoaux/comp), 1), nao_pair)
shranges = _guess_shell_ranges(mol, buflen, 's2ij')
log.debug('erifile %.8g MB, IO buf size %.8g MB',
naoaux*nao_pair*8/1e6, comp*buflen*naoaux*8/1e6)
if log.verbose >= logger.DEBUG1:
log.debug1('shranges = %s', shranges)
cintopt = gto.moleintor.make_cintopt(atm, bas, env, int3c)
bufs1 = numpy.empty((comp*max([x[2] for x in shranges]),naoaux))
for istep, sh_range in enumerate(shranges):
log.debug('int3c2e [%d/%d], AO [%d:%d], nrow = %d', \
istep+1, len(shranges), *sh_range)
bstart, bend, nrow = sh_range
shls_slice = (bstart, bend, 0, mol.nbas, mol.nbas, mol.nbas+auxmol.nbas)
buf = _ri.nr_auxe2(int3c, atm, bas, env, shls_slice, ao_loc,
aosym, comp, cintopt, bufs1)
if comp == 1:
store(buf.T, '%s/0/%d'%(dataname,istep))
else:
for icomp in range(comp):
store(buf[icomp].T, '%s/%d/%d'%(dataname,icomp,istep))
time1 = log.timer('gen CD eri [%d/%d]' % (istep+1,len(shranges)), *time1)
buf = bufs1 = None
feri.close()
return erifile
def _update(self):
def _isinvalid(x):
return isnan(x) or isinf(x)
# Update the displayed confusion matrix
if self.results is not None and self.selected_learner:
cmatrix = confusion_matrix(self.results, self.selected_learner[0])
colsum = cmatrix.sum(axis=0)
rowsum = cmatrix.sum(axis=1)
n = len(cmatrix)
diag = np.diag_indices(n)
colors = cmatrix.astype(np.double)
colors[diag] = 0
if self.selected_quantity == 0:
normalized = cmatrix.astype(np.int)
formatstr = "{}"
div = np.array([colors.max()])
else:
if self.selected_quantity == 1:
normalized = 100 * cmatrix / colsum
div = colors.max(axis=0)
else:
normalized = 100 * cmatrix / rowsum[:, np.newaxis]
div = colors.max(axis=1)[:, np.newaxis]
formatstr = "{:2.1f} %"
div[div == 0] = 1
colors /= div
maxval = normalized[diag].max()
if maxval > 0:
colors[diag] = normalized[diag] / maxval
for i in range(n):
for j in range(n):
val = normalized[i, j]
col_val = colors[i, j]
item = self._item(i + 2, j + 2)
item.setData(
"NA" if _isinvalid(val) else formatstr.format(val),
Qt.DisplayRole)
bkcolor = QColor.fromHsl(
[0, 240][i == j], 160,
255 if _isinvalid(col_val) else int(255 -
30 * col_val))
item.setData(QBrush(bkcolor), Qt.BackgroundRole)
item.setData("trbl", BorderRole)
item.setToolTip("actual: {}\npredicted: {}".format(
self.headers[i], self.headers[j]))
item.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)
item.setFlags(Qt.ItemIsEnabled | Qt.ItemIsSelectable)
self._set_item(i + 2, j + 2, item)
bold_font = self.tablemodel.invisibleRootItem().font()
bold_font.setBold(True)
def _sum_item(value, border=""):
item = QStandardItem()
item.setData(value, Qt.DisplayRole)
item.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)
item.setFlags(Qt.ItemIsEnabled)
item.setFont(bold_font)
item.setData(border, BorderRole)
item.setData(QColor(192, 192, 192), BorderColorRole)
return item
for i in range(n):
self._set_item(n + 2, i + 2, _sum_item(int(colsum[i]), "t"))
self._set_item(i + 2, n + 2, _sum_item(int(rowsum[i]), "l"))
self._set_item(n + 2, n + 2, _sum_item(int(rowsum.sum())))
def forward(self, x):
# create diagonal matrices
m = np.zeros((x.size * self.dim)).reshape(-1, self.dim, self.dim)
x = x.reshape(-1, self.dim)
m[(np.s_[:], ) + np.diag_indices(x.shape[1])] = x
return m
def run_isoc_CE(objective,
obs,
obs_er,
filters,
refmag,
prange,
sample,
itmax,
band,
alpha,
tol,
weight,
prior,
seed,
nthreads=1,
guess=False):
# Define arrays to be used
ndim = prange.shape[0]
lik = np.zeros(sample)
center = np.zeros([ndim, itmax])
sigma = np.zeros([ndim, itmax])
pars_best = 0
avg_var = 1e3
diag_ind = np.diag_indices(ndim)
midpoint = (prange[:, 1] - prange[:, 0]) / 2. + prange[:, 0]
# generate initial solution population
pars = []
for k in range(ndim):
aux = np.random.uniform(prange[k, 0], prange[k, 1], sample)
pars.append(aux)
pars = np.array(pars)
if (guess != False):
pars[:, 0] = guess
iter = 0
# enforce prange limits
for n in range(sample):
ind_low = np.where(pars[:, n] - prange[:, 0] < 0.)
pars[ind_low, n] = prange[ind_low, 0]
ind_hi = np.where(pars[:, n] - prange[:, 1] > 0.)
pars[ind_hi, n] = prange[ind_hi, 1]
while (iter < itmax and avg_var > tol):
##########################################################################################
# run liklihood calculation in parallel
pool = mp.Pool(processes=nthreads)
res = [
pool.apply_async(objective,
args=(
theta,
obs,
obs_er,
filters,
refmag,
prange,
weight,
prior,
seed,
)) for theta in pars.T
]
lik = np.array([p.get() for p in res])
pool.close()
pool.join()
###########################################################################################
# sort solution in descending likelihood
ind = np.argsort(lik)
# best solution in iteration
pars_best = np.copy(pars[:, ind[0]])
lik_best = lik[ind[0]]
# indices of band best solutions
ind_best = ind[0:int(band * sample)]
# discard indices that are out of parameter space
ind_best = ind_best[np.isfinite(lik[ind_best])]
# leaving here for now. This allows for change in convergence speed
beta = alpha #* hill(iter,4.,0.5*itmax) + 0.01
peso = np.resize(-lik, (ndim, sample))
# calculate new proposal distribution
if (iter == 0):
center[:, iter] = np.nanmean(pars[:, ind_best], axis=1)
covmat = np.cov(pars[:, ind_best])
else:
center[:,iter] = beta*np.nansum(pars[:,ind_best]*peso[:,ind_best],axis=1) \
/ np.nansum(peso[:,ind_best],axis=1) + (1.-beta)*center[:,iter-1]
covmat = beta * np.cov(pars[:, ind_best]) + (1. - beta) * covmat
# check center variance in the last 10 iterations
if (iter > 10):
avg_var = np.max(
np.std(center[:, iter - 10:iter], axis=1) / center[:, iter])
if (covmat[2, 2] < 0.1):
covmat[2, 2] = 0.2
pars = np.random.multivariate_normal(center[:, iter], covmat, sample).T
# enforce prange limits
for n in range(sample):
ind_low = np.where(pars[:, n] - prange[:, 0] < 0.)
pars[ind_low, n] = midpoint[ind_low]
ind_hi = np.where(pars[:, n] - prange[:, 1] > 0.)
pars[ind_hi, n] = midpoint[ind_hi]
# keep best solution
pars[:, 0] = pars_best
# print('center:',center[:,iter])
# print(iter, avg_var, covmat[diag_ind])
# print('best:',lik_best,pars_best)
# print('')
iter += 1
# print 'Best solution'
print(' '.join('%0.3f' % v for v in pars_best),
"{0:0.2f}".format(lik_best), iter, "{0:0.5f}".format(avg_var))
return pars_best
def _newton_rhaphson(
self,
df,
events,
start,
stop,
weights,
show_progress=False,
step_size=None,
precision=10e-6,
max_steps=50,
initial_point=None,
): # pylint: disable=too-many-arguments,too-many-locals,too-many-branches,too-many-statements
"""
Newton Rhaphson algorithm for fitting CPH model.
Parameters
----------
df: DataFrame
stop_times_events: DataFrame
meta information about the subjects history
show_progress: bool, optional (default: True)
to show verbose output of convergence
step_size: float
> 0 to determine a starting step size in NR algorithm.
precision: float
the convergence halts if the norm of delta between
successive positions is less than epsilon.
Returns
--------
beta: (1,d) numpy array.
"""
assert precision <= 1.0, "precision must be less than or equal to 1."
# soft penalizer functions, from https://www.cs.ubc.ca/cgi-bin/tr/2009/TR-2009-19.pdf
soft_abs = lambda x, a: 1 / a * (anp.logaddexp(0, -a * x) + anp.logaddexp(0, a * x))
penalizer = (
lambda beta, a: n
* 0.5
* (
self.l1_ratio * (self.penalizer * soft_abs(beta, a)).sum()
+ (1 - self.l1_ratio) * (self.penalizer * beta ** 2).sum()
)
)
d_penalizer = elementwise_grad(penalizer)
dd_penalizer = elementwise_grad(d_penalizer)
n, d = df.shape
# make sure betas are correct size.
if initial_point is not None:
beta = initial_point
else:
beta = np.zeros((d,))
i = 0
converging = True
ll, previous_ll = 0, 0
start_time = time.time()
step_sizer = StepSizer(step_size)
step_size = step_sizer.next()
while converging:
i += 1
if self.strata is None:
h, g, ll = self._get_gradients(df.values, events.values, start.values, stop.values, weights.values, beta)
else:
g = np.zeros_like(beta)
h = np.zeros((d, d))
ll = 0
for _h, _g, _ll in self._partition_by_strata_and_apply(
df, events, start, stop, weights, self._get_gradients, beta
):
g += _g
h += _h
ll += _ll
if i == 1 and np.all(beta == 0):
# this is a neat optimization, the null partial likelihood
# is the same as the full partial but evaluated at zero.
# if the user supplied a non-trivial initial point, we need to delay this.
self._log_likelihood_null = ll
if isinstance(self.penalizer, np.ndarray) or self.penalizer > 0:
ll -= penalizer(beta, 1.5 ** i)
g -= d_penalizer(beta, 1.5 ** i)
h[np.diag_indices(d)] -= dd_penalizer(beta, 1.5 ** i)
try:
# reusing a piece to make g * inv(h) * g.T faster later
inv_h_dot_g_T = spsolve(-h, g, sym_pos=True)
except ValueError as e:
if "infs or NaNs" in str(e):
raise ConvergenceError(
"""hessian or gradient contains nan or inf value(s). Convergence halted. Please see the following tips in the lifelines documentation:
https://lifelines.readthedocs.io/en/latest/Examples.html#problems-with-convergence-in-the-cox-proportional-hazard-model
""",
e,
)
else:
# something else?
raise e
except LinAlgError as e:
raise ConvergenceError(
"""Convergence halted due to matrix inversion problems. Suspicion is high colinearity. Please see the following tips in the lifelines documentation:
https://lifelines.readthedocs.io/en/latest/Examples.html#problems-with-convergence-in-the-cox-proportional-hazard-model
""",
e,
)
delta = step_size * inv_h_dot_g_T
if np.any(np.isnan(delta)):
raise ConvergenceError(
"""delta contains nan value(s). Convergence halted. Please see the following tips in the lifelines documentation:
https://lifelines.readthedocs.io/en/latest/Examples.html#problems-with-convergence-in-the-cox-proportional-hazard-model
"""
)
# Save these as pending result
hessian, gradient = h, g
norm_delta = norm(delta)
newton_decrement = g.dot(inv_h_dot_g_T) / 2
if show_progress:
print(
"\rIteration %d: norm_delta = %.5f, step_size = %.5f, ll = %.5f, newton_decrement = %.5f, seconds_since_start = %.1f"
% (i, norm_delta, step_size, ll, newton_decrement, time.time() - start_time),
end="",
)
# convergence criteria
if norm_delta < precision:
converging, completed = False, True
elif previous_ll > 0 and abs(ll - previous_ll) / (-previous_ll) < 1e-09:
# this is what R uses by default
converging, completed = False, True
elif newton_decrement < 10e-8:
converging, completed = False, True
elif i >= max_steps:
# 50 iterations steps with N-R is a lot.
# Expected convergence is less than 10 steps
converging, completed = False, False
elif step_size <= 0.0001:
converging, completed = False, False
elif abs(ll) < 0.0001 and norm_delta > 1.0:
warnings.warn(
"The log-likelihood is getting suspiciously close to 0 and the delta is still large. There may be complete separation in the dataset. This may result in incorrect inference of coefficients. \
See https://stats.stackexchange.com/questions/11109/how-to-deal-with-perfect-separation-in-logistic-regression\n",
ConvergenceWarning,
)
converging, completed = False, False
step_size = step_sizer.update(norm_delta).next()
beta += delta
self._hessian_ = hessian
self._score_ = gradient
self.log_likelihood_ = ll
if show_progress and completed:
print("Convergence completed after %d iterations." % (i))
elif show_progress and not completed:
print("Convergence failed. See any warning messages.")
# report to the user problems that we detect.
if completed and norm_delta > 0.1:
warnings.warn(
"Newton-Rhapson convergence completed but norm(delta) is still high, %.3f. This may imply non-unique solutions to the maximum likelihood. Perhaps there is colinearity or complete separation in the dataset?"
% norm_delta,
ConvergenceWarning,
)
elif not completed:
warnings.warn("Newton-Rhapson failed to converge sufficiently in %d steps." % max_steps, ConvergenceWarning)
return beta
raise RuntimeError('corrupted result file (PdB len mismatch)')
pidxRange = np.where(resf['results']['Epsilon'] == 0)[0]
# some debug output
print('Channel ID = {}\nSavefile = {}\nNum Tasks = {}'.format(
cidx, outfile, len(PdB)))
if len(pidxRange) != len(PdB):
print('Remaining Tasks = {}\n\n'.format(len(pidxRange)))
else:
print('\n\n')
sys.stdout.flush()
# start slaving away
dix = np.diag_indices(numUE)
t = WSEE(mu, Pc)
beta = np.asarray(heff[cidx], dtype=np.double)
alpha = beta[dix]
beta[dix] = 0
t.setChan(alpha, beta)
for pidx in pidxRange:
print((30 * '=' + ' PdB = {} ({}/{}) ' + 30 * '=').format(
PdB[pidx], pidx, len(PdB)))
sys.stdout.flush()
t.setPmax(Plin[pidx])
t.optimize()
def add_diagonal_limit(mat, val, max_size):
di = np.diag_indices(min(min(mat.shape), max_size), mat.ndim)
mat[di] += val
adj_mat = np.zeros((p,p))
adj_mat[0,1] = 1
elif sim_case == 1:
# transitive three-node network (0 --> 1 --> 2)
p = 3
adj_mat = np.zeros((p,p))
adj_mat[0,1] = adj_mat[1,2] = 1
else:
# large sparse/weakly-coupled random network (potentially recurrent)
p = 5
sparse = 0.2
adj_mat = np.zeros((p**2))
adj_mat[0:int(sparse*p**2)] = 1
np.random.shuffle(adj_mat)
adj_mat = adj_mat.reshape((p,p))
adj_mat[np.diag_indices(p)] = 0
connection_strength = 1.0
time_step = 0.001
time_period = 5.0
time_range = np.arange(-time_period / 2, time_period / 2, time_step)
ntime_points = int(time_period / time_step)
padding_window = 50
simulation_params = {'network' : adj_mat,
'connection_strength' : connection_strength,
'time_step' : time_step,
'time_period' : time_period,
'padding' : padding_window}
def trans_rdm1(myci, cibra, ciket, nmo=None, nocc=None):
r'''
One-particle spin density matrices dm1a, dm1b in MO basis (the
occupied-virtual blocks due to the orbital response contribution are not
included).
dm1a[p,q] = <q_alpha^\dagger p_alpha>
dm1b[p,q] = <q_beta^\dagger p_beta>
The convention of 1-pdm is based on McWeeney's book, Eq (5.4.20).
'''
if nmo is None: nmo = myci.nmo
if nocc is None: nocc = myci.nocc
c0bra, c1bra, c2bra = myci.cisdvec_to_amplitudes(cibra,
nmo,
nocc,
copy=False)
c0ket, c1ket, c2ket = myci.cisdvec_to_amplitudes(ciket,
nmo,
nocc,
copy=False)
nmoa, nmob = nmo
nocca, noccb = nocc
bra1a, bra1b = c1bra
bra2aa, bra2ab, bra2bb = c2bra
ket1a, ket1b = c1ket
ket2aa, ket2ab, ket2bb = c2ket
dvoa = c0bra.conj() * ket1a.T
dvob = c0bra.conj() * ket1b.T
dvoa += numpy.einsum('jb,ijab->ai', bra1a.conj(), ket2aa)
dvoa += numpy.einsum('jb,ijab->ai', bra1b.conj(), ket2ab)
dvob += numpy.einsum('jb,ijab->ai', bra1b.conj(), ket2bb)
dvob += numpy.einsum('jb,jiba->ai', bra1a.conj(), ket2ab)
dova = c0ket * bra1a.conj()
dovb = c0ket * bra1b.conj()
dova += numpy.einsum('jb,ijab->ia', ket1a.conj(), bra2aa)
dova += numpy.einsum('jb,ijab->ia', ket1b.conj(), bra2ab)
dovb += numpy.einsum('jb,ijab->ia', ket1b.conj(), bra2bb)
dovb += numpy.einsum('jb,jiba->ia', ket1a.conj(), bra2ab)
dooa = -numpy.einsum('ia,ka->ik', bra1a.conj(), ket1a)
doob = -numpy.einsum('ia,ka->ik', bra1b.conj(), ket1b)
dooa -= numpy.einsum('ijab,ikab->jk', bra2aa.conj(), ket2aa) * .5
dooa -= numpy.einsum('jiab,kiab->jk', bra2ab.conj(), ket2ab)
doob -= numpy.einsum('ijab,ikab->jk', bra2bb.conj(), ket2bb) * .5
doob -= numpy.einsum('ijab,ikab->jk', bra2ab.conj(), ket2ab)
dvva = numpy.einsum('ia,ic->ac', ket1a, bra1a.conj())
dvvb = numpy.einsum('ia,ic->ac', ket1b, bra1b.conj())
dvva += numpy.einsum('ijab,ijac->bc', ket2aa, bra2aa.conj()) * .5
dvva += numpy.einsum('ijba,ijca->bc', ket2ab, bra2ab.conj())
dvvb += numpy.einsum('ijba,ijca->bc', ket2bb, bra2bb.conj()) * .5
dvvb += numpy.einsum('ijab,ijac->bc', ket2ab, bra2ab.conj())
dm1a = numpy.empty((nmoa, nmoa), dtype=dooa.dtype)
dm1a[:nocca, :nocca] = dooa
dm1a[:nocca, nocca:] = dova
dm1a[nocca:, :nocca] = dvoa
dm1a[nocca:, nocca:] = dvva
norm = numpy.dot(cibra, ciket)
dm1a[numpy.diag_indices(nocca)] += norm
dm1b = numpy.empty((nmob, nmob), dtype=dooa.dtype)
dm1b[:noccb, :noccb] = doob
dm1b[:noccb, noccb:] = dovb
dm1b[noccb:, :noccb] = dvob
dm1b[noccb:, noccb:] = dvvb
dm1b[numpy.diag_indices(noccb)] += norm
if myci.frozen is not None:
nmoa = myci.mo_occ[0].size
nmob = myci.mo_occ[1].size
nocca = numpy.count_nonzero(myci.mo_occ[0] > 0)
noccb = numpy.count_nonzero(myci.mo_occ[1] > 0)
rdm1a = numpy.zeros((nmoa, nmoa), dtype=dm1a.dtype)
rdm1b = numpy.zeros((nmob, nmob), dtype=dm1b.dtype)
rdm1a[numpy.diag_indices(nocca)] = norm
rdm1b[numpy.diag_indices(noccb)] = norm
moidx = myci.get_frozen_mask()
moidxa = numpy.where(moidx[0])[0]
moidxb = numpy.where(moidx[1])[0]
rdm1a[moidxa[:, None], moidxa] = dm1a
rdm1b[moidxb[:, None], moidxb] = dm1b
dm1a = rdm1a
dm1b = rdm1b
return dm1a, dm1b
def _to_standard_index(data_array, desired_shape, is_multi_var=False):
"""
Transform swath data to a standard format where data runs along
diagonal of ND matrix and the non-diagonal data points are
masked
:param data_array: The data array to be transformed
:param desired_shape: The desired shape of the resulting array
:param is_multi_var: True if this is a multi-variable tile
:type data_array: np.array
:type desired_shape: tuple
:type is_multi_var: bool
:return: Reshaped array
:rtype: np.array
"""
if desired_shape[0] == 1:
reshaped_array = np.ma.masked_all(
(desired_shape[1], desired_shape[2]))
row, col = np.indices(data_array.shape)
reshaped_array[np.diag_indices(
desired_shape[1],
len(reshaped_array.shape))] = data_array[row.flat, col.flat]
reshaped_array.mask[np.diag_indices(
desired_shape[1],
len(reshaped_array.shape))] = data_array.mask[row.flat,
col.flat]
reshaped_array = reshaped_array[np.newaxis, :]
elif is_multi_var == True:
# Break the array up by variable. Translate shape from
# len(times) x len(latitudes) x len(longitudes) x num_vars,
# to
# num_vars x len(times) x len(latitudes) x len(longitudes)
reshaped_data_array = np.moveaxis(data_array, -1, 0)
reshaped_array = []
for variable_data_array in reshaped_data_array:
variable_reshaped_array = np.ma.masked_all(desired_shape)
row, col = np.indices(variable_data_array.shape)
variable_reshaped_array[np.diag_indices(
desired_shape[1],
len(variable_reshaped_array.shape))] = variable_data_array[
row.flat, col.flat]
variable_reshaped_array.mask[np.diag_indices(
desired_shape[1], len(variable_reshaped_array.shape)
)] = variable_data_array.mask[row.flat, col.flat]
reshaped_array.append(variable_reshaped_array)
else:
reshaped_array = np.ma.masked_all(desired_shape)
row, col = np.indices(data_array.shape)
reshaped_array[np.diag_indices(
desired_shape[1],
len(reshaped_array.shape))] = data_array[row.flat, col.flat]
reshaped_array.mask[np.diag_indices(
desired_shape[1],
len(reshaped_array.shape))] = data_array.mask[row.flat,
col.flat]
return reshaped_array
def transition_features(data):
S = np.zeros(data.shape)
for i in range(5):
S[:, i] = np.convolve(data[:, i], np.ones(9), mode='same')
S = softmax(S)
cumR = np.zeros(S.shape)
Th = 0.2
peakTh = 10
for j in range(5):
for i in range(len(S)):
if S[i - 1, j] > Th:
cumR[i, j] = cumR[i - 1, j] + S[i - 1, j]
cumR[cumR[:, j] < peakTh, j] = 0
for i in range(5):
d = cumR[:, i]
indP = HypnodensityFeatures.find_peaks(cumR[:, i])
typeP = np.ones(len(indP)) * i
if i == 0:
peaks = np.concatenate([np.expand_dims(indP, axis=1), np.expand_dims(typeP, axis=1)], axis=1)
else:
peaks = np.concatenate([peaks, np.concatenate([np.expand_dims(indP, axis=1),
np.expand_dims(typeP, axis=1)], axis=1)], axis=0)
I = [i[0] for i in sorted(enumerate(peaks[:, 0]), key=lambda x: x[1])]
peaks = peaks[I, :]
remList = np.zeros(len(peaks))
peaks[peaks[:, 1] == 0, 1] = 1
peaks[:, 1] = peaks[:, 1] - 1
if peaks.shape[0] < 2:
features = np.zeros(17)
return features
for i in range(peaks.shape[0] - 1):
if peaks[i, 1] == peaks[i + 1, 1]:
peaks[i + 1, 0] += peaks[i, 0]
remList[i] = 1
remList = remList == 0
peaks = peaks[remList, :]
transitions = np.zeros([4, 4])
for i in range(peaks.shape[0] - 1):
transitions[int(peaks[i, 1]), int(peaks[i + 1, 1])] = np.sqrt(peaks[i, 0] * peaks[i + 1, 0])
di = np.diag_indices(4)
transitions[di] = None
transitions = transitions.reshape(-1)
transitions = transitions[np.invert(np.isnan(transitions))]
nPeaks = np.zeros(5)
for i in range(4):
nPeaks[i] = np.sum(peaks[:, 1] == i)
nPeaks[-1] = peaks.shape[0]
features = np.concatenate([transitions, nPeaks], axis=0)
return features
def fn(x, L_acc, LT_acc):
x_ = K.zeros((self.nb_actions, self.nb_actions))
x_ = T.set_subtensor(x_[np.tril_indices(self.nb_actions)], x)
diag = K.exp(T.diag(x_))
x_ = T.set_subtensor(x_[np.diag_indices(self.nb_actions)], diag)
return x_, x_.T
