# Python numpy.eye() 使用实例

The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.

Example 1

```def initialize_estimate(self, estimate_id, initial_state):
"""Initialize a state estimate with identity covariance.

The initial estimate is saved in the `self.estimates` dictionary.
The timestamp in the `self.estimate_times` is updated.

Args:
estimate_id (int): ID of the tracked target.
initial_state (int): Initial state of the estimate.

Returns:
X (numpy.ndarray): Solution of equation.
"""
x = initial_state
P = self.initial_position_covariance * np.eye(6)
P[3:6, 3:6] = 0
estimate = UWBTracker.StateEstimate(x, P)
self.estimates[estimate_id] = estimate
self.estimate_times[estimate_id] = rospy.get_time()
self.ikf_prev_outlier_flags[estimate_id] = False
self.ikf_outlier_counts[estimate_id] = 0 ```

Example 2

```def _compute_process_and_covariance_matrices(self, dt):
"""Computes the transition and covariance matrix of the process model and measurement model.

Args:
dt (float): Timestep of the discrete transition.

Returns:
F (numpy.ndarray): Transition matrix.
Q (numpy.ndarray): Process covariance matrix.
R (numpy.ndarray): Measurement covariance matrix.
"""
F = np.array(np.bmat([[np.eye(3), dt * np.eye(3)], [np.zeros((3, 3)), np.eye(3)]]))
self.process_matrix = F
q_p = self.process_covariance_position
q_v = self.process_covariance_velocity
Q = np.diag([q_p, q_p, q_p, q_v, q_v, q_v]) ** 2 * dt
r = self.measurement_covariance
R = r * np.eye(4)
self.process_covariance = Q
self.measurement_covariance = R
return F, Q, R ```

Example 3

```def get_covariance(self):
"""Compute data covariance with the generative model.
``cov = components_.T * S**2 * components_ + sigma2 * eye(n_features)``
where  S**2 contains the explained variances.
Returns
-------
cov : array, shape=(n_features, n_features)
Estimated covariance of data.
"""
components_ = self.components_
exp_var = self.explained_variance_
if self.whiten:
components_ = components_ * np.sqrt(exp_var[:, np.newaxis])
exp_var_diff = np.maximum(exp_var - self.noise_variance_, 0.)
cov = np.dot(components_.T * exp_var_diff, components_)
cov.flat[::len(cov) + 1] += self.noise_variance_  # modify diag inplace
return cov ```

Example 4

```def infExact_scipy_post(self, K, covars, y, sig2e, fixedEffects):
n = y.shape[0]

#mean vector
m = covars.dot(fixedEffects)

if (K.shape[1] < K.shape[0]): K_true = K.dot(K.T)
else: K_true = K

if sig2e<1e-6:
L = la.cholesky(K_true + sig2e*np.eye(n), overwrite_a=True, check_finite=False)    	 #Cholesky factor of covariance with noise
sl =   1
pL = -self.solveChol(L, np.eye(n))         									 		 #L = -inv(K+inv(sW^2))
else:
L = la.cholesky(K_true/sig2e + np.eye(n), overwrite_a=True, check_finite=False)	  	 #Cholesky factor of B
sl = sig2e
pL = L                		   												 		 #L = chol(eye(n)+sW*sW'.*K)
alpha = self.solveChol(L, y-m, overwrite_b=False) / sl

post = dict([])
post['alpha'] = alpha					  										  		#return the posterior parameters
post['sW'] = np.ones(n) / np.sqrt(sig2e)									  			#sqrt of noise precision vector
post['L'] = pL
return post ```

Example 5

```def BorthogonalityTest(B, U):
"""
Test the frobenious norm of  U^TBU - I_k
"""
BU = np.zeros(U.shape)
Bu = Vector()
u = Vector()
B.init_vector(Bu,0)
B.init_vector(u,1)

nvec  = U.shape[1]
for i in range(0,nvec):
u.set_local(U[:,i])
B.mult(u,Bu)
BU[:,i] = Bu.get_local()

UtBU = np.dot(U.T, BU)
err = UtBU - np.eye(nvec, dtype=UtBU.dtype)
print("|| UtBU - I ||_F = ", np.linalg.norm(err, 'fro') ) ```

Example 6

```def KeyGen(**kwargs):
'''
Appendix B of BLISS paper
m_bar = m + n

o/p:
A: Public Key n x m' numpy array
S: Secret Key m'x n numpy array
'''
q, n, m, alpha = kwargs['q'], kwargs['n'], kwargs['m'], kwargs['alpha']
Aq_bar = util.crypt_secure_matrix(-(q-1)/2, (q-1)/2, n, m)
S_bar = util.crypt_secure_matrix(-(2)**alpha, (2)**alpha, m, n) # alpha is small enough, we need not reduce (modq)
S = np.vstack((S_bar, np.eye(n, dtype = int))) # dimension is m_bar x n, Elements are in Z mod(2q)
A = np.hstack((2*Aq_bar, q * np.eye(n, dtype = int) - 2*np.matmul(Aq_bar,S_bar))) # dimension is n x m_bar , Elements are in Z mod(2q)
#return util.matrix_to_Zq(A, 2*q), S, Aq_bar, S_bar
return util.matrix_to_Zq(A, 2*q), S ```

Example 7

```def test():
# Classical SIS parameters
n, m, alpha, q = 128, 872, 1, 114356107
kappa = 20

#Discrete Gaussian Parameters
sd = 300
eta = 1.2

A, S = KeyGen(q = q,n = n,m = m,alpha = alpha)
#print np.array(np.matmul(A,S) - q*np.eye(n),dtype=float)/(2*q) #to test AS = q mod(2q)
z, c = Sign(msg = "Hello Bob",A = A,S = S,m = m,n = n,sd = sd,q = q,M = 3.0,kappa = kappa)
print z
print c
print Verify(msg = "Hello Bob", A=A, m=m, n=n, sd=sd, q=q, eta=eta, z=z, c=c, kappa = kappa)
print Verify(msg = "Hello Robert", A=A, m=m, n=n, sd=sd, q=q, eta=eta, z=z, c=c, kappa = kappa)
print Verify(msg = "Hello Roberto", A=A, m=m, n=n, sd=sd, q=q, eta=eta, z=z, c=c, kappa = kappa)
print Verify(msg = "Hola Roberto", A=A, m=m, n=n, sd=sd, q=q, eta=eta, z=z, c=c, kappa = kappa) ```

Example 8

```def __init__(self, X, Y, R=None, t=None, s=None, sigma2=None, maxIterations=100, tolerance=0.001, w=0):
if X.shape[1] != Y.shape[1]:
raise 'Both point clouds must have the same number of dimensions!'

self.X             = X
self.Y             = Y
self.TY            = Y
(self.N, self.D)   = self.X.shape
(self.M, _)        = self.Y.shape
self.R             = np.eye(self.D) if R is None else R
self.t             = np.atleast_2d(np.zeros((1, self.D))) if t is None else t
self.s             = 1 if s is None else s
self.sigma2        = sigma2
self.iteration     = 0
self.maxIterations = maxIterations
self.tolerance     = tolerance
self.w             = w
self.q             = 0
self.err           = 0 ```

Example 9

```def __init__(self, X, Y, B=None, t=None, sigma2=None, maxIterations=100, tolerance=0.001, w=0):
if X.shape[1] != Y.shape[1]:
raise 'Both point clouds must have the same number of dimensions!'

self.X             = X
self.Y             = Y
self.TY            = Y
(self.N, self.D)   = self.X.shape
(self.M, _)        = self.Y.shape
self.B             = np.eye(self.D) if B is None else B
self.t             = np.atleast_2d(np.zeros((1, self.D))) if t is None else t
self.sigma2        = sigma2
self.iteration     = 0
self.maxIterations = maxIterations
self.tolerance     = tolerance
self.w             = w
self.q             = 0
self.err           = 0 ```

Example 10

```def get_loss(pred, label, end_points, reg_weight=0.001):
""" pred: B*NUM_CLASSES,
label: B, """
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label)
classify_loss = tf.reduce_mean(loss)
tf.summary.scalar('classify loss', classify_loss)

# Enforce the transformation as orthogonal matrix
transform = end_points['transform'] # BxKxK
K = transform.get_shape()[1].value
mat_diff = tf.matmul(transform, tf.transpose(transform, perm=[0,2,1]))
mat_diff -= tf.constant(np.eye(K), dtype=tf.float32)
mat_diff_loss = tf.nn.l2_loss(mat_diff)
tf.summary.scalar('mat loss', mat_diff_loss)

return classify_loss + mat_diff_loss * reg_weight ```

Example 11

```def get_loss(pred, label, end_points, reg_weight=0.001):
""" pred: BxNxC,
label: BxN, """
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label)
classify_loss = tf.reduce_mean(loss)
tf.scalar_summary('classify loss', classify_loss)

# Enforce the transformation as orthogonal matrix
transform = end_points['transform'] # BxKxK
K = transform.get_shape()[1].value
mat_diff = tf.matmul(transform, tf.transpose(transform, perm=[0,2,1]))
mat_diff -= tf.constant(np.eye(K), dtype=tf.float32)
mat_diff_loss = tf.nn.l2_loss(mat_diff)
tf.scalar_summary('mat_loss', mat_diff_loss)

return classify_loss + mat_diff_loss * reg_weight ```

Example 12

```def get_loss(l_pred, seg_pred, label, seg, weight, end_points):
per_instance_label_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=l_pred, labels=label)
label_loss = tf.reduce_mean(per_instance_label_loss)

# size of seg_pred is batch_size x point_num x part_cat_num
# size of seg is batch_size x point_num
per_instance_seg_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=seg_pred, labels=seg), axis=1)
seg_loss = tf.reduce_mean(per_instance_seg_loss)

per_instance_seg_pred_res = tf.argmax(seg_pred, 2)

# Enforce the transformation as orthogonal matrix
transform = end_points['transform'] # BxKxK
K = transform.get_shape()[1].value
mat_diff = tf.matmul(transform, tf.transpose(transform, perm=[0,2,1])) - tf.constant(np.eye(K), dtype=tf.float32)
mat_diff_loss = tf.nn.l2_loss(mat_diff)

total_loss = weight * seg_loss + (1 - weight) * label_loss + mat_diff_loss * 1e-3

Example 13

```def refit_model(self):
"""Learns a new surrogate model using the data observed so far.

"""

# only fit the model if there is data for it.
if len(self.known_models) > 0:

self._build_feature_maps(self.known_models, self.ngram_maxlen, self.thres)

X = sp.vstack([ self._compute_features(mdl)
for mdl in self.known_models], "csr")
y = np.array(self.known_scores, dtype='float64')

#A = np.dot(X.T, X) + lamb * np.eye(X.shape[1])
#b = np.dot(X.T, y)
self.surr_model = lm.Ridge(self.lamb_ridge)
self.surr_model.fit(X, y)

# NOTE: if the search space has holes, it break. needs try/except module. ```

Example 14

```def covariance_matrix(self):
if self._debug:
# return None
ka = self.k_active
if ka > 0:
C = np.eye(self.N) + np.dot(self.V[:ka].T * self.S[:ka],
self.V[:ka])
C = (C * self.D).T * self.D
else:
C = np.diag(self.D**2)
C *= self.sigma**2
else:
# Fake Covariance Matrix for Speed
C = np.ones(1)
self.B = np.ones(1)
return C ```

Example 15

```def Minibatch_Discriminator(input, num_kernels=100, dim_per_kernel=5, init=False, name='MD'):
num_inputs=df_dim*4
theta = tf.get_variable(name+"/theta",[num_inputs, num_kernels, dim_per_kernel], initializer=tf.random_normal_initializer(stddev=0.05))
log_weight_scale = tf.get_variable(name+"/lws",[num_kernels, dim_per_kernel], initializer=tf.constant_initializer(0.0))
W = tf.mul(theta, tf.expand_dims(tf.exp(log_weight_scale)/tf.sqrt(tf.reduce_sum(tf.square(theta),0)),0))
W = tf.reshape(W,[-1,num_kernels*dim_per_kernel])
x = input
x=tf.reshape(x, [batchsize,num_inputs])
activation = tf.matmul(x, W)
activation = tf.reshape(activation,[-1,num_kernels,dim_per_kernel])
abs_dif = tf.mul(tf.reduce_sum(tf.abs(tf.sub(tf.expand_dims(activation,3),tf.expand_dims(tf.transpose(activation,[1,2,0]),0))),2),
1-tf.expand_dims(tf.constant(np.eye(batchsize),dtype=np.float32),1))
f = tf.reduce_sum(tf.exp(-abs_dif),2)/tf.reduce_sum(tf.exp(-abs_dif))
print(f.get_shape())
print(input.get_shape())
return tf.concat(1,[x, f]) ```

Example 16

```def _build_policy(env, predictor, epsilon):
eye = np.eye(env.num_states)
q_values = predictor.predict(
{str(i): eye[i]
for i in range(env.num_states)}
)
policy_vector = [
env.ACTIONS[np.argmax([q_values[action][i] for action in env.ACTIONS])]
for i in range(env.num_states)
]

def policy(state) -> str:
if np.random.random() < epsilon:
return np.random.choice(env.ACTIONS)
else:
return policy_vector[state]

return policy ```

Example 17

```def export_data(prediction_dir, nii_image_dir, tfrecords_dir, export_dir, transformation=None):

for file_path in os.listdir(prediction_dir):
name, prediction, probability = read_prediction_file(os.path.join(prediction_dir, file_path))

if transformation:
image, ground_truth = get_original_image(os.path.join(tfrecords_dir, name + '.tfrecord'), False)
prediction = transformation.transform_image(prediction, probability, image)

# build cv_predictions .nii image
img = nib.Nifti1Image(prediction, np.eye(4))
img.set_data_dtype(dtype=np.uint8)

path = os.path.join(nii_image_dir, name)

i = export_image.get_data()
i[:] = img.get_data()

# set name to specification and export
_id = next(l for l in os.listdir(path) if 'MR_MTT' in l).split('.')[-1]
export_path = os.path.join(export_dir, 'SMIR.' + name + '.' + _id + '.nii')
nib.save(export_image, os.path.join(export_path)) ```

Example 18

```def eye(sites, ldim):
"""Returns a MPA representing the identity matrix

:param sites: Number of sites
:param ldim: Int-like local dimension or iterable of local dimensions
:returns: Representation of the identity matrix as MPA

>>> I = eye(4, 2)
>>> I.ranks, I.shape
((1, 1, 1), ((2, 2), (2, 2), (2, 2), (2, 2)))
>>> I = eye(3, (3, 4, 5))
>>> I.shape
((3, 3), (4, 4), (5, 5))
"""
if isinstance(ldim, collections.Iterable):
ldim = tuple(ldim)
assert len(ldim) == sites
else:
ldim = it.repeat(ldim, sites)
return mp.MPArray.from_kron(map(np.eye, ldim)) ```

Example 19

```def _embed_ltens_identity(mpa, embed_tensor=None):
"""Embed with identity matrices by default.

:param embed_tensor: If the MPAs do not have two physical legs or
have non-square physical dimensions, you must provide an
embedding tensor. The default is to use the square identity
matrix, assuming that the size of the two physical legs is the
same at each site.
:returns: `embed_tensor` with one size-one virtual leg added at each
end.

"""
if embed_tensor is None:
pdims = mpa.shape[0]
assert len(pdims) == 2 and pdims[0] == pdims[1], (
"For ndims != 2 or non-square shape, you must supply a tensor"
"for embedding")
embed_tensor = np.eye(pdims[0])
embed_ltens = embed_tensor[None, ..., None]
return embed_ltens ```

Example 20

```def test_povm_normalization_ic(dim):
for name, constructor in ALL_POVMS.items():
# Check that the POVM is normalized: elements must sum to the identity
current_povm = constructor(dim)
element_sum = sum(iter(current_povm))
assert_array_almost_equal(element_sum, np.eye(dim))

# Check that the attribute that says whether the POVM is IC is correct.
linear_inversion_recons = np.dot(current_povm.linear_inversion_map,
current_povm.probability_map)
if current_povm.informationally_complete:
assert_array_almost_equal(
linear_inversion_recons, np.eye(dim**2),
err_msg='POVM {} is not informationally complete'.format(name))
else:
assert np.abs(linear_inversion_recons - np.eye(dim**2)).max() > 0.1, \
'POVM {} is informationally complete'.format(name) ```

Example 21

```def test_povm_ic_mpa(nr_sites, local_dim, rank, rgen):
# Check that the tensor product of the PauliGen POVM is IC.
paulis = povm.pauli_povm(local_dim)
inv_map = mp_from_array_repeat(paulis.linear_inversion_map, nr_sites)
probab_map = mp_from_array_repeat(paulis.probability_map, nr_sites)
reconstruction_map = mp.dot(inv_map, probab_map)

eye = factory.eye(nr_sites, local_dim**2)
assert mp.norm(reconstruction_map - eye) < 1e-5

# Check linear inversion for a particular example MPA.
# Linear inversion works for arbitrary matrices, not only for states,
# so we test it for an arbitrary MPA.
# Normalize, otherwise the absolute error check below will not work.
mpa = factory.random_mpa(nr_sites, local_dim**2, rank,
dtype=np.complex_, randstate=rgen, normalized=True)
probabs = mp.dot(probab_map, mpa)
recons = mp.dot(inv_map, probabs)
assert mp.norm(recons - mpa) < 1e-6 ```

Example 22

```def test_randomized_svd(rows, cols, rank, dtype, transpose, n_iter, target_gen,
rgen):
rank = min(rows, cols) - 2 if rank is 'fullrank' else rank
A = target_gen(rows, cols, rank=rank, randstate=rgen, dtype=dtype)

U_ref, s_ref, V_ref = utils.truncated_svd(A, k=rank)
U, s, V = em.randomized_svd(A, rank, transpose=transpose, randstate=rgen,
n_iter=n_iter)

error_U = np.abs(U.conj().T.dot(U_ref)) - np.eye(rank)
assert_allclose(np.linalg.norm(error_U), 0, atol=1e-3)
error_V = np.abs(V.dot(V_ref.conj().T)) - np.eye(rank)
assert_allclose(np.linalg.norm(error_V), 0, atol=1e-3)
assert_allclose(s.ravel() - s_ref, 0, atol=1e-3)
# Check that singular values are returned in descending order
assert_array_equal(s, np.sort(s)[::-1]) ```

Example 23

```def hessian(self, x, d=None):
"""
Computes Hessian matrix
"""
d = calc_distances(x) if d is None else d
if d.ndim == 1: d = squareform(d)

H = np.zeros((3*len(x), 3*len(x)))
n = self.n

for i in range(len(x)):
for j in range(len(x)):

if j == i: continue

dx = x[i]-x[j]
r  = d[i,j]
h  = n / r**(0.5*n+2) * ((n+2) * np.multiply.outer(dx,dx) - np.eye(3) * r)

H[3*i:3*(i+1), 3*j:3*(j+1)]  = -h
H[3*i:3*(i+1), 3*i:3*(i+1)] +=  h

return H ```

Example 24

```def apply_gate(self,gate,on_qubit_name):
on_qubit=self.qubits.get_quantum_register_containing(on_qubit_name)
if len(on_qubit.get_noop()) > 0:
print "NOTE this qubit has been measured previously, there should be no more gates allowed but we are reverting that measurement for consistency with IBM's language"
on_qubit.set_state(on_qubit.get_noop())
on_qubit.set_noop([])
if not on_qubit.is_entangled():
if on_qubit.get_num_qubits()!=1:
raise Exception("This qubit is not marked as entangled but it has an entangled state")
on_qubit.set_state(gate*on_qubit.get_state())
else:
if not on_qubit.get_num_qubits()>1:
raise Exception("This qubit is marked as entangled but it does not have an entangled state")
n_entangled=len(on_qubit.get_entangled())
apply_gate_to_qubit_idx=[qb.name for qb in on_qubit.get_entangled()].index(on_qubit_name)
if apply_gate_to_qubit_idx==0:
entangled_gate=gate
else:
entangled_gate=Gate.eye
for i in range(1,n_entangled):
if apply_gate_to_qubit_idx==i:
entangled_gate=np.kron(entangled_gate,gate)
else:
entangled_gate=np.kron(entangled_gate,Gate.eye)
on_qubit.set_state(entangled_gate*on_qubit.get_state()) ```

Example 25

```def _matrix_inverse(self, matrix):
"""
Computes inverse of a matrix.
"""
matrix = np.array(matrix)
n_features = matrix.shape[0]
rank = np.linalg.matrix_rank(matrix)

if rank == n_features:
return np.linalg.inv(matrix)
else:
# Matrix is not full rank, so use Hadi's technique to compute inverse
# Reference: Ali S. Hadi (1992) "Identifying Multiple Outliers in Multivariate Data" eg. 2.3, 2.4
eigenValues, eigenVectors = np.linalg.eig(matrix)
eigenValues = np.abs(eigenValues)  # to deal with -0 values
idx = eigenValues.argsort()[::-1]
eigenValues = eigenValues[idx]
eigenVectors = eigenVectors[:, idx]

s = eigenValues[eigenValues != 0].min()
w = [1 / max(e, s) for e in eigenValues]
W = w * np.eye(n_features)

return eigenVectors.dot(W).dot(eigenVectors.T) ```

Example 26

```def swap_affine(axes):
""" Build a correction matrix, from the given orientation of axes to RAS.

Parameters
----------
the given orientation of the axes.

Returns
-------
rotation: array (4, 4)
the correction matrix.
"""
rotation = numpy.eye(4)
rotation[:3, 0] = CORRECTION_MATRIX_COLUMNS[axes[0]]
rotation[:3, 1] = CORRECTION_MATRIX_COLUMNS[axes[1]]
rotation[:3, 2] = CORRECTION_MATRIX_COLUMNS[axes[2]]
return rotation ```

Example 27

```def distribution_parameters(self, parameter_name):
if parameter_name=='trend':
E = dot(dot(self.derivative_matrix.T,inv(diag(self.parameters.list['omega'].current_value))),self.derivative_matrix)
mean = dot(inv(eye(self.size)+E),self.data)
cov = (self.parameters.list['sigma2'].current_value)*inv(eye(self.size)+E)
return {'mean' : mean, 'cov' : cov}
elif parameter_name=='sigma2':
E = dot(dot(self.derivative_matrix.T,inv(diag(self.parameters.list['omega'].current_value))),self.derivative_matrix)
pos = self.size
loc = 0
scale = 0.5*dot((self.data-dot(eye(self.size),self.parameters.list['trend'].current_value)).T,(self.data-dot(eye(self.size),self.parameters.list['trend'].current_value)))+0.5*dot(dot(self.parameters.list['trend'].current_value.T,E),self.parameters.list['trend'].current_value)
elif parameter_name=='lambda2':
pos = self.size-self.total_variation_order-1+self.alpha
loc = 0.5*(norm(dot(self.derivative_matrix,self.parameters.list['trend'].current_value),ord=1))/self.parameters.list['sigma2'].current_value+self.rho
scale = 1
elif parameter_name==str('omega'):
pos = [sqrt(((self.parameters.list['lambda2'].current_value**2)*self.parameters.list['sigma2'].current_value)/(dj**2)) for dj in dot(self.derivative_matrix,self.parameters.list['trend'].current_value)]
loc = 0
scale = self.parameters.list['lambda2'].current_value**2
return {'pos' : pos, 'loc' : loc, 'scale' : scale} ```

Example 28

```def estimate_time_constant(y, p=2, sn=None, lags=5, fudge_factor=1.):
"""
Estimate AR model parameters through the autocovariance function

Parameters
----------
y : array, shape (T,)
One dimensional array containing the fluorescence intensities with
one entry per time-bin.
p : positive integer
order of AR system
sn : float
sn standard deviation, estimated if not provided.
lags : positive integer
number of additional lags where he autocovariance is computed
fudge_factor : float (0< fudge_factor <= 1)
shrinkage factor to reduce bias

Returns
-------
g : estimated coefficients of the AR process
"""

if sn is None:
sn = GetSn(y)

lags += p
xc = axcov(y, lags)
xc = xc[:, np.newaxis]

A = scipy.linalg.toeplitz(xc[lags + np.arange(lags)],
xc[lags + np.arange(p)]) - sn**2 * np.eye(lags, p)
g = np.linalg.lstsq(A, xc[lags + 1:])[0]
gr = np.roots(np.concatenate([np.array([1]), -g.flatten()]))
gr = (gr + gr.conjugate()) / 2.
gr[gr > 1] = 0.95 + np.random.normal(0, 0.01, np.sum(gr > 1))
gr[gr < 0] = 0.15 + np.random.normal(0, 0.01, np.sum(gr < 0))
g = np.poly(fudge_factor * gr)
g = -g[1:]

return g.flatten() ```

Example 29

```def gaussian_function(y, dimension, ?, cov, log=False, standard=False):
"""??????????? y???(???) ??????(?????) cov??????,log????????,standard??????"""
x = y - ?
if standard:
x = np.dot(x, np.linalg.inv(cov) ** 0.5)
cov_ = np.eye(dimension)
else:
cov_ = cov
np.seterr(all='ignore')  # ??????
if log:
func = - (dimension / 2) * np.log(2 * math.pi) - 0.5 * np.log(np.linalg.det(cov_))
exp = -0.5 * np.dot(np.dot(x, np.linalg.inv(cov_)), x.T)
return func + exp
else:
sigma = (2 * math.pi) ** (dimension / 2) * np.linalg.det(cov_) ** 0.5
func = 1. / sigma
exp = np.exp(-0.5 * np.dot(np.dot(x, np.linalg.inv(cov_)), x.T))
return func * exp ```

Example 30

```def batch_works(k):
if k == n_processes - 1:
paths = all_paths[k * int(len(all_paths) / n_processes) : ]
else:
paths = all_paths[k * int(len(all_paths) / n_processes) : (k + 1) * int(len(all_paths) / n_processes)]

for path in paths:
pred = np.argmax(probs, axis=3)
fg_prob = 1 - probs[..., 0]
pred = clean_contour(fg_prob, pred)
seg = np.zeros(pred.shape, dtype=np.int16)
seg[pred == 1] = 1
seg[pred == 2] = 2
seg[pred == 3] = 4
img = nib.Nifti1Image(seg, np.eye(4))
nib.save(img, os.path.join(output_path, path.replace('_probs.npy', '.nii.gz'))) ```

Example 31

```def __prepare_controls_and_actions(self):
self.__discrete_controls_to_net = np.array([i for i in range(len(self.__discrete_controls))
if i not in self.__discrete_controls_manual])
self.__num_manual_controls = len(self.__discrete_controls_manual)
self.__net_discrete_actions = []
if not self.__opposite_button_pairs:
for perm in it.product([False, True], repeat=len(self.__discrete_controls_to_net)):
self.__net_discrete_actions.append(list(perm))
else:
for perm in it.product([False, True], repeat=len(self.__discrete_controls_to_net)):
act = list(perm)
valid = True
for button_p in self.__opposite_button_pairs:
if act[button_p[0]] and act[button_p[1]]:
valid = False
if valid:
self.__net_discrete_actions.append(act)

self.__num_net_discrete_actions = len(self.__net_discrete_actions)
self.__action_to_index = {tuple(val): ind for (ind, val) in enumerate(self.__net_discrete_actions)}
self.__net_discrete_actions = np.array(self.__net_discrete_actions)
self.__onehot_discrete_acitons = np.eye(self.__num_net_discrete_actions) ```

Example 32

```def initialize_match_matrix(self):
"""
Construct the initial match matrix.

Returns:
--------
match_matrix: array
The match matrix

"""
# TODO add possibility for slack

match_matrix = np.zeros((self.reactants_elements.size, self.products_elements.size))

# set sub blocks of the match matrix to one plus random pertubation
# followed by column normalization
for indices in self.element_type_subset_indices:
match_matrix[indices] = 1 + 1e-3 * np.random.random(match_matrix[indices].shape)
match_matrix[indices] /= match_matrix[indices].sum(0)

#match_matrix = np.eye(match_matrix.shape[0])
#for i,j in [(0,0),(0,4),(1,1),(1,3),(4,0),(4,4),(3,3),(3,1),(7,7),(7,11),(8,8),(8,10),(20,20),(20,24),(21,21),(21,23),(11,7),(11,11),(10,8),(10,10),(24,20),(24,24),(23,23),(23,21)]:
#    match_matrix[i,j] = 0.5

return match_matrix ```

Example 33

```def test_nonzero_twodim(self):
x = np.array([[0, 1, 0], [2, 0, 3]])
assert_equal(np.count_nonzero(x), 3)
assert_equal(np.nonzero(x), ([0, 1, 1], [1, 0, 2]))

x = np.eye(3)
assert_equal(np.count_nonzero(x), 3)
assert_equal(np.nonzero(x), ([0, 1, 2], [0, 1, 2]))

x = np.array([[(0, 1), (0, 0), (1, 11)],
[(1, 1), (1, 0), (0, 0)],
[(0, 0), (1, 5), (0, 1)]], dtype=[('a', 'f4'), ('b', 'u1')])
assert_equal(np.count_nonzero(x['a']), 4)
assert_equal(np.count_nonzero(x['b']), 5)
assert_equal(np.nonzero(x['a']), ([0, 1, 1, 2], [2, 0, 1, 1]))
assert_equal(np.nonzero(x['b']), ([0, 0, 1, 2, 2], [0, 2, 0, 1, 2]))

assert_(not x['a'].T.flags.aligned)
assert_equal(np.count_nonzero(x['a'].T), 4)
assert_equal(np.count_nonzero(x['b'].T), 5)
assert_equal(np.nonzero(x['a'].T), ([0, 1, 1, 2], [1, 1, 2, 0]))
assert_equal(np.nonzero(x['b'].T), ([0, 0, 1, 2, 2], [0, 1, 2, 0, 2])) ```

Example 34

```def test_matrix_rank(self):
# Full rank matrix
yield assert_equal, 4, matrix_rank(np.eye(4))
# rank deficient matrix
I = np.eye(4)
I[-1, -1] = 0.
yield assert_equal, matrix_rank(I), 3
# All zeros - zero rank
yield assert_equal, matrix_rank(np.zeros((4, 4))), 0
# 1 dimension - rank 1 unless all 0
yield assert_equal, matrix_rank([1, 0, 0, 0]), 1
yield assert_equal, matrix_rank(np.zeros((4,))), 0
# accepts array-like
yield assert_equal, matrix_rank([1]), 1
# greater than 2 dimensions raises error
yield assert_raises, TypeError, matrix_rank, np.zeros((2, 2, 2))
# works on scalar
yield assert_equal, matrix_rank(1), 1 ```

Example 35

```def test_byteorder_check():
# Byte order check should pass for native order
if sys.byteorder == 'little':
native = '<'
else:
native = '>'

for dtt in (np.float32, np.float64):
arr = np.eye(4, dtype=dtt)
n_arr = arr.newbyteorder(native)
sw_arr = arr.newbyteorder('S').byteswap()
assert_equal(arr.dtype.byteorder, '=')
for routine in (linalg.inv, linalg.det, linalg.pinv):
# Normal call
res = routine(arr)
# Native but not '='
assert_array_equal(res, routine(n_arr))
# Swapped
assert_array_equal(res, routine(sw_arr)) ```

Example 36

```def test_100(self):
x, w = leg.leggauss(100)

# test orthogonality. Note that the results need to be normalized,
# otherwise the huge values that can arise from fast growing
# functions like Laguerre can be very confusing.
v = leg.legvander(x, 99)
vv = np.dot(v.T * w, v)
vd = 1/np.sqrt(vv.diagonal())
vv = vd[:, None] * vv * vd
assert_almost_equal(vv, np.eye(100))

# check that the integral of 1 is correct
tgt = 2.0
assert_almost_equal(w.sum(), tgt) ```

Example 37

```def _fwlinear(self, args, output_size, scope=None):
if args is None or (nest.is_sequence(args) and not args):
raise ValueError("`args` must be specified")
if not nest.is_sequence(args):
args = [args]
assert len(args) == 2
assert args[0].get_shape().as_list()[1] == output_size

dtype = [a.dtype for a in args][0]

with vs.variable_scope(scope or "Linear"):
matrixW = vs.get_variable(
"MatrixW", dtype=dtype, initializer=tf.convert_to_tensor(np.eye(output_size, dtype=np.float32) * .05))

matrixC = vs.get_variable(
"MatrixC", [args[1].get_shape().as_list()[1], output_size], dtype=dtype)

res = tf.matmul(args[0], matrixW) + tf.matmul(args[1], matrixC)
return res ```

Example 38

```def getValuesFromPose(self, P):
'''return the virtual values of the pots corresponding to the pose P'''
vals = []
for i, r, l, placement, attach_p in zip(range(3), self.rs, self.ls, self.placements, self.attach_ps):
#first pot axis
a = placement.rot * col([1, 0, 0])
#second pot axis
b = placement.rot * col([0, 1, 0])
#string axis
c = placement.rot * col([0, 0, 1])

#attach point on the joystick
p_joystick = P * attach_p
v = p_joystick - placement.trans
va = v - dot(v, a)*a
vb = v - dot(v, b)*b
#angles of the pots
alpha = math.atan2(dot(vb, a), dot(vb, c))
beta = math.atan2(dot(va, b), dot(va, c))
vals.append(alpha)
vals.append(beta)

#calculation of the derivatives
dv = np.bmat([-P.rot.mat() * quat.skew(attach_p), P.rot.mat()])
dva = (np.eye(3) - a*a.T) * dv
dvb = (np.eye(3) - b*b.T) * dv
dalpha = (1/dot(vb,vb)) * (dot(vb,c) * a.T - dot(vb,a) * c.T) * dvb
dbeta = (1/dot(va,va)) * (dot(va,c) * b.T - dot(va,b) * c.T) * dva

Example 39

```def prepare_cholesky(N=100, dtype=np.double):
N = int(N*2)
A = np.asarray(np.random.rand(N, N), dtype=dtype)
return ( A*A.transpose() + N*np.eye(N), )

################################################################################## ```

Example 40

```def update_filter(self, timestep, estimate, ranges):
"""Update position filter.

Args:
timestep (float): Time elapsed since last update.
estimate (StateEstimate): Position estimate to update.
ranges (list of floats): Range measurements.

Returns:
new_estimate (StateEstimate): Updated position estimate.
outlier_flag (bool): Flag indicating whether the measurement was rejected as an outlier.
"""
num_of_units = len(ranges)
x = estimate.state
P = estimate.covariance
# Compute process matrix and covariance matrices
F, Q, R = self._compute_process_and_covariance_matrices(timestep)
# rospy.logdebug('F: {}'.format(F))
# rospy.logdebug('Q: {}'.format(Q))
# rospy.logdebug('R: {}'.format(R))
# Prediction
x = np.dot(F, x)
P = np.dot(F, np.dot(P, F.T)) + Q
# Update
n = np.copy(x)
H = np.zeros((num_of_units, x.size))
z = np.zeros((num_of_units, 1))
h = np.zeros((num_of_units, 1))
for i in xrange(self.ikf_iterations):
n, K, outlier_flag = self._ikf_iteration(x, n, ranges, h, H, z, estimate, R)
if outlier_flag:
new_estimate = estimate
else:
new_state = n
new_covariance = np.dot((np.eye(6) - np.dot(K, H)), P)
new_estimate = UWBTracker.StateEstimate(new_state, new_covariance)
return new_estimate, outlier_flag ```

Example 41

```def get_precision(self):
"""Compute data precision matrix with the generative model.
Equals the inverse of the covariance but computed with
the matrix inversion lemma for efficiency.
Returns
-------
precision : array, shape=(n_features, n_features)
Estimated precision of data.
"""
n_features = self.components_.shape[1]

# handle corner cases first
if self.n_components_ == 0:
return np.eye(n_features) / self.noise_variance_
if self.n_components_ == n_features:
return linalg.inv(self.get_covariance())

# Get precision using matrix inversion lemma
components_ = self.components_
exp_var = self.explained_variance_
exp_var_diff = np.maximum(exp_var - self.noise_variance_, 0.)
precision = np.dot(components_, components_.T) / self.noise_variance_
precision.flat[::len(precision) + 1] += 1. / exp_var_diff
precision = np.dot(components_.T,
np.dot(linalg.inv(precision), components_))
precision /= -(self.noise_variance_ ** 2)
precision.flat[::len(precision) + 1] += 1. / self.noise_variance_
return precision ```

Example 42

```def getTrainKernel(self, params):
self.checkParams(params)
return np.eye(self.n) ```

Example 43

```def AorthogonalityCheck(A, U, d):
"""
Test the frobenious norm of  D^{-1}(U^TAU) - I_k
"""
V = np.zeros(U.shape)
AV = np.zeros(U.shape)
Av = Vector()
v = Vector()
A.init_vector(Av,0)
A.init_vector(v,1)

nvec  = U.shape[1]
for i in range(0,nvec):
v.set_local(U[:,i])
v *= 1./math.sqrt(d[i])
A.mult(v,Av)
AV[:,i] = Av.get_local()
V[:,i] = v.get_local()

VtAV = np.dot(V.T, AV)
err = VtAV - np.eye(nvec, dtype=VtAV.dtype)

#    plt.imshow(np.abs(err))
#    plt.colorbar()
#    plt.show()

print("i, ||Vt(i,:)AV(:,i) - I_i||_F, V[:,i] = 1/sqrt(lambda_i) U[:,i]")
for i in range(1,nvec+1):
print(i, np.linalg.norm(err[0:i,0:i], 'fro') ) ```

Example 44

```def chol_inv(B, lower=True):
"""
Returns the inverse of matrix A, where A = B*B.T,
ie B is the Cholesky decomposition of A.

Solves Ax = I
given B is the cholesky factorization of A.
"""
return cho_solve((B, lower), np.eye(B.shape[0])) ```

Example 45

```def test_theta_0():
rng.seed(0)
n_samples = 100
Y = rng.randn(n_samples, 5)
X = rng.randn(n_samples, 5)

sgcrf = SparseGaussianCRF(lamL=0.01, lamT=0.01)
sgcrf.fit(X, Y)

assert np.allclose(sgcrf.Lam, np.eye(5), .1, .2) ```

Example 46

```def init_layers(X, Z, dims, final_mean_function):
M = Z.shape[0]
q_mus, q_sqrts, mean_functions, Zs = [], [], [], []
X_running, Z_running = X.copy(), Z.copy()

for dim_in, dim_out in zip(dims[:-2], dims[1:-1]):
if dim_in == dim_out: # identity for same dims
W = np.eye(dim_in)
elif dim_in > dim_out: # use PCA mf for stepping down
_, _, V = np.linalg.svd(X_running, full_matrices=False)
W = V[:dim_out, :].T
elif dim_in < dim_out: # identity + pad with zeros for stepping up
I = np.eye(dim_in)
zeros = np.zeros((dim_in, dim_out - dim_in))
W = np.concatenate([I, zeros], 1)

mean_functions.append(Linear(A=W))
Zs.append(Z_running.copy())
q_mus.append(np.zeros((M, dim_out)))
q_sqrts.append(np.eye(M)[:, :, None] * np.ones((1, 1, dim_out)))

Z_running = Z_running.dot(W)
X_running = X_running.dot(W)

# final layer (as before but no mean function)
mean_functions.append(final_mean_function)
Zs.append(Z_running.copy())
q_mus.append(np.zeros((M, dims[-1])))
q_sqrts.append(np.eye(M)[:, :, None] * np.ones((1, 1, dims[-1])))

return q_mus, q_sqrts, Zs, mean_functions ```

Example 47

```def eye_mask(self, shape):
"""
"""
return ~np.eye(*shape, dtype=bool) ```

Example 48

```def gl_update_joint_matrices(self,node,parent_joint=None,parent_joint_matrix=numpy.eye(3,4,dtype=numpy.float32)):
for child in node.children:
if child.node_type == j3d.inf1.NodeType.JOINT:
joint = self.gl_joints[child.index]
joint_matrix = self.gl_joint_matrices[child.index]
joint_matrix[:] = joint.create_matrix(parent_joint,parent_joint_matrix)
self.gl_update_joint_matrices(child,joint,joint_matrix)
else:
self.gl_update_joint_matrices(child,parent_joint,parent_joint_matrix) ```

Example 49

```def KeyGen_test():
A, S, Aq_bar, S_bar = KeyGen(q = 7,n = 5, m = 7, alpha=1)
print Aq_bar
print S_bar
print A
print S
print np.matmul(A,S) - 7*np.eye(5, dtype=int) ```

Example 50

```def __init__(self, Y, R=None, t=None, maxIterations=100, gamma=0.1, ):
if Y is None:
raise 'Empty list of point clouds!'

dimensions = [cloud.shape[1] for cloud in Y]

if not all(dimension == dimensions[0] for dimension in dimensions):
raise 'All point clouds must have the same number of dimensions!'

self.Y = Y
self.M = [cloud.shape[0] for cloud in self.Y]
self.D = dimensions[0]

if R:
rotations = [rotation.shape for rotation in R]
if not all(rotation[0] == self.D and rotation[1] == self.D for rotation in rotations):
raise 'All rotation matrices need to be %d x %d matrices!' % (self.D, self.D)
self.R = R
else:
self.R = [np.eye(self.D) for cloud in Y]

if t:
translations = [translations.shape for translation in t]
if not all(translations[0] == 1 and translations[1] == self.D for translation in translations):
raise 'All translation vectors need to be 1 x %d matrices!' % (self.D)
self.t = t
else:
self.t = [np.atleast_2d(np.zeros((1, self.D))) for cloud in self.Y] ```