Bayesian inference¶
Inference for age structured compartment models using the diffusion approximation (via the van Kampen expansion). See our preprint for more details on the method.
There are two ways to do inference: manifold method (sec 3.3 in the report) and tangent space method (sec 3.4 in the report). Currently, all of the methods listed below are implemented for manifold, and infer_parameters, ‘latent_infer_parameters’, ‘obtain_minus_log_p’, ‘minus_logp_red’ are implemented for tangent space.
In various degrees of less robust but fast to more robust but slow:
tangent space method.
manifold method with few internal steps and fast integration method (det_method = RK2, lyapunov_method = euler).
manifold method with large number of internel steps and robust integration method (solve_ivp from scipy library).
Methods for full data |
|
|---|---|
infer_parameters |
Infers epidemiological parameters given all information. |
infer_control |
Infers control parameters. |
obtain_minus_log_p |
Computes -log(p) of a fully observed trajectory. |
compute_hessian |
Computes the Hessian of -log(p). |
Methods for partial data |
|
|---|---|
latent_infer_parameters |
Infers parameters and initial conditions. |
latent_infer_control |
Infers control parameters. |
minus_logp_red |
Computes -log(p) of a partially observed trajectory |
compute_hessian_latent |
Computes the Hessian of -log(p). |
Helper function |
|
|---|---|
integrate |
A wrapper around ‘simulate’ in pyross.deterministic. |
set_params |
Sets parameters. |
set_det_method |
Sets the integration method of the deterministic equation |
set_lyapunov_method |
Sets the integration method of the Lyapunov equation |
make_det_model |
Makes a pyross.deterministic model of the same class |
fill_params_dict |
Fills and returns a parameter dictionary |
fill_initial_conditions |
Generates full initial condition with partial info. |
The functions are documented under the parent class SIR_type.
SIR_type¶
-
class
pyross.inference.
SIR_type
¶ Parent class for inference for all SIR-type classes listed below
All subclasses use the same functions to perform inference, which are documented below.
-
FIM
()¶ Computes the Fisher Information Matrix (FIM) of the stochastic model. :param param_keys: A list of parameters to be inferred. :type param_keys: list :param init_fltr: True for initial conditions to be inferred.
Shape = (nClass*M) Total number of True = total no. of variables - total no. of observed
- Parameters
maps (numpy.array) – MAP parameter and initial condition estimate (computed for example with SIR_type.latent_inference).
obs0 (numpy.array) – The observed initial data
fltr (boolean sequence or array) – True for observed and False for unobserved. e.g. if only Is is known for SIR with one age group, fltr = [False, False, True]
Tf (float) – Total time of the trajectory
Nf (float) – Number of data points along the trajectory
contactMatrix (callable) – A function that takes time (t) as an argument and returns the contactMatrix
dx (float, optional) – Step size for numerical differentiation of the process mean and its full covariance matrix with respect to the parameters. If not specified, the square root of the machine epsilon for the smallest entry on the diagonal of the covariance matrix is chosen. Decreasing the step size too small can result in round-off error.
tangent (bool, optional) – Set to True to use tangent space inference. Default is false.
infer_scale_parameter (bool or list of bools (size: number of age-dependenly specified parameters)) – Decide if age-dependent parameters are supposed to be inferred separately (default) or if a scale parameter for the guess should be inferred. This can be set either globally for all age-dependent parameters or for each age-dependent parameter individually
- Returns
FIM – The Fisher Information Matrix
- Return type
2d numpy.array
-
FIM_det
()¶ Computes the Fisher Information Matrix (FIM) of the deterministic model. :param param_keys: A list of parameters to be inferred. :type param_keys: list :param init_fltr: True for initial conditions to be inferred.
Shape = (nClass*M) Total number of True = total no. of variables - total no. of observed
- Parameters
maps (numpy.array) – MAP parameter and initial condition estimate (computed for example with SIR_type.latent_inference).
obs0 (numpy.array) – The observed initial data
fltr (boolean sequence or array) – True for observed and False for unobserved. e.g. if only Is is known for SIR with one age group, fltr = [False, False, True]
Tf (float) – Total time of the trajectory
Nf (float) – Number of data points along the trajectory
contactMatrix (callable) – A function that takes time (t) as an argument and returns the contactMatrix
measurement_error (float, optional) – Standard deviation of measurements (uniform and independent Gaussian measurement error assumed). Default is 1e-2.
dx (float, optional) – Step size for numerical differentiation of the process mean and its full covariance matrix with respect to the parameters. If not specified, the square root of the machine epsilon for the smallest entry on the diagonal of the covariance matrix is chosen. Decreasing the step size too small can result in round-off error.
infer_scale_parameter (bool or list of bools (size: number of age-dependenly specified parameters)) – Decide if age-dependent parameters are supposed to be inferred separately (default) or if a scale parameter for the guess should be inferred. This can be set either globally for all age-dependent parameters or for each age-dependent parameter individually
- Returns
FIM_det – The Fisher Information Matrix
- Return type
2d numpy.array
-
compute_hessian
()¶ Computes the Hessian of the MAP estimate.
- Parameters
keys (list) – A list of parameter names that are inferred
maps (numpy.array) – MAP estimates
prior_mean (numpy.array) – The mean of the prior (should be the same as “guess” for infer_parameters)
prior_stds (numpy.array) – The standard deviations of the prior (same as “stds” for infer_parameters)
x (2d numpy.array) – Observed trajectory (number of data points x (age groups * model classes))
Tf (float) – Total time of the trajectory
Nf (float) – Number of data points along the trajectory
contactMatrix (callable) – A function that takes time (t) as an argument and returns the contactMatrix
eps (float or numpy.array, optional) – The step size of the Hessian calculation, default=1e-3
fd_method (str, optional) – The type of finite-difference scheme used to compute the hessian, supports “forward” and “central”.
- Returns
hess – The Hessian
- Return type
2d numpy.array
-
compute_hessian_latent
()¶ Computes the Hessian over the parameters and initial conditions.
- Parameters
maps (numpy.array) – MAP parameter and initial condition estimate (computed for example with SIR_type.latent_inference).
obs (numpy.array) – The observed data with the initial datapoint
fltr (boolean sequence or array) – True for observed and False for unobserved. e.g. if only Is is known for SIR with one age group, fltr = [False, False, True]
Tf (float) – Total time of the trajectory
Nf (int) – Total number of data points along the trajectory
contactMatrix (callable) – A function that returns the contact matrix at time t (input).
eps (float or numpy.array, optional) – Step size in the calculation of the Hessian.
obs0 (numpy.array, optional) – Observed initial condition, if more detailed than obs[0]
fltr0 (2d numpy.array, optional) – Matrix filter for obs0
fd_method (str, optional) – The type of finite-difference scheme used to compute the hessian, supports “forward” and “central”.
- Returns
hess – The Hessian over (flat) parameters and initial conditions.
- Return type
numpy.array
-
fill_initial_conditions
()¶ Returns the full initial condition given partial initial conditions and the observed data
- Parameters
partial_inits (1d np.array) – Partial initial conditions.
obs_inits (1d np.array) – The observed initial conditions.
init_fltr (1d np.array) – A vector boolean fltr that yields the partial initis given full initial conditions.
fltr (2d np.array) – A matrix fltr that yields the observed data from full data. Same as the one used for latent_infer_parameters.
- Returns
x0 – The full initial condition.
- Return type
1d np.array
-
fill_params_dict
()¶ Returns a full dictionary for epidemiological parameters with some changed values
- Parameters
keys (list of String) – A list of names of parameters to be changed.
params (numpy.array of list) – An array of the same size as keys for the updated value.
- Returns
full_parameters – A dictionary of epidemiological parameters. For parameter names specified in keys, set the values to be the ones in params; for the others, use the values stored in the class.
- Return type
dict
-
infer_control
()¶ Compute the maximum a-posteriori (MAP) estimate of the change of control parameters for a SIR type model in lockdown. The lockdown is modelled by scaling the contact matrices for contact at work, school, and other (but not home) uniformly in all age groups. This function infers the scaling parameters assuming that full data on all classes is available (with latent variables, use SIR_type.latent_infer_control).
- Parameters
guess (numpy.array) – Prior expectation (and initial guess) for the control parameter values
stds (numpy.array) – Standard deviations for the log normal prior of the control parameters
x (2d numpy.array) – Observed trajectory (number of data points x (age groups * model classes))
Tf (float) – Total time of the trajectory
Nf (float) – Number of data points along the trajectory
generator (pyross.contactMatrix) – A pyross.contactMatrix object that generates a contact matrix function with specified lockdown parameters.
bounds (2d numpy.array) – Bounds for the parameters (number of parameters x 2). Note that the upper bound must be smaller than the absolute physical upper bound minus epsilon
intervention_fun (callable, optional) – The calling signature is intervention_func(t, **kwargs), where t is time and kwargs are other keyword arguments for the function. The function must return (aW, aS, aO). If not set, assume intervention that’s constant in time and infer (aW, aS, aO).
tangent (bool, optional) – Set to True to use tangent space inference. Default is false.
verbose (bool, optional) – Set to True to see intermediate outputs from the optimizer.
ftol (double) – Relative tolerance of logp
eps (double) – Disallow paramters closer than eps to the boundary (to avoid numerical instabilities).
global_max_iter (int, optional) – Number of global optimisations performed.
local_max_iter (int, optional) – Number of local optimisation performed.
global_atol (float) – The absolute tolerance for global optimisation.
enable_global (bool, optional) – Set to True to enable global optimisation.
enable_local (bool, optional) – Set to True to enable local optimisation.
cma_processes (int, optional) – Number of parallel processes used for global optimisation.
cma_population (int, optional) – The number of samples used in each step of the CMA algorithm.
cma_stds (int, optional) – The standard deviation used in cma global optimisation. If not specified, cma_stds is set to stds.
- Returns
res – MAP estimate of the control parameters
- Return type
numpy.array
-
infer_parameters
()¶ Compute the maximum a-posteriori (MAP) estimate of the parameters of the SIR type model. This function assumes that full data on all classes is available (with latent variables, use SIR_type.latent_inference).
IN DEVELOPMENT: Parameters that support age-dependent values can be inferred age-dependently by setting the guess to a numpy.array of self.M initial values. By default, each age-dependent parameter is inferred independently. If the relation of the different parameters is known, a scale factor of the initial guess can be inferred instead by setting infer_scale_parameter to True for each age-dependent parameter where this is wanted. Note that computing hessians for age-dependent rates is not yet supported. This functionality might be changed in the future without warning.
- Parameters
keys (list) – A list of names for parameters to be inferred
guess (numpy.array or list) – Prior expectation (and initial guess) for the parameter values. Age-dependent rates can be inferred by supplying a guess that is an array instead a single float.
stds (numpy.array) – Standard deviations for the log normal prior of the parameters
bounds (2d numpy.array) – Bounds for the parameters (number of parameters x 2)
x (2d numpy.array) – Observed trajectory (number of data points x (age groups * model classes))
Tf (float) – Total time of the trajectory
Nf (float) – Number of data points along the trajectory
contactMatrix (callable) – A function that returns the contact matrix at time t (input).
tangent (bool, optional) – Set to True to do inference in tangent space (might be less robust but a lot faster). Default is False.
infer_scale_parameter (bool or list of bools (size: number of age-dependenly specified parameters)) – Decide if age-dependent parameters are supposed to be inferred separately (default) or if a scale parameter for the guess should be inferred. This can be set either globally for all age-dependent parameters or for each age-dependent parameter individually
verbose (bool, optional) – Set to True to see intermediate outputs from the optimizer.
ftol (float) – Relative tolerance of logp
eps (float) – Disallow parameters closer than eps to the boundary (to avoid numerical instabilities).
global_max_iter (int, optional) – Number of global optimisations performed.
local_max_iter (int, optional) – Number of local optimisation performed.
global_atol (float) – The absolute tolerance for global optimisation.
enable_global (bool, optional) – Set to True to enable global optimisation.
enable_local (bool, optional) – Set to True to enable local optimisation.
cma_processes (int, optional) – Number of parallel processes used for global optimisation.
cma_population (int, optional) – The number of samples used in each step of the CMA algorithm.
cma_stds (int, optional) – The standard deviation used in cma global optimisation. If not specified, cma_stds is set to stds.
- Returns
estimates – The MAP parameter estimate
- Return type
numpy.array
-
integrate
()¶ An light weight integrate method similar to simulate in pyross.deterministic
- Parameters
x0 (np.array) – Initial state of the given model
t1 (float) – Initial time of integrator
t2 (float) – Final time of integrator
steps (int) – Number of time steps for numerical integrator evaluation.
model (pyross model) – Model to integrate (pyross.deterministic.SIR etc)
contactMatrix (python function(t)) – The social contact matrix C_{ij} denotes the average number of contacts made per day by an individual in class i with an individual in class j
maxNumSteps – The maximum number of steps taken by the integrator.
- Returns
sol – The state of the system evaulated at the time point specified. Only used if det_method is set to ‘solve_ivp’.
- Return type
np.array
-
latent_infer_control
()¶ Compute the maximum a-posteriori (MAP) estimate of the change of control parameters for a SIR type model in lockdown with partially observed classes. The unobserved classes are treated as latent variables. The lockdown is modelled by scaling the contact matrices for contact at work, school, and other (but not home) uniformly in all age groups. This function infers the scaling parameters.
- Parameters
keys (list) – A list of keys for the control parameters to be inferred.
guess (numpy.array) – Prior expectation (and initial guess) for the control parameter values.
stds (numpy.array) – Standard deviations for the log normal prior of the control parameters
x0 (numpy.array) – Initial conditions.
obs – Observed trajectory (number of data points x (age groups * observed model classes)).
fltr (boolean sequence or array) – True for observed and False for unobserved classes. e.g. if only Is is known for SIR with one age group, fltr = [False, False, True]
Tf (float) – Total time of the trajectory
Nf (float) – Number of data points along the trajectory
generator (pyross.contactMatrix) – A pyross.contactMatrix object that generates a contact matrix function with specified lockdown parameters.
bounds (2d numpy.array) – Bounds for the parameters (number of parameters x 2). Note that the upper bound must be smaller than the absolute physical upper bound minus epsilon
intervention_fun (callable, optional) – The calling signature is intervention_func(t, **kwargs), where t is time and kwargs are other keyword arguments for the function. The function must return (aW, aS, aO). If not set, assume intervention that’s constant in time and infer (aW, aS, aO).
tangent (bool, optional) – Set to True to use tangent space inference. Default is false.
verbose (bool, optional) – Set to True to see intermediate outputs from the optimizer.
ftol (double) – Relative tolerance of logp
global_max_iter (int, optional) – Number of global optimisations performed.
local_max_iter (int, optional) – Number of local optimisation performed.
global_atol (float) – The absolute tolerance for global minimisation.
enable_global (bool, optional) – Set to True to enable global optimisation.
enable_local (bool, optional) – Set to True to enable local optimisation.
cma_processes (int, optional) – Number of parallel processes used for global optimisation.
cma_population (int, optional) – The number of samples used in each step of the CMA algorithm.
cma_stds (int, optional) – The standard deviation used in cma global optimisation. If not specified, cma_stds is set to stds.
full_output (bool, optional) – Set to True to return full minimization output
- Returns
params (numpy.array) – MAP estimate of control parameters
y_result (float (returned if full_output is True)) – logp for MAP estimates
-
latent_infer_parameters
()¶ Compute the maximum a-posteriori (MAP) estimate of the parameters and the initial conditions of a SIR type model when the classes are only partially observed. Unobserved classes are treated as latent variables.
- Parameters
param_keys (list) – A list of parameters to be inferred.
init_fltr (boolean array) – True for initial conditions to be inferred. Shape = (nClass*M) Total number of True = total no. of variables - total no. of observed
guess (numpy.array or list) – Prior expectation for the parameter values listed, and prior for initial conditions. Expect of length len(param_keys)+ (total no. of variables - total no. of observed). Age-dependent rates can be inferred by supplying a guess that is an array instead a single float.
stds (numpy.array) – Standard deviations for the log normal prior.
obs (2d numpy.array) – The observed trajectories with reduced number of variables (number of data points, (age groups * observed model classes))
fltr (2d numpy.array) – A matrix of shape (no. observed variables, no. total variables), such that obs_{ti} = fltr_{ij} * X_{tj}
Tf (float) – Total time of the trajectory
Nf (int) – Total number of data points along the trajectory
contactMatrix (callable) – A function that returns the contact matrix at time t (input).
bounds (2d numpy.array) – Bounds for the parameters + initial conditions ((number of parameters + number of initial conditions) x 2). Better bounds makes it easier to find the true global minimum.
tangent (bool, optional) – Set to True to do inference in tangent space (might be less robust but a lot faster). Default is False.
infer_scale_parameter (bool or list of bools (size: number of age-dependenly specified parameters)) – Decide if age-dependent parameters are supposed to be inferred separately (default) or if a scale parameter for the guess should be inferred. This can be set either globally for all age-dependent parameters or for each age-dependent parameter individually
verbose (bool, optional) – Set to True to see intermediate outputs from the optimizer.
ftol (float, optional) – Relative tolerance
global_max_iter (int, optional) – Number of global optimisations performed.
local_max_iter (int, optional) – Number of local optimisation performed.
global_atol (float) – The absolute tolerance for global optimisation.
enable_global (bool, optional) – Set to True to enable global optimisation.
enable_local (bool, optional) – Set to True to enable local optimisation.
cma_processes (int, optional) – Number of parallel processes used for global optimisation.
cma_population (int, optional) – The number of samples used in each step of the CMA algorithm.
cma_stds (int, optional) – The standard deviation used in cma global optimisation. If not specified, cma_stds is set to stds.
obs0 (numpy.array, optional) – Observed initial condition, if more detailed than obs[0]
fltr0 (2d numpy.array, optional) – Matrix filter for obs0
- Returns
params – MAP estimate of paramters (nested if some parameters are age dependent) and initial values of the classes.
- Return type
nested list
-
log_G_evidence
()¶ Compute the evidence using a Laplace approximation at the MAP estimate.
-
log_G_evidence_latent
()¶ Compute the evidence using a Laplace approximation at the MAP estimate.
-
make_det_model
()¶ Returns a determinisitic model of the same epidemiological class and same parameters
- Parameters
parameters (dict) – A dictionary of parameter values, same as the ones required for initialisation.
- Returns
det_model – A determinisitic model of the same epidemiological class and same parameters
- Return type
a class in pyross.deterministic
-
minus_logp_red
()¶ Computes -logp for a latent trajectory
- Parameters
parameters (dict) – A dictionary of parameter values, same as the ones required for initialisation.
x0 (numpy.array) – Initial conditions
obs (numpy.array) – The observed trajectory without the initial datapoint
fltr (boolean sequence or array) – True for observed and False for unobserved. e.g. if only Is is known for SIR with one age group, fltr = [False, False, True]
Tf (float) – The total time of the trajectory
Nf (int) – The total number of datapoints
contactMatrix (callable) – A function that returns the contact matrix at time t (input).
tangent (bool, optional) – Set to True to do inference in tangent space (might be less robust but a lot faster). Default is False.
- Returns
minus_logp – -log(p) for the observed trajectory with the given parameters and initial conditions
- Return type
float
-
nested_sampling_inference
()¶ Compute the log-evidence and weighted samples of the a-posteriori distribution of the parameters of a SIR type model using nested sampling as implemented in the nestle Python package. This function assumes that full data on all classes is available.
This function provides a computational alterantive to log_G_evidence and infer_parameters. It does not use the Laplace approximation to compute the evidence and, in addition, returns a set of representative samples that can be used to compute a posterior mean estimate (insted of the MAP estimate). This approach approach is much more resource intensive and typically only viable for small models or tangent space inference.
- Parameters
keys (list) – A list of names for parameters to be inferred
guess (numpy.array or list) – Prior expectation (and initial guess) for the parameter values. Age-dependent rates can be inferred by supplying a guess that is an array instead a single float.
stds (numpy.array) – Standard deviations for the log normal prior of the parameters
x (2d numpy.array) – Observed trajectory (number of data points x (age groups * model classes))
Tf (float) – Total time of the trajectory
Nf (float) – Number of data points along the trajectory
contactMatrix (callable) – A function that returns the contact matrix at time t (input).
bounds (np.array(len(guess), 2), optional) – Bound the prior within the values specified by this. This can be used to avoid sampling the posterior in regions where the solution is numerically unstable (e.g. parameters close to 0). Any bound introduces a bias to the result, therefore one must make sure that the blocked regions are negligible.
tangent (bool, optional) – Set to True to do inference in tangent space (might be less robust but a lot faster). Default is False.
infer_scale_parameter (bool or list of bools (size: number of age-dependenly specified parameters)) – Decide if age-dependent parameters are supposed to be inferred separately (default) or if a scale parameter for the guess should be inferred. This can be set either globally for all age-dependent parameters or for each age-dependent parameter individually
verbose (bool, optional) – Set to True to see intermediate outputs from the nested sampling procedure.
queue_size (int) – Size of the internal queue of samples of the nested sampling algorithm. The log-likelihood of these samples is computed in parallel (if queue_size > 1).
max_workers (int) – The maximal number of processes used to compute samples.
npoints (int) – Argument of nestle.sample. The number of active points used in the nested sampling algorithm. The higher the number the more accurate and expensive is the evidence computation.
method (str) – Nested sampling method used int nestle.sample, see their documentation. Default is single, for multimodel posteriors, use multi.
max_iter (int) – Maximum number of iterations of the nested sampling algorithm.
dlogz (float, optional) – Stopping threshold for the estimated error of the log-evidence. This option is mutually exclusive with decline_factor.
decline_factor (float, optional) – Stop the iteration when the weight (likelihood times prior volume) of newly saved samples has been declining for decline_factor * nsamples consecutive samples. This option is mutually exclusive with dlogz.
- Returns
log_evidence – The nested sampling estimate of the log-evidence.
(samples, weights) – A set of weighted samples approximating the posterios distribution.
-
nested_sampling_latent_inference
()¶ Compute the log-evidence and weighted samples of the a-posteriori distribution of the parameters of a SIR type model with latent variables using nested sampling as implemented in the nestle Python package.
This function provides a computational alterantive to latent_infer_parameters. It computes an estimate of the evidence and, in addition, returns a set of representative samples that can be used to compute a posterior mean estimate (insted of the MAP estimate). This approach approach is much more resource intensive and typically only viable for small models or tangent space inference.
- Parameters
param_keys (list) – A list of parameters to be inferred.
init_fltr (boolean array) – True for initial conditions to be inferred. Shape = (nClass*M) Total number of True = total no. of variables - total no. of observed
guess (numpy.array or list) – Prior expectation for the parameter values listed, and prior for initial conditions. Expect of length len(param_keys)+ (total no. of variables - total no. of observed). Age-dependent rates can be inferred by supplying a guess that is an array instead a single float.
stds (numpy.array) – Standard deviations for the log normal prior.
obs (2d numpy.array) – The observed trajectories with reduced number of variables (number of data points, (age groups * observed model classes))
fltr (2d numpy.array) – A matrix of shape (no. observed variables, no. total variables), such that obs_{ti} = fltr_{ij} * X_{tj}
Tf (float) – Total time of the trajectory
Nf (int) – Total number of data points along the trajectory
contactMatrix (callable) – A function that returns the contact matrix at time t (input).
bounds (np.array(len(guess), 2), optional) – Bound the prior within the values specified by this array. This can be used to avoid sampling the posterior in regions where the solution is numerically unstable (e.g. parameters close to 0). Any bound introduces a bias to the result, therefore one must make sure that the blocked regions are negligible.
obs0 (numpy.array, optional) – Observed initial condition, if more detailed than obs[0]
fltr0 (2d numpy.array, optional) – Matrix filter for obs0
tangent (bool, optional) – Set to True to do inference in tangent space (might be less robust but a lot faster). Default is False.
infer_scale_parameter (bool or list of bools (size: number of age-dependenly specified parameters)) – Decide if age-dependent parameters are supposed to be inferred separately (default) or if a scale parameter for the guess should be inferred. This can be set either globally for all age-dependent parameters or for each age-dependent parameter individually
verbose (bool, optional) – Set to True to see intermediate outputs from the nested sampling procedure.
queue_size (int) – Size of the internal queue of samples of the nested sampling algorithm. The log-likelihood of these samples is computed in parallel (if queue_size > 1).
max_workers (int) – The maximal number of processes used to compute samples.
npoints (int) – Argument of nestle.sample. The number of active points used in the nested sampling algorithm. The higher the number the more accurate and expensive is the evidence computation.
method (str) – Nested sampling method used int nestle.sample, see their documentation. Default is single, for multimodel posteriors, use multi.
max_iter (int) – Maximum number of iterations of the nested sampling algorithm.
dlogz (float, optional) – Stopping threshold for the estimated error of the log-evidence. This option is mutually exclusive with decline_factor.
decline_factor (float, optional) – Stop the iteration when the weight (likelihood times prior volume) of newly saved samples has been declining for decline_factor * nsamples consecutive samples. This option is mutually exclusive with dlogz.
- Returns
log_evidence – The nested sampling estimate of the log-evidence.
(samples, weights) – A set of weighted samples approximating the posterios distribution.
-
set_det_method
()¶ Sets the method used for deterministic integration for the SIR_type model
- Parameters
det_method (str) – The name of the integration method. Choose between ‘LSODA’, ‘RK45’, ‘RK2’ and ‘euler’.
-
set_lyapunov_method
()¶ Sets the method used for deterministic integration for the SIR_type model
- Parameters
det_method (str) – The name of the integration method. Choose between ‘LSODA’, ‘RK45’, ‘RK2’ and ‘euler’.
-
set_params
()¶ Sets epidemiological parameters used for evaluating -log(p)
- Parameters
parameters (dict) – A dictionary containing all epidemiological parameters. Same keys as the one used to initialise the class.
Notes
Can use fill_params_dict to generate the full dictionary if only a few parameters are changed
-
Spp¶
-
class
pyross.inference.
Spp
¶ User-defined epidemic model.
To initialise the Spp model,
- Parameters
model_spec (dict) – A dictionary specifying the model. See Examples.
parameters (dict) – A dictionary containing the model parameters. All parameters can be float if not age-dependent, and np.array(M,) if age-dependent
M (int) – Number of age groups.
fi (np.array(M) or list) – Fraction of each age group.
N (int) – Total population.
steps (int) – The number of internal integration steps performed between the observed points (not used in tangent space inference). The minimal is 4, as required by the cubic spline fit used for interpolation. For robustness, set steps to be large, det_method=’LSODA’, lyapunov_method=’LSODA’. For speed, set steps to be 4, det_method=’RK2’, lyapunov_method=’euler’. For a combination of the two, choose something in between.
det_method (str, optional) – The integration method used for deterministic integration. Choose one of ‘LSODA’, ‘RK45’, ‘RK2’ and ‘euler’. Default is ‘LSODA’.
lyapunov_method (str, optional) – The integration method used for the integration of the Lyapunov equation for the covariance. Choose one of ‘LSODA’, ‘RK45’, ‘RK2’ and ‘euler’. Default is ‘LSODA’.
See SIR_type for a table of all the methods
Examples
An example of model_spec and parameters for SIR class with a constant influx
>>> model_spec = { "classes" : ["S", "I"], "S" : { "constant" : [ ["k"] ], "infection" : [ ["I", "-beta"] ] }, "I" : { "linear" : [ ["I", "-gamma"] ], "infection" : [ ["I", "beta"] ] } } >>> parameters = { 'beta': 0.1, 'gamma': 0.1, 'k': 1, }
-
FIM_sym
()¶ Does the FIM based off symbolic expressions. In development,
-
adj_RHS_mean
()¶ RHS function for the adjoint gradient calculation of the time evolution operator.
-
construct_A_spp
()¶ construct Spp A. x is a sympy matrix
-
construct_B_spp
()¶ constructs Spp B. x is a sympy array
-
construct_J_spp
()¶ constructs Spp J. x is a sympy matrix
-
construct_l
()¶ constructs sympy l. x is a sympy matrix
-
dAd
()¶ constructs Spp B. param is a string or sympy symbol. Most likely you’ll wish to use ‘all’ string keys can be passed as a integer 0,1 numpy array which selects the parameters to be used for FIM calculation p is a sympy array which contains epi parameters. nParams*M due to age dependence [dAdp]_ij = dA_j/dp_i
-
dBd
()¶ constructs Spp B. param is a string or sympy symbol. Most likely you’ll wish to use ‘all’ string keys can be passed as a integer 0,1 numpy array which selects the parameters to be used for FIM calculation p is a sympy array which contains epi parameters. nParams*M due to age dependence [dBdp]_ijk = dB_jk/dp_i
-
lambdify_derivative_functions
()¶ Create python functions from sympy expressions
SIR¶
-
class
pyross.inference.
SIR
¶ Susceptible, Infected, Removed (SIR)
Ia: asymptomatic
Is: symptomatic
To initialise the SIR class,
- Parameters
parameters (dict) –
Contains the following keys:
- alpha: float
Ratio of asymptomatic carriers
- beta: float
Infection rate upon contact
- gIa: float
Recovery rate for asymptomatic
- gIs: float
Recovery rate for symptomatic
- fsa: float
The fraction of symptomatic people who are self-isolating
M (int) – Number of age groups
fi (float numpy.array) – Fraction of each age group
N (int) – Total population
steps (int) – The number of internal integration steps performed between the observed points (not used in tangent space inference). The minimal is 4, as required by the cubic spline fit used for interpolation. For robustness, set steps to be large, det_method=’LSODA’, lyapunov_method=’LSODA’. For speed, set steps to be 4, det_method=’RK2’, lyapunov_method=’euler’. For a combination of the two, choose something in between.
det_method (str, optional) – The integration method used for deterministic integration. Choose one of ‘LSODA’, ‘RK45’, ‘RK2’ and ‘euler’. Default is ‘LSODA’.
lyapunov_method (str, optional) – The integration method used for the integration of the Lyapunov equation for the covariance. Choose one of ‘LSODA’, ‘RK45’, ‘RK2’ and ‘euler’. Default is ‘LSODA’.
SEIR¶
-
class
pyross.inference.
SEIR
¶ Susceptible, Exposed, Infected, Removed (SEIR)
Ia: asymptomatic
Is: symptomatic
To initialise the SEIR class,
- Parameters
parameters (dict) –
Contains the following keys:
- alpha: float or np.array(M)
Fraction of infected who are asymptomatic.
- beta: float
Rate of spread of infection.
- gIa: float
Rate of removal from asymptomatic individuals.
- gIs: float
Rate of removal from symptomatic individuals.
- fsa: float
Fraction by which symptomatic individuals self isolate.
- gE: float
rate of removal from exposed individuals.
M (int) – Number of age groups
fi (float numpy.array) – Fraction of each age group
N (int) – Total population
steps (int) – The number of internal integration steps performed between the observed points (not used in tangent space inference). The minimal is 4, as required by the cubic spline fit used for interpolation. For robustness, set steps to be large, det_method=’LSODA’, lyapunov_method=’LSODA’. For speed, set steps to be 4, det_method=’RK2’, lyapunov_method=’euler’. For a combination of the two, choose something in between.
det_method (str, optional) – The integration method used for deterministic integration. Choose one of ‘LSODA’, ‘RK45’, ‘RK2’ and ‘euler’. Default is ‘LSODA’.
lyapunov_method (str, optional) – The integration method used for the integration of the Lyapunov equation for the covariance. Choose one of ‘LSODA’, ‘RK45’, ‘RK2’ and ‘euler’. Default is ‘LSODA’.
SEAIRQ¶
-
class
pyross.inference.
SEAIRQ
¶ Susceptible, Exposed, Asymptomatic and infected, Infected, Removed, Quarantined (SEAIRQ)
Ia: asymptomatic
Is: symptomatic
E: exposed
A: asymptomatic and infectious
Q: quarantined
To initialise the SEAIRQ class,
- Parameters
parameters (dict) –
Contains the following keys:
- alpha: float or np.array(M)
Fraction of infected who are asymptomatic.
- beta: float
Rate of spread of infection.
- gIa: float
Rate of removal from asymptomatic individuals.
- gIs: float
Rate of removal from symptomatic individuals.
- gE: float
rate of removal from exposed individuals.
- gA: float
rate of removal from activated individuals.
- fsa: float
fraction by which symptomatic individuals self isolate.
- tE: float
testing rate and contact tracing of exposeds
- tA: float
testing rate and contact tracing of activateds
- tIa: float
testing rate and contact tracing of asymptomatics
- tIs: float
testing rate and contact tracing of symptomatics
M (int) – Number of age groups.
fi (float numpy.array) – Fraction of each age group.
N (int) – Total population.
steps (int) – The number of internal integration steps performed between the observed points (not used in tangent space inference). The minimal is 4, as required by the cubic spline fit used for interpolation. For robustness, set steps to be large, det_method=’LSODA’, lyapunov_method=’LSODA’. For speed, set steps to be 4, det_method=’RK2’, lyapunov_method=’euler’. For a combination of the two, choose something in between.
det_method (str, optional) – The integration method used for deterministic integration. Choose one of ‘LSODA’, ‘RK45’, ‘RK2’ and ‘euler’. Default is ‘LSODA’.
lyapunov_method (str, optional) – The integration method used for the integration of the Lyapunov equation for the covariance. Choose one of ‘LSODA’, ‘RK45’, ‘RK2’ and ‘euler’. Default is ‘LSODA’.


Regular Expressions
Banned In Phase 0, Useful Going Forward!
March 29, 2015
BACK | HOME
A Regular Expression, or RegEx, is a search pattern. They're similar to wildcard searches that we've seen (using *) but are far more powerful. To search for a file ending in ".txt" using a wildcard, we'd type *.txt
. The equivalent as a RegEx is not quite as straightforward: .*\.txt$
. We use regular expressions to search within a string, so the two parts we need are the regex and a string to look inside.
In the first sentence above, "A Regular Expression, or RegEx, is a search pattern," we could search for the word "sentence" by using the regex 'sentence' to search within the string "A Regular Expression, or RegEx, is a search pattern." It would simply return the word "sentence" since that's what we were searching for. But the power of Regular Expressions lies in their flexibility and ability to search strings for unknowns and patterns, which is why I make the wildcard analogy above.
Regular Expressions have twelve "metacharacters" that must be escaped with the escape character (\
) to be used:
- Backslash:
\
- Caret:
^
- Dollar sign:
$
- Period/dot:
.
- Vertical bar/pipe:
|
- Question mark:
?
- Asterisk:
*
- Plus sign:
+
- Opening and closing parentheses:
()
- Opening square bracket:
[
- Opening curly brace:
{
a+b=c
, you need to escape the plus sign like so: a\+b=c
. This isn't too much of a stretch since we're somewhat used to using escapes from our Phase 0 work.
If you need to find instances of both "hey" and "hay," you can use a character class to search for the two variations. Character classes are enclosed in square brackets. To search for "hay" or "hey" you would simple write: h[ae]y
. In addition, you can specify a range in a character class using a hyphen. To search for any number from 0 to 9, the character class would be [0-9]
, any letter from A to Z [A-Za-z]
. You don't even need a space there, which looks weird. To match any Hex character, the regex [0-9a-fA-F]
will do it for you. If you want to find any Hex character and Z, you can just add one single letter to the character class: [0-9a-fzA-FZ]
. You can also negate characters using ^
, so in the phrase "character class," c[^h]
would return class, but not character, since we negated the h
.
Character classes have a few shorthand notations that can be useful:
\d
for digit, equals[0-9]
\w
for word, equals[A-Za-z0-9_]
(note underscore and digits)\s
for space or whitespace, equals[ \t\r\n\f]
(note the non-printable characters tab\t
, carriage return\r
, new line\n
, and form feed\f
)
The dot (.
) is like a wildcard in a regex, but its use is generally discouraged since it's so broad. An example would be gr.y
matching gray
, grey
, or gr%y
. Using a character class or negated character class should be more precise and faster.
The pipe character (|
) functions similarly to "or," as we'd expect: fish | dogs
matches either fish or dogs in a string. This is known as alternation, and has a low precedence, so it's a good idea to use parentheses for a complicated search: (cat | dog) food
will return "cat food" or "dog food," while cat | dog food
will just return "cat" or "dog food."
To differentiate between English and American spellings, like in the word "colour," we use the ?
to match the preceding token 0 or more times: colou?r
. Looks pretty straightforward. We can use parentheses here again to create a "capturing group" if we have a word or multiple letters that may be optional: go(ing)?
.
Regular expressions can be confusing at first, since often chained together they look like nonsense. They aren't intuitive at first glance. Hopefully this very cursory introduction will at least provide some structure and ideas to make them look a little clearer! Of course the best way to become skillful with these is practice, practice, practice.
See Regular-Expressions.info for more information, this is where I got most of my info. Wikipedia also has a lot to offer. Get some practice at RegExr, too.
Thanks for reading!


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Henry, Portrait of a Serial Killer (1990) A movie review by Serdar Yegulalp Copyright 1997 by Serdar Yegulalp
CAPSULE: Bleak and point-blank -- just the way it should be. Makes its case with cold-blooded precision and intelligence.
"Serial killer chic" is a relatively new entry to the cultural vocabulary, and one of the more stomach-turning ones. I've never found someone automatically admirable or even interesting because they killed creatively or in great numbers, and because of that I had apprehensions about seeing HENRY. I wasn't sure I wanted to see the story of multiple murderer Henry Lee Lucas portrayed as some kind of nihilistic hero/antihero.
Thankfully, that's not the way HENRY works. The director, John McNaughton, took a small budget and a gallery of actor friends and created a chilling and intelligent piece of work. It doesn't provide cheap-jack explanations for Henry's behavior: it shows him up for what he is, an unrepentant and un-idolizable man who killed when the mood struck him.
At the film's opening, Henry's friend Ottis Toole has a sister, who comes by to stay while she tries to find work in Chicago. In a viciously riveting scene, he admits matter-of-factly to having murdered his own mother, and she covers his hand with hers. They are damned together from that moment on.
 The movie is especially good at showing how they seem to have drifted casually into it: there's a scene where the two go to buy a new TV. The pawnbroker they talk to goads Henry with ugly words, and Henry responds by stabbing the man to death with a soldering iron. No preface -- it smashes into us as suddenly as it does the broker.
In a scene which many damned the movie for, unseen, they steal a video camera and film the murder of a whole household, playing it back over and over again for their own enjoyment. The movie itself, however, does not glorify the killings, but shows instead that Henry feels no joy at his work, only irritation.
HENRY succeeds thanks to excellent writing and direction as well as acting. Michael Rooker is credible as Henry from the first frame, just standing there, and the rest of the cast works by being unaffected and casual as well. The producers had originally comissioned a slasher film from McNaughton, but he decided to give them something more memorable, and consequently the film nearly didn't see release.
The film does not glorify or justify -- it just shows us, with unflinching coldness, what Henry was. I'm hard pressed to see how anyone can see this and consider Henry any kind of "victim" or "hero". Then again, probably, so would Henry.
Three and a half out of four motels.
<EMAIL_ADDRESS>EFNet IRC: GinRei http://www.io.com/~syegul another worldly device... you can crush me as I speak/write on rocks what you feel/now feel this truth =smilin' in your face, all the time wanna take your place, the BACKSTABBERS=
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It has been quite often observed that there has been a bumper crop in a particular region at a particular time due to which the price of the crop falls heavily.There have been innumerable such scenarios where a big amount of crop yield gets wasted due to bumper crop in one season and then rises heavily in another season. All this happens due to poor management and storage facilities by government due to lack of contingency measures and planning for such situations. These calamities causes massive losses to both general citizens as well as the farmers. We as a team are planning to use soil data, weather information and market information for prices of crop to build predictive models that will tackle these situations by making the government aware in advance to take necessary action to save such crops in times of bumper crop and to use them at times of shortages. In this way the government can pre plan to build effective storage solutions for the particular crop that may have bumper production and use this at times of crisis. This will save a lot of farmers and citizens from market exploitation and thus will create a balance to meet the food demands of the nation.


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Before Change
if self.verbose:
print("Here are all the hyperparameters that were tried:")
raw_scores = gs.cv_results_
print("raw_scores")
print(raw_scores)
df_raw_scores = pd.DataFrame(raw_scores)
df_raw_scores = df_raw_scores.sort_values(by="mean_test_score", ascending=False)
col_name_map = {
After Change
pass
cleaned_params = list(df_raw_scores["params"].apply(utils.clean_params))
df_params = pd.DataFrame(cleaned_params)
df_scores = pd.concat([df_raw_scores.mean_score, df_params], axis=1)
print("Score in the following columns always refers to cross-validation score")
print(tabulate(df_scores, headers="keys", floatfmt=".4f", tablefmt="psql", showindex=False))


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Sabine Kienzer (editorial management), Tina Frank, Marianne Pührerfellner, Matthias Steinbauer, Ismail Khalil, Lukas Leitner, Stefan Ehrenmüller, Elisabeth Egger-Mann, Katharina Mayrhofer, Nataša Sienčnik, Victoria Tanczos
Editorial office: Martina Aichhorn, Translation: Jonathan Quinn, Nataša Sienčnik, David Westacott
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| Opcode/Instruction | Op/En | 64/32 bit Mode Support | CPUID Feature Flag | Description |
|---|---|---|---|---|
| NP 0F 2B /r MOVNTPS m128, xmm1 | A | V/V | SSE | Move packed single-precision values xmm1 to mem using non-temporal hint. |
| VEX.128.0F.WIG 2B /r VMOVNTPS m128, xmm1 | A | V/V | AVX | Move packed single-precision values xmm1 to mem using non-temporal hint. |
| VEX.256.0F.WIG 2B /r VMOVNTPS m256, ymm1 | A | V/V | AVX | Move packed single-precision values ymm1 to mem using non-temporal hint. |
| EVEX.128.0F.W0 2B /r VMOVNTPS m128, xmm1 | B | V/V | AVX512VL AVX512F | Move packed single-precision values in xmm1 to m128 using non-temporal hint. |
| EVEX.256.0F.W0 2B /r VMOVNTPS m256, ymm1 | B | V/V | AVX512VL AVX512F | Move packed single-precision values in ymm1 to m256 using non-temporal hint. |
| EVEX.512.0F.W0 2B /r VMOVNTPS m512, zmm1 | B | V/V | AVX512F | Move packed single-precision values in zmm1 to m512 using non-temporal hint. |
1. ModRM.MOD != 011B
| Op/En | Tuple Type | Operand 1 | Operand 2 | Operand 3 | Operand 4 |
| A | NA | ModRM:r/m (w) | ModRM:reg (r) | NA | NA |
| B | Full Mem | ModRM:r/m (w) | ModRM:reg (r) | NA | NA |
Moves the packed single-precision floating-point values in the source operand (second operand) to the destination operand (first operand) using a non-temporal hint to prevent caching of the data during the write to memory. The source operand is an XMM register, YMM register or ZMM register, which is assumed to contain packed single-precision, floating-pointing. The destination operand is a 128-bit, 256-bit or 512-bit memory location. The memory operand must be aligned on a 16-byte (128-bit version), 32-byte (VEX.256 encoded version) or 64-byte (EVEX.512 encoded version) boundary otherwise a general-protection exception (#GP) will be generated.
The non-temporal hint is implemented by using a write combining (WC) memory type protocol when writing the data to memory. Using this protocol, the processor does not write the data into the cache hierarchy, nor does it fetch the corresponding cache line from memory into the cache hierarchy. The memory type of the region being written to can override the non-temporal hint, if the memory address specified for the non-temporal store is in an uncacheable (UC) or write protected (WP) memory region. For more information on non-temporal stores, see “Caching of Temporal vs. Non-Temporal Data” in Chapter 10 in the IA-32 Intel Architecture Software Developer’s Manual, Volume 1.
Because the WC protocol uses a weakly-ordered memory consistency model, a fencing operation implemented with the SFENCE or MFENCE instruction should be used in conjunction with MOVNTPS instructions if multiple processors might use different memory types to read/write the destination memory locations.
Note: VEX.vvvv and EVEX.vvvv are reserved and must be 1111b otherwise instructions will #UD.
VL = 128, 256, 512 DEST[VL-1:0] ← SRC[VL-1:0] DEST[MAXVL-1:VL] ← 0
DEST ← SRC
VMOVNTPS void _mm512_stream_ps(float * p, __m512d a);
MOVNTPS void _mm_stream_ps (float * p, __m128d a);
VMOVNTPS void _mm256_stream_ps (float * p, __m256 a);
None
Non-EVEX-encoded instruction, see Exceptions Type1.SSE; additionally
EVEX-encoded instruction, see Exceptions Type E1NF.
| #UD | If VEX.vvvv != 1111B or EVEX.vvvv != 1111B. |


{$+ "views/tmpl.master.html" $} {$/ Middle-End Boilerplate (tmpl.index.html) (c) Kyle Simpson MIT License /$} {$: "#content" }
This is a boiler-plate for Middle-End web architecture.
{$= data.results $}


Viewing Your Event Enrollments
How to view details your event enrollments using the Events module.
- Select My Enrollments from the module actions menu - OR - Click the My Enrollments button in the Event module icon bar ( if displayed ).
The following details are displayed for each event you have enrolled for:
- Select: and then click the Cancel Selected Enrollments link to cancel one or more enrollments. This option may not be available.
- Start: The start date and time of the event.
- End: The end date and time of the event.
- Event: The title of the event which links to more event details.
- Approved: Displays if your enrollment has been approved or is unapproved .
- Amount: Displays the amount ( if any ) that has been paid to attend the event.
- Quantity: The number of attendees you have enrolled for this event.
- Total: Total amount for this enrollment.


We ease your biggest doubts with personalized Video Counselling from our
Curated Experts and Answers from the student community
We ease your biggest doubts with personalized Video Counselling from our
Curated Experts and Answers from the student community


On 29 October 2015, the National IHR Focal Point of the Republic of Korea notified WHO of a cluster of cases with respiratory symptoms in Seoul.
Between 19 and 29 October, 3 people who work in the same facility developed symptoms of pneumonia. From 22 to 24 October, they were admitted to hospital. On 27 October, the hospital isolated the patients and alerted local health authorities, which then informed the Korean Centers for Disease Control and Prevention (KCDC).
To date, 84 cases with respiratory symptoms have been identified; 55 of them were diagnosed with pneumonia and 29 cases were mildly symptomatic. The 55 patients with pneumonia were isolated in 7 different hospitals while the 29 patients with mild symptoms were under home quarantine.
As of 11 November, only 1 of the 55 patients with pneumonia was still hospitalized; the other 54 cases had all been discharged. Currently, none of the patients is in critical condition.
In response to the outbreak, KCDC and the government of the Republic of Korea have implemented a series of public health measures:
So far, all PCR tests carried out by the KCDC have been negative, including for Adenovirus, Respiratory syncytial virus, Parainfluenza virus, Metapneumo virus, Boca virus, Influenza virus, and Coronavirus (including MERS-CoV), as well as for Mycoplasma, Chlamydia, Corynebacterium, Bordatella, Brucella, Coxiella and Legionella.
At present, there is no evidence of human-to-human transmission. Investigation on the cause of the outbreak is ongoing.


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In Gremlin, the same path expression can be written in numerous ways. Gremlin is an imperative language in that the developer explicitly instructs Gremlin which path to take through the graph (as defined by an abstract path description). However, Gremlin doesn’t always do as he is told. There are various optimizations that Gremlin will automatically take advantage of. This section describes such automatic optimizations.
In Blueprints, their is a method called Vertex.query()
. This method returns a Query
object that can be configured to filter the edges/vertices associated with that Vertex
according to edge labels, edge properties, edge property intervals, etc. Whenever a pattern such as outE....inV
is seen by Gremlin, Gremlin will automatically compile that statement into a Query
object.
gremlin> g.v(1).outE('knows','created').interval('weight',0.5,1.0).has('date',2012).inV.name.toString()
==>[StartPipe, QueryPipe(out,[knows, created],has:true,interval:true,edge), IdentityPipe, IdentityPipe, InVertexPipe, PropertyPipe(name)]
However, Gremlin ensures that the pipeline length stays the same by introducing IdentityPipe
steps. This ensure that numbered step constructs (e.g. loop(2)
, back(1)
) remain faithful. Though it is always a good idea to toString()
an expression when using numbered steps to ensure proper indexing.
If the query optimization is not desired, there is the method GremlinPipeline.optimize(boolean)
. This method makes it possible to turn off query optimizations.
gremlin> g.v(1).optimize(false).outE('knows','created').interval('weight',0.5,1.0).has('date',2012).inV.name.toString()
==>[StartPipe, OutEdgesPipe(knows,created), IntervalFilterPipe, PropertyFilterPipe(date,EQUAL,2012), InVertexPipe, PropertyPipe(name)]
Pipes natively supports the recording of the history of a particular traversal. By default, Gremlin does not assume that path information will be required of the traversal unless a path-specific step is called — e.g. path
, simplePath
. If the path information is required internal to a closure, Gremlin doesn’t know that as it can not interpret what is in a closure. As such, be sure to GremlinPipeline.enablePath()
if path information will be required by the expression.
gremlin> g.v(1).out.loop(1){it.loops < 3}{it.path.contains(g.v(4))}
Cannot invoke method contains() on null object
Display stack trace? [yN]
gremlin> g.v(1).out.loop(1){it.loops < 3}{it.path.contains(g.v(4))}.enablePath()
==>v[5]
==>v[3]


{% extends 'portal.html' %} {% block title %} {% include "hawc/siteTitle.html" with crumbs=object.get_crumbs crud=crud %} {% endblock title %} {% block breadcrumbs %} {% include "hawc/breadcrumbs.html" with crumbs=object.get_crumbs crud=crud %} {% endblock %} {% block content %}
| Assessment name | {{object.name}} |
|---|---|
| CAS number | {{object.cas}} |
| Year | {{object.year}} |
| Version | {{object.version}} |
| Assessment objective | {{object.assessment_objective|safe}} |
| Project manager(s) |
|
| Team member(s) |
|
| Reviewer(s) |
|
| Editable | {{object.editable}} |
| Public | {{object.public}} |
| Hidden on public page? | {{object.hide_from_public_page}} |
| Conflicts of interest | {{object.conflicts_of_interest|safe}} |
| Funding source | {{object.funding_source|safe}} |


Tari Protocol Discussion 22
On Monday, the Tari community held a brief discussion continuing on the topic of namespace registration. Specifically, there are two competing proposals that were assessed for feasibility. Below is the TL;DR on Monday’s conversation (full transcript included below):
Proposal 1:
- Asset issuers claim text identifiers that are registered on the base layer
- Easily readable
- Suffers from domain squatting attacks
Proposal 2:
- RAINs are random text labels that are mapped to IRL entities using an out of band lookup mechanism, such as Open Alias
- Open Alias doesn’t support reverse lookups
Join us for our next discussion on Freenode in #tari-dev.
Discussion times proposed by the Tari community:
Mondays: 6pm CAT (11am EST)
Thursdays: 11am CAT (4am EST)
To keep up with the latest Tari developments, you can follow the project on Twitter.
Transcript of Monday’s discussion
4:19 AM <@cjs77> Hey everyone, sorry for the late start
4:24 AM <@cjs77> I think there's still some discussion to be had around the RAIN idea. There are currently 2 proposals as I see it: One where Asset Issuers claim text identifiers that are registered on the base layer. This is easily readable, but suffer from domain squatting "attacks". The proposal to mitigate this is to charge more for shorter IDs. Sammy007 makes the point that not all identifiers (regardless of length) are equal (e.g. EPIC
4:24 AM <@cjs77> >> FNPA). He suggests, aiui, that RAINS are random text labels that are mapped to IRL entities using an out of band lookup mechanism, such as open alias.
4:34 AM <simian_za> Ahh, just looked at Open Alias and this makes a lot of sense
4:35 AM <Blackwolfsa> I agree
4:37 AM <Hansie> Neat
4:44 AM <neonknight> The Open Alias Mechanism makes sense!
4:44 AM <@cjs77> Yeah, I like it too.
4:57 AM <@cjs77> Does openalias support reverse lookups? Can I get "domate.getmonero.org" from the recipient_address 44AFFq5kSiGBoZ4...QBEP3A?
4:58 AM <moneromooo> No.
4:59 AM <@cjs77> Hmm.. that's a potential spanner in the works.
5:01 AM <moneromooo> If your network DHTs, that sounds like something you could maintain this way though.
5:02 AM <@cjs77> right
5:08 AM <moneromooo> Of course, since a DNS record owner can change the TXT record at any time, it's going to be hard to keep the two up to date in the face of a malicious DNS record owner.
5:09 AM <moneromooo> ie, if I start using disney's address on my own domain name.
5:21 AM <simian_za> We were thinking that the TXT record would include a signature by the asset issuer signing a message including the domain it can live on so that the DNS records can be validated against the domain the live on
5:24 AM <simian_za> signed using the private key belonging to the public key published as part of the RAIN registration transaction on the base layer
6:19 AM <@fluffypony> yeah you could sign it
8:09 AM → rottensox joined (~rottensox@unaffiliated/rottensox)
9:19 AM <sammy007> cjs77 yep just some random or with some useful data but not TLD-like
9:31 AM <sammy007> moneromooo assuming you wanna buy a PONY asset, you wouldn't trust RAIN anyway, you will go and validate it on internet, go to website and check if this asset is valid, just like with any erc20 where we can see tons of DAOs and only one is authentic, the openalias could help you to add that asset into your own registry in a wallet (just like with erc20 and MEW) bc you will know that issuer is running a website on mylittlepony.biz domain
9:33 AM <sammy007> or check for shitcointalk ANN thread, etc. Otherwise I can claim AMZN on chain and phish people with my fake shares for example
9:35 AM — moneromooo is just the peanut gallery here
9:37 AM ↔ rottensox nipped out
9:39 AM <@fluffypony> yeah you'd hit like Disney.com and it would pull the record from there
9:39 AM <@fluffypony> if you go in that route then namespaces almost doesn't matter


Dutch ace gets British F4 season with Villeneuve-backed prize
by Stephen LickorishBrought to you by
Dutch karting champion Marijn Kremers has secured a funded seat with Carlin in British Formula 4 next season after winning a shootout co-organised by 1997 Formula 1 champion Jacques Villeneuve.
Kremers, 21, triumphed in the competition held by racing school FEED Racing France at Magny-Cours earlier this week.
Villeneuve founded the school alongside Patrick Lemarie - a test driver for the BAR F1 team for four years while Villeneuve drove for the outfit - and both were part of a judging panel for the shootout that also featured 1996 Monaco Grand Prix winner Olivier Panis.
Nearly 100 drivers registered for the scholarship and these were whittled down to a final six that contested the shootout in Mygale F4 cars.
Kremers, who won the CIK-FIA World KZ karting title this year, will now receive a funded British F4 season with leading squad Carlin - which has run the champion driver in every year it has been in the series.
"It feels great!" said Kremers of his win. "I see the season in British F4 as a great opportunity for me to boost my racing career to the next level and it is a great feeling to have secured a seat with an amazing team as Carlin.
"I'm very excited, Carlin is such a big name in motorsport and I'm sure I will be able to learn a lot from their experience.
"Hopefully I'll be able to add another championship for them."
Kremers becomes the sixth driver to commit to racing in British F4 next season.
Ginetta Junior racer Casper Stevenson has also recently won a scholarship - this time a prize pot worth £35,000 from British F4 and the series' partners.
His fellow Ginetta Junior driver Roman Bilinski has already signed up and will drive for Arden alongside karter Frederick Lubin.
Elsewhere, Brazilian Roberto Faria will continue with Fortec for a second season, while Peruvian karting graduate Matias Zagazeta will be a team-mate of Kremers at Carlin.


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Terminal usage
Vico includes a command line tool that can be used to launch vico from the shell.
To use the tool from the command line, create a link from the application bundle to a directory in your PATH. If you have a bin directory in your home directory, create it as:
ln -s /Applications/Vico.app/Contents/MacOS/vicotool ~/bin/vico
If you want to install it for all users on the machine, create the link in a global directory (this requires administrator privileges):
sudo ln -s /Applications/Vico.app/Contents/MacOS/vicotool /usr/local/bin/vico
If Vico is not stored in your /Applications folder, adjust the command appropriately. Once the link is created, it will be kept up-to-date when Vico is updated.
To open a file with Vico from the shell, simply type:
vico filename
You can open multiple files at once, also using globbing characters (eg, vico *.py). If you specify a directory, Vico will display a new window with the directory selected in the explorer sidebar.
If you want to use Vico in your $EDITOR variable to edit commit messages, you need to use the -w switch. This makes Vico wait until the document is closed to return. The return code from vicotool is 0 if the document saved successfully before closing, and non-zero if it wasn't saved.
To see a quick description of the command line usage, use the -h option:
$ vico -h
syntax: vicotool [-hrw] [-e string] [-f file] [-p params] [file ...]
options:
-h show this help
-e string evaluate the string as a Nu script
-f file read file and evaluate as a Nu script
-p params read script parameters as a JSON string
-p - read script parameters as JSON from standard input
-r enter runloop (don't exit script immediately)
-w wait for document to close


Как я себя вижу
“I’m looking for the unexpected. I’m looking for things I’ve never seen before.”Robert Mapplethorpe
Services
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Visit MeUnerdwear cookie liquorice. Cake donut cupcake lollipop soufflé candy. Chocolate oat cake @cheesecake tootsie roll.
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Applicake chocolate cake wafer toffee pie soufflé wafer. Tart marshmallow wafer macaroon cheesecake jelly. Gingerbread cookie soufflé sweet roll sweet roll jelly-o.
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Finatra is a Scala service framework built on top of twitter-sever and Finagle.
This template shows the scaffolding for a simple Http server with a single controller. See the Finatra Http overview for more information and examples.
Full documentation available on the Finatra project site.
The build.sbt contains the sbt build configuration; you'll want to edit at least the project name and version.
This example by default will run your external service on port :9999 -- you can override this by either changing the code in the ExampleServer class or by passing a different value in the -http.port flag.
More examples are available in the Finatra Examples project on github.


Class: Rango::Router
Constructor Summary
public
initialize
[View source]
30 31 32 |
# File 'lib/rango/router/router.rb', line 30 def initialize self.routes = Array.new end |
Public Visibility
Public Instance Method Summary
| #find(uri) | |
|---|---|
| #include(*args) | |
| #inspect | |
| #match(pattern, params = Hash.new, &block) |
match(“kontakt/”, :method => “get”). |
| #redirect(url) |
Public Class Method Details
find
public
find(uri)
[View source]
22 23 24 25 26 |
# File 'lib/rango/router/router.rb', line 22 def find(uri) routers.each do |router| router.find(uri) end end |
register
public
register(&block)
[View source]
14 15 16 17 18 19 20 |
# File 'lib/rango/router/router.rb', line 14 def register(&block) router = self.new router.instance_eval(&block) routers.push(router) Project.router = router # FIXME return router end |
Public Instance Method Details
find
public
find(uri)
[View source]
59 60 61 62 63 |
# File 'lib/rango/router/router.rb', line 59 def find(uri) route = @routes.find { |route| route.match?(uri) } route = Error404.new unless route return route end |
include
public
include(*args)
[View source]
47 48 49 |
# File 'lib/rango/router/router.rb', line 47 def include(*args) return self end |
inspect
public
inspect
[View source]
55 56 57 |
# File 'lib/rango/router/router.rb', line 55 def inspect @routes.inspect end |
match
public
match(pattern, params = Hash.new, &block)
match(“kontakt/”, :method => “get”)
[View source]
35 36 37 38 39 40 41 42 43 44 45 |
# File 'lib/rango/router/router.rb', line 35 def match(pattern, params = Hash.new, &block) if pattern.is_a?(String) escaped = Regexp::quote(pattern) pattern = %r[^#{escaped}] end route = Route.new route.match_pattern = pattern route.match_params = params @routes.push(route) return route end |
redirect
public
redirect(url)
[View source]
51 52 53 |
# File 'lib/rango/router/router.rb', line 51 def redirect(url) # TODO end |


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http://meteor.redandivory.com/
Created by Pieter Soudan / @Sewdn
Presented 31th of march 2014 on 2nd edition of Meteor Meetup Antwerp
http://meteor.redandivory.com/
Created by Pieter Soudan / @Sewdn
Presented 11th of february on Meteor Meetup Antwerp


GIFDIFF(1) GIFDIFF(1) NAME gifdiff - compares GIF images SYNOPSIS gifdiff [options] GIF-file-1 GIF-file-2 DESCRIPTION gifdiff compares two GIF files and determines if they appear identical. Differences that don't affect appearance (like colormap ordering or how much an animation is optimized) are not reported. gifdiff prints details of any differences it finds. If the GIFs are the same, it prints nothing. It exits with status 0 if there were no differ‐ ences, 1 if there were some differences, and 2 if there was trouble. OPTIONS --brief, -q Report only whether the GIFs differ, not the details of the differ‐ ences. --ignore-redundancy, -w Do not report differences in the numbers of redundant frames (frames which do not change the displayed image). --help Print usage information and exit. --version Print the version number and some quickie warranty information and exit. SEE ALSO gifsicle(1) BUGS Please email suggestions, additions, patches and bugs to<EMAIL_ADDRESS>AUTHORS Eddie Kohler<EMAIL_ADDRESS>http://www.read.seas.harvard.edu/~kohler/ http://www.lcdf.org/gifsicle/ The gifsicle home page. Version 1.67 31 August 1998 GIFDIFF(1)


[gecode-users] Is it possible to support gecode gcc411?
Guido Tack
tack at gecode.org
Wed Nov 17 14:34:57 CET 2010
debbie lin wrote:
> Gecode is really an amazing great CSP solver. Thanks for your effect great work.
> I'm planning to integrate gecode with my current work. However, I can use
> gcc411 compiler only.
> Is it possible to support gcc411 compiler?
We cannot support gcc 4.1.1 officially, it has serious bugs that are impossible to work around and that lead to code in Gecode not functioning correctly.
If you really want to try, you can work around the compilation issues using the attached patch (apply using patch -0 < gcc4patch, then run autoconf, then simply configure and make). It makes some things public that shouldn't have to be public (but gcc < 4.2 does not implement the C++ specs correctly). No guarantees, though - the code should compile and it may work, but it will contain subtle bugs, in particular in the set constraints.
Cheers,
Guido
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--
Guido Tack, http://people.cs.kuleuven.be/~guido.tack/
More information about the users
mailing list


Chocolate Pizza Posted On 15 Dec 2013 / Desserts
print
For the fig swirl: Melt butter over medium heat in a sauce pan. Add brown sugar and stir to dissolve. Halve all of the figs and toss in the saucepan with water and lemon juice. Cook over medium heat, stiring frequently, until you have a chuncky-jammy mixture. Add salt with one or two stirs, set aside and let cool completely.
Ice cream: In a small pot over medium heat, combine milk, and granulated sugar until sugar is completely dissolved and the milk is just barely lukewarm. Whisk in the egg yolks. Set mixture in the fridge and wait until the fig mixture is cooled.
Using an ice cream machine, pour liquids into the frozen basin and process according to manufacture instructions, i.e., let spin and thicken for 20 minutes before adding mascarpone, fig jam, and the nuts. Continue to process for +/- 10 minutes. Pour semi-frozen mixture into a pyrex dish or glass tupperware. Freeze for at least two hours before serving.


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Normal Text outside the Blockquote
This is a block quote
This is inside the blockquote Inside of _it again_
Back to the top this is the
initialblockquote
Other normal text
And finally a looong blockquote that is only triggered by one long line.
This is a blockquote
- list level 1
- list level 1
- list level 2
This is a nested quote block.
Blockquote 1
Blockquote 2 blocquote 2
Blockquote 3 Blockquote 3
Blockquote 4
Blockquote 2


Support ERP (Oracle EBS R12) on both technical /functional sides
Strong experience on Procure-to-Pay cycles.
Long experience with Develop, maintain, update, troubleshoot and support software applications.
Monitor, follow up, handle, analyze and resolve IT problems and requests for end users.
Build interfaces and conversions, develop and customize reports.
Analyze, clean, develop, administer, query and maintain databases.
Handle data collection, analysis, validation and correction to ensure the data accuracy.
Support and maintain internal business applications. Responsible for configuration, setup, making changes to existing process and adding new functionalities. Implements roles and access levels.
Lead and engage with the business and IT users at strategic levels to understand business needs. Document the requirements. Analyze and evaluate the requirements and make solution recommendations.
Troubleshoot problems, Implement, maintain, monitor and tune Systems and Applications.
Initiate, develop and implement automated scripts and hence decreasing manual intervention in systems.
Provide Function guidelines to users on current and new features of system software.
Manage and Collaborate in the planning, analyze, design, development, configure and deployment of new applications, and enhancements to existing applications.
Direct and manage project development from beginning to end.
ACADEMIC QUALIFICATIONS:
B.Sc. in Computer & Software Engineering (University of Technology), Iraq- Baghdad, 1998-2002 (High school’s Government Scholarship Award).
CERTIFICATIONS:
OCP (Oracle Forms Developer Certified Professional) with 100% score Recognized by Oracle Certification Program.
OCA (Oracle PL/SQL Developer Certified Associate) with 100% score Recognized by Oracle Certification Program. .


Main #2 This class is so awesome!!! This class is so awesome!!! This class is so awesome!!! This class is so awesome!!!


Non-parametric permutation tests or randomization tests are often recommended because they require fewer assumptions of the data. In the recent paper by Eklund et al. non-parametric permutation testing was the only method that consistently gave false positive rates in the expected range.
But how many permutations or randomizations should you perform? Answers range from “all of them” to “as many as you can” to “no more than a couple hundred”. Below I briefly describe the relationship between number of permutations performed and the confidence you can have in the resultant p-value.
When performing all possible permutations, the randomization test of \(H_0\) is guaranteed to control the probability of Type I error under the assumption of exchangeability of the randomized conditions. However, due to computational limits, enumeration of all possible permutations is not feasible in some (many) cases. Using a Monte Carlo sampling of the permutation distribution (a subset of all possible permutations), we can estimate the p value as
where \(M\) is the number of permutations performed, \(t^*\) is the observed test statistic and \(t_i\) is the test statistic found on the \(i^{th}\) permutation. The Monte Carlo p value \(\hat{p}\) will vary depending on which subset of all permutations are selected. Since \(\hat{p}\) is an unbiased estimator of \(p\), we can use \(\hat{p}\) to construct a confidence interval for \(p\) based on a Normal approximation of the Binomial distribution. (Note that we could do this part non-parametrically also, but let’s not).
where \(z\) is the critical value derived from the Normal curve and defines the level of confidence.
For example, let’s say I performed 1000 permutations and achieved a \(\hat{p}\) of 0.048 (true story). Should I reject the null hypothesis? To calculate a 95% confidence interval, I will plug in \(z = 1.96\) to the equations above, which gives the interval \([0.035, 0.061]\). For an \(\alpha\) of 0.05, I cannot reject the null hypothesis because the extreme of my confidence interval exceeds 0.05. The size of the confidence interval will increase with \(\hat{p}\) and decrease with the number of permutations. So for very small \(\hat{p}\), only few permutations are needed. But for \(\hat{p}\) near \(\alpha\), it may be impossible to perform sufficient permutations to confidently reject \(H_0\).
We can calculate the number of permutations required to reject \(H_0\) for a given \(\hat{p}\) by setting \(e^+ = \alpha - \epsilon\) and solving for M.
Thus, for the example above, if I choose \(\alpha=0.05\) and \(z=1.96\) for 95% confidence, I would need to perform 43,884 permutations in order to reject \(H_0\) with \(p=0.048\). Alternatively, had I found \(p=0.03\), I would only have needed 280 permutations.
So how many permutations should you perform? Perhaps unsatisfactorily, it does seem prudent to perform “as many as you can” to maximize the precision of your p value since you won’t know \(\hat{p}\) ahead of time. How to maximize precision while minimizing the number of required permutations appears to be an open area of research. I wonder if there could be some procedure to estimate \(\hat{p}\) with say a parametric test first, and then use that estimate to decide how many permutations to perform, but this seems akin to p-hacking somehow. In any case, we now know how to reason about the confidence of our p-values, whatever number of permutations we choose. Please comment below if you have other ideas.
References
- Ernst, M. D. (2004). Permutation Methods: A Basis for Exact Inference. Statistical Science, 19(4), 676–685. doi:10.1214/088342304000000396
- Wallis, S. (retrieved Aug 10 2016) Binomial confidence intervals and contingency tests: mathematical fundamentals and the evaluation of alternative methods
- Calculation Of The Confidence Interval (retrieved Aug 10 2016)
- Eklund, A., Nichols, T. E., & Knutsson, H. (2016). Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. Proceedings of the National Academy of Sciences, 113(28), 7900–7905. doi:10.1073/pnas.1602413113
- Phipson, B., and Smyth, G. K. (2010). Permutation p-values should never be zero: calculating exact p-values when permutations are randomly drawn. Stat. Appl. Genet. Molec. Biol. Volume 9, Issue 1, Article 39.
- Theo A. Knijnenburg, Lodewyk F. A. Wessels, Marcel J. T. Reinders, Ilya Shmulevich (2009) Fewer permutations, more accurate P-values. Bioinformatics. 25(12): i161–i168. doi: 10.1093/bioinformatics/btp211
Note: Thanks to Giancarlo Valente for his help with this post!


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File API manual IDL tests
Either download
upload.txt
and select it below or select an arbitrary local file.


Privacy Policy of www.imf.org
Last Updated: October 30, 2007This privacy policy statement applies to www.imf.org only. It does not apply to information collected and stored by a means other than this website. You can find the International Financial Statistics Online privacy policy statement here.
Privacy of Personal Information
Out of respect for the privacy of its users, the IMF website (http://www.imf.org) refrains from obtaining personal information from users without their explicit consent, other than as described below.
The IMF website, either directly or through a third-party vendor, Omniture, stores the Internet protocol (IP) address of your computer, the browser software and operating system used, and the date and time of your visit.
This information is used by the IMF to understand which countries the users of the website visit from, when they visit, and other information about how users access the website. The intention is to inform analysis about how to improve and develop www.imf.org. The IMF also monitors network traffic to identify unauthorized attempts to upload or change information or to otherwise cause damage to the site.
The IMF does not distribute, sell or rent any personal information collected via its website. This is also true for personal information (for example, email and postal addresses) sent to the website’s email addresses. The IMF does not send unsolicited emails to users of its site. The transmission of personal information entered via the site’s registration forms (e.g. IMF Online Employment Application form, Annual and Spring Meetings registration form) is protected by security features.
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The Mandalorian's Combat Skills Have a Surprising Weakness
by Brandon ZacharyWARNING: The following contains spoilers for the second episode of The Mandalorian, "The Child," streaming now on Disney+.
The Mandalorian has proved himself a fearsome bounty hunter, skilled enough to take on a bunker filled with mercenaries, with the help a single, scene-stealing ally. However, the first two episodes of the Star Wars live-action series have highlighted a glaring flaw to his combat skills: He's useless when confronted by animals.
ANIMAL CONTROL
When we first meet the Mandalorian (Pedro Pascal) is on an icy planet, where he apprehends a bounty and brings him back to his ship. Along the way, his ship is almost brought down by a massive sea monster called a Ravinak. It grabs onto the Mandalorian's ship, the Razor Crest, and almost drags it down into the icy waters below. But the Mandalorian is barely able to electrocute it and force it to release the landing gear.
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Blurrgs, cow-sized reptilian creatures that can be ridden, are introduced next. On the desert planet of Arvala-7, you need one just to cross the sands without dying. But when the Mandalorian isn't paying attention, one gets up close and personal with him. Despite all his technology and weapons, the Mandalorian can't escape the creature's grasp. It takes the arrival of Kuiil, who is used to the creatures, to save the Mandalorian from the Star Wars equivalent of a camel attack.
None of that compares to the Mud Horn, a large creature that resembles a rhino and becomes his target at the behest of the Jawas. To recover the parts of his ship that they stole at the beginning of the episode "Chapter 2: The Child", he has to steal an egg from the creature.
BORN TO BE WILD
It's kind of hilarious that the Mandalorian, who has been nothing but skilled when it comes to bringing down humanoid aliens, can't seem to handle creatures. He casually shoots humanoids down or captures them to gain a bounty. His armor protects him from most attacks, and he can easily win even when surrounded and ambushed.  Even the Blurrg, a minor and domesticated beast, was able to get the drop on him and throw him around like a rag doll.
It's logical to make the character fallible. The Mandalorian isn't a Jedi or even some kind of war ace. He's just a man trying to make his way in the galaxy and regularly finds himself in unexpected and dangerous situations. But even when his skirmishes turn out to be tough and he has to struggle to survive, he still manages just fine. Put him up against a wild beast though and he's useless. He's 0-3 going up against such creatures by the end of the second episode, which isn't a great showing for the bounty hunter against wild animals.
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