pyhrf.jde.nrl.bigaussian_drift.
BiGaussMixtureParams_Multi_Sess_NRLsBar_Sampler
(parameters=None, xmlHandler=None, xmlLabel=None, xmlComment=None)¶Bases: pyhrf.jde.samplerbase.GibbsSamplerVariable
#TODO : comment
I_MEAN_CA
= 0¶I_VAR_CA
= 1¶I_VAR_CI
= 2¶L_CA
= 1¶L_CI
= 0¶NB_PARAMS
= 3¶PARAMS_NAMES
= ['Mean_Activ', 'Var_Activ', 'Var_Inactiv']¶P_ACTIV_THRESH
= 'mean_activation_threshold'¶P_HYPER_PRIOR
= 'hyperPriorType'¶P_MEAN_CA_PR_MEAN
= 'meanCAPrMean'¶P_MEAN_CA_PR_VAR
= 'meanCAPrVar'¶P_SAMPLE_FLAG
= 'sampleFlag'¶P_USE_TRUE_VALUE
= 'useTrueValue'¶P_VAL_INI
= 'initialValue'¶P_VAR_CA_PR_ALPHA
= 'varCAPrAlpha'¶P_VAR_CA_PR_BETA
= 'varCAPrBeta'¶P_VAR_CI_PR_ALPHA
= 'varCIPrAlpha'¶P_VAR_CI_PR_BETA
= 'varCIPrBeta'¶checkAndSetInitValue
(variables)¶computeWithJeffreyPriors
(j, cardCIj, cardCAj)¶computeWithProperPriors
(j, cardCIj, cardCAj)¶defaultParameters
= {'hyperPriorType': 'Jeffrey', 'initialValue': None, 'meanCAPrMean': 5.0, 'meanCAPrVar': 20.0, 'mean_activation_threshold': 4.0, 'sampleFlag': True, 'useTrueValue': False, 'varCAPrAlpha': 2.01, 'varCAPrBeta': 0.5, 'varCIPrAlpha': 2.04, 'varCIPrBeta': 2.08}¶finalizeSampling
()¶getCurrentMeans
()¶getCurrentVars
()¶getOutputs
()¶get_string_value
(v)¶linkToData
(dataInput)¶parametersComments
= {'hyperPriorType': "Either 'proper' or 'Jeffrey'", 'mean_activation_threshold': 'Threshold for the max activ mean above which the region is considered activating'}¶parametersToShow
= ['initialValue', 'sampleFlag', 'mean_activation_threshold', 'useTrueValue', 'hyperPriorType', 'meanCAPrMean', 'meanCAPrVar', 'varCIPrAlpha', 'varCIPrBeta', 'varCAPrAlpha', 'varCAPrBeta']¶sampleNextInternal
(variables)¶updateObsersables
()¶pyhrf.jde.nrl.bigaussian_drift.
NRL_Drift_Sampler
(do_sampling=True, val_ini=None, contrasts={}, do_label_sampling=True, use_true_nrls=False, use_true_labels=False, labels_ini=None, ppm_proba_threshold=0.05, ppm_value_threshold=0, ppm_value_multi_threshold=array([ 0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1., 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2., 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4. ]), mean_activation_threshold=4, rescale_results=False, wip_variance_computation=False)¶Bases: pyhrf.jde.nrl.bigaussian.NRLSampler
Class handling the Gibbs sampling of Neural Response Levels in the case of joint drift sampling.
computeVarYTildeOpt
(varXh)¶sampleNextInternal
(variables)¶Define the behaviour of the variable at each sampling step when its sampling is not activated. Must be overriden in child classes.
sampleNrlsSerial
(rb, h, varCI, varCA, meanCA, gTg, variables)¶pyhrf.jde.nrl.bigaussian_drift.
NRL_Drift_SamplerWithRelVar
(do_sampling=True, val_ini=None, contrasts={}, do_label_sampling=True, use_true_nrls=False, use_true_labels=False, labels_ini=None, ppm_proba_threshold=0.05, ppm_value_threshold=0, ppm_value_multi_threshold=array([ 0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1., 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2., 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4. ]), mean_activation_threshold=4, rescale_results=False, wip_variance_computation=False)¶Bases: pyhrf.jde.nrl.bigaussian.NRLSamplerWithRelVar
Class handling the Gibbs sampling of Neural Response Levels in the case of joint drift sampling and relevant variable.
computeVarYTildeOptWithRelVar
(varXh, w)¶sampleNextInternal
(variables)¶Define the behaviour of the variable at each sampling step when its sampling is not activated. Must be overriden in child classes.
sampleNrlsSerialWithRelVar
(rb, h, gTg, variables, w, t1, t2)¶pyhrf.jde.nrl.bigaussian_drift.
NRLsBar_Drift_Multi_Sess_Sampler
(do_sampling=True, val_ini=None, contrasts={}, do_label_sampling=True, use_true_nrls=False, use_true_labels=False, labels_ini=None, ppm_proba_threshold=0.05, ppm_value_threshold=0, ppm_value_multi_threshold=array([ 0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1., 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2., 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4. ]), mean_activation_threshold=4, rescale_results=False, wip_variance_computation=False)¶Bases: pyhrf.jde.nrl.bigaussian.NRLSampler
Class handling the Gibbs sampling of Neural Response Levels in the case of joint drift sampling.
checkAndSetInitValue
(variables)¶linkToData
(dataInput)¶sampleNextAlt
(variables)¶Define the behaviour of the variable at each sampling step when its sampling is not activated.
sampleNextInternal
(variables)¶Define the behaviour of the variable at each sampling step when its sampling is not activated. Must be overriden in child classes.
sampleNrlsSerial
(varCI, varCA, meanCA, variables)¶samplingWarmUp
(variables)¶#TODO : comment
pyhrf.jde.nrl.bigaussian_drift.
permutation
(x)¶Randomly permute a sequence, or return a permuted range.
If x is a multi-dimensional array, it is only shuffled along its first index.
Parameters: | x (int or array_like) – If x is an integer, randomly permute np.arange(x) .
If x is an array, make a copy and shuffle the elements
randomly. |
---|---|
Returns: | out – Permuted sequence or array range. |
Return type: | ndarray |
Examples
>>> np.random.permutation(10)
array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6])
>>> np.random.permutation([1, 4, 9, 12, 15])
array([15, 1, 9, 4, 12])
>>> arr = np.arange(9).reshape((3, 3))
>>> np.random.permutation(arr)
array([[6, 7, 8],
[0, 1, 2],
[3, 4, 5]])
pyhrf.jde.nrl.bigaussian_drift.
rand
(d0, d1, ..., dn)¶Random values in a given shape.
Create an array of the given shape and populate it with
random samples from a uniform distribution
over [0, 1)
.
Parameters: | d1, .., dn (d0,) – The dimensions of the returned array, should all be positive. If no argument is given a single Python float is returned. |
---|---|
Returns: | out – Random values. |
Return type: | ndarray, shape (d0, d1, ..., dn) |
See also
random()
Notes
This is a convenience function. If you want an interface that takes a shape-tuple as the first argument, refer to np.random.random_sample .
Examples
>>> np.random.rand(3,2)
array([[ 0.14022471, 0.96360618], #random
[ 0.37601032, 0.25528411], #random
[ 0.49313049, 0.94909878]]) #random
pyhrf.jde.nrl.bigaussian_drift.
randn
(d0, d1, ..., dn)¶Return a sample (or samples) from the “standard normal” distribution.
If positive, int_like or int-convertible arguments are provided,
randn generates an array of shape (d0, d1, ..., dn)
, filled
with random floats sampled from a univariate “normal” (Gaussian)
distribution of mean 0 and variance 1 (if any of the are
floats, they are first converted to integers by truncation). A single
float randomly sampled from the distribution is returned if no
argument is provided.
This is a convenience function. If you want an interface that takes a tuple as the first argument, use numpy.random.standard_normal instead.
Parameters: | d1, .., dn (d0,) – The dimensions of the returned array, should be all positive. If no argument is given a single Python float is returned. |
---|---|
Returns: | Z – A (d0, d1, ..., dn) -shaped array of floating-point samples from
the standard normal distribution, or a single such float if
no parameters were supplied. |
Return type: | ndarray or float |
See also
random.standard_normal()
Notes
For random samples from , use:
sigma * np.random.randn(...) + mu
Examples
>>> np.random.randn()
2.1923875335537315 #random
Two-by-four array of samples from N(3, 6.25):
>>> 2.5 * np.random.randn(2, 4) + 3
array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], #random
[ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) #random