pyhrf.validation.valid_beta_estim.
ObsField2DTest
(methodName='runTest')¶Bases: unittest.case.TestCase
Test estimation of beta with on observed 2D fields
MC_comp_pfmethods_ML
(shape)¶MC_comp_pfmethods_ML_3C
(shape)¶setUp
()¶Hook method for setting up the test fixture before exercising it.
test_MC_comp_pfmethods_ML_100x100
()¶test_MC_comp_pfmethods_ML_10x10
()¶test_MC_comp_pfmethods_ML_30x30
()¶test_MC_comp_pfmethods_ML_3C_10x10
()¶test_MC_comp_pfmethods_ML_3C_20x20
()¶test_MC_comp_pfmethods_ML_3C_30x30
()¶test_MC_comp_pfmethods_ML_3C_50x50
()¶test_single_Onsager_MAP
()¶PF method: Onsager. MAP on p(label|beta).
test_single_Onsager_ML
()¶PF method: Onsager. ML on p(beta|label).
test_single_PFES_MAP
()¶PF estimation method : extrapolation scheme. MAP on p(beta|label).
test_single_PFES_ML
()¶PF estimation method : extrapolation scheme. ML on p(label|beta).
test_single_PFPS_MAP
()¶PF estimation method : path sampling. MAP on p(beta|label).
test_single_PFPS_ML
()¶PF estimation method : path sampling. ML on p(label|beta).
test_single_surface_PFPS_ML
()¶PF estimation method : path sampling. ML on p(label|beta). topology from a surfacic RDI
pyhrf.validation.valid_beta_estim.
beta_estim_obs_field_mc
(graph, nbClasses, beta, gridLnz, mcit=1, cachePotts=False)¶pyhrf.validation.valid_beta_estim.
dist
(x, y)¶pyhrf.validation.valid_beta_estim.
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
pyhrf.validation.valid_beta_estim.
test_beta_estim_obs_fields
(graphs, betas, nbLabels, pfmethod, mcit=1)¶