dense1.density_estimator module¶
- class Model(density, correlated_field, select_subset, amplitude_total_offset, normalized_amplitudes)¶
Bases:
tuple
- amplitude_total_offset¶
Alias for field number 3
Alias for field number 1
- density¶
Alias for field number 0
- normalized_amplitudes¶
Alias for field number 4
- select_subset¶
Alias for field number 2
- build_density_estimator(shape, pixel_distances=None, pad=1.0, cf_fluctuations=None, cf_azm_uniform=None)[source]¶
Construct the model for a density estimator
- Parameters
shape (int or tuple of int or tuple of tuple of int) – Shape of the to-be-modeled data. Note, the level of nesting matters! While ((128, 128), ) denotes a shared kernel for both axis, (128, 128) denotes separate kernels for both axis.
pixel_distances (float or tuple of float, optional) – Distances between individual pixels.
pad (float or tuple of float, optional) – Padding factor.
cf_fluctuations (dict or tuple of dict, optional) – Parameters for the Matern kernel of the density field.
cf_azm_uniform (tuple of float, optional) – Parameters for the a-priori uniform amplitude total offset.
- Returns
model – Collection of operators that represent the model. Most importantly model features an attribute .density which corresponds to the NIFTy representation of the density estimator.
- Return type
- draw_synthetic_sample(model, seed=60, pretty=False)[source]¶
Draw synthetic data (i.e. produce a prior sample in data space)
- Parameters
model (Model) – Collection of operators representing the model.
seed (int or np.random.SeedSequence, optional) – Random state with which to draw the synthetic truth and the noise.
pretty (bool or set) – Set of latent parameters which are artificially dampened as to produce untrue prior samples that look prettier. If a boolean and true then a sane set of latent parameters is chosen.
- Returns
synth_data (np.ndarray) – Array of integers for the synthetic Poissonian counts.
synth_truth (np.ndarray) – Array of the true expectation value underlying the Poissonian counts.
- fit(model, data, n_samples=5, n_max_iterations=20, init_position=None, init_seed=31617, kl_kw=None, sampling_kw=None, minimizer_kw=None)[source]¶
Fit the model to data and return approximate posterior samples
- Parameters
model (Model) – Collection of operators representing the model.
data (np.ndarray) – Poissonian count data to which to fit the model to.
init_position (ift.MultiField, optional) – Optional starting position in the form of a MultiField from NIFTy.
init_seed (int or np.random.SeedSequence, optional) – Random state with which to draw the synthetic truth and the noise.
kl_kw (dict, optional) – Parameters for the Kullback-Leibler computation.
sampling_kw (dict, optional) – Parameters for the sampling iteration controller.
minimizer_kw (dict, optional) – Parameters for the minimizer iteration controller.
- Returns
density_samples (tuple of np.ndarray) – Samples from the approximate posterior distribution.
lat_samples (tuple of ift.MultiField) – Latent samples compatible with the collection of operators representation in the model. These can be used to access intermediate posteriors within the model itself.