Ms quantity distributions #98
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src/mlmc/quantity_concept.py
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| def _samples(self, i_chunk): | ||
| @clearable_cache(custom_key_maker=get_cache_key) |
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? why clearable
? can we use max_size=1
? simplification of get_cache_key:
- just return tuple
- no need to hash also input quantities as these are unique for self anyway
src/mlmc/moments.py
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| @@ -77,6 +77,9 @@ def __call__(self, value): | |||
| def eval(self, i, value): | |||
| return self._eval_all(value, i+1)[:, -1] | |||
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| def eval_fine_coarse(self, i, value): | |||
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K cemu se toto pouziva? Je to neefektivni pocitat vsechny momenty az do i a pak vybrat jen ten posledni.
src/mlmc/sample_storage.py
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| @@ -187,14 +200,30 @@ def load_scheduled_samples(self): | |||
| def sample_pairs(self): | |||
| """ | |||
| Sample results split to numpy arrays | |||
| :param i_chunk: identifier of chunk | |||
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Ma sample_pairs jeste smysl? Zejmena kdyz neni podporovano i_chunk?
src/mlmc/sample_storage_hdf.py
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| :param i_chunk: int, chunk id | ||
| :return: | ||
| """ | ||
| if i_chunk != 0: |
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To je nejake divne, nema tu byt:
if i_chunk==0:?
Podobne pro memory storage.
Mas nejaky test kde se pocita s vice chunkama?
src/mlmc/quantity_estimate.py
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| # cov = (cov_fine - cov_coarse) / self.n_samples | ||
| # | ||
| # return cov | ||
| def estimate_covariance(self, moments_fn, stable=False): |
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We should do the cov matrix estimate through quantities and the estimate_mean algorithm.
The stable summation algorithm tries to reduce arithmetic errors. That can also be an issue of the general estimation algorithm. See, e.g. https://en.wikipedia.org/wiki/Kahan_summation_algorithm
That can have some effect if we try to obtain very precise estimates using millions of samples.
src/mlmc/sim/synth_simulation.py
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| @@ -116,7 +116,10 @@ def calculate(config, seed): | |||
| for result in [fine_result, coarse_result]: | |||
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General not to synth_simulation.py currently this is just a test mock class and should be in test directory possibly as a simple tutorial. But we can possibly implement a SyntheticSimulation based on given exact distribution.
src/mlmc/quantity_concept.py
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| return self.frame0._copy(*[q for t, q in self.time_frames], operation=max_op) | ||
| def __getitem__(self, key): |
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Should be reused from quantity or rather from QType descendants. See the note above.
src/mlmc/quantity_concept.py
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| new_shape = np.empty(self._shape)[key].shape | ||
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| # One selected item is considered to be a scalar QType | ||
| if len(new_shape) == 1 and new_shape[0] == 1: |
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We should keep the produced new_shape. Be as close to the numpy behavior as possible.
src/mlmc/quantity_concept.py
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| # Result is also array | ||
| if len(new_shape) > 0: | ||
| q_type = ArrayType(new_shape, qtype=copy.deepcopy(self._qtype)) |
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Why we need deep copy of the type?
src/mlmc/quantity_concept.py
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| def custom_copy(quantities): |
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Possibly make it a private static method of Quantity and use appropriate name. 'custom' says nothing.
test/test_quantity_concept.py
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| @@ -0,0 +1,468 @@ | |||
| import os | |||
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I have to go through the test yet...
MS_quantity branch contains code for approximation of the distribution function using the maximum entropy method and also using BSpline and other stuff which were used for the Diploma Thesis.
Quantity is supported.