Feature Construction Moments #438
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Codecov Report
@@ Coverage Diff @@
## develop #438 +/- ##
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+ Coverage 80.45% 80.49% +0.04%
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Files 95 95
Lines 7417 7433 +16
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+ Hits 5967 5983 +16
Misses 1450 1450
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| import numpy as np | ||
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| from ..._utils import check_is_univariate | ||
| from ...representation import FDataBasis, FDataGrid | ||
| from ...representation._typing import NDArrayFloat, NDArrayInt | ||
| from ...representation import FDataGrid |
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[pep8] reported by reviewdog 🐶
F811 redefinition of unused 'FDataGrid' from line 10
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| import numpy as np | ||
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| from ..._utils import check_is_univariate | ||
| from ...representation import FDataBasis, FDataGrid | ||
| from ...representation._typing import NDArrayFloat, NDArrayInt | ||
| from ...representation import FDataGrid |
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[pep8] reported by reviewdog 🐶
I001 isort found an import in the wrong position
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| import numpy as np | ||
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| from ..._utils import check_is_univariate | ||
| from ...representation import FDataBasis, FDataGrid | ||
| from ...representation._typing import NDArrayFloat, NDArrayInt | ||
| from ...representation import FDataGrid |
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[pep8] reported by reviewdog 🐶
WPS440 Found block variables overlap: FDataGrid
| @@ -342,3 +343,138 @@ def number_up_crossings( | |||
| points_greater & points_smaller_rotated, | |||
| axis=2, | |||
| ).T | |||
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| def moments_of_norm( | |||
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[pep8] reported by reviewdog 🐶
E302 expected 2 blank lines, found 1
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| [ 0.03, 0.01]]) | ||
| """ | ||
| mean = np.mean(data.data_matrix, axis=1) |
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This doesn't work for FDataBasis.
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Also, it should be the mean in the integral sense, IMHO, to consider the position of the grid points too.
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| if isinstance(data, FDataGrid): | ||
| return scipy.integrate.simpson( | ||
| function(data.data_matrix), |
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You can apply the function directly to the FDataGrid and use its integrate method afterwards.
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Then the function should be a Callable[[FDataGrid], FDataGrid] so a FDataGrid is returned and the integrate method could be applied later
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You can apply a ndarray function over a FDataGrid. Mypy probably won't like it but it will work at runtime.
| [ 4.9 ], | ||
| [ 4.84]]) | ||
| """ | ||
| domain_range = data.domain_range[0][1] - data.domain_range[0][0] |
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Should this be extended to general functions?
| Calculate the specified unconditional moment of a dataset. | ||
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| It performs the following map: | ||
| :math:`f(X)=\int_a^b f_1(X(t))dt,\dots,f_p(X(t))dt=\int_a^b f^p(X(t))dt`. |
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Where is the n in this formula? What is p?
| if isinstance(data, FDataGrid): | ||
| return function(data).integrate() / domain_range | ||
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| return nquad_vec( |
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I don't think this is ok. You seem to compute the integral of the function instead of the function applied to the random variable. Please add a test using the same data in both discretized and basis form to check if it works.
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| def unconditional_expected_value( | ||
| data: Union[FDataBasis, FDataGrid], |
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You can use FData instead of a Union.
| and p observations, how the unconditional expected values are calculated: | ||
| .. math:: | ||
| f_1(x(t))=\frac{1}{\left( b-a\right)}\int_a^b g | ||
| \left(x_1(t)\right)dt,\dots, |
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[pep8] reported by reviewdog 🐶
WPS430 Found nested function: integrand
| if isinstance(data, FDataGrid): | ||
| return function(data).integrate() / lebesgue_measure | ||
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| def integrand(*args: NDArrayFloat): |
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[pep8] reported by reviewdog 🐶
WPS430 Found nested function: integrand
No description provided.