cross_validate_pce_degree¶
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pyapprox.approximate.cross_validate_pce_degree(pce, train_samples, train_vals, min_degree=1, max_degree=3, hcross_strength=1, cv=10, solver_type='lasso_lars', verbosity=0)[source]¶ Use cross validation to find the polynomial degree which best fits the data. A polynomial is constructed for each degree and the degree with the highest cross validation score is returned.
- Parameters
- train_samplesnp.ndarray (nvars,nsamples)
The inputs of the function used to train the approximation
- train_valsnp.ndarray (nvars,nsamples)
The values of the function at
train_samples- min_degreeinteger
The minimum degree to consider
- min_degreeinteger
The maximum degree to consider. All degrees in
range(min_degree,max_deree+1)are considered- hcross_strengthfloat
The strength of the hyperbolic cross index set. hcross_strength must be in (0,1]. A value of 1 produces total degree polynomials
- cvinteger
The number of cross validation folds used to compute the cross validation error
- solver_typestring
The type of regression used to train the polynomial
‘lasso_lars’
‘lars’
‘lasso’
‘omp’
- verbosityinteger
Controls the amount of information printed to screen
- Returns
- result
pyapprox.approximate.ApproximateResult Result object with the following attributes
- approx
pyapprox.multivariate_polynomials.PolynomialChaosExpansion The PCE approximation
- scoresnp.ndarray (nqoi)
The best cross validation score for each QoI
- degreesnp.ndarray (nqoi)
The best degree for each QoI
- result