U Kv fwD@sdgZddlZddlmZmZmZddlmZer:ddl Z ddZ ddZ d d Z Gd d d e ZeeZd dZde_ddZddZdS)bsN) have_pandas no_picklingassert_no_pickling)stateful_transformcCs<zddlm}Wntk r,tdYnXttj|td}|jdksPt| t |}t|}|jdkr|j ddkr|dddf}|jdkstt |t |kst |t |krtdt||d}tj|j d|ftd}t|D]6}t|f}d||<|||||f|dd|f<q|S)Nr)splevz#spline functionality requires scipy)Zdtypezksome data points fall outside the outermost knots, and I'm not sure how to handle them. (Patches accepted!))Zscipy.interpolater ImportErrornp atleast_1dasarrayfloatndimAssertionErrorsortintshapeminmaxNotImplementedErrorlenemptyrangezeros)xknotsdegreerZn_basesbasisiZcoefsr 1/tmp/pip-unpacked-wheel-68fdvdus/patsy/splines.py_eval_bspline_basiss* (   r"cs:t|}tfdd|jddD}|j|jddS)Ncsg|]}td|qS)d)r Z percentile).0probrr r! Asz&_R_compat_quantile..C)order)r r ZravelZreshaper)rZprobsZ quantilesr r&r!_R_compat_quantile>s   r*cCs`dd}|ddgdd|ddgdd|ddgdd gdd g|ttddd gd d gdS) NcSstt|||stdSN)r Zallcloser*r)rr%expectedr r r!tFsz"test__R_compat_quantile..t g?g333333? gffffff?g@g333333@)listr)r-r r r!test__R_compat_quantileEs r4c@s8eZdZdZddZd ddZd d Zdd d ZeZ dS)BSa3 bs(x, df=None, knots=None, degree=3, include_intercept=False, lower_bound=None, upper_bound=None) Generates a B-spline basis for ``x``, allowing non-linear fits. The usual usage is something like:: y ~ 1 + bs(x, 4) to fit ``y`` as a smooth function of ``x``, with 4 degrees of freedom given to the smooth. :arg df: The number of degrees of freedom to use for this spline. The return value will have this many columns. You must specify at least one of ``df`` and ``knots``. :arg knots: The interior knots to use for the spline. If unspecified, then equally spaced quantiles of the input data are used. You must specify at least one of ``df`` and ``knots``. :arg degree: The degree of the spline to use. :arg include_intercept: If ``True``, then the resulting spline basis will span the intercept term (i.e., the constant function). If ``False`` (the default) then this will not be the case, which is useful for avoiding overspecification in models that include multiple spline terms and/or an intercept term. :arg lower_bound: The lower exterior knot location. :arg upper_bound: The upper exterior knot location. A spline with ``degree=0`` is piecewise constant with breakpoints at each knot, and the default knot positions are quantiles of the input. So if you find yourself in the situation of wanting to quantize a continuous variable into ``num_bins`` equal-sized bins with a constant effect across each bin, you can use ``bs(x, num_bins - 1, degree=0)``. (The ``- 1`` is because one degree of freedom will be taken by the intercept; alternatively, you could leave the intercept term out of your model and use ``bs(x, num_bins, degree=0, include_intercept=True)``. A spline with ``degree=1`` is piecewise linear with breakpoints at each knot. The default is ``degree=3``, which gives a cubic b-spline. This is a stateful transform (for details see :ref:`stateful-transforms`). If ``knots``, ``lower_bound``, or ``upper_bound`` are not specified, they will be calculated from the data and then the chosen values will be remembered and re-used for prediction from the fitted model. Using this function requires scipy be installed. .. note:: This function is very similar to the R function of the same name. In cases where both return output at all (e.g., R's ``bs`` will raise an error if ``degree=0``, while patsy's will not), they should produce identical output given identical input and parameter settings. .. warning:: I'm not sure on what the proper handling of points outside the lower/upper bounds is, so for now attempting to evaluate a spline basis at such points produces an error. 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