/DjIBdgZddlZddlmZmZmZddlmZerddl Z dZ dZ dZ Gdd e ZeeZd Zd e_d Zd Zy)bsN) have_pandas no_picklingassert_no_pickling)stateful_transformcL ddlm}tjtj |t }|jdk(sJ|jt|}tj|}|jdk(r|jddk(r |dddf}|jdk(sJtj|tj|ks+tj|tj|kDr tdt||dzz }tj|jd|ft }t!|D]0}tj"|f}d||<|||||f|dd|f<2|S#t$r tdwxYw)Nr)splevz#spline functionality requires scipy)dtypezksome data points fall outside the outermost knots, and I'm not sure how to handle them. (Patches accepted!))scipy.interpolater ImportErrornp atleast_1dasarrayfloatndimsortintshapeminmaxNotImplementedErrorlenemptyrangezeros)xknotsdegreer n_basesbasisicoefss QC:\Crop_Prediction\Backend\crop-ai-system\venv\Lib\site-packages\patsy/splines.py_eval_bspline_basisr&sjA+ MM"**U%8 9E ::? JJL [F aAvv{qwwqzQ adG 66Q;  vvay266%= BFF1Iu $=! 2  "%jFQJ'G HHaggaj'*% 8E 7^7'$aAuf56ad 7 LS A?@@As FF#c tj|}tj|jdDcgc]}tj|d|zc}}|j |j dScc}w)NC)orderd)rrravel percentilereshaper)rprobsprob quantiless r%_R_compat_quantiler1Bsh JJu E 27++C+2HI$q#* %II   U[[  44 Js A=cd}|ddgdd|ddgdd|ddgddgdd g|ttdddgd d gy) NcHtjt|||sJyN)rallcloser1)rr/expecteds r%tz"test__R_compat_quantile..tLs{{-a6AAA g?g333333? gffffff?g@g333333@)listr)r7s r%test__R_compat_quantiler?Ks^Br2hRr2hRr2hc RH%d59oSzC:.r8cHeZdZdZdZ ddZdZ ddZeZ y)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. Patches gratefully accepted. .. versionadded:: 0.2.0 c.i|_d|_d|_yr4)_tmp_degree _all_knots)selfs r%__init__z BS.__init__s  r8Nc<||||||d}||jd<tj|}|jdk(r|jddk(r |dddf}|jdkDr t d|jj dgj|y)N)dfrr include_intercept lower_bound upper_boundargsr r rz1input to 'bs' must be 1-d, or a 2-d column vectorxs)rCrrrr ValueError setdefaultappend) rFrrIrr rJrKrLrMs r%memorize_chunkzBS.memorize_chunks!2&&  ! & MM!  66Q;1771:?!Q$A 66A:PQ Q T2&--a0r8c |j}|d}|`|ddkrtd|ddt|d|dk7rtd|jdt j |d}|d|d  td |dd z}|d|d|z }|d s|d z }|dkr&td |dd|dd|d d|d|z |d ;t |d |k7rStd |dd|dd|dt |d d t jdd |dzd d}t||}|d |d }|d|d}nt j|}|d|d} nt j|} || kDrtd|d| dt j}|jd kDr tdt j||krtd|||kd|dt j|| kDrtd||| kDd| dt j || g|z|f} | j|d|_| |_y)NrMr rz#degree must be greater than 0 (not )zdegree must be an integer (not rNrIrzmust specify either df or knotsr rJzdf=z is too small for degree=z and include_intercept=z ; must be >= z with degree=z implies z knots, but z knots were providedr rKrLzlower_bound > upper_bound (z > zknots must be 1 dimensionalzsome knot values (z) fall below lower bound (z) fall above upper bound ()rCrOrrDr concatenaterlinspacer1rrrranyrrE) rFtmprMrr) n_inner_knotsknot_quantiles inner_knotsrKrL all_knotss r%memorize_finishzBS.memorize_finishsii6{ I >A <@NL  tH~ $x. 0T\\ST T NN3t9 % : $w-"7>? ?X" : ! J.M+," q  T X01T ]2   G}(tG}%6$!J N)W .   "$Q=13D!Ea!K0NC = $w-K   *}-K&&)K   *}-K&&)K  $9DkR jj-   a :; ; 66+ + ,%kK&?@+O  66+ + ,%kK&?@+O NN[+$>$F #TU H~ #r8ct||j|j}|s |ddddf}trPt |t j t jfr&t j|}|j|_|S)Nr ) r&rErDr isinstancepandasSeries DataFrameindex) rFrrIrr rJrKrLr"s r% transformz BS.transformsh$At E !QR%LE !fmmV-=-=>?((/gg  r8)NNFNN) __name__ __module__ __qualname____doc__rGrRr^rer __getstate__r8r%rArAUsM9v 1:R$n &Lr8rAclddlm}ddlm}m}m}|j d}d}|jd} ||dk(sn|dz }|jd|}|||}i} |D]} | j dd\} } | | | <t| d t| d t| d d } | d dk7rt| d \}}|| d<|| d<| ddk(| d<tjt| d}| d |jd| d k(sJ|td||fi| |dz }|dz}||k(sJy)Nr)check_stateful) R_bs_test_xR_bs_test_dataR_bs_num_tests z--BEGIN TEST CASE--r z--END TEST CASE--=r rIr)r rIrzBoundary.knotsNonerKrL interceptTRUErJoutputF)patsy.test_staternpatsy.test_splines_bs_datarorprqsplitrdrevalrrrrA)rnrorprqlines tests_ran start_idxstop_idxblock test_datalinekeyvaluekwargslowerupperrws r%test_bs_compatr s/VV   &EI 12I Y#88 Q ;;2I>i)  #DC+JC"IcN # )H-.y')G,-   % && 0 *: ;>&A, 33"J%&JQ1q5!" >>&A, 33"JJqAv >>&A, 33 >> 9QqcTB Q!Q!Q   AaVAFJqAqEJM >>*QU+] ;; ;r8c ddl}tjddd}|jtt |dd|jtt |dd|jt t |t |dddgd z t |dd dgd z t |dddgd zdt |dd dgdzd|jt t |dddgdz |jt t |dd dgd z |jt t |dddgdzd|jt t |dd dgd zd|jt t |dddgd z |jt t |dd dgdz |jt t |dddgdzd|jt t |dd dgd zd|jt t |dd|jt t |dd|jt t |dd|jt t |dd|jt t |ddd|jt t tj||fdtjt |ddgt |ddgsJ|jt t |dgdgg|jt t |ddg|jt t |ddgd|jt t |ddg|jt t |ddgdy)Nrir9r:rf)rK)rLF)rIrJrT r )rIrJrr )rIr rUg?)rKrLr)r)rrLi)rrK) pytestrrWraisesrrrO column_stackr)rrs r%test_bs_errorsrms CR A MM%r1aQM? MM%r1aQM? MM*b!$qR5a8qR4sQw7qR5aBqR4sQwqA MM*b!eA3QR7MS MM*b!d1#PQ'MR MMBbE!rRS MMBbDaPQ MM*b!eA3QR7MS MM*b!d1#PQ'MR MMBbE!qQR MMBbDaPQ MM*b!!M4 MM*b!!M4 MM*b!2M6 MM*b!3M7 MM*b!QA2MF MM*b"//1a&"91= >>"Qq!f-r!Aq6/B CC MM*b!QC";M7 MM*b!Ar7M3 MM*b!Aq6qMA MM*b!C8M4 MM*b!B7MCr8)__all__numpyr patsy.utilrrr patsy.staterrar&r1r?objectrArrslowrrrlr8r%rsf &CC*,^5/EEP*'Z <65Dr8