Djv?ddlmZddlZddlZddlmZddl m Z gdZ dZ d Z ed d d fd Zed d fd ZdZdZddZdZdZddZdZy)) validationN)DTYPE) C_intgrt_vec) as_seriesc check_endog check_exogdiffdiff_inv is_iterablec t|tjr|Stjtj|fi|S)aCast as pandas Series. Cast an iterable to a Pandas Series object. Note that the index will simply be a positional ``arange`` and cannot be set in this function. Parameters ---------- x : array-like, shape=(n_samples,) The 1d array on which to compute the auto correlation. Examples -------- >>> as_series([1, 2, 3]) 0 1 1 2 2 3 dtype: int64 >>> as_series(as_series((1, 2, 3))) 0 1 1 2 2 3 dtype: int64 >>> import pandas as pd >>> as_series(pd.Series([4, 5, 6], index=['a', 'b', 'c'])) a 4 b 5 c 6 dtype: int64 Returns ------- s : pd.Series A pandas Series object. ) isinstancepdSeriesskval column_or_1d)xkwargss XC:\Crop_Prediction\Backend\crop-ai-system\venv\Lib\site-packages\pmdarima/utils/array.pyrrs7L!RYY 99U''* 5f 55c|syt|dk(r;|d}t|rtj|Stj|gStj|Dcgc]}t|r|n|gc}Scc}w)aImitates the ``c`` function from R. Since this whole library is aimed at re-creating in Python what R has already done so well, the ``c`` function was created to wrap ``numpy.concatenate`` and mimic the R functionality. Similar to R, this works with scalars, iterables, and any mix therein. Note that using the ``c`` function on multi-nested lists or iterables will fail! Examples -------- Using ``c`` with varargs will yield a single array: >>> c(1, 2, 3, 4) array([1, 2, 3, 4]) Using ``c`` with nested lists and scalars will also yield a single array: >>> c([1, 2], 4, c(5, 4)) array([1, 2, 4, 5, 4]) However, using ``c`` with multi-level lists will fail! >>> c([1, 2, 3], [[1, 2]]) # doctest: +SKIP ValueError: all the input arrays must have same number of dimensions References ---------- .. [1] https://stat.ethz.ch/R-manual/R-devel/library/base/html/c.html Nrr)lenrnpasarray concatenate)argselementas rr r EsyB  4yA~q' w ::g& &zz7)$$0 >>FA A1QC7F GGFs A>TFc6tj|d|||}tj|}|s|St|tj r|j }t|tjr!t j||j}|S)a+Wrapper for ``check_array`` and ``column_or_1d`` from sklearn Parameters ---------- y : array-like, shape=(n_samples,) The 1d endogenous array. dtype : string, type or None (default=np.float64) Data type of result. If None, the dtype of the input is preserved. If "numeric", dtype is preserved unless array.dtype is object. copy : bool, optional (default=False) Whether a forced copy will be triggered. If copy=False, a copy might still be triggered by a conversion. force_all_finite : bool, optional (default=False) Whether to raise an error on np.inf and np.nan in an array. The possibilities are: - True: Force all values of array to be finite. - False: accept both np.inf and np.nan in array. preserve_series : bool, optional Whether to preserve a ``pd.Series`` object. Will also attempt to squeeze a dataframe into a ``pd.Series``. Returns ------- y : np.ndarray or pd.Series, shape=(n_samples,) A 1d numpy ndarray F) ensure_2dforce_all_finitecopydtype)index) r check_arrayrrr DataFramesqueezerr&)yr%r$r#preserve_seriesendogs rr r sL    )   E   u %E  !R\\" IIK!RYY %qww/ Lrc~t|dr|jdk7r tdt|tj r`|r||j |}|rG|jtjjjr td|Stj|dt||S)aA wrapper for ``check_array`` for 2D arrays Parameters ---------- X : array-like, shape=(n_samples, n_features) The exogenous array. If a Pandas frame, a Pandas frame will be returned as well. Otherwise, a numpy array will be returned. dtype : string, type or None (default=np.float64) Data type of result. If None, the dtype of the input is preserved. If "numeric", dtype is preserved unless array.dtype is object. copy : bool, optional (default=True) Whether a forced copy will be triggered. If copy=False, a copy might still be triggered by a conversion. force_all_finite : bool, optional (default=True) Whether to raise an error on np.inf and np.nan in an array. The possibilities are: - True: Force all values of array to be finite. - False: accept both np.inf and np.nan in array. Returns ------- X : pd.DataFrame or np.ndarray, shape=(n_samples, n_features) Either a 2-d numpy array or pd.DataFrame ndimrz Must be a 2-d array or dataframez$Found non-finite values in dataframeT)r"r%r$r#)hasattrr. ValueErrorrrr(astypeapplyrisfiniteanyrr'r)Xr%r$r#s rr r s:q&affk;<<!R\\" E%A !''"++"6!6 ; ; = A A CCD D     )  rcT|jd}t||}||||d||z z S)Nrshapemin)rlagns r _diff_vectorr<s5  A a+C S!9q1S5z !!rcl|j\}}t||}|||ddf|d||z ddfz S)Nr7)rr:m_s r _diff_matrixr@sA 77DAq a+C S!VQY>> x = c(10, 4, 2, 9, 34) >>> diff(x, 1, 1) array([ -6., -2., 7., 25.], dtype=float32) Where ``lag=1`` and ``differences=2``: >>> x = c(10, 4, 2, 9, 34) >>> diff(x, 1, 2) array([ 4., 9., 18.], dtype=float32) Where ``lag=3`` and ``differences=1``: >>> x = c(10, 4, 2, 9, 34) >>> diff(x, 3, 1) array([ -1., 30.], dtype=float32) Where ``lag=6`` (larger than the array is) and ``differences=1``: >>> x = c(10, 4, 2, 9, 34) >>> diff(x, 6, 1) array([], dtype=float32) For a 2d array with ``lag=1`` and ``differences=1``: >>> import numpy as np >>> >>> x = np.arange(1, 10).reshape((3, 3)).T >>> diff(x, 1, 1) array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) Parameters ---------- x : array-like, shape=(n_samples, [n_features]) The array to difference. lag : int, optional (default=1) An integer > 0 indicating which lag to use. differences : int, optional (default=1) An integer > 0 indicating the order of the difference. Returns ------- res : np.ndarray, shape=(n_samples, [n_features]) The result of the differenced arrays. References ---------- .. [1] https://stat.ethz.ch/R-manual/R-devel/library/base/html/diff.html c3&K|] }|dk ywrN.0vs r zdiff..J -Q1q5 -3lag and differences must be positive (> 0) integersF)r"r%r$rr) r4r0rr'rr.r<r@ranger8)rr: differencesfunresis rr r sB -3 , --NOO !uEFA&&A+,$3x   rc |j\}}tj|||zz|ft}|dk\r|!tj||z|ft}n=t j |tddd}|j||z|fk7r t dt|D]%}t|dd|f|||dd|f|dd|f<'|S)NrRrFTr%r$r#r"z""xi" does not have the right shape) r8rrUrrr'rVrLrW)rr:rMrSr;r>r*rPs r_diff_inv_matrixrZ{s 77DAq !cK''+59AAv :3,a0>B""!& BxxC+-q11 !EFFq LA&qAw["QT(KAadG L Hrctj|tddd}td||fDr t d|j dk(rt ||||S|j dk(rt||||St d)a Inverse the difference of an array. A python implementation of the R ``diffinv`` function [1]. This computes the inverse of lag differences from an array given a ``lag`` and ``differencing`` term. If ``x`` is a vector of length :math:`n`, ``lag=1`` and ``differences=1``, then the computed result is equal to the cumulative sum plus left-padding of zeros equal to ``lag * differences``. Examples -------- Where ``lag=1`` and ``differences=1``: >>> x = c(10, 4, 2, 9, 34) >>> diff_inv(x, 1, 1) array([ 0., 10., 14., 16., 25., 59.]) Where ``lag=1`` and ``differences=2``: >>> x = c(10, 4, 2, 9, 34) >>> diff_inv(x, 1, 2) array([ 0., 0., 10., 24., 40., 65., 124.]) Where ``lag=3`` and ``differences=1``: >>> x = c(10, 4, 2, 9, 34) >>> diff_inv(x, 3, 1) array([ 0., 0., 0., 10., 4., 2., 19., 38.]) Where ``lag=6`` (larger than the array is) and ``differences=1``: >>> x = c(10, 4, 2, 9, 34) >>> diff_inv(x, 6, 1) array([ 0., 0., 0., 0., 0., 0., 10., 4., 2., 9., 34.]) For a 2d array with ``lag=1`` and ``differences=1``: >>> import numpy as np >>> >>> x = np.arange(1, 10).reshape((3, 3)).T >>> diff_inv(x, 1, 1) array([[ 0., 0., 0.], [ 1., 4., 7.], [ 3., 9., 15.], [ 6., 15., 24.]]) Parameters ---------- x : array-like, shape=(n_samples, [n_features]) The array to difference. lag : int, optional (default=1) An integer > 0 indicating which lag to use. differences : int, optional (default=1) An integer > 0 indicating the order of the difference. Returns ------- res : np.ndarray, shape=(n_samples, [n_features]) The result of the inverse of the difference arrays. References ---------- .. [1] https://stat.ethz.ch/R-manual/R-devel/library/stats/html/diffinv.html FrYc3&K|] }|dk ywrCrDrEs rrHzdiff_inv..rIrJrKrrz9only vector and matrix inverse differencing are supported)rr'rr4r0r.rWrZrTs rr r sJ     A -3 , --NOOvv{3 R88 13 R88 % &&rc<t|tryt|dS)aTest a variable for iterability. Determine whether an object ``x`` is iterable. In Python 2, this was as simple as checking for the ``__iter__`` attribute. However, in Python 3, strings became iterable. Therefore, this function checks for the ``__iter__`` attribute, returning True if present (except for strings, for which it will return False). Parameters ---------- x : str, iterable or object The object in question. Examples -------- Strings and other objects are not iterable: >>> x = "not me" >>> y = 123 >>> any(is_iterable(v) for v in (x, y)) False Tuples, lists and other structures with ``__iter__`` are: >>> x = ('a', 'tuple') >>> y = ['a', 'list'] >>> all(is_iterable(v) for v in (x, y)) True This even applies to numpy arrays: >>> import numpy as np >>> is_iterable(np.arange(10)) True Returns ------- isiter : bool True if iterable, else False. F__iter__)rstrr/)rs rrrsR!S 1j !!r)rr)rrN) sklearn.utilsrrnumpyrpandasrcompatr_arrayr__all__rr r r r<r@r rWrZr rrDrrrfsv.  (6VEHT   9xD4/d"(Od @ 4W&t+"r