o
    tBh`                     @   s  d dl Zd dlmZ d dlmZ d dlZd dlmZ d dl	m
Z
 d dlmZ d dlmZmZ dd gd d	gd	d	gd	d	ggZeeZg d
Zg dZejdddgejdddgejdddgejdddgdd Zdd Zd5ddZdd  Zd!d" Zd#Zejd$d%d&ed'fgd(d) Zejd*ded	 gd+d, Zejd-ed.fd/gd0d1 Zejd2ded	 gd3d4 Z dS )6    N)sparse)stats)l1_min_c)	LinearSVC)LogisticRegressionset_seed_wrapbounded_rand_int_wrap   )r   r   r   r   )   r   r   r   losssquared_hingelogX_labelr   denseY_labeltwo-classesmulti-classintercept_labelno-interceptfit-interceptc           
      C   sX   t td}ttd}ddidddd}|| }|| }|| }	t||| fi |	 d S )	N)r   r   )r   r   fit_interceptFT
   )r   intercept_scaling)r   r   )sparse_Xdense_XY1Y2check_l1_min_c)
r   r   r   r   XsYs
interceptsXYintercept_params r&   t/var/www/html/riverr-enterprise-integrations-main/venv/lib/python3.10/site-packages/sklearn/svm/tests/test_bounds.pytest_l1_min_c   s   

r(   c                  C   sF   d} t jt| d tttdd W d    d S 1 sw   Y  d S )Nzloss type not in)matchl2r   pytestraises
ValueErrorr   r   r   )msgr&   r&   r'   test_l1_min_c_l2_loss&   s   "r1   Tc                 C   s   t | ||||d}tdddtddddd| }||_||_||_|| | t|j	d	k
 s4J t|jd	k
 s@J |d
 |_|| | t|j	d	k sat|jd	k scJ d S d S )N)r   r   r   l1	liblinear)penaltysolverr   F)r   r4   dual)r   r   r   g)\(?)r   r   r   r   r   Cfitnpasarraycoef_all
intercept_any)r#   yr   r   r   min_cclfr&   r&   r'   r   -   s*   
	
4r   c                  C   sR   ddgddgg} ddg}t t t| | W d    d S 1 s"w   Y  d S )Nr   r   )r-   r.   r/   r   )r#   r?   r&   r&   r'   test_ill_posed_min_cH   s
   "rB   c                   C   s>   t t tttdd W d    d S 1 sw   Y  d S )Nr2   r+   r,   r&   r&   r&   r'   test_unsupported_lossO   s   "rC   l    z	seed, val)NQ   )r   6   	   c                 C   s:   | durt |  td}||ksJ d| d| ddS )z3Test that `set_seed` produces deterministic resultsNd   z	Expected z	 but got z insteadr   )seedvalxr&   r&   r'   test_newrand_set_seedW   s   "rK   rH   c                 C   8   t t t|  W d   dS 1 sw   Y  dS )z=Test that `set_seed_wrap` is defined for unsigned 32bits intsN)r-   r.   OverflowErrorr   )rH   r&   r&   r'   test_newrand_set_seed_overflow`      
"rN   zrange_, n_ptsi'  )rG      c                    s   d}g }t jd d}t|D ]} fddt|D }t ||j}||j qt jddd}t ||j}	|	jdksFJ d|	j d	tj|d
d}
|
dksYJ d|
 ddS )z;Test that `bounded_rand_int` follows a uniform distributionrG   r   )locscalec                    s   g | ]}t  qS r&   )r	   ).0_range_r&   r'   
<listcomp>p   s    z1test_newrand_bounded_rand_int.<locals>.<listcomp>r   g?zNull hypothesis rejected: generated random numbers are not uniform. Details: the (meta) p-value of the test of uniform distribution of p-values is z which is not > 0.05r   )qzlNull hypothesis rejected: generated random numbers are not uniform. Details: lower 10th quantile p-value of z not > 0.05.N)	r   uniformrangekstestcdfappendpvaluer9   
percentile)rV   n_ptsn_iterks_pvalsuniform_distrT   sampleresuniform_p_vals_dist	res_pvalsmin_10pct_pvalr&   rU   r'   test_newrand_bounded_rand_intg   s(   
ri   rV   c                 C   rL   )zETest that `bounded_rand_int_wrap` is defined for unsigned 32bits intsN)r-   r.   rM   r	   rU   r&   r&   r'   $test_newrand_bounded_rand_int_limits   rO   rj   )TN)!numpyr9   scipyr   spr   r-   sklearn.svm._boundsr   sklearn.svmr   sklearn.linear_modelr   sklearn.svm._newrandr   r	   r   
csr_matrixr   r   r   markparametrizer(   r1   r   rB   rC   _MAX_UNSIGNED_INTrK   rN   ri   rj   r&   r&   r&   r'   <module>   s<    




$