U
    |e3                     @   s  d dl mZ d dlZd dlZd dlZd dlZd dlm	Z	m
Z
mZmZmZ d dlmZmZ d dlZd dlmZ eejd dZeejjd Zeejjd Zed	d
ddddgZejddd Zej dddddf Z!ej Z"e#ededfZ$ejddd Z%ej&ej'(ejddd ejddd ee"fejdddddf ejddd ej)ddd ej*ej*ejddd ejejej*ej*ej*ej*d	dddddd Z+ej&ej'(ejddd ejddd ee"fejdddddf ejddd ej)ddd ej*ej*ejddd ejejej*ej*ej*ej*d	dddddd Z,ej&dddej'jddd ej'jddd ej'jej'j*dddd Z-ej&dddddd Z.ej&dej'jej'jdddbd"d#Z/ej&dej'0e$ej'jej'jd$ddcd%d&Z1ej&dej'jej'jddddd'd(Z2ej&dej'jej'jddded)d*Z3ej&dd+dfd-d.Z4ej&dd+dgd/d0Z5ej&d1ej'6ej'j7ej'jdddd2ej'jej'j7ej'jdddd2ej'j7ej'j)ddd,d2gdej'jej'j8ej'j9d3dd4d5d6 Z:ej&d7ej'j7ej'jdddd2ej'j7ej'jdddd2ej'j7ej'jd8ddd2ej'j7ej'jdddd2ej'j7ej'jd8ddd2ej'j7ej'jdddd2ej'j7ej'j)ddd,d2gej'j*ej'j6d9dd:d;d< Z;ej&ddd=d>d? Z<ej&d@ej'j*idd:dAdB Z=dhdCdDZ>ej&dddEdFdG Z?dHdI Z@dJdK ZAe& dLdM ZBe& dNdO ZCej&ddPdQdR ZDdSdT ZEd ZFdZGd8ZHdUZIdVZJdWdX ZKdYdZ ZLej&dej)ddd ejej*d[d,d\d]d^ ZMej&dd@ejid,d_d`da ZNdS )i    )warnN)
sparse_mulsparse_diff
sparse_sumarr_intersectsparse_dot_product)tau_rand_intnorm)
namedtupleCg:0yE>   FlatTreehyperplanesoffsetschildrenindices	leaf_size)	n_leftn_righthyperplane_vectorhyperplane_offsetmargindi
left_indexright_indexT)localsfastmathnogilcachec                 C   s  | j d }t||j d  }t||j d  }|||k7 }||j d  }|| }|| }t| | }t| | }	t|tk rd}t|	tk rd}	tj|tjd}
t|D ](}| ||f | | ||f |	  |
|< qt|
}t|tk rd}t|D ]}|
| | |
|< qd}d}t|j d tj	}t|j d D ]}d}t|D ]"}||
| | || |f  7 }qBt|tk rt|d ||< || dkr|d7 }n|d7 }n,|dkrd||< |d7 }nd||< |d7 }q2|dks|dkr8d}d}t|j d D ]6}t|d ||< || dkr,|d7 }n|d7 }q tj|tj
d}tj|tj
d}d}d}t|j d D ]>}|| dkr|| ||< |d7 }n|| ||< |d7 }qn|||
dfS )aM  Given a set of ``graph_indices`` for graph_data points from ``graph_data``, create
    a random hyperplane to split the graph_data, returning two arrays graph_indices
    that fall on either side of the hyperplane. This is the basis for a
    random projection tree, which simply uses this splitting recursively.
    This particular split uses cosine distance to determine the hyperplane
    and which side each graph_data sample falls on.
    Parameters
    ----------
    data: array of shape (n_samples, n_features)
        The original graph_data to be split
    indices: array of shape (tree_node_size,)
        The graph_indices of the elements in the ``graph_data`` array that are to
        be split in the current operation.
    rng_state: array of int64, shape (3,)
        The internal state of the rng
    Returns
    -------
    indices_left: array
        The elements of ``graph_indices`` that fall on the "left" side of the
        random hyperplane.
    indices_right: array
        The elements of ``graph_indices`` that fall on the "left" side of the
        random hyperplane.
    r   r         ?dtype           )shaper   r	   absEPSnpemptyfloat32rangeint8int32)datar   	rng_statedimr   r   leftright	left_norm
right_normr   r   hyperplane_normr   r   sider   r   indices_leftindices_right r:   Q/var/www/website-v5/atlas_env/lib/python3.8/site-packages/pynndescent/rp_trees.pyangular_random_projection_split)   sv    ,

 





r<   c                 C   sp  | j d }t||j d  }t||j d  }|||k7 }||j d  }|| }|| }d}tj|tjd}	t|D ]H}
| ||
f | ||
f  |	|
< ||	|
 | ||
f | ||
f   d 8 }qtd}d}t|j d tj}t|j d D ]}|}t|D ] }
||	|
 | || |
f  7 }qt|tk r^tt|d ||< || dkrT|d7 }n|d7 }q|dkrzd||< |d7 }qd||< |d7 }q|dks|dkrd}d}t|j d D ]6}t|d ||< || dkr|d7 }n|d7 }qtj|tj	d}tj|tj	d}d}d}t|j d D ]>}|| dkrL|| ||< |d7 }n|| ||< |d7 }q$|||	|fS )aP  Given a set of ``graph_indices`` for graph_data points from ``graph_data``, create
    a random hyperplane to split the graph_data, returning two arrays graph_indices
    that fall on either side of the hyperplane. This is the basis for a
    random projection tree, which simply uses this splitting recursively.
    This particular split uses euclidean distance to determine the hyperplane
    and which side each graph_data sample falls on.
    Parameters
    ----------
    data: array of shape (n_samples, n_features)
        The original graph_data to be split
    indices: array of shape (tree_node_size,)
        The graph_indices of the elements in the ``graph_data`` array that are to
        be split in the current operation.
    rng_state: array of int64, shape (3,)
        The internal state of the rng
    Returns
    -------
    indices_left: array
        The elements of ``graph_indices`` that fall on the "left" side of the
        random hyperplane.
    indices_right: array
        The elements of ``graph_indices`` that fall on the "left" side of the
        random hyperplane.
    r   r   r$   r"          @r%   )
r&   r   r)   r*   r+   r,   r-   r'   r(   r.   )r/   r   r0   r1   r   r   r2   r3   r   r   r   r   r   r7   r   r   r8   r9   r:   r:   r;   !euclidean_random_projection_split   sd    ,
"






r>   )normalized_left_datanormalized_right_datar6   r   )r   r   r    r   c           "      C   sJ  t ||jd  }t ||jd  }|||k7 }||jd  }|| }|| }| || ||d   }	||| ||d   }
| || ||d   }||| ||d   }t|
}t|}t|tk rd}t|tk rd}|
| tj}|| tj}t|	|||\}}t|}t|tk r*d}t	|jd D ]}|| | ||< q8d}d}t
|jd tj}t	|jd D ]}d}| |||  ||| d   }||||  ||| d   }t||||\}}|D ]}||7 }qt|tk r(t |d ||< || dkr|d7 }n|d7 }n,|dkrDd||< |d7 }nd||< |d7 }qz|dksl|dkrd}d}t	|jd D ]6}t |d ||< || dkr|d7 }n|d7 }qtj
|tjd}tj
|tjd} d}d}t	|jd D ]>}|| dkr|| ||< |d7 }n|| | |< |d7 }qt||f}!|| |!dfS )  Given a set of ``graph_indices`` for graph_data points from a sparse graph_data set
    presented in csr sparse format as inds, graph_indptr and graph_data, create
    a random hyperplane to split the graph_data, returning two arrays graph_indices
    that fall on either side of the hyperplane. This is the basis for a
    random projection tree, which simply uses this splitting recursively.
    This particular split uses cosine distance to determine the hyperplane
    and which side each graph_data sample falls on.
    Parameters
    ----------
    inds: array
        CSR format index array of the matrix
    indptr: array
        CSR format index pointer array of the matrix
    data: array
        CSR format graph_data array of the matrix
    indices: array of shape (tree_node_size,)
        The graph_indices of the elements in the ``graph_data`` array that are to
        be split in the current operation.
    rng_state: array of int64, shape (3,)
        The internal state of the rng
    Returns
    -------
    indices_left: array
        The elements of ``graph_indices`` that fall on the "left" side of the
        random hyperplane.
    indices_right: array
        The elements of ``graph_indices`` that fall on the "left" side of the
        random hyperplane.
    r   r   r!   r$   r%   r"   )r   r&   r	   r'   r(   astyper)   r+   r   r,   r*   r-   r   r.   vstack)"indsindptrr/   r   r0   r   r   r2   r3   	left_inds	left_data
right_inds
right_datar4   r5   r?   r@   hyperplane_indshyperplane_datar6   r   r   r   r7   r   r   i_indsi_data_mul_datavalr8   r9   
hyperplaner:   r:   r;   &sparse_angular_random_projection_split%  s    *     





rR   )r   r   r    c                 C   s  t t||jd  }t t||jd  }|||k7 }||jd  }|| }|| }| || ||d   }	||| ||d   }
| || ||d   }||| ||d   }d}t|	|
||\}}t|	|
||\}}|d }t||||t j\}}|D ]}||8 }qd}d}t 	|jd t j
}t|jd D ]}|}| |||  ||| d   }||||  ||| d   }t||||\}}|D ]}||7 }qt|tk rtt|d ||< || dkr|d7 }n|d7 }n,|dkrd||< |d7 }nd||< |d7 }qB|dks8|dkrd}d}t|jd D ]:}tt|d ||< || dkr~|d7 }n|d7 }qNt j	|t jd}t j	|t jd}d}d}t|jd D ]>}|| dkr|| ||< |d7 }n|| ||< |d7 }qt ||f}||||fS )rA   r   r   r$   r=   r%   r"   )r)   r'   r   r&   r   r   r   rB   r+   r*   r-   r,   r(   r.   rC   )rD   rE   r/   r   r0   r   r   r2   r3   rF   rG   rH   rI   r   rJ   rK   offset_indsoffset_datarP   r   r   r7   r   r   rL   rM   rN   rO   r8   r9   rQ   r:   r:   r;   (sparse_euclidean_random_projection_split  s           
  





rU   )left_node_numright_node_num)r   r      d   c	                 C   s  |j d |kr|dkrt| ||\}	}
}}t| |	|||||||d 	 t|d }t| |
|||||||d 	 t|d }|| || |t|t|f |tjdgtjd nJ|tjdgtjd |tj	  |tdtdf || d S Nr   r   r   r"   g      )
r&   r>   make_euclidean_treelenappendr)   r.   arrayr+   infr/   r   r   r   r   point_indicesr0   r   	max_depthleft_indicesright_indicesrQ   offsetrV   rW   r:   r:   r;   r[   &  sP    



r[   )r   rV   rW   c	                 C   s  |j d |kr|dkrt| ||\}	}
}}t| |	|||||||d 	 t|d }t| |
|||||||d 	 t|d }|| || |t|t|f |tjdgtjd nJ|tjdgtjd |tj	  |tdtdf || d S rZ   )
r&   r<   make_angular_treer\   r]   r)   r.   r^   r+   r_   r`   r:   r:   r;   rf   f  sP    



rf   c                 C   s"  |j d |	kr|
dkrt| ||||\}}}}t| |||||||||	|
d  t|d }t| |||||||||	|
d  t|d }|| || |t|t|f |tjdgtjd nP|tjdgdggtjd |tj	  |tdtdf || d S rZ   )
r&   rU   make_sparse_euclidean_treer\   r]   r)   r.   r^   float64r_   rD   rE   r/   r   r   r   r   ra   r0   r   rb   rc   rd   rQ   re   rV   rW   r:   r:   r;   rg     sd        


rg   c                 C   s"  |j d |	kr|
dkrt| ||||\}}}}t| |||||||||	|
d  t|d }t| |||||||||	|
d  t|d }|| || |t|t|f |tjdgtjd nP|tjdgdggtjd |tj	  |tdtdf || d S rZ   )
r&   rR   make_sparse_angular_treer\   r]   r)   r.   r^   rh   r_   ri   r:   r:   r;   rj     sb        

rj   )r   Fc           
   	   C   s   t | jd t j}tjjt	}tjjt
}tjjt}tjjt}|rlt| ||||||| nt| ||||||| t|||||}	|	S )Nr   )r)   aranger&   rB   r.   numbatypedList
empty_listdense_hyperplane_typeoffset_typechildren_typepoint_indices_typerf   r[   r   )
r/   r0   r   angularr   r   r   r   ra   resultr:   r:   r;   make_dense_tree8  s8    rv   c                 C   s   t |jd d t j}tjjt	}tjjt
}tjjt}	tjjt}
|rtt| ||||||	|
||
 nt| ||||||	|
||
 t|||	|
|S Nr   r   )r)   rk   r&   rB   r.   rl   rm   rn   ro   sparse_hyperplane_typerq   rr   rs   rj   rg   r   )rD   rE   Zspdatar0   r   rt   r   r   r   r   ra   r:   r:   r;   make_sparse_tree\  s>    ry   zb1(f4[::1],f4,f4[::1],i8[::1]))readonly)r   r1   r   )r   r   r    c                 C   st   |}|j d }t|D ]}|| | ||  7 }qt|tk r`tt|d }|dkrZdS dS n|dkrldS dS d S )Nr   r%   r   )r&   r,   r'   r(   r)   r   )rQ   re   pointr0   r   r1   r   r7   r:   r:   r;   select_side  s    
r|   z<i4[::1](f4[::1],f4[:,::1],f4[::1],i4[:,::1],i4[::1],i8[::1])r%   )noder7   )r   r    c                 C   sn   d}||df dkrNt || || | |}|dkr@||df }q||df }q|||df  ||df   S rw   )r|   )r{   r   r   r   r   r0   r}   r7   r:   r:   r;   search_flat_tree  s    r~   )r   r    c           
      C   s   |}| j d }| d|d f dk r,|d8 }q| dd |f tj}| dd |f }|t||||7 }t|tk rt|d }	|	dkrdS dS n|dkrdS dS d S )Nr   r   r$   r%   )r&   rB   r)   r.   r   r'   r(   r   )
rQ   re   
point_inds
point_datar0   r   Zhyperplane_sizerJ   rK   r7   r:   r:   r;   sparse_select_side  s(    

   r   r}   c           	      C   sp   d}||df dkrPt || || | ||}|dkrB||df }q||df }q|||df  ||df   S rw   )r   )	r   r   r   r   r   r   r0   r}   r7   r:   r:   r;   search_sparse_flat_tree  s        r   c           	   
      s   g }dkrt dt||dkr(d}|jtt|dfdtjzftj	
r~tj|dd fdd	t|D }n*tj|dd fd
d	t|D }W n" tttfk
r   td Y nX t|S )zBuild a random projection forest with ``n_trees``.

    Parameters
    ----------
    data
    n_neighbors
    n_trees
    leaf_size
    rng_state
    angular

    Returns
    -------
    forest: list
        A list of random projection trees.
    N
   r      )size	sharedmemn_jobsrequirec                 3   s0   | ](}t tjjj|  V  qd S N)joblibdelayedry   r   rE   r/   .0r   rt   r/   r   Z
rng_statesr:   r;   	<genexpr>  s   	zmake_forest.<locals>.<genexpr>c                 3   s&   | ]}t t|  V  qd S r   )r   r   rv   r   r   r:   r;   r      s   zRandom Projection forest initialisation failed due to recursionlimit being reached. Something is a little strange with your graph_data, and this may take longer than normal to compute.)maxr)   r.   randint	INT32_MIN	INT32_MAXrB   int64scipysparseisspmatrix_csrr   Parallelr,   RuntimeErrorRecursionErrorSystemErrorr   tuple)	r/   n_neighborsn_treesr   r0   random_stater   rt   ru   r:   r   r;   make_forest  s*    	

r   )r   r    c                 C   s   d}t t| jD ]0}| j| d dkr| j| d dkr|d7 }qtj|| jfdtjd}d}t t| jD ]V}| j| d dks| j| d dkrn| j| jd }| j| ||d |f< |d7 }qn|S )Nr   r   r   r"   )	r,   r\   r   r)   fullr   r.   r   r&   )treen_leavesr   ru   Z
leaf_indexr   r:   r:   r;   get_leaves_from_tree.  s    $
$
r   c                 C   s    t jddddd | D }|S )Nr   r   r   c                 s   s   | ]}t t|V  qd S r   )r   r   r   )r   Zrp_treer:   r:   r;   r   A  s    z-rptree_leaf_array_parallel.<locals>.<genexpr>)r   r   )	rp_forestru   r:   r:   r;   rptree_leaf_array_parallel@  s    r   c                 C   s,   t | dkrtt| S tdggS d S )Nr   r   )r\   r)   rC   r   r^   )r   r:   r:   r;   rptree_leaf_arrayG  s    r   c           
   
   C   s   | j | d dk rZ|t| j|  }| ||df< | ||df< | j| |||< ||fS | j| ||< | j| ||< |d ||df< |}	t| |||||d || j | d \}}|d ||	df< t| |||||d || j | d \}}||fS d S rw   )r   r\   r   r   r   recursive_convert
r   r   r   r   r   Znode_numZ
leaf_startZ	tree_nodeZleaf_endZold_node_numr:   r:   r;   r   N  s@    

r   c           
   
   C   s  | j | d dk rZ|t| j|  }| ||df< | ||df< | j| |||< ||fS | j| ||d d d | j| jd f< | j| ||< |d ||df< |}	t| |||||d || j | d \}}|d ||	df< t| |||||d || j | d \}}||fS d S rw   )r   r\   r   r   r&   r   recursive_convert_sparser   r:   r:   r;   r   u  sJ    

r   )r    c                 C   sP   d}d}t t| jD ]0}| j| d dk r>|d7 }|d7 }q|d7 }q||fS rw   )r,   r\   r   )r   n_nodesr   r   r:   r:   r;   num_nodes_and_leaves  s    

r   c              
   C   s  t | \}}d}| jd jdkr:|}tj||ftjd}n4d}|}tj|d|ftjd}d|d d dd d f< tj|tjd}tdtj|dftjd }	tdtj|tjd }
|rt| |||	|
ddt	| j
d  n t| |||	|
ddt	| j
d  t|||	|
| jS )NFr   r   r"   Tr%   r   )r   r   ndimr)   zerosr+   r.   onesr   r\   r   r   r   r   )r   	data_sizeZdata_dimr   r   	is_sparseZhyperplane_dimr   r   r   r   r:   r:   r;   convert_tree_format  sD                  r   r      c                 C   s   | j | j| j| j| jf}|S r   )r   r   r   r   r   r   ru   r:   r:   r;   denumbaify_tree  s    r   c                 C   s(   t | t | t | t | t | t }|S r   )r   FLAT_TREE_HYPERPLANESFLAT_TREE_OFFSETSFLAT_TREE_CHILDRENFLAT_TREE_INDICESFLAT_TREE_LEAF_SIZEr   r:   r:   r;   renumbaify_tree  s    r   )intersectionru   r   )parallelr   r    c                 C   sr   d}t |jd D ]H}t|| | j| j| j| j|}t|| |}|t 	|jd dk7 }q|t 	|jd  S )Nr$   r   r   )
rl   pranger&   r~   r   r   r   r   r   r+   )r   neighbor_indicesr/   r0   ru   r   Zleaf_indicesr   r:   r:   r;   
score_tree  s    
r   )r   r   r    c                 C   s   d}t | j}t|D ]}t|}| j| d }| j| d }|dkr|dkrt| j| jd D ]>}| j| | }	t||	 | j| }
|t|
jd dk7 }qdq|t|jd  S )Nr$   r   r   r   )	r\   r   r,   rl   r.   r   r&   r   r+   )r   r   ru   r   r   r}   
left_childright_childjidxr   r:   r:   r;   score_linked_tree  s    

r   )rX   rY   )rX   rY   )rX   rY   )rX   rY   )rX   F)rX   F)NF)Owarningsr   localenumpyr)   rl   scipy.sparser   pynndescent.sparser   r   r   r   r   pynndescent.utilsr   r	   r   collectionsr
   	setlocale
LC_NUMERICr(   iinfor.   minr   r   r   r   r+   rp   rh   rx   rq   typeofrr   rs   njittypesTupler   uint32r<   r>   rR   rU   r[   ListTyperf   rg   rj   rv   ry   booleanArrayintpuint16r|   r~   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r:   r:   r:   r;   <module>   sn   "  
r"  
d

v  <
  <  D  B
#
&	



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