`ihSddlZdZdZ ddlmZdZn$#e$r ddlmZdZn#e$rdZdZYnwxYwYnwxYwdZdZGddZ e d d h e d hd  e d hd e dddh e dddh e dhdddge dhd e dddh e dhd e ddhdge dhd  e d!d!h e d"hd# g Z d$e DZ e d%e DZ ee Zd&Zd'Zd(Zd)Zd*Zd+Zd,Zd-Zd.Zd/Zd0Zd1Zd2Zd3Zd9d4Zd:dd5d6Z d;d8Z!dS)<NF)pairwise_distanceTc.tj||S)Ndtype)cupyascontiguousarray)Xout_types p/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/cupyx/scipy/spatial/distance.py_convert_to_typer s  !!8 4 4 44c |j}|j|vr |||jn|d}t||}|j}|r ||||fi|}|||fS)Nr)r )typesrindexr validator)r mn metric_infokwargsrtyp_validate_kwargss r _validate_pdist_inputrs  E)*E)9)9% AG$$ % %uQxCS)))A#,5!!!Q44V44 c6>r ceZdZ ddZdS) MetricInfoNc>||_||_||_||_dSN)canonical_name_aka_ validator_types_)selfcanonical_nameakarrs r __init__zMetricInfo.__init__'s#- # r )NNNN)__name__ __module__ __qualname__r$r r rr%s.04'+r rcanberra)r"r# chebyshev>chchebcheby chebychevr* cityblock>ccbcblockr/ correlationcocosinecoshamming>hhahammr7matchingdoublebool)r"r#r euclidean>eeueuclidr> jensenshannonjs minkowski>rmipnormrD russellrao sqeuclidean>sqesqeuclidrH hellinger kl_divergence>kldkl_divrLci|] }|j| Sr()r).0infos r rRjs A A A4D $ A A Ar c#2K|]}|jD]}||fV dSr)r)rPrQaliass r rUksQ--"&)--T]-------r cDtststddSdS)NzJcuVS >= 24.12 or pylibraft < 24.12 should be installed to use this feature)cuvs_availablepylibraft_available RuntimeErrorr(r r check_soft_dependenciesrZrsG P" P OPP PPP P Pr c"ttj|rdnd}tj|rdnd}||krtd|d|tjd|j|}t |||d||dS) aCompute the Minkowski distance between two 1-D arrays. Args: u (array_like): Input array of size (N,) v (array_like): Input array of size (N,) p (float): The order of the norm of the difference :math:`{\|u-v\|}_p`. Note that for :math:`0 < p < 1`, the triangle inequality only holds with an additional multiplicative factor, i.e. it is only a quasi-metric. Returns: minkowski (double): The Minkowski distance between vectors `u` and `v`. FC+u and v must have the same layout (u.order= , v.order=rarorderrDrrrZr isfortran ValueErroremptyrr)uvpu_orderv_order output_arrs r rDrDys^A&&/ccCG^A&&/ccCG'j6=ggwwHII IF!'AAAJaJ Q777 d r c ttj|rdnd}tj|rdnd}||krtd|d|tjd|j|}t |||d|dS) azCompute the Canberra distance between two 1-D arrays. The Canberra distance is defined as .. math:: d(u, v) = \sum_{i} \frac{| u_i - v_i |}{|u_i| + |v_i|} Args: u (array_like): Input array of size (N,) v (array_like): Input array of size (N,) Returns: canberra (double): The Canberra distance between vectors `u` and `v`. r\r]r^r_r`rbr)rdrerirjrlrmrns r r)r)s^A&&/ccCG^A&&/ccCG'j6=ggwwHII IF!'AAAJaJ 333 d r c ttj|rdnd}tj|rdnd}||krtd|d|tjd|j|}t |||d|dS) afCompute the Chebyshev distance between two 1-D arrays. The Chebyshev distance is defined as .. math:: d(u, v) = \max_{i} |u_i - v_i| Args: u (array_like): Input array of size (N,) v (array_like): Input array of size (N,) Returns: chebyshev (double): The Chebyshev distance between vectors `u` and `v`. r\r]r^r_r`rbr*rdrerps r r*r*^A&&/ccCG^A&&/ccCG'j6=ggwwHII IF!'AAAJaJ 444 d r c ttj|rdnd}tj|rdnd}||krtd|d|tjd|j|}t |||d|dS) a}Compute the City Block (Manhattan) distance between two 1-D arrays. The City Block distance is defined as .. math:: d(u, v) = \sum_{i} |u_i - v_i| Args: u (array_like): Input array of size (N,) v (array_like): Input array of size (N,) Returns: cityblock (double): The City Block distance between vectors `u` and `v`. r\r]r^r_r`rbr/rdrerps r r/r/s ^A&&/ccCG^A&&/ccCG'j6=ggwwHII IF!'AAAJaJ 444 d r c ttj|rdnd}tj|rdnd}||krtd|d|tjd|j|}t |||d|dS) a2Compute the correlation distance between two 1-D arrays. The correlation distance is defined as .. math:: d(u, v) = 1 - \frac{(u - \bar{u}) \cdot (v - \bar{v})}{ \|(u - \bar{u})\|_2 \|(v - \bar{v})\|_2} where :math:`\bar{u}` is the mean of the elements of :math:`u` and :math:`x \cdot y` is the dot product. Args: u (array_like): Input array of size (N,) v (array_like): Input array of size (N,) Returns: correlation (double): The correlation distance between vectors `u` and `v`. r\r]r^r_r`rbr3rdrerps r r3r3s(^A&&/ccCG^A&&/ccCG'j6=ggwwHII IF!'AAAJaJ 666 d r c ttj|rdnd}tj|rdnd}||krtd|d|tjd|j|}t |||d|dS) aCompute the Cosine distance between two 1-D arrays. The Cosine distance is defined as .. math:: d(u, v) = 1 - \frac{u \cdot v}{\|u\|_2 \|v\|_2} where :math:`x \cdot y` is the dot product. Args: u (array_like): Input array of size (N,) v (array_like): Input array of size (N,) Returns: cosine (double): The Cosine distance between vectors `u` and `v`. r\r]r^r_r`rbr5rdrerps r r5r5s"^A&&/ccCG^A&&/ccCG'j6=ggwwHII IF!'AAAJaJ111 d r c ttj|rdnd}tj|rdnd}||krtd|d|tjd|j|}t |||d|dS) a/Compute the Hamming distance between two 1-D arrays. The Hamming distance is defined as the proportion of elements in both `u` and `v` that are not in the exact same position: .. math:: d(u, v) = \frac{1}{n} \sum_{k=0}^n u_i \neq v_i where :math:`x \neq y` is one if :math:`x` is different from :math:`y` and zero otherwise. Args: u (array_like): Input array of size (N,) v (array_like): Input array of size (N,) Returns: hamming (double): The Hamming distance between vectors `u` and `v`. r\r]r^r_r`rbr7rdrerps r r7r7;s&^A&&/ccCG^A&&/ccCG'j6=ggwwHII IF!'AAAJaJ 222 d r c ttj|rdnd}tj|rdnd}||krtd|d|tjd|j|}t |||d|dS) aCompute the Euclidean distance between two 1-D arrays. The Euclidean distance is defined as .. math:: d(u, v) = \left(\sum_{i} (u_i - v_i)^2\right)^{\sfrac{1}{2}} Args: u (array_like): Input array of size (N,) v (array_like): Input array of size (N,) Returns: euclidean (double): The Euclidean distance between vectors `u` and `v`. r\r]r^r_r`rbr>rdrerps r r>r>^rrr c ttj|rdnd}tj|rdnd}||krtd|d|tjd|j|}t |||d|dS) aCompute the Jensen-Shannon distance between two 1-D arrays. The Jensen-Shannon distance is defined as .. math:: d(u, v) = \sqrt{\frac{KL(u \| m) + KL(v \| m)}{2}} where :math:`KL` is the Kullback-Leibler divergence and :math:`m` is the pointwise mean of `u` and `v`. Args: u (array_like): Input array of size (N,) v (array_like): Input array of size (N,) Returns: jensenshannon (double): The Jensen-Shannon distance between vectors `u` and `v`. r\r]r^r_r`rbrBrdrerps r rBrB}s&^A&&/ccCG^A&&/ccCG'j6=ggwwHII IF!'AAAJaJ888 d r c ttj|rdnd}tj|rdnd}||krtd|d|tjd|j|}t |||d|dS) a-Compute the Russell-Rao distance between two 1-D arrays. The Russell-Rao distance is defined as the proportion of elements in both `u` and `v` that are in the exact same position: .. math:: d(u, v) = \frac{1}{n} \sum_{k=0}^n u_i = v_i where :math:`x = y` is one if :math:`x` is different from :math:`y` and zero otherwise. Args: u (array_like): Input array of size (N,) v (array_like): Input array of size (N,) Returns: hamming (double): The Hamming distance between vectors `u` and `v`. r\r]r^r_r`rbrGrdrerps r rGrGs&^A&&/ccCG^A&&/ccCG'j6=ggwwHII IF!'AAAJaJ 555 d r c ttj|rdnd}tj|rdnd}||krtd|d|tjd|j|}t |||d|dS) aCompute the squared Euclidean distance between two 1-D arrays. The squared Euclidean distance is defined as .. math:: d(u, v) = \sum_{i} (u_i - v_i)^2 Args: u (array_like): Input array of size (N,) v (array_like): Input array of size (N,) Returns: sqeuclidean (double): The squared Euclidean distance between vectors `u` and `v`. r\r]r^r_r`rbrHrdrerps r rHrHs ^A&&/ccCG^A&&/ccCG'j6=ggwwHII IF!'AAAJaJ 666 d r c ttj|rdnd}tj|rdnd}||krtd|d|tjd|j|}t |||d|dS) aCompute the Hellinger distance between two 1-D arrays. The Hellinger distance is defined as .. math:: d(u, v) = \frac{1}{\sqrt{2}} \sqrt{ \sum_{i} (\sqrt{u_i} - \sqrt{v_i})^2} Args: u (array_like): Input array of size (N,) v (array_like): Input array of size (N,) Returns: hellinger (double): The Hellinger distance between vectors `u` and `v`. r\r]r^r_r`rbrKrdrerps r rKrKs"^A&&/ccCG^A&&/ccCG'j6=ggwwHII IF!'AAAJaJ 444 d r cpttj|}tj|}tj|rdnd}tj|rdnd}||krt d|d|tjd|j|}t|||d|dS) aCompute the Kullback-Leibler divergence between two 1-D arrays. The Kullback-Leibler divergence is defined as .. math:: KL(U \| V) = \sum_{i} U_i \log{\left(\frac{U_i}{V_i}\right)} Args: u (array_like): Input array of size (N,) v (array_like): Input array of size (N,) Returns: kl_divergence (double): The Kullback-Leibler divergence between vectors `u` and `v`. r\r]r^r_r`rbrLrd)rZrasarrayrfrgrhrrrps r rLrLs  QA QA^A&&/ccCG^A&&/ccCG'j6=ggwwHII IF!'AAAJaJ888 d r c ttstr|jdvrt j|d}tstr|jdvrt j|d}t j|rdnd}t j|rdnd}||krtd|d||j}|j}t|dkrtd t|dkrtd |d |d krtd |d } |d } d|vr|dnd} |tr |jdkstrP|jdvrGt j|rdnd} | |krtd| z| dd}|j| | fkrt j || | ft|trz|} t| d}|7||nt j| | f|j|}t%||||| |Std| zt'd)aCompute distance between each pair of the two collections of inputs. Args: XA (array_like): An :math:`m_A` by :math:`n` array of :math:`m_A` original observations in an :math:`n`-dimensional space. Inputs are converted to float type. XB (array_like): An :math:`m_B` by :math:`n` array of :math:`m_B` original observations in an :math:`n`-dimensional space. Inputs are converted to float type. metric (str, optional): The distance metric to use. The distance function can be 'canberra', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'euclidean', 'hamming', 'hellinger', 'jensenshannon', 'kl_divergence', 'matching', 'minkowski', 'russellrao', 'sqeuclidean'. out (cupy.ndarray, optional): The output array. If not None, the distance matrix Y is stored in this array. **kwargs (dict, optional): Extra arguments to `metric`: refer to each metric documentation for a list of all possible arguments. Some possible arguments: p (float): The p-norm to apply for Minkowski, weighted and unweighted. Default: 2.0 Returns: Y (cupy.ndarray): A :math:`m_A` by :math:`m_B` distance matrix is returned. For each :math:`i` and :math:`j`, the metric ``dist(u=XA[i], v=XB[j])`` is computed and stored in the :math:`ij` th entry. )float32float64rrr\r]z.XA and XB must have the same layout (XA.order=z , XB.order=z!XA must be a 2-dimensional array.z!XB must be a 2-dimensional array.razHXA and XB must have the same number of columns (i.e. feature dimension.)rrk@Nz1out must have same layout as input (out.order=%s)F)copyrbzUnknown Distance Metric: %sz/2nd argument metric must be a string identifier)rZrXrWrrr}rfrgshapelenastyperesize isinstancestrlower _METRIC_ALIASgetrhr TypeError)XAXBmetricoutrXA_orderXB_orderssBmAmBrk out_ordermstrrrns r cdistr&s:/ / "0F F F \"I . . ./ / "0F F F \"I . . .nR((1sscHnR((1sscH8j8@((LMM M A B 1vv{{<=== 2ww!||<===tr!u}}566 6 1B ABf}}s #A   4CI$:$:%;$'I5K$K$K#~c22;IH$$ "24=">???**YU*33C 9R Kb"X & & &&# K||~~#''d33  " #TZRFHhFN6P6P6PJ b"j&! < < < :TABB BIJJJr )rc ^t||f||d|}tj|d}||S)a[Compute distance between observations in n-dimensional space. Args: X (array_like): An :math:`m` by :math:`n` array of :math:`m` original observations in an :math:`n`-dimensional space. Inputs are converted to float type. metric (str, optional): The distance metric to use. The distance function can be 'canberra', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'euclidean', 'hamming', 'hellinger', 'jensenshannon', 'kl_divergence', 'matching', 'minkowski', 'russellrao', 'sqeuclidean'. out (cupy.ndarray, optional): The output array. If not None, the distance matrix Y is stored in this array. **kwargs (dict, optional): Extra arguments to `metric`: refer to each metric documentation for a list of all possible arguments. Some possible arguments: p (float): The p-norm to apply for Minkowski, weighted and unweighted. Default: 2.0 Returns: Y (cupy.ndarray): Returns a condensed distance matrix Y. For each :math:`i` and :math:`j` and (where :math:`i < j < m`), where m is the number of original observations. The metric ``dist(u=X[i], v=X[j])`` is computed and stored in entry ``m * i + j - ((i + 2) * (i + 1)) // 2``. )rrra)rrtriu_indices_from)r rrrall_distup_idxs r pdistr~s?8Q<&c<?WEFF F AkQ / / //r )r>N)r>)r)"rrWrX cuvs.distancer ImportErrorpylibraft.distancer rr _METRIC_INFOS_METRICSdictrlistkeys_METRICS_NAMESrZrDr)r*r/r3r5r7r>rBrGrHrKrLrrrr(r r rs=  $//////NN$$$$888888" $$$#$ $555   J! LJ" = = =J" . . .J$ D !J u J 6 6 6  J" . . .J& d #J" - - -J# Nh J$ . . .J" MJ& . . .g9 v B A= A A A--!.----- hmmoo&&PPP>>>@!!!HB   F>   F   F@BDUKUKUKUKpB000000s$4"4 .4.44