`iPddlZddlZddlZddlZddlmZeZejddddZejddd d Z d Z d Z d Z ddZ ddZddZejddddZejddddZddZddZdS) N)_corezS x, raw T bins, int32 n_binszraw U yaD if (x < bins[0] or bins[n_bins - 1] < x) { return; } int high = n_bins - 1; int low = 0; while (high - low > 1) { int mid = (high + low) / 2; if (bins[mid] <= x) { low = mid; } else { high = mid; } } atomicAdd(&y[low], U(1)); cupy_histogram_kernelz,S x, raw T bins, int32 n_bins, raw W weightszraw Y yaM if (x < bins[0] or bins[n_bins - 1] < x) { return; } int high = n_bins - 1; int low = 0; while (high - low > 1) { int mid = (high + low) / 2; if (bins[mid] <= x) { low = mid; } else { high = mid; } } atomicAdd(&y[low], (Y)weights[i]); cupy_weighted_histogram_kernelc|jtjkr^tjd|jtjtd|tj}|\t|tj std|j |j krtd| }| }||fS)z: Check a and weights have matching shapes, and ravel both z1Converting input from {} to {} for compatibility.) stacklevelNzweights must be a cupy.ndarrayz(weights should have the same shape as a.)dtypecupybool_warningswarnformatuint8RuntimeWarningastype isinstancendarray ValueErrorshaperavel)aweightss n/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/cupy/_statistics/histogram.py_ravel_and_check_weightsr=s w$* Ivagtz22$ 4 4 4 4 HHTZ '4<00 ?=>> > =AG # #:<< <--//  A g:c4|f|\}}||krtdtj|rtj|s#td||n|jdkrd}d}nt |}t |}tj|rtj|s#td||||kr |dz }|dz}||fS) z^ Determine the outer bin edges to use, from either the data or the range argument Nz/max must be larger than min in range parameter.z(supplied range of [{}, {}] is not finitergg?z,autodetected range of [{}, {}] is not finiteg?) rnumpyisfinitersizefloatminmaxr )rrange first_edge last_edges r_get_outer_edgesr&Rs@   % I  ! !ACC Cz** ,u~i/H/H ,:AA ++,, , , 1  15577^^ !%%''NN  j)) ,dmI.F.F ,>EE ++,, , Y#% O y  rcd}d}t|trtdt|tjst j|dkrt|tjr|}nt j|}|dd|ddkrtdt|t jrtj|}nt j|dkr\ tj |}n#t$rtdwxYw|dkrtdt||\}}ntd |ktj|||}tj|tjrtj|t"}tj|||dzd | }|S) a5 Computes the bins used internally by `histogram`. Args: a (ndarray): Ravelled data array bins (int or ndarray): Forwarded argument from `histogram`. range (None or tuple): Forwarded argument from `histogram`. Returns: bin_edges (ndarray): Array of bin edges Nz+only integer and array bins are implementedz1`bins` must increase monotonically, when an arrayrz0`bins` must be an integer, a string, or an arrayz(`bins` must be positive, when an integerz `bins` must be 1d, when an arrayT)endpointr )rstrNotImplementedErrorr rrndimasarrayanyroperatorindex TypeErrorr& result_type issubdtypeintegerr linspace)rbinsr# n_equal_bins bin_edgesr$r%bin_types r_get_bin_edgesr;ssLI$=! 9;; ; D$, ' '=5:d+;+;q+@+@ dDL ) ) ,II d++I crcNYqrr] * / / 1 1 ECEE E i / / 0 Y//I D  Q   D#>$//LL D D DBDD D D !  GHH H 0E : : II;<<<#J 1== ?8T\ 2 2 9'%88HM  A-T_EEEA &9inglAF D D D &9inglAF D D D D}!U**Jy~1===Jy~1FFF &9ingq : : :+ Z ),,dl ; ;2v** i<rcttjr,jdkrddtjfj\}}n tjdj\}}tj|t}|dgz|dgz}|tj |} t|} | |krtdn#t$r ||gz}YnwxYw|d|z}n"t||krtdt|D]} tj|| dkr|| dkr"td | t!dd| f|| \} } t|| dz} tj| | | | <ntj|| dkrt|| tjstd || | <| dd| ddkr"td | n"td | t| dz|| <tj| || <t)fd t|D}t|D]4} dd| f| dk}|| |xxdzcc<5tj||}tj||tj|}||}|t4}|t7ddfz}||}|r]|}t|D]4} dg|z}|| dz || <||| |z }5||z}t%|jtj |dz krt;d|fS)aCompute the multidimensional histogram of some data. Args: sample (cupy.ndarray): The data to be histogrammed. (N, D) or (D, N) array Note the unusual interpretation of sample when an array_like: * When an array, each row is a coordinate in a D-dimensional space - such as ``histogramdd(cupy.array([p1, p2, p3]))``. * When an array_like, each element is the list of values for single coordinate - such as ``histogramdd((X, Y, Z))``. The first form should be preferred. bins (int or tuple of int or cupy.ndarray): The bin specification: * A sequence of arrays describing the monotonically increasing bin edges along each dimension. * The number of bins for each dimension (nx, ny, ... =bins) * The number of bins for all dimensions (nx=ny=...=bins). range (sequence, optional): A sequence of length D, each an optional (lower, upper) tuple giving the outer bin edges to be used if the edges are not given explicitly in `bins`. An entry of None in the sequence results in the minimum and maximum values being used for the corresponding dimension. The default, None, is equivalent to passing a tuple of D None values. weights (cupy.ndarray): An array of values `w_i` weighing each sample `(x_i, y_i, z_i, ...)`. The values of the returned histogram are equal to the sum of the weights belonging to the samples falling into each bin. density (bool, optional): If False, the default, returns the number of samples in each bin. If True, returns the probability *density* function at the bin, ``bin_count / sample_count / bin_volume``. Returns: tuple: H (cupy.ndarray): The multidimensional histogram of sample x. See normed and weights for the different possible semantics. edges (list of cupy.ndarray): A list of D arrays describing the bin edges for each dimension. .. warning:: This function may synchronize the device. .. seealso:: :func:`numpy.histogramdd` r(Nr))axiszFThe dimension of bins must be equal to the dimension of the sample x.)Nz0range argument must have one entry per dimensionrz,`bins[{}]` must be positive, when an integerzarray-like bins not supportedz:`bins[{}]` must be monotonically increasing, when an arrayz'`bins[{}]` must be a scalar or 1d arrayc3fK|]+}tj|dd|fdV,dS)Nright)side)r searchsorted).0iedgessamples r zhistogramdd..ks[  %(F111a4Lw???r) minlengthzInternal Shape Error)rr rr-newaxisrstackremptyrKr.lenrr2_rangerr&r6r/rMtupleravel_multi_indexbincountprodreshaperr slicerN RuntimeError)r^r7r#rrPnsamplesr-nbindedgesnbinsr\sminsmaxnumncounton_edgexyhistcoresrr]s` @r histogramddr{sd&$,''& ;!  AAAt|O,F$$F,,,$ ;tS ! !D D6ME TF]F,w'' D  D==   tf}  }$ Ut  KLLLD\\(( 9T!W   " "Aw{{ BII!LL*&A,aAAJD$d1gk""C}T455E!HH YtAw  1 $ $d1gt|44 B !@AAAAwE!Ha" a ,1133  "F1II  9@@CC eAh--!#QIeAh''q FD\\  A,%(2,.q 'a   - -B =W 40@0@ A A AD <<  D ;;u  D 5B<> > tx{{  a D4## Jwej 1 1 1 A Jwem 4 4 4$Q333 Hrc|jjdkrtd|jdkrt d|jdkrt d|rdnd}t jj|||dd S) a~Finds the indices of the bins to which each value in input array belongs. .. note:: In order to avoid device synchronization, digitize does not raise an exception when the array is not monotonic Args: x (cupy.ndarray): Input array. bins (cupy.ndarray): Array of bins. It has to be 1-dimensional and monotonic increasing or decreasing. right (bool): Indicates whether the intervals include the right or the left bin edge. Returns: cupy.ndarray: Output array of indices, of same shape as ``x``. .. seealso:: :func:`numpy.digitize` r>zx may not be complexr(rrleftrXNF) r rAr2r-rr _sortingsearch _searchsorted)rOr7rXrYs rdigitizers, w|s./// y1}}<=== y1}}FGGG '66D =  - -dAtT5 I IIr)r<NNF)r<NNN)NN)F)r0r rr rr#rfElementwiseKernelrDrHrr&r;rTr{rrrrirrrrs  ,E+# ").5U42 "%)&&.*!!!B888vLLLL^ZZZZz8$8$8$8$v+5* ; 7u6 & ( ( 1 1 1 1 h J J J J J Jr