U %|eK@sddlmZddlZddlmZmZ m Z ddl m Z ddZdd Zd d Zd d Zd4ddZddddZddZddZddZddZddZddZd d!Zd"d#Zd$d%Zd&d'Zd(d)Zd5d*d+Zd6d,d-Z d.d/Z!d0d1Z"d2d3Z#dS)7N)csr_mean_variance_axis0csc_mean_variance_axis0incr_mean_variance_axis0)_check_sample_weightcCs,t|r|jnt|}d|}t|dS)z2Raises a TypeError if X is not a CSR or CSC matrixz,Expected a CSR or CSC sparse matrix, got %s.N)spissparseformattype TypeError)X input_typeerrrV/var/www/website-v5/atlas_env/lib/python3.8/site-packages/sklearn/utils/sparsefuncs.py_raise_typeerrorsrcCs|dkrtd|dS)N)rrz8Unknown axis value: %d. Use 0 for rows, or 1 for columns) ValueErroraxisrrr_raise_error_wrong_axissrcCs6|jd|jdkst|j|j|jdd9_dS)a Inplace column scaling of a CSR matrix. Scale each feature of the data matrix by multiplying with specific scale provided by the caller assuming a (n_samples, n_features) shape. Parameters ---------- X : sparse matrix of shape (n_samples, n_features) Matrix to normalize using the variance of the features. It should be of CSR format. scale : ndarray of shape (n_features,), dtype={np.float32, np.float64} Array of precomputed feature-wise values to use for scaling. rrclip)modeN)shapeAssertionErrordatatakeindicesr scalerrrinplace_csr_column_scalesr cCs:|jd|jdkst|jt|t|j9_dS)aInplace row scaling of a CSR matrix. Scale each sample of the data matrix by multiplying with specific scale provided by the caller assuming a (n_samples, n_features) shape. Parameters ---------- X : sparse matrix of shape (n_samples, n_features) Matrix to be scaled. It should be of CSR format. scale : ndarray of float of shape (n_samples,) Array of precomputed sample-wise values to use for scaling. rN)rrrnprepeatdiffindptrrrrrinplace_csr_row_scale2sr%FcCs|t|t|tjr<|dkr*t|||dSt|j||dSnt|tjrt|j|n t|tjr2t||nt|dS)aInplace column scaling of a CSC/CSR matrix. Scale each feature of the data matrix by multiplying with specific scale provided by the caller assuming a (n_samples, n_features) shape. Parameters ---------- X : sparse matrix of shape (n_samples, n_features) Matrix to normalize using the variance of the features. It should be of CSC or CSR format. scale : ndarray of shape (n_features,), dtype={np.float32, np.float64} Array of precomputed feature-wise values to use for scaling. N)r(rr-r%r,r)r rrrrrinplace_column_scales    r7cCs>t|tjrt|j|n t|tjr2t||nt|dS)aInplace row scaling of a CSR or CSC matrix. Scale each row of the data matrix by multiplying with specific scale provided by the caller assuming a (n_samples, n_features) shape. Parameters ---------- X : sparse matrix of shape (n_samples, n_features) Matrix to be scaled. It should be of CSR or CSC format. scale : ndarray of shape (n_features,), dtype={np.float32, np.float64} Array of precomputed sample-wise values to use for scaling. N)r(rr-r r,r)r%rrrrrinplace_row_scales    r8cCsv||fD]}t|tjrtdq|dkr8||jd7}|dkrN||jd7}|j|k}||j|j|k<||j|<dS)aKSwap two rows of a CSC matrix in-place. Parameters ---------- X : sparse matrix of shape (n_samples, n_features) Matrix whose two rows are to be swapped. It should be of CSC format. m : int Index of the row of X to be swapped. n : int Index of the row of X to be swapped. m and n should be valid integersrN)r(r!ndarrayr rr)r mntZm_maskrrrinplace_swap_row_csc s    r>c Csx||fD]}t|tjrtdq|dkr8||jd7}|dkrN||jd7}||kr`||}}|j}||}||d}||}||d}||} ||} | | kr|j|d|| | 7<|| |j|d<|| |j|<t|jd||j|||j|||j|||j|dg|_t|jd||j|||j|||j|||j|dg|_dS)aKSwap two rows of a CSR matrix in-place. Parameters ---------- X : sparse matrix of shape (n_samples, n_features) Matrix whose two rows are to be swapped. It should be of CSR format. m : int Index of the row of X to be swapped. n : int Index of the row of X to be swapped. r9rrrN) r(r!r:r rr$ concatenaterr) r r;r<r=r$m_startZm_stopn_startZn_stopZnz_mZnz_nrrrinplace_swap_row_csr(sH                 rBcCs@t|tjrt|||n"t|tjr4t|||nt|dS)a[ Swap two rows of a CSC/CSR matrix in-place. Parameters ---------- X : sparse matrix of shape (n_samples, n_features) Matrix whose two rows are to be swapped. It should be of CSR or CSC format. m : int Index of the row of X to be swapped. n : int Index of the row of X to be swapped. N)r(rr-r>r)rBrr r;r<rrrinplace_swap_rowgs   rDcCsl|dkr||jd7}|dkr,||jd7}t|tjrFt|||n"t|tjr`t|||nt|dS)ag Swap two columns of a CSC/CSR matrix in-place. Parameters ---------- X : sparse matrix of shape (n_samples, n_features) Matrix whose two columns are to be swapped. It should be of CSR or CSC format. m : int Index of the column of X to be swapped. n : int Index of the column of X to be swapped. rrN)rr(rr-rBr)r>rrCrrrinplace_swap_columns  rEcCsLtt|j}t||j|j|jf|jd}||j|j|}||fS)N)r) r! flatnonzeror#r$r rrrreduceat)r ufunc major_indexvaluerrr _minor_reducesrKc Cs|j|}|dkrtd|jd|}|dkr8|n|}|t||\}}t|j||k}|||d||<|dk} t | |}t | |}|dkrt j |t t ||ff|jd|fd} n(t j ||t t |ff|j|dfd} | jS)Nr&zero-size array to reduction operationr)r/r)rrtocsctocsrsum_duplicatesrKr!r#r$compressr coo_matrixzeroslenr/Aravel) r r min_or_maxNMmatrIrJnot_fullmaskresrrr_min_or_max_axiss0   r]cCs|dkrdd|jkrtd|jd}|jdkr4|S||j}|jt |jkr`|||}|S|dkrt|d7}|dks|dkrt |||StddS)NrrLrrz.invalid axis, use 0 for rows, or 1 for columns) rrr/r nnzreducerrUr!productr])r rrVzeror;rrr_sparse_min_or_maxs     rbcCst||tjt||tjfSN)rbr!minimummaximumr rrrr_sparse_min_maxs  rgcCst||tjt||tjfSrc)rbr!fminfmaxrfrrr_sparse_nan_min_maxsrjcCs<t|tjtjfr0|r"t||dSt||dSnt|dS)aCompute minimium and maximum along an axis on a CSR or CSC matrix. Optionally ignore NaN values. Parameters ---------- X : sparse matrix of shape (n_samples, n_features) Input data. It should be of CSR or CSC format. axis : {0, 1} Axis along which the axis should be computed. ignore_nan : bool, default=False Ignore or passing through NaN values. .. versionadded:: 0.20 Returns ------- mins : ndarray of shape (n_features,), dtype={np.float32, np.float64} Feature-wise minima. maxs : ndarray of shape (n_features,), dtype={np.float32, np.float64} Feature-wise maxima. rN)r(rr)r-rjrgr)r rZ ignore_nanrrr min_max_axiss  rkcCs|dkrd}n(|dkrd}n|jdkr6td|j|dkrb|dkrL|jStt|j|Sn|dkrt|j}|dkr|dS||S|dkr|dkrtj|j |j dd St |t|j}tj|j |j d|d Snt d |dS) aA variant of X.getnnz() with extension to weighting on axis 0. Useful in efficiently calculating multilabel metrics. Parameters ---------- X : sparse matrix of shape (n_samples, n_labels) Input data. It should be of CSR format. axis : {0, 1}, default=None The axis on which the data is aggregated. sample_weight : array-like of shape (n_samples,), default=None Weight for each row of X. Returns ------- nnz : int, float, ndarray of shape (n_samples,) or ndarray of shape (n_features,) Number of non-zero values in the array along a given axis. Otherwise, the total number of non-zero values in the array is returned. rrcsrz#Expected CSR sparse format, got {0}Nintp) minlength)rpr&zUnsupported axis: {0}) r r r^r!dotr#r$astypebincountrrr"r)r r sample_weightoutr&rrr count_nonzeros*   rvcCspt||}|stjSt|dk}t|d\}}||rLt||||St|d|||t||||dS)zCompute the median of data with n_zeros additional zeros. This function is used to support sparse matrices; it modifies data in-place. rrrg@)rSr!nanrvdivmodsort_get_elem_at_rank)rn_zerosZn_elems n_negativemiddleZis_oddrrr _get_median7s  r~cCs,||kr||S|||kr dS|||S)z@Find the value in data augmented with n_zeros for the given rankrr)rankrr|r{rrrrzMs  rzc Cst|tjstd|j|j}|j\}}t|}t t |dd|ddD]8\}\}}t |j ||}||j } t|| ||<qR|S)aCFind the median across axis 0 of a CSC matrix. It is equivalent to doing np.median(X, axis=0). Parameters ---------- X : sparse matrix of shape (n_samples, n_features) Input data. It should be of CSC format. Returns ------- median : ndarray of shape (n_features,) Median. z%Expected matrix of CSC format, got %sNrlr)r(rr-r r r$rr!rR enumeratezipcopyrr3r~) r r$ n_samples n_featuresmedianZf_indstartendrnzrrrcsc_median_axis_0Vs   * r)NF)F)NN)$ scipy.sparsesparsernumpyr!sparsefuncs_fastrr*rr+rr5utils.validationrrrr r%r.r6r7r8r>rBrDrErKr]rbrgrjrkrvr~rzrrrrrs2   <^?  $ 6