a a7j@s$dZdZgdZddlZddlmZddlZddlm Z m Z ddl m Z m Z mZdd lmZdd lmZdd lmZdd lmZdd lmZddlmZd,ddZd-ddZd.ddZddedfddZd/ddZ d0ddZ!ddZ"d1ddZ#d2d d!Z$d3d"d#Z%d4d$d%Z&d5d(d)Z'd6d*d+Z(dS)7z'Functions to construct sparse matrices zrestructuredtext en) spdiagseyeidentitykronkronsumhstackvstackbmatrandrandomdiags block_diagN)partial)check_random_state rng_integers)upcastget_index_dtype isscalarlike) csr_matrix) csc_matrix) bsr_matrix) coo_matrix) dia_matrix)issparsecCst||f||fd|S)a Return a sparse matrix from diagonals. Parameters ---------- data : array_like matrix diagonals stored row-wise diags : diagonals to set - k = 0 the main diagonal - k > 0 the k-th upper diagonal - k < 0 the k-th lower diagonal m, n : int shape of the result format : str, optional Format of the result. By default (format=None) an appropriate sparse matrix format is returned. This choice is subject to change. See Also -------- diags : more convenient form of this function dia_matrix : the sparse DIAgonal format. Examples -------- >>> from scipy.sparse import spdiags >>> data = np.array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]]) >>> diags = np.array([0, -1, 2]) >>> spdiags(data, diags, 4, 4).toarray() array([[1, 0, 3, 0], [1, 2, 0, 4], [0, 2, 3, 0], [0, 0, 3, 4]]) shape)rasformat)datar mnformatr"f/Users/vegardjervell/Documents/master/model/venv/lib/python3.9/site-packages/scipy/sparse/construct.pyrs#rc st|r8t|dks t|dr.t|g}qHtdntttj|}t|}t|t|krjtd|durt|dtt|df}|durtj |}|\t fdd|D}t d|}tj t||f|d}t }t |D]\}} ||} t d| } t | | |} | dkrBtd| |fz$| dd| f||| | | f<Wqty} zFt| | krt| d krtd |t| | f| WYd} ~ qd} ~ 00qt||ffd |S) a Construct a sparse matrix from diagonals. Parameters ---------- diagonals : sequence of array_like Sequence of arrays containing the matrix diagonals, corresponding to `offsets`. offsets : sequence of int or an int, optional Diagonals to set: - k = 0 the main diagonal (default) - k > 0 the kth upper diagonal - k < 0 the kth lower diagonal shape : tuple of int, optional Shape of the result. If omitted, a square matrix large enough to contain the diagonals is returned. format : {"dia", "csr", "csc", "lil", ...}, optional Matrix format of the result. By default (format=None) an appropriate sparse matrix format is returned. This choice is subject to change. dtype : dtype, optional Data type of the matrix. See Also -------- spdiags : construct matrix from diagonals Notes ----- This function differs from `spdiags` in the way it handles off-diagonals. The result from `diags` is the sparse equivalent of:: np.diag(diagonals[0], offsets[0]) + ... + np.diag(diagonals[k], offsets[k]) Repeated diagonal offsets are disallowed. .. versionadded:: 0.11 Examples -------- >>> from scipy.sparse import diags >>> diagonals = [[1, 2, 3, 4], [1, 2, 3], [1, 2]] >>> diags(diagonals, [0, -1, 2]).toarray() array([[1, 0, 1, 0], [1, 2, 0, 2], [0, 2, 3, 0], [0, 0, 3, 4]]) Broadcasting of scalars is supported (but shape needs to be specified): >>> diags([1, -2, 1], [-1, 0, 1], shape=(4, 4)).toarray() array([[-2., 1., 0., 0.], [ 1., -2., 1., 0.], [ 0., 1., -2., 1.], [ 0., 0., 1., -2.]]) If only one diagonal is wanted (as in `numpy.diag`), the following works as well: >>> diags([1, 2, 3], 1).toarray() array([[ 0., 1., 0., 0.], [ 0., 0., 2., 0.], [ 0., 0., 0., 3.], [ 0., 0., 0., 0.]]) r z*Different number of diagonals and offsets.Ncs(g|] }t||td|qS)r )minmax).0offsetrr r"r# szdiags..dtypez"Offset %d (index %d) out of bounds.rzUDiagonal length (index %d: %d at offset %d) does not agree with matrix size (%d, %d).r)rlennpZ atleast_1d ValueErrorlistmapabsintZ common_typer%zerosr$ enumeraterr)Z diagonalsoffsetsrr!r+MZdata_arrKjZdiagonalr'klengther"r(r#r ?sPI       $r dcCst||||dS)aIdentity matrix in sparse format Returns an identity matrix with shape (n,n) using a given sparse format and dtype. Parameters ---------- n : int Shape of the identity matrix. dtype : dtype, optional Data type of the matrix format : str, optional Sparse format of the result, e.g., format="csr", etc. Examples -------- >>> from scipy.sparse import identity >>> identity(3).toarray() array([[ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.]]) >>> identity(3, dtype='int8', format='dia') <3x3 sparse matrix of type '' with 3 stored elements (1 diagonals) in DIAgonal format> )r+r!)r)r r+r!r"r"r#rsrc Cs|dur |}t|t|}}||kr|dkr|dvrt|d}tj|d|d}tj||d}tj||d}ttd|} | |||f||fS|dkrt|d}tj||d} tj||d} tj||d}t|| | ff||fStjdtdt |||f|d} t | ||| |S)a[Sparse matrix with ones on diagonal Returns a sparse (m x n) matrix where the kth diagonal is all ones and everything else is zeros. Parameters ---------- m : int Number of rows in the matrix. n : int, optional Number of columns. Default: `m`. k : int, optional Diagonal to place ones on. Default: 0 (main diagonal). dtype : dtype, optional Data type of the matrix. format : str, optional Sparse format of the result, e.g., format="csr", etc. Examples -------- >>> from scipy import sparse >>> sparse.eye(3).toarray() array([[ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.]]) >>> sparse.eye(3, dtype=np.int8) <3x3 sparse matrix of type '' with 3 stored elements (1 diagonals) in DIAgonal format> Nr )csrcscmaxvalrr*coo) r2rr-arangeZonesrrrr%r$rr) rr r9r+r! idx_dtypeindptrindicesrclsrowcolr r"r"r#rs&  "rcCsFt|}|dus|dkrd|j|jd|jdkrt|dd}|jd|jd|jd|jdf}|jdks~|jdkrt||S|}|j|j d|jd|jd}||}t ||j |j f|d St|}|jd|jd|jd|jdf}|jdks|jdkr,t||S|j |j}|j|j}|j|j}t|jd|jd|jd|jdtd jkr|tj}|tj}||jd9}||jd9}| d|j| d|j}}||j 7}||j7}| d| d}}| d|j|j}| d}t|||ff|d |SdS) aYkronecker product of sparse matrices A and B Parameters ---------- A : sparse or dense matrix first matrix of the product B : sparse or dense matrix second matrix of the product format : str, optional format of the result (e.g. "csr") Returns ------- kronecker product in a sparse matrix format Examples -------- >>> from scipy import sparse >>> A = sparse.csr_matrix(np.array([[0, 2], [5, 0]])) >>> B = sparse.csr_matrix(np.array([[1, 2], [3, 4]])) >>> sparse.kron(A, B).toarray() array([[ 0, 0, 2, 4], [ 0, 0, 6, 8], [ 5, 10, 0, 0], [15, 20, 0, 0]]) >>> sparse.kron(A, [[1, 2], [3, 4]]).toarray() array([[ 0, 0, 2, 4], [ 0, 0, 6, 8], [ 5, 10, 0, 0], [15, 20, 0, 0]]) Nbsrr rTcopyrZint32)rnnzrrrZtoarrayrrepeatsizeZreshaperrErDrGrHr%r-iinfoastypeint64)ABr!Z output_shaperrGrHr"r"r#rs:#. ($(8     rcCst|}t|}|jd|jdkr,td|jd|jdkrHtdt|j|j}tt|jd|d||d}t|t|jd|d|d}|||S)akronecker sum of sparse matrices A and B Kronecker sum of two sparse matrices is a sum of two Kronecker products kron(I_n,A) + kron(B,I_m) where A has shape (m,m) and B has shape (n,n) and I_m and I_n are identity matrices of shape (m,m) and (n,n), respectively. Parameters ---------- A square matrix B square matrix format : str format of the result (e.g. "csr") Returns ------- kronecker sum in a sparse matrix format Examples -------- r rzA is not squarezB is not squarer*)r!)rrr.rr+rrr)rTrUr!r+LRr"r"r#rhsrc sndkr dnd}tdd|D}|dj|}tdd|Dt|j|d}tj|j|d}tjtfdd |Dd|d}|d}d} d} |D]} | j||krtd || j || | | j j<| | j j7} t | | | j} | j d d || <|| |7<| | j7} || j d 7}q||d <dkrTt |||f| |fd St |||f|| fd Sd S)z^ Stacking fast path for CSR/CSC matrices (i) vstack for CSR, (ii) hstack for CSC. r rcSsg|] }|jqSr")rr&br"r"r#r)z,_compressed_sparse_stack..cSsg|] }|jqSr")rDrXr"r"r#r)rZ)Zarraysr@r*c3s|]}|jVqdSNrrXaxisr"r# rZz+_compressed_sparse_stack..z#incompatible dimensions for axis %dNrMr)r- concatenaterrr%rPemptysumr.rEslicerDrr) blocksr]Z other_axisrZ constant_dimrCrErDZ last_indptrZsum_dimZ sum_indicesrYZidxsr"r\r#_compressed_sparse_stacks: $     rdcCst|g||dS)a Stack sparse matrices horizontally (column wise) Parameters ---------- blocks sequence of sparse matrices with compatible shapes format : str sparse format of the result (e.g., "csr") by default an appropriate sparse matrix format is returned. This choice is subject to change. dtype : dtype, optional The data-type of the output matrix. If not given, the dtype is determined from that of `blocks`. See Also -------- vstack : stack sparse matrices vertically (row wise) Examples -------- >>> from scipy.sparse import coo_matrix, hstack >>> A = coo_matrix([[1, 2], [3, 4]]) >>> B = coo_matrix([[5], [6]]) >>> hstack([A,B]).toarray() array([[1, 2, 5], [3, 4, 6]]) r!r+rrcr!r+r"r"r#rsrcCstdd|D||dS)a0 Stack sparse matrices vertically (row wise) Parameters ---------- blocks sequence of sparse matrices with compatible shapes format : str, optional sparse format of the result (e.g., "csr") by default an appropriate sparse matrix format is returned. This choice is subject to change. dtype : dtype, optional The data-type of the output matrix. If not given, the dtype is determined from that of `blocks`. See Also -------- hstack : stack sparse matrices horizontally (column wise) Examples -------- >>> from scipy.sparse import coo_matrix, vstack >>> A = coo_matrix([[1, 2], [3, 4]]) >>> B = coo_matrix([[5, 6]]) >>> vstack([A, B]).toarray() array([[1, 2], [3, 4], [5, 6]]) cSsg|] }|gqSr"r"rXr"r"r#r)rZzvstack..rerfrgr"r"r#rsrcCsFtj|dd}|jdkr td|j\}}|dkrz|dvrztdd|jDrzt|d d d fd }|d urv||}|S|dkr|d vrtd d|jDrt|d d d fd}|d ur||}|Stj |jt d}tj |tj d}tj |tj d}t |D]} t |D]} || | fd urt || | f}||| | f<d || | f<|| d krj|jd || <n8|| |jd krdj| | || |jd d} t| || d kr|jd|| <n8|| |jdkrdj| | || |jdd} t| qqtdd||D} |d urDdd||D} | r@t| nd }td t|}td t|}|d|df}tj| |d}tt|d}tj| |d}tj| |d}d } t|\}}t||D]^\} } || | f}t| | |j}|j||<|j|| ||<|j|| ||<| |j7} qt |||ff|d|S)aQ Build a sparse matrix from sparse sub-blocks Parameters ---------- blocks : array_like Grid of sparse matrices with compatible shapes. An entry of None implies an all-zero matrix. format : {'bsr', 'coo', 'csc', 'csr', 'dia', 'dok', 'lil'}, optional The sparse format of the result (e.g. "csr"). By default an appropriate sparse matrix format is returned. This choice is subject to change. dtype : dtype, optional The data-type of the output matrix. If not given, the dtype is determined from that of `blocks`. Returns ------- bmat : sparse matrix See Also -------- block_diag, diags Examples -------- >>> from scipy.sparse import coo_matrix, bmat >>> A = coo_matrix([[1, 2], [3, 4]]) >>> B = coo_matrix([[5], [6]]) >>> C = coo_matrix([[7]]) >>> bmat([[A, B], [None, C]]).toarray() array([[1, 2, 5], [3, 4, 6], [0, 0, 7]]) >>> bmat([[A, None], [None, C]]).toarray() array([[1, 2, 0], [3, 4, 0], [0, 0, 7]]) objectr*rJzblocks must be 2-Dr)Nr=css|]}t|tVqdSr[) isinstancerrXr"r"r#r^+szbmat..Nr )Nr>css|]}t|tVqdSr[)rirrXr"r"r#r^2rZTzeblocks[{i},:] has incompatible row dimensions. Got blocks[{i},{j}].shape[0] == {got}, expected {exp}.)ir8expgotzeblocks[:,{j}] has incompatible row dimensions. Got blocks[{i},{j}].shape[1] == {got}, expected {exp}.css|] }|jVqdSr[)rN)r&blockr"r"r#r^XrZcSsg|] }|jqSr"r*)r&Zblkr"r"r#r)ZrZzbmat..rMr?r)r-Zasarrayndimr.rallZflatrdrRr3boolrSrangerr!rarappendZcumsumr`rr%ZnonzeroziprbrNrrGrHr)rcr!r+r6NrTZ block_maskZ brow_lengthsZ bcol_lengthsrjr8msgrNZ all_dtypesZ row_offsetsZ col_offsetsrrrCrGrHiiZjjrUidxr"r"r#rs+           rc Csg}g}g}d}d}|D]}t|ttjfr4t|}|j\} } t|r||}||j |||j |||j nDt t | | | \} } || ||| ||||| 7}|| 7}qt |}t |}t |}t|||ff||f|d|S)a Build a block diagonal sparse matrix from provided matrices. Parameters ---------- mats : sequence of matrices Input matrices. format : str, optional The sparse format of the result (e.g., "csr"). If not given, the matrix is returned in "coo" format. dtype : dtype specifier, optional The data-type of the output matrix. If not given, the dtype is determined from that of `blocks`. Returns ------- res : sparse matrix Notes ----- .. versionadded:: 0.11.0 See Also -------- bmat, diags Examples -------- >>> from scipy.sparse import coo_matrix, block_diag >>> A = coo_matrix([[1, 2], [3, 4]]) >>> B = coo_matrix([[5], [6]]) >>> C = coo_matrix([[7]]) >>> block_diag((A, B, C)).toarray() array([[1, 2, 0, 0], [3, 4, 0, 0], [0, 0, 5, 0], [0, 0, 6, 0], [0, 0, 0, 7]]) r )rr+)rir/numbersNumberrrrZtocoorrrGrHrr-divmodrBZravelr_r) Zmatsr!r+rGrHrZr_idxZc_idxaZnrowsZncolsZa_rowZa_colr"r"r#r ts:*      r {Gz?rAcsP|dks|dkrtdt||}tj}|t|jkrFtj}|t|jkrnd} t| t|jtt|||} t |durt tj rfdd}n*t tj ṙfdd}nt jd d }j|| d d } t| d |j|d d } | | |j|d d } || jd d }t|| | ff||fdj|d d S)a Generate a sparse matrix of the given shape and density with randomly distributed values. Parameters ---------- m, n : int shape of the matrix density : real, optional density of the generated matrix: density equal to one means a full matrix, density of 0 means a matrix with no non-zero items. format : str, optional sparse matrix format. dtype : dtype, optional type of the returned matrix values. random_state : {None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional If `seed` is None (or `np.random`), the `numpy.random.RandomState` singleton is used. If `seed` is an int, a new ``RandomState`` instance is used, seeded with `seed`. If `seed` is already a ``Generator`` or ``RandomState`` instance then that instance is used. This random state will be used for sampling the sparsity structure, but not necessarily for sampling the values of the structurally nonzero entries of the matrix. data_rvs : callable, optional Samples a requested number of random values. This function should take a single argument specifying the length of the ndarray that it will return. The structurally nonzero entries of the sparse random matrix will be taken from the array sampled by this function. By default, uniform [0, 1) random values will be sampled using the same random state as is used for sampling the sparsity structure. Returns ------- res : sparse matrix Notes ----- Only float types are supported for now. Examples -------- >>> from scipy.sparse import random >>> from scipy import stats >>> from numpy.random import default_rng >>> rng = default_rng() >>> rvs = stats.poisson(25, loc=10).rvs >>> S = random(3, 4, density=0.25, random_state=rng, data_rvs=rvs) >>> S.A array([[ 36., 0., 33., 0.], # random [ 0., 0., 0., 0.], [ 0., 0., 36., 0.]]) >>> from scipy.sparse import random >>> from scipy.stats import rv_continuous >>> class CustomDistribution(rv_continuous): ... def _rvs(self, size=None, random_state=None): ... return random_state.standard_normal(size) >>> X = CustomDistribution(seed=rng) >>> Y = X() # get a frozen version of the distribution >>> S = random(3, 4, density=0.25, random_state=rng, data_rvs=Y.rvs) >>> S.A array([[ 0. , 0. , 0. , 0. ], # random [ 0.13569738, 1.9467163 , -0.81205367, 0. ], [ 0. , 0. , 0. , 0. ]]) r rz(density expected to be 0 <= density <= 1zTrying to generate a random sparse matrix such as the product of dimensions is greater than %d - this is not supported on this machine Ncs"ttjtj|dS)Nr*)rr-rQr$r%r r+ random_stater"r#data_rvss   zrandom..data_rvscsj|dj|ddS)N)rPy?)uniformr})rr"r#r"s gg?F)rPreplacerKr)r.r-r+ZintcrQr%rSr2roundrZ issubdtypeintegerZcomplexfloatingrrchoicefloorrRrr)rr densityr!r+rrmntprur9indr8rjvalsr"r~r#r s2H r cCst||||||S)aYGenerate a sparse matrix of the given shape and density with uniformly distributed values. Parameters ---------- m, n : int shape of the matrix density : real, optional density of the generated matrix: density equal to one means a full matrix, density of 0 means a matrix with no non-zero items. format : str, optional sparse matrix format. dtype : dtype, optional type of the returned matrix values. random_state : {None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional If `seed` is None (or `np.random`), the `numpy.random.RandomState` singleton is used. If `seed` is an int, a new ``RandomState`` instance is used, seeded with `seed`. If `seed` is already a ``Generator`` or ``RandomState`` instance then that instance is used. Returns ------- res : sparse matrix Notes ----- Only float types are supported for now. See Also -------- scipy.sparse.random : Similar function that allows a user-specified random data source. Examples -------- >>> from scipy.sparse import rand >>> matrix = rand(3, 4, density=0.25, format="csr", random_state=42) >>> matrix <3x4 sparse matrix of type '' with 3 stored elements in Compressed Sparse Row format> >>> matrix.todense() matrix([[0.05641158, 0. , 0. , 0.65088847], [0. , 0. , 0. , 0.14286682], [0. , 0. , 0. , 0. ]]) )r )rr rr!r+rr"r"r#r 1s3r )N)r NNN)r<N)N)N)NN)NN)NN)NN)r|rANNN)r|rANN))__doc__Z __docformat____all__rx functoolsrZnumpyr-Zscipy._lib._utilrrZsputilsrrrr=rr>rrIrrArZdiarbaserrr rfloatrrrrdrrrr r r r"r"r"r#s8        & ~ 7 V +" ! " | G v