val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?shape:int list ->
?dtype:Py.Object.t ->
?copy:Py.Object.t ->
arg1:Py.Object.t ->
unit ->
tCompressed Sparse Row matrix
This can be instantiated in several ways: csr_matrix(D) with a dense matrix or rank-2 ndarray D
csr_matrix(S) with another sparse matrix S (equivalent to S.tocsr())
csr_matrix((M, N), dtype) to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype='d'.
csr_matrix((data, (row_ind, col_ind)), shape=(M, N)) where ``data``, ``row_ind`` and ``col_ind`` satisfy the relationship ``arow_ind[k], col_ind[k] = datak``.
csr_matrix((data, indices, indptr), shape=(M, N)) is the standard CSR representation where the column indices for row i are stored in ``indicesindptr[i]:indptr[i+1]`` and their corresponding values are stored in ``dataindptr[i]:indptr[i+1]``. If the shape parameter is not supplied, the matrix dimensions are inferred from the index arrays.
Attributes ---------- dtype : dtype Data type of the matrix shape : 2-tuple Shape of the matrix ndim : int Number of dimensions (this is always 2) nnz Number of stored values, including explicit zeros data CSR format data array of the matrix indices CSR format index array of the matrix indptr CSR format index pointer array of the matrix has_sorted_indices Whether indices are sorted
Notes -----
Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power.
Advantages of the CSR format
Disadvantages of the CSR format
Examples --------
>>> import numpy as np >>> from scipy.sparse import csr_matrix >>> csr_matrix((3, 4), dtype=np.int8).toarray() array([0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0], dtype=int8)
>>> row = np.array(0, 0, 1, 2, 2, 2) >>> col = np.array(0, 2, 2, 0, 1, 2) >>> data = np.array(1, 2, 3, 4, 5, 6) >>> csr_matrix((data, (row, col)), shape=(3, 3)).toarray() array([1, 0, 2],
[0, 0, 3],
[4, 5, 6])
>>> indptr = np.array(0, 2, 3, 6) >>> indices = np.array(0, 2, 2, 0, 1, 2) >>> data = np.array(1, 2, 3, 4, 5, 6) >>> csr_matrix((data, indices, indptr), shape=(3, 3)).toarray() array([1, 0, 2],
[0, 0, 3],
[4, 5, 6])
As an example of how to construct a CSR matrix incrementally, the following snippet builds a term-document matrix from texts:
>>> docs = ["hello", "world", "hello"], ["goodbye", "cruel", "world"] >>> indptr = 0 >>> indices = >>> data = >>> vocabulary = {
}
>>> for d in docs: ... for term in d: ... index = vocabulary.setdefault(term, len(vocabulary)) ... indices.append(index) ... data.append(1) ... indptr.append(len(indices)) ... >>> csr_matrix((data, indices, indptr), dtype=int).toarray() array([2, 1, 0, 0],
[0, 1, 1, 1])
val get_item : key:Py.Object.t -> t -> Py.Object.tNone
val arcsin : t -> Py.Object.tElement-wise arcsin.
See numpy.arcsin for more information.
val arcsinh : t -> Py.Object.tElement-wise arcsinh.
See numpy.arcsinh for more information.
val arctan : t -> Py.Object.tElement-wise arctan.
See numpy.arctan for more information.
val arctanh : t -> Py.Object.tElement-wise arctanh.
See numpy.arctanh for more information.
val argmax :
?axis:[ `Zero | `One | `PyObject of Py.Object.t ] ->
?out:Py.Object.t ->
t ->
[ `Ndarray of Ndarray.t | `I of int ]Return indices of maximum elements along an axis.
Implicit zero elements are also taken into account. If there are several maximum values, the index of the first occurrence is returned.
Parameters ---------- axis :
2, -1, 0, 1, None
}
, optional Axis along which the argmax is computed. If None (default), index of the maximum element in the flatten data is returned. out : None, optional This argument is in the signature *solely* for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used.
Returns ------- ind : numpy.matrix or int Indices of maximum elements. If matrix, its size along `axis` is 1.
val argmin :
?axis:[ `Zero | `One | `PyObject of Py.Object.t ] ->
?out:Py.Object.t ->
t ->
[ `Ndarray of Ndarray.t | `I of int ]Return indices of minimum elements along an axis.
Implicit zero elements are also taken into account. If there are several minimum values, the index of the first occurrence is returned.
Parameters ---------- axis :
2, -1, 0, 1, None
}
, optional Axis along which the argmin is computed. If None (default), index of the minimum element in the flatten data is returned. out : None, optional This argument is in the signature *solely* for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used.
Returns ------- ind : numpy.matrix or int Indices of minimum elements. If matrix, its size along `axis` is 1.
val asformat :
?copy:Py.Object.t ->
format:[ `S of string | `None ] ->
t ->
Py.Object.tReturn this matrix in the passed format.
Parameters ---------- format : str, None The desired matrix format ("csr", "csc", "lil", "dok", "array", ...) or None for no conversion. copy : bool, optional If True, the result is guaranteed to not share data with self.
Returns ------- A : This matrix in the passed format.
val asfptype : t -> Py.Object.tUpcast matrix to a floating point format (if necessary)
val astype :
?casting:Py.Object.t ->
?copy:Py.Object.t ->
dtype:[ `S of string | `Dtype of Py.Object.t ] ->
t ->
Py.Object.tCast the matrix elements to a specified type.
Parameters ---------- dtype : string or numpy dtype Typecode or data-type to which to cast the data. casting : 'no', 'equiv', 'safe', 'same_kind', 'unsafe', optional Controls what kind of data casting may occur. Defaults to 'unsafe' for backwards compatibility. 'no' means the data types should not be cast at all. 'equiv' means only byte-order changes are allowed. 'safe' means only casts which can preserve values are allowed. 'same_kind' means only safe casts or casts within a kind, like float64 to float32, are allowed. 'unsafe' means any data conversions may be done. copy : bool, optional If `copy` is `False`, the result might share some memory with this matrix. If `copy` is `True`, it is guaranteed that the result and this matrix do not share any memory.
val ceil : t -> Py.Object.tElement-wise ceil.
See numpy.ceil for more information.
val check_format : ?full_check:bool -> t -> Py.Object.tcheck whether the matrix format is valid
Parameters ---------- full_check : bool, optional If `True`, rigorous check, O(N) operations. Otherwise basic check, O(1) operations (default True).
val conj : ?copy:bool -> t -> Py.Object.tElement-wise complex conjugation.
If the matrix is of non-complex data type and `copy` is False, this method does nothing and the data is not copied.
Parameters ---------- copy : bool, optional If True, the result is guaranteed to not share data with self.
Returns ------- A : The element-wise complex conjugate.
val conjugate : ?copy:bool -> t -> Py.Object.tElement-wise complex conjugation.
If the matrix is of non-complex data type and `copy` is False, this method does nothing and the data is not copied.
Parameters ---------- copy : bool, optional If True, the result is guaranteed to not share data with self.
Returns ------- A : The element-wise complex conjugate.
val copy : t -> Py.Object.tReturns a copy of this matrix.
No data/indices will be shared between the returned value and current matrix.
val count_nonzero : t -> Py.Object.tNumber of non-zero entries, equivalent to
np.count_nonzero(a.toarray())
Unlike getnnz() and the nnz property, which return the number of stored entries (the length of the data attribute), this method counts the actual number of non-zero entries in data.
val deg2rad : t -> Py.Object.tElement-wise deg2rad.
See numpy.deg2rad for more information.
val diagonal : ?k:int -> t -> Py.Object.tReturns the k-th diagonal of the matrix.
Parameters ---------- k : int, optional Which diagonal to get, corresponding to elements ai, i+k. Default: 0 (the main diagonal).
.. versionadded:: 1.0
See also -------- numpy.diagonal : Equivalent numpy function.
Examples -------- >>> from scipy.sparse import csr_matrix >>> A = csr_matrix([1, 2, 0], [0, 0, 3], [4, 0, 5]) >>> A.diagonal() array(1, 0, 5) >>> A.diagonal(k=1) array(2, 3)
val dot : other:Py.Object.t -> t -> Py.Object.tOrdinary dot product
Examples -------- >>> import numpy as np >>> from scipy.sparse import csr_matrix >>> A = csr_matrix([1, 2, 0], [0, 0, 3], [4, 0, 5]) >>> v = np.array(1, 0, -1) >>> A.dot(v) array( 1, -3, -1, dtype=int64)
val eliminate_zeros : t -> Py.Object.tRemove zero entries from the matrix
This is an *in place* operation
val expm1 : t -> Py.Object.tElement-wise expm1.
See numpy.expm1 for more information.
val floor : t -> Py.Object.tElement-wise floor.
See numpy.floor for more information.
val getH : t -> Py.Object.tReturn the Hermitian transpose of this matrix.
See Also -------- numpy.matrix.getH : NumPy's implementation of `getH` for matrices
val get_shape : t -> Py.Object.tGet shape of a matrix.
val getcol : i:Py.Object.t -> t -> Py.Object.tReturns a copy of column i of the matrix, as a (m x 1) CSR matrix (column vector).
val getformat : t -> Py.Object.tFormat of a matrix representation as a string.
val getmaxprint : t -> Py.Object.tMaximum number of elements to display when printed.
val getnnz : ?axis:[ `Zero | `One ] -> t -> Py.Object.tNumber of stored values, including explicit zeros.
Parameters ---------- axis : None, 0, or 1 Select between the number of values across the whole matrix, in each column, or in each row.
See also -------- count_nonzero : Number of non-zero entries
val getrow : i:Py.Object.t -> t -> Py.Object.tReturns a copy of row i of the matrix, as a (1 x n) CSR matrix (row vector).
val log1p : t -> Py.Object.tElement-wise log1p.
See numpy.log1p for more information.
val max :
?axis:[ `Zero | `One | `PyObject of Py.Object.t ] ->
?out:Py.Object.t ->
t ->
Py.Object.tReturn the maximum of the matrix or maximum along an axis. This takes all elements into account, not just the non-zero ones.
Parameters ---------- axis :
2, -1, 0, 1, None
}
optional Axis along which the sum is computed. The default is to compute the maximum over all the matrix elements, returning a scalar (i.e. `axis` = `None`).
out : None, optional This argument is in the signature *solely* for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used.
Returns ------- amax : coo_matrix or scalar Maximum of `a`. If `axis` is None, the result is a scalar value. If `axis` is given, the result is a sparse.coo_matrix of dimension ``a.ndim - 1``.
See Also -------- min : The minimum value of a sparse matrix along a given axis. numpy.matrix.max : NumPy's implementation of 'max' for matrices
val maximum : other:Py.Object.t -> t -> Py.Object.tElement-wise maximum between this and another matrix.
val mean :
?axis:[ `Zero | `One | `PyObject of Py.Object.t ] ->
?dtype:Py.Object.t ->
?out:Ndarray.t ->
t ->
Ndarray.tCompute the arithmetic mean along the specified axis.
Returns the average of the matrix elements. The average is taken over all elements in the matrix by default, otherwise over the specified axis. `float64` intermediate and return values are used for integer inputs.
Parameters ---------- axis :
2, -1, 0, 1, None
}
optional Axis along which the mean is computed. The default is to compute the mean of all elements in the matrix (i.e. `axis` = `None`). dtype : data-type, optional Type to use in computing the mean. For integer inputs, the default is `float64`; for floating point inputs, it is the same as the input dtype.
.. versionadded:: 0.18.0
out : np.matrix, optional Alternative output matrix in which to place the result. It must have the same shape as the expected output, but the type of the output values will be cast if necessary.
.. versionadded:: 0.18.0
Returns ------- m : np.matrix
See Also -------- numpy.matrix.mean : NumPy's implementation of 'mean' for matrices
val min :
?axis:[ `Zero | `One | `PyObject of Py.Object.t ] ->
?out:Py.Object.t ->
t ->
Py.Object.tReturn the minimum of the matrix or maximum along an axis. This takes all elements into account, not just the non-zero ones.
Parameters ---------- axis :
2, -1, 0, 1, None
}
optional Axis along which the sum is computed. The default is to compute the minimum over all the matrix elements, returning a scalar (i.e. `axis` = `None`).
out : None, optional This argument is in the signature *solely* for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used.
Returns ------- amin : coo_matrix or scalar Minimum of `a`. If `axis` is None, the result is a scalar value. If `axis` is given, the result is a sparse.coo_matrix of dimension ``a.ndim - 1``.
See Also -------- max : The maximum value of a sparse matrix along a given axis. numpy.matrix.min : NumPy's implementation of 'min' for matrices
val minimum : other:Py.Object.t -> t -> Py.Object.tElement-wise minimum between this and another matrix.
val multiply : other:Py.Object.t -> t -> Py.Object.tPoint-wise multiplication by another matrix, vector, or scalar.
val nonzero : t -> Py.Object.tnonzero indices
Returns a tuple of arrays (row,col) containing the indices of the non-zero elements of the matrix.
Examples -------- >>> from scipy.sparse import csr_matrix >>> A = csr_matrix([1,2,0],[0,0,3],[4,0,5]) >>> A.nonzero() (array(0, 0, 1, 2, 2), array(0, 1, 2, 0, 2))
val power : ?dtype:Py.Object.t -> n:Py.Object.t -> t -> Py.Object.tThis function performs element-wise power.
Parameters ---------- n : n is a scalar
dtype : If dtype is not specified, the current dtype will be preserved.
val prune : t -> Py.Object.tRemove empty space after all non-zero elements.
val rad2deg : t -> Py.Object.tElement-wise rad2deg.
See numpy.rad2deg for more information.
val reshape : ?kwargs:(string * Py.Object.t) list -> Py.Object.t list -> t -> treshape(self, shape, order='C', copy=False)
Gives a new shape to a sparse matrix without changing its data.
Parameters ---------- shape : length-2 tuple of ints The new shape should be compatible with the original shape. order : 'C', 'F', optional Read the elements using this index order. 'C' means to read and write the elements using C-like index order; e.g. read entire first row, then second row, etc. 'F' means to read and write the elements using Fortran-like index order; e.g. read entire first column, then second column, etc. copy : bool, optional Indicates whether or not attributes of self should be copied whenever possible. The degree to which attributes are copied varies depending on the type of sparse matrix being used.
Returns ------- reshaped_matrix : sparse matrix A sparse matrix with the given `shape`, not necessarily of the same format as the current object.
See Also -------- numpy.matrix.reshape : NumPy's implementation of 'reshape' for matrices
val resize : int list -> t -> Py.Object.tResize the matrix in-place to dimensions given by ``shape``
Any elements that lie within the new shape will remain at the same indices, while non-zero elements lying outside the new shape are removed.
Parameters ---------- shape : (int, int) number of rows and columns in the new matrix
Notes ----- The semantics are not identical to `numpy.ndarray.resize` or `numpy.resize`. Here, the same data will be maintained at each index before and after reshape, if that index is within the new bounds. In numpy, resizing maintains contiguity of the array, moving elements around in the logical matrix but not within a flattened representation.
We give no guarantees about whether the underlying data attributes (arrays, etc.) will be modified in place or replaced with new objects.
val rint : t -> Py.Object.tElement-wise rint.
See numpy.rint for more information.
val set_shape : shape:int list -> t -> Py.Object.tSee `reshape`.
val setdiag : ?k:int -> values:Ndarray.t -> t -> Py.Object.tSet diagonal or off-diagonal elements of the array.
Parameters ---------- values : array_like New values of the diagonal elements.
Values may have any length. If the diagonal is longer than values, then the remaining diagonal entries will not be set. If values if longer than the diagonal, then the remaining values are ignored.
If a scalar value is given, all of the diagonal is set to it.
k : int, optional Which off-diagonal to set, corresponding to elements ai,i+k. Default: 0 (the main diagonal).
val sign : t -> Py.Object.tElement-wise sign.
See numpy.sign for more information.
val sin : t -> Py.Object.tElement-wise sin.
See numpy.sin for more information.
val sinh : t -> Py.Object.tElement-wise sinh.
See numpy.sinh for more information.
val sort_indices : t -> Py.Object.tSort the indices of this matrix *in place*
val sorted_indices : t -> Py.Object.tReturn a copy of this matrix with sorted indices
val sqrt : t -> Py.Object.tElement-wise sqrt.
See numpy.sqrt for more information.
val sum :
?axis:[ `Zero | `One | `PyObject of Py.Object.t ] ->
?dtype:Py.Object.t ->
?out:Ndarray.t ->
t ->
Ndarray.tSum the matrix elements over a given axis.
Parameters ---------- axis :
2, -1, 0, 1, None
}
optional Axis along which the sum is computed. The default is to compute the sum of all the matrix elements, returning a scalar (i.e. `axis` = `None`). dtype : dtype, optional The type of the returned matrix and of the accumulator in which the elements are summed. The dtype of `a` is used by default unless `a` has an integer dtype of less precision than the default platform integer. In that case, if `a` is signed then the platform integer is used while if `a` is unsigned then an unsigned integer of the same precision as the platform integer is used.
.. versionadded:: 0.18.0
out : np.matrix, optional Alternative output matrix in which to place the result. It must have the same shape as the expected output, but the type of the output values will be cast if necessary.
.. versionadded:: 0.18.0
Returns ------- sum_along_axis : np.matrix A matrix with the same shape as `self`, with the specified axis removed.
See Also -------- numpy.matrix.sum : NumPy's implementation of 'sum' for matrices
val sum_duplicates : t -> Py.Object.tEliminate duplicate matrix entries by adding them together
The is an *in place* operation
val tan : t -> Py.Object.tElement-wise tan.
See numpy.tan for more information.
val tanh : t -> Py.Object.tElement-wise tanh.
See numpy.tanh for more information.
Return a dense ndarray representation of this matrix.
Parameters ---------- order : 'C', 'F', optional Whether to store multi-dimensional data in C (row-major) or Fortran (column-major) order in memory. The default is 'None', indicating the NumPy default of C-ordered. Cannot be specified in conjunction with the `out` argument.
out : ndarray, 2-dimensional, optional If specified, uses this array as the output buffer instead of allocating a new array to return. The provided array must have the same shape and dtype as the sparse matrix on which you are calling the method. For most sparse types, `out` is required to be memory contiguous (either C or Fortran ordered).
Returns ------- arr : ndarray, 2-dimensional An array with the same shape and containing the same data represented by the sparse matrix, with the requested memory order. If `out` was passed, the same object is returned after being modified in-place to contain the appropriate values.
val tobsr : ?blocksize:Py.Object.t -> ?copy:Py.Object.t -> t -> Py.Object.tConvert this matrix to Block Sparse Row format.
With copy=False, the data/indices may be shared between this matrix and the resultant bsr_matrix.
When blocksize=(R, C) is provided, it will be used for construction of the bsr_matrix.
val tocoo : ?copy:Py.Object.t -> t -> Py.Object.tConvert this matrix to COOrdinate format.
With copy=False, the data/indices may be shared between this matrix and the resultant coo_matrix.
val tocsc : ?copy:Py.Object.t -> t -> Py.Object.tConvert this matrix to Compressed Sparse Column format.
With copy=False, the data/indices may be shared between this matrix and the resultant csc_matrix.
val tocsr : ?copy:Py.Object.t -> t -> Py.Object.tConvert this matrix to Compressed Sparse Row format.
With copy=False, the data/indices may be shared between this matrix and the resultant csr_matrix.
Return a dense matrix representation of this matrix.
Parameters ---------- order : 'C', 'F', optional Whether to store multi-dimensional data in C (row-major) or Fortran (column-major) order in memory. The default is 'None', indicating the NumPy default of C-ordered. Cannot be specified in conjunction with the `out` argument.
out : ndarray, 2-dimensional, optional If specified, uses this array (or `numpy.matrix`) as the output buffer instead of allocating a new array to return. The provided array must have the same shape and dtype as the sparse matrix on which you are calling the method.
Returns ------- arr : numpy.matrix, 2-dimensional A NumPy matrix object with the same shape and containing the same data represented by the sparse matrix, with the requested memory order. If `out` was passed and was an array (rather than a `numpy.matrix`), it will be filled with the appropriate values and returned wrapped in a `numpy.matrix` object that shares the same memory.
val todia : ?copy:Py.Object.t -> t -> Py.Object.tConvert this matrix to sparse DIAgonal format.
With copy=False, the data/indices may be shared between this matrix and the resultant dia_matrix.
val todok : ?copy:Py.Object.t -> t -> Py.Object.tConvert this matrix to Dictionary Of Keys format.
With copy=False, the data/indices may be shared between this matrix and the resultant dok_matrix.
val tolil : ?copy:Py.Object.t -> t -> Py.Object.tConvert this matrix to List of Lists format.
With copy=False, the data/indices may be shared between this matrix and the resultant lil_matrix.
val transpose : ?axes:Py.Object.t -> ?copy:Py.Object.t -> t -> Py.Object.tReverses the dimensions of the sparse matrix.
Parameters ---------- axes : None, optional This argument is in the signature *solely* for NumPy compatibility reasons. Do not pass in anything except for the default value. copy : bool, optional Indicates whether or not attributes of `self` should be copied whenever possible. The degree to which attributes are copied varies depending on the type of sparse matrix being used.
Returns ------- p : `self` with the dimensions reversed.
See Also -------- numpy.matrix.transpose : NumPy's implementation of 'transpose' for matrices
val trunc : t -> Py.Object.tElement-wise trunc.
See numpy.trunc for more information.
val dtype : t -> Py.Object.tAttribute dtype: get value or raise Not_found if None.
val dtype_opt : t -> Py.Object.t optionAttribute dtype: get value as an option.
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.