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Package sage :: Package linalg :: Module sparse_matrix_pyx :: Class Matrix_mpq |
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object
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Matrix_mpq
Method Summary | |
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x.__init__(...) initializes x; see x.__class__.__doc__ for signature | |
x.__delitem__(y) <==> del x[y] | |
x.__getitem__(y) <==> x[y] | |
T.__new__(S, ...) -> a new object with type S, a subtype of T | |
x.__repr__() <==> repr(x) | |
x.__setitem__(i, y) <==> x[i]=y | |
Return an associative arrow of pairs (i,j):x where the keys (i,j) run through the nonzero positions of the matrix and the x are nonzero and of type Integer. | |
Replace self by its reduction to reduced row echelon form. | |
linear_combination_of_rows(...)
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list(...)
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ncols(...)
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nrows(...)
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parent(...)
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pivots(...)
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randomize(self, int sparcity): | |
Return an associative arrow of pairs n:x where the keys n run through the nonzero positions of the row, and the x are nonzero and of type Integer. | |
Swap the rows in positions n1 and n2 | |
Inherited from object | |
x.__delattr__('name') <==> del x.name | |
x.__getattribute__('name') <==> x.name | |
x.__hash__() <==> hash(x) | |
helper for pickle | |
helper for pickle | |
x.__setattr__('name', value) <==> x.name = value | |
x.__str__() <==> str(x) |
Class Variable Summary | |
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member_descriptor |
nc = <member 'nc' of 'sparse_matrix_pyx.Matrix_mpq' obje...
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member_descriptor |
nr = <member 'nr' of 'sparse_matrix_pyx.Matrix_mpq' obje...
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Method Details |
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__init__(...)
x.__init__(...) initializes x; see x.__class__.__doc__ for
signature
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__delitem__(x,
y)
x.__delitem__(y) <==> del x[y]
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__getitem__(x,
y)
x.__getitem__(y) <==> x[y]
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__new__(T, S, ...)T.__new__(S, ...) -> a new object with type S, a subtype of T
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__repr__(x)
x.__repr__() <==> repr(x)
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__setitem__(x,
i,
y)
x.__setitem__(i, y) <==> x[i]=y
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dict(...)Return an associative arrow of pairs (i,j):x where the keys (i,j) run through the nonzero positions of the matrix and the x are nonzero and of type Integer. |
echelon(...)Replace self by its reduction to reduced row echelon form. ALGORITHM: We use Gauss elimination, which is slightly intelligent, in these sense that we clear each column using a row with the minimum number of nonzero entries. WARNING: There is no reason to use the code below, except for testing this class. It is *vastly* faster to use the multi-modular method, which is implemented in sparse_matrix.Sparse_matrix_rational |
randomize(...)randomize(self, int sparcity): The sparcity is a bound on the number of nonzeros per row. |
row_to_dict(...)Return an associative arrow of pairs n:x where the keys n run through the nonzero positions of the row, and the x are nonzero and of type Integer. |
swap_rows(...)Swap the rows in positions n1 and n2 |
Class Variable Details |
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nc
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nr
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