Python Matrix.rank方法代码示例

# 需要导入模块: from sympy.matrices import Matrix [as 别名]
# 或者: from sympy.matrices.Matrix import rank [as 别名]
 def is_scalar_multiple(p1, p2):
     """Returns whether `p1` and `p2` are scalar multiples
     of eachother.
     """
     # if the vectors p1 and p2 are linearly dependent, then they must
     # be scalar multiples of eachother
     m = Matrix([p1.args, p2.args])
     # XXX: issue #9480 we need `simplify=True` otherwise the
     # rank may be computed incorrectly
     return m.rank(simplify=True) < 2

# 需要导入模块: from sympy.matrices import Matrix [as 别名]
# 或者: from sympy.matrices.Matrix import rank [as 别名]
    def is_scalar_multiple(self, p):
        """Returns whether each coordinate of `self` is a scalar
        multiple of the corresponding coordinate in point p.
        """
        s, o = Point._normalize_dimension(self, Point(p))
        # 2d points happen a lot, so optimize this function call
        if s.ambient_dimension == 2:
            (x1, y1), (x2, y2) = s.args, o.args
            rv = (x1*y2 - x2*y1).equals(0)
            if rv is None:
                raise Undecidable(filldedent(
                    '''can't determine if %s is a scalar multiple of
                    %s''' % (s, o)))

        # if the vectors p1 and p2 are linearly dependent, then they must
        # be scalar multiples of each other
        m = Matrix([s.args, o.args])
        return m.rank() < 2

# 需要导入模块: from sympy.matrices import Matrix [as 别名]
# 或者: from sympy.matrices.Matrix import rank [as 别名]
    def affine_rank(*args):
        """The affine rank of a set of points is the dimension
        of the smallest affine space containing all the points.
        For example, if the points lie on a line (and are not all
        the same) their affine rank is 1.  If the points lie on a plane
        but not a line, their affine rank is 2.  By convention, the empty
        set has affine rank -1."""

        if len(args) == 0:
            return -1
        # make sure we're genuinely points
        # and translate every point to the origin
        points = Point._normalize_dimension(*[Point(i) for i in args])
        origin = points[0]
        points = [i - origin for i in points[1:]]

        m = Matrix([i.args for i in points])
        return m.rank()