Dense real double vectors using a NumPy backend¶
EXAMPLES:
sage: # needs sage.symbolic
sage: v = vector(RDF, [1, pi, sqrt(2)]); v
(1.0, 3.141592653589793, 1.414213562373095)
sage: type(v)
<class 'sage.modules.vector_real_double_dense.Vector_real_double_dense'>
sage: parent(v)
Vector space of dimension 3 over Real Double Field
sage: v[0] = 5
sage: v
(5.0, 3.141592653589793, 1.414213562373095)
sage: loads(dumps(v)) == v
True
>>> from sage.all import *
>>> # needs sage.symbolic
>>> v = vector(RDF, [Integer(1), pi, sqrt(Integer(2))]); v
(1.0, 3.141592653589793, 1.414213562373095)
>>> type(v)
<class 'sage.modules.vector_real_double_dense.Vector_real_double_dense'>
>>> parent(v)
Vector space of dimension 3 over Real Double Field
>>> v[Integer(0)] = Integer(5)
>>> v
(5.0, 3.141592653589793, 1.414213562373095)
>>> loads(dumps(v)) == v
True
- AUTHORS:
- – Jason Grout, Oct 2008: switch to numpy backend, factored out
- Vector_double_dense class 
 
- class sage.modules.vector_real_double_dense.Vector_real_double_dense[source]¶
- Bases: - Vector_double_dense- Vectors over the Real Double Field. These are supposed to be fast vector operations using C doubles. Most operations are implemented using numpy which will call the underlying BLAS, if needed, on the system. - EXAMPLES: - sage: v = vector(RDF, [1,2,3,4]); v (1.0, 2.0, 3.0, 4.0) sage: v*v 30.0 - >>> from sage.all import * >>> v = vector(RDF, [Integer(1),Integer(2),Integer(3),Integer(4)]); v (1.0, 2.0, 3.0, 4.0) >>> v*v 30.0 - stats_skew()[source]¶
- Compute the skewness of a data set. - For normally distributed data, the skewness should be about 0. A skewness value > 0 means that there is more weight in the left tail of the distribution. (Paragraph from the scipy.stats docstring.) - EXAMPLES: - sage: v = vector(RDF, range(9)) sage: v.stats_skew() # needs scipy 0.0 - >>> from sage.all import * >>> v = vector(RDF, range(Integer(9))) >>> v.stats_skew() # needs scipy 0.0 
 
- sage.modules.vector_real_double_dense.unpickle_v0(parent, entries, degree)[source]¶
- Create a real double vector containing the entries. - EXAMPLES: - sage: v = vector(RDF, [1,2,3]) sage: w = sage.modules.vector_real_double_dense.unpickle_v0(v.parent(), list(v), v.degree()) sage: v == w True - >>> from sage.all import * >>> v = vector(RDF, [Integer(1),Integer(2),Integer(3)]) >>> w = sage.modules.vector_real_double_dense.unpickle_v0(v.parent(), list(v), v.degree()) >>> v == w True 
- sage.modules.vector_real_double_dense.unpickle_v1(parent, entries, degree, is_mutable=None)[source]¶
- Create a real double vector with the given parent, entries, degree, and mutability. - EXAMPLES: - sage: v = vector(RDF, [1,2,3]) sage: w = sage.modules.vector_real_double_dense.unpickle_v1(v.parent(), list(v), v.degree(), v.is_immutable()) sage: v == w True - >>> from sage.all import * >>> v = vector(RDF, [Integer(1),Integer(2),Integer(3)]) >>> w = sage.modules.vector_real_double_dense.unpickle_v1(v.parent(), list(v), v.degree(), v.is_immutable()) >>> v == w True