Dense vectors using a NumPy backend#
This serves as a base class for dense vectors over Real Double Field and Complex Double Field
EXAMPLES:
sage: # needs sage.symbolic
sage: v = vector(CDF,[(1,-1), (2,pi), (3,5)]); v
(1.0 - 1.0*I, 2.0 + 3.141592653589793*I, 3.0 + 5.0*I)
sage: type(v)
<class 'sage.modules.vector_complex_double_dense.Vector_complex_double_dense'>
sage: parent(v)
Vector space of dimension 3 over Complex Double Field
sage: v[0] = 5
sage: v
(5.0, 2.0 + 3.141592653589793*I, 3.0 + 5.0*I)
sage: loads(dumps(v)) == v
True
sage: v = vector(RDF, [1,2,3,4]); v
(1.0, 2.0, 3.0, 4.0)
sage: loads(dumps(v)) == v
True
AUTHORS:
Jason Grout, Oct 2008: switch to numpy backend, factored out
Vector_double_dense
classJosh Kantor
William Stein
- class sage.modules.vector_double_dense.Vector_double_dense#
Bases:
Vector_numpy_dense
Base class for vectors over the Real Double Field and the Complex 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.
This class cannot be instantiated on its own. The numpy vector creation depends on several variables that are set in the subclasses.
EXAMPLES:
sage: v = vector(RDF, [1,2,3,4]); v (1.0, 2.0, 3.0, 4.0) sage: v*v 30.0
- complex_vector()#
Return the associated complex vector, i.e., this vector but with coefficients viewed as complex numbers.
EXAMPLES:
sage: v = vector(RDF,4,range(4)); v (0.0, 1.0, 2.0, 3.0) sage: v.complex_vector() (0.0, 1.0, 2.0, 3.0) sage: v = vector(RDF,0) sage: v.complex_vector() ()
- fft(direction='forward', algorithm='radix2', inplace=False)#
This performs a fast Fourier transform on the vector.
INPUT:
direction – ‘forward’ (default) or ‘backward’
The algorithm and inplace arguments are ignored.
This function is fastest if the vector’s length is a power of 2.
EXAMPLES:
sage: # needs scipy sage: v = vector(CDF,[1+2*I,2,3*I,4]) sage: v.fft() (7.0 + 5.0*I, 1.0 + 1.0*I, -5.0 + 5.0*I, 1.0 - 3.0*I) sage: v.fft(direction='backward') (1.75 + 1.25*I, 0.25 - 0.75*I, -1.25 + 1.25*I, 0.25 + 0.25*I) sage: v.fft().fft(direction='backward') (1.0 + 2.0*I, 2.0, 3.0*I, 4.0) sage: v.fft().parent() Vector space of dimension 4 over Complex Double Field sage: v.fft(inplace=True) sage: v (7.0 + 5.0*I, 1.0 + 1.0*I, -5.0 + 5.0*I, 1.0 - 3.0*I) sage: # needs scipy sage: v = vector(RDF,4,range(4)); v (0.0, 1.0, 2.0, 3.0) sage: v.fft() (6.0, -2.0 + 2.0*I, -2.0, -2.0 - 2.0*I) sage: v.fft(direction='backward') (1.5, -0.5 - 0.5*I, -0.5, -0.5 + 0.5*I) sage: v.fft().fft(direction='backward') (0.0, 1.0, 2.0, 3.0) sage: v.fft().parent() Vector space of dimension 4 over Complex Double Field sage: v.fft(inplace=True) Traceback (most recent call last): ... ValueError: inplace can only be True for CDF vectors
- inv_fft(algorithm='radix2', inplace=False)#
This performs the inverse fast Fourier transform on the vector.
The Fourier transform can be done in place using the keyword inplace=True
This will be fastest if the vector’s length is a power of 2.
EXAMPLES:
sage: # needs scipy sage: v = vector(CDF,[1,2,3,4]) sage: w = v.fft() sage: max(v - w.inv_fft()) < 1e-12 True
- mean()#
Calculate the arithmetic mean of the vector.
EXAMPLES:
sage: v = vector(RDF, range(9)) sage: w = vector(CDF, [k+(9-k)*I for k in range(9)]) sage: v.mean() 4.0 sage: w.mean() 4.0 + 5.0*I
- norm(p=2)#
Returns the norm (or related computations) of the vector.
INPUT:
p
- default: 2 - controls which norm is computed, allowable values are any real number and positive and negative infinity. See output discussion for specifics.
OUTPUT:
Returned value is a double precision floating point value in
RDF
(or an integer whenp=0
). The default value ofp = 2
is the “usual” Euclidean norm. For other values:p = Infinity
orp = oo
: the maximum of the absolute values of the entries, where the absolute value of the complex number \(a+bi\) is \(\sqrt{a^2+b^2}\).p = -Infinity
orp = -oo
: the minimum of the absolute values of the entries.p = 0
: the number of nonzero entries in the vector.p
is any other real number: for a vector \(\vec{x}\) this method computes\[\left(\sum_i x_i^p\right)^{1/p}\]For
p < 0
this function is not a norm, but the above computation may be useful for other purposes.
ALGORITHM:
Computation is performed by the
norm()
function of the SciPy/NumPy library.EXAMPLES:
First over the reals.
sage: v = vector(RDF, range(9)) sage: v.norm() 14.28285685... sage: v.norm(p=2) 14.28285685... sage: v.norm(p=6) 8.744039097... sage: v.norm(p=Infinity) 8.0 sage: v.norm(p=-oo) 0.0 sage: v.norm(p=0) 8.0 sage: v.norm(p=0.3) 4099.153615...
And over the complex numbers.
sage: w = vector(CDF, [3-4*I, 0, 5+12*I]) sage: w.norm() 13.9283882... sage: w.norm(p=2) 13.9283882... sage: w.norm(p=0) 2.0 sage: w.norm(p=4.2) 13.0555695... sage: w.norm(p=oo) 13.0
Negative values of
p
are allowed and will provide the same computation as for positive values. A zero entry in the vector will raise a warning and return zero.sage: v = vector(CDF, range(1,10)) sage: v.norm(p=-3.2) 0.953760808... sage: w = vector(CDF, [-1,0,1]) sage: w.norm(p=-1.6) doctest:...: RuntimeWarning: divide by zero encountered in power 0.0
Return values are in
RDF
, or an integer whenp = 0
.sage: v = vector(RDF, [1,2,4,8]) sage: v.norm() in RDF True sage: v.norm(p=0) in ZZ True
Improper values of
p
are caught.sage: w = vector(CDF, [-1,0,1]) sage: w.norm(p='junk') Traceback (most recent call last): ... ValueError: vector norm 'p' must be +/- infinity or a real number, not junk
- prod()#
Return the product of the entries of self.
EXAMPLES:
sage: v = vector(RDF, range(9)) sage: w = vector(CDF, [k+(9-k)*I for k in range(9)]) sage: v.prod() 0.0 sage: w.prod() 57204225.0*I
- standard_deviation(population=True)#
Calculate the standard deviation of entries of the vector.
- INPUT:
population – If False, calculate the sample standard deviation.
EXAMPLES:
sage: v = vector(RDF, range(9)) sage: w = vector(CDF, [k+(9-k)*I for k in range(9)]) sage: v.standard_deviation() 2.7386127875258306 sage: v.standard_deviation(population=False) 2.581988897471611 sage: w.standard_deviation() 3.872983346207417 sage: w.standard_deviation(population=False) 3.6514837167011076
- stats_kurtosis()#
Compute the kurtosis of a dataset.
Kurtosis is the fourth central moment divided by the square of the variance. Since we use Fisher’s definition, 3.0 is subtracted from the result to give 0.0 for a normal distribution. (Paragraph from the scipy.stats docstring.)
EXAMPLES:
sage: # needs scipy sage: v = vector(RDF, range(9)) sage: w = vector(CDF, [k+(9-k)*I for k in range(9)]) sage: v.stats_kurtosis() # rel tol 5e-15 -1.2300000000000000 sage: w.stats_kurtosis() # rel tol 5e-15 -1.2300000000000000
- sum()#
Return the sum of the entries of self.
EXAMPLES:
sage: v = vector(RDF, range(9)) sage: w = vector(CDF, [k+(9-k)*I for k in range(9)]) sage: v.sum() 36.0 sage: w.sum() 36.0 + 45.0*I
- variance(population=True)#
Calculate the variance of entries of the vector.
INPUT:
population
– If False, calculate the sample variance.
EXAMPLES:
sage: v = vector(RDF, range(9)) sage: w = vector(CDF, [k+(9-k)*I for k in range(9)]) sage: v.variance() 7.5 sage: v.variance(population=False) 6.666666666666667 sage: w.variance() 15.0 sage: w.variance(population=False) 13.333333333333334
- zero_at(eps)#
Returns a copy with small entries replaced by zeros.
This is useful for modifying output from algorithms which have large relative errors when producing zero elements, e.g. to create reliable doctests.
INPUT:
eps
- cutoff value
OUTPUT:
A modified copy of the vector. Elements smaller than or equal to
eps
are replaced with zeroes. For complex vectors, the real and imaginary parts are considered individually.EXAMPLES:
sage: v = vector(RDF, [1.0, 2.0, 10^-10, 3.0]) sage: v.zero_at(1e-8) (1.0, 2.0, 0.0, 3.0) sage: v.zero_at(1e-12) (1.0, 2.0, 1e-10, 3.0)
For complex numbers the real and imaginary parts are considered separately.
sage: w = vector(CDF, [10^-6 + 5*I, 5 + 10^-6*I, 5 + 5*I, 10^-6 + 10^-6*I]) sage: w.zero_at(1.0e-4) (5.0*I, 5.0, 5.0 + 5.0*I, 0.0) sage: w.zero_at(1.0e-8) (1e-06 + 5.0*I, 5.0 + 1e-06*I, 5.0 + 5.0*I, 1e-06 + 1e-06*I)