You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
1039 lines
29 KiB
Python
1039 lines
29 KiB
Python
6 years ago
|
from __future__ import division, print_function, absolute_import
|
||
|
|
|
||
|
|
import math
|
||
|
|
import numpy as np
|
||
|
|
from scipy._lib.six import xrange
|
||
|
|
from scipy._lib.six import string_types
|
||
|
|
from numpy.lib.stride_tricks import as_strided
|
||
|
|
|
||
|
|
|
||
|
|
__all__ = ['tri', 'tril', 'triu', 'toeplitz', 'circulant', 'hankel',
|
||
|
|
'hadamard', 'leslie', 'kron', 'block_diag', 'companion',
|
||
|
|
'helmert', 'hilbert', 'invhilbert', 'pascal', 'invpascal', 'dft']
|
||
|
|
|
||
|
|
|
||
|
|
#-----------------------------------------------------------------------------
|
||
|
|
# matrix construction functions
|
||
|
|
#-----------------------------------------------------------------------------
|
||
|
|
|
||
|
|
#
|
||
|
|
# *Note*: tri{,u,l} is implemented in numpy, but an important bug was fixed in
|
||
|
|
# 2.0.0.dev-1af2f3, the following tri{,u,l} definitions are here for backwards
|
||
|
|
# compatibility.
|
||
|
|
|
||
|
|
def tri(N, M=None, k=0, dtype=None):
|
||
|
|
"""
|
||
|
|
Construct (N, M) matrix filled with ones at and below the k-th diagonal.
|
||
|
|
|
||
|
|
The matrix has A[i,j] == 1 for i <= j + k
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
N : int
|
||
|
|
The size of the first dimension of the matrix.
|
||
|
|
M : int or None, optional
|
||
|
|
The size of the second dimension of the matrix. If `M` is None,
|
||
|
|
`M = N` is assumed.
|
||
|
|
k : int, optional
|
||
|
|
Number of subdiagonal below which matrix is filled with ones.
|
||
|
|
`k` = 0 is the main diagonal, `k` < 0 subdiagonal and `k` > 0
|
||
|
|
superdiagonal.
|
||
|
|
dtype : dtype, optional
|
||
|
|
Data type of the matrix.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
tri : (N, M) ndarray
|
||
|
|
Tri matrix.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from scipy.linalg import tri
|
||
|
|
>>> tri(3, 5, 2, dtype=int)
|
||
|
|
array([[1, 1, 1, 0, 0],
|
||
|
|
[1, 1, 1, 1, 0],
|
||
|
|
[1, 1, 1, 1, 1]])
|
||
|
|
>>> tri(3, 5, -1, dtype=int)
|
||
|
|
array([[0, 0, 0, 0, 0],
|
||
|
|
[1, 0, 0, 0, 0],
|
||
|
|
[1, 1, 0, 0, 0]])
|
||
|
|
|
||
|
|
"""
|
||
|
|
if M is None:
|
||
|
|
M = N
|
||
|
|
if isinstance(M, string_types):
|
||
|
|
#pearu: any objections to remove this feature?
|
||
|
|
# As tri(N,'d') is equivalent to tri(N,dtype='d')
|
||
|
|
dtype = M
|
||
|
|
M = N
|
||
|
|
m = np.greater_equal.outer(np.arange(k, N+k), np.arange(M))
|
||
|
|
if dtype is None:
|
||
|
|
return m
|
||
|
|
else:
|
||
|
|
return m.astype(dtype)
|
||
|
|
|
||
|
|
|
||
|
|
def tril(m, k=0):
|
||
|
|
"""
|
||
|
|
Make a copy of a matrix with elements above the k-th diagonal zeroed.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
m : array_like
|
||
|
|
Matrix whose elements to return
|
||
|
|
k : int, optional
|
||
|
|
Diagonal above which to zero elements.
|
||
|
|
`k` == 0 is the main diagonal, `k` < 0 subdiagonal and
|
||
|
|
`k` > 0 superdiagonal.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
tril : ndarray
|
||
|
|
Return is the same shape and type as `m`.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from scipy.linalg import tril
|
||
|
|
>>> tril([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1)
|
||
|
|
array([[ 0, 0, 0],
|
||
|
|
[ 4, 0, 0],
|
||
|
|
[ 7, 8, 0],
|
||
|
|
[10, 11, 12]])
|
||
|
|
|
||
|
|
"""
|
||
|
|
m = np.asarray(m)
|
||
|
|
out = tri(m.shape[0], m.shape[1], k=k, dtype=m.dtype.char) * m
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
def triu(m, k=0):
|
||
|
|
"""
|
||
|
|
Make a copy of a matrix with elements below the k-th diagonal zeroed.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
m : array_like
|
||
|
|
Matrix whose elements to return
|
||
|
|
k : int, optional
|
||
|
|
Diagonal below which to zero elements.
|
||
|
|
`k` == 0 is the main diagonal, `k` < 0 subdiagonal and
|
||
|
|
`k` > 0 superdiagonal.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
triu : ndarray
|
||
|
|
Return matrix with zeroed elements below the k-th diagonal and has
|
||
|
|
same shape and type as `m`.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from scipy.linalg import triu
|
||
|
|
>>> triu([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1)
|
||
|
|
array([[ 1, 2, 3],
|
||
|
|
[ 4, 5, 6],
|
||
|
|
[ 0, 8, 9],
|
||
|
|
[ 0, 0, 12]])
|
||
|
|
|
||
|
|
"""
|
||
|
|
m = np.asarray(m)
|
||
|
|
out = (1 - tri(m.shape[0], m.shape[1], k - 1, m.dtype.char)) * m
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
def toeplitz(c, r=None):
|
||
|
|
"""
|
||
|
|
Construct a Toeplitz matrix.
|
||
|
|
|
||
|
|
The Toeplitz matrix has constant diagonals, with c as its first column
|
||
|
|
and r as its first row. If r is not given, ``r == conjugate(c)`` is
|
||
|
|
assumed.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
c : array_like
|
||
|
|
First column of the matrix. Whatever the actual shape of `c`, it
|
||
|
|
will be converted to a 1-D array.
|
||
|
|
r : array_like, optional
|
||
|
|
First row of the matrix. If None, ``r = conjugate(c)`` is assumed;
|
||
|
|
in this case, if c[0] is real, the result is a Hermitian matrix.
|
||
|
|
r[0] is ignored; the first row of the returned matrix is
|
||
|
|
``[c[0], r[1:]]``. Whatever the actual shape of `r`, it will be
|
||
|
|
converted to a 1-D array.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
A : (len(c), len(r)) ndarray
|
||
|
|
The Toeplitz matrix. Dtype is the same as ``(c[0] + r[0]).dtype``.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
circulant : circulant matrix
|
||
|
|
hankel : Hankel matrix
|
||
|
|
solve_toeplitz : Solve a Toeplitz system.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
The behavior when `c` or `r` is a scalar, or when `c` is complex and
|
||
|
|
`r` is None, was changed in version 0.8.0. The behavior in previous
|
||
|
|
versions was undocumented and is no longer supported.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from scipy.linalg import toeplitz
|
||
|
|
>>> toeplitz([1,2,3], [1,4,5,6])
|
||
|
|
array([[1, 4, 5, 6],
|
||
|
|
[2, 1, 4, 5],
|
||
|
|
[3, 2, 1, 4]])
|
||
|
|
>>> toeplitz([1.0, 2+3j, 4-1j])
|
||
|
|
array([[ 1.+0.j, 2.-3.j, 4.+1.j],
|
||
|
|
[ 2.+3.j, 1.+0.j, 2.-3.j],
|
||
|
|
[ 4.-1.j, 2.+3.j, 1.+0.j]])
|
||
|
|
|
||
|
|
"""
|
||
|
|
c = np.asarray(c).ravel()
|
||
|
|
if r is None:
|
||
|
|
r = c.conjugate()
|
||
|
|
else:
|
||
|
|
r = np.asarray(r).ravel()
|
||
|
|
# Form a 1D array containing a reversed c followed by r[1:] that could be
|
||
|
|
# strided to give us toeplitz matrix.
|
||
|
|
vals = np.concatenate((c[::-1], r[1:]))
|
||
|
|
out_shp = len(c), len(r)
|
||
|
|
n = vals.strides[0]
|
||
|
|
return as_strided(vals[len(c)-1:], shape=out_shp, strides=(-n, n)).copy()
|
||
|
|
|
||
|
|
|
||
|
|
def circulant(c):
|
||
|
|
"""
|
||
|
|
Construct a circulant matrix.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
c : (N,) array_like
|
||
|
|
1-D array, the first column of the matrix.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
A : (N, N) ndarray
|
||
|
|
A circulant matrix whose first column is `c`.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
toeplitz : Toeplitz matrix
|
||
|
|
hankel : Hankel matrix
|
||
|
|
solve_circulant : Solve a circulant system.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
.. versionadded:: 0.8.0
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from scipy.linalg import circulant
|
||
|
|
>>> circulant([1, 2, 3])
|
||
|
|
array([[1, 3, 2],
|
||
|
|
[2, 1, 3],
|
||
|
|
[3, 2, 1]])
|
||
|
|
|
||
|
|
"""
|
||
|
|
c = np.asarray(c).ravel()
|
||
|
|
# Form an extended array that could be strided to give circulant version
|
||
|
|
c_ext = np.concatenate((c[::-1], c[:0:-1]))
|
||
|
|
L = len(c)
|
||
|
|
n = c_ext.strides[0]
|
||
|
|
return as_strided(c_ext[L-1:], shape=(L, L), strides=(-n, n)).copy()
|
||
|
|
|
||
|
|
|
||
|
|
def hankel(c, r=None):
|
||
|
|
"""
|
||
|
|
Construct a Hankel matrix.
|
||
|
|
|
||
|
|
The Hankel matrix has constant anti-diagonals, with `c` as its
|
||
|
|
first column and `r` as its last row. If `r` is not given, then
|
||
|
|
`r = zeros_like(c)` is assumed.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
c : array_like
|
||
|
|
First column of the matrix. Whatever the actual shape of `c`, it
|
||
|
|
will be converted to a 1-D array.
|
||
|
|
r : array_like, optional
|
||
|
|
Last row of the matrix. If None, ``r = zeros_like(c)`` is assumed.
|
||
|
|
r[0] is ignored; the last row of the returned matrix is
|
||
|
|
``[c[-1], r[1:]]``. Whatever the actual shape of `r`, it will be
|
||
|
|
converted to a 1-D array.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
A : (len(c), len(r)) ndarray
|
||
|
|
The Hankel matrix. Dtype is the same as ``(c[0] + r[0]).dtype``.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
toeplitz : Toeplitz matrix
|
||
|
|
circulant : circulant matrix
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from scipy.linalg import hankel
|
||
|
|
>>> hankel([1, 17, 99])
|
||
|
|
array([[ 1, 17, 99],
|
||
|
|
[17, 99, 0],
|
||
|
|
[99, 0, 0]])
|
||
|
|
>>> hankel([1,2,3,4], [4,7,7,8,9])
|
||
|
|
array([[1, 2, 3, 4, 7],
|
||
|
|
[2, 3, 4, 7, 7],
|
||
|
|
[3, 4, 7, 7, 8],
|
||
|
|
[4, 7, 7, 8, 9]])
|
||
|
|
|
||
|
|
"""
|
||
|
|
c = np.asarray(c).ravel()
|
||
|
|
if r is None:
|
||
|
|
r = np.zeros_like(c)
|
||
|
|
else:
|
||
|
|
r = np.asarray(r).ravel()
|
||
|
|
# Form a 1D array of values to be used in the matrix, containing `c`
|
||
|
|
# followed by r[1:].
|
||
|
|
vals = np.concatenate((c, r[1:]))
|
||
|
|
# Stride on concatenated array to get hankel matrix
|
||
|
|
out_shp = len(c), len(r)
|
||
|
|
n = vals.strides[0]
|
||
|
|
return as_strided(vals, shape=out_shp, strides=(n, n)).copy()
|
||
|
|
|
||
|
|
|
||
|
|
def hadamard(n, dtype=int):
|
||
|
|
"""
|
||
|
|
Construct a Hadamard matrix.
|
||
|
|
|
||
|
|
Constructs an n-by-n Hadamard matrix, using Sylvester's
|
||
|
|
construction. `n` must be a power of 2.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
n : int
|
||
|
|
The order of the matrix. `n` must be a power of 2.
|
||
|
|
dtype : dtype, optional
|
||
|
|
The data type of the array to be constructed.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
H : (n, n) ndarray
|
||
|
|
The Hadamard matrix.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
.. versionadded:: 0.8.0
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from scipy.linalg import hadamard
|
||
|
|
>>> hadamard(2, dtype=complex)
|
||
|
|
array([[ 1.+0.j, 1.+0.j],
|
||
|
|
[ 1.+0.j, -1.-0.j]])
|
||
|
|
>>> hadamard(4)
|
||
|
|
array([[ 1, 1, 1, 1],
|
||
|
|
[ 1, -1, 1, -1],
|
||
|
|
[ 1, 1, -1, -1],
|
||
|
|
[ 1, -1, -1, 1]])
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
# This function is a slightly modified version of the
|
||
|
|
# function contributed by Ivo in ticket #675.
|
||
|
|
|
||
|
|
if n < 1:
|
||
|
|
lg2 = 0
|
||
|
|
else:
|
||
|
|
lg2 = int(math.log(n, 2))
|
||
|
|
if 2 ** lg2 != n:
|
||
|
|
raise ValueError("n must be an positive integer, and n must be "
|
||
|
|
"a power of 2")
|
||
|
|
|
||
|
|
H = np.array([[1]], dtype=dtype)
|
||
|
|
|
||
|
|
# Sylvester's construction
|
||
|
|
for i in range(0, lg2):
|
||
|
|
H = np.vstack((np.hstack((H, H)), np.hstack((H, -H))))
|
||
|
|
|
||
|
|
return H
|
||
|
|
|
||
|
|
|
||
|
|
def leslie(f, s):
|
||
|
|
"""
|
||
|
|
Create a Leslie matrix.
|
||
|
|
|
||
|
|
Given the length n array of fecundity coefficients `f` and the length
|
||
|
|
n-1 array of survival coefficients `s`, return the associated Leslie matrix.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
f : (N,) array_like
|
||
|
|
The "fecundity" coefficients.
|
||
|
|
s : (N-1,) array_like
|
||
|
|
The "survival" coefficients, has to be 1-D. The length of `s`
|
||
|
|
must be one less than the length of `f`, and it must be at least 1.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
L : (N, N) ndarray
|
||
|
|
The array is zero except for the first row,
|
||
|
|
which is `f`, and the first sub-diagonal, which is `s`.
|
||
|
|
The data-type of the array will be the data-type of ``f[0]+s[0]``.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
.. versionadded:: 0.8.0
|
||
|
|
|
||
|
|
The Leslie matrix is used to model discrete-time, age-structured
|
||
|
|
population growth [1]_ [2]_. In a population with `n` age classes, two sets
|
||
|
|
of parameters define a Leslie matrix: the `n` "fecundity coefficients",
|
||
|
|
which give the number of offspring per-capita produced by each age
|
||
|
|
class, and the `n` - 1 "survival coefficients", which give the
|
||
|
|
per-capita survival rate of each age class.
|
||
|
|
|
||
|
|
References
|
||
|
|
----------
|
||
|
|
.. [1] P. H. Leslie, On the use of matrices in certain population
|
||
|
|
mathematics, Biometrika, Vol. 33, No. 3, 183--212 (Nov. 1945)
|
||
|
|
.. [2] P. H. Leslie, Some further notes on the use of matrices in
|
||
|
|
population mathematics, Biometrika, Vol. 35, No. 3/4, 213--245
|
||
|
|
(Dec. 1948)
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from scipy.linalg import leslie
|
||
|
|
>>> leslie([0.1, 2.0, 1.0, 0.1], [0.2, 0.8, 0.7])
|
||
|
|
array([[ 0.1, 2. , 1. , 0.1],
|
||
|
|
[ 0.2, 0. , 0. , 0. ],
|
||
|
|
[ 0. , 0.8, 0. , 0. ],
|
||
|
|
[ 0. , 0. , 0.7, 0. ]])
|
||
|
|
|
||
|
|
"""
|
||
|
|
f = np.atleast_1d(f)
|
||
|
|
s = np.atleast_1d(s)
|
||
|
|
if f.ndim != 1:
|
||
|
|
raise ValueError("Incorrect shape for f. f must be one-dimensional")
|
||
|
|
if s.ndim != 1:
|
||
|
|
raise ValueError("Incorrect shape for s. s must be one-dimensional")
|
||
|
|
if f.size != s.size + 1:
|
||
|
|
raise ValueError("Incorrect lengths for f and s. The length"
|
||
|
|
" of s must be one less than the length of f.")
|
||
|
|
if s.size == 0:
|
||
|
|
raise ValueError("The length of s must be at least 1.")
|
||
|
|
|
||
|
|
tmp = f[0] + s[0]
|
||
|
|
n = f.size
|
||
|
|
a = np.zeros((n, n), dtype=tmp.dtype)
|
||
|
|
a[0] = f
|
||
|
|
a[list(range(1, n)), list(range(0, n - 1))] = s
|
||
|
|
return a
|
||
|
|
|
||
|
|
|
||
|
|
def kron(a, b):
|
||
|
|
"""
|
||
|
|
Kronecker product.
|
||
|
|
|
||
|
|
The result is the block matrix::
|
||
|
|
|
||
|
|
a[0,0]*b a[0,1]*b ... a[0,-1]*b
|
||
|
|
a[1,0]*b a[1,1]*b ... a[1,-1]*b
|
||
|
|
...
|
||
|
|
a[-1,0]*b a[-1,1]*b ... a[-1,-1]*b
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
a : (M, N) ndarray
|
||
|
|
Input array
|
||
|
|
b : (P, Q) ndarray
|
||
|
|
Input array
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
A : (M*P, N*Q) ndarray
|
||
|
|
Kronecker product of `a` and `b`.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from numpy import array
|
||
|
|
>>> from scipy.linalg import kron
|
||
|
|
>>> kron(array([[1,2],[3,4]]), array([[1,1,1]]))
|
||
|
|
array([[1, 1, 1, 2, 2, 2],
|
||
|
|
[3, 3, 3, 4, 4, 4]])
|
||
|
|
|
||
|
|
"""
|
||
|
|
if not a.flags['CONTIGUOUS']:
|
||
|
|
a = np.reshape(a, a.shape)
|
||
|
|
if not b.flags['CONTIGUOUS']:
|
||
|
|
b = np.reshape(b, b.shape)
|
||
|
|
o = np.outer(a, b)
|
||
|
|
o = o.reshape(a.shape + b.shape)
|
||
|
|
return np.concatenate(np.concatenate(o, axis=1), axis=1)
|
||
|
|
|
||
|
|
|
||
|
|
def block_diag(*arrs):
|
||
|
|
"""
|
||
|
|
Create a block diagonal matrix from provided arrays.
|
||
|
|
|
||
|
|
Given the inputs `A`, `B` and `C`, the output will have these
|
||
|
|
arrays arranged on the diagonal::
|
||
|
|
|
||
|
|
[[A, 0, 0],
|
||
|
|
[0, B, 0],
|
||
|
|
[0, 0, C]]
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
A, B, C, ... : array_like, up to 2-D
|
||
|
|
Input arrays. A 1-D array or array_like sequence of length `n` is
|
||
|
|
treated as a 2-D array with shape ``(1,n)``.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
D : ndarray
|
||
|
|
Array with `A`, `B`, `C`, ... on the diagonal. `D` has the
|
||
|
|
same dtype as `A`.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
If all the input arrays are square, the output is known as a
|
||
|
|
block diagonal matrix.
|
||
|
|
|
||
|
|
Empty sequences (i.e., array-likes of zero size) will not be ignored.
|
||
|
|
Noteworthy, both [] and [[]] are treated as matrices with shape ``(1,0)``.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from scipy.linalg import block_diag
|
||
|
|
>>> A = [[1, 0],
|
||
|
|
... [0, 1]]
|
||
|
|
>>> B = [[3, 4, 5],
|
||
|
|
... [6, 7, 8]]
|
||
|
|
>>> C = [[7]]
|
||
|
|
>>> P = np.zeros((2, 0), dtype='int32')
|
||
|
|
>>> block_diag(A, B, C)
|
||
|
|
array([[1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 0],
|
||
|
|
[0, 0, 3, 4, 5, 0],
|
||
|
|
[0, 0, 6, 7, 8, 0],
|
||
|
|
[0, 0, 0, 0, 0, 7]])
|
||
|
|
>>> block_diag(A, P, B, C)
|
||
|
|
array([[1, 0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 3, 4, 5, 0],
|
||
|
|
[0, 0, 6, 7, 8, 0],
|
||
|
|
[0, 0, 0, 0, 0, 7]])
|
||
|
|
>>> block_diag(1.0, [2, 3], [[4, 5], [6, 7]])
|
||
|
|
array([[ 1., 0., 0., 0., 0.],
|
||
|
|
[ 0., 2., 3., 0., 0.],
|
||
|
|
[ 0., 0., 0., 4., 5.],
|
||
|
|
[ 0., 0., 0., 6., 7.]])
|
||
|
|
|
||
|
|
"""
|
||
|
|
if arrs == ():
|
||
|
|
arrs = ([],)
|
||
|
|
arrs = [np.atleast_2d(a) for a in arrs]
|
||
|
|
|
||
|
|
bad_args = [k for k in range(len(arrs)) if arrs[k].ndim > 2]
|
||
|
|
if bad_args:
|
||
|
|
raise ValueError("arguments in the following positions have dimension "
|
||
|
|
"greater than 2: %s" % bad_args)
|
||
|
|
|
||
|
|
shapes = np.array([a.shape for a in arrs])
|
||
|
|
out_dtype = np.find_common_type([arr.dtype for arr in arrs], [])
|
||
|
|
out = np.zeros(np.sum(shapes, axis=0), dtype=out_dtype)
|
||
|
|
|
||
|
|
r, c = 0, 0
|
||
|
|
for i, (rr, cc) in enumerate(shapes):
|
||
|
|
out[r:r + rr, c:c + cc] = arrs[i]
|
||
|
|
r += rr
|
||
|
|
c += cc
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
def companion(a):
|
||
|
|
"""
|
||
|
|
Create a companion matrix.
|
||
|
|
|
||
|
|
Create the companion matrix [1]_ associated with the polynomial whose
|
||
|
|
coefficients are given in `a`.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
a : (N,) array_like
|
||
|
|
1-D array of polynomial coefficients. The length of `a` must be
|
||
|
|
at least two, and ``a[0]`` must not be zero.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
c : (N-1, N-1) ndarray
|
||
|
|
The first row of `c` is ``-a[1:]/a[0]``, and the first
|
||
|
|
sub-diagonal is all ones. The data-type of the array is the same
|
||
|
|
as the data-type of ``1.0*a[0]``.
|
||
|
|
|
||
|
|
Raises
|
||
|
|
------
|
||
|
|
ValueError
|
||
|
|
If any of the following are true: a) ``a.ndim != 1``;
|
||
|
|
b) ``a.size < 2``; c) ``a[0] == 0``.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
.. versionadded:: 0.8.0
|
||
|
|
|
||
|
|
References
|
||
|
|
----------
|
||
|
|
.. [1] R. A. Horn & C. R. Johnson, *Matrix Analysis*. Cambridge, UK:
|
||
|
|
Cambridge University Press, 1999, pp. 146-7.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from scipy.linalg import companion
|
||
|
|
>>> companion([1, -10, 31, -30])
|
||
|
|
array([[ 10., -31., 30.],
|
||
|
|
[ 1., 0., 0.],
|
||
|
|
[ 0., 1., 0.]])
|
||
|
|
|
||
|
|
"""
|
||
|
|
a = np.atleast_1d(a)
|
||
|
|
|
||
|
|
if a.ndim != 1:
|
||
|
|
raise ValueError("Incorrect shape for `a`. `a` must be "
|
||
|
|
"one-dimensional.")
|
||
|
|
|
||
|
|
if a.size < 2:
|
||
|
|
raise ValueError("The length of `a` must be at least 2.")
|
||
|
|
|
||
|
|
if a[0] == 0:
|
||
|
|
raise ValueError("The first coefficient in `a` must not be zero.")
|
||
|
|
|
||
|
|
first_row = -a[1:] / (1.0 * a[0])
|
||
|
|
n = a.size
|
||
|
|
c = np.zeros((n - 1, n - 1), dtype=first_row.dtype)
|
||
|
|
c[0] = first_row
|
||
|
|
c[list(range(1, n - 1)), list(range(0, n - 2))] = 1
|
||
|
|
return c
|
||
|
|
|
||
|
|
|
||
|
|
def helmert(n, full=False):
|
||
|
|
"""
|
||
|
|
Create a Helmert matrix of order `n`.
|
||
|
|
|
||
|
|
This has applications in statistics, compositional or simplicial analysis,
|
||
|
|
and in Aitchison geometry.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
n : int
|
||
|
|
The size of the array to create.
|
||
|
|
full : bool, optional
|
||
|
|
If True the (n, n) ndarray will be returned.
|
||
|
|
Otherwise the submatrix that does not include the first
|
||
|
|
row will be returned.
|
||
|
|
Default: False.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
M : ndarray
|
||
|
|
The Helmert matrix.
|
||
|
|
The shape is (n, n) or (n-1, n) depending on the `full` argument.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from scipy.linalg import helmert
|
||
|
|
>>> helmert(5, full=True)
|
||
|
|
array([[ 0.4472136 , 0.4472136 , 0.4472136 , 0.4472136 , 0.4472136 ],
|
||
|
|
[ 0.70710678, -0.70710678, 0. , 0. , 0. ],
|
||
|
|
[ 0.40824829, 0.40824829, -0.81649658, 0. , 0. ],
|
||
|
|
[ 0.28867513, 0.28867513, 0.28867513, -0.8660254 , 0. ],
|
||
|
|
[ 0.2236068 , 0.2236068 , 0.2236068 , 0.2236068 , -0.89442719]])
|
||
|
|
|
||
|
|
"""
|
||
|
|
H = np.tril(np.ones((n, n)), -1) - np.diag(np.arange(n))
|
||
|
|
d = np.arange(n) * np.arange(1, n+1)
|
||
|
|
H[0] = 1
|
||
|
|
d[0] = n
|
||
|
|
H_full = H / np.sqrt(d)[:, np.newaxis]
|
||
|
|
if full:
|
||
|
|
return H_full
|
||
|
|
else:
|
||
|
|
return H_full[1:]
|
||
|
|
|
||
|
|
|
||
|
|
def hilbert(n):
|
||
|
|
"""
|
||
|
|
Create a Hilbert matrix of order `n`.
|
||
|
|
|
||
|
|
Returns the `n` by `n` array with entries `h[i,j] = 1 / (i + j + 1)`.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
n : int
|
||
|
|
The size of the array to create.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
h : (n, n) ndarray
|
||
|
|
The Hilbert matrix.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
invhilbert : Compute the inverse of a Hilbert matrix.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
.. versionadded:: 0.10.0
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from scipy.linalg import hilbert
|
||
|
|
>>> hilbert(3)
|
||
|
|
array([[ 1. , 0.5 , 0.33333333],
|
||
|
|
[ 0.5 , 0.33333333, 0.25 ],
|
||
|
|
[ 0.33333333, 0.25 , 0.2 ]])
|
||
|
|
|
||
|
|
"""
|
||
|
|
values = 1.0 / (1.0 + np.arange(2 * n - 1))
|
||
|
|
h = hankel(values[:n], r=values[n - 1:])
|
||
|
|
return h
|
||
|
|
|
||
|
|
|
||
|
|
def invhilbert(n, exact=False):
|
||
|
|
"""
|
||
|
|
Compute the inverse of the Hilbert matrix of order `n`.
|
||
|
|
|
||
|
|
The entries in the inverse of a Hilbert matrix are integers. When `n`
|
||
|
|
is greater than 14, some entries in the inverse exceed the upper limit
|
||
|
|
of 64 bit integers. The `exact` argument provides two options for
|
||
|
|
dealing with these large integers.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
n : int
|
||
|
|
The order of the Hilbert matrix.
|
||
|
|
exact : bool, optional
|
||
|
|
If False, the data type of the array that is returned is np.float64,
|
||
|
|
and the array is an approximation of the inverse.
|
||
|
|
If True, the array is the exact integer inverse array. To represent
|
||
|
|
the exact inverse when n > 14, the returned array is an object array
|
||
|
|
of long integers. For n <= 14, the exact inverse is returned as an
|
||
|
|
array with data type np.int64.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
invh : (n, n) ndarray
|
||
|
|
The data type of the array is np.float64 if `exact` is False.
|
||
|
|
If `exact` is True, the data type is either np.int64 (for n <= 14)
|
||
|
|
or object (for n > 14). In the latter case, the objects in the
|
||
|
|
array will be long integers.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
hilbert : Create a Hilbert matrix.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
.. versionadded:: 0.10.0
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from scipy.linalg import invhilbert
|
||
|
|
>>> invhilbert(4)
|
||
|
|
array([[ 16., -120., 240., -140.],
|
||
|
|
[ -120., 1200., -2700., 1680.],
|
||
|
|
[ 240., -2700., 6480., -4200.],
|
||
|
|
[ -140., 1680., -4200., 2800.]])
|
||
|
|
>>> invhilbert(4, exact=True)
|
||
|
|
array([[ 16, -120, 240, -140],
|
||
|
|
[ -120, 1200, -2700, 1680],
|
||
|
|
[ 240, -2700, 6480, -4200],
|
||
|
|
[ -140, 1680, -4200, 2800]], dtype=int64)
|
||
|
|
>>> invhilbert(16)[7,7]
|
||
|
|
4.2475099528537506e+19
|
||
|
|
>>> invhilbert(16, exact=True)[7,7]
|
||
|
|
42475099528537378560L
|
||
|
|
|
||
|
|
"""
|
||
|
|
from scipy.special import comb
|
||
|
|
if exact:
|
||
|
|
if n > 14:
|
||
|
|
dtype = object
|
||
|
|
else:
|
||
|
|
dtype = np.int64
|
||
|
|
else:
|
||
|
|
dtype = np.float64
|
||
|
|
invh = np.empty((n, n), dtype=dtype)
|
||
|
|
for i in xrange(n):
|
||
|
|
for j in xrange(0, i + 1):
|
||
|
|
s = i + j
|
||
|
|
invh[i, j] = ((-1) ** s * (s + 1) *
|
||
|
|
comb(n + i, n - j - 1, exact) *
|
||
|
|
comb(n + j, n - i - 1, exact) *
|
||
|
|
comb(s, i, exact) ** 2)
|
||
|
|
if i != j:
|
||
|
|
invh[j, i] = invh[i, j]
|
||
|
|
return invh
|
||
|
|
|
||
|
|
|
||
|
|
def pascal(n, kind='symmetric', exact=True):
|
||
|
|
"""
|
||
|
|
Returns the n x n Pascal matrix.
|
||
|
|
|
||
|
|
The Pascal matrix is a matrix containing the binomial coefficients as
|
||
|
|
its elements.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
n : int
|
||
|
|
The size of the matrix to create; that is, the result is an n x n
|
||
|
|
matrix.
|
||
|
|
kind : str, optional
|
||
|
|
Must be one of 'symmetric', 'lower', or 'upper'.
|
||
|
|
Default is 'symmetric'.
|
||
|
|
exact : bool, optional
|
||
|
|
If `exact` is True, the result is either an array of type
|
||
|
|
numpy.uint64 (if n < 35) or an object array of Python long integers.
|
||
|
|
If `exact` is False, the coefficients in the matrix are computed using
|
||
|
|
`scipy.special.comb` with `exact=False`. The result will be a floating
|
||
|
|
point array, and the values in the array will not be the exact
|
||
|
|
coefficients, but this version is much faster than `exact=True`.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
p : (n, n) ndarray
|
||
|
|
The Pascal matrix.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
invpascal
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
See https://en.wikipedia.org/wiki/Pascal_matrix for more information
|
||
|
|
about Pascal matrices.
|
||
|
|
|
||
|
|
.. versionadded:: 0.11.0
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from scipy.linalg import pascal
|
||
|
|
>>> pascal(4)
|
||
|
|
array([[ 1, 1, 1, 1],
|
||
|
|
[ 1, 2, 3, 4],
|
||
|
|
[ 1, 3, 6, 10],
|
||
|
|
[ 1, 4, 10, 20]], dtype=uint64)
|
||
|
|
>>> pascal(4, kind='lower')
|
||
|
|
array([[1, 0, 0, 0],
|
||
|
|
[1, 1, 0, 0],
|
||
|
|
[1, 2, 1, 0],
|
||
|
|
[1, 3, 3, 1]], dtype=uint64)
|
||
|
|
>>> pascal(50)[-1, -1]
|
||
|
|
25477612258980856902730428600L
|
||
|
|
>>> from scipy.special import comb
|
||
|
|
>>> comb(98, 49, exact=True)
|
||
|
|
25477612258980856902730428600L
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
from scipy.special import comb
|
||
|
|
if kind not in ['symmetric', 'lower', 'upper']:
|
||
|
|
raise ValueError("kind must be 'symmetric', 'lower', or 'upper'")
|
||
|
|
|
||
|
|
if exact:
|
||
|
|
if n >= 35:
|
||
|
|
L_n = np.empty((n, n), dtype=object)
|
||
|
|
L_n.fill(0)
|
||
|
|
else:
|
||
|
|
L_n = np.zeros((n, n), dtype=np.uint64)
|
||
|
|
for i in range(n):
|
||
|
|
for j in range(i + 1):
|
||
|
|
L_n[i, j] = comb(i, j, exact=True)
|
||
|
|
else:
|
||
|
|
L_n = comb(*np.ogrid[:n, :n])
|
||
|
|
|
||
|
|
if kind == 'lower':
|
||
|
|
p = L_n
|
||
|
|
elif kind == 'upper':
|
||
|
|
p = L_n.T
|
||
|
|
else:
|
||
|
|
p = np.dot(L_n, L_n.T)
|
||
|
|
|
||
|
|
return p
|
||
|
|
|
||
|
|
|
||
|
|
def invpascal(n, kind='symmetric', exact=True):
|
||
|
|
"""
|
||
|
|
Returns the inverse of the n x n Pascal matrix.
|
||
|
|
|
||
|
|
The Pascal matrix is a matrix containing the binomial coefficients as
|
||
|
|
its elements.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
n : int
|
||
|
|
The size of the matrix to create; that is, the result is an n x n
|
||
|
|
matrix.
|
||
|
|
kind : str, optional
|
||
|
|
Must be one of 'symmetric', 'lower', or 'upper'.
|
||
|
|
Default is 'symmetric'.
|
||
|
|
exact : bool, optional
|
||
|
|
If `exact` is True, the result is either an array of type
|
||
|
|
`numpy.int64` (if `n` <= 35) or an object array of Python integers.
|
||
|
|
If `exact` is False, the coefficients in the matrix are computed using
|
||
|
|
`scipy.special.comb` with `exact=False`. The result will be a floating
|
||
|
|
point array, and for large `n`, the values in the array will not be the
|
||
|
|
exact coefficients.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
invp : (n, n) ndarray
|
||
|
|
The inverse of the Pascal matrix.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
pascal
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
|
||
|
|
.. versionadded:: 0.16.0
|
||
|
|
|
||
|
|
References
|
||
|
|
----------
|
||
|
|
.. [1] "Pascal matrix", https://en.wikipedia.org/wiki/Pascal_matrix
|
||
|
|
.. [2] Cohen, A. M., "The inverse of a Pascal matrix", Mathematical
|
||
|
|
Gazette, 59(408), pp. 111-112, 1975.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from scipy.linalg import invpascal, pascal
|
||
|
|
>>> invp = invpascal(5)
|
||
|
|
>>> invp
|
||
|
|
array([[ 5, -10, 10, -5, 1],
|
||
|
|
[-10, 30, -35, 19, -4],
|
||
|
|
[ 10, -35, 46, -27, 6],
|
||
|
|
[ -5, 19, -27, 17, -4],
|
||
|
|
[ 1, -4, 6, -4, 1]])
|
||
|
|
|
||
|
|
>>> p = pascal(5)
|
||
|
|
>>> p.dot(invp)
|
||
|
|
array([[ 1., 0., 0., 0., 0.],
|
||
|
|
[ 0., 1., 0., 0., 0.],
|
||
|
|
[ 0., 0., 1., 0., 0.],
|
||
|
|
[ 0., 0., 0., 1., 0.],
|
||
|
|
[ 0., 0., 0., 0., 1.]])
|
||
|
|
|
||
|
|
An example of the use of `kind` and `exact`:
|
||
|
|
|
||
|
|
>>> invpascal(5, kind='lower', exact=False)
|
||
|
|
array([[ 1., -0., 0., -0., 0.],
|
||
|
|
[-1., 1., -0., 0., -0.],
|
||
|
|
[ 1., -2., 1., -0., 0.],
|
||
|
|
[-1., 3., -3., 1., -0.],
|
||
|
|
[ 1., -4., 6., -4., 1.]])
|
||
|
|
|
||
|
|
"""
|
||
|
|
from scipy.special import comb
|
||
|
|
|
||
|
|
if kind not in ['symmetric', 'lower', 'upper']:
|
||
|
|
raise ValueError("'kind' must be 'symmetric', 'lower' or 'upper'.")
|
||
|
|
|
||
|
|
if kind == 'symmetric':
|
||
|
|
if exact:
|
||
|
|
if n > 34:
|
||
|
|
dt = object
|
||
|
|
else:
|
||
|
|
dt = np.int64
|
||
|
|
else:
|
||
|
|
dt = np.float64
|
||
|
|
invp = np.empty((n, n), dtype=dt)
|
||
|
|
for i in range(n):
|
||
|
|
for j in range(0, i + 1):
|
||
|
|
v = 0
|
||
|
|
for k in range(n - i):
|
||
|
|
v += comb(i + k, k, exact=exact) * comb(i + k, i + k - j,
|
||
|
|
exact=exact)
|
||
|
|
invp[i, j] = (-1)**(i - j) * v
|
||
|
|
if i != j:
|
||
|
|
invp[j, i] = invp[i, j]
|
||
|
|
else:
|
||
|
|
# For the 'lower' and 'upper' cases, we computer the inverse by
|
||
|
|
# changing the sign of every other diagonal of the pascal matrix.
|
||
|
|
invp = pascal(n, kind=kind, exact=exact)
|
||
|
|
if invp.dtype == np.uint64:
|
||
|
|
# This cast from np.uint64 to int64 OK, because if `kind` is not
|
||
|
|
# "symmetric", the values in invp are all much less than 2**63.
|
||
|
|
invp = invp.view(np.int64)
|
||
|
|
|
||
|
|
# The toeplitz matrix has alternating bands of 1 and -1.
|
||
|
|
invp *= toeplitz((-1)**np.arange(n)).astype(invp.dtype)
|
||
|
|
|
||
|
|
return invp
|
||
|
|
|
||
|
|
|
||
|
|
def dft(n, scale=None):
|
||
|
|
"""
|
||
|
|
Discrete Fourier transform matrix.
|
||
|
|
|
||
|
|
Create the matrix that computes the discrete Fourier transform of a
|
||
|
|
sequence [1]_. The n-th primitive root of unity used to generate the
|
||
|
|
matrix is exp(-2*pi*i/n), where i = sqrt(-1).
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
n : int
|
||
|
|
Size the matrix to create.
|
||
|
|
scale : str, optional
|
||
|
|
Must be None, 'sqrtn', or 'n'.
|
||
|
|
If `scale` is 'sqrtn', the matrix is divided by `sqrt(n)`.
|
||
|
|
If `scale` is 'n', the matrix is divided by `n`.
|
||
|
|
If `scale` is None (the default), the matrix is not normalized, and the
|
||
|
|
return value is simply the Vandermonde matrix of the roots of unity.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
m : (n, n) ndarray
|
||
|
|
The DFT matrix.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
When `scale` is None, multiplying a vector by the matrix returned by
|
||
|
|
`dft` is mathematically equivalent to (but much less efficient than)
|
||
|
|
the calculation performed by `scipy.fftpack.fft`.
|
||
|
|
|
||
|
|
.. versionadded:: 0.14.0
|
||
|
|
|
||
|
|
References
|
||
|
|
----------
|
||
|
|
.. [1] "DFT matrix", https://en.wikipedia.org/wiki/DFT_matrix
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from scipy.linalg import dft
|
||
|
|
>>> np.set_printoptions(precision=5, suppress=True)
|
||
|
|
>>> x = np.array([1, 2, 3, 0, 3, 2, 1, 0])
|
||
|
|
>>> m = dft(8)
|
||
|
|
>>> m.dot(x) # Compute the DFT of x
|
||
|
|
array([ 12.+0.j, -2.-2.j, 0.-4.j, -2.+2.j, 4.+0.j, -2.-2.j,
|
||
|
|
-0.+4.j, -2.+2.j])
|
||
|
|
|
||
|
|
Verify that ``m.dot(x)`` is the same as ``fft(x)``.
|
||
|
|
|
||
|
|
>>> from scipy.fftpack import fft
|
||
|
|
>>> fft(x) # Same result as m.dot(x)
|
||
|
|
array([ 12.+0.j, -2.-2.j, 0.-4.j, -2.+2.j, 4.+0.j, -2.-2.j,
|
||
|
|
0.+4.j, -2.+2.j])
|
||
|
|
"""
|
||
|
|
if scale not in [None, 'sqrtn', 'n']:
|
||
|
|
raise ValueError("scale must be None, 'sqrtn', or 'n'; "
|
||
|
|
"%r is not valid." % (scale,))
|
||
|
|
|
||
|
|
omegas = np.exp(-2j * np.pi * np.arange(n) / n).reshape(-1, 1)
|
||
|
|
m = omegas ** np.arange(n)
|
||
|
|
if scale == 'sqrtn':
|
||
|
|
m /= math.sqrt(n)
|
||
|
|
elif scale == 'n':
|
||
|
|
m /= n
|
||
|
|
return m
|