def __init__(self, n, d, real_dtype="d", integer_dtype="l"):
"""
Input arguments:
n -- Number of maxima and minima to remember
d -- Minimum gap between two hits
real_dtype -- dtype of sequence items
integer_dtype -- dtype of sequence indices
Note: be careful with dtypes!
"""
self.n = int(n)
self.d = int(d)
self.iM = numx.zeros((n, ), dtype=integer_dtype)
self.im = numx.zeros((n, ), dtype=integer_dtype)
real_dtype = numx.dtype(real_dtype)
if real_dtype in mdp.utils.get_dtypes('AllInteger'):
max_num = numx.iinfo(real_dtype).max
min_num = numx.iinfo(real_dtype).min
else:
max_num = numx.finfo(real_dtype).max
min_num = numx.finfo(real_dtype).min
self.M = numx.array([min_num]*n, dtype=real_dtype)
self.m = numx.array([max_num]*n, dtype=real_dtype)
self.lM = 0
self.lm = 0
def __init__(self, n, d, real_dtype="d", integer_dtype="l"):
"""
Input arguments:
n -- Number of maxima and minima to remember
d -- Minimum gap between two hits
real_dtype -- dtype of sequence items
integer_dtype -- dtype of sequence indices
Note: be careful with dtypes!
"""
self.n = int(n)
self.d = int(d)
self.iM = numx.zeros((n, ), dtype=integer_dtype)
self.im = numx.zeros((n, ), dtype=integer_dtype)
real_dtype = numx.dtype(real_dtype)
if real_dtype in mdp.utils.get_dtypes('AllInteger'):
max_num = numx.iinfo(real_dtype).max
min_num = numx.iinfo(real_dtype).min
else:
max_num = numx.finfo(real_dtype).max
min_num = numx.finfo(real_dtype).min
self.M = numx.array([min_num]*n, dtype=real_dtype)
self.m = numx.array([max_num]*n, dtype=real_dtype)
self.lM = 0
self.lm = 0
def __init__(self, n, d, real_dtype="d", integer_dtype="l"):
"""Initializes an object of type 'OneDimensionalHitParade'.
:param n: Number of maxima and minima to remember.
:type n: int
:param d: Minimum gap between two hits.
:type d: int
:param real_dtype: Datatype of sequence items
:type real_dtype: numpy.dtype or str
:param integer_dtype: Datatype of sequence indices
:type integer_dtype: numpy.dtype or str
"""
self.n = int(n)
self.d = int(d)
self.iM = numx.zeros((n, ), dtype=integer_dtype)
self.im = numx.zeros((n, ), dtype=integer_dtype)
real_dtype = numx.dtype(real_dtype)
if real_dtype in mdp.utils.get_dtypes('AllInteger'):
max_num = numx.iinfo(real_dtype).max
min_num = numx.iinfo(real_dtype).min
else:
max_num = numx.finfo(real_dtype).max
min_num = numx.finfo(real_dtype).min
self.M = numx.array([min_num]*n, dtype=real_dtype)
self.m = numx.array([max_num]*n, dtype=real_dtype)
self.lM = 0
self.lm = 0
def __init__(self, n, d, real_dtype="d", integer_dtype="l"):
"""Initializes an object of type 'OneDimensionalHitParade'.
:param n: Number of maxima and minima to remember.
:type n: int
:param d: Minimum gap between two hits.
:type d: int
:param real_dtype: Datatype of sequence items
:type real_dtype: numpy.dtype or str
:param integer_dtype: Datatype of sequence indices
:type integer_dtype: numpy.dtype or str
"""
self.n = int(n)
self.d = int(d)
self.iM = numx.zeros((n, ), dtype=integer_dtype)
self.im = numx.zeros((n, ), dtype=integer_dtype)
real_dtype = numx.dtype(real_dtype)
if real_dtype in mdp.utils.get_dtypes('AllInteger'):
max_num = numx.iinfo(real_dtype).max
min_num = numx.iinfo(real_dtype).min
else:
max_num = numx.finfo(real_dtype).max
min_num = numx.finfo(real_dtype).min
self.M = numx.array([min_num] * n, dtype=real_dtype)
self.m = numx.array([max_num] * n, dtype=real_dtype)
self.lM = 0
self.lm = 0
def _assert_eigenvalues_real_and_positive(w, dtype):
tol = numx.finfo(dtype.type).eps * 100
if abs(w.imag).max() > tol:
err = "Some eigenvalues have significant imaginary part: %s " % str(w)
raise mdp.SymeigException(err)
from past.utils import old_div
__docformat__ = "restructuredtext en"
import sys as _sys
import mdp
from mdp import Node, NodeException, numx, numx_rand
from mdp.nodes import WhiteningNode
from mdp.utils import (DelayCovarianceMatrix, MultipleCovarianceMatrices,
rotate, mult)
# TODO: support floats of size different than 64-bit; will need to change SQRT_EPS_D
# rename often used functions
sum, cos, sin, PI = numx.sum, numx.cos, numx.sin, numx.pi
SQRT_EPS_D = numx.sqrt(numx.finfo('d').eps)
def _triu(m, k=0):
""" returns the elements on and above the k-th diagonal of m. k=0 is the
main diagonal, k > 0 is above and k < 0 is below the main diagonal."""
N = m.shape[0]
M = m.shape[1]
x = numx.greater_equal(numx.subtract.outer(numx.arange(N),
numx.arange(M)),1-k)
out = (1-x)*m
return out
#############
class ISFANode(Node):
"""
Perform Independent Slow Feature Analysis on the input data.
def _assert_eigenvalues_real_and_positive(w, dtype):
tol = numx.finfo(dtype.type).eps * 100
if abs(w.imag).max() > tol:
err = "Some eigenvalues have significant imaginary part: %s " % str(w)
raise mdp.SymeigException(err)
from past.utils import old_div
__docformat__ = "restructuredtext en"
import sys as _sys
import mdp
from mdp import Node, NodeException, numx, numx_rand
from mdp.nodes import WhiteningNode
from mdp.utils import (DelayCovarianceMatrix, MultipleCovarianceMatrices,
rotate, mult)
# Licensed under the BSD License, see Copyright file for details.
# TODO: support floats of size different than 64-bit; will need to change SQRT_EPS_D
# rename often used functions
sum, cos, sin, PI = numx.sum, numx.cos, numx.sin, numx.pi
SQRT_EPS_D = numx.sqrt(numx.finfo('d').eps)
def _triu(m, k=0):
"""Reduces a matrix to a triangular part.
:param m: A Matrix.
:param k: Index of diagonal.
:type k: int
:return: Elements on and above the k-th diagonal of m. k=0 is the
main diagonal, k > 0 is above and k < 0 is below the main diagonal.
"""
N = m.shape[0]
M = m.shape[1]
x = numx.greater_equal(numx.subtract.outer(numx.arange(N),
__docformat__ = "restructuredtext en"
import sys as _sys
import mdp
from mdp import Node, NodeException, numx, numx_rand
from mdp.nodes import WhiteningNode
from mdp.utils import DelayCovarianceMatrix, MultipleCovarianceMatrices, rotate, mult
# TODO: support floats of size different than 64-bit; will need to change SQRT_EPS_D
# rename often used functions
sum, cos, sin, PI = numx.sum, numx.cos, numx.sin, numx.pi
SQRT_EPS_D = numx.sqrt(numx.finfo("d").eps)
def _triu(m, k=0):
""" returns the elements on and above the k-th diagonal of m. k=0 is the
main diagonal, k > 0 is above and k < 0 is below the main diagonal."""
N = m.shape[0]
M = m.shape[1]
x = numx.greater_equal(numx.subtract.outer(numx.arange(N), numx.arange(M)), 1 - k)
out = (1 - x) * m
return out
#############
class ISFANode(Node):
"""
Perform Independent Slow Feature Analysis on the input data.