loiseaujc

# --> Import standard Python libraries.
import numpy as np
from scipy.special import expit
from scipy.linalg import norm

# --> Import sklearn utility functions.
from sklearn.base import BaseEstimator, ClassifierMixin


class LogisticRegression_Newton(BaseEstimator, ClassifierMixin):

loiseaujc

# --> Import standard Python libraries.
import numpy as np
from scipy.special import expit
from scipy.linalg import norm

# --> Import sklearn utility functions.
from sklearn.base import BaseEstimator, ClassifierMixin


class LogisticRegression_GD(BaseEstimator, ClassifierMixin):

loiseaujc

# --> Import standard Python libraries.
import numpy as np

# --> Import sklearn utility functions to create derived-class objects.
from sklearn.base import BaseEstimator, ClassifierMixin

# --> Redefine the Heaviside function.
def H(x): return np.heaviside(x-0.5, 1).astype(np.int)

loiseaujc

"""
Spectral Proper Orthogonal Decomposition
-----------------------------------------

This module implements the Spectral Proper Orthogonal Decomposition class. The
present implementation corresponds to the batch algorithm originally proposed
in [1]. Note that a streaming algorithm has also been proposed in [2].

References
----------

loiseaujc

# --> Import standard Python libraries.
import numpy as np

# --> Import sklearn utility functions to create derived-class objects.
from sklearn.base import BaseEstimator, ClassifierMixin

# --> Redefine the Heavisde function.
H = lambda x: np.heaviside(x, 1).astype(np.int)

class Rosenblatt(BaseEstimator, ClassifierMixin):