ahwillia

import numpy as np
from scipy.optimize import linear_sum_assignment
from sklearn.utils import check_random_state
import scipy.sparse

def perm_alignment(X, Y):
    """
    Given two matrix X and Y. Returns sparse matrix P, holding permutation
    matrix that minimizes norm(X @ P - Y).

ahwillia

"""
Supervised PCA model.

Ritchie, A., Balzano, L., Kessler, D., Sripada, C. S., & Scott, C.
(2020). Supervised PCA: A Multiobjective Approach. arXiv:2011.05309.
"""

import numpy as onp
import autograd.numpy as np
from pymanopt.manifolds import Grassmann, Euclidean, Product

ahwillia

import numpy as np
import torch
import matplotlib.pyplot as plt
from torch_nonneg_linesearch import nonneg_projected_gradient_step

# Data dimensions
m, n = 100, 101
rank = 3

# Data matrix, detached from the graph.

ahwillia

"""
Python code to generate M-splines.

References
----------
Ramsay, J. O. (1988). Monotone regression splines in action.
Statistical science, 3(4), 425-441.
"""

import numpy as np

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from matplotlib.colors import LinearSegmentedColormap, colorConverter

def simple_cmap(colors, name='none'):
    """Create a colormap from a sequence of rgb values.
    cmap = simple_cmap([(1,1,1), (1,0,0)]) # white to red colormap
    cmap = simple_cmap(['w', 'r'])         # white to red colormap
    cmap = simple_cmap(['r', 'b', 'r'])    # red to blue to red
    """

    # check inputs

ahwillia

"""
A simple implementation of a permutation test among two
independent samples.
"""

import numpy as np
from sklearn.utils.validation import check_random_state
from more_itertools import distinct_permutations
from scipy.stats import percentileofscore
from math import factorial

ahwillia

import scipy.io as spio
import numpy as np

def loadmat(filename):
    '''
    this function should be called instead of direct spio.loadmat
    as it cures the problem of not properly recovering python dictionaries
    from mat files. It calls the function check keys to cure all entries
    which are still mat-objects
    '''

ahwillia

from sklearn.datasets import make_biclusters
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

def resort_rows_hclust(U):
    """Sorts the rows of a matrix by hierarchical clustering

    Parameters:
        U (ndarray) : matrix of data

ahwillia

import numpy as np
from sklearn.utils.extmath import randomized_svd


def partial_whiten(X, alpha, eigval_tol=1e-7):
    """
    Return regularized whitening transform for a matrix X.

    Parameters
    ----------

ahwillia

# The MIT License (MIT)
# 
# Copyright (c) Alex H. Williams
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation
# files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy,
# modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the