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

import numpy as np
import scipy.linalg

def elastic_net(A, B, x=None, l1=1, l2=1, lam=1, tol=1e-6, maxiter=10000):
    """Performs elastic net regression by ADMM

    minimize ||A*x - B|| + l1*|x| + l2*||x||

    Args:
      A (ndarray) : m x n matrix

ahwillia

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

import numpy as np
import tensorflow as tf

# N, size of matrix. R, rank of data
N = 100
R = 5

# generate data
W_true = np.random.randn(N,R)
C_true = np.random.randn(R,N)

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

"""
References:

  - B. Plateau, On the stochastic structure of parallelism and synchronization models for distributed algorithms.
  Perform. Eval. Rev., 13 (1985), pp. 147–154.

  - Dayar, T., & Orhan, M. C. (2015). On vector-Kronecker product multiplication with rectangular factors.
  SIAM Journal on Scientific Computing, 37(5), S526-S543.
"""

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

using PyPlot
using Distributions

function credible_interval(D::UnivariateDistribution; c=0.95, nx=1000)
	# Discretize over the support
	r = support(D)
	lb,ub = r.lb,r.ub

	# Histogram approximation of area under pdf
	x = linspace(lb,ub,nx)