ahwillia

import numpy as np
from scipy.spatial.distance import pdist, squareform
from scipy.sparse import coo_matrix
from scipy.sparse.csgraph import laplacian
import matplotlib.pyplot as plt

def symmetric_knn_graph(X, k):
    """
    Parameters
    ----------

ahwillia

"""
NOTE: This code has not been rigorously tested.
"""
import matplotlib.pyplot as plt
import jax.numpy as jnp
import jax
from tqdm import trange


def elliptical_slice_update(x, log_density, sigmas, key):

ahwillia

import matplotlib.pyplot as plt
import numpy as np

def raised_cos(t, loc, scale, amp):
    """
    Raised, 1d-cosine basis functions tiling [0, 2 * pi)
    These functions have the property of summing to a
    constant amplitude at all points (i.e. uniform
    tiling of space).

ahwillia

import numba
import numpy as np
from scipy.spatial.distance import pdist, squareform
from math import comb

@numba.jit(nopython=True)
def index(n, i, j):
    """
    Computes linear index of (i, j) from the (n x n) distance matrix.
    """

ahwillia

import numpy as np
from scipy.spatial.distance import cdist, pdist

def mmd_two_sample_test(X, Y):
    """
    Implements Gretton's test for equality of
    distributions in high-dimensional settings
    using concentration bounds on the maximum
    mean discrepancy (MMD). This function uses
    the unbiased estimator of the MMD (see

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

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