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

ahwillia

import numpy as np
import numpy.random as npr
from functools import reduce

# Goal
# ----
# Compute (As[0] kron As[1] kron ... As[-1]) @ v

# ==== HELPER FUNCTIONS ==== #

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 numpy as np
from scipy.linalg import solve_circulant, circulant
from numpy.testing import assert_array_almost_equal
import numba


@numba.jit(nopython=True, cache=True)
def rojo_method(c, a, f, x, z):
    """
    Solves symmetric, tridiagonal circulant system, assuming diagonal

ahwillia

def bandpass(x, lowcut, highcut, fs, order=5, axis=-1, kind='butter'):
    """
    Parameters
    ----------
    x : ndarray
        1d time series data
    lowcut : float
        Defines lower frequency cutoff (e.g. in Hz)
    highcut : float
        Defines upper frequency cutoff (e.g. in Hz)

ahwillia

"""
Computing hessian-vector products in tensorflow.

For simplicity, we demonstrate the idea on a Poisson regression model.
"""

import tensorflow as tf
import numpy as np
from scipy.optimize import minimize

ahwillia

"""
A simple implementation of Poisson regression.
"""

import numpy as np
from scipy.optimize import minimize

n = 1000   # number of datapoints
p = 5     # number of features