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

ahwillia

import numpy as np
from tqdm import trange
import matplotlib.pyplot as plt


# TODO: subclass np.ndarray?
class ShiftMatrix(object):
    """
    Thin wrapper around a numpy matrix to support shifting along the second
    axis and padding with zeros.

ahwillia

import numpy as np
from numpy.random import randn, rand
from scipy.optimize import minimize
import matplotlib.pyplot as plt
from nnls import nnlsm_blockpivot as nnlstsq
import itertools
from scipy.spatial.distance import cdist

def censored_lstsq(A, B, M):
    """Solves least squares problem with missing data in B

ahwillia

def reverse_segment(path, n1, n2):
    """Reverse the nodes between n1 and n2.
    """
    q = path.copy()
    if n2 > n1:
        q[n1:(n2+1)] = path[n1:(n2+1)][::-1]
        return q
    else:
        seg = np.hstack((path[n1:], path[:(n2+1)]))[::-1]
        brk = len(q) - n1

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 pca_crossval import * 
from tqdm import tqdm
import itertools
np.random.seed(1111)

m, n, r = 100, 101, 3
Utrue, Vtrue = rand(m,r), rand(r,n)
data = np.dot(Utrue, Vtrue) + .25*randn(m,n)
data[data < 0] = 0
ranks = []

ahwillia

import numpy as np
import tensorflow as tf
from tqdm import tqdm

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

# generate data
W_true = np.random.randn(N,R).astype(np.float32)

ahwillia

import tensorflow as tf # works on version 1.0.0
import numpy as np
from tqdm import trange

# create fake data (low-rank matrix X)
A = np.random.randn(100, 3).astype(np.float32)
B = np.random.randn(3, 100).astype(np.float32)
X = np.dot(A, B)

# create tensorflow variables to predict low-rank decomposition

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

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)