galenseilis

from itertools import combinations
from time import ctime, time

import numpy as np
import matplotlib.pyplot as plt
from multiprocessing import Pool

def corr(X, method='pearson'):
    if method == 'pearson':
        X = X - np.mean(X, axis=0)

galenseilis

import matplotlib.pyplot as plt
import numpy as np

N = 10**2
x = np.random.normal(0,100,size=N)

means = []
stderrors = []
for i in range(2, N+1):
    sample = np.random.choice(x, size=i, replace=False)

galenseilis

# https://stats.stackexchange.com/questions/557381/how-do-i-interpret-or-explain-loess-plot
from statsmodels.nonparametric.smoothers_lowess import lowess
import matplotlib.pyplot as plt
import numpy as np
from p_tqdm import p_map
from astropy.stats import bootstrap

x = np.linspace(0,80,1000)
y =  np.sin(x) + 0.1 * x + np.random.normal(0, 0.1, size=1000)

galenseilis

from semopy import Model, semplot

desc = '''
Y ~ X
Y ~ Z
'''

model = Model(desc)
semplot(model, 'test.png', plot_covs=True)

galenseilis

import matplotlib.pyplot as plt

from time import time
import numpy as np
import tensorly as tl
from tensorly.random import random_cp
from tensorly.decomposition import CP, parafac

tol = np.logspace(-1, -9)
err = np.empty_like(tol)

galenseilis

import hypernetx as hnx
import matplotlib.pyplot as plt
from itertools import permutations

values = set(range(10))


solutions = {}
sol_count = 0
for perm in permutations(values, r=6):

galenseilis

from codecs import decode
import struct

class SimpleLinearRegression():
    '''
    Simple linear regression trained with bit flipping.
    '''
    
    def __init__(self):
        self.slope = self.random_bin()

galenseilis

import numpy as np
import struct
import pandas as pd
import matplotlib.pyplot as plt

def binary(num):
    return ''.join('{:0>8b}'.format(c) for c in struct.pack('!f', num))

binary = np.vectorize(binary)

galenseilis

from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np

def V(x,y,z):
     return x * y * z

X,Y = np.mgrid[-1:1:100j, -1:1:100j]
Z_vals = [ -0.5, 0.0, 0.5]

galenseilis

from itertools import product
import numpy as np


class Interval(object):

    def __init__(self, lower, upper):
        assert lower <= upper
        self.a = lower
        self.b = upper