galenseilis

from itertools import combinations

import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
from sklearn.datasets import load_iris
from sklearn.manifold import Isomap

X,y = load_iris(return_X_y=True)
pca = Isomap(n_components=2)

galenseilis

from itertools import combinations

import matplotlib.pyplot as plt
import networkx as nx
import numpy as np

X = np.random.normal(size=1000).reshape((500,2))
pos = {i:X[i] for i in range(X.shape[0])}

g = nx.Graph()

galenseilis

import numpy as np
from scipy.odr import *
import pandas as pd

n = 10000
true_X = np.concatenate(tuple([np.eye(3)]*n))
true_X[:,0] = 1.0
true_beta = np.array([3, 5, 7])

true_y = np.matmul(true_X, true_beta)

galenseilis

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns

x = np.random.uniform(-10,10,size=10000)
y = x**2
dydx = np.exp(x) / (1 + np.exp(-x)) ** 2
d = {'x':x, 'y':y, 'dydx':dydx}
df = pd.DataFrame(d)

galenseilis

from graphviz import Digraph

def brack_depth_map(file, bracks=('{', '}')):
    open_brack, close_brack = bracks
    d = {}
    depth = 0
    with open(source_path) as f:
        for line_index, line in enumerate(f):
            d[line] = depth
            if open_brack in line:

galenseilis

import numpy as np
import sympy as sp
from sympy.abc import a, x, y, z


def reflect(exp):
    for symbol in exp.free_symbols:
        exp = exp.subs(symbol, -symbol)
    return exp

galenseilis

import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD

X = np.array([[0,0],[0,1],[1,0],[1,1]], "float32")
y = np.array([[0],[1],[1],[0]], "float32")

galenseilis

import matplotlib.pyplot as plt
from sympy import cos, pi, sin
from sympy.abc import phi, theta
from sympy.plotting.plot import plot3d_parametric_surface

plt.rcParams['figure.figsize'] = 10,10
p1 = plot3d_parametric_surface(sin(phi) * cos(theta), sin(phi) * sin(theta), cos(phi),
                          (phi, 0, pi), (theta, 0, 2 * pi))

galenseilis

from itertools import combinations, chain, product
import matplotlib.pyplot as plt
import numpy as np

def full_frame(width=19.20, height=10.80):
    import matplotlib as mpl
    mpl.rcParams['savefig.pad_inches'] = 0
    figsize = None if width is None else (width, height)
    fig = plt.figure(figsize=figsize)
    ax = plt.axes([0,0,1,1], frameon=False)

galenseilis

import numpy as np
import matplotlib.pyplot as plt

shape=(1080, 1920, 3)
img = np.zeros(shape)

for i in range(1, shape[0]):
    for j in range(1, shape[1]):
        img[i,j,0] = i%j
        img[i,j,1] = j%i