detrin

import requests

test_data = [
        [8.0, 390.0, 190.0, 3850.0, 8.5, 70.0, 0.0, 0.0, 1.0],
        [8.0, 360.0, 215.0, 4615.0, 14.0, 70.0, 0.0, 0.0, 1.0],
        [8.0, 304.0, 193.0, 4732.0, 18.5, 70.0, 0.0, 0.0, 1.0],
        [4.0, 113.0, 95.0, 2228.0, 14.0, 71.0, 0.0, 1.0, 0.0],
        [6.0, 232.0, 100.0, 2634.0, 13.0, 71.0, 0.0, 0.0, 1.0],
    ]

detrin

import sys
from flask import Flask, request, jsonify
import traceback
import pandas as pd
import numpy as np
import json
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing

detrin

normalizer = preprocessing.Normalization(axis=-1)
normalizer.adapt(np.array(train_features))

model = keras.Sequential(
    [
        normalizer,
        layers.Dense(64, activation="relu"),
        layers.Dense(64, activation="relu"),
        layers.Dense(1),
    ]

detrin

url = "http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data"
column_names = [
    "MPG",
    "Cylinders",
    "Displacement",
    "Horsepower",
    "Weight",
    "Acceleration",
    "Model Year",
    "Origin",

detrin

from pyvirtualdisplay import Display
from selenium import webdriver
import time
import numpy as np

display = Display(visible=0, size=(1920, 1080))
display.start()

options = webdriver.ChromeOptions()
options.add_argument("--kiosk")

detrin

import numpy as np
import math
from mayavi import mlab

mlab.clf()
x, y, z = np.mgrid[-3:3:50j, -3:3:50j, -3:3:50j]

# Plot a sphere of radius 1
values = x * x + y * y + z * z - np.sqrt(3)
mlab.contour3d(x, y, z, values, contours=20, colormap="jet", opacity=0.5)

detrin

import numpy as np
import matplotlib.pyplot as plt

from mpl_toolkits import mplot3d


theta = np.linspace(0, 8 * np.pi, 120)
w = np.linspace(-0.25, 0.25, 8)
w, theta = np.meshgrid(w, theta)

detrin

import numpy as np
import matplotlib.pyplot as plt
import random

from matplotlib import rc
rc('font',**{'family':'serif','serif':['Helvetica']})
rc('text', usetex=True)

start, stop, step = 1, 10, 0.4
data_x = [x for x in np.arange(start, stop, step)]

detrin

# -*- coding: utf-8 -*-

import random
import time
from concurrent.futures import ProcessPoolExecutor, as_completed
from multiprocessing import Pool

import numpy
from tqdm import tqdm