utahka

import random
from copy import deepcopy
from deap import base, creator, tools

def eval_one_max(individual) -> tuple:
    return sum(individual),
  
def main():
    # creator.FitnessMax クラスと、creator.Individual クラスを作成
    creator.create("FitnessMax", base.Fitness, weights=(1.0,))

utahka

import numpy as np
import scipy.stats

# %matplotlib inline
import matplotlib.pyplot as plt
from matplotlib import mlab
plt.style.use("ggplot")
plt.rcParams["font.size"] = 16
plt.rcParams["figure.figsize"] = 10, 8

utahka

import numpy as np

from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

def salt_and_pepper_noise(x, ratio):
    x_noise = x.copy()

utahka

import numpy as np

def intersect2d(x, y):
  _, ncols = x.shape
  
  dtype = {
    "names": [f"f{i}" for i in range(ncols)],
    "formats": ncols * [predict.dtype],
  }
        

utahka

from IPython.display import clear_output, Image, display, HTML
import tensorflow as tf
import numpy as np

def strip_consts(graph_def, max_const_size=32):
    """Strip large constant values from graph_def."""
    strip_def = tf.GraphDef()
    for n0 in graph_def.node:
        n = strip_def.node.add() 
        n.MergeFrom(n0)

utahka

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# for matplotlib
plt.style.use("ggplot")
plt.rcParams["font.size"] = 13
plt.rcParams["figure.figsize"] = 10, 8

utahka

import datetime
from logging import getLogger, Formatter, FileHandler, StreamHandler, DEBUG

today = datetime.date.today()
today = today.strftime("%Y%m%d")

# Specify a path to log file which will be written log messages
path_to_log = f"log/{today}.log"
fmt = "%(asctime)s %(name)s %(lineno)d [%(levelname)s][%(funcName)s] %(message)s"
log_fmt = Formatter(fmt)

utahka

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import animation

plt.style.use("ggplot")
plt.rcParams["font.size"] = 13
plt.rcParams["figure.figsize"] = 16, 8

class GradientDescent(object):
    def __init__(self, f, grad, init_x, n_iter=100, learning_ratio=0.01, delta=0.1**8):

utahka

import pandas as pd

cols = ['col1', 'col2']
df = pd.DataFrame(index=[], columns=cols)

record = pd.Series(['hoge', 'fuga'], index=df.columns)

for _ in range(5):
  df = df.append(record, ignore_index=True)
  

utahka

import numpy as np
import matplotlib.pyplot as plt

plt.style.use("ggplot")
plt.rcParams["font.size"] = 13
plt.rcParams["figure.figsize"] = 16, 12

class Newton(object):
    def __init__(self, f, d1f, d2f, f2):
        self.f = f