lockdef

はじめに

Python3を用いたグラフ描写プログラムを書いていきます。

環境

Python 3.6.6
matplotlib
numpy

lockdef

はじめに

数学基礎第四章のまとめです。
公式のみを扱います。

指数法則

$a^ba^c=a^{b+c}$

$(a^b)^c=a^{bc}$

$(ab)^c=a^cb^c$

lockdef

from sklearn.datasets import load_digits
import matplotlib.pyplot as plt

data = load_digits()

for num, image, label in zip(range(10), data.images[:10], data.target[:10]):
    plt.subplot(2, 5, num+1)
    plt.imshow(image)
    plt.title(label)

lockdef

n=int(input());print(('YES' if len([_ for _ in range(1,n+1) if n%_==0])==2 else 'NO'))

lockdef

n=int(input());print('YNEOS'[n-len([_ for _ in range(1,n+1) if n%_])==2::2])

lockdef

import requests
from io import BytesIO

from PIL import Image
import numpy as np

def url_to_image(url):
    """
    url -> np.array (RGB)
    """

lockdef

from tensorflow.keras.utils import to_categorical

def preprocessing(data, ch=3, label=False):
    if label:
        return to_categorical(data)
    data = data / 255.0
    w, h = data[0:3]
    data = data.reshape((-1, w, h, ch))
    return data

lockdef

from io import BytesIO

from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import cv2

cap = cv2.VideoCapture(0)

ret, frame = cap.read()

lockdef

import cv2

ESC_KEY = 27

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()

    # 

lockdef

import math

def avg_entropy(num_arr):
    num_arr = list(map(lambda x: x * math.log2(x), num_arr))
    return -sum(num_arr)

if __name__=='__main__':
    input_num = [0.05, 0.25, 0.70]
    output = avg_entropy(input_num)
    print('output :', output)