[メモ]Pythonプログラミングパーフェクトマスター

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def sequence_sound(*args):
    for a in(args):
        print(a)

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def attacks(**kwargs):
    print(kwargs)

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def run_something(func):
    func()

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def outer(a, b):
    def inner(c, d):
        return c + d
    return inner(a, b)

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class CapsuleCoarch:
    def __init__(self, max = 5, count = 0):
        self.__max = max
        self.__count = count

    def get_max(self):
        return self.__max

    def set_max(self, max):
        self.__max = max

    max = property(get_max, set_max)

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file = open('dictionary.txt', 'r', encoding = 'utf_8')
data =file.read()
file.close()

lines = data.split('\n')
for line in lines:
    print(line)

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file = open('dictionary.txt', 'r', encoding = 'utf_8')
lines = file.readlines()
file.close()

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with open('dictionary.txt', 'r', encoding = 'utf-8') as f:
    for line in f:
        print(line)

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for line in lines:
    str = line.rstrip('\n')

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"""
以下を実行すると...
{
    'pattern': []
    'phrases': []
}
となる
"""
patternDictionary = {}
for line in lines:
    ptn, prs = line.split('\t')
    patternDictionary.setdefault('pattern', []).append(ptn)
    patternDictionary.setdefault('phrases', []).append(prs)

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5##かわいい|可愛い|カワイイ|きれい|綺麗|キレイ  %match%って言った？

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class Emotion:
    MOOD_MIN = -15
    MOOD_MAX = 15
    MOOD_RECOVERY = 0.5

    def __init__(self, pattern):
        self.pattern = pattern
        self.mood = 0

    def update(self, input):
        if self.mood < 0:
            self.mood += Emotion.MOOD_RECOVERY
        elif self.mood > 0:
            self.mood -= Emotion.MOOD_RECOVERY

        for ptn_item in self.pattern:
            if ptn_item.match(input):
                self.adjust_mood(ptn_item.modify)
                break

    def adjust_mood(self, val):
        self.mood += int(val)
        if self.mood > Emotion.MOOD_MAX:
            self.mood = Emotion.MOOD_MAX
        elif self.mood < Emotion.MOOD_MIN:
            self.mood = Emotion.MOOD_MIN

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class PatternItem:
    SEPARATOR = '((-?\d+)##)?(.*)$'

    def __ init__(self, pattern, phrases):
        self.initModifyAndPattern(pattern)
        self.initPhrases(phrases)

    def initModifyAndPattern(self, pattern):
        m = re.findall(PatternItem.SEPARATOR, pattern)
        self.modify = 0
        if m[0][1]:
            self.modify = int(m[0][1])
        self.pattern = m[0][2]

    def initPhrases(self, phrases):
        self.phrases = []
        dic = {}
        for phrase in phrases.split('|'):
            m = re.findall(PatternItem.SEPARATOR, phrase)
            dic['need'] = 0
            if m[0][1]:
                dic['need'] = int(m[0][1])
            dic['phrase'] = m[0][2]
            self.phrases.append(dic.copy())

    def match(self, str):
        return re.search(self.pattern, str)

    def choice(self, mood):
        choices = []
        for p in self.phrases:
            if(self.suitable(p['need'], mood)):
                choices.append(p['phrase'])
        if(len(choices) == 0):
            return None
        return random.choice(choices)

    def suitable(self, need, mood):
        if(need == 0):
            return True
        elif(need > 0):
            return (mood > need)
        else:
            return (mood < need)

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re.findall('(a(bc))x(yz)', '123abcxyz') # [('abc', 'bc', 'yz')]

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import re
import janome.tokenizer

def analyze(text):
    t = janome.tokenizer.Tokenizer()
    tokens = t.tokenize(text)
    result = []

    for token in tokens:
        result.append(
            [token.surface, # 形態素取得
            token.part_of_speech] # 品詞情報取得
        )
    return(result)

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import requests
import pprint

API_KEY = ''
URL = 'http://api.openweathermap.org/data/2.5/'
place = 'TOKYO'
data = requests.get(URL + 'weather?lang=ja&q=' + place + '&appid=' + API_KEY).json()

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import requests
from bs4 import BeautifulSoup

xml = requests.get('http://news.yahoo.co.jp/pickup/science/rss.xml')
soup = BeautifulSoup(xml.text, 'html.parser')
for news in soup.findAll('item'):
    print(news.title.string)

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from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.optimizers import Adam

(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(60000, 784)
x_train = x_train/255
x_test = x_test/255

num_classes = 10
y_train = np.utils.to_categorical(y_train, num_classes)
y_test = np.utils.to_categorical(y_test, num_classes)

model = Sequential()

model.add(Dense(512, input_shape(784,), activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy'])

model.summary()

batch = 32
epochs = 10

history = model.fit(x_train, y_train, batch_size=batch, epochs=epochs, verbose=1, validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)